diff --git a/.eslintignore b/.eslintignore new file mode 100644 index 0000000000000000000000000000000000000000..1cfd9487674ff4db01a4285097f5eae74010b2ae --- /dev/null +++ b/.eslintignore @@ -0,0 +1,4 @@ +extensions +extensions-disabled +repositories +venv \ No newline at end of file diff --git a/.eslintrc.js b/.eslintrc.js new file mode 100644 index 0000000000000000000000000000000000000000..cf8397695e1d59a9a12de4cfa908cae3bbfad0d9 --- /dev/null +++ b/.eslintrc.js @@ -0,0 +1,98 @@ +/* global module */ +module.exports = { + env: { + browser: true, + es2021: true, + }, + extends: "eslint:recommended", + parserOptions: { + ecmaVersion: "latest", + }, + rules: { + "arrow-spacing": "error", + "block-spacing": "error", + "brace-style": "error", + "comma-dangle": ["error", "only-multiline"], + "comma-spacing": "error", + "comma-style": ["error", "last"], + "curly": ["error", "multi-line", "consistent"], + "eol-last": "error", + "func-call-spacing": "error", + "function-call-argument-newline": ["error", "consistent"], + "function-paren-newline": ["error", "consistent"], + "indent": ["error", 4], + "key-spacing": "error", + "keyword-spacing": "error", + "linebreak-style": ["error", "unix"], + "no-extra-semi": "error", + "no-mixed-spaces-and-tabs": "error", + "no-multi-spaces": "error", + "no-redeclare": ["error", {builtinGlobals: false}], + "no-trailing-spaces": "error", + "no-unused-vars": "off", + "no-whitespace-before-property": "error", + "object-curly-newline": ["error", {consistent: true, multiline: true}], + "object-curly-spacing": ["error", "never"], + "operator-linebreak": ["error", "after"], + "quote-props": ["error", "consistent-as-needed"], + "semi": ["error", "always"], + "semi-spacing": "error", + "semi-style": ["error", "last"], + "space-before-blocks": "error", + "space-before-function-paren": ["error", "never"], + "space-in-parens": ["error", "never"], + "space-infix-ops": "error", + "space-unary-ops": "error", + "switch-colon-spacing": "error", + "template-curly-spacing": ["error", "never"], + "unicode-bom": "error", + }, + globals: { + //script.js + gradioApp: "readonly", + executeCallbacks: "readonly", + onAfterUiUpdate: "readonly", + onOptionsChanged: "readonly", + onUiLoaded: "readonly", + onUiUpdate: "readonly", + uiCurrentTab: "writable", + uiElementInSight: "readonly", + uiElementIsVisible: "readonly", + //ui.js + opts: "writable", + all_gallery_buttons: "readonly", + selected_gallery_button: "readonly", + selected_gallery_index: "readonly", + switch_to_txt2img: "readonly", + switch_to_img2img_tab: "readonly", + switch_to_img2img: "readonly", + switch_to_sketch: "readonly", + switch_to_inpaint: "readonly", + switch_to_inpaint_sketch: "readonly", + switch_to_extras: "readonly", + get_tab_index: "readonly", + create_submit_args: "readonly", + restart_reload: "readonly", + updateInput: "readonly", + onEdit: "readonly", + //extraNetworks.js + requestGet: "readonly", + popup: "readonly", + // from python + localization: "readonly", + // progrssbar.js + randomId: "readonly", + requestProgress: "readonly", + // imageviewer.js + modalPrevImage: "readonly", + modalNextImage: "readonly", + // token-counters.js + setupTokenCounters: "readonly", + // localStorage.js + localSet: "readonly", + localGet: "readonly", + localRemove: "readonly", + // resizeHandle.js + setupResizeHandle: "writable" + } +}; diff --git a/.git-blame-ignore-revs b/.git-blame-ignore-revs new file mode 100644 index 0000000000000000000000000000000000000000..4104da632b8fcacf3a6f52eba093e63059749725 --- /dev/null +++ b/.git-blame-ignore-revs @@ -0,0 +1,2 @@ +# Apply ESlint +9c54b78d9dde5601e916f308d9a9d6953ec39430 \ No newline at end of file diff --git a/.github/ISSUE_TEMPLATE/bug_report.yml b/.github/ISSUE_TEMPLATE/bug_report.yml new file mode 100644 index 0000000000000000000000000000000000000000..5876e941085d256cc6a3f4d9ec560d19e782e16e --- /dev/null +++ b/.github/ISSUE_TEMPLATE/bug_report.yml @@ -0,0 +1,105 @@ +name: Bug Report +description: You think something is broken in the UI +title: "[Bug]: " +labels: ["bug-report"] + +body: + - type: markdown + attributes: + value: | + > The title of the bug report should be short and descriptive. + > Use relevant keywords for searchability. + > Do not leave it blank, but also do not put an entire error log in it. + - type: checkboxes + attributes: + label: Checklist + description: | + Please perform basic debugging to see if extensions or configuration is the cause of the issue. + Basic debug procedure + 　1. Disable all third-party extensions - check if extension is the cause + 　2. Update extensions and webui - sometimes things just need to be updated + 　3. Backup and remove your config.json and ui-config.json - check if the issue is caused by bad configuration + 　4. Delete venv with third-party extensions disabled - sometimes extensions might cause wrong libraries to be installed + 　5. Try a fresh installation webui in a different directory - see if a clean installation solves the issue + Before making a issue report please, check that the issue hasn't been reported recently. + options: + - label: The issue exists after disabling all extensions + - label: The issue exists on a clean installation of webui + - label: The issue is caused by an extension, but I believe it is caused by a bug in the webui + - label: The issue exists in the current version of the webui + - label: The issue has not been reported before recently + - label: The issue has been reported before but has not been fixed yet + - type: markdown + attributes: + value: | + > Please fill this form with as much information as possible. Don't forget to "Upload Sysinfo" and "What browsers" and provide screenshots if possible + - type: textarea + id: what-did + attributes: + label: What happened? + description: Tell us what happened in a very clear and simple way + placeholder: | + txt2img is not working as intended. + validations: + required: true + - type: textarea + id: steps + attributes: + label: Steps to reproduce the problem + description: Please provide us with precise step by step instructions on how to reproduce the bug + placeholder: | + 1. Go to ... + 2. Press ... + 3. ... + validations: + required: true + - type: textarea + id: what-should + attributes: + label: What should have happened? + description: Tell us what you think the normal behavior should be + placeholder: | + WebUI should ... + validations: + required: true + - type: dropdown + id: browsers + attributes: + label: What browsers do you use to access the UI ? + multiple: true + options: + - Mozilla Firefox + - Google Chrome + - Brave + - Apple Safari + - Microsoft Edge + - Android + - iOS + - Other + - type: textarea + id: sysinfo + attributes: + label: Sysinfo + description: System info file, generated by WebUI. You can generate it in settings, on the Sysinfo page. Drag the file into the field to upload it. If you submit your report without including the sysinfo file, the report will be closed. If needed, review the report to make sure it includes no personal information you don't want to share. If you can't start WebUI, you can use --dump-sysinfo commandline argument to generate the file. + placeholder: | + 1. Go to WebUI Settings -> Sysinfo -> Download system info. + If WebUI fails to launch, use --dump-sysinfo commandline argument to generate the file + 2. Upload the Sysinfo as a attached file, Do NOT paste it in as plain text. + validations: + required: true + - type: textarea + id: logs + attributes: + label: Console logs + description: Please provide **full** cmd/terminal logs from the moment you started UI to the end of it, after the bug occured. If it's very long, provide a link to pastebin or similar service. + render: Shell + validations: + required: true + - type: textarea + id: misc + attributes: + label: Additional information + description: | + Please provide us with any relevant additional info or context. + Examples: + 　I have updated my GPU driver recently. diff --git a/.github/ISSUE_TEMPLATE/config.yml b/.github/ISSUE_TEMPLATE/config.yml new file mode 100644 index 0000000000000000000000000000000000000000..f58c94a9be6847193a971ac67aa83e9a6d75c0ae --- /dev/null +++ b/.github/ISSUE_TEMPLATE/config.yml @@ -0,0 +1,5 @@ +blank_issues_enabled: false +contact_links: + - name: WebUI Community Support + url: https://github.com/AUTOMATIC1111/stable-diffusion-webui/discussions + about: Please ask and answer questions here. diff --git a/.github/ISSUE_TEMPLATE/feature_request.yml b/.github/ISSUE_TEMPLATE/feature_request.yml new file mode 100644 index 0000000000000000000000000000000000000000..35a887408c1a0cb7d5bbf0a8444d0903a708be75 --- /dev/null +++ b/.github/ISSUE_TEMPLATE/feature_request.yml @@ -0,0 +1,40 @@ +name: Feature request +description: Suggest an idea for this project +title: "[Feature Request]: " +labels: ["enhancement"] + +body: + - type: checkboxes + attributes: + label: Is there an existing issue for this? + description: Please search to see if an issue already exists for the feature you want, and that it's not implemented in a recent build/commit. + options: + - label: I have searched the existing issues and checked the recent builds/commits + required: true + - type: markdown + attributes: + value: | + *Please fill this form with as much information as possible, provide screenshots and/or illustrations of the feature if possible* + - type: textarea + id: feature + attributes: + label: What would your feature do ? + description: Tell us about your feature in a very clear and simple way, and what problem it would solve + validations: + required: true + - type: textarea + id: workflow + attributes: + label: Proposed workflow + description: Please provide us with step by step information on how you'd like the feature to be accessed and used + value: | + 1. Go to .... + 2. Press .... + 3. ... + validations: + required: true + - type: textarea + id: misc + attributes: + label: Additional information + description: Add any other context or screenshots about the feature request here. diff --git a/.github/pull_request_template.md b/.github/pull_request_template.md new file mode 100644 index 0000000000000000000000000000000000000000..c9fcda2e2790861c7bf4aa4cb37e01545c48fb95 --- /dev/null +++ b/.github/pull_request_template.md @@ -0,0 +1,15 @@ +## Description + +* a simple description of what you're trying to accomplish +* a summary of changes in code +* which issues it fixes, if any + +## Screenshots/videos: + + +## Checklist: + +- [ ] I have read [contributing wiki page](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Contributing) +- [ ] I have performed a self-review of my own code +- [ ] My code follows the [style guidelines](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Contributing#code-style) +- [ ] My code passes [tests](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Tests) diff --git a/.github/workflows/on_pull_request.yaml b/.github/workflows/on_pull_request.yaml new file mode 100644 index 0000000000000000000000000000000000000000..9e44c806ab353cf3166b6ddbc54df63a16995ff5 --- /dev/null +++ b/.github/workflows/on_pull_request.yaml @@ -0,0 +1,38 @@ +name: Linter + +on: + - push + - pull_request + +jobs: + lint-python: + name: ruff + runs-on: ubuntu-latest + if: github.event_name != 'pull_request' || github.event.pull_request.head.repo.full_name != github.event.pull_request.base.repo.full_name + steps: + - name: Checkout Code + uses: actions/checkout@v3 + - uses: actions/setup-python@v4 + with: + python-version: 3.11 + # NB: there's no cache: pip here since we're not installing anything + # from the requirements.txt file(s) in the repository; it's faster + # not to have GHA download an (at the time of writing) 4 GB cache + # of PyTorch and other dependencies. + - name: Install Ruff + run: pip install ruff==0.1.6 + - name: Run Ruff + run: ruff . + lint-js: + name: eslint + runs-on: ubuntu-latest + if: github.event_name != 'pull_request' || github.event.pull_request.head.repo.full_name != github.event.pull_request.base.repo.full_name + steps: + - name: Checkout Code + uses: actions/checkout@v3 + - name: Install Node.js + uses: actions/setup-node@v3 + with: + node-version: 18 + - run: npm i --ci + - run: npm run lint diff --git a/.github/workflows/run_tests.yaml b/.github/workflows/run_tests.yaml new file mode 100644 index 0000000000000000000000000000000000000000..3dafaf8dcfcd14fd7a7ca3385806efad5550b871 --- /dev/null +++ b/.github/workflows/run_tests.yaml @@ -0,0 +1,73 @@ +name: Tests + +on: + - push + - pull_request + +jobs: + test: + name: tests on CPU with empty model + runs-on: ubuntu-latest + if: github.event_name != 'pull_request' || github.event.pull_request.head.repo.full_name != github.event.pull_request.base.repo.full_name + steps: + - name: Checkout Code + uses: actions/checkout@v3 + - name: Set up Python 3.10 + uses: actions/setup-python@v4 + with: + python-version: 3.10.6 + cache: pip + cache-dependency-path: | + **/requirements*txt + launch.py + - name: Install test dependencies + run: pip install wait-for-it -r requirements-test.txt + env: + PIP_DISABLE_PIP_VERSION_CHECK: "1" + PIP_PROGRESS_BAR: "off" + - name: Setup environment + run: python launch.py --skip-torch-cuda-test --exit + env: + PIP_DISABLE_PIP_VERSION_CHECK: "1" + PIP_PROGRESS_BAR: "off" + TORCH_INDEX_URL: https://download.pytorch.org/whl/cpu + WEBUI_LAUNCH_LIVE_OUTPUT: "1" + PYTHONUNBUFFERED: "1" + - name: Start test server + run: > + python -m coverage run + --data-file=.coverage.server + launch.py + --skip-prepare-environment + --skip-torch-cuda-test + --test-server + --do-not-download-clip + --no-half + --disable-opt-split-attention + --use-cpu all + --api-server-stop + 2>&1 | tee output.txt & + - name: Run tests + run: | + wait-for-it --service 127.0.0.1:7860 -t 600 + python -m pytest -vv --junitxml=test/results.xml --cov . --cov-report=xml --verify-base-url test + - name: Kill test server + if: always() + run: curl -vv -XPOST http://127.0.0.1:7860/sdapi/v1/server-stop && sleep 10 + - name: Show coverage + run: | + python -m coverage combine .coverage* + python -m coverage report -i + python -m coverage html -i + - name: Upload main app output + uses: actions/upload-artifact@v3 + if: always() + with: + name: output + path: output.txt + - name: Upload coverage HTML + uses: actions/upload-artifact@v3 + if: always() + with: + name: htmlcov + path: htmlcov diff --git a/.github/workflows/warns_merge_master.yml b/.github/workflows/warns_merge_master.yml new file mode 100644 index 0000000000000000000000000000000000000000..ae2aab6ba8ce5684755b5fb4083267111bcd23cd --- /dev/null +++ b/.github/workflows/warns_merge_master.yml @@ -0,0 +1,19 @@ +name: Pull requests can't target master branch + +"on": + pull_request: + types: + - opened + - synchronize + - reopened + branches: + - master + +jobs: + check: + runs-on: ubuntu-latest + steps: + - name: Warning marge into master + run: | + echo -e "::warning::This pull request directly merge into \"master\" branch, normally development happens on \"dev\" branch." + exit 1 diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..09734267ff5c4d51c2f9f1c85f6f8bf2cc225fb9 --- /dev/null +++ b/.gitignore @@ -0,0 +1,39 @@ +__pycache__ +*.ckpt +*.safetensors +*.pth +/ESRGAN/* +/SwinIR/* +/repositories +/venv +/tmp +/model.ckpt +/models/**/* +/GFPGANv1.3.pth +/gfpgan/weights/*.pth +/ui-config.json +/outputs +/config.json +/log +/webui.settings.bat +/embeddings +/styles.csv +/params.txt +/styles.csv.bak +/webui-user.bat +/webui-user.sh +/interrogate +/user.css +/.idea +notification.mp3 +/SwinIR +/textual_inversion +.vscode +/extensions +/test/stdout.txt +/test/stderr.txt +/cache.json* +/config_states/ +/node_modules +/package-lock.json +/.coverage* diff --git a/.pylintrc b/.pylintrc new file mode 100644 index 0000000000000000000000000000000000000000..53254e5dcfd871c8c0f0f4dec9dceeb1ba967eda --- /dev/null +++ b/.pylintrc @@ -0,0 +1,3 @@ +# See https://pylint.pycqa.org/en/latest/user_guide/messages/message_control.html +[MESSAGES CONTROL] +disable=C,R,W,E,I diff --git a/CHANGELOG.md b/CHANGELOG.md new file mode 100644 index 0000000000000000000000000000000000000000..b274aa428097b8f43f91a71aa9e887062ff71faf --- /dev/null +++ b/CHANGELOG.md @@ -0,0 +1,674 @@ +## 1.7.0 + +### Features: +* settings tab rework: add search field, add categories, split UI settings page into many +* add altdiffusion-m18 support ([#13364](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13364)) +* support inference with LyCORIS GLora networks ([#13610](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13610)) +* add lora-embedding bundle system ([#13568](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13568)) +* option to move prompt from top row into generation parameters +* add support for SSD-1B ([#13865](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13865)) +* support inference with OFT networks ([#13692](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13692)) +* script metadata and DAG sorting mechanism ([#13944](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13944)) +* support HyperTile optimization ([#13948](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13948)) +* add support for SD 2.1 Turbo ([#14170](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14170)) +* remove Train->Preprocessing tab and put all its functionality into Extras tab +* initial IPEX support for Intel Arc GPU ([#14171](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14171)) + +### Minor: +* allow reading model hash from images in img2img batch mode ([#12767](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12767)) +* add option to align with sgm repo's sampling implementation ([#12818](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12818)) +* extra field for lora metadata viewer: `ss_output_name` ([#12838](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12838)) +* add action in settings page to calculate all SD checkpoint hashes ([#12909](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12909)) +* add button to copy prompt to style editor ([#12975](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12975)) +* add --skip-load-model-at-start option ([#13253](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13253)) +* write infotext to gif images +* read infotext from gif images ([#13068](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13068)) +* allow configuring the initial state of InputAccordion in ui-config.json ([#13189](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13189)) +* allow editing whitespace delimiters for ctrl+up/ctrl+down prompt editing ([#13444](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13444)) +* prevent accidentally closing popup dialogs ([#13480](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13480)) +* added option to play notification sound or not ([#13631](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13631)) +* show the preview image in the full screen image viewer if available ([#13459](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13459)) +* support for webui.settings.bat ([#13638](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13638)) +* add an option to not print stack traces on ctrl+c +* start/restart generation by Ctrl (Alt) + Enter ([#13644](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13644)) +* update prompts_from_file script to allow concatenating entries with the general prompt ([#13733](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13733)) +* added a visible checkbox to input accordion +* added an option to hide all txt2img/img2img parameters in an accordion ([#13826](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13826)) +* added 'Path' sorting option for Extra network cards ([#13968](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13968)) +* enable prompt hotkeys in style editor ([#13931](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13931)) +* option to show batch img2img results in UI ([#14009](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14009)) +* infotext updates: add option to disregard certain infotext fields, add option to not include VAE in infotext, add explanation to infotext settings page, move some options to infotext settings page +* add FP32 fallback support on sd_vae_approx ([#14046](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14046)) +* support XYZ scripts / split hires path from unet ([#14126](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14126)) +* allow use of mutiple styles csv files ([#14125](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14125)) + +### Extensions and API: +* update gradio to 3.41.2 +* support installed extensions list api ([#12774](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12774)) +* update pnginfo API to return dict with parsed values +* add noisy latent to `ExtraNoiseParams` for callback ([#12856](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12856)) +* show extension datetime in UTC ([#12864](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12864), [#12865](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12865), [#13281](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13281)) +* add an option to choose how to combine hires fix and refiner +* include program version in info response. ([#13135](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13135)) +* sd_unet support for SDXL +* patch DDPM.register_betas so that users can put given_betas in model yaml ([#13276](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13276)) +* xyz_grid: add prepare ([#13266](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13266)) +* allow multiple localization files with same language in extensions ([#13077](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13077)) +* add onEdit function for js and rework token-counter.js to use it +* fix the key error exception when processing override_settings keys ([#13567](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13567)) +* ability for extensions to return custom data via api in response.images ([#13463](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13463)) +* call state.jobnext() before postproces*() ([#13762](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13762)) +* add option to set notification sound volume ([#13884](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13884)) +* update Ruff to 0.1.6 ([#14059](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14059)) +* add Block component creation callback ([#14119](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14119)) +* catch uncaught exception with ui creation scripts ([#14120](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14120)) +* use extension name for determining an extension is installed in the index ([#14063](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14063)) +* update is_installed() from launch_utils.py to fix reinstalling already installed packages ([#14192](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14192)) + +### Bug Fixes: +* fix pix2pix producing bad results +* fix defaults settings page breaking when any of main UI tabs are hidden +* fix error that causes some extra networks to be disabled if both and are present in the prompt +* fix for Reload UI function: if you reload UI on one tab, other opened tabs will no longer stop working +* prevent duplicate resize handler ([#12795](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12795)) +* small typo: vae resolve bug ([#12797](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12797)) +* hide broken image crop tool ([#12792](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12792)) +* don't show hidden samplers in dropdown for XYZ script ([#12780](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12780)) +* fix style editing dialog breaking if it's opened in both img2img and txt2img tabs +* hide --gradio-auth and --api-auth values from /internal/sysinfo report +* add missing infotext for RNG in options ([#12819](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12819)) +* fix notification not playing when built-in webui tab is inactive ([#12834](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12834)) +* honor `--skip-install` for extension installers ([#12832](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12832)) +* don't print blank stdout in extension installers ([#12833](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12833), [#12855](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12855)) +* get progressbar to display correctly in extensions tab +* keep order in list of checkpoints when loading model that doesn't have a checksum +* fix inpainting models in txt2img creating black pictures +* fix generation params regex ([#12876](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12876)) +* fix batch img2img output dir with script ([#12926](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12926)) +* fix #13080 - Hypernetwork/TI preview generation ([#13084](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13084)) +* fix bug with sigma min/max overrides. ([#12995](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12995)) +* more accurate check for enabling cuDNN benchmark on 16XX cards ([#12924](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12924)) +* don't use multicond parser for negative prompt counter ([#13118](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13118)) +* fix data-sort-name containing spaces ([#13412](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13412)) +* update card on correct tab when editing metadata ([#13411](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13411)) +* fix viewing/editing metadata when filename contains an apostrophe ([#13395](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13395)) +* fix: --sd_model in "Prompts from file or textbox" script is not working ([#13302](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13302)) +* better Support for Portable Git ([#13231](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13231)) +* fix issues when webui_dir is not work_dir ([#13210](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13210)) +* fix: lora-bias-backup don't reset cache ([#13178](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13178)) +* account for customizable extra network separators whyen removing extra network text from the prompt ([#12877](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12877)) +* re fix batch img2img output dir with script ([#13170](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13170)) +* fix `--ckpt-dir` path separator and option use `short name` for checkpoint dropdown ([#13139](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13139)) +* consolidated allowed preview formats, Fix extra network `.gif` not woking as preview ([#13121](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13121)) +* fix venv_dir=- environment variable not working as expected on linux ([#13469](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13469)) +* repair unload sd checkpoint button +* edit-attention fixes ([#13533](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13533)) +* fix bug when using --gfpgan-models-path ([#13718](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13718)) +* properly apply sort order for extra network cards when selected from dropdown +* fixes generation restart not working for some users when 'Ctrl+Enter' is pressed ([#13962](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13962)) +* thread safe extra network list_items ([#13014](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13014)) +* fix not able to exit metadata popup when pop up is too big ([#14156](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14156)) +* fix auto focal point crop for opencv >= 4.8 ([#14121](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14121)) +* make 'use-cpu all' actually apply to 'all' ([#14131](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14131)) +* extras tab batch: actually use original filename +* make webui not crash when running with --disable-all-extensions option + +### Other: +* non-local condition ([#12814](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12814)) +* fix minor typos ([#12827](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12827)) +* remove xformers Python version check ([#12842](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12842)) +* style: file-metadata word-break ([#12837](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12837)) +* revert SGM noise multiplier change for img2img because it breaks hires fix +* do not change quicksettings dropdown option when value returned is `None` ([#12854](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12854)) +* [RC 1.6.0 - zoom is partly hidden] Update style.css ([#12839](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12839)) +* chore: change extension time format ([#12851](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12851)) +* WEBUI.SH - Use torch 2.1.0 release candidate for Navi 3 ([#12929](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12929)) +* add Fallback at images.read_info_from_image if exif data was invalid ([#13028](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13028)) +* update cmd arg description ([#12986](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12986)) +* fix: update shared.opts.data when add_option ([#12957](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12957), [#13213](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13213)) +* restore missing tooltips ([#12976](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12976)) +* use default dropdown padding on mobile ([#12880](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12880)) +* put enable console prompts option into settings from commandline args ([#13119](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13119)) +* fix some deprecated types ([#12846](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12846)) +* bump to torchsde==0.2.6 ([#13418](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13418)) +* update dragdrop.js ([#13372](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13372)) +* use orderdict as lru cache:opt/bug ([#13313](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13313)) +* XYZ if not include sub grids do not save sub grid ([#13282](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13282)) +* initialize state.time_start befroe state.job_count ([#13229](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13229)) +* fix fieldname regex ([#13458](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13458)) +* change denoising_strength default to None. ([#13466](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13466)) +* fix regression ([#13475](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13475)) +* fix IndexError ([#13630](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13630)) +* fix: checkpoints_loaded:{checkpoint:state_dict}, model.load_state_dict issue in dict value empty ([#13535](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13535)) +* update bug_report.yml ([#12991](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12991)) +* requirements_versions httpx==0.24.1 ([#13839](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13839)) +* fix parenthesis auto selection ([#13829](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13829)) +* fix #13796 ([#13797](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13797)) +* corrected a typo in `modules/cmd_args.py` ([#13855](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13855)) +* feat: fix randn found element of type float at pos 2 ([#14004](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14004)) +* adds tqdm handler to logging_config.py for progress bar integration ([#13996](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13996)) +* hotfix: call shared.state.end() after postprocessing done ([#13977](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13977)) +* fix dependency address patch 1 ([#13929](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13929)) +* save sysinfo as .json ([#14035](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14035)) +* move exception_records related methods to errors.py ([#14084](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14084)) +* compatibility ([#13936](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13936)) +* json.dump(ensure_ascii=False) ([#14108](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14108)) +* dir buttons start with / so only the correct dir will be shown and no… ([#13957](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13957)) +* alternate implementation for unet forward replacement that does not depend on hijack being applied +* re-add `keyedit_delimiters_whitespace` setting lost as part of commit e294e46 ([#14178](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14178)) +* fix `save_samples` being checked early when saving masked composite ([#14177](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14177)) +* slight optimization for mask and mask_composite ([#14181](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14181)) +* add import_hook hack to work around basicsr/torchvision incompatibility ([#14186](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14186)) + +## 1.6.1 + +### Bug Fixes: + * fix an error causing the webui to fail to start ([#13839](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13839)) + +## 1.6.0 + +### Features: + * refiner support [#12371](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12371) + * add NV option for Random number generator source setting, which allows to generate same pictures on CPU/AMD/Mac as on NVidia videocards + * add style editor dialog + * hires fix: add an option to use a different checkpoint for second pass ([#12181](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12181)) + * option to keep multiple loaded models in memory ([#12227](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12227)) + * new samplers: Restart, DPM++ 2M SDE Exponential, DPM++ 2M SDE Heun, DPM++ 2M SDE Heun Karras, DPM++ 2M SDE Heun Exponential, DPM++ 3M SDE, DPM++ 3M SDE Karras, DPM++ 3M SDE Exponential ([#12300](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12300), [#12519](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12519), [#12542](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12542)) + * rework DDIM, PLMS, UniPC to use CFG denoiser same as in k-diffusion samplers: + * makes all of them work with img2img + * makes prompt composition posssible (AND) + * makes them available for SDXL + * always show extra networks tabs in the UI ([#11808](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/11808)) + * use less RAM when creating models ([#11958](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/11958), [#12599](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12599)) + * textual inversion inference support for SDXL + * extra networks UI: show metadata for SD checkpoints + * checkpoint merger: add metadata support + * prompt editing and attention: add support for whitespace after the number ([ red : green : 0.5 ]) (seed breaking change) ([#12177](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12177)) + * VAE: allow selecting own VAE for each checkpoint (in user metadata editor) + * VAE: add selected VAE to infotext + * options in main UI: add own separate setting for txt2img and img2img, correctly read values from pasted infotext, add setting for column count ([#12551](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12551)) + * add resize handle to txt2img and img2img tabs, allowing to change the amount of horizontable space given to generation parameters and resulting image gallery ([#12687](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12687), [#12723](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12723)) + * change default behavior for batching cond/uncond -- now it's on by default, and is disabled by an UI setting (Optimizatios -> Batch cond/uncond) - if you are on lowvram/medvram and are getting OOM exceptions, you will need to enable it + * show current position in queue and make it so that requests are processed in the order of arrival ([#12707](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12707)) + * add `--medvram-sdxl` flag that only enables `--medvram` for SDXL models + * prompt editing timeline has separate range for first pass and hires-fix pass (seed breaking change) ([#12457](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12457)) + +### Minor: + * img2img batch: RAM savings, VRAM savings, .tif, .tiff in img2img batch ([#12120](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12120), [#12514](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12514), [#12515](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12515)) + * postprocessing/extras: RAM savings ([#12479](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12479)) + * XYZ: in the axis labels, remove pathnames from model filenames + * XYZ: support hires sampler ([#12298](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12298)) + * XYZ: new option: use text inputs instead of dropdowns ([#12491](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12491)) + * add gradio version warning + * sort list of VAE checkpoints ([#12297](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12297)) + * use transparent white for mask in inpainting, along with an option to select the color ([#12326](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12326)) + * move some settings to their own section: img2img, VAE + * add checkbox to show/hide dirs for extra networks + * Add TAESD(or more) options for all the VAE encode/decode operation ([#12311](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12311)) + * gradio theme cache, new gradio themes, along with explanation that the user can input his own values ([#12346](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12346), [#12355](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12355)) + * sampler fixes/tweaks: s_tmax, s_churn, s_noise, s_tmax ([#12354](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12354), [#12356](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12356), [#12357](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12357), [#12358](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12358), [#12375](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12375), [#12521](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12521)) + * update README.md with correct instructions for Linux installation ([#12352](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12352)) + * option to not save incomplete images, on by default ([#12338](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12338)) + * enable cond cache by default + * git autofix for repos that are corrupted ([#12230](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12230)) + * allow to open images in new browser tab by middle mouse button ([#12379](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12379)) + * automatically open webui in browser when running "locally" ([#12254](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12254)) + * put commonly used samplers on top, make DPM++ 2M Karras the default choice + * zoom and pan: option to auto-expand a wide image, improved integration ([#12413](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12413), [#12727](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12727)) + * option to cache Lora networks in memory + * rework hires fix UI to use accordion + * face restoration and tiling moved to settings - use "Options in main UI" setting if you want them back + * change quicksettings items to have variable width + * Lora: add Norm module, add support for bias ([#12503](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12503)) + * Lora: output warnings in UI rather than fail for unfitting loras; switch to logging for error output in console + * support search and display of hashes for all extra network items ([#12510](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12510)) + * add extra noise param for img2img operations ([#12564](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12564)) + * support for Lora with bias ([#12584](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12584)) + * make interrupt quicker ([#12634](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12634)) + * configurable gallery height ([#12648](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12648)) + * make results column sticky ([#12645](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12645)) + * more hash filename patterns ([#12639](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12639)) + * make image viewer actually fit the whole page ([#12635](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12635)) + * make progress bar work independently from live preview display which results in it being updated a lot more often + * forbid Full live preview method for medvram and add a setting to undo the forbidding + * make it possible to localize tooltips and placeholders + * add option to align with sgm repo's sampling implementation ([#12818](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12818)) + * Restore faces and Tiling generation parameters have been moved to settings out of main UI + * if you want to put them back into main UI, use `Options in main UI` setting on the UI page. + +### Extensions and API: + * gradio 3.41.2 + * also bump versions for packages: transformers, GitPython, accelerate, scikit-image, timm, tomesd + * support tooltip kwarg for gradio elements: gr.Textbox(label='hello', tooltip='world') + * properly clear the total console progressbar when using txt2img and img2img from API + * add cmd_arg --disable-extra-extensions and --disable-all-extensions ([#12294](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12294)) + * shared.py and webui.py split into many files + * add --loglevel commandline argument for logging + * add a custom UI element that combines accordion and checkbox + * avoid importing gradio in tests because it spams warnings + * put infotext label for setting into OptionInfo definition rather than in a separate list + * make `StableDiffusionProcessingImg2Img.mask_blur` a property, make more inline with PIL `GaussianBlur` ([#12470](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12470)) + * option to make scripts UI without gr.Group + * add a way for scripts to register a callback for before/after just a single component's creation + * use dataclass for StableDiffusionProcessing + * store patches for Lora in a specialized module instead of inside torch + * support http/https URLs in API ([#12663](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12663), [#12698](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12698)) + * add extra noise callback ([#12616](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12616)) + * dump current stack traces when exiting with SIGINT + * add type annotations for extra fields of shared.sd_model + +### Bug Fixes: + * Don't crash if out of local storage quota for javascriot localStorage + * XYZ plot do not fail if an exception occurs + * fix missing TI hash in infotext if generation uses both negative and positive TI ([#12269](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12269)) + * localization fixes ([#12307](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12307)) + * fix sdxl model invalid configuration after the hijack + * correctly toggle extras checkbox for infotext paste ([#12304](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12304)) + * open raw sysinfo link in new page ([#12318](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12318)) + * prompt parser: Account for empty field in alternating words syntax ([#12319](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12319)) + * add tab and carriage return to invalid filename chars ([#12327](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12327)) + * fix api only Lora not working ([#12387](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12387)) + * fix options in main UI misbehaving when there's just one element + * make it possible to use a sampler from infotext even if it's hidden in the dropdown + * fix styles missing from the prompt in infotext when making a grid of batch of multiplie images + * prevent bogus progress output in console when calculating hires fix dimensions + * fix --use-textbox-seed + * fix broken `Lora/Networks: use old method` option ([#12466](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12466)) + * properly return `None` for VAE hash when using `--no-hashing` ([#12463](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12463)) + * MPS/macOS fixes and optimizations ([#12526](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12526)) + * add second_order to samplers that mistakenly didn't have it + * when refreshing cards in extra networks UI, do not discard user's custom resolution + * fix processing error that happens if batch_size is not a multiple of how many prompts/negative prompts there are ([#12509](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12509)) + * fix inpaint upload for alpha masks ([#12588](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12588)) + * fix exception when image sizes are not integers ([#12586](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12586)) + * fix incorrect TAESD Latent scale ([#12596](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12596)) + * auto add data-dir to gradio-allowed-path ([#12603](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12603)) + * fix exception if extensuions dir is missing ([#12607](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12607)) + * fix issues with api model-refresh and vae-refresh ([#12638](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12638)) + * fix img2img background color for transparent images option not being used ([#12633](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12633)) + * attempt to resolve NaN issue with unstable VAEs in fp32 mk2 ([#12630](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12630)) + * implement missing undo hijack for SDXL + * fix xyz swap axes ([#12684](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12684)) + * fix errors in backup/restore tab if any of config files are broken ([#12689](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12689)) + * fix SD VAE switch error after model reuse ([#12685](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12685)) + * fix trying to create images too large for the chosen format ([#12667](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12667)) + * create Gradio temp directory if necessary ([#12717](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12717)) + * prevent possible cache loss if exiting as it's being written by using an atomic operation to replace the cache with the new version + * set devices.dtype_unet correctly + * run RealESRGAN on GPU for non-CUDA devices ([#12737](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12737)) + * prevent extra network buttons being obscured by description for very small card sizes ([#12745](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12745)) + * fix error that causes some extra networks to be disabled if both and are present in the prompt + * fix defaults settings page breaking when any of main UI tabs are hidden + * fix incorrect save/display of new values in Defaults page in settings + * fix for Reload UI function: if you reload UI on one tab, other opened tabs will no longer stop working + * fix an error that prevents VAE being reloaded after an option change if a VAE near the checkpoint exists ([#12797](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12737)) + * hide broken image crop tool ([#12792](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12737)) + * don't show hidden samplers in dropdown for XYZ script ([#12780](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12737)) + * fix style editing dialog breaking if it's opened in both img2img and txt2img tabs + * fix a bug allowing users to bypass gradio and API authentication (reported by vysecurity) + * fix notification not playing when built-in webui tab is inactive ([#12834](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12834)) + * honor `--skip-install` for extension installers ([#12832](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12832)) + * don't print blank stdout in extension installers ([#12833](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12832), [#12855](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12855)) + * do not change quicksettings dropdown option when value returned is `None` ([#12854](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12854)) + * get progressbar to display correctly in extensions tab + + +## 1.5.2 + +### Bug Fixes: + * fix memory leak when generation fails + * update doggettx cross attention optimization to not use an unreasonable amount of memory in some edge cases -- suggestion by MorkTheOrk + + +## 1.5.1 + +### Minor: + * support parsing text encoder blocks in some new LoRAs + * delete scale checker script due to user demand + +### Extensions and API: + * add postprocess_batch_list script callback + +### Bug Fixes: + * fix TI training for SD1 + * fix reload altclip model error + * prepend the pythonpath instead of overriding it + * fix typo in SD_WEBUI_RESTARTING + * if txt2img/img2img raises an exception, finally call state.end() + * fix composable diffusion weight parsing + * restyle Startup profile for black users + * fix webui not launching with --nowebui + * catch exception for non git extensions + * fix some options missing from /sdapi/v1/options + * fix for extension update status always saying "unknown" + * fix display of extra network cards that have `<>` in the name + * update lora extension to work with python 3.8 + + +## 1.5.0 + +### Features: + * SD XL support + * user metadata system for custom networks + * extended Lora metadata editor: set activation text, default weight, view tags, training info + * Lora extension rework to include other types of networks (all that were previously handled by LyCORIS extension) + * show github stars for extenstions + * img2img batch mode can read extra stuff from png info + * img2img batch works with subdirectories + * hotkeys to move prompt elements: alt+left/right + * restyle time taken/VRAM display + * add textual inversion hashes to infotext + * optimization: cache git extension repo information + * move generate button next to the generated picture for mobile clients + * hide cards for networks of incompatible Stable Diffusion version in Lora extra networks interface + * skip installing packages with pip if they all are already installed - startup speedup of about 2 seconds + +### Minor: + * checkbox to check/uncheck all extensions in the Installed tab + * add gradio user to infotext and to filename patterns + * allow gif for extra network previews + * add options to change colors in grid + * use natural sort for items in extra networks + * Mac: use empty_cache() from torch 2 to clear VRAM + * added automatic support for installing the right libraries for Navi3 (AMD) + * add option SWIN_torch_compile to accelerate SwinIR upscale + * suppress printing TI embedding info at start to console by default + * speedup extra networks listing + * added `[none]` filename token. + * removed thumbs extra networks view mode (use settings tab to change width/height/scale to get thumbs) + * add always_discard_next_to_last_sigma option to XYZ plot + * automatically switch to 32-bit float VAE if the generated picture has NaNs without the need for `--no-half-vae` commandline flag. + +### Extensions and API: + * api endpoints: /sdapi/v1/server-kill, /sdapi/v1/server-restart, /sdapi/v1/server-stop + * allow Script to have custom metaclass + * add model exists status check /sdapi/v1/options + * rename --add-stop-route to --api-server-stop + * add `before_hr` script callback + * add callback `after_extra_networks_activate` + * disable rich exception output in console for API by default, use WEBUI_RICH_EXCEPTIONS env var to enable + * return http 404 when thumb file not found + * allow replacing extensions index with environment variable + +### Bug Fixes: + * fix for catch errors when retrieving extension index #11290 + * fix very slow loading speed of .safetensors files when reading from network drives + * API cache cleanup + * fix UnicodeEncodeError when writing to file CLIP Interrogator batch mode + * fix warning of 'has_mps' deprecated from PyTorch + * fix problem with extra network saving images as previews losing generation info + * fix throwing exception when trying to resize image with I;16 mode + * fix for #11534: canvas zoom and pan extension hijacking shortcut keys + * fixed launch script to be runnable from any directory + * don't add "Seed Resize: -1x-1" to API image metadata + * correctly remove end parenthesis with ctrl+up/down + * fixing --subpath on newer gradio version + * fix: check fill size none zero when resize (fixes #11425) + * use submit and blur for quick settings textbox + * save img2img batch with images.save_image() + * prevent running preload.py for disabled extensions + * fix: previously, model name was added together with directory name to infotext and to [model_name] filename pattern; directory name is now not included + + +## 1.4.1 + +### Bug Fixes: + * add queue lock for refresh-checkpoints + +## 1.4.0 + +### Features: + * zoom controls for inpainting + * run basic torch calculation at startup in parallel to reduce the performance impact of first generation + * option to pad prompt/neg prompt to be same length + * remove taming_transformers dependency + * custom k-diffusion scheduler settings + * add an option to show selected settings in main txt2img/img2img UI + * sysinfo tab in settings + * infer styles from prompts when pasting params into the UI + * an option to control the behavior of the above + +### Minor: + * bump Gradio to 3.32.0 + * bump xformers to 0.0.20 + * Add option to disable token counters + * tooltip fixes & optimizations + * make it possible to configure filename for the zip download + * `[vae_filename]` pattern for filenames + * Revert discarding penultimate sigma for DPM-Solver++(2M) SDE + * change UI reorder setting to multiselect + * read version info form CHANGELOG.md if git version info is not available + * link footer API to Wiki when API is not active + * persistent conds cache (opt-in optimization) + +### Extensions: + * After installing extensions, webui properly restarts the process rather than reloads the UI + * Added VAE listing to web API. Via: /sdapi/v1/sd-vae + * custom unet support + * Add onAfterUiUpdate callback + * refactor EmbeddingDatabase.register_embedding() to allow unregistering + * add before_process callback for scripts + * add ability for alwayson scripts to specify section and let user reorder those sections + +### Bug Fixes: + * Fix dragging text to prompt + * fix incorrect quoting for infotext values with colon in them + * fix "hires. fix" prompt sharing same labels with txt2img_prompt + * Fix s_min_uncond default type int + * Fix for #10643 (Inpainting mask sometimes not working) + * fix bad styling for thumbs view in extra networks #10639 + * fix for empty list of optimizations #10605 + * small fixes to prepare_tcmalloc for Debian/Ubuntu compatibility + * fix --ui-debug-mode exit + * patch GitPython to not use leaky persistent processes + * fix duplicate Cross attention optimization after UI reload + * torch.cuda.is_available() check for SdOptimizationXformers + * fix hires fix using wrong conds in second pass if using Loras. + * handle exception when parsing generation parameters from png info + * fix upcast attention dtype error + * forcing Torch Version to 1.13.1 for RX 5000 series GPUs + * split mask blur into X and Y components, patch Outpainting MK2 accordingly + * don't die when a LoRA is a broken symlink + * allow activation of Generate Forever during generation + + +## 1.3.2 + +### Bug Fixes: + * fix files served out of tmp directory even if they are saved to disk + * fix postprocessing overwriting parameters + +## 1.3.1 + +### Features: + * revert default cross attention optimization to Doggettx + +### Bug Fixes: + * fix bug: LoRA don't apply on dropdown list sd_lora + * fix png info always added even if setting is not enabled + * fix some fields not applying in xyz plot + * fix "hires. fix" prompt sharing same labels with txt2img_prompt + * fix lora hashes not being added properly to infotex if there is only one lora + * fix --use-cpu failing to work properly at startup + * make --disable-opt-split-attention command line option work again + +## 1.3.0 + +### Features: + * add UI to edit defaults + * token merging (via dbolya/tomesd) + * settings tab rework: add a lot of additional explanations and links + * load extensions' Git metadata in parallel to loading the main program to save a ton of time during startup + * update extensions table: show branch, show date in separate column, and show version from tags if available + * TAESD - another option for cheap live previews + * allow choosing sampler and prompts for second pass of hires fix - hidden by default, enabled in settings + * calculate hashes for Lora + * add lora hashes to infotext + * when pasting infotext, use infotext's lora hashes to find local loras for `` entries whose hashes match loras the user has + * select cross attention optimization from UI + +### Minor: + * bump Gradio to 3.31.0 + * bump PyTorch to 2.0.1 for macOS and Linux AMD + * allow setting defaults for elements in extensions' tabs + * allow selecting file type for live previews + * show "Loading..." for extra networks when displaying for the first time + * suppress ENSD infotext for samplers that don't use it + * clientside optimizations + * add options to show/hide hidden files and dirs in extra networks, and to not list models/files in hidden directories + * allow whitespace in styles.csv + * add option to reorder tabs + * move some functionality (swap resolution and set seed to -1) to client + * option to specify editor height for img2img + * button to copy image resolution into img2img width/height sliders + * switch from pyngrok to ngrok-py + * lazy-load images in extra networks UI + * set "Navigate image viewer with gamepad" option to false by default, by request + * change upscalers to download models into user-specified directory (from commandline args) rather than the default models/<...> + * allow hiding buttons in ui-config.json + +### Extensions: + * add /sdapi/v1/script-info api + * use Ruff to lint Python code + * use ESlint to lint Javascript code + * add/modify CFG callbacks for Self-Attention Guidance extension + * add command and endpoint for graceful server stopping + * add some locals (prompts/seeds/etc) from processing function into the Processing class as fields + * rework quoting for infotext items that have commas in them to use JSON (should be backwards compatible except for cases where it didn't work previously) + * add /sdapi/v1/refresh-loras api checkpoint post request + * tests overhaul + +### Bug Fixes: + * fix an issue preventing the program from starting if the user specifies a bad Gradio theme + * fix broken prompts from file script + * fix symlink scanning for extra networks + * fix --data-dir ignored when launching via webui-user.bat COMMANDLINE_ARGS + * allow web UI to be ran fully offline + * fix inability to run with --freeze-settings + * fix inability to merge checkpoint without adding metadata + * fix extra networks' save preview image not adding infotext for jpeg/webm + * remove blinking effect from text in hires fix and scale resolution preview + * make links to `http://<...>.git` extensions work in the extension tab + * fix bug with webui hanging at startup due to hanging git process + + +## 1.2.1 + +### Features: + * add an option to always refer to LoRA by filenames + +### Bug Fixes: + * never refer to LoRA by an alias if multiple LoRAs have same alias or the alias is called none + * fix upscalers disappearing after the user reloads UI + * allow bf16 in safe unpickler (resolves problems with loading some LoRAs) + * allow web UI to be ran fully offline + * fix localizations not working + * fix error for LoRAs: `'LatentDiffusion' object has no attribute 'lora_layer_mapping'` + +## 1.2.0 + +### Features: + * do not wait for Stable Diffusion model to load at startup + * add filename patterns: `[denoising]` + * directory hiding for extra networks: dirs starting with `.` will hide their cards on extra network tabs unless specifically searched for + * LoRA: for the `<...>` text in prompt, use name of LoRA that is in the metdata of the file, if present, instead of filename (both can be used to activate LoRA) + * LoRA: read infotext params from kohya-ss's extension parameters if they are present and if his extension is not active + * LoRA: fix some LoRAs not working (ones that have 3x3 convolution layer) + * LoRA: add an option to use old method of applying LoRAs (producing same results as with kohya-ss) + * add version to infotext, footer and console output when starting + * add links to wiki for filename pattern settings + * add extended info for quicksettings setting and use multiselect input instead of a text field + +### Minor: + * bump Gradio to 3.29.0 + * bump PyTorch to 2.0.1 + * `--subpath` option for gradio for use with reverse proxy + * Linux/macOS: use existing virtualenv if already active (the VIRTUAL_ENV environment variable) + * do not apply localizations if there are none (possible frontend optimization) + * add extra `None` option for VAE in XYZ plot + * print error to console when batch processing in img2img fails + * create HTML for extra network pages only on demand + * allow directories starting with `.` to still list their models for LoRA, checkpoints, etc + * put infotext options into their own category in settings tab + * do not show licenses page when user selects Show all pages in settings + +### Extensions: + * tooltip localization support + * add API method to get LoRA models with prompt + +### Bug Fixes: + * re-add `/docs` endpoint + * fix gamepad navigation + * make the lightbox fullscreen image function properly + * fix squished thumbnails in extras tab + * keep "search" filter for extra networks when user refreshes the tab (previously it showed everthing after you refreshed) + * fix webui showing the same image if you configure the generation to always save results into same file + * fix bug with upscalers not working properly + * fix MPS on PyTorch 2.0.1, Intel Macs + * make it so that custom context menu from contextMenu.js only disappears after user's click, ignoring non-user click events + * prevent Reload UI button/link from reloading the page when it's not yet ready + * fix prompts from file script failing to read contents from a drag/drop file + + +## 1.1.1 +### Bug Fixes: + * fix an error that prevents running webui on PyTorch<2.0 without --disable-safe-unpickle + +## 1.1.0 +### Features: + * switch to PyTorch 2.0.0 (except for AMD GPUs) + * visual improvements to custom code scripts + * add filename patterns: `[clip_skip]`, `[hasprompt<>]`, `[batch_number]`, `[generation_number]` + * add support for saving init images in img2img, and record their hashes in infotext for reproducability + * automatically select current word when adjusting weight with ctrl+up/down + * add dropdowns for X/Y/Z plot + * add setting: Stable Diffusion/Random number generator source: makes it possible to make images generated from a given manual seed consistent across different GPUs + * support Gradio's theme API + * use TCMalloc on Linux by default; possible fix for memory leaks + * add optimization option to remove negative conditioning at low sigma values #9177 + * embed model merge metadata in .safetensors file + * extension settings backup/restore feature #9169 + * add "resize by" and "resize to" tabs to img2img + * add option "keep original size" to textual inversion images preprocess + * image viewer scrolling via analog stick + * button to restore the progress from session lost / tab reload + +### Minor: + * bump Gradio to 3.28.1 + * change "scale to" to sliders in Extras tab + * add labels to tool buttons to make it possible to hide them + * add tiled inference support for ScuNET + * add branch support for extension installation + * change Linux installation script to install into current directory rather than `/home/username` + * sort textual inversion embeddings by name (case-insensitive) + * allow styles.csv to be symlinked or mounted in docker + * remove the "do not add watermark to images" option + * make selected tab configurable with UI config + * make the extra networks UI fixed height and scrollable + * add `disable_tls_verify` arg for use with self-signed certs + +### Extensions: + * add reload callback + * add `is_hr_pass` field for processing + +### Bug Fixes: + * fix broken batch image processing on 'Extras/Batch Process' tab + * add "None" option to extra networks dropdowns + * fix FileExistsError for CLIP Interrogator + * fix /sdapi/v1/txt2img endpoint not working on Linux #9319 + * fix disappearing live previews and progressbar during slow tasks + * fix fullscreen image view not working properly in some cases + * prevent alwayson_scripts args param resizing script_arg list when they are inserted in it + * fix prompt schedule for second order samplers + * fix image mask/composite for weird resolutions #9628 + * use correct images for previews when using AND (see #9491) + * one broken image in img2img batch won't stop all processing + * fix image orientation bug in train/preprocess + * fix Ngrok recreating tunnels every reload + * fix `--realesrgan-models-path` and `--ldsr-models-path` not working + * fix `--skip-install` not working + * use SAMPLE file format in Outpainting Mk2 & Poorman + * do not fail all LoRAs if some have failed to load when making a picture + +## 1.0.0 + * everything diff --git a/CITATION.cff b/CITATION.cff new file mode 100644 index 0000000000000000000000000000000000000000..2c781aff450c8604eb3cf876d2c3585a96a5a590 --- /dev/null +++ b/CITATION.cff @@ -0,0 +1,7 @@ +cff-version: 1.2.0 +message: "If you use this software, please cite it as below." +authors: + - given-names: AUTOMATIC1111 +title: "Stable Diffusion Web UI" +date-released: 2022-08-22 +url: "https://github.com/AUTOMATIC1111/stable-diffusion-webui" diff --git a/CODEOWNERS b/CODEOWNERS new file mode 100644 index 0000000000000000000000000000000000000000..2c937f6f1e519f864d15d5233e1fb86c6cdfac2f --- /dev/null +++ b/CODEOWNERS @@ -0,0 +1,12 @@ +* @AUTOMATIC1111 + +# if you were managing a localization and were removed from this file, this is because +# the intended way to do localizations now is via extensions. See: +# https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Developing-extensions +# Make a repo with your localization and since you are still listed as a collaborator +# you can add it to the wiki page yourself. This change is because some people complained +# the git commit log is cluttered with things unrelated to almost everyone and +# because I believe this is the best overall for the project to handle localizations almost +# entirely without my oversight. + + diff --git a/LICENSE.txt b/LICENSE.txt new file mode 100644 index 0000000000000000000000000000000000000000..211d32e752cb61bd056436e8f7a806f12a626bb7 --- /dev/null +++ b/LICENSE.txt @@ -0,0 +1,663 @@ + GNU AFFERO GENERAL PUBLIC LICENSE + Version 3, 19 November 2007 + + Copyright (c) 2023 AUTOMATIC1111 + + Copyright (C) 2007 Free Software Foundation, Inc. + Everyone is permitted to copy and distribute verbatim copies + of this license document, but changing it is not allowed. + + Preamble + + The GNU Affero General Public License is a free, copyleft license for +software and other kinds of works, specifically designed to ensure +cooperation with the community in the case of network server software. + + The licenses for most software and other practical works are designed +to take away your freedom to share and change the works. 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There are many ways you could offer source, and different +solutions will be better for different programs; see section 13 for the +specific requirements. + + You should also get your employer (if you work as a programmer) or school, +if any, to sign a "copyright disclaimer" for the program, if necessary. +For more information on this, and how to apply and follow the GNU AGPL, see +. diff --git a/README.md b/README.md new file mode 100644 index 0000000000000000000000000000000000000000..329a86e2a4eba6b3f1ebf14181e09bac2af7ff59 --- /dev/null +++ b/README.md @@ -0,0 +1,181 @@ +# Stable Diffusion web UI +A browser interface based on Gradio library for Stable Diffusion. + +![](screenshot.png) + +## Features +[Detailed feature showcase with images](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features): +- Original txt2img and img2img modes +- One click install and run script (but you still must install python and git) +- Outpainting +- Inpainting +- Color Sketch +- Prompt Matrix +- Stable Diffusion Upscale +- Attention, specify parts of text that the model should pay more attention to + - a man in a `((tuxedo))` - will pay more attention to tuxedo + - a man in a `(tuxedo:1.21)` - alternative syntax + - select text and press `Ctrl+Up` or `Ctrl+Down` (or `Command+Up` or `Command+Down` if you're on a MacOS) to automatically adjust attention to selected text (code contributed by anonymous user) +- Loopback, run img2img processing multiple times +- X/Y/Z plot, a way to draw a 3 dimensional plot of images with different parameters +- Textual Inversion + - have as many embeddings as you want and use any names you like for them + - use multiple embeddings with different numbers of vectors per token + - works with half precision floating point numbers + - train embeddings on 8GB (also reports of 6GB working) +- Extras tab with: + - GFPGAN, neural network that fixes faces + - CodeFormer, face restoration tool as an alternative to GFPGAN + - RealESRGAN, neural network upscaler + - ESRGAN, neural network upscaler with a lot of third party models + - SwinIR and Swin2SR ([see here](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/2092)), neural network upscalers + - LDSR, Latent diffusion super resolution upscaling +- Resizing aspect ratio options +- Sampling method selection + - Adjust sampler eta values (noise multiplier) + - More advanced noise setting options +- Interrupt processing at any time +- 4GB video card support (also reports of 2GB working) +- Correct seeds for batches +- Live prompt token length validation +- Generation parameters + - parameters you used to generate images are saved with that image + - in PNG chunks for PNG, in EXIF for JPEG + - can drag the image to PNG info tab to restore generation parameters and automatically copy them into UI + - can be disabled in settings + - drag and drop an image/text-parameters to promptbox +- Read Generation Parameters Button, loads parameters in promptbox to UI +- Settings page +- Running arbitrary python code from UI (must run with `--allow-code` to enable) +- Mouseover hints for most UI elements +- Possible to change defaults/mix/max/step values for UI elements via text config +- Tiling support, a checkbox to create images that can be tiled like textures +- Progress bar and live image generation preview + - Can use a separate neural network to produce previews with almost none VRAM or compute requirement +- Negative prompt, an extra text field that allows you to list what you don't want to see in generated image +- Styles, a way to save part of prompt and easily apply them via dropdown later +- Variations, a way to generate same image but with tiny differences +- Seed resizing, a way to generate same image but at slightly different resolution +- CLIP interrogator, a button that tries to guess prompt from an image +- Prompt Editing, a way to change prompt mid-generation, say to start making a watermelon and switch to anime girl midway +- Batch Processing, process a group of files using img2img +- Img2img Alternative, reverse Euler method of cross attention control +- Highres Fix, a convenience option to produce high resolution pictures in one click without usual distortions +- Reloading checkpoints on the fly +- Checkpoint Merger, a tab that allows you to merge up to 3 checkpoints into one +- [Custom scripts](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Custom-Scripts) with many extensions from community +- [Composable-Diffusion](https://energy-based-model.github.io/Compositional-Visual-Generation-with-Composable-Diffusion-Models/), a way to use multiple prompts at once + - separate prompts using uppercase `AND` + - also supports weights for prompts: `a cat :1.2 AND a dog AND a penguin :2.2` +- No token limit for prompts (original stable diffusion lets you use up to 75 tokens) +- DeepDanbooru integration, creates danbooru style tags for anime prompts +- [xformers](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Xformers), major speed increase for select cards: (add `--xformers` to commandline args) +- via extension: [History tab](https://github.com/yfszzx/stable-diffusion-webui-images-browser): view, direct and delete images conveniently within the UI +- Generate forever option +- Training tab + - hypernetworks and embeddings options + - Preprocessing images: cropping, mirroring, autotagging using BLIP or deepdanbooru (for anime) +- Clip skip +- Hypernetworks +- Loras (same as Hypernetworks but more pretty) +- A separate UI where you can choose, with preview, which embeddings, hypernetworks or Loras to add to your prompt +- Can select to load a different VAE from settings screen +- Estimated completion time in progress bar +- API +- Support for dedicated [inpainting model](https://github.com/runwayml/stable-diffusion#inpainting-with-stable-diffusion) by RunwayML +- via extension: [Aesthetic Gradients](https://github.com/AUTOMATIC1111/stable-diffusion-webui-aesthetic-gradients), a way to generate images with a specific aesthetic by using clip images embeds (implementation of [https://github.com/vicgalle/stable-diffusion-aesthetic-gradients](https://github.com/vicgalle/stable-diffusion-aesthetic-gradients)) +- [Stable Diffusion 2.0](https://github.com/Stability-AI/stablediffusion) support - see [wiki](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features#stable-diffusion-20) for instructions +- [Alt-Diffusion](https://arxiv.org/abs/2211.06679) support - see [wiki](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features#alt-diffusion) for instructions +- Now without any bad letters! +- Load checkpoints in safetensors format +- Eased resolution restriction: generated image's dimensions must be a multiple of 8 rather than 64 +- Now with a license! +- Reorder elements in the UI from settings screen +- [Segmind Stable Diffusion](https://huggingface.co/segmind/SSD-1B) support + +## Installation and Running +Make sure the required [dependencies](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Dependencies) are met and follow the instructions available for: +- [NVidia](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-NVidia-GPUs) (recommended) +- [AMD](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-AMD-GPUs) GPUs. +- [Intel CPUs, Intel GPUs (both integrated and discrete)](https://github.com/openvinotoolkit/stable-diffusion-webui/wiki/Installation-on-Intel-Silicon) (external wiki page) + +Alternatively, use online services (like Google Colab): + +- [List of Online Services](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Online-Services) + +### Installation on Windows 10/11 with NVidia-GPUs using release package +1. Download `sd.webui.zip` from [v1.0.0-pre](https://github.com/AUTOMATIC1111/stable-diffusion-webui/releases/tag/v1.0.0-pre) and extract its contents. +2. Run `update.bat`. +3. Run `run.bat`. +> For more details see [Install-and-Run-on-NVidia-GPUs](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-NVidia-GPUs) + +### Automatic Installation on Windows +1. Install [Python 3.10.6](https://www.python.org/downloads/release/python-3106/) (Newer version of Python does not support torch), checking "Add Python to PATH". +2. Install [git](https://git-scm.com/download/win). +3. Download the stable-diffusion-webui repository, for example by running `git clone https://github.com/AUTOMATIC1111/stable-diffusion-webui.git`. +4. Run `webui-user.bat` from Windows Explorer as normal, non-administrator, user. + +### Automatic Installation on Linux +1. Install the dependencies: +```bash +# Debian-based: +sudo apt install wget git python3 python3-venv libgl1 libglib2.0-0 +# Red Hat-based: +sudo dnf install wget git python3 gperftools-libs libglvnd-glx +# openSUSE-based: +sudo zypper install wget git python3 libtcmalloc4 libglvnd +# Arch-based: +sudo pacman -S wget git python3 +``` +2. Navigate to the directory you would like the webui to be installed and execute the following command: +```bash +wget -q https://raw.githubusercontent.com/AUTOMATIC1111/stable-diffusion-webui/master/webui.sh +``` +3. Run `webui.sh`. +4. Check `webui-user.sh` for options. +### Installation on Apple Silicon + +Find the instructions [here](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Installation-on-Apple-Silicon). + +## Contributing +Here's how to add code to this repo: [Contributing](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Contributing) + +## Documentation + +The documentation was moved from this README over to the project's [wiki](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki). + +For the purposes of getting Google and other search engines to crawl the wiki, here's a link to the (not for humans) [crawlable wiki](https://github-wiki-see.page/m/AUTOMATIC1111/stable-diffusion-webui/wiki). + +## Credits +Licenses for borrowed code can be found in `Settings -> Licenses` screen, and also in `html/licenses.html` file. + +- Stable Diffusion - https://github.com/Stability-AI/stablediffusion, https://github.com/CompVis/taming-transformers +- k-diffusion - https://github.com/crowsonkb/k-diffusion.git +- GFPGAN - https://github.com/TencentARC/GFPGAN.git +- CodeFormer - https://github.com/sczhou/CodeFormer +- ESRGAN - https://github.com/xinntao/ESRGAN +- SwinIR - https://github.com/JingyunLiang/SwinIR +- Swin2SR - https://github.com/mv-lab/swin2sr +- LDSR - https://github.com/Hafiidz/latent-diffusion +- MiDaS - https://github.com/isl-org/MiDaS +- Ideas for optimizations - https://github.com/basujindal/stable-diffusion +- Cross Attention layer optimization - Doggettx - https://github.com/Doggettx/stable-diffusion, original idea for prompt editing. +- Cross Attention layer optimization - InvokeAI, lstein - https://github.com/invoke-ai/InvokeAI (originally http://github.com/lstein/stable-diffusion) +- Sub-quadratic Cross Attention layer optimization - Alex Birch (https://github.com/Birch-san/diffusers/pull/1), Amin Rezaei (https://github.com/AminRezaei0x443/memory-efficient-attention) +- Textual Inversion - Rinon Gal - https://github.com/rinongal/textual_inversion (we're not using his code, but we are using his ideas). +- Idea for SD upscale - https://github.com/jquesnelle/txt2imghd +- Noise generation for outpainting mk2 - https://github.com/parlance-zz/g-diffuser-bot +- CLIP interrogator idea and borrowing some code - https://github.com/pharmapsychotic/clip-interrogator +- Idea for Composable Diffusion - https://github.com/energy-based-model/Compositional-Visual-Generation-with-Composable-Diffusion-Models-PyTorch +- xformers - https://github.com/facebookresearch/xformers +- DeepDanbooru - interrogator for anime diffusers https://github.com/KichangKim/DeepDanbooru +- Sampling in float32 precision from a float16 UNet - marunine for the idea, Birch-san for the example Diffusers implementation (https://github.com/Birch-san/diffusers-play/tree/92feee6) +- Instruct pix2pix - Tim Brooks (star), Aleksander Holynski (star), Alexei A. Efros (no star) - https://github.com/timothybrooks/instruct-pix2pix +- Security advice - RyotaK +- UniPC sampler - Wenliang Zhao - https://github.com/wl-zhao/UniPC +- TAESD - Ollin Boer Bohan - https://github.com/madebyollin/taesd +- LyCORIS - KohakuBlueleaf +- Restart sampling - lambertae - https://github.com/Newbeeer/diffusion_restart_sampling +- Hypertile - tfernd - https://github.com/tfernd/HyperTile +- Initial Gradio script - posted on 4chan by an Anonymous user. Thank you Anonymous user. +- (You) diff --git a/configs/alt-diffusion-inference.yaml b/configs/alt-diffusion-inference.yaml new file mode 100644 index 0000000000000000000000000000000000000000..cfbee72d71bfd7deed2075e423ca51bd1da0521c --- /dev/null +++ b/configs/alt-diffusion-inference.yaml @@ -0,0 +1,72 @@ +model: + base_learning_rate: 1.0e-04 + target: ldm.models.diffusion.ddpm.LatentDiffusion + params: + linear_start: 0.00085 + linear_end: 0.0120 + num_timesteps_cond: 1 + log_every_t: 200 + timesteps: 1000 + first_stage_key: "jpg" + cond_stage_key: "txt" + image_size: 64 + channels: 4 + cond_stage_trainable: false # Note: different from the one we trained before + conditioning_key: crossattn + monitor: val/loss_simple_ema + scale_factor: 0.18215 + use_ema: False + + scheduler_config: # 10000 warmup steps + target: ldm.lr_scheduler.LambdaLinearScheduler + params: + warm_up_steps: [ 10000 ] + cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases + f_start: [ 1.e-6 ] + f_max: [ 1. ] + f_min: [ 1. ] + + unet_config: + target: ldm.modules.diffusionmodules.openaimodel.UNetModel + params: + image_size: 32 # unused + in_channels: 4 + out_channels: 4 + model_channels: 320 + attention_resolutions: [ 4, 2, 1 ] + num_res_blocks: 2 + channel_mult: [ 1, 2, 4, 4 ] + num_heads: 8 + use_spatial_transformer: True + transformer_depth: 1 + context_dim: 768 + use_checkpoint: True + legacy: False + + first_stage_config: + target: ldm.models.autoencoder.AutoencoderKL + params: + embed_dim: 4 + monitor: val/rec_loss + ddconfig: + double_z: true + z_channels: 4 + resolution: 256 + in_channels: 3 + out_ch: 3 + ch: 128 + ch_mult: + - 1 + - 2 + - 4 + - 4 + num_res_blocks: 2 + attn_resolutions: [] + dropout: 0.0 + lossconfig: + target: torch.nn.Identity + + cond_stage_config: + target: modules.xlmr.BertSeriesModelWithTransformation + params: + name: "XLMR-Large" \ No newline at end of file diff --git a/configs/alt-diffusion-m18-inference.yaml b/configs/alt-diffusion-m18-inference.yaml new file mode 100644 index 0000000000000000000000000000000000000000..41a031d55f03b9946b543e930b881017c7e1cca6 --- /dev/null +++ b/configs/alt-diffusion-m18-inference.yaml @@ -0,0 +1,73 @@ +model: + base_learning_rate: 1.0e-04 + target: ldm.models.diffusion.ddpm.LatentDiffusion + params: + linear_start: 0.00085 + linear_end: 0.0120 + num_timesteps_cond: 1 + log_every_t: 200 + timesteps: 1000 + first_stage_key: "jpg" + cond_stage_key: "txt" + image_size: 64 + channels: 4 + cond_stage_trainable: false # Note: different from the one we trained before + conditioning_key: crossattn + monitor: val/loss_simple_ema + scale_factor: 0.18215 + use_ema: False + + scheduler_config: # 10000 warmup steps + target: ldm.lr_scheduler.LambdaLinearScheduler + params: + warm_up_steps: [ 10000 ] + cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases + f_start: [ 1.e-6 ] + f_max: [ 1. ] + f_min: [ 1. ] + + unet_config: + target: ldm.modules.diffusionmodules.openaimodel.UNetModel + params: + image_size: 32 # unused + in_channels: 4 + out_channels: 4 + model_channels: 320 + attention_resolutions: [ 4, 2, 1 ] + num_res_blocks: 2 + channel_mult: [ 1, 2, 4, 4 ] + num_head_channels: 64 + use_spatial_transformer: True + use_linear_in_transformer: True + transformer_depth: 1 + context_dim: 1024 + use_checkpoint: True + legacy: False + + first_stage_config: + target: ldm.models.autoencoder.AutoencoderKL + params: + embed_dim: 4 + monitor: val/rec_loss + ddconfig: + double_z: true + z_channels: 4 + resolution: 256 + in_channels: 3 + out_ch: 3 + ch: 128 + ch_mult: + - 1 + - 2 + - 4 + - 4 + num_res_blocks: 2 + attn_resolutions: [] + dropout: 0.0 + lossconfig: + target: torch.nn.Identity + + cond_stage_config: + target: modules.xlmr_m18.BertSeriesModelWithTransformation + params: + name: "XLMR-Large" diff --git a/configs/instruct-pix2pix.yaml b/configs/instruct-pix2pix.yaml new file mode 100644 index 0000000000000000000000000000000000000000..4e896879dd7ac5697b89cb323ec43eb41c03596c --- /dev/null +++ b/configs/instruct-pix2pix.yaml @@ -0,0 +1,98 @@ +# File modified by authors of InstructPix2Pix from original (https://github.com/CompVis/stable-diffusion). +# See more details in LICENSE. + +model: + base_learning_rate: 1.0e-04 + target: modules.models.diffusion.ddpm_edit.LatentDiffusion + params: + linear_start: 0.00085 + linear_end: 0.0120 + num_timesteps_cond: 1 + log_every_t: 200 + timesteps: 1000 + first_stage_key: edited + cond_stage_key: edit + # image_size: 64 + # image_size: 32 + image_size: 16 + channels: 4 + cond_stage_trainable: false # Note: different from the one we trained before + conditioning_key: hybrid + monitor: val/loss_simple_ema + scale_factor: 0.18215 + use_ema: false + + scheduler_config: # 10000 warmup steps + target: ldm.lr_scheduler.LambdaLinearScheduler + params: + warm_up_steps: [ 0 ] + cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases + f_start: [ 1.e-6 ] + f_max: [ 1. ] + f_min: [ 1. ] + + unet_config: + target: ldm.modules.diffusionmodules.openaimodel.UNetModel + params: + image_size: 32 # unused + in_channels: 8 + out_channels: 4 + model_channels: 320 + attention_resolutions: [ 4, 2, 1 ] + num_res_blocks: 2 + channel_mult: [ 1, 2, 4, 4 ] + num_heads: 8 + use_spatial_transformer: True + transformer_depth: 1 + context_dim: 768 + use_checkpoint: True + legacy: False + + first_stage_config: + target: ldm.models.autoencoder.AutoencoderKL + params: + embed_dim: 4 + monitor: val/rec_loss + ddconfig: + double_z: true + z_channels: 4 + resolution: 256 + in_channels: 3 + out_ch: 3 + ch: 128 + ch_mult: + - 1 + - 2 + - 4 + - 4 + num_res_blocks: 2 + attn_resolutions: [] + dropout: 0.0 + lossconfig: + target: torch.nn.Identity + + cond_stage_config: + target: ldm.modules.encoders.modules.FrozenCLIPEmbedder + +data: + target: main.DataModuleFromConfig + params: + batch_size: 128 + num_workers: 1 + wrap: false + validation: + target: edit_dataset.EditDataset + params: + path: data/clip-filtered-dataset + cache_dir: data/ + cache_name: data_10k + split: val + min_text_sim: 0.2 + min_image_sim: 0.75 + min_direction_sim: 0.2 + max_samples_per_prompt: 1 + min_resize_res: 512 + max_resize_res: 512 + crop_res: 512 + output_as_edit: False + real_input: True diff --git a/configs/v1-inference.yaml b/configs/v1-inference.yaml new file mode 100644 index 0000000000000000000000000000000000000000..d4effe569e897369918625f9d8be5603a0e6a0d6 --- /dev/null +++ b/configs/v1-inference.yaml @@ -0,0 +1,70 @@ +model: + base_learning_rate: 1.0e-04 + target: ldm.models.diffusion.ddpm.LatentDiffusion + params: + linear_start: 0.00085 + linear_end: 0.0120 + num_timesteps_cond: 1 + log_every_t: 200 + timesteps: 1000 + first_stage_key: "jpg" + cond_stage_key: "txt" + image_size: 64 + channels: 4 + cond_stage_trainable: false # Note: different from the one we trained before + conditioning_key: crossattn + monitor: val/loss_simple_ema + scale_factor: 0.18215 + use_ema: False + + scheduler_config: # 10000 warmup steps + target: ldm.lr_scheduler.LambdaLinearScheduler + params: + warm_up_steps: [ 10000 ] + cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases + f_start: [ 1.e-6 ] + f_max: [ 1. ] + f_min: [ 1. ] + + unet_config: + target: ldm.modules.diffusionmodules.openaimodel.UNetModel + params: + image_size: 32 # unused + in_channels: 4 + out_channels: 4 + model_channels: 320 + attention_resolutions: [ 4, 2, 1 ] + num_res_blocks: 2 + channel_mult: [ 1, 2, 4, 4 ] + num_heads: 8 + use_spatial_transformer: True + transformer_depth: 1 + context_dim: 768 + use_checkpoint: True + legacy: False + + first_stage_config: + target: ldm.models.autoencoder.AutoencoderKL + params: + embed_dim: 4 + monitor: val/rec_loss + ddconfig: + double_z: true + z_channels: 4 + resolution: 256 + in_channels: 3 + out_ch: 3 + ch: 128 + ch_mult: + - 1 + - 2 + - 4 + - 4 + num_res_blocks: 2 + attn_resolutions: [] + dropout: 0.0 + lossconfig: + target: torch.nn.Identity + + cond_stage_config: + target: ldm.modules.encoders.modules.FrozenCLIPEmbedder diff --git a/configs/v1-inpainting-inference.yaml b/configs/v1-inpainting-inference.yaml new file mode 100644 index 0000000000000000000000000000000000000000..f9eec37d24bce33ce92320a782d16ae72308190a --- /dev/null +++ b/configs/v1-inpainting-inference.yaml @@ -0,0 +1,70 @@ +model: + base_learning_rate: 7.5e-05 + target: ldm.models.diffusion.ddpm.LatentInpaintDiffusion + params: + linear_start: 0.00085 + linear_end: 0.0120 + num_timesteps_cond: 1 + log_every_t: 200 + timesteps: 1000 + first_stage_key: "jpg" + cond_stage_key: "txt" + image_size: 64 + channels: 4 + cond_stage_trainable: false # Note: different from the one we trained before + conditioning_key: hybrid # important + monitor: val/loss_simple_ema + scale_factor: 0.18215 + finetune_keys: null + + scheduler_config: # 10000 warmup steps + target: ldm.lr_scheduler.LambdaLinearScheduler + params: + warm_up_steps: [ 2500 ] # NOTE for resuming. use 10000 if starting from scratch + cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases + f_start: [ 1.e-6 ] + f_max: [ 1. ] + f_min: [ 1. ] + + unet_config: + target: ldm.modules.diffusionmodules.openaimodel.UNetModel + params: + image_size: 32 # unused + in_channels: 9 # 4 data + 4 downscaled image + 1 mask + out_channels: 4 + model_channels: 320 + attention_resolutions: [ 4, 2, 1 ] + num_res_blocks: 2 + channel_mult: [ 1, 2, 4, 4 ] + num_heads: 8 + use_spatial_transformer: True + transformer_depth: 1 + context_dim: 768 + use_checkpoint: True + legacy: False + + first_stage_config: + target: ldm.models.autoencoder.AutoencoderKL + params: + embed_dim: 4 + monitor: val/rec_loss + ddconfig: + double_z: true + z_channels: 4 + resolution: 256 + in_channels: 3 + out_ch: 3 + ch: 128 + ch_mult: + - 1 + - 2 + - 4 + - 4 + num_res_blocks: 2 + attn_resolutions: [] + dropout: 0.0 + lossconfig: + target: torch.nn.Identity + + cond_stage_config: + target: ldm.modules.encoders.modules.FrozenCLIPEmbedder diff --git a/embeddings/Place Textual Inversion embeddings here.txt b/embeddings/Place Textual Inversion embeddings here.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/environment-wsl2.yaml b/environment-wsl2.yaml new file mode 100644 index 0000000000000000000000000000000000000000..0c4ae6809997ec38e7cf62cf0f71360b8cb61a7e --- /dev/null +++ b/environment-wsl2.yaml @@ -0,0 +1,11 @@ +name: automatic +channels: + - pytorch + - defaults +dependencies: + - python=3.10 + - pip=23.0 + - cudatoolkit=11.8 + - pytorch=2.0 + - torchvision=0.15 + - numpy=1.23 diff --git a/extensions-builtin/LDSR/ldsr_model_arch.py b/extensions-builtin/LDSR/ldsr_model_arch.py new file mode 100644 index 0000000000000000000000000000000000000000..7cac36ce55ae295c6d0e444a93ea12bf8cfe893c --- /dev/null +++ b/extensions-builtin/LDSR/ldsr_model_arch.py @@ -0,0 +1,250 @@ +import os +import gc +import time + +import numpy as np +import torch +import torchvision +from PIL import Image +from einops import rearrange, repeat +from omegaconf import OmegaConf +import safetensors.torch + +from ldm.models.diffusion.ddim import DDIMSampler +from ldm.util import instantiate_from_config, ismap +from modules import shared, sd_hijack, devices + +cached_ldsr_model: torch.nn.Module = None + + +# Create LDSR Class +class LDSR: + def load_model_from_config(self, half_attention): + global cached_ldsr_model + + if shared.opts.ldsr_cached and cached_ldsr_model is not None: + print("Loading model from cache") + model: torch.nn.Module = cached_ldsr_model + else: + print(f"Loading model from {self.modelPath}") + _, extension = os.path.splitext(self.modelPath) + if extension.lower() == ".safetensors": + pl_sd = safetensors.torch.load_file(self.modelPath, device="cpu") + else: + pl_sd = torch.load(self.modelPath, map_location="cpu") + sd = pl_sd["state_dict"] if "state_dict" in pl_sd else pl_sd + config = OmegaConf.load(self.yamlPath) + config.model.target = "ldm.models.diffusion.ddpm.LatentDiffusionV1" + model: torch.nn.Module = instantiate_from_config(config.model) + model.load_state_dict(sd, strict=False) + model = model.to(shared.device) + if half_attention: + model = model.half() + if shared.cmd_opts.opt_channelslast: + model = model.to(memory_format=torch.channels_last) + + sd_hijack.model_hijack.hijack(model) # apply optimization + model.eval() + + if shared.opts.ldsr_cached: + cached_ldsr_model = model + + return {"model": model} + + def __init__(self, model_path, yaml_path): + self.modelPath = model_path + self.yamlPath = yaml_path + + @staticmethod + def run(model, selected_path, custom_steps, eta): + example = get_cond(selected_path) + + n_runs = 1 + guider = None + ckwargs = None + ddim_use_x0_pred = False + temperature = 1. + eta = eta + custom_shape = None + + height, width = example["image"].shape[1:3] + split_input = height >= 128 and width >= 128 + + if split_input: + ks = 128 + stride = 64 + vqf = 4 # + model.split_input_params = {"ks": (ks, ks), "stride": (stride, stride), + "vqf": vqf, + "patch_distributed_vq": True, + "tie_braker": False, + "clip_max_weight": 0.5, + "clip_min_weight": 0.01, + "clip_max_tie_weight": 0.5, + "clip_min_tie_weight": 0.01} + else: + if hasattr(model, "split_input_params"): + delattr(model, "split_input_params") + + x_t = None + logs = None + for _ in range(n_runs): + if custom_shape is not None: + x_t = torch.randn(1, custom_shape[1], custom_shape[2], custom_shape[3]).to(model.device) + x_t = repeat(x_t, '1 c h w -> b c h w', b=custom_shape[0]) + + logs = make_convolutional_sample(example, model, + custom_steps=custom_steps, + eta=eta, quantize_x0=False, + custom_shape=custom_shape, + temperature=temperature, noise_dropout=0., + corrector=guider, corrector_kwargs=ckwargs, x_T=x_t, + ddim_use_x0_pred=ddim_use_x0_pred + ) + return logs + + def super_resolution(self, image, steps=100, target_scale=2, half_attention=False): + model = self.load_model_from_config(half_attention) + + # Run settings + diffusion_steps = int(steps) + eta = 1.0 + + + gc.collect() + devices.torch_gc() + + im_og = image + width_og, height_og = im_og.size + # If we can adjust the max upscale size, then the 4 below should be our variable + down_sample_rate = target_scale / 4 + wd = width_og * down_sample_rate + hd = height_og * down_sample_rate + width_downsampled_pre = int(np.ceil(wd)) + height_downsampled_pre = int(np.ceil(hd)) + + if down_sample_rate != 1: + print( + f'Downsampling from [{width_og}, {height_og}] to [{width_downsampled_pre}, {height_downsampled_pre}]') + im_og = im_og.resize((width_downsampled_pre, height_downsampled_pre), Image.LANCZOS) + else: + print(f"Down sample rate is 1 from {target_scale} / 4 (Not downsampling)") + + # pad width and height to multiples of 64, pads with the edge values of image to avoid artifacts + pad_w, pad_h = np.max(((2, 2), np.ceil(np.array(im_og.size) / 64).astype(int)), axis=0) * 64 - im_og.size + im_padded = Image.fromarray(np.pad(np.array(im_og), ((0, pad_h), (0, pad_w), (0, 0)), mode='edge')) + + logs = self.run(model["model"], im_padded, diffusion_steps, eta) + + sample = logs["sample"] + sample = sample.detach().cpu() + sample = torch.clamp(sample, -1., 1.) + sample = (sample + 1.) / 2. * 255 + sample = sample.numpy().astype(np.uint8) + sample = np.transpose(sample, (0, 2, 3, 1)) + a = Image.fromarray(sample[0]) + + # remove padding + a = a.crop((0, 0) + tuple(np.array(im_og.size) * 4)) + + del model + gc.collect() + devices.torch_gc() + + return a + + +def get_cond(selected_path): + example = {} + up_f = 4 + c = selected_path.convert('RGB') + c = torch.unsqueeze(torchvision.transforms.ToTensor()(c), 0) + c_up = torchvision.transforms.functional.resize(c, size=[up_f * c.shape[2], up_f * c.shape[3]], + antialias=True) + c_up = rearrange(c_up, '1 c h w -> 1 h w c') + c = rearrange(c, '1 c h w -> 1 h w c') + c = 2. * c - 1. + + c = c.to(shared.device) + example["LR_image"] = c + example["image"] = c_up + + return example + + +@torch.no_grad() +def convsample_ddim(model, cond, steps, shape, eta=1.0, callback=None, normals_sequence=None, + mask=None, x0=None, quantize_x0=False, temperature=1., score_corrector=None, + corrector_kwargs=None, x_t=None + ): + ddim = DDIMSampler(model) + bs = shape[0] + shape = shape[1:] + print(f"Sampling with eta = {eta}; steps: {steps}") + samples, intermediates = ddim.sample(steps, batch_size=bs, shape=shape, conditioning=cond, callback=callback, + normals_sequence=normals_sequence, quantize_x0=quantize_x0, eta=eta, + mask=mask, x0=x0, temperature=temperature, verbose=False, + score_corrector=score_corrector, + corrector_kwargs=corrector_kwargs, x_t=x_t) + + return samples, intermediates + + +@torch.no_grad() +def make_convolutional_sample(batch, model, custom_steps=None, eta=1.0, quantize_x0=False, custom_shape=None, temperature=1., noise_dropout=0., corrector=None, + corrector_kwargs=None, x_T=None, ddim_use_x0_pred=False): + log = {} + + z, c, x, xrec, xc = model.get_input(batch, model.first_stage_key, + return_first_stage_outputs=True, + force_c_encode=not (hasattr(model, 'split_input_params') + and model.cond_stage_key == 'coordinates_bbox'), + return_original_cond=True) + + if custom_shape is not None: + z = torch.randn(custom_shape) + print(f"Generating {custom_shape[0]} samples of shape {custom_shape[1:]}") + + z0 = None + + log["input"] = x + log["reconstruction"] = xrec + + if ismap(xc): + log["original_conditioning"] = model.to_rgb(xc) + if hasattr(model, 'cond_stage_key'): + log[model.cond_stage_key] = model.to_rgb(xc) + + else: + log["original_conditioning"] = xc if xc is not None else torch.zeros_like(x) + if model.cond_stage_model: + log[model.cond_stage_key] = xc if xc is not None else torch.zeros_like(x) + if model.cond_stage_key == 'class_label': + log[model.cond_stage_key] = xc[model.cond_stage_key] + + with model.ema_scope("Plotting"): + t0 = time.time() + + sample, intermediates = convsample_ddim(model, c, steps=custom_steps, shape=z.shape, + eta=eta, + quantize_x0=quantize_x0, mask=None, x0=z0, + temperature=temperature, score_corrector=corrector, corrector_kwargs=corrector_kwargs, + x_t=x_T) + t1 = time.time() + + if ddim_use_x0_pred: + sample = intermediates['pred_x0'][-1] + + x_sample = model.decode_first_stage(sample) + + try: + x_sample_noquant = model.decode_first_stage(sample, force_not_quantize=True) + log["sample_noquant"] = x_sample_noquant + log["sample_diff"] = torch.abs(x_sample_noquant - x_sample) + except Exception: + pass + + log["sample"] = x_sample + log["time"] = t1 - t0 + + return log diff --git a/extensions-builtin/LDSR/preload.py b/extensions-builtin/LDSR/preload.py new file mode 100644 index 0000000000000000000000000000000000000000..cfd478d545ed12ef74e73fa40b6defe0156859da --- /dev/null +++ b/extensions-builtin/LDSR/preload.py @@ -0,0 +1,6 @@ +import os +from modules import paths + + +def preload(parser): + parser.add_argument("--ldsr-models-path", type=str, help="Path to directory with LDSR model file(s).", default=os.path.join(paths.models_path, 'LDSR')) diff --git a/extensions-builtin/LDSR/scripts/ldsr_model.py b/extensions-builtin/LDSR/scripts/ldsr_model.py new file mode 100644 index 0000000000000000000000000000000000000000..bd78decea1ec9fc66d61d66ee64457458a290f72 --- /dev/null +++ b/extensions-builtin/LDSR/scripts/ldsr_model.py @@ -0,0 +1,68 @@ +import os + +from modules.modelloader import load_file_from_url +from modules.upscaler import Upscaler, UpscalerData +from ldsr_model_arch import LDSR +from modules import shared, script_callbacks, errors +import sd_hijack_autoencoder # noqa: F401 +import sd_hijack_ddpm_v1 # noqa: F401 + + +class UpscalerLDSR(Upscaler): + def __init__(self, user_path): + self.name = "LDSR" + self.user_path = user_path + self.model_url = "https://heibox.uni-heidelberg.de/f/578df07c8fc04ffbadf3/?dl=1" + self.yaml_url = "https://heibox.uni-heidelberg.de/f/31a76b13ea27482981b4/?dl=1" + super().__init__() + scaler_data = UpscalerData("LDSR", None, self) + self.scalers = [scaler_data] + + def load_model(self, path: str): + # Remove incorrect project.yaml file if too big + yaml_path = os.path.join(self.model_path, "project.yaml") + old_model_path = os.path.join(self.model_path, "model.pth") + new_model_path = os.path.join(self.model_path, "model.ckpt") + + local_model_paths = self.find_models(ext_filter=[".ckpt", ".safetensors"]) + local_ckpt_path = next(iter([local_model for local_model in local_model_paths if local_model.endswith("model.ckpt")]), None) + local_safetensors_path = next(iter([local_model for local_model in local_model_paths if local_model.endswith("model.safetensors")]), None) + local_yaml_path = next(iter([local_model for local_model in local_model_paths if local_model.endswith("project.yaml")]), None) + + if os.path.exists(yaml_path): + statinfo = os.stat(yaml_path) + if statinfo.st_size >= 10485760: + print("Removing invalid LDSR YAML file.") + os.remove(yaml_path) + + if os.path.exists(old_model_path): + print("Renaming model from model.pth to model.ckpt") + os.rename(old_model_path, new_model_path) + + if local_safetensors_path is not None and os.path.exists(local_safetensors_path): + model = local_safetensors_path + else: + model = local_ckpt_path or load_file_from_url(self.model_url, model_dir=self.model_download_path, file_name="model.ckpt") + + yaml = local_yaml_path or load_file_from_url(self.yaml_url, model_dir=self.model_download_path, file_name="project.yaml") + + return LDSR(model, yaml) + + def do_upscale(self, img, path): + try: + ldsr = self.load_model(path) + except Exception: + errors.report(f"Failed loading LDSR model {path}", exc_info=True) + return img + ddim_steps = shared.opts.ldsr_steps + return ldsr.super_resolution(img, ddim_steps, self.scale) + + +def on_ui_settings(): + import gradio as gr + + shared.opts.add_option("ldsr_steps", shared.OptionInfo(100, "LDSR processing steps. Lower = faster", gr.Slider, {"minimum": 1, "maximum": 200, "step": 1}, section=('upscaling', "Upscaling"))) + shared.opts.add_option("ldsr_cached", shared.OptionInfo(False, "Cache LDSR model in memory", gr.Checkbox, {"interactive": True}, section=('upscaling', "Upscaling"))) + + +script_callbacks.on_ui_settings(on_ui_settings) diff --git a/extensions-builtin/LDSR/sd_hijack_autoencoder.py b/extensions-builtin/LDSR/sd_hijack_autoencoder.py new file mode 100644 index 0000000000000000000000000000000000000000..c29d274da825d2500b77a2022db3421b40b18886 --- /dev/null +++ b/extensions-builtin/LDSR/sd_hijack_autoencoder.py @@ -0,0 +1,293 @@ +# The content of this file comes from the ldm/models/autoencoder.py file of the compvis/stable-diffusion repo +# The VQModel & VQModelInterface were subsequently removed from ldm/models/autoencoder.py when we moved to the stability-ai/stablediffusion repo +# As the LDSR upscaler relies on VQModel & VQModelInterface, the hijack aims to put them back into the ldm.models.autoencoder +import numpy as np +import torch +import pytorch_lightning as pl +import torch.nn.functional as F +from contextlib import contextmanager + +from torch.optim.lr_scheduler import LambdaLR + +from ldm.modules.ema import LitEma +from vqvae_quantize import VectorQuantizer2 as VectorQuantizer +from ldm.modules.diffusionmodules.model import Encoder, Decoder +from ldm.util import instantiate_from_config + +import ldm.models.autoencoder +from packaging import version + +class VQModel(pl.LightningModule): + def __init__(self, + ddconfig, + lossconfig, + n_embed, + embed_dim, + ckpt_path=None, + ignore_keys=None, + image_key="image", + colorize_nlabels=None, + monitor=None, + batch_resize_range=None, + scheduler_config=None, + lr_g_factor=1.0, + remap=None, + sane_index_shape=False, # tell vector quantizer to return indices as bhw + use_ema=False + ): + super().__init__() + self.embed_dim = embed_dim + self.n_embed = n_embed + self.image_key = image_key + self.encoder = Encoder(**ddconfig) + self.decoder = Decoder(**ddconfig) + self.loss = instantiate_from_config(lossconfig) + self.quantize = VectorQuantizer(n_embed, embed_dim, beta=0.25, + remap=remap, + sane_index_shape=sane_index_shape) + self.quant_conv = torch.nn.Conv2d(ddconfig["z_channels"], embed_dim, 1) + self.post_quant_conv = torch.nn.Conv2d(embed_dim, ddconfig["z_channels"], 1) + if colorize_nlabels is not None: + assert type(colorize_nlabels)==int + self.register_buffer("colorize", torch.randn(3, colorize_nlabels, 1, 1)) + if monitor is not None: + self.monitor = monitor + self.batch_resize_range = batch_resize_range + if self.batch_resize_range is not None: + print(f"{self.__class__.__name__}: Using per-batch resizing in range {batch_resize_range}.") + + self.use_ema = use_ema + if self.use_ema: + self.model_ema = LitEma(self) + print(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.") + + if ckpt_path is not None: + self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys or []) + self.scheduler_config = scheduler_config + self.lr_g_factor = lr_g_factor + + @contextmanager + def ema_scope(self, context=None): + if self.use_ema: + self.model_ema.store(self.parameters()) + self.model_ema.copy_to(self) + if context is not None: + print(f"{context}: Switched to EMA weights") + try: + yield None + finally: + if self.use_ema: + self.model_ema.restore(self.parameters()) + if context is not None: + print(f"{context}: Restored training weights") + + def init_from_ckpt(self, path, ignore_keys=None): + sd = torch.load(path, map_location="cpu")["state_dict"] + keys = list(sd.keys()) + for k in keys: + for ik in ignore_keys or []: + if k.startswith(ik): + print("Deleting key {} from state_dict.".format(k)) + del sd[k] + missing, unexpected = self.load_state_dict(sd, strict=False) + print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys") + if missing: + print(f"Missing Keys: {missing}") + if unexpected: + print(f"Unexpected Keys: {unexpected}") + + def on_train_batch_end(self, *args, **kwargs): + if self.use_ema: + self.model_ema(self) + + def encode(self, x): + h = self.encoder(x) + h = self.quant_conv(h) + quant, emb_loss, info = self.quantize(h) + return quant, emb_loss, info + + def encode_to_prequant(self, x): + h = self.encoder(x) + h = self.quant_conv(h) + return h + + def decode(self, quant): + quant = self.post_quant_conv(quant) + dec = self.decoder(quant) + return dec + + def decode_code(self, code_b): + quant_b = self.quantize.embed_code(code_b) + dec = self.decode(quant_b) + return dec + + def forward(self, input, return_pred_indices=False): + quant, diff, (_,_,ind) = self.encode(input) + dec = self.decode(quant) + if return_pred_indices: + return dec, diff, ind + return dec, diff + + def get_input(self, batch, k): + x = batch[k] + if len(x.shape) == 3: + x = x[..., None] + x = x.permute(0, 3, 1, 2).to(memory_format=torch.contiguous_format).float() + if self.batch_resize_range is not None: + lower_size = self.batch_resize_range[0] + upper_size = self.batch_resize_range[1] + if self.global_step <= 4: + # do the first few batches with max size to avoid later oom + new_resize = upper_size + else: + new_resize = np.random.choice(np.arange(lower_size, upper_size+16, 16)) + if new_resize != x.shape[2]: + x = F.interpolate(x, size=new_resize, mode="bicubic") + x = x.detach() + return x + + def training_step(self, batch, batch_idx, optimizer_idx): + # https://github.com/pytorch/pytorch/issues/37142 + # try not to fool the heuristics + x = self.get_input(batch, self.image_key) + xrec, qloss, ind = self(x, return_pred_indices=True) + + if optimizer_idx == 0: + # autoencode + aeloss, log_dict_ae = self.loss(qloss, x, xrec, optimizer_idx, self.global_step, + last_layer=self.get_last_layer(), split="train", + predicted_indices=ind) + + self.log_dict(log_dict_ae, prog_bar=False, logger=True, on_step=True, on_epoch=True) + return aeloss + + if optimizer_idx == 1: + # discriminator + discloss, log_dict_disc = self.loss(qloss, x, xrec, optimizer_idx, self.global_step, + last_layer=self.get_last_layer(), split="train") + self.log_dict(log_dict_disc, prog_bar=False, logger=True, on_step=True, on_epoch=True) + return discloss + + def validation_step(self, batch, batch_idx): + log_dict = self._validation_step(batch, batch_idx) + with self.ema_scope(): + self._validation_step(batch, batch_idx, suffix="_ema") + return log_dict + + def _validation_step(self, batch, batch_idx, suffix=""): + x = self.get_input(batch, self.image_key) + xrec, qloss, ind = self(x, return_pred_indices=True) + aeloss, log_dict_ae = self.loss(qloss, x, xrec, 0, + self.global_step, + last_layer=self.get_last_layer(), + split="val"+suffix, + predicted_indices=ind + ) + + discloss, log_dict_disc = self.loss(qloss, x, xrec, 1, + self.global_step, + last_layer=self.get_last_layer(), + split="val"+suffix, + predicted_indices=ind + ) + rec_loss = log_dict_ae[f"val{suffix}/rec_loss"] + self.log(f"val{suffix}/rec_loss", rec_loss, + prog_bar=True, logger=True, on_step=False, on_epoch=True, sync_dist=True) + self.log(f"val{suffix}/aeloss", aeloss, + prog_bar=True, logger=True, on_step=False, on_epoch=True, sync_dist=True) + if version.parse(pl.__version__) >= version.parse('1.4.0'): + del log_dict_ae[f"val{suffix}/rec_loss"] + self.log_dict(log_dict_ae) + self.log_dict(log_dict_disc) + return self.log_dict + + def configure_optimizers(self): + lr_d = self.learning_rate + lr_g = self.lr_g_factor*self.learning_rate + print("lr_d", lr_d) + print("lr_g", lr_g) + opt_ae = torch.optim.Adam(list(self.encoder.parameters())+ + list(self.decoder.parameters())+ + list(self.quantize.parameters())+ + list(self.quant_conv.parameters())+ + list(self.post_quant_conv.parameters()), + lr=lr_g, betas=(0.5, 0.9)) + opt_disc = torch.optim.Adam(self.loss.discriminator.parameters(), + lr=lr_d, betas=(0.5, 0.9)) + + if self.scheduler_config is not None: + scheduler = instantiate_from_config(self.scheduler_config) + + print("Setting up LambdaLR scheduler...") + scheduler = [ + { + 'scheduler': LambdaLR(opt_ae, lr_lambda=scheduler.schedule), + 'interval': 'step', + 'frequency': 1 + }, + { + 'scheduler': LambdaLR(opt_disc, lr_lambda=scheduler.schedule), + 'interval': 'step', + 'frequency': 1 + }, + ] + return [opt_ae, opt_disc], scheduler + return [opt_ae, opt_disc], [] + + def get_last_layer(self): + return self.decoder.conv_out.weight + + def log_images(self, batch, only_inputs=False, plot_ema=False, **kwargs): + log = {} + x = self.get_input(batch, self.image_key) + x = x.to(self.device) + if only_inputs: + log["inputs"] = x + return log + xrec, _ = self(x) + if x.shape[1] > 3: + # colorize with random projection + assert xrec.shape[1] > 3 + x = self.to_rgb(x) + xrec = self.to_rgb(xrec) + log["inputs"] = x + log["reconstructions"] = xrec + if plot_ema: + with self.ema_scope(): + xrec_ema, _ = self(x) + if x.shape[1] > 3: + xrec_ema = self.to_rgb(xrec_ema) + log["reconstructions_ema"] = xrec_ema + return log + + def to_rgb(self, x): + assert self.image_key == "segmentation" + if not hasattr(self, "colorize"): + self.register_buffer("colorize", torch.randn(3, x.shape[1], 1, 1).to(x)) + x = F.conv2d(x, weight=self.colorize) + x = 2.*(x-x.min())/(x.max()-x.min()) - 1. + return x + + +class VQModelInterface(VQModel): + def __init__(self, embed_dim, *args, **kwargs): + super().__init__(*args, embed_dim=embed_dim, **kwargs) + self.embed_dim = embed_dim + + def encode(self, x): + h = self.encoder(x) + h = self.quant_conv(h) + return h + + def decode(self, h, force_not_quantize=False): + # also go through quantization layer + if not force_not_quantize: + quant, emb_loss, info = self.quantize(h) + else: + quant = h + quant = self.post_quant_conv(quant) + dec = self.decoder(quant) + return dec + +ldm.models.autoencoder.VQModel = VQModel +ldm.models.autoencoder.VQModelInterface = VQModelInterface diff --git a/extensions-builtin/LDSR/sd_hijack_ddpm_v1.py b/extensions-builtin/LDSR/sd_hijack_ddpm_v1.py new file mode 100644 index 0000000000000000000000000000000000000000..04adc5eb2cfe9aa1d5f75e5653624456c5e37a47 --- /dev/null +++ b/extensions-builtin/LDSR/sd_hijack_ddpm_v1.py @@ -0,0 +1,1443 @@ +# This script is copied from the compvis/stable-diffusion repo (aka the SD V1 repo) +# Original filename: ldm/models/diffusion/ddpm.py +# The purpose to reinstate the old DDPM logic which works with VQ, whereas the V2 one doesn't +# Some models such as LDSR require VQ to work correctly +# The classes are suffixed with "V1" and added back to the "ldm.models.diffusion.ddpm" module + +import torch +import torch.nn as nn +import numpy as np +import pytorch_lightning as pl +from torch.optim.lr_scheduler import LambdaLR +from einops import rearrange, repeat +from contextlib import contextmanager +from functools import partial +from tqdm import tqdm +from torchvision.utils import make_grid +from pytorch_lightning.utilities.distributed import rank_zero_only + +from ldm.util import log_txt_as_img, exists, default, ismap, isimage, mean_flat, count_params, instantiate_from_config +from ldm.modules.ema import LitEma +from ldm.modules.distributions.distributions import normal_kl, DiagonalGaussianDistribution +from ldm.models.autoencoder import VQModelInterface, IdentityFirstStage, AutoencoderKL +from ldm.modules.diffusionmodules.util import make_beta_schedule, extract_into_tensor, noise_like +from ldm.models.diffusion.ddim import DDIMSampler + +import ldm.models.diffusion.ddpm + +__conditioning_keys__ = {'concat': 'c_concat', + 'crossattn': 'c_crossattn', + 'adm': 'y'} + + +def disabled_train(self, mode=True): + """Overwrite model.train with this function to make sure train/eval mode + does not change anymore.""" + return self + + +def uniform_on_device(r1, r2, shape, device): + return (r1 - r2) * torch.rand(*shape, device=device) + r2 + + +class DDPMV1(pl.LightningModule): + # classic DDPM with Gaussian diffusion, in image space + def __init__(self, + unet_config, + timesteps=1000, + beta_schedule="linear", + loss_type="l2", + ckpt_path=None, + ignore_keys=None, + load_only_unet=False, + monitor="val/loss", + use_ema=True, + first_stage_key="image", + image_size=256, + channels=3, + log_every_t=100, + clip_denoised=True, + linear_start=1e-4, + linear_end=2e-2, + cosine_s=8e-3, + given_betas=None, + original_elbo_weight=0., + v_posterior=0., # weight for choosing posterior variance as sigma = (1-v) * beta_tilde + v * beta + l_simple_weight=1., + conditioning_key=None, + parameterization="eps", # all assuming fixed variance schedules + scheduler_config=None, + use_positional_encodings=False, + learn_logvar=False, + logvar_init=0., + ): + super().__init__() + assert parameterization in ["eps", "x0"], 'currently only supporting "eps" and "x0"' + self.parameterization = parameterization + print(f"{self.__class__.__name__}: Running in {self.parameterization}-prediction mode") + self.cond_stage_model = None + self.clip_denoised = clip_denoised + self.log_every_t = log_every_t + self.first_stage_key = first_stage_key + self.image_size = image_size # try conv? + self.channels = channels + self.use_positional_encodings = use_positional_encodings + self.model = DiffusionWrapperV1(unet_config, conditioning_key) + count_params(self.model, verbose=True) + self.use_ema = use_ema + if self.use_ema: + self.model_ema = LitEma(self.model) + print(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.") + + self.use_scheduler = scheduler_config is not None + if self.use_scheduler: + self.scheduler_config = scheduler_config + + self.v_posterior = v_posterior + self.original_elbo_weight = original_elbo_weight + self.l_simple_weight = l_simple_weight + + if monitor is not None: + self.monitor = monitor + if ckpt_path is not None: + self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys or [], only_model=load_only_unet) + + self.register_schedule(given_betas=given_betas, beta_schedule=beta_schedule, timesteps=timesteps, + linear_start=linear_start, linear_end=linear_end, cosine_s=cosine_s) + + self.loss_type = loss_type + + self.learn_logvar = learn_logvar + self.logvar = torch.full(fill_value=logvar_init, size=(self.num_timesteps,)) + if self.learn_logvar: + self.logvar = nn.Parameter(self.logvar, requires_grad=True) + + + def register_schedule(self, given_betas=None, beta_schedule="linear", timesteps=1000, + linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3): + if exists(given_betas): + betas = given_betas + else: + betas = make_beta_schedule(beta_schedule, timesteps, linear_start=linear_start, linear_end=linear_end, + cosine_s=cosine_s) + alphas = 1. - betas + alphas_cumprod = np.cumprod(alphas, axis=0) + alphas_cumprod_prev = np.append(1., alphas_cumprod[:-1]) + + timesteps, = betas.shape + self.num_timesteps = int(timesteps) + self.linear_start = linear_start + self.linear_end = linear_end + assert alphas_cumprod.shape[0] == self.num_timesteps, 'alphas have to be defined for each timestep' + + to_torch = partial(torch.tensor, dtype=torch.float32) + + self.register_buffer('betas', to_torch(betas)) + self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod)) + self.register_buffer('alphas_cumprod_prev', to_torch(alphas_cumprod_prev)) + + # calculations for diffusion q(x_t | x_{t-1}) and others + self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod))) + self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod))) + self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod))) + self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod))) + self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod - 1))) + + # calculations for posterior q(x_{t-1} | x_t, x_0) + posterior_variance = (1 - self.v_posterior) * betas * (1. - alphas_cumprod_prev) / ( + 1. - alphas_cumprod) + self.v_posterior * betas + # above: equal to 1. / (1. / (1. - alpha_cumprod_tm1) + alpha_t / beta_t) + self.register_buffer('posterior_variance', to_torch(posterior_variance)) + # below: log calculation clipped because the posterior variance is 0 at the beginning of the diffusion chain + self.register_buffer('posterior_log_variance_clipped', to_torch(np.log(np.maximum(posterior_variance, 1e-20)))) + self.register_buffer('posterior_mean_coef1', to_torch( + betas * np.sqrt(alphas_cumprod_prev) / (1. - alphas_cumprod))) + self.register_buffer('posterior_mean_coef2', to_torch( + (1. - alphas_cumprod_prev) * np.sqrt(alphas) / (1. - alphas_cumprod))) + + if self.parameterization == "eps": + lvlb_weights = self.betas ** 2 / ( + 2 * self.posterior_variance * to_torch(alphas) * (1 - self.alphas_cumprod)) + elif self.parameterization == "x0": + lvlb_weights = 0.5 * np.sqrt(torch.Tensor(alphas_cumprod)) / (2. * 1 - torch.Tensor(alphas_cumprod)) + else: + raise NotImplementedError("mu not supported") + # TODO how to choose this term + lvlb_weights[0] = lvlb_weights[1] + self.register_buffer('lvlb_weights', lvlb_weights, persistent=False) + assert not torch.isnan(self.lvlb_weights).all() + + @contextmanager + def ema_scope(self, context=None): + if self.use_ema: + self.model_ema.store(self.model.parameters()) + self.model_ema.copy_to(self.model) + if context is not None: + print(f"{context}: Switched to EMA weights") + try: + yield None + finally: + if self.use_ema: + self.model_ema.restore(self.model.parameters()) + if context is not None: + print(f"{context}: Restored training weights") + + def init_from_ckpt(self, path, ignore_keys=None, only_model=False): + sd = torch.load(path, map_location="cpu") + if "state_dict" in list(sd.keys()): + sd = sd["state_dict"] + keys = list(sd.keys()) + for k in keys: + for ik in ignore_keys or []: + if k.startswith(ik): + print("Deleting key {} from state_dict.".format(k)) + del sd[k] + missing, unexpected = self.load_state_dict(sd, strict=False) if not only_model else self.model.load_state_dict( + sd, strict=False) + print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys") + if missing: + print(f"Missing Keys: {missing}") + if unexpected: + print(f"Unexpected Keys: {unexpected}") + + def q_mean_variance(self, x_start, t): + """ + Get the distribution q(x_t | x_0). + :param x_start: the [N x C x ...] tensor of noiseless inputs. + :param t: the number of diffusion steps (minus 1). Here, 0 means one step. + :return: A tuple (mean, variance, log_variance), all of x_start's shape. + """ + mean = (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start) + variance = extract_into_tensor(1.0 - self.alphas_cumprod, t, x_start.shape) + log_variance = extract_into_tensor(self.log_one_minus_alphas_cumprod, t, x_start.shape) + return mean, variance, log_variance + + def predict_start_from_noise(self, x_t, t, noise): + return ( + extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t - + extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape) * noise + ) + + def q_posterior(self, x_start, x_t, t): + posterior_mean = ( + extract_into_tensor(self.posterior_mean_coef1, t, x_t.shape) * x_start + + extract_into_tensor(self.posterior_mean_coef2, t, x_t.shape) * x_t + ) + posterior_variance = extract_into_tensor(self.posterior_variance, t, x_t.shape) + posterior_log_variance_clipped = extract_into_tensor(self.posterior_log_variance_clipped, t, x_t.shape) + return posterior_mean, posterior_variance, posterior_log_variance_clipped + + def p_mean_variance(self, x, t, clip_denoised: bool): + model_out = self.model(x, t) + if self.parameterization == "eps": + x_recon = self.predict_start_from_noise(x, t=t, noise=model_out) + elif self.parameterization == "x0": + x_recon = model_out + if clip_denoised: + x_recon.clamp_(-1., 1.) + + model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t) + return model_mean, posterior_variance, posterior_log_variance + + @torch.no_grad() + def p_sample(self, x, t, clip_denoised=True, repeat_noise=False): + b, *_, device = *x.shape, x.device + model_mean, _, model_log_variance = self.p_mean_variance(x=x, t=t, clip_denoised=clip_denoised) + noise = noise_like(x.shape, device, repeat_noise) + # no noise when t == 0 + nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1))) + return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise + + @torch.no_grad() + def p_sample_loop(self, shape, return_intermediates=False): + device = self.betas.device + b = shape[0] + img = torch.randn(shape, device=device) + intermediates = [img] + for i in tqdm(reversed(range(0, self.num_timesteps)), desc='Sampling t', total=self.num_timesteps): + img = self.p_sample(img, torch.full((b,), i, device=device, dtype=torch.long), + clip_denoised=self.clip_denoised) + if i % self.log_every_t == 0 or i == self.num_timesteps - 1: + intermediates.append(img) + if return_intermediates: + return img, intermediates + return img + + @torch.no_grad() + def sample(self, batch_size=16, return_intermediates=False): + image_size = self.image_size + channels = self.channels + return self.p_sample_loop((batch_size, channels, image_size, image_size), + return_intermediates=return_intermediates) + + def q_sample(self, x_start, t, noise=None): + noise = default(noise, lambda: torch.randn_like(x_start)) + return (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start + + extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise) + + def get_loss(self, pred, target, mean=True): + if self.loss_type == 'l1': + loss = (target - pred).abs() + if mean: + loss = loss.mean() + elif self.loss_type == 'l2': + if mean: + loss = torch.nn.functional.mse_loss(target, pred) + else: + loss = torch.nn.functional.mse_loss(target, pred, reduction='none') + else: + raise NotImplementedError("unknown loss type '{loss_type}'") + + return loss + + def p_losses(self, x_start, t, noise=None): + noise = default(noise, lambda: torch.randn_like(x_start)) + x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise) + model_out = self.model(x_noisy, t) + + loss_dict = {} + if self.parameterization == "eps": + target = noise + elif self.parameterization == "x0": + target = x_start + else: + raise NotImplementedError(f"Paramterization {self.parameterization} not yet supported") + + loss = self.get_loss(model_out, target, mean=False).mean(dim=[1, 2, 3]) + + log_prefix = 'train' if self.training else 'val' + + loss_dict.update({f'{log_prefix}/loss_simple': loss.mean()}) + loss_simple = loss.mean() * self.l_simple_weight + + loss_vlb = (self.lvlb_weights[t] * loss).mean() + loss_dict.update({f'{log_prefix}/loss_vlb': loss_vlb}) + + loss = loss_simple + self.original_elbo_weight * loss_vlb + + loss_dict.update({f'{log_prefix}/loss': loss}) + + return loss, loss_dict + + def forward(self, x, *args, **kwargs): + # b, c, h, w, device, img_size, = *x.shape, x.device, self.image_size + # assert h == img_size and w == img_size, f'height and width of image must be {img_size}' + t = torch.randint(0, self.num_timesteps, (x.shape[0],), device=self.device).long() + return self.p_losses(x, t, *args, **kwargs) + + def get_input(self, batch, k): + x = batch[k] + if len(x.shape) == 3: + x = x[..., None] + x = rearrange(x, 'b h w c -> b c h w') + x = x.to(memory_format=torch.contiguous_format).float() + return x + + def shared_step(self, batch): + x = self.get_input(batch, self.first_stage_key) + loss, loss_dict = self(x) + return loss, loss_dict + + def training_step(self, batch, batch_idx): + loss, loss_dict = self.shared_step(batch) + + self.log_dict(loss_dict, prog_bar=True, + logger=True, on_step=True, on_epoch=True) + + self.log("global_step", self.global_step, + prog_bar=True, logger=True, on_step=True, on_epoch=False) + + if self.use_scheduler: + lr = self.optimizers().param_groups[0]['lr'] + self.log('lr_abs', lr, prog_bar=True, logger=True, on_step=True, on_epoch=False) + + return loss + + @torch.no_grad() + def validation_step(self, batch, batch_idx): + _, loss_dict_no_ema = self.shared_step(batch) + with self.ema_scope(): + _, loss_dict_ema = self.shared_step(batch) + loss_dict_ema = {key + '_ema': loss_dict_ema[key] for key in loss_dict_ema} + self.log_dict(loss_dict_no_ema, prog_bar=False, logger=True, on_step=False, on_epoch=True) + self.log_dict(loss_dict_ema, prog_bar=False, logger=True, on_step=False, on_epoch=True) + + def on_train_batch_end(self, *args, **kwargs): + if self.use_ema: + self.model_ema(self.model) + + def _get_rows_from_list(self, samples): + n_imgs_per_row = len(samples) + denoise_grid = rearrange(samples, 'n b c h w -> b n c h w') + denoise_grid = rearrange(denoise_grid, 'b n c h w -> (b n) c h w') + denoise_grid = make_grid(denoise_grid, nrow=n_imgs_per_row) + return denoise_grid + + @torch.no_grad() + def log_images(self, batch, N=8, n_row=2, sample=True, return_keys=None, **kwargs): + log = {} + x = self.get_input(batch, self.first_stage_key) + N = min(x.shape[0], N) + n_row = min(x.shape[0], n_row) + x = x.to(self.device)[:N] + log["inputs"] = x + + # get diffusion row + diffusion_row = [] + x_start = x[:n_row] + + for t in range(self.num_timesteps): + if t % self.log_every_t == 0 or t == self.num_timesteps - 1: + t = repeat(torch.tensor([t]), '1 -> b', b=n_row) + t = t.to(self.device).long() + noise = torch.randn_like(x_start) + x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise) + diffusion_row.append(x_noisy) + + log["diffusion_row"] = self._get_rows_from_list(diffusion_row) + + if sample: + # get denoise row + with self.ema_scope("Plotting"): + samples, denoise_row = self.sample(batch_size=N, return_intermediates=True) + + log["samples"] = samples + log["denoise_row"] = self._get_rows_from_list(denoise_row) + + if return_keys: + if np.intersect1d(list(log.keys()), return_keys).shape[0] == 0: + return log + else: + return {key: log[key] for key in return_keys} + return log + + def configure_optimizers(self): + lr = self.learning_rate + params = list(self.model.parameters()) + if self.learn_logvar: + params = params + [self.logvar] + opt = torch.optim.AdamW(params, lr=lr) + return opt + + +class LatentDiffusionV1(DDPMV1): + """main class""" + def __init__(self, + first_stage_config, + cond_stage_config, + num_timesteps_cond=None, + cond_stage_key="image", + cond_stage_trainable=False, + concat_mode=True, + cond_stage_forward=None, + conditioning_key=None, + scale_factor=1.0, + scale_by_std=False, + *args, **kwargs): + self.num_timesteps_cond = default(num_timesteps_cond, 1) + self.scale_by_std = scale_by_std + assert self.num_timesteps_cond <= kwargs['timesteps'] + # for backwards compatibility after implementation of DiffusionWrapper + if conditioning_key is None: + conditioning_key = 'concat' if concat_mode else 'crossattn' + if cond_stage_config == '__is_unconditional__': + conditioning_key = None + ckpt_path = kwargs.pop("ckpt_path", None) + ignore_keys = kwargs.pop("ignore_keys", []) + super().__init__(*args, conditioning_key=conditioning_key, **kwargs) + self.concat_mode = concat_mode + self.cond_stage_trainable = cond_stage_trainable + self.cond_stage_key = cond_stage_key + try: + self.num_downs = len(first_stage_config.params.ddconfig.ch_mult) - 1 + except Exception: + self.num_downs = 0 + if not scale_by_std: + self.scale_factor = scale_factor + else: + self.register_buffer('scale_factor', torch.tensor(scale_factor)) + self.instantiate_first_stage(first_stage_config) + self.instantiate_cond_stage(cond_stage_config) + self.cond_stage_forward = cond_stage_forward + self.clip_denoised = False + self.bbox_tokenizer = None + + self.restarted_from_ckpt = False + if ckpt_path is not None: + self.init_from_ckpt(ckpt_path, ignore_keys) + self.restarted_from_ckpt = True + + def make_cond_schedule(self, ): + self.cond_ids = torch.full(size=(self.num_timesteps,), fill_value=self.num_timesteps - 1, dtype=torch.long) + ids = torch.round(torch.linspace(0, self.num_timesteps - 1, self.num_timesteps_cond)).long() + self.cond_ids[:self.num_timesteps_cond] = ids + + @rank_zero_only + @torch.no_grad() + def on_train_batch_start(self, batch, batch_idx, dataloader_idx): + # only for very first batch + if self.scale_by_std and self.current_epoch == 0 and self.global_step == 0 and batch_idx == 0 and not self.restarted_from_ckpt: + assert self.scale_factor == 1., 'rather not use custom rescaling and std-rescaling simultaneously' + # set rescale weight to 1./std of encodings + print("### USING STD-RESCALING ###") + x = super().get_input(batch, self.first_stage_key) + x = x.to(self.device) + encoder_posterior = self.encode_first_stage(x) + z = self.get_first_stage_encoding(encoder_posterior).detach() + del self.scale_factor + self.register_buffer('scale_factor', 1. / z.flatten().std()) + print(f"setting self.scale_factor to {self.scale_factor}") + print("### USING STD-RESCALING ###") + + def register_schedule(self, + given_betas=None, beta_schedule="linear", timesteps=1000, + linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3): + super().register_schedule(given_betas, beta_schedule, timesteps, linear_start, linear_end, cosine_s) + + self.shorten_cond_schedule = self.num_timesteps_cond > 1 + if self.shorten_cond_schedule: + self.make_cond_schedule() + + def instantiate_first_stage(self, config): + model = instantiate_from_config(config) + self.first_stage_model = model.eval() + self.first_stage_model.train = disabled_train + for param in self.first_stage_model.parameters(): + param.requires_grad = False + + def instantiate_cond_stage(self, config): + if not self.cond_stage_trainable: + if config == "__is_first_stage__": + print("Using first stage also as cond stage.") + self.cond_stage_model = self.first_stage_model + elif config == "__is_unconditional__": + print(f"Training {self.__class__.__name__} as an unconditional model.") + self.cond_stage_model = None + # self.be_unconditional = True + else: + model = instantiate_from_config(config) + self.cond_stage_model = model.eval() + self.cond_stage_model.train = disabled_train + for param in self.cond_stage_model.parameters(): + param.requires_grad = False + else: + assert config != '__is_first_stage__' + assert config != '__is_unconditional__' + model = instantiate_from_config(config) + self.cond_stage_model = model + + def _get_denoise_row_from_list(self, samples, desc='', force_no_decoder_quantization=False): + denoise_row = [] + for zd in tqdm(samples, desc=desc): + denoise_row.append(self.decode_first_stage(zd.to(self.device), + force_not_quantize=force_no_decoder_quantization)) + n_imgs_per_row = len(denoise_row) + denoise_row = torch.stack(denoise_row) # n_log_step, n_row, C, H, W + denoise_grid = rearrange(denoise_row, 'n b c h w -> b n c h w') + denoise_grid = rearrange(denoise_grid, 'b n c h w -> (b n) c h w') + denoise_grid = make_grid(denoise_grid, nrow=n_imgs_per_row) + return denoise_grid + + def get_first_stage_encoding(self, encoder_posterior): + if isinstance(encoder_posterior, DiagonalGaussianDistribution): + z = encoder_posterior.sample() + elif isinstance(encoder_posterior, torch.Tensor): + z = encoder_posterior + else: + raise NotImplementedError(f"encoder_posterior of type '{type(encoder_posterior)}' not yet implemented") + return self.scale_factor * z + + def get_learned_conditioning(self, c): + if self.cond_stage_forward is None: + if hasattr(self.cond_stage_model, 'encode') and callable(self.cond_stage_model.encode): + c = self.cond_stage_model.encode(c) + if isinstance(c, DiagonalGaussianDistribution): + c = c.mode() + else: + c = self.cond_stage_model(c) + else: + assert hasattr(self.cond_stage_model, self.cond_stage_forward) + c = getattr(self.cond_stage_model, self.cond_stage_forward)(c) + return c + + def meshgrid(self, h, w): + y = torch.arange(0, h).view(h, 1, 1).repeat(1, w, 1) + x = torch.arange(0, w).view(1, w, 1).repeat(h, 1, 1) + + arr = torch.cat([y, x], dim=-1) + return arr + + def delta_border(self, h, w): + """ + :param h: height + :param w: width + :return: normalized distance to image border, + wtith min distance = 0 at border and max dist = 0.5 at image center + """ + lower_right_corner = torch.tensor([h - 1, w - 1]).view(1, 1, 2) + arr = self.meshgrid(h, w) / lower_right_corner + dist_left_up = torch.min(arr, dim=-1, keepdims=True)[0] + dist_right_down = torch.min(1 - arr, dim=-1, keepdims=True)[0] + edge_dist = torch.min(torch.cat([dist_left_up, dist_right_down], dim=-1), dim=-1)[0] + return edge_dist + + def get_weighting(self, h, w, Ly, Lx, device): + weighting = self.delta_border(h, w) + weighting = torch.clip(weighting, self.split_input_params["clip_min_weight"], + self.split_input_params["clip_max_weight"], ) + weighting = weighting.view(1, h * w, 1).repeat(1, 1, Ly * Lx).to(device) + + if self.split_input_params["tie_braker"]: + L_weighting = self.delta_border(Ly, Lx) + L_weighting = torch.clip(L_weighting, + self.split_input_params["clip_min_tie_weight"], + self.split_input_params["clip_max_tie_weight"]) + + L_weighting = L_weighting.view(1, 1, Ly * Lx).to(device) + weighting = weighting * L_weighting + return weighting + + def get_fold_unfold(self, x, kernel_size, stride, uf=1, df=1): # todo load once not every time, shorten code + """ + :param x: img of size (bs, c, h, w) + :return: n img crops of size (n, bs, c, kernel_size[0], kernel_size[1]) + """ + bs, nc, h, w = x.shape + + # number of crops in image + Ly = (h - kernel_size[0]) // stride[0] + 1 + Lx = (w - kernel_size[1]) // stride[1] + 1 + + if uf == 1 and df == 1: + fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride) + unfold = torch.nn.Unfold(**fold_params) + + fold = torch.nn.Fold(output_size=x.shape[2:], **fold_params) + + weighting = self.get_weighting(kernel_size[0], kernel_size[1], Ly, Lx, x.device).to(x.dtype) + normalization = fold(weighting).view(1, 1, h, w) # normalizes the overlap + weighting = weighting.view((1, 1, kernel_size[0], kernel_size[1], Ly * Lx)) + + elif uf > 1 and df == 1: + fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride) + unfold = torch.nn.Unfold(**fold_params) + + fold_params2 = dict(kernel_size=(kernel_size[0] * uf, kernel_size[0] * uf), + dilation=1, padding=0, + stride=(stride[0] * uf, stride[1] * uf)) + fold = torch.nn.Fold(output_size=(x.shape[2] * uf, x.shape[3] * uf), **fold_params2) + + weighting = self.get_weighting(kernel_size[0] * uf, kernel_size[1] * uf, Ly, Lx, x.device).to(x.dtype) + normalization = fold(weighting).view(1, 1, h * uf, w * uf) # normalizes the overlap + weighting = weighting.view((1, 1, kernel_size[0] * uf, kernel_size[1] * uf, Ly * Lx)) + + elif df > 1 and uf == 1: + fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride) + unfold = torch.nn.Unfold(**fold_params) + + fold_params2 = dict(kernel_size=(kernel_size[0] // df, kernel_size[0] // df), + dilation=1, padding=0, + stride=(stride[0] // df, stride[1] // df)) + fold = torch.nn.Fold(output_size=(x.shape[2] // df, x.shape[3] // df), **fold_params2) + + weighting = self.get_weighting(kernel_size[0] // df, kernel_size[1] // df, Ly, Lx, x.device).to(x.dtype) + normalization = fold(weighting).view(1, 1, h // df, w // df) # normalizes the overlap + weighting = weighting.view((1, 1, kernel_size[0] // df, kernel_size[1] // df, Ly * Lx)) + + else: + raise NotImplementedError + + return fold, unfold, normalization, weighting + + @torch.no_grad() + def get_input(self, batch, k, return_first_stage_outputs=False, force_c_encode=False, + cond_key=None, return_original_cond=False, bs=None): + x = super().get_input(batch, k) + if bs is not None: + x = x[:bs] + x = x.to(self.device) + encoder_posterior = self.encode_first_stage(x) + z = self.get_first_stage_encoding(encoder_posterior).detach() + + if self.model.conditioning_key is not None: + if cond_key is None: + cond_key = self.cond_stage_key + if cond_key != self.first_stage_key: + if cond_key in ['caption', 'coordinates_bbox']: + xc = batch[cond_key] + elif cond_key == 'class_label': + xc = batch + else: + xc = super().get_input(batch, cond_key).to(self.device) + else: + xc = x + if not self.cond_stage_trainable or force_c_encode: + if isinstance(xc, dict) or isinstance(xc, list): + # import pudb; pudb.set_trace() + c = self.get_learned_conditioning(xc) + else: + c = self.get_learned_conditioning(xc.to(self.device)) + else: + c = xc + if bs is not None: + c = c[:bs] + + if self.use_positional_encodings: + pos_x, pos_y = self.compute_latent_shifts(batch) + ckey = __conditioning_keys__[self.model.conditioning_key] + c = {ckey: c, 'pos_x': pos_x, 'pos_y': pos_y} + + else: + c = None + xc = None + if self.use_positional_encodings: + pos_x, pos_y = self.compute_latent_shifts(batch) + c = {'pos_x': pos_x, 'pos_y': pos_y} + out = [z, c] + if return_first_stage_outputs: + xrec = self.decode_first_stage(z) + out.extend([x, xrec]) + if return_original_cond: + out.append(xc) + return out + + @torch.no_grad() + def decode_first_stage(self, z, predict_cids=False, force_not_quantize=False): + if predict_cids: + if z.dim() == 4: + z = torch.argmax(z.exp(), dim=1).long() + z = self.first_stage_model.quantize.get_codebook_entry(z, shape=None) + z = rearrange(z, 'b h w c -> b c h w').contiguous() + + z = 1. / self.scale_factor * z + + if hasattr(self, "split_input_params"): + if self.split_input_params["patch_distributed_vq"]: + ks = self.split_input_params["ks"] # eg. (128, 128) + stride = self.split_input_params["stride"] # eg. (64, 64) + uf = self.split_input_params["vqf"] + bs, nc, h, w = z.shape + if ks[0] > h or ks[1] > w: + ks = (min(ks[0], h), min(ks[1], w)) + print("reducing Kernel") + + if stride[0] > h or stride[1] > w: + stride = (min(stride[0], h), min(stride[1], w)) + print("reducing stride") + + fold, unfold, normalization, weighting = self.get_fold_unfold(z, ks, stride, uf=uf) + + z = unfold(z) # (bn, nc * prod(**ks), L) + # 1. Reshape to img shape + z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1])) # (bn, nc, ks[0], ks[1], L ) + + # 2. apply model loop over last dim + if isinstance(self.first_stage_model, VQModelInterface): + output_list = [self.first_stage_model.decode(z[:, :, :, :, i], + force_not_quantize=predict_cids or force_not_quantize) + for i in range(z.shape[-1])] + else: + + output_list = [self.first_stage_model.decode(z[:, :, :, :, i]) + for i in range(z.shape[-1])] + + o = torch.stack(output_list, axis=-1) # # (bn, nc, ks[0], ks[1], L) + o = o * weighting + # Reverse 1. reshape to img shape + o = o.view((o.shape[0], -1, o.shape[-1])) # (bn, nc * ks[0] * ks[1], L) + # stitch crops together + decoded = fold(o) + decoded = decoded / normalization # norm is shape (1, 1, h, w) + return decoded + else: + if isinstance(self.first_stage_model, VQModelInterface): + return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize) + else: + return self.first_stage_model.decode(z) + + else: + if isinstance(self.first_stage_model, VQModelInterface): + return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize) + else: + return self.first_stage_model.decode(z) + + # same as above but without decorator + def differentiable_decode_first_stage(self, z, predict_cids=False, force_not_quantize=False): + if predict_cids: + if z.dim() == 4: + z = torch.argmax(z.exp(), dim=1).long() + z = self.first_stage_model.quantize.get_codebook_entry(z, shape=None) + z = rearrange(z, 'b h w c -> b c h w').contiguous() + + z = 1. / self.scale_factor * z + + if hasattr(self, "split_input_params"): + if self.split_input_params["patch_distributed_vq"]: + ks = self.split_input_params["ks"] # eg. (128, 128) + stride = self.split_input_params["stride"] # eg. (64, 64) + uf = self.split_input_params["vqf"] + bs, nc, h, w = z.shape + if ks[0] > h or ks[1] > w: + ks = (min(ks[0], h), min(ks[1], w)) + print("reducing Kernel") + + if stride[0] > h or stride[1] > w: + stride = (min(stride[0], h), min(stride[1], w)) + print("reducing stride") + + fold, unfold, normalization, weighting = self.get_fold_unfold(z, ks, stride, uf=uf) + + z = unfold(z) # (bn, nc * prod(**ks), L) + # 1. Reshape to img shape + z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1])) # (bn, nc, ks[0], ks[1], L ) + + # 2. apply model loop over last dim + if isinstance(self.first_stage_model, VQModelInterface): + output_list = [self.first_stage_model.decode(z[:, :, :, :, i], + force_not_quantize=predict_cids or force_not_quantize) + for i in range(z.shape[-1])] + else: + + output_list = [self.first_stage_model.decode(z[:, :, :, :, i]) + for i in range(z.shape[-1])] + + o = torch.stack(output_list, axis=-1) # # (bn, nc, ks[0], ks[1], L) + o = o * weighting + # Reverse 1. reshape to img shape + o = o.view((o.shape[0], -1, o.shape[-1])) # (bn, nc * ks[0] * ks[1], L) + # stitch crops together + decoded = fold(o) + decoded = decoded / normalization # norm is shape (1, 1, h, w) + return decoded + else: + if isinstance(self.first_stage_model, VQModelInterface): + return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize) + else: + return self.first_stage_model.decode(z) + + else: + if isinstance(self.first_stage_model, VQModelInterface): + return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize) + else: + return self.first_stage_model.decode(z) + + @torch.no_grad() + def encode_first_stage(self, x): + if hasattr(self, "split_input_params"): + if self.split_input_params["patch_distributed_vq"]: + ks = self.split_input_params["ks"] # eg. (128, 128) + stride = self.split_input_params["stride"] # eg. (64, 64) + df = self.split_input_params["vqf"] + self.split_input_params['original_image_size'] = x.shape[-2:] + bs, nc, h, w = x.shape + if ks[0] > h or ks[1] > w: + ks = (min(ks[0], h), min(ks[1], w)) + print("reducing Kernel") + + if stride[0] > h or stride[1] > w: + stride = (min(stride[0], h), min(stride[1], w)) + print("reducing stride") + + fold, unfold, normalization, weighting = self.get_fold_unfold(x, ks, stride, df=df) + z = unfold(x) # (bn, nc * prod(**ks), L) + # Reshape to img shape + z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1])) # (bn, nc, ks[0], ks[1], L ) + + output_list = [self.first_stage_model.encode(z[:, :, :, :, i]) + for i in range(z.shape[-1])] + + o = torch.stack(output_list, axis=-1) + o = o * weighting + + # Reverse reshape to img shape + o = o.view((o.shape[0], -1, o.shape[-1])) # (bn, nc * ks[0] * ks[1], L) + # stitch crops together + decoded = fold(o) + decoded = decoded / normalization + return decoded + + else: + return self.first_stage_model.encode(x) + else: + return self.first_stage_model.encode(x) + + def shared_step(self, batch, **kwargs): + x, c = self.get_input(batch, self.first_stage_key) + loss = self(x, c) + return loss + + def forward(self, x, c, *args, **kwargs): + t = torch.randint(0, self.num_timesteps, (x.shape[0],), device=self.device).long() + if self.model.conditioning_key is not None: + assert c is not None + if self.cond_stage_trainable: + c = self.get_learned_conditioning(c) + if self.shorten_cond_schedule: # TODO: drop this option + tc = self.cond_ids[t].to(self.device) + c = self.q_sample(x_start=c, t=tc, noise=torch.randn_like(c.float())) + return self.p_losses(x, c, t, *args, **kwargs) + + def apply_model(self, x_noisy, t, cond, return_ids=False): + + if isinstance(cond, dict): + # hybrid case, cond is exptected to be a dict + pass + else: + if not isinstance(cond, list): + cond = [cond] + key = 'c_concat' if self.model.conditioning_key == 'concat' else 'c_crossattn' + cond = {key: cond} + + if hasattr(self, "split_input_params"): + assert len(cond) == 1 # todo can only deal with one conditioning atm + assert not return_ids + ks = self.split_input_params["ks"] # eg. (128, 128) + stride = self.split_input_params["stride"] # eg. (64, 64) + + h, w = x_noisy.shape[-2:] + + fold, unfold, normalization, weighting = self.get_fold_unfold(x_noisy, ks, stride) + + z = unfold(x_noisy) # (bn, nc * prod(**ks), L) + # Reshape to img shape + z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1])) # (bn, nc, ks[0], ks[1], L ) + z_list = [z[:, :, :, :, i] for i in range(z.shape[-1])] + + if self.cond_stage_key in ["image", "LR_image", "segmentation", + 'bbox_img'] and self.model.conditioning_key: # todo check for completeness + c_key = next(iter(cond.keys())) # get key + c = next(iter(cond.values())) # get value + assert (len(c) == 1) # todo extend to list with more than one elem + c = c[0] # get element + + c = unfold(c) + c = c.view((c.shape[0], -1, ks[0], ks[1], c.shape[-1])) # (bn, nc, ks[0], ks[1], L ) + + cond_list = [{c_key: [c[:, :, :, :, i]]} for i in range(c.shape[-1])] + + elif self.cond_stage_key == 'coordinates_bbox': + assert 'original_image_size' in self.split_input_params, 'BoudingBoxRescaling is missing original_image_size' + + # assuming padding of unfold is always 0 and its dilation is always 1 + n_patches_per_row = int((w - ks[0]) / stride[0] + 1) + full_img_h, full_img_w = self.split_input_params['original_image_size'] + # as we are operating on latents, we need the factor from the original image size to the + # spatial latent size to properly rescale the crops for regenerating the bbox annotations + num_downs = self.first_stage_model.encoder.num_resolutions - 1 + rescale_latent = 2 ** (num_downs) + + # get top left postions of patches as conforming for the bbbox tokenizer, therefore we + # need to rescale the tl patch coordinates to be in between (0,1) + tl_patch_coordinates = [(rescale_latent * stride[0] * (patch_nr % n_patches_per_row) / full_img_w, + rescale_latent * stride[1] * (patch_nr // n_patches_per_row) / full_img_h) + for patch_nr in range(z.shape[-1])] + + # patch_limits are tl_coord, width and height coordinates as (x_tl, y_tl, h, w) + patch_limits = [(x_tl, y_tl, + rescale_latent * ks[0] / full_img_w, + rescale_latent * ks[1] / full_img_h) for x_tl, y_tl in tl_patch_coordinates] + # patch_values = [(np.arange(x_tl,min(x_tl+ks, 1.)),np.arange(y_tl,min(y_tl+ks, 1.))) for x_tl, y_tl in tl_patch_coordinates] + + # tokenize crop coordinates for the bounding boxes of the respective patches + patch_limits_tknzd = [torch.LongTensor(self.bbox_tokenizer._crop_encoder(bbox))[None].to(self.device) + for bbox in patch_limits] # list of length l with tensors of shape (1, 2) + print(patch_limits_tknzd[0].shape) + # cut tknzd crop position from conditioning + assert isinstance(cond, dict), 'cond must be dict to be fed into model' + cut_cond = cond['c_crossattn'][0][..., :-2].to(self.device) + print(cut_cond.shape) + + adapted_cond = torch.stack([torch.cat([cut_cond, p], dim=1) for p in patch_limits_tknzd]) + adapted_cond = rearrange(adapted_cond, 'l b n -> (l b) n') + print(adapted_cond.shape) + adapted_cond = self.get_learned_conditioning(adapted_cond) + print(adapted_cond.shape) + adapted_cond = rearrange(adapted_cond, '(l b) n d -> l b n d', l=z.shape[-1]) + print(adapted_cond.shape) + + cond_list = [{'c_crossattn': [e]} for e in adapted_cond] + + else: + cond_list = [cond for i in range(z.shape[-1])] # Todo make this more efficient + + # apply model by loop over crops + output_list = [self.model(z_list[i], t, **cond_list[i]) for i in range(z.shape[-1])] + assert not isinstance(output_list[0], + tuple) # todo cant deal with multiple model outputs check this never happens + + o = torch.stack(output_list, axis=-1) + o = o * weighting + # Reverse reshape to img shape + o = o.view((o.shape[0], -1, o.shape[-1])) # (bn, nc * ks[0] * ks[1], L) + # stitch crops together + x_recon = fold(o) / normalization + + else: + x_recon = self.model(x_noisy, t, **cond) + + if isinstance(x_recon, tuple) and not return_ids: + return x_recon[0] + else: + return x_recon + + def _predict_eps_from_xstart(self, x_t, t, pred_xstart): + return (extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t - pred_xstart) / \ + extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape) + + def _prior_bpd(self, x_start): + """ + Get the prior KL term for the variational lower-bound, measured in + bits-per-dim. + This term can't be optimized, as it only depends on the encoder. + :param x_start: the [N x C x ...] tensor of inputs. + :return: a batch of [N] KL values (in bits), one per batch element. + """ + batch_size = x_start.shape[0] + t = torch.tensor([self.num_timesteps - 1] * batch_size, device=x_start.device) + qt_mean, _, qt_log_variance = self.q_mean_variance(x_start, t) + kl_prior = normal_kl(mean1=qt_mean, logvar1=qt_log_variance, mean2=0.0, logvar2=0.0) + return mean_flat(kl_prior) / np.log(2.0) + + def p_losses(self, x_start, cond, t, noise=None): + noise = default(noise, lambda: torch.randn_like(x_start)) + x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise) + model_output = self.apply_model(x_noisy, t, cond) + + loss_dict = {} + prefix = 'train' if self.training else 'val' + + if self.parameterization == "x0": + target = x_start + elif self.parameterization == "eps": + target = noise + else: + raise NotImplementedError() + + loss_simple = self.get_loss(model_output, target, mean=False).mean([1, 2, 3]) + loss_dict.update({f'{prefix}/loss_simple': loss_simple.mean()}) + + logvar_t = self.logvar[t].to(self.device) + loss = loss_simple / torch.exp(logvar_t) + logvar_t + # loss = loss_simple / torch.exp(self.logvar) + self.logvar + if self.learn_logvar: + loss_dict.update({f'{prefix}/loss_gamma': loss.mean()}) + loss_dict.update({'logvar': self.logvar.data.mean()}) + + loss = self.l_simple_weight * loss.mean() + + loss_vlb = self.get_loss(model_output, target, mean=False).mean(dim=(1, 2, 3)) + loss_vlb = (self.lvlb_weights[t] * loss_vlb).mean() + loss_dict.update({f'{prefix}/loss_vlb': loss_vlb}) + loss += (self.original_elbo_weight * loss_vlb) + loss_dict.update({f'{prefix}/loss': loss}) + + return loss, loss_dict + + def p_mean_variance(self, x, c, t, clip_denoised: bool, return_codebook_ids=False, quantize_denoised=False, + return_x0=False, score_corrector=None, corrector_kwargs=None): + t_in = t + model_out = self.apply_model(x, t_in, c, return_ids=return_codebook_ids) + + if score_corrector is not None: + assert self.parameterization == "eps" + model_out = score_corrector.modify_score(self, model_out, x, t, c, **corrector_kwargs) + + if return_codebook_ids: + model_out, logits = model_out + + if self.parameterization == "eps": + x_recon = self.predict_start_from_noise(x, t=t, noise=model_out) + elif self.parameterization == "x0": + x_recon = model_out + else: + raise NotImplementedError() + + if clip_denoised: + x_recon.clamp_(-1., 1.) + if quantize_denoised: + x_recon, _, [_, _, indices] = self.first_stage_model.quantize(x_recon) + model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t) + if return_codebook_ids: + return model_mean, posterior_variance, posterior_log_variance, logits + elif return_x0: + return model_mean, posterior_variance, posterior_log_variance, x_recon + else: + return model_mean, posterior_variance, posterior_log_variance + + @torch.no_grad() + def p_sample(self, x, c, t, clip_denoised=False, repeat_noise=False, + return_codebook_ids=False, quantize_denoised=False, return_x0=False, + temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None): + b, *_, device = *x.shape, x.device + outputs = self.p_mean_variance(x=x, c=c, t=t, clip_denoised=clip_denoised, + return_codebook_ids=return_codebook_ids, + quantize_denoised=quantize_denoised, + return_x0=return_x0, + score_corrector=score_corrector, corrector_kwargs=corrector_kwargs) + if return_codebook_ids: + raise DeprecationWarning("Support dropped.") + model_mean, _, model_log_variance, logits = outputs + elif return_x0: + model_mean, _, model_log_variance, x0 = outputs + else: + model_mean, _, model_log_variance = outputs + + noise = noise_like(x.shape, device, repeat_noise) * temperature + if noise_dropout > 0.: + noise = torch.nn.functional.dropout(noise, p=noise_dropout) + # no noise when t == 0 + nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1))) + + if return_codebook_ids: + return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise, logits.argmax(dim=1) + if return_x0: + return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise, x0 + else: + return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise + + @torch.no_grad() + def progressive_denoising(self, cond, shape, verbose=True, callback=None, quantize_denoised=False, + img_callback=None, mask=None, x0=None, temperature=1., noise_dropout=0., + score_corrector=None, corrector_kwargs=None, batch_size=None, x_T=None, start_T=None, + log_every_t=None): + if not log_every_t: + log_every_t = self.log_every_t + timesteps = self.num_timesteps + if batch_size is not None: + b = batch_size if batch_size is not None else shape[0] + shape = [batch_size] + list(shape) + else: + b = batch_size = shape[0] + if x_T is None: + img = torch.randn(shape, device=self.device) + else: + img = x_T + intermediates = [] + if cond is not None: + if isinstance(cond, dict): + cond = {key: cond[key][:batch_size] if not isinstance(cond[key], list) else + [x[:batch_size] for x in cond[key]] for key in cond} + else: + cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size] + + if start_T is not None: + timesteps = min(timesteps, start_T) + iterator = tqdm(reversed(range(0, timesteps)), desc='Progressive Generation', + total=timesteps) if verbose else reversed( + range(0, timesteps)) + if type(temperature) == float: + temperature = [temperature] * timesteps + + for i in iterator: + ts = torch.full((b,), i, device=self.device, dtype=torch.long) + if self.shorten_cond_schedule: + assert self.model.conditioning_key != 'hybrid' + tc = self.cond_ids[ts].to(cond.device) + cond = self.q_sample(x_start=cond, t=tc, noise=torch.randn_like(cond)) + + img, x0_partial = self.p_sample(img, cond, ts, + clip_denoised=self.clip_denoised, + quantize_denoised=quantize_denoised, return_x0=True, + temperature=temperature[i], noise_dropout=noise_dropout, + score_corrector=score_corrector, corrector_kwargs=corrector_kwargs) + if mask is not None: + assert x0 is not None + img_orig = self.q_sample(x0, ts) + img = img_orig * mask + (1. - mask) * img + + if i % log_every_t == 0 or i == timesteps - 1: + intermediates.append(x0_partial) + if callback: + callback(i) + if img_callback: + img_callback(img, i) + return img, intermediates + + @torch.no_grad() + def p_sample_loop(self, cond, shape, return_intermediates=False, + x_T=None, verbose=True, callback=None, timesteps=None, quantize_denoised=False, + mask=None, x0=None, img_callback=None, start_T=None, + log_every_t=None): + + if not log_every_t: + log_every_t = self.log_every_t + device = self.betas.device + b = shape[0] + if x_T is None: + img = torch.randn(shape, device=device) + else: + img = x_T + + intermediates = [img] + if timesteps is None: + timesteps = self.num_timesteps + + if start_T is not None: + timesteps = min(timesteps, start_T) + iterator = tqdm(reversed(range(0, timesteps)), desc='Sampling t', total=timesteps) if verbose else reversed( + range(0, timesteps)) + + if mask is not None: + assert x0 is not None + assert x0.shape[2:3] == mask.shape[2:3] # spatial size has to match + + for i in iterator: + ts = torch.full((b,), i, device=device, dtype=torch.long) + if self.shorten_cond_schedule: + assert self.model.conditioning_key != 'hybrid' + tc = self.cond_ids[ts].to(cond.device) + cond = self.q_sample(x_start=cond, t=tc, noise=torch.randn_like(cond)) + + img = self.p_sample(img, cond, ts, + clip_denoised=self.clip_denoised, + quantize_denoised=quantize_denoised) + if mask is not None: + img_orig = self.q_sample(x0, ts) + img = img_orig * mask + (1. - mask) * img + + if i % log_every_t == 0 or i == timesteps - 1: + intermediates.append(img) + if callback: + callback(i) + if img_callback: + img_callback(img, i) + + if return_intermediates: + return img, intermediates + return img + + @torch.no_grad() + def sample(self, cond, batch_size=16, return_intermediates=False, x_T=None, + verbose=True, timesteps=None, quantize_denoised=False, + mask=None, x0=None, shape=None,**kwargs): + if shape is None: + shape = (batch_size, self.channels, self.image_size, self.image_size) + if cond is not None: + if isinstance(cond, dict): + cond = {key: cond[key][:batch_size] if not isinstance(cond[key], list) else + [x[:batch_size] for x in cond[key]] for key in cond} + else: + cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size] + return self.p_sample_loop(cond, + shape, + return_intermediates=return_intermediates, x_T=x_T, + verbose=verbose, timesteps=timesteps, quantize_denoised=quantize_denoised, + mask=mask, x0=x0) + + @torch.no_grad() + def sample_log(self,cond,batch_size,ddim, ddim_steps,**kwargs): + + if ddim: + ddim_sampler = DDIMSampler(self) + shape = (self.channels, self.image_size, self.image_size) + samples, intermediates =ddim_sampler.sample(ddim_steps,batch_size, + shape,cond,verbose=False,**kwargs) + + else: + samples, intermediates = self.sample(cond=cond, batch_size=batch_size, + return_intermediates=True,**kwargs) + + return samples, intermediates + + + @torch.no_grad() + def log_images(self, batch, N=8, n_row=4, sample=True, ddim_steps=200, ddim_eta=1., return_keys=None, + quantize_denoised=True, inpaint=True, plot_denoise_rows=False, plot_progressive_rows=True, + plot_diffusion_rows=True, **kwargs): + + use_ddim = ddim_steps is not None + + log = {} + z, c, x, xrec, xc = self.get_input(batch, self.first_stage_key, + return_first_stage_outputs=True, + force_c_encode=True, + return_original_cond=True, + bs=N) + N = min(x.shape[0], N) + n_row = min(x.shape[0], n_row) + log["inputs"] = x + log["reconstruction"] = xrec + if self.model.conditioning_key is not None: + if hasattr(self.cond_stage_model, "decode"): + xc = self.cond_stage_model.decode(c) + log["conditioning"] = xc + elif self.cond_stage_key in ["caption"]: + xc = log_txt_as_img((x.shape[2], x.shape[3]), batch["caption"]) + log["conditioning"] = xc + elif self.cond_stage_key == 'class_label': + xc = log_txt_as_img((x.shape[2], x.shape[3]), batch["human_label"]) + log['conditioning'] = xc + elif isimage(xc): + log["conditioning"] = xc + if ismap(xc): + log["original_conditioning"] = self.to_rgb(xc) + + if plot_diffusion_rows: + # get diffusion row + diffusion_row = [] + z_start = z[:n_row] + for t in range(self.num_timesteps): + if t % self.log_every_t == 0 or t == self.num_timesteps - 1: + t = repeat(torch.tensor([t]), '1 -> b', b=n_row) + t = t.to(self.device).long() + noise = torch.randn_like(z_start) + z_noisy = self.q_sample(x_start=z_start, t=t, noise=noise) + diffusion_row.append(self.decode_first_stage(z_noisy)) + + diffusion_row = torch.stack(diffusion_row) # n_log_step, n_row, C, H, W + diffusion_grid = rearrange(diffusion_row, 'n b c h w -> b n c h w') + diffusion_grid = rearrange(diffusion_grid, 'b n c h w -> (b n) c h w') + diffusion_grid = make_grid(diffusion_grid, nrow=diffusion_row.shape[0]) + log["diffusion_row"] = diffusion_grid + + if sample: + # get denoise row + with self.ema_scope("Plotting"): + samples, z_denoise_row = self.sample_log(cond=c,batch_size=N,ddim=use_ddim, + ddim_steps=ddim_steps,eta=ddim_eta) + # samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True) + x_samples = self.decode_first_stage(samples) + log["samples"] = x_samples + if plot_denoise_rows: + denoise_grid = self._get_denoise_row_from_list(z_denoise_row) + log["denoise_row"] = denoise_grid + + if quantize_denoised and not isinstance(self.first_stage_model, AutoencoderKL) and not isinstance( + self.first_stage_model, IdentityFirstStage): + # also display when quantizing x0 while sampling + with self.ema_scope("Plotting Quantized Denoised"): + samples, z_denoise_row = self.sample_log(cond=c,batch_size=N,ddim=use_ddim, + ddim_steps=ddim_steps,eta=ddim_eta, + quantize_denoised=True) + # samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True, + # quantize_denoised=True) + x_samples = self.decode_first_stage(samples.to(self.device)) + log["samples_x0_quantized"] = x_samples + + if inpaint: + # make a simple center square + h, w = z.shape[2], z.shape[3] + mask = torch.ones(N, h, w).to(self.device) + # zeros will be filled in + mask[:, h // 4:3 * h // 4, w // 4:3 * w // 4] = 0. + mask = mask[:, None, ...] + with self.ema_scope("Plotting Inpaint"): + + samples, _ = self.sample_log(cond=c,batch_size=N,ddim=use_ddim, eta=ddim_eta, + ddim_steps=ddim_steps, x0=z[:N], mask=mask) + x_samples = self.decode_first_stage(samples.to(self.device)) + log["samples_inpainting"] = x_samples + log["mask"] = mask + + # outpaint + with self.ema_scope("Plotting Outpaint"): + samples, _ = self.sample_log(cond=c, batch_size=N, ddim=use_ddim,eta=ddim_eta, + ddim_steps=ddim_steps, x0=z[:N], mask=mask) + x_samples = self.decode_first_stage(samples.to(self.device)) + log["samples_outpainting"] = x_samples + + if plot_progressive_rows: + with self.ema_scope("Plotting Progressives"): + img, progressives = self.progressive_denoising(c, + shape=(self.channels, self.image_size, self.image_size), + batch_size=N) + prog_row = self._get_denoise_row_from_list(progressives, desc="Progressive Generation") + log["progressive_row"] = prog_row + + if return_keys: + if np.intersect1d(list(log.keys()), return_keys).shape[0] == 0: + return log + else: + return {key: log[key] for key in return_keys} + return log + + def configure_optimizers(self): + lr = self.learning_rate + params = list(self.model.parameters()) + if self.cond_stage_trainable: + print(f"{self.__class__.__name__}: Also optimizing conditioner params!") + params = params + list(self.cond_stage_model.parameters()) + if self.learn_logvar: + print('Diffusion model optimizing logvar') + params.append(self.logvar) + opt = torch.optim.AdamW(params, lr=lr) + if self.use_scheduler: + assert 'target' in self.scheduler_config + scheduler = instantiate_from_config(self.scheduler_config) + + print("Setting up LambdaLR scheduler...") + scheduler = [ + { + 'scheduler': LambdaLR(opt, lr_lambda=scheduler.schedule), + 'interval': 'step', + 'frequency': 1 + }] + return [opt], scheduler + return opt + + @torch.no_grad() + def to_rgb(self, x): + x = x.float() + if not hasattr(self, "colorize"): + self.colorize = torch.randn(3, x.shape[1], 1, 1).to(x) + x = nn.functional.conv2d(x, weight=self.colorize) + x = 2. * (x - x.min()) / (x.max() - x.min()) - 1. + return x + + +class DiffusionWrapperV1(pl.LightningModule): + def __init__(self, diff_model_config, conditioning_key): + super().__init__() + self.diffusion_model = instantiate_from_config(diff_model_config) + self.conditioning_key = conditioning_key + assert self.conditioning_key in [None, 'concat', 'crossattn', 'hybrid', 'adm'] + + def forward(self, x, t, c_concat: list = None, c_crossattn: list = None): + if self.conditioning_key is None: + out = self.diffusion_model(x, t) + elif self.conditioning_key == 'concat': + xc = torch.cat([x] + c_concat, dim=1) + out = self.diffusion_model(xc, t) + elif self.conditioning_key == 'crossattn': + cc = torch.cat(c_crossattn, 1) + out = self.diffusion_model(x, t, context=cc) + elif self.conditioning_key == 'hybrid': + xc = torch.cat([x] + c_concat, dim=1) + cc = torch.cat(c_crossattn, 1) + out = self.diffusion_model(xc, t, context=cc) + elif self.conditioning_key == 'adm': + cc = c_crossattn[0] + out = self.diffusion_model(x, t, y=cc) + else: + raise NotImplementedError() + + return out + + +class Layout2ImgDiffusionV1(LatentDiffusionV1): + # TODO: move all layout-specific hacks to this class + def __init__(self, cond_stage_key, *args, **kwargs): + assert cond_stage_key == 'coordinates_bbox', 'Layout2ImgDiffusion only for cond_stage_key="coordinates_bbox"' + super().__init__(*args, cond_stage_key=cond_stage_key, **kwargs) + + def log_images(self, batch, N=8, *args, **kwargs): + logs = super().log_images(*args, batch=batch, N=N, **kwargs) + + key = 'train' if self.training else 'validation' + dset = self.trainer.datamodule.datasets[key] + mapper = dset.conditional_builders[self.cond_stage_key] + + bbox_imgs = [] + map_fn = lambda catno: dset.get_textual_label(dset.get_category_id(catno)) + for tknzd_bbox in batch[self.cond_stage_key][:N]: + bboximg = mapper.plot(tknzd_bbox.detach().cpu(), map_fn, (256, 256)) + bbox_imgs.append(bboximg) + + cond_img = torch.stack(bbox_imgs, dim=0) + logs['bbox_image'] = cond_img + return logs + +ldm.models.diffusion.ddpm.DDPMV1 = DDPMV1 +ldm.models.diffusion.ddpm.LatentDiffusionV1 = LatentDiffusionV1 +ldm.models.diffusion.ddpm.DiffusionWrapperV1 = DiffusionWrapperV1 +ldm.models.diffusion.ddpm.Layout2ImgDiffusionV1 = Layout2ImgDiffusionV1 diff --git a/extensions-builtin/LDSR/vqvae_quantize.py b/extensions-builtin/LDSR/vqvae_quantize.py new file mode 100644 index 0000000000000000000000000000000000000000..dd14b8fda5ce25a8cea8b70eb1d387b9c46c80d8 --- /dev/null +++ b/extensions-builtin/LDSR/vqvae_quantize.py @@ -0,0 +1,147 @@ +# Vendored from https://raw.githubusercontent.com/CompVis/taming-transformers/24268930bf1dce879235a7fddd0b2355b84d7ea6/taming/modules/vqvae/quantize.py, +# where the license is as follows: +# +# Copyright (c) 2020 Patrick Esser and Robin Rombach and Björn Ommer +# +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: +# +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. +# +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, +# EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF +# MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. +# IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, +# DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR +# OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE +# OR OTHER DEALINGS IN THE SOFTWARE./ + +import torch +import torch.nn as nn +import numpy as np +from einops import rearrange + + +class VectorQuantizer2(nn.Module): + """ + Improved version over VectorQuantizer, can be used as a drop-in replacement. Mostly + avoids costly matrix multiplications and allows for post-hoc remapping of indices. + """ + + # NOTE: due to a bug the beta term was applied to the wrong term. for + # backwards compatibility we use the buggy version by default, but you can + # specify legacy=False to fix it. + def __init__(self, n_e, e_dim, beta, remap=None, unknown_index="random", + sane_index_shape=False, legacy=True): + super().__init__() + self.n_e = n_e + self.e_dim = e_dim + self.beta = beta + self.legacy = legacy + + self.embedding = nn.Embedding(self.n_e, self.e_dim) + self.embedding.weight.data.uniform_(-1.0 / self.n_e, 1.0 / self.n_e) + + self.remap = remap + if self.remap is not None: + self.register_buffer("used", torch.tensor(np.load(self.remap))) + self.re_embed = self.used.shape[0] + self.unknown_index = unknown_index # "random" or "extra" or integer + if self.unknown_index == "extra": + self.unknown_index = self.re_embed + self.re_embed = self.re_embed + 1 + print(f"Remapping {self.n_e} indices to {self.re_embed} indices. " + f"Using {self.unknown_index} for unknown indices.") + else: + self.re_embed = n_e + + self.sane_index_shape = sane_index_shape + + def remap_to_used(self, inds): + ishape = inds.shape + assert len(ishape) > 1 + inds = inds.reshape(ishape[0], -1) + used = self.used.to(inds) + match = (inds[:, :, None] == used[None, None, ...]).long() + new = match.argmax(-1) + unknown = match.sum(2) < 1 + if self.unknown_index == "random": + new[unknown] = torch.randint(0, self.re_embed, size=new[unknown].shape).to(device=new.device) + else: + new[unknown] = self.unknown_index + return new.reshape(ishape) + + def unmap_to_all(self, inds): + ishape = inds.shape + assert len(ishape) > 1 + inds = inds.reshape(ishape[0], -1) + used = self.used.to(inds) + if self.re_embed > self.used.shape[0]: # extra token + inds[inds >= self.used.shape[0]] = 0 # simply set to zero + back = torch.gather(used[None, :][inds.shape[0] * [0], :], 1, inds) + return back.reshape(ishape) + + def forward(self, z, temp=None, rescale_logits=False, return_logits=False): + assert temp is None or temp == 1.0, "Only for interface compatible with Gumbel" + assert rescale_logits is False, "Only for interface compatible with Gumbel" + assert return_logits is False, "Only for interface compatible with Gumbel" + # reshape z -> (batch, height, width, channel) and flatten + z = rearrange(z, 'b c h w -> b h w c').contiguous() + z_flattened = z.view(-1, self.e_dim) + # distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z + + d = torch.sum(z_flattened ** 2, dim=1, keepdim=True) + \ + torch.sum(self.embedding.weight ** 2, dim=1) - 2 * \ + torch.einsum('bd,dn->bn', z_flattened, rearrange(self.embedding.weight, 'n d -> d n')) + + min_encoding_indices = torch.argmin(d, dim=1) + z_q = self.embedding(min_encoding_indices).view(z.shape) + perplexity = None + min_encodings = None + + # compute loss for embedding + if not self.legacy: + loss = self.beta * torch.mean((z_q.detach() - z) ** 2) + \ + torch.mean((z_q - z.detach()) ** 2) + else: + loss = torch.mean((z_q.detach() - z) ** 2) + self.beta * \ + torch.mean((z_q - z.detach()) ** 2) + + # preserve gradients + z_q = z + (z_q - z).detach() + + # reshape back to match original input shape + z_q = rearrange(z_q, 'b h w c -> b c h w').contiguous() + + if self.remap is not None: + min_encoding_indices = min_encoding_indices.reshape(z.shape[0], -1) # add batch axis + min_encoding_indices = self.remap_to_used(min_encoding_indices) + min_encoding_indices = min_encoding_indices.reshape(-1, 1) # flatten + + if self.sane_index_shape: + min_encoding_indices = min_encoding_indices.reshape( + z_q.shape[0], z_q.shape[2], z_q.shape[3]) + + return z_q, loss, (perplexity, min_encodings, min_encoding_indices) + + def get_codebook_entry(self, indices, shape): + # shape specifying (batch, height, width, channel) + if self.remap is not None: + indices = indices.reshape(shape[0], -1) # add batch axis + indices = self.unmap_to_all(indices) + indices = indices.reshape(-1) # flatten again + + # get quantized latent vectors + z_q = self.embedding(indices) + + if shape is not None: + z_q = z_q.view(shape) + # reshape back to match original input shape + z_q = z_q.permute(0, 3, 1, 2).contiguous() + + return z_q diff --git a/extensions-builtin/Lora/extra_networks_lora.py b/extensions-builtin/Lora/extra_networks_lora.py new file mode 100644 index 0000000000000000000000000000000000000000..88425009c7150f303b10bec8a42a3aa7a8c4ff93 --- /dev/null +++ b/extensions-builtin/Lora/extra_networks_lora.py @@ -0,0 +1,67 @@ +from modules import extra_networks, shared +import networks + + +class ExtraNetworkLora(extra_networks.ExtraNetwork): + def __init__(self): + super().__init__('lora') + + self.errors = {} + """mapping of network names to the number of errors the network had during operation""" + + def activate(self, p, params_list): + additional = shared.opts.sd_lora + + self.errors.clear() + + if additional != "None" and additional in networks.available_networks and not any(x for x in params_list if x.items[0] == additional): + p.all_prompts = [x + f"" for x in p.all_prompts] + params_list.append(extra_networks.ExtraNetworkParams(items=[additional, shared.opts.extra_networks_default_multiplier])) + + names = [] + te_multipliers = [] + unet_multipliers = [] + dyn_dims = [] + for params in params_list: + assert params.items + + names.append(params.positional[0]) + + te_multiplier = float(params.positional[1]) if len(params.positional) > 1 else 1.0 + te_multiplier = float(params.named.get("te", te_multiplier)) + + unet_multiplier = float(params.positional[2]) if len(params.positional) > 2 else te_multiplier + unet_multiplier = float(params.named.get("unet", unet_multiplier)) + + dyn_dim = int(params.positional[3]) if len(params.positional) > 3 else None + dyn_dim = int(params.named["dyn"]) if "dyn" in params.named else dyn_dim + + te_multipliers.append(te_multiplier) + unet_multipliers.append(unet_multiplier) + dyn_dims.append(dyn_dim) + + networks.load_networks(names, te_multipliers, unet_multipliers, dyn_dims) + + if shared.opts.lora_add_hashes_to_infotext: + network_hashes = [] + for item in networks.loaded_networks: + shorthash = item.network_on_disk.shorthash + if not shorthash: + continue + + alias = item.mentioned_name + if not alias: + continue + + alias = alias.replace(":", "").replace(",", "") + + network_hashes.append(f"{alias}: {shorthash}") + + if network_hashes: + p.extra_generation_params["Lora hashes"] = ", ".join(network_hashes) + + def deactivate(self, p): + if self.errors: + p.comment("Networks with errors: " + ", ".join(f"{k} ({v})" for k, v in self.errors.items())) + + self.errors.clear() diff --git a/extensions-builtin/Lora/lora.py b/extensions-builtin/Lora/lora.py new file mode 100644 index 0000000000000000000000000000000000000000..6186538e956e39c843a2a22a77c5ab53fdfec3c7 --- /dev/null +++ b/extensions-builtin/Lora/lora.py @@ -0,0 +1,9 @@ +import networks + +list_available_loras = networks.list_available_networks + +available_loras = networks.available_networks +available_lora_aliases = networks.available_network_aliases +available_lora_hash_lookup = networks.available_network_hash_lookup +forbidden_lora_aliases = networks.forbidden_network_aliases +loaded_loras = networks.loaded_networks diff --git a/extensions-builtin/Lora/lora_logger.py b/extensions-builtin/Lora/lora_logger.py new file mode 100644 index 0000000000000000000000000000000000000000..d51de29704f72b80958dbabda021c6648aef8177 --- /dev/null +++ b/extensions-builtin/Lora/lora_logger.py @@ -0,0 +1,33 @@ +import sys +import copy +import logging + + +class ColoredFormatter(logging.Formatter): + COLORS = { + "DEBUG": "\033[0;36m", # CYAN + "INFO": "\033[0;32m", # GREEN + "WARNING": "\033[0;33m", # YELLOW + "ERROR": "\033[0;31m", # RED + "CRITICAL": "\033[0;37;41m", # WHITE ON RED + "RESET": "\033[0m", # RESET COLOR + } + + def format(self, record): + colored_record = copy.copy(record) + levelname = colored_record.levelname + seq = self.COLORS.get(levelname, self.COLORS["RESET"]) + colored_record.levelname = f"{seq}{levelname}{self.COLORS['RESET']}" + return super().format(colored_record) + + +logger = logging.getLogger("lora") +logger.propagate = False + + +if not logger.handlers: + handler = logging.StreamHandler(sys.stdout) + handler.setFormatter( + ColoredFormatter("[%(name)s]-%(levelname)s: %(message)s") + ) + logger.addHandler(handler) diff --git a/extensions-builtin/Lora/lora_patches.py b/extensions-builtin/Lora/lora_patches.py new file mode 100644 index 0000000000000000000000000000000000000000..59859e6f94f434a032c2c07f040bceb79fcd1dc2 --- /dev/null +++ b/extensions-builtin/Lora/lora_patches.py @@ -0,0 +1,31 @@ +import torch + +import networks +from modules import patches + + +class LoraPatches: + def __init__(self): + self.Linear_forward = patches.patch(__name__, torch.nn.Linear, 'forward', networks.network_Linear_forward) + self.Linear_load_state_dict = patches.patch(__name__, torch.nn.Linear, '_load_from_state_dict', networks.network_Linear_load_state_dict) + self.Conv2d_forward = patches.patch(__name__, torch.nn.Conv2d, 'forward', networks.network_Conv2d_forward) + self.Conv2d_load_state_dict = patches.patch(__name__, torch.nn.Conv2d, '_load_from_state_dict', networks.network_Conv2d_load_state_dict) + self.GroupNorm_forward = patches.patch(__name__, torch.nn.GroupNorm, 'forward', networks.network_GroupNorm_forward) + self.GroupNorm_load_state_dict = patches.patch(__name__, torch.nn.GroupNorm, '_load_from_state_dict', networks.network_GroupNorm_load_state_dict) + self.LayerNorm_forward = patches.patch(__name__, torch.nn.LayerNorm, 'forward', networks.network_LayerNorm_forward) + self.LayerNorm_load_state_dict = patches.patch(__name__, torch.nn.LayerNorm, '_load_from_state_dict', networks.network_LayerNorm_load_state_dict) + self.MultiheadAttention_forward = patches.patch(__name__, torch.nn.MultiheadAttention, 'forward', networks.network_MultiheadAttention_forward) + self.MultiheadAttention_load_state_dict = patches.patch(__name__, torch.nn.MultiheadAttention, '_load_from_state_dict', networks.network_MultiheadAttention_load_state_dict) + + def undo(self): + self.Linear_forward = patches.undo(__name__, torch.nn.Linear, 'forward') + self.Linear_load_state_dict = patches.undo(__name__, torch.nn.Linear, '_load_from_state_dict') + self.Conv2d_forward = patches.undo(__name__, torch.nn.Conv2d, 'forward') + self.Conv2d_load_state_dict = patches.undo(__name__, torch.nn.Conv2d, '_load_from_state_dict') + self.GroupNorm_forward = patches.undo(__name__, torch.nn.GroupNorm, 'forward') + self.GroupNorm_load_state_dict = patches.undo(__name__, torch.nn.GroupNorm, '_load_from_state_dict') + self.LayerNorm_forward = patches.undo(__name__, torch.nn.LayerNorm, 'forward') + self.LayerNorm_load_state_dict = patches.undo(__name__, torch.nn.LayerNorm, '_load_from_state_dict') + self.MultiheadAttention_forward = patches.undo(__name__, torch.nn.MultiheadAttention, 'forward') + self.MultiheadAttention_load_state_dict = patches.undo(__name__, torch.nn.MultiheadAttention, '_load_from_state_dict') + diff --git a/extensions-builtin/Lora/lyco_helpers.py b/extensions-builtin/Lora/lyco_helpers.py new file mode 100644 index 0000000000000000000000000000000000000000..e0e1afa7887aae587f854b6f3ea3d231f2e8f6eb --- /dev/null +++ b/extensions-builtin/Lora/lyco_helpers.py @@ -0,0 +1,68 @@ +import torch + + +def make_weight_cp(t, wa, wb): + temp = torch.einsum('i j k l, j r -> i r k l', t, wb) + return torch.einsum('i j k l, i r -> r j k l', temp, wa) + + +def rebuild_conventional(up, down, shape, dyn_dim=None): + up = up.reshape(up.size(0), -1) + down = down.reshape(down.size(0), -1) + if dyn_dim is not None: + up = up[:, :dyn_dim] + down = down[:dyn_dim, :] + return (up @ down).reshape(shape) + + +def rebuild_cp_decomposition(up, down, mid): + up = up.reshape(up.size(0), -1) + down = down.reshape(down.size(0), -1) + return torch.einsum('n m k l, i n, m j -> i j k l', mid, up, down) + + +# copied from https://github.com/KohakuBlueleaf/LyCORIS/blob/dev/lycoris/modules/lokr.py +def factorization(dimension: int, factor:int=-1) -> tuple[int, int]: + ''' + return a tuple of two value of input dimension decomposed by the number closest to factor + second value is higher or equal than first value. + + In LoRA with Kroneckor Product, first value is a value for weight scale. + secon value is a value for weight. + + Becuase of non-commutative property, A⊗B ≠ B⊗A. Meaning of two matrices is slightly different. + + examples) + factor + -1 2 4 8 16 ... + 127 -> 1, 127 127 -> 1, 127 127 -> 1, 127 127 -> 1, 127 127 -> 1, 127 + 128 -> 8, 16 128 -> 2, 64 128 -> 4, 32 128 -> 8, 16 128 -> 8, 16 + 250 -> 10, 25 250 -> 2, 125 250 -> 2, 125 250 -> 5, 50 250 -> 10, 25 + 360 -> 8, 45 360 -> 2, 180 360 -> 4, 90 360 -> 8, 45 360 -> 12, 30 + 512 -> 16, 32 512 -> 2, 256 512 -> 4, 128 512 -> 8, 64 512 -> 16, 32 + 1024 -> 32, 32 1024 -> 2, 512 1024 -> 4, 256 1024 -> 8, 128 1024 -> 16, 64 + ''' + + if factor > 0 and (dimension % factor) == 0: + m = factor + n = dimension // factor + if m > n: + n, m = m, n + return m, n + if factor < 0: + factor = dimension + m, n = 1, dimension + length = m + n + while m length or new_m>factor: + break + else: + m, n = new_m, new_n + if m > n: + n, m = m, n + return m, n + diff --git a/extensions-builtin/Lora/network.py b/extensions-builtin/Lora/network.py new file mode 100644 index 0000000000000000000000000000000000000000..e14cc67c9104c88bb222cf72e162a4d99b77b0a8 --- /dev/null +++ b/extensions-builtin/Lora/network.py @@ -0,0 +1,159 @@ +from __future__ import annotations +import os +from collections import namedtuple +import enum + +from modules import sd_models, cache, errors, hashes, shared + +NetworkWeights = namedtuple('NetworkWeights', ['network_key', 'sd_key', 'w', 'sd_module']) + +metadata_tags_order = {"ss_sd_model_name": 1, "ss_resolution": 2, "ss_clip_skip": 3, "ss_num_train_images": 10, "ss_tag_frequency": 20} + + +class SdVersion(enum.Enum): + Unknown = 1 + SD1 = 2 + SD2 = 3 + SDXL = 4 + + +class NetworkOnDisk: + def __init__(self, name, filename): + self.name = name + self.filename = filename + self.metadata = {} + self.is_safetensors = os.path.splitext(filename)[1].lower() == ".safetensors" + + def read_metadata(): + metadata = sd_models.read_metadata_from_safetensors(filename) + metadata.pop('ssmd_cover_images', None) # those are cover images, and they are too big to display in UI as text + + return metadata + + if self.is_safetensors: + try: + self.metadata = cache.cached_data_for_file('safetensors-metadata', "lora/" + self.name, filename, read_metadata) + except Exception as e: + errors.display(e, f"reading lora {filename}") + + if self.metadata: + m = {} + for k, v in sorted(self.metadata.items(), key=lambda x: metadata_tags_order.get(x[0], 999)): + m[k] = v + + self.metadata = m + + self.alias = self.metadata.get('ss_output_name', self.name) + + self.hash = None + self.shorthash = None + self.set_hash( + self.metadata.get('sshs_model_hash') or + hashes.sha256_from_cache(self.filename, "lora/" + self.name, use_addnet_hash=self.is_safetensors) or + '' + ) + + self.sd_version = self.detect_version() + + def detect_version(self): + if str(self.metadata.get('ss_base_model_version', "")).startswith("sdxl_"): + return SdVersion.SDXL + elif str(self.metadata.get('ss_v2', "")) == "True": + return SdVersion.SD2 + elif len(self.metadata): + return SdVersion.SD1 + + return SdVersion.Unknown + + def set_hash(self, v): + self.hash = v + self.shorthash = self.hash[0:12] + + if self.shorthash: + import networks + networks.available_network_hash_lookup[self.shorthash] = self + + def read_hash(self): + if not self.hash: + self.set_hash(hashes.sha256(self.filename, "lora/" + self.name, use_addnet_hash=self.is_safetensors) or '') + + def get_alias(self): + import networks + if shared.opts.lora_preferred_name == "Filename" or self.alias.lower() in networks.forbidden_network_aliases: + return self.name + else: + return self.alias + + +class Network: # LoraModule + def __init__(self, name, network_on_disk: NetworkOnDisk): + self.name = name + self.network_on_disk = network_on_disk + self.te_multiplier = 1.0 + self.unet_multiplier = 1.0 + self.dyn_dim = None + self.modules = {} + self.bundle_embeddings = {} + self.mtime = None + + self.mentioned_name = None + """the text that was used to add the network to prompt - can be either name or an alias""" + + +class ModuleType: + def create_module(self, net: Network, weights: NetworkWeights) -> Network | None: + return None + + +class NetworkModule: + def __init__(self, net: Network, weights: NetworkWeights): + self.network = net + self.network_key = weights.network_key + self.sd_key = weights.sd_key + self.sd_module = weights.sd_module + + if hasattr(self.sd_module, 'weight'): + self.shape = self.sd_module.weight.shape + + self.dim = None + self.bias = weights.w.get("bias") + self.alpha = weights.w["alpha"].item() if "alpha" in weights.w else None + self.scale = weights.w["scale"].item() if "scale" in weights.w else None + + def multiplier(self): + if 'transformer' in self.sd_key[:20]: + return self.network.te_multiplier + else: + return self.network.unet_multiplier + + def calc_scale(self): + if self.scale is not None: + return self.scale + if self.dim is not None and self.alpha is not None: + return self.alpha / self.dim + + return 1.0 + + def finalize_updown(self, updown, orig_weight, output_shape, ex_bias=None): + if self.bias is not None: + updown = updown.reshape(self.bias.shape) + updown += self.bias.to(orig_weight.device, dtype=orig_weight.dtype) + updown = updown.reshape(output_shape) + + if len(output_shape) == 4: + updown = updown.reshape(output_shape) + + if orig_weight.size().numel() == updown.size().numel(): + updown = updown.reshape(orig_weight.shape) + + if ex_bias is not None: + ex_bias = ex_bias * self.multiplier() + + return updown * self.calc_scale() * self.multiplier(), ex_bias + + def calc_updown(self, target): + raise NotImplementedError() + + def forward(self, x, y): + raise NotImplementedError() + diff --git a/extensions-builtin/Lora/network_full.py b/extensions-builtin/Lora/network_full.py new file mode 100644 index 0000000000000000000000000000000000000000..545e254e0c675c8b81668cbd234d41edaba524e7 --- /dev/null +++ b/extensions-builtin/Lora/network_full.py @@ -0,0 +1,27 @@ +import network + + +class ModuleTypeFull(network.ModuleType): + def create_module(self, net: network.Network, weights: network.NetworkWeights): + if all(x in weights.w for x in ["diff"]): + return NetworkModuleFull(net, weights) + + return None + + +class NetworkModuleFull(network.NetworkModule): + def __init__(self, net: network.Network, weights: network.NetworkWeights): + super().__init__(net, weights) + + self.weight = weights.w.get("diff") + self.ex_bias = weights.w.get("diff_b") + + def calc_updown(self, orig_weight): + output_shape = self.weight.shape + updown = self.weight.to(orig_weight.device, dtype=orig_weight.dtype) + if self.ex_bias is not None: + ex_bias = self.ex_bias.to(orig_weight.device, dtype=orig_weight.dtype) + else: + ex_bias = None + + return self.finalize_updown(updown, orig_weight, output_shape, ex_bias) diff --git a/extensions-builtin/Lora/network_glora.py b/extensions-builtin/Lora/network_glora.py new file mode 100644 index 0000000000000000000000000000000000000000..492d487078de426074066b0176840fed9bb66c29 --- /dev/null +++ b/extensions-builtin/Lora/network_glora.py @@ -0,0 +1,33 @@ + +import network + +class ModuleTypeGLora(network.ModuleType): + def create_module(self, net: network.Network, weights: network.NetworkWeights): + if all(x in weights.w for x in ["a1.weight", "a2.weight", "alpha", "b1.weight", "b2.weight"]): + return NetworkModuleGLora(net, weights) + + return None + +# adapted from https://github.com/KohakuBlueleaf/LyCORIS +class NetworkModuleGLora(network.NetworkModule): + def __init__(self, net: network.Network, weights: network.NetworkWeights): + super().__init__(net, weights) + + if hasattr(self.sd_module, 'weight'): + self.shape = self.sd_module.weight.shape + + self.w1a = weights.w["a1.weight"] + self.w1b = weights.w["b1.weight"] + self.w2a = weights.w["a2.weight"] + self.w2b = weights.w["b2.weight"] + + def calc_updown(self, orig_weight): + w1a = self.w1a.to(orig_weight.device, dtype=orig_weight.dtype) + w1b = self.w1b.to(orig_weight.device, dtype=orig_weight.dtype) + w2a = self.w2a.to(orig_weight.device, dtype=orig_weight.dtype) + w2b = self.w2b.to(orig_weight.device, dtype=orig_weight.dtype) + + output_shape = [w1a.size(0), w1b.size(1)] + updown = ((w2b @ w1b) + ((orig_weight @ w2a) @ w1a)) + + return self.finalize_updown(updown, orig_weight, output_shape) diff --git a/extensions-builtin/Lora/network_hada.py b/extensions-builtin/Lora/network_hada.py new file mode 100644 index 0000000000000000000000000000000000000000..b62e88840866f2801b5bafa657cfd9b0377054b7 --- /dev/null +++ b/extensions-builtin/Lora/network_hada.py @@ -0,0 +1,55 @@ +import lyco_helpers +import network + + +class ModuleTypeHada(network.ModuleType): + def create_module(self, net: network.Network, weights: network.NetworkWeights): + if all(x in weights.w for x in ["hada_w1_a", "hada_w1_b", "hada_w2_a", "hada_w2_b"]): + return NetworkModuleHada(net, weights) + + return None + + +class NetworkModuleHada(network.NetworkModule): + def __init__(self, net: network.Network, weights: network.NetworkWeights): + super().__init__(net, weights) + + if hasattr(self.sd_module, 'weight'): + self.shape = self.sd_module.weight.shape + + self.w1a = weights.w["hada_w1_a"] + self.w1b = weights.w["hada_w1_b"] + self.dim = self.w1b.shape[0] + self.w2a = weights.w["hada_w2_a"] + self.w2b = weights.w["hada_w2_b"] + + self.t1 = weights.w.get("hada_t1") + self.t2 = weights.w.get("hada_t2") + + def calc_updown(self, orig_weight): + w1a = self.w1a.to(orig_weight.device, dtype=orig_weight.dtype) + w1b = self.w1b.to(orig_weight.device, dtype=orig_weight.dtype) + w2a = self.w2a.to(orig_weight.device, dtype=orig_weight.dtype) + w2b = self.w2b.to(orig_weight.device, dtype=orig_weight.dtype) + + output_shape = [w1a.size(0), w1b.size(1)] + + if self.t1 is not None: + output_shape = [w1a.size(1), w1b.size(1)] + t1 = self.t1.to(orig_weight.device, dtype=orig_weight.dtype) + updown1 = lyco_helpers.make_weight_cp(t1, w1a, w1b) + output_shape += t1.shape[2:] + else: + if len(w1b.shape) == 4: + output_shape += w1b.shape[2:] + updown1 = lyco_helpers.rebuild_conventional(w1a, w1b, output_shape) + + if self.t2 is not None: + t2 = self.t2.to(orig_weight.device, dtype=orig_weight.dtype) + updown2 = lyco_helpers.make_weight_cp(t2, w2a, w2b) + else: + updown2 = lyco_helpers.rebuild_conventional(w2a, w2b, output_shape) + + updown = updown1 * updown2 + + return self.finalize_updown(updown, orig_weight, output_shape) diff --git a/extensions-builtin/Lora/network_ia3.py b/extensions-builtin/Lora/network_ia3.py new file mode 100644 index 0000000000000000000000000000000000000000..ddf5d68983c3b8d57ad3d58b293e6bc462d52159 --- /dev/null +++ b/extensions-builtin/Lora/network_ia3.py @@ -0,0 +1,30 @@ +import network + + +class ModuleTypeIa3(network.ModuleType): + def create_module(self, net: network.Network, weights: network.NetworkWeights): + if all(x in weights.w for x in ["weight"]): + return NetworkModuleIa3(net, weights) + + return None + + +class NetworkModuleIa3(network.NetworkModule): + def __init__(self, net: network.Network, weights: network.NetworkWeights): + super().__init__(net, weights) + + self.w = weights.w["weight"] + self.on_input = weights.w["on_input"].item() + + def calc_updown(self, orig_weight): + w = self.w.to(orig_weight.device, dtype=orig_weight.dtype) + + output_shape = [w.size(0), orig_weight.size(1)] + if self.on_input: + output_shape.reverse() + else: + w = w.reshape(-1, 1) + + updown = orig_weight * w + + return self.finalize_updown(updown, orig_weight, output_shape) diff --git a/extensions-builtin/Lora/network_lokr.py b/extensions-builtin/Lora/network_lokr.py new file mode 100644 index 0000000000000000000000000000000000000000..87fbafa1b406de73cc394a3a0c9068da4119b0d8 --- /dev/null +++ b/extensions-builtin/Lora/network_lokr.py @@ -0,0 +1,64 @@ +import torch + +import lyco_helpers +import network + + +class ModuleTypeLokr(network.ModuleType): + def create_module(self, net: network.Network, weights: network.NetworkWeights): + has_1 = "lokr_w1" in weights.w or ("lokr_w1_a" in weights.w and "lokr_w1_b" in weights.w) + has_2 = "lokr_w2" in weights.w or ("lokr_w2_a" in weights.w and "lokr_w2_b" in weights.w) + if has_1 and has_2: + return NetworkModuleLokr(net, weights) + + return None + + +def make_kron(orig_shape, w1, w2): + if len(w2.shape) == 4: + w1 = w1.unsqueeze(2).unsqueeze(2) + w2 = w2.contiguous() + return torch.kron(w1, w2).reshape(orig_shape) + + +class NetworkModuleLokr(network.NetworkModule): + def __init__(self, net: network.Network, weights: network.NetworkWeights): + super().__init__(net, weights) + + self.w1 = weights.w.get("lokr_w1") + self.w1a = weights.w.get("lokr_w1_a") + self.w1b = weights.w.get("lokr_w1_b") + self.dim = self.w1b.shape[0] if self.w1b is not None else self.dim + self.w2 = weights.w.get("lokr_w2") + self.w2a = weights.w.get("lokr_w2_a") + self.w2b = weights.w.get("lokr_w2_b") + self.dim = self.w2b.shape[0] if self.w2b is not None else self.dim + self.t2 = weights.w.get("lokr_t2") + + def calc_updown(self, orig_weight): + if self.w1 is not None: + w1 = self.w1.to(orig_weight.device, dtype=orig_weight.dtype) + else: + w1a = self.w1a.to(orig_weight.device, dtype=orig_weight.dtype) + w1b = self.w1b.to(orig_weight.device, dtype=orig_weight.dtype) + w1 = w1a @ w1b + + if self.w2 is not None: + w2 = self.w2.to(orig_weight.device, dtype=orig_weight.dtype) + elif self.t2 is None: + w2a = self.w2a.to(orig_weight.device, dtype=orig_weight.dtype) + w2b = self.w2b.to(orig_weight.device, dtype=orig_weight.dtype) + w2 = w2a @ w2b + else: + t2 = self.t2.to(orig_weight.device, dtype=orig_weight.dtype) + w2a = self.w2a.to(orig_weight.device, dtype=orig_weight.dtype) + w2b = self.w2b.to(orig_weight.device, dtype=orig_weight.dtype) + w2 = lyco_helpers.make_weight_cp(t2, w2a, w2b) + + output_shape = [w1.size(0) * w2.size(0), w1.size(1) * w2.size(1)] + if len(orig_weight.shape) == 4: + output_shape = orig_weight.shape + + updown = make_kron(output_shape, w1, w2) + + return self.finalize_updown(updown, orig_weight, output_shape) diff --git a/extensions-builtin/Lora/network_lora.py b/extensions-builtin/Lora/network_lora.py new file mode 100644 index 0000000000000000000000000000000000000000..cb63807a09a6883fa636822ebc01753e2cb4848f --- /dev/null +++ b/extensions-builtin/Lora/network_lora.py @@ -0,0 +1,86 @@ +import torch + +import lyco_helpers +import network +from modules import devices + + +class ModuleTypeLora(network.ModuleType): + def create_module(self, net: network.Network, weights: network.NetworkWeights): + if all(x in weights.w for x in ["lora_up.weight", "lora_down.weight"]): + return NetworkModuleLora(net, weights) + + return None + + +class NetworkModuleLora(network.NetworkModule): + def __init__(self, net: network.Network, weights: network.NetworkWeights): + super().__init__(net, weights) + + self.up_model = self.create_module(weights.w, "lora_up.weight") + self.down_model = self.create_module(weights.w, "lora_down.weight") + self.mid_model = self.create_module(weights.w, "lora_mid.weight", none_ok=True) + + self.dim = weights.w["lora_down.weight"].shape[0] + + def create_module(self, weights, key, none_ok=False): + weight = weights.get(key) + + if weight is None and none_ok: + return None + + is_linear = type(self.sd_module) in [torch.nn.Linear, torch.nn.modules.linear.NonDynamicallyQuantizableLinear, torch.nn.MultiheadAttention] + is_conv = type(self.sd_module) in [torch.nn.Conv2d] + + if is_linear: + weight = weight.reshape(weight.shape[0], -1) + module = torch.nn.Linear(weight.shape[1], weight.shape[0], bias=False) + elif is_conv and key == "lora_down.weight" or key == "dyn_up": + if len(weight.shape) == 2: + weight = weight.reshape(weight.shape[0], -1, 1, 1) + + if weight.shape[2] != 1 or weight.shape[3] != 1: + module = torch.nn.Conv2d(weight.shape[1], weight.shape[0], self.sd_module.kernel_size, self.sd_module.stride, self.sd_module.padding, bias=False) + else: + module = torch.nn.Conv2d(weight.shape[1], weight.shape[0], (1, 1), bias=False) + elif is_conv and key == "lora_mid.weight": + module = torch.nn.Conv2d(weight.shape[1], weight.shape[0], self.sd_module.kernel_size, self.sd_module.stride, self.sd_module.padding, bias=False) + elif is_conv and key == "lora_up.weight" or key == "dyn_down": + module = torch.nn.Conv2d(weight.shape[1], weight.shape[0], (1, 1), bias=False) + else: + raise AssertionError(f'Lora layer {self.network_key} matched a layer with unsupported type: {type(self.sd_module).__name__}') + + with torch.no_grad(): + if weight.shape != module.weight.shape: + weight = weight.reshape(module.weight.shape) + module.weight.copy_(weight) + + module.to(device=devices.cpu, dtype=devices.dtype) + module.weight.requires_grad_(False) + + return module + + def calc_updown(self, orig_weight): + up = self.up_model.weight.to(orig_weight.device, dtype=orig_weight.dtype) + down = self.down_model.weight.to(orig_weight.device, dtype=orig_weight.dtype) + + output_shape = [up.size(0), down.size(1)] + if self.mid_model is not None: + # cp-decomposition + mid = self.mid_model.weight.to(orig_weight.device, dtype=orig_weight.dtype) + updown = lyco_helpers.rebuild_cp_decomposition(up, down, mid) + output_shape += mid.shape[2:] + else: + if len(down.shape) == 4: + output_shape += down.shape[2:] + updown = lyco_helpers.rebuild_conventional(up, down, output_shape, self.network.dyn_dim) + + return self.finalize_updown(updown, orig_weight, output_shape) + + def forward(self, x, y): + self.up_model.to(device=devices.device) + self.down_model.to(device=devices.device) + + return y + self.up_model(self.down_model(x)) * self.multiplier() * self.calc_scale() + + diff --git a/extensions-builtin/Lora/network_norm.py b/extensions-builtin/Lora/network_norm.py new file mode 100644 index 0000000000000000000000000000000000000000..ce450158068ef85ebe11cc60756ed991465c0e54 --- /dev/null +++ b/extensions-builtin/Lora/network_norm.py @@ -0,0 +1,28 @@ +import network + + +class ModuleTypeNorm(network.ModuleType): + def create_module(self, net: network.Network, weights: network.NetworkWeights): + if all(x in weights.w for x in ["w_norm", "b_norm"]): + return NetworkModuleNorm(net, weights) + + return None + + +class NetworkModuleNorm(network.NetworkModule): + def __init__(self, net: network.Network, weights: network.NetworkWeights): + super().__init__(net, weights) + + self.w_norm = weights.w.get("w_norm") + self.b_norm = weights.w.get("b_norm") + + def calc_updown(self, orig_weight): + output_shape = self.w_norm.shape + updown = self.w_norm.to(orig_weight.device, dtype=orig_weight.dtype) + + if self.b_norm is not None: + ex_bias = self.b_norm.to(orig_weight.device, dtype=orig_weight.dtype) + else: + ex_bias = None + + return self.finalize_updown(updown, orig_weight, output_shape, ex_bias) diff --git a/extensions-builtin/Lora/network_oft.py b/extensions-builtin/Lora/network_oft.py new file mode 100644 index 0000000000000000000000000000000000000000..fa647020f0a3cad281e74596d749daf3ee412c20 --- /dev/null +++ b/extensions-builtin/Lora/network_oft.py @@ -0,0 +1,82 @@ +import torch +import network +from lyco_helpers import factorization +from einops import rearrange + + +class ModuleTypeOFT(network.ModuleType): + def create_module(self, net: network.Network, weights: network.NetworkWeights): + if all(x in weights.w for x in ["oft_blocks"]) or all(x in weights.w for x in ["oft_diag"]): + return NetworkModuleOFT(net, weights) + + return None + +# Supports both kohya-ss' implementation of COFT https://github.com/kohya-ss/sd-scripts/blob/main/networks/oft.py +# and KohakuBlueleaf's implementation of OFT/COFT https://github.com/KohakuBlueleaf/LyCORIS/blob/dev/lycoris/modules/diag_oft.py +class NetworkModuleOFT(network.NetworkModule): + def __init__(self, net: network.Network, weights: network.NetworkWeights): + + super().__init__(net, weights) + + self.lin_module = None + self.org_module: list[torch.Module] = [self.sd_module] + + self.scale = 1.0 + + # kohya-ss + if "oft_blocks" in weights.w.keys(): + self.is_kohya = True + self.oft_blocks = weights.w["oft_blocks"] # (num_blocks, block_size, block_size) + self.alpha = weights.w["alpha"] # alpha is constraint + self.dim = self.oft_blocks.shape[0] # lora dim + # LyCORIS + elif "oft_diag" in weights.w.keys(): + self.is_kohya = False + self.oft_blocks = weights.w["oft_diag"] + # self.alpha is unused + self.dim = self.oft_blocks.shape[1] # (num_blocks, block_size, block_size) + + is_linear = type(self.sd_module) in [torch.nn.Linear, torch.nn.modules.linear.NonDynamicallyQuantizableLinear] + is_conv = type(self.sd_module) in [torch.nn.Conv2d] + is_other_linear = type(self.sd_module) in [torch.nn.MultiheadAttention] # unsupported + + if is_linear: + self.out_dim = self.sd_module.out_features + elif is_conv: + self.out_dim = self.sd_module.out_channels + elif is_other_linear: + self.out_dim = self.sd_module.embed_dim + + if self.is_kohya: + self.constraint = self.alpha * self.out_dim + self.num_blocks = self.dim + self.block_size = self.out_dim // self.dim + else: + self.constraint = None + self.block_size, self.num_blocks = factorization(self.out_dim, self.dim) + + def calc_updown(self, orig_weight): + oft_blocks = self.oft_blocks.to(orig_weight.device, dtype=orig_weight.dtype) + eye = torch.eye(self.block_size, device=self.oft_blocks.device) + + if self.is_kohya: + block_Q = oft_blocks - oft_blocks.transpose(1, 2) # ensure skew-symmetric orthogonal matrix + norm_Q = torch.norm(block_Q.flatten()) + new_norm_Q = torch.clamp(norm_Q, max=self.constraint) + block_Q = block_Q * ((new_norm_Q + 1e-8) / (norm_Q + 1e-8)) + oft_blocks = torch.matmul(eye + block_Q, (eye - block_Q).float().inverse()) + + R = oft_blocks.to(orig_weight.device, dtype=orig_weight.dtype) + + # This errors out for MultiheadAttention, might need to be handled up-stream + merged_weight = rearrange(orig_weight, '(k n) ... -> k n ...', k=self.num_blocks, n=self.block_size) + merged_weight = torch.einsum( + 'k n m, k n ... -> k m ...', + R, + merged_weight + ) + merged_weight = rearrange(merged_weight, 'k m ... -> (k m) ...') + + updown = merged_weight.to(orig_weight.device, dtype=orig_weight.dtype) - orig_weight + output_shape = orig_weight.shape + return self.finalize_updown(updown, orig_weight, output_shape) diff --git a/extensions-builtin/Lora/networks.py b/extensions-builtin/Lora/networks.py new file mode 100644 index 0000000000000000000000000000000000000000..c905e51d03f4e419b738b72589fa4fa4e6dba9b1 --- /dev/null +++ b/extensions-builtin/Lora/networks.py @@ -0,0 +1,629 @@ +import logging +import os +import re + +import lora_patches +import network +import network_lora +import network_glora +import network_hada +import network_ia3 +import network_lokr +import network_full +import network_norm +import network_oft + +import torch +from typing import Union + +from modules import shared, devices, sd_models, errors, scripts, sd_hijack +import modules.textual_inversion.textual_inversion as textual_inversion + +from lora_logger import logger + +module_types = [ + network_lora.ModuleTypeLora(), + network_hada.ModuleTypeHada(), + network_ia3.ModuleTypeIa3(), + network_lokr.ModuleTypeLokr(), + network_full.ModuleTypeFull(), + network_norm.ModuleTypeNorm(), + network_glora.ModuleTypeGLora(), + network_oft.ModuleTypeOFT(), +] + + +re_digits = re.compile(r"\d+") +re_x_proj = re.compile(r"(.*)_([qkv]_proj)$") +re_compiled = {} + +suffix_conversion = { + "attentions": {}, + "resnets": { + "conv1": "in_layers_2", + "conv2": "out_layers_3", + "norm1": "in_layers_0", + "norm2": "out_layers_0", + "time_emb_proj": "emb_layers_1", + "conv_shortcut": "skip_connection", + } +} + + +def convert_diffusers_name_to_compvis(key, is_sd2): + def match(match_list, regex_text): + regex = re_compiled.get(regex_text) + if regex is None: + regex = re.compile(regex_text) + re_compiled[regex_text] = regex + + r = re.match(regex, key) + if not r: + return False + + match_list.clear() + match_list.extend([int(x) if re.match(re_digits, x) else x for x in r.groups()]) + return True + + m = [] + + if match(m, r"lora_unet_conv_in(.*)"): + return f'diffusion_model_input_blocks_0_0{m[0]}' + + if match(m, r"lora_unet_conv_out(.*)"): + return f'diffusion_model_out_2{m[0]}' + + if match(m, r"lora_unet_time_embedding_linear_(\d+)(.*)"): + return f"diffusion_model_time_embed_{m[0] * 2 - 2}{m[1]}" + + if match(m, r"lora_unet_down_blocks_(\d+)_(attentions|resnets)_(\d+)_(.+)"): + suffix = suffix_conversion.get(m[1], {}).get(m[3], m[3]) + return f"diffusion_model_input_blocks_{1 + m[0] * 3 + m[2]}_{1 if m[1] == 'attentions' else 0}_{suffix}" + + if match(m, r"lora_unet_mid_block_(attentions|resnets)_(\d+)_(.+)"): + suffix = suffix_conversion.get(m[0], {}).get(m[2], m[2]) + return f"diffusion_model_middle_block_{1 if m[0] == 'attentions' else m[1] * 2}_{suffix}" + + if match(m, r"lora_unet_up_blocks_(\d+)_(attentions|resnets)_(\d+)_(.+)"): + suffix = suffix_conversion.get(m[1], {}).get(m[3], m[3]) + return f"diffusion_model_output_blocks_{m[0] * 3 + m[2]}_{1 if m[1] == 'attentions' else 0}_{suffix}" + + if match(m, r"lora_unet_down_blocks_(\d+)_downsamplers_0_conv"): + return f"diffusion_model_input_blocks_{3 + m[0] * 3}_0_op" + + if match(m, r"lora_unet_up_blocks_(\d+)_upsamplers_0_conv"): + return f"diffusion_model_output_blocks_{2 + m[0] * 3}_{2 if m[0]>0 else 1}_conv" + + if match(m, r"lora_te_text_model_encoder_layers_(\d+)_(.+)"): + if is_sd2: + if 'mlp_fc1' in m[1]: + return f"model_transformer_resblocks_{m[0]}_{m[1].replace('mlp_fc1', 'mlp_c_fc')}" + elif 'mlp_fc2' in m[1]: + return f"model_transformer_resblocks_{m[0]}_{m[1].replace('mlp_fc2', 'mlp_c_proj')}" + else: + return f"model_transformer_resblocks_{m[0]}_{m[1].replace('self_attn', 'attn')}" + + return f"transformer_text_model_encoder_layers_{m[0]}_{m[1]}" + + if match(m, r"lora_te2_text_model_encoder_layers_(\d+)_(.+)"): + if 'mlp_fc1' in m[1]: + return f"1_model_transformer_resblocks_{m[0]}_{m[1].replace('mlp_fc1', 'mlp_c_fc')}" + elif 'mlp_fc2' in m[1]: + return f"1_model_transformer_resblocks_{m[0]}_{m[1].replace('mlp_fc2', 'mlp_c_proj')}" + else: + return f"1_model_transformer_resblocks_{m[0]}_{m[1].replace('self_attn', 'attn')}" + + return key + + +def assign_network_names_to_compvis_modules(sd_model): + network_layer_mapping = {} + + if shared.sd_model.is_sdxl: + for i, embedder in enumerate(shared.sd_model.conditioner.embedders): + if not hasattr(embedder, 'wrapped'): + continue + + for name, module in embedder.wrapped.named_modules(): + network_name = f'{i}_{name.replace(".", "_")}' + network_layer_mapping[network_name] = module + module.network_layer_name = network_name + else: + for name, module in shared.sd_model.cond_stage_model.wrapped.named_modules(): + network_name = name.replace(".", "_") + network_layer_mapping[network_name] = module + module.network_layer_name = network_name + + for name, module in shared.sd_model.model.named_modules(): + network_name = name.replace(".", "_") + network_layer_mapping[network_name] = module + module.network_layer_name = network_name + + sd_model.network_layer_mapping = network_layer_mapping + + +def load_network(name, network_on_disk): + net = network.Network(name, network_on_disk) + net.mtime = os.path.getmtime(network_on_disk.filename) + + sd = sd_models.read_state_dict(network_on_disk.filename) + + # this should not be needed but is here as an emergency fix for an unknown error people are experiencing in 1.2.0 + if not hasattr(shared.sd_model, 'network_layer_mapping'): + assign_network_names_to_compvis_modules(shared.sd_model) + + keys_failed_to_match = {} + is_sd2 = 'model_transformer_resblocks' in shared.sd_model.network_layer_mapping + + matched_networks = {} + bundle_embeddings = {} + + for key_network, weight in sd.items(): + key_network_without_network_parts, _, network_part = key_network.partition(".") + + if key_network_without_network_parts == "bundle_emb": + emb_name, vec_name = network_part.split(".", 1) + emb_dict = bundle_embeddings.get(emb_name, {}) + if vec_name.split('.')[0] == 'string_to_param': + _, k2 = vec_name.split('.', 1) + emb_dict['string_to_param'] = {k2: weight} + else: + emb_dict[vec_name] = weight + bundle_embeddings[emb_name] = emb_dict + + key = convert_diffusers_name_to_compvis(key_network_without_network_parts, is_sd2) + sd_module = shared.sd_model.network_layer_mapping.get(key, None) + + if sd_module is None: + m = re_x_proj.match(key) + if m: + sd_module = shared.sd_model.network_layer_mapping.get(m.group(1), None) + + # SDXL loras seem to already have correct compvis keys, so only need to replace "lora_unet" with "diffusion_model" + if sd_module is None and "lora_unet" in key_network_without_network_parts: + key = key_network_without_network_parts.replace("lora_unet", "diffusion_model") + sd_module = shared.sd_model.network_layer_mapping.get(key, None) + elif sd_module is None and "lora_te1_text_model" in key_network_without_network_parts: + key = key_network_without_network_parts.replace("lora_te1_text_model", "0_transformer_text_model") + sd_module = shared.sd_model.network_layer_mapping.get(key, None) + + # some SD1 Loras also have correct compvis keys + if sd_module is None: + key = key_network_without_network_parts.replace("lora_te1_text_model", "transformer_text_model") + sd_module = shared.sd_model.network_layer_mapping.get(key, None) + + # kohya_ss OFT module + elif sd_module is None and "oft_unet" in key_network_without_network_parts: + key = key_network_without_network_parts.replace("oft_unet", "diffusion_model") + sd_module = shared.sd_model.network_layer_mapping.get(key, None) + + # KohakuBlueLeaf OFT module + if sd_module is None and "oft_diag" in key: + key = key_network_without_network_parts.replace("lora_unet", "diffusion_model") + key = key_network_without_network_parts.replace("lora_te1_text_model", "0_transformer_text_model") + sd_module = shared.sd_model.network_layer_mapping.get(key, None) + + if sd_module is None: + keys_failed_to_match[key_network] = key + continue + + if key not in matched_networks: + matched_networks[key] = network.NetworkWeights(network_key=key_network, sd_key=key, w={}, sd_module=sd_module) + + matched_networks[key].w[network_part] = weight + + for key, weights in matched_networks.items(): + net_module = None + for nettype in module_types: + net_module = nettype.create_module(net, weights) + if net_module is not None: + break + + if net_module is None: + raise AssertionError(f"Could not find a module type (out of {', '.join([x.__class__.__name__ for x in module_types])}) that would accept those keys: {', '.join(weights.w)}") + + net.modules[key] = net_module + + embeddings = {} + for emb_name, data in bundle_embeddings.items(): + embedding = textual_inversion.create_embedding_from_data(data, emb_name, filename=network_on_disk.filename + "/" + emb_name) + embedding.loaded = None + embeddings[emb_name] = embedding + + net.bundle_embeddings = embeddings + + if keys_failed_to_match: + logging.debug(f"Network {network_on_disk.filename} didn't match keys: {keys_failed_to_match}") + + return net + + +def purge_networks_from_memory(): + while len(networks_in_memory) > shared.opts.lora_in_memory_limit and len(networks_in_memory) > 0: + name = next(iter(networks_in_memory)) + networks_in_memory.pop(name, None) + + devices.torch_gc() + + +def load_networks(names, te_multipliers=None, unet_multipliers=None, dyn_dims=None): + emb_db = sd_hijack.model_hijack.embedding_db + already_loaded = {} + + for net in loaded_networks: + if net.name in names: + already_loaded[net.name] = net + for emb_name, embedding in net.bundle_embeddings.items(): + if embedding.loaded: + emb_db.register_embedding_by_name(None, shared.sd_model, emb_name) + + loaded_networks.clear() + + networks_on_disk = [available_network_aliases.get(name, None) for name in names] + if any(x is None for x in networks_on_disk): + list_available_networks() + + networks_on_disk = [available_network_aliases.get(name, None) for name in names] + + failed_to_load_networks = [] + + for i, (network_on_disk, name) in enumerate(zip(networks_on_disk, names)): + net = already_loaded.get(name, None) + + if network_on_disk is not None: + if net is None: + net = networks_in_memory.get(name) + + if net is None or os.path.getmtime(network_on_disk.filename) > net.mtime: + try: + net = load_network(name, network_on_disk) + + networks_in_memory.pop(name, None) + networks_in_memory[name] = net + except Exception as e: + errors.display(e, f"loading network {network_on_disk.filename}") + continue + + net.mentioned_name = name + + network_on_disk.read_hash() + + if net is None: + failed_to_load_networks.append(name) + logging.info(f"Couldn't find network with name {name}") + continue + + net.te_multiplier = te_multipliers[i] if te_multipliers else 1.0 + net.unet_multiplier = unet_multipliers[i] if unet_multipliers else 1.0 + net.dyn_dim = dyn_dims[i] if dyn_dims else 1.0 + loaded_networks.append(net) + + for emb_name, embedding in net.bundle_embeddings.items(): + if embedding.loaded is None and emb_name in emb_db.word_embeddings: + logger.warning( + f'Skip bundle embedding: "{emb_name}"' + ' as it was already loaded from embeddings folder' + ) + continue + + embedding.loaded = False + if emb_db.expected_shape == -1 or emb_db.expected_shape == embedding.shape: + embedding.loaded = True + emb_db.register_embedding(embedding, shared.sd_model) + else: + emb_db.skipped_embeddings[name] = embedding + + if failed_to_load_networks: + sd_hijack.model_hijack.comments.append("Networks not found: " + ", ".join(failed_to_load_networks)) + + purge_networks_from_memory() + + +def network_restore_weights_from_backup(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn.GroupNorm, torch.nn.LayerNorm, torch.nn.MultiheadAttention]): + weights_backup = getattr(self, "network_weights_backup", None) + bias_backup = getattr(self, "network_bias_backup", None) + + if weights_backup is None and bias_backup is None: + return + + if weights_backup is not None: + if isinstance(self, torch.nn.MultiheadAttention): + self.in_proj_weight.copy_(weights_backup[0]) + self.out_proj.weight.copy_(weights_backup[1]) + else: + self.weight.copy_(weights_backup) + + if bias_backup is not None: + if isinstance(self, torch.nn.MultiheadAttention): + self.out_proj.bias.copy_(bias_backup) + else: + self.bias.copy_(bias_backup) + else: + if isinstance(self, torch.nn.MultiheadAttention): + self.out_proj.bias = None + else: + self.bias = None + + +def network_apply_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn.GroupNorm, torch.nn.LayerNorm, torch.nn.MultiheadAttention]): + """ + Applies the currently selected set of networks to the weights of torch layer self. + If weights already have this particular set of networks applied, does nothing. + If not, restores orginal weights from backup and alters weights according to networks. + """ + + network_layer_name = getattr(self, 'network_layer_name', None) + if network_layer_name is None: + return + + current_names = getattr(self, "network_current_names", ()) + wanted_names = tuple((x.name, x.te_multiplier, x.unet_multiplier, x.dyn_dim) for x in loaded_networks) + + weights_backup = getattr(self, "network_weights_backup", None) + if weights_backup is None and wanted_names != (): + if current_names != (): + raise RuntimeError("no backup weights found and current weights are not unchanged") + + if isinstance(self, torch.nn.MultiheadAttention): + weights_backup = (self.in_proj_weight.to(devices.cpu, copy=True), self.out_proj.weight.to(devices.cpu, copy=True)) + else: + weights_backup = self.weight.to(devices.cpu, copy=True) + + self.network_weights_backup = weights_backup + + bias_backup = getattr(self, "network_bias_backup", None) + if bias_backup is None: + if isinstance(self, torch.nn.MultiheadAttention) and self.out_proj.bias is not None: + bias_backup = self.out_proj.bias.to(devices.cpu, copy=True) + elif getattr(self, 'bias', None) is not None: + bias_backup = self.bias.to(devices.cpu, copy=True) + else: + bias_backup = None + self.network_bias_backup = bias_backup + + if current_names != wanted_names: + network_restore_weights_from_backup(self) + + for net in loaded_networks: + module = net.modules.get(network_layer_name, None) + if module is not None and hasattr(self, 'weight'): + try: + with torch.no_grad(): + updown, ex_bias = module.calc_updown(self.weight) + + if len(self.weight.shape) == 4 and self.weight.shape[1] == 9: + # inpainting model. zero pad updown to make channel[1] 4 to 9 + updown = torch.nn.functional.pad(updown, (0, 0, 0, 0, 0, 5)) + + self.weight += updown + if ex_bias is not None and hasattr(self, 'bias'): + if self.bias is None: + self.bias = torch.nn.Parameter(ex_bias) + else: + self.bias += ex_bias + except RuntimeError as e: + logging.debug(f"Network {net.name} layer {network_layer_name}: {e}") + extra_network_lora.errors[net.name] = extra_network_lora.errors.get(net.name, 0) + 1 + + continue + + module_q = net.modules.get(network_layer_name + "_q_proj", None) + module_k = net.modules.get(network_layer_name + "_k_proj", None) + module_v = net.modules.get(network_layer_name + "_v_proj", None) + module_out = net.modules.get(network_layer_name + "_out_proj", None) + + if isinstance(self, torch.nn.MultiheadAttention) and module_q and module_k and module_v and module_out: + try: + with torch.no_grad(): + updown_q, _ = module_q.calc_updown(self.in_proj_weight) + updown_k, _ = module_k.calc_updown(self.in_proj_weight) + updown_v, _ = module_v.calc_updown(self.in_proj_weight) + updown_qkv = torch.vstack([updown_q, updown_k, updown_v]) + updown_out, ex_bias = module_out.calc_updown(self.out_proj.weight) + + self.in_proj_weight += updown_qkv + self.out_proj.weight += updown_out + if ex_bias is not None: + if self.out_proj.bias is None: + self.out_proj.bias = torch.nn.Parameter(ex_bias) + else: + self.out_proj.bias += ex_bias + + except RuntimeError as e: + logging.debug(f"Network {net.name} layer {network_layer_name}: {e}") + extra_network_lora.errors[net.name] = extra_network_lora.errors.get(net.name, 0) + 1 + + continue + + if module is None: + continue + + logging.debug(f"Network {net.name} layer {network_layer_name}: couldn't find supported operation") + extra_network_lora.errors[net.name] = extra_network_lora.errors.get(net.name, 0) + 1 + + self.network_current_names = wanted_names + + +def network_forward(module, input, original_forward): + """ + Old way of applying Lora by executing operations during layer's forward. + Stacking many loras this way results in big performance degradation. + """ + + if len(loaded_networks) == 0: + return original_forward(module, input) + + input = devices.cond_cast_unet(input) + + network_restore_weights_from_backup(module) + network_reset_cached_weight(module) + + y = original_forward(module, input) + + network_layer_name = getattr(module, 'network_layer_name', None) + for lora in loaded_networks: + module = lora.modules.get(network_layer_name, None) + if module is None: + continue + + y = module.forward(input, y) + + return y + + +def network_reset_cached_weight(self: Union[torch.nn.Conv2d, torch.nn.Linear]): + self.network_current_names = () + self.network_weights_backup = None + self.network_bias_backup = None + + +def network_Linear_forward(self, input): + if shared.opts.lora_functional: + return network_forward(self, input, originals.Linear_forward) + + network_apply_weights(self) + + return originals.Linear_forward(self, input) + + +def network_Linear_load_state_dict(self, *args, **kwargs): + network_reset_cached_weight(self) + + return originals.Linear_load_state_dict(self, *args, **kwargs) + + +def network_Conv2d_forward(self, input): + if shared.opts.lora_functional: + return network_forward(self, input, originals.Conv2d_forward) + + network_apply_weights(self) + + return originals.Conv2d_forward(self, input) + + +def network_Conv2d_load_state_dict(self, *args, **kwargs): + network_reset_cached_weight(self) + + return originals.Conv2d_load_state_dict(self, *args, **kwargs) + + +def network_GroupNorm_forward(self, input): + if shared.opts.lora_functional: + return network_forward(self, input, originals.GroupNorm_forward) + + network_apply_weights(self) + + return originals.GroupNorm_forward(self, input) + + +def network_GroupNorm_load_state_dict(self, *args, **kwargs): + network_reset_cached_weight(self) + + return originals.GroupNorm_load_state_dict(self, *args, **kwargs) + + +def network_LayerNorm_forward(self, input): + if shared.opts.lora_functional: + return network_forward(self, input, originals.LayerNorm_forward) + + network_apply_weights(self) + + return originals.LayerNorm_forward(self, input) + + +def network_LayerNorm_load_state_dict(self, *args, **kwargs): + network_reset_cached_weight(self) + + return originals.LayerNorm_load_state_dict(self, *args, **kwargs) + + +def network_MultiheadAttention_forward(self, *args, **kwargs): + network_apply_weights(self) + + return originals.MultiheadAttention_forward(self, *args, **kwargs) + + +def network_MultiheadAttention_load_state_dict(self, *args, **kwargs): + network_reset_cached_weight(self) + + return originals.MultiheadAttention_load_state_dict(self, *args, **kwargs) + + +def list_available_networks(): + available_networks.clear() + available_network_aliases.clear() + forbidden_network_aliases.clear() + available_network_hash_lookup.clear() + forbidden_network_aliases.update({"none": 1, "Addams": 1}) + + os.makedirs(shared.cmd_opts.lora_dir, exist_ok=True) + + candidates = list(shared.walk_files(shared.cmd_opts.lora_dir, allowed_extensions=[".pt", ".ckpt", ".safetensors"])) + candidates += list(shared.walk_files(shared.cmd_opts.lyco_dir_backcompat, allowed_extensions=[".pt", ".ckpt", ".safetensors"])) + for filename in candidates: + if os.path.isdir(filename): + continue + + name = os.path.splitext(os.path.basename(filename))[0] + try: + entry = network.NetworkOnDisk(name, filename) + except OSError: # should catch FileNotFoundError and PermissionError etc. + errors.report(f"Failed to load network {name} from {filename}", exc_info=True) + continue + + available_networks[name] = entry + + if entry.alias in available_network_aliases: + forbidden_network_aliases[entry.alias.lower()] = 1 + + available_network_aliases[name] = entry + available_network_aliases[entry.alias] = entry + + +re_network_name = re.compile(r"(.*)\s*\([0-9a-fA-F]+\)") + + +def infotext_pasted(infotext, params): + if "AddNet Module 1" in [x[1] for x in scripts.scripts_txt2img.infotext_fields]: + return # if the other extension is active, it will handle those fields, no need to do anything + + added = [] + + for k in params: + if not k.startswith("AddNet Model "): + continue + + num = k[13:] + + if params.get("AddNet Module " + num) != "LoRA": + continue + + name = params.get("AddNet Model " + num) + if name is None: + continue + + m = re_network_name.match(name) + if m: + name = m.group(1) + + multiplier = params.get("AddNet Weight A " + num, "1.0") + + added.append(f"") + + if added: + params["Prompt"] += "\n" + "".join(added) + + +originals: lora_patches.LoraPatches = None + +extra_network_lora = None + +available_networks = {} +available_network_aliases = {} +loaded_networks = [] +loaded_bundle_embeddings = {} +networks_in_memory = {} +available_network_hash_lookup = {} +forbidden_network_aliases = {} + +list_available_networks() diff --git a/extensions-builtin/Lora/preload.py b/extensions-builtin/Lora/preload.py new file mode 100644 index 0000000000000000000000000000000000000000..1f85bc5338d77df91e60f35ebb4ce11d2573f01f --- /dev/null +++ b/extensions-builtin/Lora/preload.py @@ -0,0 +1,7 @@ +import os +from modules import paths + + +def preload(parser): + parser.add_argument("--lora-dir", type=str, help="Path to directory with Lora networks.", default=os.path.join(paths.models_path, 'Lora')) + parser.add_argument("--lyco-dir-backcompat", type=str, help="Path to directory with LyCORIS networks (for backawards compatibility; can also use --lyco-dir).", default=os.path.join(paths.models_path, 'LyCORIS')) diff --git a/extensions-builtin/Lora/scripts/lora_script.py b/extensions-builtin/Lora/scripts/lora_script.py new file mode 100644 index 0000000000000000000000000000000000000000..83b6678db128f44a7a556221e02022fe8e416beb --- /dev/null +++ b/extensions-builtin/Lora/scripts/lora_script.py @@ -0,0 +1,99 @@ +import re + +import gradio as gr +from fastapi import FastAPI + +import network +import networks +import lora # noqa:F401 +import lora_patches +import extra_networks_lora +import ui_extra_networks_lora +from modules import script_callbacks, ui_extra_networks, extra_networks, shared + + +def unload(): + networks.originals.undo() + + +def before_ui(): + ui_extra_networks.register_page(ui_extra_networks_lora.ExtraNetworksPageLora()) + + networks.extra_network_lora = extra_networks_lora.ExtraNetworkLora() + extra_networks.register_extra_network(networks.extra_network_lora) + extra_networks.register_extra_network_alias(networks.extra_network_lora, "lyco") + + +networks.originals = lora_patches.LoraPatches() + +script_callbacks.on_model_loaded(networks.assign_network_names_to_compvis_modules) +script_callbacks.on_script_unloaded(unload) +script_callbacks.on_before_ui(before_ui) +script_callbacks.on_infotext_pasted(networks.infotext_pasted) + + +shared.options_templates.update(shared.options_section(('extra_networks', "Extra Networks"), { + "sd_lora": shared.OptionInfo("None", "Add network to prompt", gr.Dropdown, lambda: {"choices": ["None", *networks.available_networks]}, refresh=networks.list_available_networks), + "lora_preferred_name": shared.OptionInfo("Alias from file", "When adding to prompt, refer to Lora by", gr.Radio, {"choices": ["Alias from file", "Filename"]}), + "lora_add_hashes_to_infotext": shared.OptionInfo(True, "Add Lora hashes to infotext"), + "lora_show_all": shared.OptionInfo(False, "Always show all networks on the Lora page").info("otherwise, those detected as for incompatible version of Stable Diffusion will be hidden"), + "lora_hide_unknown_for_versions": shared.OptionInfo([], "Hide networks of unknown versions for model versions", gr.CheckboxGroup, {"choices": ["SD1", "SD2", "SDXL"]}), + "lora_in_memory_limit": shared.OptionInfo(0, "Number of Lora networks to keep cached in memory", gr.Number, {"precision": 0}), +})) + + +shared.options_templates.update(shared.options_section(('compatibility', "Compatibility"), { + "lora_functional": shared.OptionInfo(False, "Lora/Networks: use old method that takes longer when you have multiple Loras active and produces same results as kohya-ss/sd-webui-additional-networks extension"), +})) + + +def create_lora_json(obj: network.NetworkOnDisk): + return { + "name": obj.name, + "alias": obj.alias, + "path": obj.filename, + "metadata": obj.metadata, + } + + +def api_networks(_: gr.Blocks, app: FastAPI): + @app.get("/sdapi/v1/loras") + async def get_loras(): + return [create_lora_json(obj) for obj in networks.available_networks.values()] + + @app.post("/sdapi/v1/refresh-loras") + async def refresh_loras(): + return networks.list_available_networks() + + +script_callbacks.on_app_started(api_networks) + +re_lora = re.compile("= 16 + + +re_word = re.compile(r"[-_\w']+") +re_comma = re.compile(r" *, *") + + +def build_tags(metadata): + tags = {} + + for _, tags_dict in metadata.get("ss_tag_frequency", {}).items(): + for tag, tag_count in tags_dict.items(): + tag = tag.strip() + tags[tag] = tags.get(tag, 0) + int(tag_count) + + if tags and is_non_comma_tagset(tags): + new_tags = {} + + for text, text_count in tags.items(): + for word in re.findall(re_word, text): + if len(word) < 3: + continue + + new_tags[word] = new_tags.get(word, 0) + text_count + + tags = new_tags + + ordered_tags = sorted(tags.keys(), key=tags.get, reverse=True) + + return [(tag, tags[tag]) for tag in ordered_tags] + + +class LoraUserMetadataEditor(ui_extra_networks_user_metadata.UserMetadataEditor): + def __init__(self, ui, tabname, page): + super().__init__(ui, tabname, page) + + self.select_sd_version = None + + self.taginfo = None + self.edit_activation_text = None + self.slider_preferred_weight = None + self.edit_notes = None + + def save_lora_user_metadata(self, name, desc, sd_version, activation_text, preferred_weight, notes): + user_metadata = self.get_user_metadata(name) + user_metadata["description"] = desc + user_metadata["sd version"] = sd_version + user_metadata["activation text"] = activation_text + user_metadata["preferred weight"] = preferred_weight + user_metadata["notes"] = notes + + self.write_user_metadata(name, user_metadata) + + def get_metadata_table(self, name): + table = super().get_metadata_table(name) + item = self.page.items.get(name, {}) + metadata = item.get("metadata") or {} + + keys = { + 'ss_output_name': "Output name:", + 'ss_sd_model_name': "Model:", + 'ss_clip_skip': "Clip skip:", + 'ss_network_module': "Kohya module:", + } + + for key, label in keys.items(): + value = metadata.get(key, None) + if value is not None and str(value) != "None": + table.append((label, html.escape(value))) + + ss_training_started_at = metadata.get('ss_training_started_at') + if ss_training_started_at: + table.append(("Date trained:", datetime.datetime.utcfromtimestamp(float(ss_training_started_at)).strftime('%Y-%m-%d %H:%M'))) + + ss_bucket_info = metadata.get("ss_bucket_info") + if ss_bucket_info and "buckets" in ss_bucket_info: + resolutions = {} + for _, bucket in ss_bucket_info["buckets"].items(): + resolution = bucket["resolution"] + resolution = f'{resolution[1]}x{resolution[0]}' + + resolutions[resolution] = resolutions.get(resolution, 0) + int(bucket["count"]) + + resolutions_list = sorted(resolutions.keys(), key=resolutions.get, reverse=True) + resolutions_text = html.escape(", ".join(resolutions_list[0:4])) + if len(resolutions) > 4: + resolutions_text += ", ..." + resolutions_text = f"{resolutions_text}" + + table.append(('Resolutions:' if len(resolutions_list) > 1 else 'Resolution:', resolutions_text)) + + image_count = 0 + for _, params in metadata.get("ss_dataset_dirs", {}).items(): + image_count += int(params.get("img_count", 0)) + + if image_count: + table.append(("Dataset size:", image_count)) + + return table + + def put_values_into_components(self, name): + user_metadata = self.get_user_metadata(name) + values = super().put_values_into_components(name) + + item = self.page.items.get(name, {}) + metadata = item.get("metadata") or {} + + tags = build_tags(metadata) + gradio_tags = [(tag, str(count)) for tag, count in tags[0:24]] + + return [ + *values[0:5], + item.get("sd_version", "Unknown"), + gr.HighlightedText.update(value=gradio_tags, visible=True if tags else False), + user_metadata.get('activation text', ''), + float(user_metadata.get('preferred weight', 0.0)), + gr.update(visible=True if tags else False), + gr.update(value=self.generate_random_prompt_from_tags(tags), visible=True if tags else False), + ] + + def generate_random_prompt(self, name): + item = self.page.items.get(name, {}) + metadata = item.get("metadata") or {} + tags = build_tags(metadata) + + return self.generate_random_prompt_from_tags(tags) + + def generate_random_prompt_from_tags(self, tags): + max_count = None + res = [] + for tag, count in tags: + if not max_count: + max_count = count + + v = random.random() * max_count + if count > v: + res.append(tag) + + return ", ".join(sorted(res)) + + def create_extra_default_items_in_left_column(self): + + # this would be a lot better as gr.Radio but I can't make it work + self.select_sd_version = gr.Dropdown(['SD1', 'SD2', 'SDXL', 'Unknown'], value='Unknown', label='Stable Diffusion version', interactive=True) + + def create_editor(self): + self.create_default_editor_elems() + + self.taginfo = gr.HighlightedText(label="Training dataset tags") + self.edit_activation_text = gr.Text(label='Activation text', info="Will be added to prompt along with Lora") + self.slider_preferred_weight = gr.Slider(label='Preferred weight', info="Set to 0 to disable", minimum=0.0, maximum=2.0, step=0.01) + + with gr.Row() as row_random_prompt: + with gr.Column(scale=8): + random_prompt = gr.Textbox(label='Random prompt', lines=4, max_lines=4, interactive=False) + + with gr.Column(scale=1, min_width=120): + generate_random_prompt = gr.Button('Generate', size="lg", scale=1) + + self.edit_notes = gr.TextArea(label='Notes', lines=4) + + generate_random_prompt.click(fn=self.generate_random_prompt, inputs=[self.edit_name_input], outputs=[random_prompt], show_progress=False) + + def select_tag(activation_text, evt: gr.SelectData): + tag = evt.value[0] + + words = re.split(re_comma, activation_text) + if tag in words: + words = [x for x in words if x != tag and x.strip()] + return ", ".join(words) + + return activation_text + ", " + tag if activation_text else tag + + self.taginfo.select(fn=select_tag, inputs=[self.edit_activation_text], outputs=[self.edit_activation_text], show_progress=False) + + self.create_default_buttons() + + viewed_components = [ + self.edit_name, + self.edit_description, + self.html_filedata, + self.html_preview, + self.edit_notes, + self.select_sd_version, + self.taginfo, + self.edit_activation_text, + self.slider_preferred_weight, + row_random_prompt, + random_prompt, + ] + + self.button_edit\ + .click(fn=self.put_values_into_components, inputs=[self.edit_name_input], outputs=viewed_components)\ + .then(fn=lambda: gr.update(visible=True), inputs=[], outputs=[self.box]) + + edited_components = [ + self.edit_description, + self.select_sd_version, + self.edit_activation_text, + self.slider_preferred_weight, + self.edit_notes, + ] + + self.setup_save_handler(self.button_save, self.save_lora_user_metadata, edited_components) diff --git a/extensions-builtin/Lora/ui_extra_networks_lora.py b/extensions-builtin/Lora/ui_extra_networks_lora.py new file mode 100644 index 0000000000000000000000000000000000000000..ba930378242285e8342e6e8dcb73cf12009ed014 --- /dev/null +++ b/extensions-builtin/Lora/ui_extra_networks_lora.py @@ -0,0 +1,82 @@ +import os + +import network +import networks + +from modules import shared, ui_extra_networks +from modules.ui_extra_networks import quote_js +from ui_edit_user_metadata import LoraUserMetadataEditor + + +class ExtraNetworksPageLora(ui_extra_networks.ExtraNetworksPage): + def __init__(self): + super().__init__('Lora') + + def refresh(self): + networks.list_available_networks() + + def create_item(self, name, index=None, enable_filter=True): + lora_on_disk = networks.available_networks.get(name) + if lora_on_disk is None: + return + + path, ext = os.path.splitext(lora_on_disk.filename) + + alias = lora_on_disk.get_alias() + + item = { + "name": name, + "filename": lora_on_disk.filename, + "shorthash": lora_on_disk.shorthash, + "preview": self.find_preview(path), + "description": self.find_description(path), + "search_term": self.search_terms_from_path(lora_on_disk.filename) + " " + (lora_on_disk.hash or ""), + "local_preview": f"{path}.{shared.opts.samples_format}", + "metadata": lora_on_disk.metadata, + "sort_keys": {'default': index, **self.get_sort_keys(lora_on_disk.filename)}, + "sd_version": lora_on_disk.sd_version.name, + } + + self.read_user_metadata(item) + activation_text = item["user_metadata"].get("activation text") + preferred_weight = item["user_metadata"].get("preferred weight", 0.0) + item["prompt"] = quote_js(f"") + + if activation_text: + item["prompt"] += " + " + quote_js(" " + activation_text) + + sd_version = item["user_metadata"].get("sd version") + if sd_version in network.SdVersion.__members__: + item["sd_version"] = sd_version + sd_version = network.SdVersion[sd_version] + else: + sd_version = lora_on_disk.sd_version + + if shared.opts.lora_show_all or not enable_filter: + pass + elif sd_version == network.SdVersion.Unknown: + model_version = network.SdVersion.SDXL if shared.sd_model.is_sdxl else network.SdVersion.SD2 if shared.sd_model.is_sd2 else network.SdVersion.SD1 + if model_version.name in shared.opts.lora_hide_unknown_for_versions: + return None + elif shared.sd_model.is_sdxl and sd_version != network.SdVersion.SDXL: + return None + elif shared.sd_model.is_sd2 and sd_version != network.SdVersion.SD2: + return None + elif shared.sd_model.is_sd1 and sd_version != network.SdVersion.SD1: + return None + + return item + + def list_items(self): + # instantiate a list to protect against concurrent modification + names = list(networks.available_networks) + for index, name in enumerate(names): + item = self.create_item(name, index) + if item is not None: + yield item + + def allowed_directories_for_previews(self): + return [shared.cmd_opts.lora_dir, shared.cmd_opts.lyco_dir_backcompat] + + def create_user_metadata_editor(self, ui, tabname): + return LoraUserMetadataEditor(ui, tabname, self) diff --git a/extensions-builtin/ScuNET/preload.py b/extensions-builtin/ScuNET/preload.py new file mode 100644 index 0000000000000000000000000000000000000000..4ce82b1d4349b24192b1915d022ed4fda9f31e5c --- /dev/null +++ b/extensions-builtin/ScuNET/preload.py @@ -0,0 +1,6 @@ +import os +from modules import paths + + +def preload(parser): + parser.add_argument("--scunet-models-path", type=str, help="Path to directory with ScuNET model file(s).", default=os.path.join(paths.models_path, 'ScuNET')) diff --git a/extensions-builtin/ScuNET/scripts/scunet_model.py b/extensions-builtin/ScuNET/scripts/scunet_model.py new file mode 100644 index 0000000000000000000000000000000000000000..167d2f64b8e8ef1c506d89026e5d2ac8687d8098 --- /dev/null +++ b/extensions-builtin/ScuNET/scripts/scunet_model.py @@ -0,0 +1,144 @@ +import sys + +import PIL.Image +import numpy as np +import torch +from tqdm import tqdm + +import modules.upscaler +from modules import devices, modelloader, script_callbacks, errors +from scunet_model_arch import SCUNet + +from modules.modelloader import load_file_from_url +from modules.shared import opts + + +class UpscalerScuNET(modules.upscaler.Upscaler): + def __init__(self, dirname): + self.name = "ScuNET" + self.model_name = "ScuNET GAN" + self.model_name2 = "ScuNET PSNR" + self.model_url = "https://github.com/cszn/KAIR/releases/download/v1.0/scunet_color_real_gan.pth" + self.model_url2 = "https://github.com/cszn/KAIR/releases/download/v1.0/scunet_color_real_psnr.pth" + self.user_path = dirname + super().__init__() + model_paths = self.find_models(ext_filter=[".pth"]) + scalers = [] + add_model2 = True + for file in model_paths: + if file.startswith("http"): + name = self.model_name + else: + name = modelloader.friendly_name(file) + if name == self.model_name2 or file == self.model_url2: + add_model2 = False + try: + scaler_data = modules.upscaler.UpscalerData(name, file, self, 4) + scalers.append(scaler_data) + except Exception: + errors.report(f"Error loading ScuNET model: {file}", exc_info=True) + if add_model2: + scaler_data2 = modules.upscaler.UpscalerData(self.model_name2, self.model_url2, self) + scalers.append(scaler_data2) + self.scalers = scalers + + @staticmethod + @torch.no_grad() + def tiled_inference(img, model): + # test the image tile by tile + h, w = img.shape[2:] + tile = opts.SCUNET_tile + tile_overlap = opts.SCUNET_tile_overlap + if tile == 0: + return model(img) + + device = devices.get_device_for('scunet') + assert tile % 8 == 0, "tile size should be a multiple of window_size" + sf = 1 + + stride = tile - tile_overlap + h_idx_list = list(range(0, h - tile, stride)) + [h - tile] + w_idx_list = list(range(0, w - tile, stride)) + [w - tile] + E = torch.zeros(1, 3, h * sf, w * sf, dtype=img.dtype, device=device) + W = torch.zeros_like(E, dtype=devices.dtype, device=device) + + with tqdm(total=len(h_idx_list) * len(w_idx_list), desc="ScuNET tiles") as pbar: + for h_idx in h_idx_list: + + for w_idx in w_idx_list: + + in_patch = img[..., h_idx: h_idx + tile, w_idx: w_idx + tile] + + out_patch = model(in_patch) + out_patch_mask = torch.ones_like(out_patch) + + E[ + ..., h_idx * sf: (h_idx + tile) * sf, w_idx * sf: (w_idx + tile) * sf + ].add_(out_patch) + W[ + ..., h_idx * sf: (h_idx + tile) * sf, w_idx * sf: (w_idx + tile) * sf + ].add_(out_patch_mask) + pbar.update(1) + output = E.div_(W) + + return output + + def do_upscale(self, img: PIL.Image.Image, selected_file): + + devices.torch_gc() + + try: + model = self.load_model(selected_file) + except Exception as e: + print(f"ScuNET: Unable to load model from {selected_file}: {e}", file=sys.stderr) + return img + + device = devices.get_device_for('scunet') + tile = opts.SCUNET_tile + h, w = img.height, img.width + np_img = np.array(img) + np_img = np_img[:, :, ::-1] # RGB to BGR + np_img = np_img.transpose((2, 0, 1)) / 255 # HWC to CHW + torch_img = torch.from_numpy(np_img).float().unsqueeze(0).to(device) # type: ignore + + if tile > h or tile > w: + _img = torch.zeros(1, 3, max(h, tile), max(w, tile), dtype=torch_img.dtype, device=torch_img.device) + _img[:, :, :h, :w] = torch_img # pad image + torch_img = _img + + torch_output = self.tiled_inference(torch_img, model).squeeze(0) + torch_output = torch_output[:, :h * 1, :w * 1] # remove padding, if any + np_output: np.ndarray = torch_output.float().cpu().clamp_(0, 1).numpy() + del torch_img, torch_output + devices.torch_gc() + + output = np_output.transpose((1, 2, 0)) # CHW to HWC + output = output[:, :, ::-1] # BGR to RGB + return PIL.Image.fromarray((output * 255).astype(np.uint8)) + + def load_model(self, path: str): + device = devices.get_device_for('scunet') + if path.startswith("http"): + # TODO: this doesn't use `path` at all? + filename = load_file_from_url(self.model_url, model_dir=self.model_download_path, file_name=f"{self.name}.pth") + else: + filename = path + model = SCUNet(in_nc=3, config=[4, 4, 4, 4, 4, 4, 4], dim=64) + model.load_state_dict(torch.load(filename), strict=True) + model.eval() + for _, v in model.named_parameters(): + v.requires_grad = False + model = model.to(device) + + return model + + +def on_ui_settings(): + import gradio as gr + from modules import shared + + shared.opts.add_option("SCUNET_tile", shared.OptionInfo(256, "Tile size for SCUNET upscalers.", gr.Slider, {"minimum": 0, "maximum": 512, "step": 16}, section=('upscaling', "Upscaling")).info("0 = no tiling")) + shared.opts.add_option("SCUNET_tile_overlap", shared.OptionInfo(8, "Tile overlap for SCUNET upscalers.", gr.Slider, {"minimum": 0, "maximum": 64, "step": 1}, section=('upscaling', "Upscaling")).info("Low values = visible seam")) + + +script_callbacks.on_ui_settings(on_ui_settings) diff --git a/extensions-builtin/ScuNET/scunet_model_arch.py b/extensions-builtin/ScuNET/scunet_model_arch.py new file mode 100644 index 0000000000000000000000000000000000000000..b51a880629baa492ffcbebe682bcf101f06699a6 --- /dev/null +++ b/extensions-builtin/ScuNET/scunet_model_arch.py @@ -0,0 +1,268 @@ +# -*- coding: utf-8 -*- +import numpy as np +import torch +import torch.nn as nn +from einops import rearrange +from einops.layers.torch import Rearrange +from timm.models.layers import trunc_normal_, DropPath + + +class WMSA(nn.Module): + """ Self-attention module in Swin Transformer + """ + + def __init__(self, input_dim, output_dim, head_dim, window_size, type): + super(WMSA, self).__init__() + self.input_dim = input_dim + self.output_dim = output_dim + self.head_dim = head_dim + self.scale = self.head_dim ** -0.5 + self.n_heads = input_dim // head_dim + self.window_size = window_size + self.type = type + self.embedding_layer = nn.Linear(self.input_dim, 3 * self.input_dim, bias=True) + + self.relative_position_params = nn.Parameter( + torch.zeros((2 * window_size - 1) * (2 * window_size - 1), self.n_heads)) + + self.linear = nn.Linear(self.input_dim, self.output_dim) + + trunc_normal_(self.relative_position_params, std=.02) + self.relative_position_params = torch.nn.Parameter( + self.relative_position_params.view(2 * window_size - 1, 2 * window_size - 1, self.n_heads).transpose(1, + 2).transpose( + 0, 1)) + + def generate_mask(self, h, w, p, shift): + """ generating the mask of SW-MSA + Args: + shift: shift parameters in CyclicShift. + Returns: + attn_mask: should be (1 1 w p p), + """ + # supporting square. + attn_mask = torch.zeros(h, w, p, p, p, p, dtype=torch.bool, device=self.relative_position_params.device) + if self.type == 'W': + return attn_mask + + s = p - shift + attn_mask[-1, :, :s, :, s:, :] = True + attn_mask[-1, :, s:, :, :s, :] = True + attn_mask[:, -1, :, :s, :, s:] = True + attn_mask[:, -1, :, s:, :, :s] = True + attn_mask = rearrange(attn_mask, 'w1 w2 p1 p2 p3 p4 -> 1 1 (w1 w2) (p1 p2) (p3 p4)') + return attn_mask + + def forward(self, x): + """ Forward pass of Window Multi-head Self-attention module. + Args: + x: input tensor with shape of [b h w c]; + attn_mask: attention mask, fill -inf where the value is True; + Returns: + output: tensor shape [b h w c] + """ + if self.type != 'W': + x = torch.roll(x, shifts=(-(self.window_size // 2), -(self.window_size // 2)), dims=(1, 2)) + + x = rearrange(x, 'b (w1 p1) (w2 p2) c -> b w1 w2 p1 p2 c', p1=self.window_size, p2=self.window_size) + h_windows = x.size(1) + w_windows = x.size(2) + # square validation + # assert h_windows == w_windows + + x = rearrange(x, 'b w1 w2 p1 p2 c -> b (w1 w2) (p1 p2) c', p1=self.window_size, p2=self.window_size) + qkv = self.embedding_layer(x) + q, k, v = rearrange(qkv, 'b nw np (threeh c) -> threeh b nw np c', c=self.head_dim).chunk(3, dim=0) + sim = torch.einsum('hbwpc,hbwqc->hbwpq', q, k) * self.scale + # Adding learnable relative embedding + sim = sim + rearrange(self.relative_embedding(), 'h p q -> h 1 1 p q') + # Using Attn Mask to distinguish different subwindows. + if self.type != 'W': + attn_mask = self.generate_mask(h_windows, w_windows, self.window_size, shift=self.window_size // 2) + sim = sim.masked_fill_(attn_mask, float("-inf")) + + probs = nn.functional.softmax(sim, dim=-1) + output = torch.einsum('hbwij,hbwjc->hbwic', probs, v) + output = rearrange(output, 'h b w p c -> b w p (h c)') + output = self.linear(output) + output = rearrange(output, 'b (w1 w2) (p1 p2) c -> b (w1 p1) (w2 p2) c', w1=h_windows, p1=self.window_size) + + if self.type != 'W': + output = torch.roll(output, shifts=(self.window_size // 2, self.window_size // 2), dims=(1, 2)) + + return output + + def relative_embedding(self): + cord = torch.tensor(np.array([[i, j] for i in range(self.window_size) for j in range(self.window_size)])) + relation = cord[:, None, :] - cord[None, :, :] + self.window_size - 1 + # negative is allowed + return self.relative_position_params[:, relation[:, :, 0].long(), relation[:, :, 1].long()] + + +class Block(nn.Module): + def __init__(self, input_dim, output_dim, head_dim, window_size, drop_path, type='W', input_resolution=None): + """ SwinTransformer Block + """ + super(Block, self).__init__() + self.input_dim = input_dim + self.output_dim = output_dim + assert type in ['W', 'SW'] + self.type = type + if input_resolution <= window_size: + self.type = 'W' + + self.ln1 = nn.LayerNorm(input_dim) + self.msa = WMSA(input_dim, input_dim, head_dim, window_size, self.type) + self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity() + self.ln2 = nn.LayerNorm(input_dim) + self.mlp = nn.Sequential( + nn.Linear(input_dim, 4 * input_dim), + nn.GELU(), + nn.Linear(4 * input_dim, output_dim), + ) + + def forward(self, x): + x = x + self.drop_path(self.msa(self.ln1(x))) + x = x + self.drop_path(self.mlp(self.ln2(x))) + return x + + +class ConvTransBlock(nn.Module): + def __init__(self, conv_dim, trans_dim, head_dim, window_size, drop_path, type='W', input_resolution=None): + """ SwinTransformer and Conv Block + """ + super(ConvTransBlock, self).__init__() + self.conv_dim = conv_dim + self.trans_dim = trans_dim + self.head_dim = head_dim + self.window_size = window_size + self.drop_path = drop_path + self.type = type + self.input_resolution = input_resolution + + assert self.type in ['W', 'SW'] + if self.input_resolution <= self.window_size: + self.type = 'W' + + self.trans_block = Block(self.trans_dim, self.trans_dim, self.head_dim, self.window_size, self.drop_path, + self.type, self.input_resolution) + self.conv1_1 = nn.Conv2d(self.conv_dim + self.trans_dim, self.conv_dim + self.trans_dim, 1, 1, 0, bias=True) + self.conv1_2 = nn.Conv2d(self.conv_dim + self.trans_dim, self.conv_dim + self.trans_dim, 1, 1, 0, bias=True) + + self.conv_block = nn.Sequential( + nn.Conv2d(self.conv_dim, self.conv_dim, 3, 1, 1, bias=False), + nn.ReLU(True), + nn.Conv2d(self.conv_dim, self.conv_dim, 3, 1, 1, bias=False) + ) + + def forward(self, x): + conv_x, trans_x = torch.split(self.conv1_1(x), (self.conv_dim, self.trans_dim), dim=1) + conv_x = self.conv_block(conv_x) + conv_x + trans_x = Rearrange('b c h w -> b h w c')(trans_x) + trans_x = self.trans_block(trans_x) + trans_x = Rearrange('b h w c -> b c h w')(trans_x) + res = self.conv1_2(torch.cat((conv_x, trans_x), dim=1)) + x = x + res + + return x + + +class SCUNet(nn.Module): + # def __init__(self, in_nc=3, config=[2, 2, 2, 2, 2, 2, 2], dim=64, drop_path_rate=0.0, input_resolution=256): + def __init__(self, in_nc=3, config=None, dim=64, drop_path_rate=0.0, input_resolution=256): + super(SCUNet, self).__init__() + if config is None: + config = [2, 2, 2, 2, 2, 2, 2] + self.config = config + self.dim = dim + self.head_dim = 32 + self.window_size = 8 + + # drop path rate for each layer + dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(config))] + + self.m_head = [nn.Conv2d(in_nc, dim, 3, 1, 1, bias=False)] + + begin = 0 + self.m_down1 = [ConvTransBlock(dim // 2, dim // 2, self.head_dim, self.window_size, dpr[i + begin], + 'W' if not i % 2 else 'SW', input_resolution) + for i in range(config[0])] + \ + [nn.Conv2d(dim, 2 * dim, 2, 2, 0, bias=False)] + + begin += config[0] + self.m_down2 = [ConvTransBlock(dim, dim, self.head_dim, self.window_size, dpr[i + begin], + 'W' if not i % 2 else 'SW', input_resolution // 2) + for i in range(config[1])] + \ + [nn.Conv2d(2 * dim, 4 * dim, 2, 2, 0, bias=False)] + + begin += config[1] + self.m_down3 = [ConvTransBlock(2 * dim, 2 * dim, self.head_dim, self.window_size, dpr[i + begin], + 'W' if not i % 2 else 'SW', input_resolution // 4) + for i in range(config[2])] + \ + [nn.Conv2d(4 * dim, 8 * dim, 2, 2, 0, bias=False)] + + begin += config[2] + self.m_body = [ConvTransBlock(4 * dim, 4 * dim, self.head_dim, self.window_size, dpr[i + begin], + 'W' if not i % 2 else 'SW', input_resolution // 8) + for i in range(config[3])] + + begin += config[3] + self.m_up3 = [nn.ConvTranspose2d(8 * dim, 4 * dim, 2, 2, 0, bias=False), ] + \ + [ConvTransBlock(2 * dim, 2 * dim, self.head_dim, self.window_size, dpr[i + begin], + 'W' if not i % 2 else 'SW', input_resolution // 4) + for i in range(config[4])] + + begin += config[4] + self.m_up2 = [nn.ConvTranspose2d(4 * dim, 2 * dim, 2, 2, 0, bias=False), ] + \ + [ConvTransBlock(dim, dim, self.head_dim, self.window_size, dpr[i + begin], + 'W' if not i % 2 else 'SW', input_resolution // 2) + for i in range(config[5])] + + begin += config[5] + self.m_up1 = [nn.ConvTranspose2d(2 * dim, dim, 2, 2, 0, bias=False), ] + \ + [ConvTransBlock(dim // 2, dim // 2, self.head_dim, self.window_size, dpr[i + begin], + 'W' if not i % 2 else 'SW', input_resolution) + for i in range(config[6])] + + self.m_tail = [nn.Conv2d(dim, in_nc, 3, 1, 1, bias=False)] + + self.m_head = nn.Sequential(*self.m_head) + self.m_down1 = nn.Sequential(*self.m_down1) + self.m_down2 = nn.Sequential(*self.m_down2) + self.m_down3 = nn.Sequential(*self.m_down3) + self.m_body = nn.Sequential(*self.m_body) + self.m_up3 = nn.Sequential(*self.m_up3) + self.m_up2 = nn.Sequential(*self.m_up2) + self.m_up1 = nn.Sequential(*self.m_up1) + self.m_tail = nn.Sequential(*self.m_tail) + # self.apply(self._init_weights) + + def forward(self, x0): + + h, w = x0.size()[-2:] + paddingBottom = int(np.ceil(h / 64) * 64 - h) + paddingRight = int(np.ceil(w / 64) * 64 - w) + x0 = nn.ReplicationPad2d((0, paddingRight, 0, paddingBottom))(x0) + + x1 = self.m_head(x0) + x2 = self.m_down1(x1) + x3 = self.m_down2(x2) + x4 = self.m_down3(x3) + x = self.m_body(x4) + x = self.m_up3(x + x4) + x = self.m_up2(x + x3) + x = self.m_up1(x + x2) + x = self.m_tail(x + x1) + + x = x[..., :h, :w] + + return x + + def _init_weights(self, m): + if isinstance(m, nn.Linear): + trunc_normal_(m.weight, std=.02) + if m.bias is not None: + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.LayerNorm): + nn.init.constant_(m.bias, 0) + nn.init.constant_(m.weight, 1.0) diff --git a/extensions-builtin/SwinIR/preload.py b/extensions-builtin/SwinIR/preload.py new file mode 100644 index 0000000000000000000000000000000000000000..e912c6402bc80faa797cf2e95183101fb9a10286 --- /dev/null +++ b/extensions-builtin/SwinIR/preload.py @@ -0,0 +1,6 @@ +import os +from modules import paths + + +def preload(parser): + parser.add_argument("--swinir-models-path", type=str, help="Path to directory with SwinIR model file(s).", default=os.path.join(paths.models_path, 'SwinIR')) diff --git a/extensions-builtin/SwinIR/scripts/swinir_model.py b/extensions-builtin/SwinIR/scripts/swinir_model.py new file mode 100644 index 0000000000000000000000000000000000000000..ae0d0e6a8ea04f3054c1e8e5baefd2f76b57f246 --- /dev/null +++ b/extensions-builtin/SwinIR/scripts/swinir_model.py @@ -0,0 +1,192 @@ +import sys +import platform + +import numpy as np +import torch +from PIL import Image +from tqdm import tqdm + +from modules import modelloader, devices, script_callbacks, shared +from modules.shared import opts, state +from swinir_model_arch import SwinIR +from swinir_model_arch_v2 import Swin2SR +from modules.upscaler import Upscaler, UpscalerData + +SWINIR_MODEL_URL = "https://github.com/JingyunLiang/SwinIR/releases/download/v0.0/003_realSR_BSRGAN_DFOWMFC_s64w8_SwinIR-L_x4_GAN.pth" + +device_swinir = devices.get_device_for('swinir') + + +class UpscalerSwinIR(Upscaler): + def __init__(self, dirname): + self._cached_model = None # keep the model when SWIN_torch_compile is on to prevent re-compile every runs + self._cached_model_config = None # to clear '_cached_model' when changing model (v1/v2) or settings + self.name = "SwinIR" + self.model_url = SWINIR_MODEL_URL + self.model_name = "SwinIR 4x" + self.user_path = dirname + super().__init__() + scalers = [] + model_files = self.find_models(ext_filter=[".pt", ".pth"]) + for model in model_files: + if model.startswith("http"): + name = self.model_name + else: + name = modelloader.friendly_name(model) + model_data = UpscalerData(name, model, self) + scalers.append(model_data) + self.scalers = scalers + + def do_upscale(self, img, model_file): + use_compile = hasattr(opts, 'SWIN_torch_compile') and opts.SWIN_torch_compile \ + and int(torch.__version__.split('.')[0]) >= 2 and platform.system() != "Windows" + current_config = (model_file, opts.SWIN_tile) + + if use_compile and self._cached_model_config == current_config: + model = self._cached_model + else: + self._cached_model = None + try: + model = self.load_model(model_file) + except Exception as e: + print(f"Failed loading SwinIR model {model_file}: {e}", file=sys.stderr) + return img + model = model.to(device_swinir, dtype=devices.dtype) + if use_compile: + model = torch.compile(model) + self._cached_model = model + self._cached_model_config = current_config + img = upscale(img, model) + devices.torch_gc() + return img + + def load_model(self, path, scale=4): + if path.startswith("http"): + filename = modelloader.load_file_from_url( + url=path, + model_dir=self.model_download_path, + file_name=f"{self.model_name.replace(' ', '_')}.pth", + ) + else: + filename = path + if filename.endswith(".v2.pth"): + model = Swin2SR( + upscale=scale, + in_chans=3, + img_size=64, + window_size=8, + img_range=1.0, + depths=[6, 6, 6, 6, 6, 6], + embed_dim=180, + num_heads=[6, 6, 6, 6, 6, 6], + mlp_ratio=2, + upsampler="nearest+conv", + resi_connection="1conv", + ) + params = None + else: + model = SwinIR( + upscale=scale, + in_chans=3, + img_size=64, + window_size=8, + img_range=1.0, + depths=[6, 6, 6, 6, 6, 6, 6, 6, 6], + embed_dim=240, + num_heads=[8, 8, 8, 8, 8, 8, 8, 8, 8], + mlp_ratio=2, + upsampler="nearest+conv", + resi_connection="3conv", + ) + params = "params_ema" + + pretrained_model = torch.load(filename) + if params is not None: + model.load_state_dict(pretrained_model[params], strict=True) + else: + model.load_state_dict(pretrained_model, strict=True) + return model + + +def upscale( + img, + model, + tile=None, + tile_overlap=None, + window_size=8, + scale=4, +): + tile = tile or opts.SWIN_tile + tile_overlap = tile_overlap or opts.SWIN_tile_overlap + + + img = np.array(img) + img = img[:, :, ::-1] + img = np.moveaxis(img, 2, 0) / 255 + img = torch.from_numpy(img).float() + img = img.unsqueeze(0).to(device_swinir, dtype=devices.dtype) + with torch.no_grad(), devices.autocast(): + _, _, h_old, w_old = img.size() + h_pad = (h_old // window_size + 1) * window_size - h_old + w_pad = (w_old // window_size + 1) * window_size - w_old + img = torch.cat([img, torch.flip(img, [2])], 2)[:, :, : h_old + h_pad, :] + img = torch.cat([img, torch.flip(img, [3])], 3)[:, :, :, : w_old + w_pad] + output = inference(img, model, tile, tile_overlap, window_size, scale) + output = output[..., : h_old * scale, : w_old * scale] + output = output.data.squeeze().float().cpu().clamp_(0, 1).numpy() + if output.ndim == 3: + output = np.transpose( + output[[2, 1, 0], :, :], (1, 2, 0) + ) # CHW-RGB to HCW-BGR + output = (output * 255.0).round().astype(np.uint8) # float32 to uint8 + return Image.fromarray(output, "RGB") + + +def inference(img, model, tile, tile_overlap, window_size, scale): + # test the image tile by tile + b, c, h, w = img.size() + tile = min(tile, h, w) + assert tile % window_size == 0, "tile size should be a multiple of window_size" + sf = scale + + stride = tile - tile_overlap + h_idx_list = list(range(0, h - tile, stride)) + [h - tile] + w_idx_list = list(range(0, w - tile, stride)) + [w - tile] + E = torch.zeros(b, c, h * sf, w * sf, dtype=devices.dtype, device=device_swinir).type_as(img) + W = torch.zeros_like(E, dtype=devices.dtype, device=device_swinir) + + with tqdm(total=len(h_idx_list) * len(w_idx_list), desc="SwinIR tiles") as pbar: + for h_idx in h_idx_list: + if state.interrupted or state.skipped: + break + + for w_idx in w_idx_list: + if state.interrupted or state.skipped: + break + + in_patch = img[..., h_idx: h_idx + tile, w_idx: w_idx + tile] + out_patch = model(in_patch) + out_patch_mask = torch.ones_like(out_patch) + + E[ + ..., h_idx * sf: (h_idx + tile) * sf, w_idx * sf: (w_idx + tile) * sf + ].add_(out_patch) + W[ + ..., h_idx * sf: (h_idx + tile) * sf, w_idx * sf: (w_idx + tile) * sf + ].add_(out_patch_mask) + pbar.update(1) + output = E.div_(W) + + return output + + +def on_ui_settings(): + import gradio as gr + + shared.opts.add_option("SWIN_tile", shared.OptionInfo(192, "Tile size for all SwinIR.", gr.Slider, {"minimum": 16, "maximum": 512, "step": 16}, section=('upscaling', "Upscaling"))) + shared.opts.add_option("SWIN_tile_overlap", shared.OptionInfo(8, "Tile overlap, in pixels for SwinIR. Low values = visible seam.", gr.Slider, {"minimum": 0, "maximum": 48, "step": 1}, section=('upscaling', "Upscaling"))) + if int(torch.__version__.split('.')[0]) >= 2 and platform.system() != "Windows": # torch.compile() require pytorch 2.0 or above, and not on Windows + shared.opts.add_option("SWIN_torch_compile", shared.OptionInfo(False, "Use torch.compile to accelerate SwinIR.", gr.Checkbox, {"interactive": True}, section=('upscaling', "Upscaling")).info("Takes longer on first run")) + + +script_callbacks.on_ui_settings(on_ui_settings) diff --git a/extensions-builtin/SwinIR/swinir_model_arch.py b/extensions-builtin/SwinIR/swinir_model_arch.py new file mode 100644 index 0000000000000000000000000000000000000000..93b9327473a6e77c3a3dc6a7743e932c9083a996 --- /dev/null +++ b/extensions-builtin/SwinIR/swinir_model_arch.py @@ -0,0 +1,867 @@ +# ----------------------------------------------------------------------------------- +# SwinIR: Image Restoration Using Swin Transformer, https://arxiv.org/abs/2108.10257 +# Originally Written by Ze Liu, Modified by Jingyun Liang. +# ----------------------------------------------------------------------------------- + +import math +import torch +import torch.nn as nn +import torch.nn.functional as F +import torch.utils.checkpoint as checkpoint +from timm.models.layers import DropPath, to_2tuple, trunc_normal_ + + +class Mlp(nn.Module): + def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.): + super().__init__() + out_features = out_features or in_features + hidden_features = hidden_features or in_features + self.fc1 = nn.Linear(in_features, hidden_features) + self.act = act_layer() + self.fc2 = nn.Linear(hidden_features, out_features) + self.drop = nn.Dropout(drop) + + def forward(self, x): + x = self.fc1(x) + x = self.act(x) + x = self.drop(x) + x = self.fc2(x) + x = self.drop(x) + return x + + +def window_partition(x, window_size): + """ + Args: + x: (B, H, W, C) + window_size (int): window size + + Returns: + windows: (num_windows*B, window_size, window_size, C) + """ + B, H, W, C = x.shape + x = x.view(B, H // window_size, window_size, W // window_size, window_size, C) + windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C) + return windows + + +def window_reverse(windows, window_size, H, W): + """ + Args: + windows: (num_windows*B, window_size, window_size, C) + window_size (int): Window size + H (int): Height of image + W (int): Width of image + + Returns: + x: (B, H, W, C) + """ + B = int(windows.shape[0] / (H * W / window_size / window_size)) + x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1) + x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1) + return x + + +class WindowAttention(nn.Module): + r""" Window based multi-head self attention (W-MSA) module with relative position bias. + It supports both of shifted and non-shifted window. + + Args: + dim (int): Number of input channels. + window_size (tuple[int]): The height and width of the window. + num_heads (int): Number of attention heads. + qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True + qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set + attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0 + proj_drop (float, optional): Dropout ratio of output. Default: 0.0 + """ + + def __init__(self, dim, window_size, num_heads, qkv_bias=True, qk_scale=None, attn_drop=0., proj_drop=0.): + + super().__init__() + self.dim = dim + self.window_size = window_size # Wh, Ww + self.num_heads = num_heads + head_dim = dim // num_heads + self.scale = qk_scale or head_dim ** -0.5 + + # define a parameter table of relative position bias + self.relative_position_bias_table = nn.Parameter( + torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads)) # 2*Wh-1 * 2*Ww-1, nH + + # get pair-wise relative position index for each token inside the window + coords_h = torch.arange(self.window_size[0]) + coords_w = torch.arange(self.window_size[1]) + coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww + coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww + relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] # 2, Wh*Ww, Wh*Ww + relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2 + relative_coords[:, :, 0] += self.window_size[0] - 1 # shift to start from 0 + relative_coords[:, :, 1] += self.window_size[1] - 1 + relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1 + relative_position_index = relative_coords.sum(-1) # Wh*Ww, Wh*Ww + self.register_buffer("relative_position_index", relative_position_index) + + self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) + self.attn_drop = nn.Dropout(attn_drop) + self.proj = nn.Linear(dim, dim) + + self.proj_drop = nn.Dropout(proj_drop) + + trunc_normal_(self.relative_position_bias_table, std=.02) + self.softmax = nn.Softmax(dim=-1) + + def forward(self, x, mask=None): + """ + Args: + x: input features with shape of (num_windows*B, N, C) + mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None + """ + B_, N, C = x.shape + qkv = self.qkv(x).reshape(B_, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) + q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple) + + q = q * self.scale + attn = (q @ k.transpose(-2, -1)) + + relative_position_bias = self.relative_position_bias_table[self.relative_position_index.view(-1)].view( + self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1) # Wh*Ww,Wh*Ww,nH + relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous() # nH, Wh*Ww, Wh*Ww + attn = attn + relative_position_bias.unsqueeze(0) + + if mask is not None: + nW = mask.shape[0] + attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0) + attn = attn.view(-1, self.num_heads, N, N) + attn = self.softmax(attn) + else: + attn = self.softmax(attn) + + attn = self.attn_drop(attn) + + x = (attn @ v).transpose(1, 2).reshape(B_, N, C) + x = self.proj(x) + x = self.proj_drop(x) + return x + + def extra_repr(self) -> str: + return f'dim={self.dim}, window_size={self.window_size}, num_heads={self.num_heads}' + + def flops(self, N): + # calculate flops for 1 window with token length of N + flops = 0 + # qkv = self.qkv(x) + flops += N * self.dim * 3 * self.dim + # attn = (q @ k.transpose(-2, -1)) + flops += self.num_heads * N * (self.dim // self.num_heads) * N + # x = (attn @ v) + flops += self.num_heads * N * N * (self.dim // self.num_heads) + # x = self.proj(x) + flops += N * self.dim * self.dim + return flops + + +class SwinTransformerBlock(nn.Module): + r""" Swin Transformer Block. + + Args: + dim (int): Number of input channels. + input_resolution (tuple[int]): Input resolution. + num_heads (int): Number of attention heads. + window_size (int): Window size. + shift_size (int): Shift size for SW-MSA. + mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. + qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True + qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set. + drop (float, optional): Dropout rate. Default: 0.0 + attn_drop (float, optional): Attention dropout rate. Default: 0.0 + drop_path (float, optional): Stochastic depth rate. Default: 0.0 + act_layer (nn.Module, optional): Activation layer. Default: nn.GELU + norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm + """ + + def __init__(self, dim, input_resolution, num_heads, window_size=7, shift_size=0, + mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0., drop_path=0., + act_layer=nn.GELU, norm_layer=nn.LayerNorm): + super().__init__() + self.dim = dim + self.input_resolution = input_resolution + self.num_heads = num_heads + self.window_size = window_size + self.shift_size = shift_size + self.mlp_ratio = mlp_ratio + if min(self.input_resolution) <= self.window_size: + # if window size is larger than input resolution, we don't partition windows + self.shift_size = 0 + self.window_size = min(self.input_resolution) + assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size" + + self.norm1 = norm_layer(dim) + self.attn = WindowAttention( + dim, window_size=to_2tuple(self.window_size), num_heads=num_heads, + qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop) + + self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity() + self.norm2 = norm_layer(dim) + mlp_hidden_dim = int(dim * mlp_ratio) + self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop) + + if self.shift_size > 0: + attn_mask = self.calculate_mask(self.input_resolution) + else: + attn_mask = None + + self.register_buffer("attn_mask", attn_mask) + + def calculate_mask(self, x_size): + # calculate attention mask for SW-MSA + H, W = x_size + img_mask = torch.zeros((1, H, W, 1)) # 1 H W 1 + h_slices = (slice(0, -self.window_size), + slice(-self.window_size, -self.shift_size), + slice(-self.shift_size, None)) + w_slices = (slice(0, -self.window_size), + slice(-self.window_size, -self.shift_size), + slice(-self.shift_size, None)) + cnt = 0 + for h in h_slices: + for w in w_slices: + img_mask[:, h, w, :] = cnt + cnt += 1 + + mask_windows = window_partition(img_mask, self.window_size) # nW, window_size, window_size, 1 + mask_windows = mask_windows.view(-1, self.window_size * self.window_size) + attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2) + attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0)) + + return attn_mask + + def forward(self, x, x_size): + H, W = x_size + B, L, C = x.shape + # assert L == H * W, "input feature has wrong size" + + shortcut = x + x = self.norm1(x) + x = x.view(B, H, W, C) + + # cyclic shift + if self.shift_size > 0: + shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2)) + else: + shifted_x = x + + # partition windows + x_windows = window_partition(shifted_x, self.window_size) # nW*B, window_size, window_size, C + x_windows = x_windows.view(-1, self.window_size * self.window_size, C) # nW*B, window_size*window_size, C + + # W-MSA/SW-MSA (to be compatible for testing on images whose shapes are the multiple of window size + if self.input_resolution == x_size: + attn_windows = self.attn(x_windows, mask=self.attn_mask) # nW*B, window_size*window_size, C + else: + attn_windows = self.attn(x_windows, mask=self.calculate_mask(x_size).to(x.device)) + + # merge windows + attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C) + shifted_x = window_reverse(attn_windows, self.window_size, H, W) # B H' W' C + + # reverse cyclic shift + if self.shift_size > 0: + x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2)) + else: + x = shifted_x + x = x.view(B, H * W, C) + + # FFN + x = shortcut + self.drop_path(x) + x = x + self.drop_path(self.mlp(self.norm2(x))) + + return x + + def extra_repr(self) -> str: + return f"dim={self.dim}, input_resolution={self.input_resolution}, num_heads={self.num_heads}, " \ + f"window_size={self.window_size}, shift_size={self.shift_size}, mlp_ratio={self.mlp_ratio}" + + def flops(self): + flops = 0 + H, W = self.input_resolution + # norm1 + flops += self.dim * H * W + # W-MSA/SW-MSA + nW = H * W / self.window_size / self.window_size + flops += nW * self.attn.flops(self.window_size * self.window_size) + # mlp + flops += 2 * H * W * self.dim * self.dim * self.mlp_ratio + # norm2 + flops += self.dim * H * W + return flops + + +class PatchMerging(nn.Module): + r""" Patch Merging Layer. + + Args: + input_resolution (tuple[int]): Resolution of input feature. + dim (int): Number of input channels. + norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm + """ + + def __init__(self, input_resolution, dim, norm_layer=nn.LayerNorm): + super().__init__() + self.input_resolution = input_resolution + self.dim = dim + self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False) + self.norm = norm_layer(4 * dim) + + def forward(self, x): + """ + x: B, H*W, C + """ + H, W = self.input_resolution + B, L, C = x.shape + assert L == H * W, "input feature has wrong size" + assert H % 2 == 0 and W % 2 == 0, f"x size ({H}*{W}) are not even." + + x = x.view(B, H, W, C) + + x0 = x[:, 0::2, 0::2, :] # B H/2 W/2 C + x1 = x[:, 1::2, 0::2, :] # B H/2 W/2 C + x2 = x[:, 0::2, 1::2, :] # B H/2 W/2 C + x3 = x[:, 1::2, 1::2, :] # B H/2 W/2 C + x = torch.cat([x0, x1, x2, x3], -1) # B H/2 W/2 4*C + x = x.view(B, -1, 4 * C) # B H/2*W/2 4*C + + x = self.norm(x) + x = self.reduction(x) + + return x + + def extra_repr(self) -> str: + return f"input_resolution={self.input_resolution}, dim={self.dim}" + + def flops(self): + H, W = self.input_resolution + flops = H * W * self.dim + flops += (H // 2) * (W // 2) * 4 * self.dim * 2 * self.dim + return flops + + +class BasicLayer(nn.Module): + """ A basic Swin Transformer layer for one stage. + + Args: + dim (int): Number of input channels. + input_resolution (tuple[int]): Input resolution. + depth (int): Number of blocks. + num_heads (int): Number of attention heads. + window_size (int): Local window size. + mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. + qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True + qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set. + drop (float, optional): Dropout rate. Default: 0.0 + attn_drop (float, optional): Attention dropout rate. Default: 0.0 + drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0 + norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm + downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None + use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False. + """ + + def __init__(self, dim, input_resolution, depth, num_heads, window_size, + mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0., + drop_path=0., norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False): + + super().__init__() + self.dim = dim + self.input_resolution = input_resolution + self.depth = depth + self.use_checkpoint = use_checkpoint + + # build blocks + self.blocks = nn.ModuleList([ + SwinTransformerBlock(dim=dim, input_resolution=input_resolution, + num_heads=num_heads, window_size=window_size, + shift_size=0 if (i % 2 == 0) else window_size // 2, + mlp_ratio=mlp_ratio, + qkv_bias=qkv_bias, qk_scale=qk_scale, + drop=drop, attn_drop=attn_drop, + drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path, + norm_layer=norm_layer) + for i in range(depth)]) + + # patch merging layer + if downsample is not None: + self.downsample = downsample(input_resolution, dim=dim, norm_layer=norm_layer) + else: + self.downsample = None + + def forward(self, x, x_size): + for blk in self.blocks: + if self.use_checkpoint: + x = checkpoint.checkpoint(blk, x, x_size) + else: + x = blk(x, x_size) + if self.downsample is not None: + x = self.downsample(x) + return x + + def extra_repr(self) -> str: + return f"dim={self.dim}, input_resolution={self.input_resolution}, depth={self.depth}" + + def flops(self): + flops = 0 + for blk in self.blocks: + flops += blk.flops() + if self.downsample is not None: + flops += self.downsample.flops() + return flops + + +class RSTB(nn.Module): + """Residual Swin Transformer Block (RSTB). + + Args: + dim (int): Number of input channels. + input_resolution (tuple[int]): Input resolution. + depth (int): Number of blocks. + num_heads (int): Number of attention heads. + window_size (int): Local window size. + mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. + qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True + qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set. + drop (float, optional): Dropout rate. Default: 0.0 + attn_drop (float, optional): Attention dropout rate. Default: 0.0 + drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0 + norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm + downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None + use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False. + img_size: Input image size. + patch_size: Patch size. + resi_connection: The convolutional block before residual connection. + """ + + def __init__(self, dim, input_resolution, depth, num_heads, window_size, + mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0., + drop_path=0., norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False, + img_size=224, patch_size=4, resi_connection='1conv'): + super(RSTB, self).__init__() + + self.dim = dim + self.input_resolution = input_resolution + + self.residual_group = BasicLayer(dim=dim, + input_resolution=input_resolution, + depth=depth, + num_heads=num_heads, + window_size=window_size, + mlp_ratio=mlp_ratio, + qkv_bias=qkv_bias, qk_scale=qk_scale, + drop=drop, attn_drop=attn_drop, + drop_path=drop_path, + norm_layer=norm_layer, + downsample=downsample, + use_checkpoint=use_checkpoint) + + if resi_connection == '1conv': + self.conv = nn.Conv2d(dim, dim, 3, 1, 1) + elif resi_connection == '3conv': + # to save parameters and memory + self.conv = nn.Sequential(nn.Conv2d(dim, dim // 4, 3, 1, 1), nn.LeakyReLU(negative_slope=0.2, inplace=True), + nn.Conv2d(dim // 4, dim // 4, 1, 1, 0), + nn.LeakyReLU(negative_slope=0.2, inplace=True), + nn.Conv2d(dim // 4, dim, 3, 1, 1)) + + self.patch_embed = PatchEmbed( + img_size=img_size, patch_size=patch_size, in_chans=0, embed_dim=dim, + norm_layer=None) + + self.patch_unembed = PatchUnEmbed( + img_size=img_size, patch_size=patch_size, in_chans=0, embed_dim=dim, + norm_layer=None) + + def forward(self, x, x_size): + return self.patch_embed(self.conv(self.patch_unembed(self.residual_group(x, x_size), x_size))) + x + + def flops(self): + flops = 0 + flops += self.residual_group.flops() + H, W = self.input_resolution + flops += H * W * self.dim * self.dim * 9 + flops += self.patch_embed.flops() + flops += self.patch_unembed.flops() + + return flops + + +class PatchEmbed(nn.Module): + r""" Image to Patch Embedding + + Args: + img_size (int): Image size. Default: 224. + patch_size (int): Patch token size. Default: 4. + in_chans (int): Number of input image channels. Default: 3. + embed_dim (int): Number of linear projection output channels. Default: 96. + norm_layer (nn.Module, optional): Normalization layer. Default: None + """ + + def __init__(self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None): + super().__init__() + img_size = to_2tuple(img_size) + patch_size = to_2tuple(patch_size) + patches_resolution = [img_size[0] // patch_size[0], img_size[1] // patch_size[1]] + self.img_size = img_size + self.patch_size = patch_size + self.patches_resolution = patches_resolution + self.num_patches = patches_resolution[0] * patches_resolution[1] + + self.in_chans = in_chans + self.embed_dim = embed_dim + + if norm_layer is not None: + self.norm = norm_layer(embed_dim) + else: + self.norm = None + + def forward(self, x): + x = x.flatten(2).transpose(1, 2) # B Ph*Pw C + if self.norm is not None: + x = self.norm(x) + return x + + def flops(self): + flops = 0 + H, W = self.img_size + if self.norm is not None: + flops += H * W * self.embed_dim + return flops + + +class PatchUnEmbed(nn.Module): + r""" Image to Patch Unembedding + + Args: + img_size (int): Image size. Default: 224. + patch_size (int): Patch token size. Default: 4. + in_chans (int): Number of input image channels. Default: 3. + embed_dim (int): Number of linear projection output channels. Default: 96. + norm_layer (nn.Module, optional): Normalization layer. Default: None + """ + + def __init__(self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None): + super().__init__() + img_size = to_2tuple(img_size) + patch_size = to_2tuple(patch_size) + patches_resolution = [img_size[0] // patch_size[0], img_size[1] // patch_size[1]] + self.img_size = img_size + self.patch_size = patch_size + self.patches_resolution = patches_resolution + self.num_patches = patches_resolution[0] * patches_resolution[1] + + self.in_chans = in_chans + self.embed_dim = embed_dim + + def forward(self, x, x_size): + B, HW, C = x.shape + x = x.transpose(1, 2).view(B, self.embed_dim, x_size[0], x_size[1]) # B Ph*Pw C + return x + + def flops(self): + flops = 0 + return flops + + +class Upsample(nn.Sequential): + """Upsample module. + + Args: + scale (int): Scale factor. Supported scales: 2^n and 3. + num_feat (int): Channel number of intermediate features. + """ + + def __init__(self, scale, num_feat): + m = [] + if (scale & (scale - 1)) == 0: # scale = 2^n + for _ in range(int(math.log(scale, 2))): + m.append(nn.Conv2d(num_feat, 4 * num_feat, 3, 1, 1)) + m.append(nn.PixelShuffle(2)) + elif scale == 3: + m.append(nn.Conv2d(num_feat, 9 * num_feat, 3, 1, 1)) + m.append(nn.PixelShuffle(3)) + else: + raise ValueError(f'scale {scale} is not supported. ' 'Supported scales: 2^n and 3.') + super(Upsample, self).__init__(*m) + + +class UpsampleOneStep(nn.Sequential): + """UpsampleOneStep module (the difference with Upsample is that it always only has 1conv + 1pixelshuffle) + Used in lightweight SR to save parameters. + + Args: + scale (int): Scale factor. Supported scales: 2^n and 3. + num_feat (int): Channel number of intermediate features. + + """ + + def __init__(self, scale, num_feat, num_out_ch, input_resolution=None): + self.num_feat = num_feat + self.input_resolution = input_resolution + m = [] + m.append(nn.Conv2d(num_feat, (scale ** 2) * num_out_ch, 3, 1, 1)) + m.append(nn.PixelShuffle(scale)) + super(UpsampleOneStep, self).__init__(*m) + + def flops(self): + H, W = self.input_resolution + flops = H * W * self.num_feat * 3 * 9 + return flops + + +class SwinIR(nn.Module): + r""" SwinIR + A PyTorch impl of : `SwinIR: Image Restoration Using Swin Transformer`, based on Swin Transformer. + + Args: + img_size (int | tuple(int)): Input image size. Default 64 + patch_size (int | tuple(int)): Patch size. Default: 1 + in_chans (int): Number of input image channels. Default: 3 + embed_dim (int): Patch embedding dimension. Default: 96 + depths (tuple(int)): Depth of each Swin Transformer layer. + num_heads (tuple(int)): Number of attention heads in different layers. + window_size (int): Window size. Default: 7 + mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4 + qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True + qk_scale (float): Override default qk scale of head_dim ** -0.5 if set. Default: None + drop_rate (float): Dropout rate. Default: 0 + attn_drop_rate (float): Attention dropout rate. Default: 0 + drop_path_rate (float): Stochastic depth rate. Default: 0.1 + norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm. + ape (bool): If True, add absolute position embedding to the patch embedding. Default: False + patch_norm (bool): If True, add normalization after patch embedding. Default: True + use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False + upscale: Upscale factor. 2/3/4/8 for image SR, 1 for denoising and compress artifact reduction + img_range: Image range. 1. or 255. + upsampler: The reconstruction reconstruction module. 'pixelshuffle'/'pixelshuffledirect'/'nearest+conv'/None + resi_connection: The convolutional block before residual connection. '1conv'/'3conv' + """ + + def __init__(self, img_size=64, patch_size=1, in_chans=3, + embed_dim=96, depths=(6, 6, 6, 6), num_heads=(6, 6, 6, 6), + window_size=7, mlp_ratio=4., qkv_bias=True, qk_scale=None, + drop_rate=0., attn_drop_rate=0., drop_path_rate=0.1, + norm_layer=nn.LayerNorm, ape=False, patch_norm=True, + use_checkpoint=False, upscale=2, img_range=1., upsampler='', resi_connection='1conv', + **kwargs): + super(SwinIR, self).__init__() + num_in_ch = in_chans + num_out_ch = in_chans + num_feat = 64 + self.img_range = img_range + if in_chans == 3: + rgb_mean = (0.4488, 0.4371, 0.4040) + self.mean = torch.Tensor(rgb_mean).view(1, 3, 1, 1) + else: + self.mean = torch.zeros(1, 1, 1, 1) + self.upscale = upscale + self.upsampler = upsampler + self.window_size = window_size + + ##################################################################################################### + ################################### 1, shallow feature extraction ################################### + self.conv_first = nn.Conv2d(num_in_ch, embed_dim, 3, 1, 1) + + ##################################################################################################### + ################################### 2, deep feature extraction ###################################### + self.num_layers = len(depths) + self.embed_dim = embed_dim + self.ape = ape + self.patch_norm = patch_norm + self.num_features = embed_dim + self.mlp_ratio = mlp_ratio + + # split image into non-overlapping patches + self.patch_embed = PatchEmbed( + img_size=img_size, patch_size=patch_size, in_chans=embed_dim, embed_dim=embed_dim, + norm_layer=norm_layer if self.patch_norm else None) + num_patches = self.patch_embed.num_patches + patches_resolution = self.patch_embed.patches_resolution + self.patches_resolution = patches_resolution + + # merge non-overlapping patches into image + self.patch_unembed = PatchUnEmbed( + img_size=img_size, patch_size=patch_size, in_chans=embed_dim, embed_dim=embed_dim, + norm_layer=norm_layer if self.patch_norm else None) + + # absolute position embedding + if self.ape: + self.absolute_pos_embed = nn.Parameter(torch.zeros(1, num_patches, embed_dim)) + trunc_normal_(self.absolute_pos_embed, std=.02) + + self.pos_drop = nn.Dropout(p=drop_rate) + + # stochastic depth + dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))] # stochastic depth decay rule + + # build Residual Swin Transformer blocks (RSTB) + self.layers = nn.ModuleList() + for i_layer in range(self.num_layers): + layer = RSTB(dim=embed_dim, + input_resolution=(patches_resolution[0], + patches_resolution[1]), + depth=depths[i_layer], + num_heads=num_heads[i_layer], + window_size=window_size, + mlp_ratio=self.mlp_ratio, + qkv_bias=qkv_bias, qk_scale=qk_scale, + drop=drop_rate, attn_drop=attn_drop_rate, + drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])], # no impact on SR results + norm_layer=norm_layer, + downsample=None, + use_checkpoint=use_checkpoint, + img_size=img_size, + patch_size=patch_size, + resi_connection=resi_connection + + ) + self.layers.append(layer) + self.norm = norm_layer(self.num_features) + + # build the last conv layer in deep feature extraction + if resi_connection == '1conv': + self.conv_after_body = nn.Conv2d(embed_dim, embed_dim, 3, 1, 1) + elif resi_connection == '3conv': + # to save parameters and memory + self.conv_after_body = nn.Sequential(nn.Conv2d(embed_dim, embed_dim // 4, 3, 1, 1), + nn.LeakyReLU(negative_slope=0.2, inplace=True), + nn.Conv2d(embed_dim // 4, embed_dim // 4, 1, 1, 0), + nn.LeakyReLU(negative_slope=0.2, inplace=True), + nn.Conv2d(embed_dim // 4, embed_dim, 3, 1, 1)) + + ##################################################################################################### + ################################ 3, high quality image reconstruction ################################ + if self.upsampler == 'pixelshuffle': + # for classical SR + self.conv_before_upsample = nn.Sequential(nn.Conv2d(embed_dim, num_feat, 3, 1, 1), + nn.LeakyReLU(inplace=True)) + self.upsample = Upsample(upscale, num_feat) + self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1) + elif self.upsampler == 'pixelshuffledirect': + # for lightweight SR (to save parameters) + self.upsample = UpsampleOneStep(upscale, embed_dim, num_out_ch, + (patches_resolution[0], patches_resolution[1])) + elif self.upsampler == 'nearest+conv': + # for real-world SR (less artifacts) + self.conv_before_upsample = nn.Sequential(nn.Conv2d(embed_dim, num_feat, 3, 1, 1), + nn.LeakyReLU(inplace=True)) + self.conv_up1 = nn.Conv2d(num_feat, num_feat, 3, 1, 1) + if self.upscale == 4: + self.conv_up2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1) + self.conv_hr = nn.Conv2d(num_feat, num_feat, 3, 1, 1) + self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1) + self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True) + else: + # for image denoising and JPEG compression artifact reduction + self.conv_last = nn.Conv2d(embed_dim, num_out_ch, 3, 1, 1) + + self.apply(self._init_weights) + + def _init_weights(self, m): + if isinstance(m, nn.Linear): + trunc_normal_(m.weight, std=.02) + if isinstance(m, nn.Linear) and m.bias is not None: + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.LayerNorm): + nn.init.constant_(m.bias, 0) + nn.init.constant_(m.weight, 1.0) + + @torch.jit.ignore + def no_weight_decay(self): + return {'absolute_pos_embed'} + + @torch.jit.ignore + def no_weight_decay_keywords(self): + return {'relative_position_bias_table'} + + def check_image_size(self, x): + _, _, h, w = x.size() + mod_pad_h = (self.window_size - h % self.window_size) % self.window_size + mod_pad_w = (self.window_size - w % self.window_size) % self.window_size + x = F.pad(x, (0, mod_pad_w, 0, mod_pad_h), 'reflect') + return x + + def forward_features(self, x): + x_size = (x.shape[2], x.shape[3]) + x = self.patch_embed(x) + if self.ape: + x = x + self.absolute_pos_embed + x = self.pos_drop(x) + + for layer in self.layers: + x = layer(x, x_size) + + x = self.norm(x) # B L C + x = self.patch_unembed(x, x_size) + + return x + + def forward(self, x): + H, W = x.shape[2:] + x = self.check_image_size(x) + + self.mean = self.mean.type_as(x) + x = (x - self.mean) * self.img_range + + if self.upsampler == 'pixelshuffle': + # for classical SR + x = self.conv_first(x) + x = self.conv_after_body(self.forward_features(x)) + x + x = self.conv_before_upsample(x) + x = self.conv_last(self.upsample(x)) + elif self.upsampler == 'pixelshuffledirect': + # for lightweight SR + x = self.conv_first(x) + x = self.conv_after_body(self.forward_features(x)) + x + x = self.upsample(x) + elif self.upsampler == 'nearest+conv': + # for real-world SR + x = self.conv_first(x) + x = self.conv_after_body(self.forward_features(x)) + x + x = self.conv_before_upsample(x) + x = self.lrelu(self.conv_up1(torch.nn.functional.interpolate(x, scale_factor=2, mode='nearest'))) + if self.upscale == 4: + x = self.lrelu(self.conv_up2(torch.nn.functional.interpolate(x, scale_factor=2, mode='nearest'))) + x = self.conv_last(self.lrelu(self.conv_hr(x))) + else: + # for image denoising and JPEG compression artifact reduction + x_first = self.conv_first(x) + res = self.conv_after_body(self.forward_features(x_first)) + x_first + x = x + self.conv_last(res) + + x = x / self.img_range + self.mean + + return x[:, :, :H*self.upscale, :W*self.upscale] + + def flops(self): + flops = 0 + H, W = self.patches_resolution + flops += H * W * 3 * self.embed_dim * 9 + flops += self.patch_embed.flops() + for layer in self.layers: + flops += layer.flops() + flops += H * W * 3 * self.embed_dim * self.embed_dim + flops += self.upsample.flops() + return flops + + +if __name__ == '__main__': + upscale = 4 + window_size = 8 + height = (1024 // upscale // window_size + 1) * window_size + width = (720 // upscale // window_size + 1) * window_size + model = SwinIR(upscale=2, img_size=(height, width), + window_size=window_size, img_range=1., depths=[6, 6, 6, 6], + embed_dim=60, num_heads=[6, 6, 6, 6], mlp_ratio=2, upsampler='pixelshuffledirect') + print(model) + print(height, width, model.flops() / 1e9) + + x = torch.randn((1, 3, height, width)) + x = model(x) + print(x.shape) diff --git a/extensions-builtin/SwinIR/swinir_model_arch_v2.py b/extensions-builtin/SwinIR/swinir_model_arch_v2.py new file mode 100644 index 0000000000000000000000000000000000000000..59219f69a9a7f8365628cb2f4f57f5cd0104147a --- /dev/null +++ b/extensions-builtin/SwinIR/swinir_model_arch_v2.py @@ -0,0 +1,1017 @@ +# ----------------------------------------------------------------------------------- +# Swin2SR: Swin2SR: SwinV2 Transformer for Compressed Image Super-Resolution and Restoration, https://arxiv.org/abs/ +# Written by Conde and Choi et al. +# ----------------------------------------------------------------------------------- + +import math +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F +import torch.utils.checkpoint as checkpoint +from timm.models.layers import DropPath, to_2tuple, trunc_normal_ + + +class Mlp(nn.Module): + def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.): + super().__init__() + out_features = out_features or in_features + hidden_features = hidden_features or in_features + self.fc1 = nn.Linear(in_features, hidden_features) + self.act = act_layer() + self.fc2 = nn.Linear(hidden_features, out_features) + self.drop = nn.Dropout(drop) + + def forward(self, x): + x = self.fc1(x) + x = self.act(x) + x = self.drop(x) + x = self.fc2(x) + x = self.drop(x) + return x + + +def window_partition(x, window_size): + """ + Args: + x: (B, H, W, C) + window_size (int): window size + Returns: + windows: (num_windows*B, window_size, window_size, C) + """ + B, H, W, C = x.shape + x = x.view(B, H // window_size, window_size, W // window_size, window_size, C) + windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C) + return windows + + +def window_reverse(windows, window_size, H, W): + """ + Args: + windows: (num_windows*B, window_size, window_size, C) + window_size (int): Window size + H (int): Height of image + W (int): Width of image + Returns: + x: (B, H, W, C) + """ + B = int(windows.shape[0] / (H * W / window_size / window_size)) + x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1) + x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1) + return x + +class WindowAttention(nn.Module): + r""" Window based multi-head self attention (W-MSA) module with relative position bias. + It supports both of shifted and non-shifted window. + Args: + dim (int): Number of input channels. + window_size (tuple[int]): The height and width of the window. + num_heads (int): Number of attention heads. + qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True + attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0 + proj_drop (float, optional): Dropout ratio of output. Default: 0.0 + pretrained_window_size (tuple[int]): The height and width of the window in pre-training. + """ + + def __init__(self, dim, window_size, num_heads, qkv_bias=True, attn_drop=0., proj_drop=0., + pretrained_window_size=(0, 0)): + + super().__init__() + self.dim = dim + self.window_size = window_size # Wh, Ww + self.pretrained_window_size = pretrained_window_size + self.num_heads = num_heads + + self.logit_scale = nn.Parameter(torch.log(10 * torch.ones((num_heads, 1, 1))), requires_grad=True) + + # mlp to generate continuous relative position bias + self.cpb_mlp = nn.Sequential(nn.Linear(2, 512, bias=True), + nn.ReLU(inplace=True), + nn.Linear(512, num_heads, bias=False)) + + # get relative_coords_table + relative_coords_h = torch.arange(-(self.window_size[0] - 1), self.window_size[0], dtype=torch.float32) + relative_coords_w = torch.arange(-(self.window_size[1] - 1), self.window_size[1], dtype=torch.float32) + relative_coords_table = torch.stack( + torch.meshgrid([relative_coords_h, + relative_coords_w])).permute(1, 2, 0).contiguous().unsqueeze(0) # 1, 2*Wh-1, 2*Ww-1, 2 + if pretrained_window_size[0] > 0: + relative_coords_table[:, :, :, 0] /= (pretrained_window_size[0] - 1) + relative_coords_table[:, :, :, 1] /= (pretrained_window_size[1] - 1) + else: + relative_coords_table[:, :, :, 0] /= (self.window_size[0] - 1) + relative_coords_table[:, :, :, 1] /= (self.window_size[1] - 1) + relative_coords_table *= 8 # normalize to -8, 8 + relative_coords_table = torch.sign(relative_coords_table) * torch.log2( + torch.abs(relative_coords_table) + 1.0) / np.log2(8) + + self.register_buffer("relative_coords_table", relative_coords_table) + + # get pair-wise relative position index for each token inside the window + coords_h = torch.arange(self.window_size[0]) + coords_w = torch.arange(self.window_size[1]) + coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww + coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww + relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] # 2, Wh*Ww, Wh*Ww + relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2 + relative_coords[:, :, 0] += self.window_size[0] - 1 # shift to start from 0 + relative_coords[:, :, 1] += self.window_size[1] - 1 + relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1 + relative_position_index = relative_coords.sum(-1) # Wh*Ww, Wh*Ww + self.register_buffer("relative_position_index", relative_position_index) + + self.qkv = nn.Linear(dim, dim * 3, bias=False) + if qkv_bias: + self.q_bias = nn.Parameter(torch.zeros(dim)) + self.v_bias = nn.Parameter(torch.zeros(dim)) + else: + self.q_bias = None + self.v_bias = None + self.attn_drop = nn.Dropout(attn_drop) + self.proj = nn.Linear(dim, dim) + self.proj_drop = nn.Dropout(proj_drop) + self.softmax = nn.Softmax(dim=-1) + + def forward(self, x, mask=None): + """ + Args: + x: input features with shape of (num_windows*B, N, C) + mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None + """ + B_, N, C = x.shape + qkv_bias = None + if self.q_bias is not None: + qkv_bias = torch.cat((self.q_bias, torch.zeros_like(self.v_bias, requires_grad=False), self.v_bias)) + qkv = F.linear(input=x, weight=self.qkv.weight, bias=qkv_bias) + qkv = qkv.reshape(B_, N, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4) + q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple) + + # cosine attention + attn = (F.normalize(q, dim=-1) @ F.normalize(k, dim=-1).transpose(-2, -1)) + logit_scale = torch.clamp(self.logit_scale, max=torch.log(torch.tensor(1. / 0.01)).to(self.logit_scale.device)).exp() + attn = attn * logit_scale + + relative_position_bias_table = self.cpb_mlp(self.relative_coords_table).view(-1, self.num_heads) + relative_position_bias = relative_position_bias_table[self.relative_position_index.view(-1)].view( + self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1) # Wh*Ww,Wh*Ww,nH + relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous() # nH, Wh*Ww, Wh*Ww + relative_position_bias = 16 * torch.sigmoid(relative_position_bias) + attn = attn + relative_position_bias.unsqueeze(0) + + if mask is not None: + nW = mask.shape[0] + attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0) + attn = attn.view(-1, self.num_heads, N, N) + attn = self.softmax(attn) + else: + attn = self.softmax(attn) + + attn = self.attn_drop(attn) + + x = (attn @ v).transpose(1, 2).reshape(B_, N, C) + x = self.proj(x) + x = self.proj_drop(x) + return x + + def extra_repr(self) -> str: + return f'dim={self.dim}, window_size={self.window_size}, ' \ + f'pretrained_window_size={self.pretrained_window_size}, num_heads={self.num_heads}' + + def flops(self, N): + # calculate flops for 1 window with token length of N + flops = 0 + # qkv = self.qkv(x) + flops += N * self.dim * 3 * self.dim + # attn = (q @ k.transpose(-2, -1)) + flops += self.num_heads * N * (self.dim // self.num_heads) * N + # x = (attn @ v) + flops += self.num_heads * N * N * (self.dim // self.num_heads) + # x = self.proj(x) + flops += N * self.dim * self.dim + return flops + +class SwinTransformerBlock(nn.Module): + r""" Swin Transformer Block. + Args: + dim (int): Number of input channels. + input_resolution (tuple[int]): Input resulotion. + num_heads (int): Number of attention heads. + window_size (int): Window size. + shift_size (int): Shift size for SW-MSA. + mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. + qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True + drop (float, optional): Dropout rate. Default: 0.0 + attn_drop (float, optional): Attention dropout rate. Default: 0.0 + drop_path (float, optional): Stochastic depth rate. Default: 0.0 + act_layer (nn.Module, optional): Activation layer. Default: nn.GELU + norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm + pretrained_window_size (int): Window size in pre-training. + """ + + def __init__(self, dim, input_resolution, num_heads, window_size=7, shift_size=0, + mlp_ratio=4., qkv_bias=True, drop=0., attn_drop=0., drop_path=0., + act_layer=nn.GELU, norm_layer=nn.LayerNorm, pretrained_window_size=0): + super().__init__() + self.dim = dim + self.input_resolution = input_resolution + self.num_heads = num_heads + self.window_size = window_size + self.shift_size = shift_size + self.mlp_ratio = mlp_ratio + if min(self.input_resolution) <= self.window_size: + # if window size is larger than input resolution, we don't partition windows + self.shift_size = 0 + self.window_size = min(self.input_resolution) + assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size" + + self.norm1 = norm_layer(dim) + self.attn = WindowAttention( + dim, window_size=to_2tuple(self.window_size), num_heads=num_heads, + qkv_bias=qkv_bias, attn_drop=attn_drop, proj_drop=drop, + pretrained_window_size=to_2tuple(pretrained_window_size)) + + self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity() + self.norm2 = norm_layer(dim) + mlp_hidden_dim = int(dim * mlp_ratio) + self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop) + + if self.shift_size > 0: + attn_mask = self.calculate_mask(self.input_resolution) + else: + attn_mask = None + + self.register_buffer("attn_mask", attn_mask) + + def calculate_mask(self, x_size): + # calculate attention mask for SW-MSA + H, W = x_size + img_mask = torch.zeros((1, H, W, 1)) # 1 H W 1 + h_slices = (slice(0, -self.window_size), + slice(-self.window_size, -self.shift_size), + slice(-self.shift_size, None)) + w_slices = (slice(0, -self.window_size), + slice(-self.window_size, -self.shift_size), + slice(-self.shift_size, None)) + cnt = 0 + for h in h_slices: + for w in w_slices: + img_mask[:, h, w, :] = cnt + cnt += 1 + + mask_windows = window_partition(img_mask, self.window_size) # nW, window_size, window_size, 1 + mask_windows = mask_windows.view(-1, self.window_size * self.window_size) + attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2) + attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0)) + + return attn_mask + + def forward(self, x, x_size): + H, W = x_size + B, L, C = x.shape + #assert L == H * W, "input feature has wrong size" + + shortcut = x + x = x.view(B, H, W, C) + + # cyclic shift + if self.shift_size > 0: + shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2)) + else: + shifted_x = x + + # partition windows + x_windows = window_partition(shifted_x, self.window_size) # nW*B, window_size, window_size, C + x_windows = x_windows.view(-1, self.window_size * self.window_size, C) # nW*B, window_size*window_size, C + + # W-MSA/SW-MSA (to be compatible for testing on images whose shapes are the multiple of window size + if self.input_resolution == x_size: + attn_windows = self.attn(x_windows, mask=self.attn_mask) # nW*B, window_size*window_size, C + else: + attn_windows = self.attn(x_windows, mask=self.calculate_mask(x_size).to(x.device)) + + # merge windows + attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C) + shifted_x = window_reverse(attn_windows, self.window_size, H, W) # B H' W' C + + # reverse cyclic shift + if self.shift_size > 0: + x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2)) + else: + x = shifted_x + x = x.view(B, H * W, C) + x = shortcut + self.drop_path(self.norm1(x)) + + # FFN + x = x + self.drop_path(self.norm2(self.mlp(x))) + + return x + + def extra_repr(self) -> str: + return f"dim={self.dim}, input_resolution={self.input_resolution}, num_heads={self.num_heads}, " \ + f"window_size={self.window_size}, shift_size={self.shift_size}, mlp_ratio={self.mlp_ratio}" + + def flops(self): + flops = 0 + H, W = self.input_resolution + # norm1 + flops += self.dim * H * W + # W-MSA/SW-MSA + nW = H * W / self.window_size / self.window_size + flops += nW * self.attn.flops(self.window_size * self.window_size) + # mlp + flops += 2 * H * W * self.dim * self.dim * self.mlp_ratio + # norm2 + flops += self.dim * H * W + return flops + +class PatchMerging(nn.Module): + r""" Patch Merging Layer. + Args: + input_resolution (tuple[int]): Resolution of input feature. + dim (int): Number of input channels. + norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm + """ + + def __init__(self, input_resolution, dim, norm_layer=nn.LayerNorm): + super().__init__() + self.input_resolution = input_resolution + self.dim = dim + self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False) + self.norm = norm_layer(2 * dim) + + def forward(self, x): + """ + x: B, H*W, C + """ + H, W = self.input_resolution + B, L, C = x.shape + assert L == H * W, "input feature has wrong size" + assert H % 2 == 0 and W % 2 == 0, f"x size ({H}*{W}) are not even." + + x = x.view(B, H, W, C) + + x0 = x[:, 0::2, 0::2, :] # B H/2 W/2 C + x1 = x[:, 1::2, 0::2, :] # B H/2 W/2 C + x2 = x[:, 0::2, 1::2, :] # B H/2 W/2 C + x3 = x[:, 1::2, 1::2, :] # B H/2 W/2 C + x = torch.cat([x0, x1, x2, x3], -1) # B H/2 W/2 4*C + x = x.view(B, -1, 4 * C) # B H/2*W/2 4*C + + x = self.reduction(x) + x = self.norm(x) + + return x + + def extra_repr(self) -> str: + return f"input_resolution={self.input_resolution}, dim={self.dim}" + + def flops(self): + H, W = self.input_resolution + flops = (H // 2) * (W // 2) * 4 * self.dim * 2 * self.dim + flops += H * W * self.dim // 2 + return flops + +class BasicLayer(nn.Module): + """ A basic Swin Transformer layer for one stage. + Args: + dim (int): Number of input channels. + input_resolution (tuple[int]): Input resolution. + depth (int): Number of blocks. + num_heads (int): Number of attention heads. + window_size (int): Local window size. + mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. + qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True + drop (float, optional): Dropout rate. Default: 0.0 + attn_drop (float, optional): Attention dropout rate. Default: 0.0 + drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0 + norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm + downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None + use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False. + pretrained_window_size (int): Local window size in pre-training. + """ + + def __init__(self, dim, input_resolution, depth, num_heads, window_size, + mlp_ratio=4., qkv_bias=True, drop=0., attn_drop=0., + drop_path=0., norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False, + pretrained_window_size=0): + + super().__init__() + self.dim = dim + self.input_resolution = input_resolution + self.depth = depth + self.use_checkpoint = use_checkpoint + + # build blocks + self.blocks = nn.ModuleList([ + SwinTransformerBlock(dim=dim, input_resolution=input_resolution, + num_heads=num_heads, window_size=window_size, + shift_size=0 if (i % 2 == 0) else window_size // 2, + mlp_ratio=mlp_ratio, + qkv_bias=qkv_bias, + drop=drop, attn_drop=attn_drop, + drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path, + norm_layer=norm_layer, + pretrained_window_size=pretrained_window_size) + for i in range(depth)]) + + # patch merging layer + if downsample is not None: + self.downsample = downsample(input_resolution, dim=dim, norm_layer=norm_layer) + else: + self.downsample = None + + def forward(self, x, x_size): + for blk in self.blocks: + if self.use_checkpoint: + x = checkpoint.checkpoint(blk, x, x_size) + else: + x = blk(x, x_size) + if self.downsample is not None: + x = self.downsample(x) + return x + + def extra_repr(self) -> str: + return f"dim={self.dim}, input_resolution={self.input_resolution}, depth={self.depth}" + + def flops(self): + flops = 0 + for blk in self.blocks: + flops += blk.flops() + if self.downsample is not None: + flops += self.downsample.flops() + return flops + + def _init_respostnorm(self): + for blk in self.blocks: + nn.init.constant_(blk.norm1.bias, 0) + nn.init.constant_(blk.norm1.weight, 0) + nn.init.constant_(blk.norm2.bias, 0) + nn.init.constant_(blk.norm2.weight, 0) + +class PatchEmbed(nn.Module): + r""" Image to Patch Embedding + Args: + img_size (int): Image size. Default: 224. + patch_size (int): Patch token size. Default: 4. + in_chans (int): Number of input image channels. Default: 3. + embed_dim (int): Number of linear projection output channels. Default: 96. + norm_layer (nn.Module, optional): Normalization layer. Default: None + """ + + def __init__(self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None): + super().__init__() + img_size = to_2tuple(img_size) + patch_size = to_2tuple(patch_size) + patches_resolution = [img_size[0] // patch_size[0], img_size[1] // patch_size[1]] + self.img_size = img_size + self.patch_size = patch_size + self.patches_resolution = patches_resolution + self.num_patches = patches_resolution[0] * patches_resolution[1] + + self.in_chans = in_chans + self.embed_dim = embed_dim + + self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size) + if norm_layer is not None: + self.norm = norm_layer(embed_dim) + else: + self.norm = None + + def forward(self, x): + B, C, H, W = x.shape + # FIXME look at relaxing size constraints + # assert H == self.img_size[0] and W == self.img_size[1], + # f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})." + x = self.proj(x).flatten(2).transpose(1, 2) # B Ph*Pw C + if self.norm is not None: + x = self.norm(x) + return x + + def flops(self): + Ho, Wo = self.patches_resolution + flops = Ho * Wo * self.embed_dim * self.in_chans * (self.patch_size[0] * self.patch_size[1]) + if self.norm is not None: + flops += Ho * Wo * self.embed_dim + return flops + +class RSTB(nn.Module): + """Residual Swin Transformer Block (RSTB). + + Args: + dim (int): Number of input channels. + input_resolution (tuple[int]): Input resolution. + depth (int): Number of blocks. + num_heads (int): Number of attention heads. + window_size (int): Local window size. + mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. + qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True + drop (float, optional): Dropout rate. Default: 0.0 + attn_drop (float, optional): Attention dropout rate. Default: 0.0 + drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0 + norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm + downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None + use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False. + img_size: Input image size. + patch_size: Patch size. + resi_connection: The convolutional block before residual connection. + """ + + def __init__(self, dim, input_resolution, depth, num_heads, window_size, + mlp_ratio=4., qkv_bias=True, drop=0., attn_drop=0., + drop_path=0., norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False, + img_size=224, patch_size=4, resi_connection='1conv'): + super(RSTB, self).__init__() + + self.dim = dim + self.input_resolution = input_resolution + + self.residual_group = BasicLayer(dim=dim, + input_resolution=input_resolution, + depth=depth, + num_heads=num_heads, + window_size=window_size, + mlp_ratio=mlp_ratio, + qkv_bias=qkv_bias, + drop=drop, attn_drop=attn_drop, + drop_path=drop_path, + norm_layer=norm_layer, + downsample=downsample, + use_checkpoint=use_checkpoint) + + if resi_connection == '1conv': + self.conv = nn.Conv2d(dim, dim, 3, 1, 1) + elif resi_connection == '3conv': + # to save parameters and memory + self.conv = nn.Sequential(nn.Conv2d(dim, dim // 4, 3, 1, 1), nn.LeakyReLU(negative_slope=0.2, inplace=True), + nn.Conv2d(dim // 4, dim // 4, 1, 1, 0), + nn.LeakyReLU(negative_slope=0.2, inplace=True), + nn.Conv2d(dim // 4, dim, 3, 1, 1)) + + self.patch_embed = PatchEmbed( + img_size=img_size, patch_size=patch_size, in_chans=dim, embed_dim=dim, + norm_layer=None) + + self.patch_unembed = PatchUnEmbed( + img_size=img_size, patch_size=patch_size, in_chans=dim, embed_dim=dim, + norm_layer=None) + + def forward(self, x, x_size): + return self.patch_embed(self.conv(self.patch_unembed(self.residual_group(x, x_size), x_size))) + x + + def flops(self): + flops = 0 + flops += self.residual_group.flops() + H, W = self.input_resolution + flops += H * W * self.dim * self.dim * 9 + flops += self.patch_embed.flops() + flops += self.patch_unembed.flops() + + return flops + +class PatchUnEmbed(nn.Module): + r""" Image to Patch Unembedding + + Args: + img_size (int): Image size. Default: 224. + patch_size (int): Patch token size. Default: 4. + in_chans (int): Number of input image channels. Default: 3. + embed_dim (int): Number of linear projection output channels. Default: 96. + norm_layer (nn.Module, optional): Normalization layer. Default: None + """ + + def __init__(self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None): + super().__init__() + img_size = to_2tuple(img_size) + patch_size = to_2tuple(patch_size) + patches_resolution = [img_size[0] // patch_size[0], img_size[1] // patch_size[1]] + self.img_size = img_size + self.patch_size = patch_size + self.patches_resolution = patches_resolution + self.num_patches = patches_resolution[0] * patches_resolution[1] + + self.in_chans = in_chans + self.embed_dim = embed_dim + + def forward(self, x, x_size): + B, HW, C = x.shape + x = x.transpose(1, 2).view(B, self.embed_dim, x_size[0], x_size[1]) # B Ph*Pw C + return x + + def flops(self): + flops = 0 + return flops + + +class Upsample(nn.Sequential): + """Upsample module. + + Args: + scale (int): Scale factor. Supported scales: 2^n and 3. + num_feat (int): Channel number of intermediate features. + """ + + def __init__(self, scale, num_feat): + m = [] + if (scale & (scale - 1)) == 0: # scale = 2^n + for _ in range(int(math.log(scale, 2))): + m.append(nn.Conv2d(num_feat, 4 * num_feat, 3, 1, 1)) + m.append(nn.PixelShuffle(2)) + elif scale == 3: + m.append(nn.Conv2d(num_feat, 9 * num_feat, 3, 1, 1)) + m.append(nn.PixelShuffle(3)) + else: + raise ValueError(f'scale {scale} is not supported. ' 'Supported scales: 2^n and 3.') + super(Upsample, self).__init__(*m) + +class Upsample_hf(nn.Sequential): + """Upsample module. + + Args: + scale (int): Scale factor. Supported scales: 2^n and 3. + num_feat (int): Channel number of intermediate features. + """ + + def __init__(self, scale, num_feat): + m = [] + if (scale & (scale - 1)) == 0: # scale = 2^n + for _ in range(int(math.log(scale, 2))): + m.append(nn.Conv2d(num_feat, 4 * num_feat, 3, 1, 1)) + m.append(nn.PixelShuffle(2)) + elif scale == 3: + m.append(nn.Conv2d(num_feat, 9 * num_feat, 3, 1, 1)) + m.append(nn.PixelShuffle(3)) + else: + raise ValueError(f'scale {scale} is not supported. ' 'Supported scales: 2^n and 3.') + super(Upsample_hf, self).__init__(*m) + + +class UpsampleOneStep(nn.Sequential): + """UpsampleOneStep module (the difference with Upsample is that it always only has 1conv + 1pixelshuffle) + Used in lightweight SR to save parameters. + + Args: + scale (int): Scale factor. Supported scales: 2^n and 3. + num_feat (int): Channel number of intermediate features. + + """ + + def __init__(self, scale, num_feat, num_out_ch, input_resolution=None): + self.num_feat = num_feat + self.input_resolution = input_resolution + m = [] + m.append(nn.Conv2d(num_feat, (scale ** 2) * num_out_ch, 3, 1, 1)) + m.append(nn.PixelShuffle(scale)) + super(UpsampleOneStep, self).__init__(*m) + + def flops(self): + H, W = self.input_resolution + flops = H * W * self.num_feat * 3 * 9 + return flops + + + +class Swin2SR(nn.Module): + r""" Swin2SR + A PyTorch impl of : `Swin2SR: SwinV2 Transformer for Compressed Image Super-Resolution and Restoration`. + + Args: + img_size (int | tuple(int)): Input image size. Default 64 + patch_size (int | tuple(int)): Patch size. Default: 1 + in_chans (int): Number of input image channels. Default: 3 + embed_dim (int): Patch embedding dimension. Default: 96 + depths (tuple(int)): Depth of each Swin Transformer layer. + num_heads (tuple(int)): Number of attention heads in different layers. + window_size (int): Window size. Default: 7 + mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4 + qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True + drop_rate (float): Dropout rate. Default: 0 + attn_drop_rate (float): Attention dropout rate. Default: 0 + drop_path_rate (float): Stochastic depth rate. Default: 0.1 + norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm. + ape (bool): If True, add absolute position embedding to the patch embedding. Default: False + patch_norm (bool): If True, add normalization after patch embedding. Default: True + use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False + upscale: Upscale factor. 2/3/4/8 for image SR, 1 for denoising and compress artifact reduction + img_range: Image range. 1. or 255. + upsampler: The reconstruction reconstruction module. 'pixelshuffle'/'pixelshuffledirect'/'nearest+conv'/None + resi_connection: The convolutional block before residual connection. '1conv'/'3conv' + """ + + def __init__(self, img_size=64, patch_size=1, in_chans=3, + embed_dim=96, depths=(6, 6, 6, 6), num_heads=(6, 6, 6, 6), + window_size=7, mlp_ratio=4., qkv_bias=True, + drop_rate=0., attn_drop_rate=0., drop_path_rate=0.1, + norm_layer=nn.LayerNorm, ape=False, patch_norm=True, + use_checkpoint=False, upscale=2, img_range=1., upsampler='', resi_connection='1conv', + **kwargs): + super(Swin2SR, self).__init__() + num_in_ch = in_chans + num_out_ch = in_chans + num_feat = 64 + self.img_range = img_range + if in_chans == 3: + rgb_mean = (0.4488, 0.4371, 0.4040) + self.mean = torch.Tensor(rgb_mean).view(1, 3, 1, 1) + else: + self.mean = torch.zeros(1, 1, 1, 1) + self.upscale = upscale + self.upsampler = upsampler + self.window_size = window_size + + ##################################################################################################### + ################################### 1, shallow feature extraction ################################### + self.conv_first = nn.Conv2d(num_in_ch, embed_dim, 3, 1, 1) + + ##################################################################################################### + ################################### 2, deep feature extraction ###################################### + self.num_layers = len(depths) + self.embed_dim = embed_dim + self.ape = ape + self.patch_norm = patch_norm + self.num_features = embed_dim + self.mlp_ratio = mlp_ratio + + # split image into non-overlapping patches + self.patch_embed = PatchEmbed( + img_size=img_size, patch_size=patch_size, in_chans=embed_dim, embed_dim=embed_dim, + norm_layer=norm_layer if self.patch_norm else None) + num_patches = self.patch_embed.num_patches + patches_resolution = self.patch_embed.patches_resolution + self.patches_resolution = patches_resolution + + # merge non-overlapping patches into image + self.patch_unembed = PatchUnEmbed( + img_size=img_size, patch_size=patch_size, in_chans=embed_dim, embed_dim=embed_dim, + norm_layer=norm_layer if self.patch_norm else None) + + # absolute position embedding + if self.ape: + self.absolute_pos_embed = nn.Parameter(torch.zeros(1, num_patches, embed_dim)) + trunc_normal_(self.absolute_pos_embed, std=.02) + + self.pos_drop = nn.Dropout(p=drop_rate) + + # stochastic depth + dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))] # stochastic depth decay rule + + # build Residual Swin Transformer blocks (RSTB) + self.layers = nn.ModuleList() + for i_layer in range(self.num_layers): + layer = RSTB(dim=embed_dim, + input_resolution=(patches_resolution[0], + patches_resolution[1]), + depth=depths[i_layer], + num_heads=num_heads[i_layer], + window_size=window_size, + mlp_ratio=self.mlp_ratio, + qkv_bias=qkv_bias, + drop=drop_rate, attn_drop=attn_drop_rate, + drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])], # no impact on SR results + norm_layer=norm_layer, + downsample=None, + use_checkpoint=use_checkpoint, + img_size=img_size, + patch_size=patch_size, + resi_connection=resi_connection + + ) + self.layers.append(layer) + + if self.upsampler == 'pixelshuffle_hf': + self.layers_hf = nn.ModuleList() + for i_layer in range(self.num_layers): + layer = RSTB(dim=embed_dim, + input_resolution=(patches_resolution[0], + patches_resolution[1]), + depth=depths[i_layer], + num_heads=num_heads[i_layer], + window_size=window_size, + mlp_ratio=self.mlp_ratio, + qkv_bias=qkv_bias, + drop=drop_rate, attn_drop=attn_drop_rate, + drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])], # no impact on SR results + norm_layer=norm_layer, + downsample=None, + use_checkpoint=use_checkpoint, + img_size=img_size, + patch_size=patch_size, + resi_connection=resi_connection + + ) + self.layers_hf.append(layer) + + self.norm = norm_layer(self.num_features) + + # build the last conv layer in deep feature extraction + if resi_connection == '1conv': + self.conv_after_body = nn.Conv2d(embed_dim, embed_dim, 3, 1, 1) + elif resi_connection == '3conv': + # to save parameters and memory + self.conv_after_body = nn.Sequential(nn.Conv2d(embed_dim, embed_dim // 4, 3, 1, 1), + nn.LeakyReLU(negative_slope=0.2, inplace=True), + nn.Conv2d(embed_dim // 4, embed_dim // 4, 1, 1, 0), + nn.LeakyReLU(negative_slope=0.2, inplace=True), + nn.Conv2d(embed_dim // 4, embed_dim, 3, 1, 1)) + + ##################################################################################################### + ################################ 3, high quality image reconstruction ################################ + if self.upsampler == 'pixelshuffle': + # for classical SR + self.conv_before_upsample = nn.Sequential(nn.Conv2d(embed_dim, num_feat, 3, 1, 1), + nn.LeakyReLU(inplace=True)) + self.upsample = Upsample(upscale, num_feat) + self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1) + elif self.upsampler == 'pixelshuffle_aux': + self.conv_bicubic = nn.Conv2d(num_in_ch, num_feat, 3, 1, 1) + self.conv_before_upsample = nn.Sequential( + nn.Conv2d(embed_dim, num_feat, 3, 1, 1), + nn.LeakyReLU(inplace=True)) + self.conv_aux = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1) + self.conv_after_aux = nn.Sequential( + nn.Conv2d(3, num_feat, 3, 1, 1), + nn.LeakyReLU(inplace=True)) + self.upsample = Upsample(upscale, num_feat) + self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1) + + elif self.upsampler == 'pixelshuffle_hf': + self.conv_before_upsample = nn.Sequential(nn.Conv2d(embed_dim, num_feat, 3, 1, 1), + nn.LeakyReLU(inplace=True)) + self.upsample = Upsample(upscale, num_feat) + self.upsample_hf = Upsample_hf(upscale, num_feat) + self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1) + self.conv_first_hf = nn.Sequential(nn.Conv2d(num_feat, embed_dim, 3, 1, 1), + nn.LeakyReLU(inplace=True)) + self.conv_after_body_hf = nn.Conv2d(embed_dim, embed_dim, 3, 1, 1) + self.conv_before_upsample_hf = nn.Sequential( + nn.Conv2d(embed_dim, num_feat, 3, 1, 1), + nn.LeakyReLU(inplace=True)) + self.conv_last_hf = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1) + + elif self.upsampler == 'pixelshuffledirect': + # for lightweight SR (to save parameters) + self.upsample = UpsampleOneStep(upscale, embed_dim, num_out_ch, + (patches_resolution[0], patches_resolution[1])) + elif self.upsampler == 'nearest+conv': + # for real-world SR (less artifacts) + assert self.upscale == 4, 'only support x4 now.' + self.conv_before_upsample = nn.Sequential(nn.Conv2d(embed_dim, num_feat, 3, 1, 1), + nn.LeakyReLU(inplace=True)) + self.conv_up1 = nn.Conv2d(num_feat, num_feat, 3, 1, 1) + self.conv_up2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1) + self.conv_hr = nn.Conv2d(num_feat, num_feat, 3, 1, 1) + self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1) + self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True) + else: + # for image denoising and JPEG compression artifact reduction + self.conv_last = nn.Conv2d(embed_dim, num_out_ch, 3, 1, 1) + + self.apply(self._init_weights) + + def _init_weights(self, m): + if isinstance(m, nn.Linear): + trunc_normal_(m.weight, std=.02) + if isinstance(m, nn.Linear) and m.bias is not None: + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.LayerNorm): + nn.init.constant_(m.bias, 0) + nn.init.constant_(m.weight, 1.0) + + @torch.jit.ignore + def no_weight_decay(self): + return {'absolute_pos_embed'} + + @torch.jit.ignore + def no_weight_decay_keywords(self): + return {'relative_position_bias_table'} + + def check_image_size(self, x): + _, _, h, w = x.size() + mod_pad_h = (self.window_size - h % self.window_size) % self.window_size + mod_pad_w = (self.window_size - w % self.window_size) % self.window_size + x = F.pad(x, (0, mod_pad_w, 0, mod_pad_h), 'reflect') + return x + + def forward_features(self, x): + x_size = (x.shape[2], x.shape[3]) + x = self.patch_embed(x) + if self.ape: + x = x + self.absolute_pos_embed + x = self.pos_drop(x) + + for layer in self.layers: + x = layer(x, x_size) + + x = self.norm(x) # B L C + x = self.patch_unembed(x, x_size) + + return x + + def forward_features_hf(self, x): + x_size = (x.shape[2], x.shape[3]) + x = self.patch_embed(x) + if self.ape: + x = x + self.absolute_pos_embed + x = self.pos_drop(x) + + for layer in self.layers_hf: + x = layer(x, x_size) + + x = self.norm(x) # B L C + x = self.patch_unembed(x, x_size) + + return x + + def forward(self, x): + H, W = x.shape[2:] + x = self.check_image_size(x) + + self.mean = self.mean.type_as(x) + x = (x - self.mean) * self.img_range + + if self.upsampler == 'pixelshuffle': + # for classical SR + x = self.conv_first(x) + x = self.conv_after_body(self.forward_features(x)) + x + x = self.conv_before_upsample(x) + x = self.conv_last(self.upsample(x)) + elif self.upsampler == 'pixelshuffle_aux': + bicubic = F.interpolate(x, size=(H * self.upscale, W * self.upscale), mode='bicubic', align_corners=False) + bicubic = self.conv_bicubic(bicubic) + x = self.conv_first(x) + x = self.conv_after_body(self.forward_features(x)) + x + x = self.conv_before_upsample(x) + aux = self.conv_aux(x) # b, 3, LR_H, LR_W + x = self.conv_after_aux(aux) + x = self.upsample(x)[:, :, :H * self.upscale, :W * self.upscale] + bicubic[:, :, :H * self.upscale, :W * self.upscale] + x = self.conv_last(x) + aux = aux / self.img_range + self.mean + elif self.upsampler == 'pixelshuffle_hf': + # for classical SR with HF + x = self.conv_first(x) + x = self.conv_after_body(self.forward_features(x)) + x + x_before = self.conv_before_upsample(x) + x_out = self.conv_last(self.upsample(x_before)) + + x_hf = self.conv_first_hf(x_before) + x_hf = self.conv_after_body_hf(self.forward_features_hf(x_hf)) + x_hf + x_hf = self.conv_before_upsample_hf(x_hf) + x_hf = self.conv_last_hf(self.upsample_hf(x_hf)) + x = x_out + x_hf + x_hf = x_hf / self.img_range + self.mean + + elif self.upsampler == 'pixelshuffledirect': + # for lightweight SR + x = self.conv_first(x) + x = self.conv_after_body(self.forward_features(x)) + x + x = self.upsample(x) + elif self.upsampler == 'nearest+conv': + # for real-world SR + x = self.conv_first(x) + x = self.conv_after_body(self.forward_features(x)) + x + x = self.conv_before_upsample(x) + x = self.lrelu(self.conv_up1(torch.nn.functional.interpolate(x, scale_factor=2, mode='nearest'))) + x = self.lrelu(self.conv_up2(torch.nn.functional.interpolate(x, scale_factor=2, mode='nearest'))) + x = self.conv_last(self.lrelu(self.conv_hr(x))) + else: + # for image denoising and JPEG compression artifact reduction + x_first = self.conv_first(x) + res = self.conv_after_body(self.forward_features(x_first)) + x_first + x = x + self.conv_last(res) + + x = x / self.img_range + self.mean + if self.upsampler == "pixelshuffle_aux": + return x[:, :, :H*self.upscale, :W*self.upscale], aux + + elif self.upsampler == "pixelshuffle_hf": + x_out = x_out / self.img_range + self.mean + return x_out[:, :, :H*self.upscale, :W*self.upscale], x[:, :, :H*self.upscale, :W*self.upscale], x_hf[:, :, :H*self.upscale, :W*self.upscale] + + else: + return x[:, :, :H*self.upscale, :W*self.upscale] + + def flops(self): + flops = 0 + H, W = self.patches_resolution + flops += H * W * 3 * self.embed_dim * 9 + flops += self.patch_embed.flops() + for layer in self.layers: + flops += layer.flops() + flops += H * W * 3 * self.embed_dim * self.embed_dim + flops += self.upsample.flops() + return flops + + +if __name__ == '__main__': + upscale = 4 + window_size = 8 + height = (1024 // upscale // window_size + 1) * window_size + width = (720 // upscale // window_size + 1) * window_size + model = Swin2SR(upscale=2, img_size=(height, width), + window_size=window_size, img_range=1., depths=[6, 6, 6, 6], + embed_dim=60, num_heads=[6, 6, 6, 6], mlp_ratio=2, upsampler='pixelshuffledirect') + print(model) + print(height, width, model.flops() / 1e9) + + x = torch.randn((1, 3, height, width)) + x = model(x) + print(x.shape) diff --git a/extensions-builtin/canvas-zoom-and-pan/javascript/zoom.js b/extensions-builtin/canvas-zoom-and-pan/javascript/zoom.js new file mode 100644 index 0000000000000000000000000000000000000000..45c7600ac5f81bc8c7b233162d73f6551c8b5e8d --- /dev/null +++ b/extensions-builtin/canvas-zoom-and-pan/javascript/zoom.js @@ -0,0 +1,962 @@ +onUiLoaded(async() => { + const elementIDs = { + img2imgTabs: "#mode_img2img .tab-nav", + inpaint: "#img2maskimg", + inpaintSketch: "#inpaint_sketch", + rangeGroup: "#img2img_column_size", + sketch: "#img2img_sketch" + }; + const tabNameToElementId = { + "Inpaint sketch": elementIDs.inpaintSketch, + "Inpaint": elementIDs.inpaint, + "Sketch": elementIDs.sketch + }; + + + // Helper functions + // Get active tab + + /** + * Waits for an element to be present in the DOM. + */ + const waitForElement = (id) => new Promise(resolve => { + const checkForElement = () => { + const element = document.querySelector(id); + if (element) return resolve(element); + setTimeout(checkForElement, 100); + }; + checkForElement(); + }); + + function getActiveTab(elements, all = false) { + const tabs = elements.img2imgTabs.querySelectorAll("button"); + + if (all) return tabs; + + for (let tab of tabs) { + if (tab.classList.contains("selected")) { + return tab; + } + } + } + + // Get tab ID + function getTabId(elements) { + const activeTab = getActiveTab(elements); + return tabNameToElementId[activeTab.innerText]; + } + + // Wait until opts loaded + async function waitForOpts() { + for (; ;) { + if (window.opts && Object.keys(window.opts).length) { + return window.opts; + } + await new Promise(resolve => setTimeout(resolve, 100)); + } + } + + // Detect whether the element has a horizontal scroll bar + function hasHorizontalScrollbar(element) { + return element.scrollWidth > element.clientWidth; + } + + // Function for defining the "Ctrl", "Shift" and "Alt" keys + function isModifierKey(event, key) { + switch (key) { + case "Ctrl": + return event.ctrlKey; + case "Shift": + return event.shiftKey; + case "Alt": + return event.altKey; + default: + return false; + } + } + + // Check if hotkey is valid + function isValidHotkey(value) { + const specialKeys = ["Ctrl", "Alt", "Shift", "Disable"]; + return ( + (typeof value === "string" && + value.length === 1 && + /[a-z]/i.test(value)) || + specialKeys.includes(value) + ); + } + + // Normalize hotkey + function normalizeHotkey(hotkey) { + return hotkey.length === 1 ? "Key" + hotkey.toUpperCase() : hotkey; + } + + // Format hotkey for display + function formatHotkeyForDisplay(hotkey) { + return hotkey.startsWith("Key") ? hotkey.slice(3) : hotkey; + } + + // Create hotkey configuration with the provided options + function createHotkeyConfig(defaultHotkeysConfig, hotkeysConfigOpts) { + const result = {}; // Resulting hotkey configuration + const usedKeys = new Set(); // Set of used hotkeys + + // Iterate through defaultHotkeysConfig keys + for (const key in defaultHotkeysConfig) { + const userValue = hotkeysConfigOpts[key]; // User-provided hotkey value + const defaultValue = defaultHotkeysConfig[key]; // Default hotkey value + + // Apply appropriate value for undefined, boolean, or object userValue + if ( + userValue === undefined || + typeof userValue === "boolean" || + typeof userValue === "object" || + userValue === "disable" + ) { + result[key] = + userValue === undefined ? defaultValue : userValue; + } else if (isValidHotkey(userValue)) { + const normalizedUserValue = normalizeHotkey(userValue); + + // Check for conflicting hotkeys + if (!usedKeys.has(normalizedUserValue)) { + usedKeys.add(normalizedUserValue); + result[key] = normalizedUserValue; + } else { + console.error( + `Hotkey: ${formatHotkeyForDisplay( + userValue + )} for ${key} is repeated and conflicts with another hotkey. The default hotkey is used: ${formatHotkeyForDisplay( + defaultValue + )}` + ); + result[key] = defaultValue; + } + } else { + console.error( + `Hotkey: ${formatHotkeyForDisplay( + userValue + )} for ${key} is not valid. The default hotkey is used: ${formatHotkeyForDisplay( + defaultValue + )}` + ); + result[key] = defaultValue; + } + } + + return result; + } + + // Disables functions in the config object based on the provided list of function names + function disableFunctions(config, disabledFunctions) { + // Bind the hasOwnProperty method to the functionMap object to avoid errors + const hasOwnProperty = + Object.prototype.hasOwnProperty.bind(functionMap); + + // Loop through the disabledFunctions array and disable the corresponding functions in the config object + disabledFunctions.forEach(funcName => { + if (hasOwnProperty(funcName)) { + const key = functionMap[funcName]; + config[key] = "disable"; + } + }); + + // Return the updated config object + return config; + } + + /** + * The restoreImgRedMask function displays a red mask around an image to indicate the aspect ratio. + * If the image display property is set to 'none', the mask breaks. To fix this, the function + * temporarily sets the display property to 'block' and then hides the mask again after 300 milliseconds + * to avoid breaking the canvas. Additionally, the function adjusts the mask to work correctly on + * very long images. + */ + function restoreImgRedMask(elements) { + const mainTabId = getTabId(elements); + + if (!mainTabId) return; + + const mainTab = gradioApp().querySelector(mainTabId); + const img = mainTab.querySelector("img"); + const imageARPreview = gradioApp().querySelector("#imageARPreview"); + + if (!img || !imageARPreview) return; + + imageARPreview.style.transform = ""; + if (parseFloat(mainTab.style.width) > 865) { + const transformString = mainTab.style.transform; + const scaleMatch = transformString.match( + /scale\(([-+]?[0-9]*\.?[0-9]+)\)/ + ); + let zoom = 1; // default zoom + + if (scaleMatch && scaleMatch[1]) { + zoom = Number(scaleMatch[1]); + } + + imageARPreview.style.transformOrigin = "0 0"; + imageARPreview.style.transform = `scale(${zoom})`; + } + + if (img.style.display !== "none") return; + + img.style.display = "block"; + + setTimeout(() => { + img.style.display = "none"; + }, 400); + } + + const hotkeysConfigOpts = await waitForOpts(); + + // Default config + const defaultHotkeysConfig = { + canvas_hotkey_zoom: "Alt", + canvas_hotkey_adjust: "Ctrl", + canvas_hotkey_reset: "KeyR", + canvas_hotkey_fullscreen: "KeyS", + canvas_hotkey_move: "KeyF", + canvas_hotkey_overlap: "KeyO", + canvas_disabled_functions: [], + canvas_show_tooltip: true, + canvas_auto_expand: true, + canvas_blur_prompt: false, + }; + + const functionMap = { + "Zoom": "canvas_hotkey_zoom", + "Adjust brush size": "canvas_hotkey_adjust", + "Moving canvas": "canvas_hotkey_move", + "Fullscreen": "canvas_hotkey_fullscreen", + "Reset Zoom": "canvas_hotkey_reset", + "Overlap": "canvas_hotkey_overlap" + }; + + // Loading the configuration from opts + const preHotkeysConfig = createHotkeyConfig( + defaultHotkeysConfig, + hotkeysConfigOpts + ); + + // Disable functions that are not needed by the user + const hotkeysConfig = disableFunctions( + preHotkeysConfig, + preHotkeysConfig.canvas_disabled_functions + ); + + let isMoving = false; + let mouseX, mouseY; + let activeElement; + + const elements = Object.fromEntries( + Object.keys(elementIDs).map(id => [ + id, + gradioApp().querySelector(elementIDs[id]) + ]) + ); + const elemData = {}; + + // Apply functionality to the range inputs. Restore redmask and correct for long images. + const rangeInputs = elements.rangeGroup ? + Array.from(elements.rangeGroup.querySelectorAll("input")) : + [ + gradioApp().querySelector("#img2img_width input[type='range']"), + gradioApp().querySelector("#img2img_height input[type='range']") + ]; + + for (const input of rangeInputs) { + input?.addEventListener("input", () => restoreImgRedMask(elements)); + } + + function applyZoomAndPan(elemId, isExtension = true) { + const targetElement = gradioApp().querySelector(elemId); + + if (!targetElement) { + console.log("Element not found"); + return; + } + + targetElement.style.transformOrigin = "0 0"; + + elemData[elemId] = { + zoom: 1, + panX: 0, + panY: 0 + }; + let fullScreenMode = false; + + // Create tooltip + function createTooltip() { + const toolTipElemnt = + targetElement.querySelector(".image-container"); + const tooltip = document.createElement("div"); + tooltip.className = "canvas-tooltip"; + + // Creating an item of information + const info = document.createElement("i"); + info.className = "canvas-tooltip-info"; + info.textContent = ""; + + // Create a container for the contents of the tooltip + const tooltipContent = document.createElement("div"); + tooltipContent.className = "canvas-tooltip-content"; + + // Define an array with hotkey information and their actions + const hotkeysInfo = [ + { + configKey: "canvas_hotkey_zoom", + action: "Zoom canvas", + keySuffix: " + wheel" + }, + { + configKey: "canvas_hotkey_adjust", + action: "Adjust brush size", + keySuffix: " + wheel" + }, + {configKey: "canvas_hotkey_reset", action: "Reset zoom"}, + { + configKey: "canvas_hotkey_fullscreen", + action: "Fullscreen mode" + }, + {configKey: "canvas_hotkey_move", action: "Move canvas"}, + {configKey: "canvas_hotkey_overlap", action: "Overlap"} + ]; + + // Create hotkeys array with disabled property based on the config values + const hotkeys = hotkeysInfo.map(info => { + const configValue = hotkeysConfig[info.configKey]; + const key = info.keySuffix ? + `${configValue}${info.keySuffix}` : + configValue.charAt(configValue.length - 1); + return { + key, + action: info.action, + disabled: configValue === "disable" + }; + }); + + for (const hotkey of hotkeys) { + if (hotkey.disabled) { + continue; + } + + const p = document.createElement("p"); + p.innerHTML = `${hotkey.key} - ${hotkey.action}`; + tooltipContent.appendChild(p); + } + + // Add information and content elements to the tooltip element + tooltip.appendChild(info); + tooltip.appendChild(tooltipContent); + + // Add a hint element to the target element + toolTipElemnt.appendChild(tooltip); + } + + //Show tool tip if setting enable + if (hotkeysConfig.canvas_show_tooltip) { + createTooltip(); + } + + // In the course of research, it was found that the tag img is very harmful when zooming and creates white canvases. This hack allows you to almost never think about this problem, it has no effect on webui. + function fixCanvas() { + const activeTab = getActiveTab(elements).textContent.trim(); + + if (activeTab !== "img2img") { + const img = targetElement.querySelector(`${elemId} img`); + + if (img && img.style.display !== "none") { + img.style.display = "none"; + img.style.visibility = "hidden"; + } + } + } + + // Reset the zoom level and pan position of the target element to their initial values + function resetZoom() { + elemData[elemId] = { + zoomLevel: 1, + panX: 0, + panY: 0 + }; + + if (isExtension) { + targetElement.style.overflow = "hidden"; + } + + targetElement.isZoomed = false; + + fixCanvas(); + targetElement.style.transform = `scale(${elemData[elemId].zoomLevel}) translate(${elemData[elemId].panX}px, ${elemData[elemId].panY}px)`; + + const canvas = gradioApp().querySelector( + `${elemId} canvas[key="interface"]` + ); + + toggleOverlap("off"); + fullScreenMode = false; + + const closeBtn = targetElement.querySelector("button[aria-label='Remove Image']"); + if (closeBtn) { + closeBtn.addEventListener("click", resetZoom); + } + + if (canvas && isExtension) { + const parentElement = targetElement.closest('[id^="component-"]'); + if ( + canvas && + parseFloat(canvas.style.width) > parentElement.offsetWidth && + parseFloat(targetElement.style.width) > parentElement.offsetWidth + ) { + fitToElement(); + return; + } + + } + + if ( + canvas && + !isExtension && + parseFloat(canvas.style.width) > 865 && + parseFloat(targetElement.style.width) > 865 + ) { + fitToElement(); + return; + } + + targetElement.style.width = ""; + } + + // Toggle the zIndex of the target element between two values, allowing it to overlap or be overlapped by other elements + function toggleOverlap(forced = "") { + const zIndex1 = "0"; + const zIndex2 = "998"; + + targetElement.style.zIndex = + targetElement.style.zIndex !== zIndex2 ? zIndex2 : zIndex1; + + if (forced === "off") { + targetElement.style.zIndex = zIndex1; + } else if (forced === "on") { + targetElement.style.zIndex = zIndex2; + } + } + + // Adjust the brush size based on the deltaY value from a mouse wheel event + function adjustBrushSize( + elemId, + deltaY, + withoutValue = false, + percentage = 5 + ) { + const input = + gradioApp().querySelector( + `${elemId} input[aria-label='Brush radius']` + ) || + gradioApp().querySelector( + `${elemId} button[aria-label="Use brush"]` + ); + + if (input) { + input.click(); + if (!withoutValue) { + const maxValue = + parseFloat(input.getAttribute("max")) || 100; + const changeAmount = maxValue * (percentage / 100); + const newValue = + parseFloat(input.value) + + (deltaY > 0 ? -changeAmount : changeAmount); + input.value = Math.min(Math.max(newValue, 0), maxValue); + input.dispatchEvent(new Event("change")); + } + } + } + + // Reset zoom when uploading a new image + const fileInput = gradioApp().querySelector( + `${elemId} input[type="file"][accept="image/*"].svelte-116rqfv` + ); + fileInput.addEventListener("click", resetZoom); + + // Update the zoom level and pan position of the target element based on the values of the zoomLevel, panX and panY variables + function updateZoom(newZoomLevel, mouseX, mouseY) { + newZoomLevel = Math.max(0.1, Math.min(newZoomLevel, 15)); + + elemData[elemId].panX += + mouseX - (mouseX * newZoomLevel) / elemData[elemId].zoomLevel; + elemData[elemId].panY += + mouseY - (mouseY * newZoomLevel) / elemData[elemId].zoomLevel; + + targetElement.style.transformOrigin = "0 0"; + targetElement.style.transform = `translate(${elemData[elemId].panX}px, ${elemData[elemId].panY}px) scale(${newZoomLevel})`; + + toggleOverlap("on"); + if (isExtension) { + targetElement.style.overflow = "visible"; + } + + return newZoomLevel; + } + + // Change the zoom level based on user interaction + function changeZoomLevel(operation, e) { + if (isModifierKey(e, hotkeysConfig.canvas_hotkey_zoom)) { + e.preventDefault(); + + let zoomPosX, zoomPosY; + let delta = 0.2; + if (elemData[elemId].zoomLevel > 7) { + delta = 0.9; + } else if (elemData[elemId].zoomLevel > 2) { + delta = 0.6; + } + + zoomPosX = e.clientX; + zoomPosY = e.clientY; + + fullScreenMode = false; + elemData[elemId].zoomLevel = updateZoom( + elemData[elemId].zoomLevel + + (operation === "+" ? delta : -delta), + zoomPosX - targetElement.getBoundingClientRect().left, + zoomPosY - targetElement.getBoundingClientRect().top + ); + + targetElement.isZoomed = true; + } + } + + /** + * This function fits the target element to the screen by calculating + * the required scale and offsets. It also updates the global variables + * zoomLevel, panX, and panY to reflect the new state. + */ + + function fitToElement() { + //Reset Zoom + targetElement.style.transform = `translate(${0}px, ${0}px) scale(${1})`; + + let parentElement; + + if (isExtension) { + parentElement = targetElement.closest('[id^="component-"]'); + } else { + parentElement = targetElement.parentElement; + } + + + // Get element and screen dimensions + const elementWidth = targetElement.offsetWidth; + const elementHeight = targetElement.offsetHeight; + + const screenWidth = parentElement.clientWidth; + const screenHeight = parentElement.clientHeight; + + // Get element's coordinates relative to the parent element + const elementRect = targetElement.getBoundingClientRect(); + const parentRect = parentElement.getBoundingClientRect(); + const elementX = elementRect.x - parentRect.x; + + // Calculate scale and offsets + const scaleX = screenWidth / elementWidth; + const scaleY = screenHeight / elementHeight; + const scale = Math.min(scaleX, scaleY); + + const transformOrigin = + window.getComputedStyle(targetElement).transformOrigin; + const [originX, originY] = transformOrigin.split(" "); + const originXValue = parseFloat(originX); + const originYValue = parseFloat(originY); + + const offsetX = + (screenWidth - elementWidth * scale) / 2 - + originXValue * (1 - scale); + const offsetY = + (screenHeight - elementHeight * scale) / 2.5 - + originYValue * (1 - scale); + + // Apply scale and offsets to the element + targetElement.style.transform = `translate(${offsetX}px, ${offsetY}px) scale(${scale})`; + + // Update global variables + elemData[elemId].zoomLevel = scale; + elemData[elemId].panX = offsetX; + elemData[elemId].panY = offsetY; + + fullScreenMode = false; + toggleOverlap("off"); + } + + /** + * This function fits the target element to the screen by calculating + * the required scale and offsets. It also updates the global variables + * zoomLevel, panX, and panY to reflect the new state. + */ + + // Fullscreen mode + function fitToScreen() { + const canvas = gradioApp().querySelector( + `${elemId} canvas[key="interface"]` + ); + + if (!canvas) return; + + if (canvas.offsetWidth > 862 || isExtension) { + targetElement.style.width = (canvas.offsetWidth + 2) + "px"; + } + + if (isExtension) { + targetElement.style.overflow = "visible"; + } + + if (fullScreenMode) { + resetZoom(); + fullScreenMode = false; + return; + } + + //Reset Zoom + targetElement.style.transform = `translate(${0}px, ${0}px) scale(${1})`; + + // Get scrollbar width to right-align the image + const scrollbarWidth = + window.innerWidth - document.documentElement.clientWidth; + + // Get element and screen dimensions + const elementWidth = targetElement.offsetWidth; + const elementHeight = targetElement.offsetHeight; + const screenWidth = window.innerWidth - scrollbarWidth; + const screenHeight = window.innerHeight; + + // Get element's coordinates relative to the page + const elementRect = targetElement.getBoundingClientRect(); + const elementY = elementRect.y; + const elementX = elementRect.x; + + // Calculate scale and offsets + const scaleX = screenWidth / elementWidth; + const scaleY = screenHeight / elementHeight; + const scale = Math.min(scaleX, scaleY); + + // Get the current transformOrigin + const computedStyle = window.getComputedStyle(targetElement); + const transformOrigin = computedStyle.transformOrigin; + const [originX, originY] = transformOrigin.split(" "); + const originXValue = parseFloat(originX); + const originYValue = parseFloat(originY); + + // Calculate offsets with respect to the transformOrigin + const offsetX = + (screenWidth - elementWidth * scale) / 2 - + elementX - + originXValue * (1 - scale); + const offsetY = + (screenHeight - elementHeight * scale) / 2 - + elementY - + originYValue * (1 - scale); + + // Apply scale and offsets to the element + targetElement.style.transform = `translate(${offsetX}px, ${offsetY}px) scale(${scale})`; + + // Update global variables + elemData[elemId].zoomLevel = scale; + elemData[elemId].panX = offsetX; + elemData[elemId].panY = offsetY; + + fullScreenMode = true; + toggleOverlap("on"); + } + + // Handle keydown events + function handleKeyDown(event) { + // Disable key locks to make pasting from the buffer work correctly + if ((event.ctrlKey && event.code === 'KeyV') || (event.ctrlKey && event.code === 'KeyC') || event.code === "F5") { + return; + } + + // before activating shortcut, ensure user is not actively typing in an input field + if (!hotkeysConfig.canvas_blur_prompt) { + if (event.target.nodeName === 'TEXTAREA' || event.target.nodeName === 'INPUT') { + return; + } + } + + + const hotkeyActions = { + [hotkeysConfig.canvas_hotkey_reset]: resetZoom, + [hotkeysConfig.canvas_hotkey_overlap]: toggleOverlap, + [hotkeysConfig.canvas_hotkey_fullscreen]: fitToScreen + }; + + const action = hotkeyActions[event.code]; + if (action) { + event.preventDefault(); + action(event); + } + + if ( + isModifierKey(event, hotkeysConfig.canvas_hotkey_zoom) || + isModifierKey(event, hotkeysConfig.canvas_hotkey_adjust) + ) { + event.preventDefault(); + } + } + + // Get Mouse position + function getMousePosition(e) { + mouseX = e.offsetX; + mouseY = e.offsetY; + } + + // Simulation of the function to put a long image into the screen. + // We detect if an image has a scroll bar or not, make a fullscreen to reveal the image, then reduce it to fit into the element. + // We hide the image and show it to the user when it is ready. + + targetElement.isExpanded = false; + function autoExpand() { + const canvas = document.querySelector(`${elemId} canvas[key="interface"]`); + if (canvas) { + if (hasHorizontalScrollbar(targetElement) && targetElement.isExpanded === false) { + targetElement.style.visibility = "hidden"; + setTimeout(() => { + fitToScreen(); + resetZoom(); + targetElement.style.visibility = "visible"; + targetElement.isExpanded = true; + }, 10); + } + } + } + + targetElement.addEventListener("mousemove", getMousePosition); + + //observers + // Creating an observer with a callback function to handle DOM changes + const observer = new MutationObserver((mutationsList, observer) => { + for (let mutation of mutationsList) { + // If the style attribute of the canvas has changed, by observation it happens only when the picture changes + if (mutation.type === 'attributes' && mutation.attributeName === 'style' && + mutation.target.tagName.toLowerCase() === 'canvas') { + targetElement.isExpanded = false; + setTimeout(resetZoom, 10); + } + } + }); + + // Apply auto expand if enabled + if (hotkeysConfig.canvas_auto_expand) { + targetElement.addEventListener("mousemove", autoExpand); + // Set up an observer to track attribute changes + observer.observe(targetElement, {attributes: true, childList: true, subtree: true}); + } + + // Handle events only inside the targetElement + let isKeyDownHandlerAttached = false; + + function handleMouseMove() { + if (!isKeyDownHandlerAttached) { + document.addEventListener("keydown", handleKeyDown); + isKeyDownHandlerAttached = true; + + activeElement = elemId; + } + } + + function handleMouseLeave() { + if (isKeyDownHandlerAttached) { + document.removeEventListener("keydown", handleKeyDown); + isKeyDownHandlerAttached = false; + + activeElement = null; + } + } + + // Add mouse event handlers + targetElement.addEventListener("mousemove", handleMouseMove); + targetElement.addEventListener("mouseleave", handleMouseLeave); + + // Reset zoom when click on another tab + elements.img2imgTabs.addEventListener("click", resetZoom); + elements.img2imgTabs.addEventListener("click", () => { + // targetElement.style.width = ""; + if (parseInt(targetElement.style.width) > 865) { + setTimeout(fitToElement, 0); + } + }); + + targetElement.addEventListener("wheel", e => { + // change zoom level + const operation = e.deltaY > 0 ? "-" : "+"; + changeZoomLevel(operation, e); + + // Handle brush size adjustment with ctrl key pressed + if (isModifierKey(e, hotkeysConfig.canvas_hotkey_adjust)) { + e.preventDefault(); + + // Increase or decrease brush size based on scroll direction + adjustBrushSize(elemId, e.deltaY); + } + }); + + // Handle the move event for pan functionality. Updates the panX and panY variables and applies the new transform to the target element. + function handleMoveKeyDown(e) { + + // Disable key locks to make pasting from the buffer work correctly + if ((e.ctrlKey && e.code === 'KeyV') || (e.ctrlKey && event.code === 'KeyC') || e.code === "F5") { + return; + } + + // before activating shortcut, ensure user is not actively typing in an input field + if (!hotkeysConfig.canvas_blur_prompt) { + if (e.target.nodeName === 'TEXTAREA' || e.target.nodeName === 'INPUT') { + return; + } + } + + + if (e.code === hotkeysConfig.canvas_hotkey_move) { + if (!e.ctrlKey && !e.metaKey && isKeyDownHandlerAttached) { + e.preventDefault(); + document.activeElement.blur(); + isMoving = true; + } + } + } + + function handleMoveKeyUp(e) { + if (e.code === hotkeysConfig.canvas_hotkey_move) { + isMoving = false; + } + } + + document.addEventListener("keydown", handleMoveKeyDown); + document.addEventListener("keyup", handleMoveKeyUp); + + // Detect zoom level and update the pan speed. + function updatePanPosition(movementX, movementY) { + let panSpeed = 2; + + if (elemData[elemId].zoomLevel > 8) { + panSpeed = 3.5; + } + + elemData[elemId].panX += movementX * panSpeed; + elemData[elemId].panY += movementY * panSpeed; + + // Delayed redraw of an element + requestAnimationFrame(() => { + targetElement.style.transform = `translate(${elemData[elemId].panX}px, ${elemData[elemId].panY}px) scale(${elemData[elemId].zoomLevel})`; + toggleOverlap("on"); + }); + } + + function handleMoveByKey(e) { + if (isMoving && elemId === activeElement) { + updatePanPosition(e.movementX, e.movementY); + targetElement.style.pointerEvents = "none"; + + if (isExtension) { + targetElement.style.overflow = "visible"; + } + + } else { + targetElement.style.pointerEvents = "auto"; + } + } + + // Prevents sticking to the mouse + window.onblur = function() { + isMoving = false; + }; + + // Checks for extension + function checkForOutBox() { + const parentElement = targetElement.closest('[id^="component-"]'); + if (parentElement.offsetWidth < targetElement.offsetWidth && !targetElement.isExpanded) { + resetZoom(); + targetElement.isExpanded = true; + } + + if (parentElement.offsetWidth < targetElement.offsetWidth && elemData[elemId].zoomLevel == 1) { + resetZoom(); + } + + if (parentElement.offsetWidth < targetElement.offsetWidth && targetElement.offsetWidth * elemData[elemId].zoomLevel > parentElement.offsetWidth && elemData[elemId].zoomLevel < 1 && !targetElement.isZoomed) { + resetZoom(); + } + } + + if (isExtension) { + targetElement.addEventListener("mousemove", checkForOutBox); + } + + + window.addEventListener('resize', (e) => { + resetZoom(); + + if (isExtension) { + targetElement.isExpanded = false; + targetElement.isZoomed = false; + } + }); + + gradioApp().addEventListener("mousemove", handleMoveByKey); + + + } + + applyZoomAndPan(elementIDs.sketch, false); + applyZoomAndPan(elementIDs.inpaint, false); + applyZoomAndPan(elementIDs.inpaintSketch, false); + + // Make the function global so that other extensions can take advantage of this solution + const applyZoomAndPanIntegration = async(id, elementIDs) => { + const mainEl = document.querySelector(id); + if (id.toLocaleLowerCase() === "none") { + for (const elementID of elementIDs) { + const el = await waitForElement(elementID); + if (!el) break; + applyZoomAndPan(elementID); + } + return; + } + + if (!mainEl) return; + mainEl.addEventListener("click", async() => { + for (const elementID of elementIDs) { + const el = await waitForElement(elementID); + if (!el) break; + applyZoomAndPan(elementID); + } + }, {once: true}); + }; + + window.applyZoomAndPan = applyZoomAndPan; // Only 1 elements, argument elementID, for example applyZoomAndPan("#txt2img_controlnet_ControlNet_input_image") + + window.applyZoomAndPanIntegration = applyZoomAndPanIntegration; // for any extension + + /* + The function `applyZoomAndPanIntegration` takes two arguments: + + 1. `id`: A string identifier for the element to which zoom and pan functionality will be applied on click. + If the `id` value is "none", the functionality will be applied to all elements specified in the second argument without a click event. + + 2. `elementIDs`: An array of string identifiers for elements. Zoom and pan functionality will be applied to each of these elements on click of the element specified by the first argument. + If "none" is specified in the first argument, the functionality will be applied to each of these elements without a click event. + + Example usage: + applyZoomAndPanIntegration("#txt2img_controlnet", ["#txt2img_controlnet_ControlNet_input_image"]); + In this example, zoom and pan functionality will be applied to the element with the identifier "txt2img_controlnet_ControlNet_input_image" upon clicking the element with the identifier "txt2img_controlnet". + */ + + // More examples + // Add integration with ControlNet txt2img One TAB + // applyZoomAndPanIntegration("#txt2img_controlnet", ["#txt2img_controlnet_ControlNet_input_image"]); + + // Add integration with ControlNet txt2img Tabs + // applyZoomAndPanIntegration("#txt2img_controlnet",Array.from({ length: 10 }, (_, i) => `#txt2img_controlnet_ControlNet-${i}_input_image`)); + + // Add integration with Inpaint Anything + // applyZoomAndPanIntegration("None", ["#ia_sam_image", "#ia_sel_mask"]); +}); diff --git a/extensions-builtin/canvas-zoom-and-pan/scripts/hotkey_config.py b/extensions-builtin/canvas-zoom-and-pan/scripts/hotkey_config.py new file mode 100644 index 0000000000000000000000000000000000000000..2d8d2d1c014be5dc1bac24b2c71079351fe1177e --- /dev/null +++ b/extensions-builtin/canvas-zoom-and-pan/scripts/hotkey_config.py @@ -0,0 +1,15 @@ +import gradio as gr +from modules import shared + +shared.options_templates.update(shared.options_section(('canvas_hotkey', "Canvas Hotkeys"), { + "canvas_hotkey_zoom": shared.OptionInfo("Alt", "Zoom canvas", gr.Radio, {"choices": ["Shift","Ctrl", "Alt"]}).info("If you choose 'Shift' you cannot scroll horizontally, 'Alt' can cause a little trouble in firefox"), + "canvas_hotkey_adjust": shared.OptionInfo("Ctrl", "Adjust brush size", gr.Radio, {"choices": ["Shift","Ctrl", "Alt"]}).info("If you choose 'Shift' you cannot scroll horizontally, 'Alt' can cause a little trouble in firefox"), + "canvas_hotkey_move": shared.OptionInfo("F", "Moving the canvas").info("To work correctly in firefox, turn off 'Automatically search the page text when typing' in the browser settings"), + "canvas_hotkey_fullscreen": shared.OptionInfo("S", "Fullscreen Mode, maximizes the picture so that it fits into the screen and stretches it to its full width "), + "canvas_hotkey_reset": shared.OptionInfo("R", "Reset zoom and canvas positon"), + "canvas_hotkey_overlap": shared.OptionInfo("O", "Toggle overlap").info("Technical button, neededs for testing"), + "canvas_show_tooltip": shared.OptionInfo(True, "Enable tooltip on the canvas"), + "canvas_auto_expand": shared.OptionInfo(True, "Automatically expands an image that does not fit completely in the canvas area, similar to manually pressing the S and R buttons"), + "canvas_blur_prompt": shared.OptionInfo(False, "Take the focus off the prompt when working with a canvas"), + "canvas_disabled_functions": shared.OptionInfo(["Overlap"], "Disable function that you don't use", gr.CheckboxGroup, {"choices": ["Zoom","Adjust brush size", "Moving canvas","Fullscreen","Reset Zoom","Overlap"]}), +})) diff --git a/extensions-builtin/canvas-zoom-and-pan/style.css b/extensions-builtin/canvas-zoom-and-pan/style.css new file mode 100644 index 0000000000000000000000000000000000000000..5d8054e65196408c97791727088088650f102b21 --- /dev/null +++ b/extensions-builtin/canvas-zoom-and-pan/style.css @@ -0,0 +1,66 @@ +.canvas-tooltip-info { + position: absolute; + top: 10px; + left: 10px; + cursor: help; + background-color: rgba(0, 0, 0, 0.3); + width: 20px; + height: 20px; + border-radius: 50%; + display: flex; + align-items: center; + justify-content: center; + flex-direction: column; + + z-index: 100; +} + +.canvas-tooltip-info::after { + content: ''; + display: block; + width: 2px; + height: 7px; + background-color: white; + margin-top: 2px; +} + +.canvas-tooltip-info::before { + content: ''; + display: block; + width: 2px; + height: 2px; + background-color: white; +} + +.canvas-tooltip-content { + display: none; + background-color: #f9f9f9; + color: #333; + border: 1px solid #ddd; + padding: 15px; + position: absolute; + top: 40px; + left: 10px; + width: 250px; + font-size: 16px; + opacity: 0; + border-radius: 8px; + box-shadow: 0px 8px 16px 0px rgba(0,0,0,0.2); + + z-index: 100; +} + +.canvas-tooltip:hover .canvas-tooltip-content { + display: block; + animation: fadeIn 0.5s; + opacity: 1; +} + +@keyframes fadeIn { + from {opacity: 0;} + to {opacity: 1;} +} + +.styler { + overflow:inherit !important; +} \ No newline at end of file diff --git a/extensions-builtin/extra-options-section/scripts/extra_options_section.py b/extensions-builtin/extra-options-section/scripts/extra_options_section.py new file mode 100644 index 0000000000000000000000000000000000000000..96797033b7c6f9ef4bf43a6ba54e6d55933872c2 --- /dev/null +++ b/extensions-builtin/extra-options-section/scripts/extra_options_section.py @@ -0,0 +1,78 @@ +import math + +import gradio as gr +from modules import scripts, shared, ui_components, ui_settings, generation_parameters_copypaste +from modules.ui_components import FormColumn + + +class ExtraOptionsSection(scripts.Script): + section = "extra_options" + + def __init__(self): + self.comps = None + self.setting_names = None + + def title(self): + return "Extra options" + + def show(self, is_img2img): + return scripts.AlwaysVisible + + def ui(self, is_img2img): + self.comps = [] + self.setting_names = [] + self.infotext_fields = [] + extra_options = shared.opts.extra_options_img2img if is_img2img else shared.opts.extra_options_txt2img + elem_id_tabname = "extra_options_" + ("img2img" if is_img2img else "txt2img") + + mapping = {k: v for v, k in generation_parameters_copypaste.infotext_to_setting_name_mapping} + + with gr.Blocks() as interface: + with gr.Accordion("Options", open=False, elem_id=elem_id_tabname) if shared.opts.extra_options_accordion and extra_options else gr.Group(elem_id=elem_id_tabname): + + row_count = math.ceil(len(extra_options) / shared.opts.extra_options_cols) + + for row in range(row_count): + with gr.Row(): + for col in range(shared.opts.extra_options_cols): + index = row * shared.opts.extra_options_cols + col + if index >= len(extra_options): + break + + setting_name = extra_options[index] + + with FormColumn(): + comp = ui_settings.create_setting_component(setting_name) + + self.comps.append(comp) + self.setting_names.append(setting_name) + + setting_infotext_name = mapping.get(setting_name) + if setting_infotext_name is not None: + self.infotext_fields.append((comp, setting_infotext_name)) + + def get_settings_values(): + res = [ui_settings.get_value_for_setting(key) for key in self.setting_names] + return res[0] if len(res) == 1 else res + + interface.load(fn=get_settings_values, inputs=[], outputs=self.comps, queue=False, show_progress=False) + + return self.comps + + def before_process(self, p, *args): + for name, value in zip(self.setting_names, args): + if name not in p.override_settings: + p.override_settings[name] = value + + +shared.options_templates.update(shared.options_section(('settings_in_ui', "Settings in UI", "ui"), { + "settings_in_ui": shared.OptionHTML(""" +This page allows you to add some settings to the main interface of txt2img and img2img tabs. +"""), + "extra_options_txt2img": shared.OptionInfo([], "Settings for txt2img", ui_components.DropdownMulti, lambda: {"choices": list(shared.opts.data_labels.keys())}).js("info", "settingsHintsShowQuicksettings").info("setting entries that also appear in txt2img interfaces").needs_reload_ui(), + "extra_options_img2img": shared.OptionInfo([], "Settings for img2img", ui_components.DropdownMulti, lambda: {"choices": list(shared.opts.data_labels.keys())}).js("info", "settingsHintsShowQuicksettings").info("setting entries that also appear in img2img interfaces").needs_reload_ui(), + "extra_options_cols": shared.OptionInfo(1, "Number of columns for added settings", gr.Slider, {"step": 1, "minimum": 1, "maximum": 20}).info("displayed amount will depend on the actual browser window width").needs_reload_ui(), + "extra_options_accordion": shared.OptionInfo(False, "Place added settings into an accordion").needs_reload_ui() +})) + + diff --git a/extensions-builtin/hypertile/hypertile.py b/extensions-builtin/hypertile/hypertile.py new file mode 100644 index 0000000000000000000000000000000000000000..0f40e2d39256deb69a3694adb627f726a568de8f --- /dev/null +++ b/extensions-builtin/hypertile/hypertile.py @@ -0,0 +1,351 @@ +""" +Hypertile module for splitting attention layers in SD-1.5 U-Net and SD-1.5 VAE +Warn: The patch works well only if the input image has a width and height that are multiples of 128 +Original author: @tfernd Github: https://github.com/tfernd/HyperTile +""" + +from __future__ import annotations + +from dataclasses import dataclass +from typing import Callable + +from functools import wraps, cache + +import math +import torch.nn as nn +import random + +from einops import rearrange + + +@dataclass +class HypertileParams: + depth = 0 + layer_name = "" + tile_size: int = 0 + swap_size: int = 0 + aspect_ratio: float = 1.0 + forward = None + enabled = False + + + +# TODO add SD-XL layers +DEPTH_LAYERS = { + 0: [ + # SD 1.5 U-Net (diffusers) + "down_blocks.0.attentions.0.transformer_blocks.0.attn1", + "down_blocks.0.attentions.1.transformer_blocks.0.attn1", + "up_blocks.3.attentions.0.transformer_blocks.0.attn1", + "up_blocks.3.attentions.1.transformer_blocks.0.attn1", + "up_blocks.3.attentions.2.transformer_blocks.0.attn1", + # SD 1.5 U-Net (ldm) + "input_blocks.1.1.transformer_blocks.0.attn1", + "input_blocks.2.1.transformer_blocks.0.attn1", + "output_blocks.9.1.transformer_blocks.0.attn1", + "output_blocks.10.1.transformer_blocks.0.attn1", + "output_blocks.11.1.transformer_blocks.0.attn1", + # SD 1.5 VAE + "decoder.mid_block.attentions.0", + "decoder.mid.attn_1", + ], + 1: [ + # SD 1.5 U-Net (diffusers) + "down_blocks.1.attentions.0.transformer_blocks.0.attn1", + "down_blocks.1.attentions.1.transformer_blocks.0.attn1", + "up_blocks.2.attentions.0.transformer_blocks.0.attn1", + "up_blocks.2.attentions.1.transformer_blocks.0.attn1", + "up_blocks.2.attentions.2.transformer_blocks.0.attn1", + # SD 1.5 U-Net (ldm) + "input_blocks.4.1.transformer_blocks.0.attn1", + "input_blocks.5.1.transformer_blocks.0.attn1", + "output_blocks.6.1.transformer_blocks.0.attn1", + "output_blocks.7.1.transformer_blocks.0.attn1", + "output_blocks.8.1.transformer_blocks.0.attn1", + ], + 2: [ + # SD 1.5 U-Net (diffusers) + "down_blocks.2.attentions.0.transformer_blocks.0.attn1", + "down_blocks.2.attentions.1.transformer_blocks.0.attn1", + "up_blocks.1.attentions.0.transformer_blocks.0.attn1", + "up_blocks.1.attentions.1.transformer_blocks.0.attn1", + "up_blocks.1.attentions.2.transformer_blocks.0.attn1", + # SD 1.5 U-Net (ldm) + "input_blocks.7.1.transformer_blocks.0.attn1", + "input_blocks.8.1.transformer_blocks.0.attn1", + "output_blocks.3.1.transformer_blocks.0.attn1", + "output_blocks.4.1.transformer_blocks.0.attn1", + "output_blocks.5.1.transformer_blocks.0.attn1", + ], + 3: [ + # SD 1.5 U-Net (diffusers) + "mid_block.attentions.0.transformer_blocks.0.attn1", + # SD 1.5 U-Net (ldm) + "middle_block.1.transformer_blocks.0.attn1", + ], +} +# XL layers, thanks for GitHub@gel-crabs for the help +DEPTH_LAYERS_XL = { + 0: [ + # SD 1.5 U-Net (diffusers) + "down_blocks.0.attentions.0.transformer_blocks.0.attn1", + "down_blocks.0.attentions.1.transformer_blocks.0.attn1", + "up_blocks.3.attentions.0.transformer_blocks.0.attn1", + "up_blocks.3.attentions.1.transformer_blocks.0.attn1", + "up_blocks.3.attentions.2.transformer_blocks.0.attn1", + # SD 1.5 U-Net (ldm) + "input_blocks.4.1.transformer_blocks.0.attn1", + "input_blocks.5.1.transformer_blocks.0.attn1", + "output_blocks.3.1.transformer_blocks.0.attn1", + "output_blocks.4.1.transformer_blocks.0.attn1", + "output_blocks.5.1.transformer_blocks.0.attn1", + # SD 1.5 VAE + "decoder.mid_block.attentions.0", + "decoder.mid.attn_1", + ], + 1: [ + # SD 1.5 U-Net (diffusers) + #"down_blocks.1.attentions.0.transformer_blocks.0.attn1", + #"down_blocks.1.attentions.1.transformer_blocks.0.attn1", + #"up_blocks.2.attentions.0.transformer_blocks.0.attn1", + #"up_blocks.2.attentions.1.transformer_blocks.0.attn1", + #"up_blocks.2.attentions.2.transformer_blocks.0.attn1", + # SD 1.5 U-Net (ldm) + "input_blocks.4.1.transformer_blocks.1.attn1", + "input_blocks.5.1.transformer_blocks.1.attn1", + "output_blocks.3.1.transformer_blocks.1.attn1", + "output_blocks.4.1.transformer_blocks.1.attn1", + "output_blocks.5.1.transformer_blocks.1.attn1", + "input_blocks.7.1.transformer_blocks.0.attn1", + "input_blocks.8.1.transformer_blocks.0.attn1", + "output_blocks.0.1.transformer_blocks.0.attn1", + "output_blocks.1.1.transformer_blocks.0.attn1", + "output_blocks.2.1.transformer_blocks.0.attn1", + "input_blocks.7.1.transformer_blocks.1.attn1", + "input_blocks.8.1.transformer_blocks.1.attn1", + "output_blocks.0.1.transformer_blocks.1.attn1", + "output_blocks.1.1.transformer_blocks.1.attn1", + "output_blocks.2.1.transformer_blocks.1.attn1", + "input_blocks.7.1.transformer_blocks.2.attn1", + "input_blocks.8.1.transformer_blocks.2.attn1", + "output_blocks.0.1.transformer_blocks.2.attn1", + "output_blocks.1.1.transformer_blocks.2.attn1", + "output_blocks.2.1.transformer_blocks.2.attn1", + "input_blocks.7.1.transformer_blocks.3.attn1", + "input_blocks.8.1.transformer_blocks.3.attn1", + "output_blocks.0.1.transformer_blocks.3.attn1", + "output_blocks.1.1.transformer_blocks.3.attn1", + "output_blocks.2.1.transformer_blocks.3.attn1", + "input_blocks.7.1.transformer_blocks.4.attn1", + "input_blocks.8.1.transformer_blocks.4.attn1", + "output_blocks.0.1.transformer_blocks.4.attn1", + "output_blocks.1.1.transformer_blocks.4.attn1", + "output_blocks.2.1.transformer_blocks.4.attn1", + "input_blocks.7.1.transformer_blocks.5.attn1", + "input_blocks.8.1.transformer_blocks.5.attn1", + "output_blocks.0.1.transformer_blocks.5.attn1", + "output_blocks.1.1.transformer_blocks.5.attn1", + "output_blocks.2.1.transformer_blocks.5.attn1", + "input_blocks.7.1.transformer_blocks.6.attn1", + "input_blocks.8.1.transformer_blocks.6.attn1", + "output_blocks.0.1.transformer_blocks.6.attn1", + "output_blocks.1.1.transformer_blocks.6.attn1", + "output_blocks.2.1.transformer_blocks.6.attn1", + "input_blocks.7.1.transformer_blocks.7.attn1", + "input_blocks.8.1.transformer_blocks.7.attn1", + "output_blocks.0.1.transformer_blocks.7.attn1", + "output_blocks.1.1.transformer_blocks.7.attn1", + "output_blocks.2.1.transformer_blocks.7.attn1", + "input_blocks.7.1.transformer_blocks.8.attn1", + "input_blocks.8.1.transformer_blocks.8.attn1", + "output_blocks.0.1.transformer_blocks.8.attn1", + "output_blocks.1.1.transformer_blocks.8.attn1", + "output_blocks.2.1.transformer_blocks.8.attn1", + "input_blocks.7.1.transformer_blocks.9.attn1", + "input_blocks.8.1.transformer_blocks.9.attn1", + "output_blocks.0.1.transformer_blocks.9.attn1", + "output_blocks.1.1.transformer_blocks.9.attn1", + "output_blocks.2.1.transformer_blocks.9.attn1", + ], + 2: [ + # SD 1.5 U-Net (diffusers) + "mid_block.attentions.0.transformer_blocks.0.attn1", + # SD 1.5 U-Net (ldm) + "middle_block.1.transformer_blocks.0.attn1", + "middle_block.1.transformer_blocks.1.attn1", + "middle_block.1.transformer_blocks.2.attn1", + "middle_block.1.transformer_blocks.3.attn1", + "middle_block.1.transformer_blocks.4.attn1", + "middle_block.1.transformer_blocks.5.attn1", + "middle_block.1.transformer_blocks.6.attn1", + "middle_block.1.transformer_blocks.7.attn1", + "middle_block.1.transformer_blocks.8.attn1", + "middle_block.1.transformer_blocks.9.attn1", + ], + 3 : [] # TODO - separate layers for SD-XL +} + + +RNG_INSTANCE = random.Random() + +@cache +def get_divisors(value: int, min_value: int, /, max_options: int = 1) -> list[int]: + """ + Returns divisors of value that + x * min_value <= value + in big -> small order, amount of divisors is limited by max_options + """ + max_options = max(1, max_options) # at least 1 option should be returned + min_value = min(min_value, value) + divisors = [i for i in range(min_value, value + 1) if value % i == 0] # divisors in small -> big order + ns = [value // i for i in divisors[:max_options]] # has at least 1 element # big -> small order + return ns + + +def random_divisor(value: int, min_value: int, /, max_options: int = 1) -> int: + """ + Returns a random divisor of value that + x * min_value <= value + if max_options is 1, the behavior is deterministic + """ + ns = get_divisors(value, min_value, max_options=max_options) # get cached divisors + idx = RNG_INSTANCE.randint(0, len(ns) - 1) + + return ns[idx] + + +def set_hypertile_seed(seed: int) -> None: + RNG_INSTANCE.seed(seed) + + +@cache +def largest_tile_size_available(width: int, height: int) -> int: + """ + Calculates the largest tile size available for a given width and height + Tile size is always a power of 2 + """ + gcd = math.gcd(width, height) + largest_tile_size_available = 1 + while gcd % (largest_tile_size_available * 2) == 0: + largest_tile_size_available *= 2 + return largest_tile_size_available + + +def iterative_closest_divisors(hw:int, aspect_ratio:float) -> tuple[int, int]: + """ + Finds h and w such that h*w = hw and h/w = aspect_ratio + We check all possible divisors of hw and return the closest to the aspect ratio + """ + divisors = [i for i in range(2, hw + 1) if hw % i == 0] # all divisors of hw + pairs = [(i, hw // i) for i in divisors] # all pairs of divisors of hw + ratios = [w/h for h, w in pairs] # all ratios of pairs of divisors of hw + closest_ratio = min(ratios, key=lambda x: abs(x - aspect_ratio)) # closest ratio to aspect_ratio + closest_pair = pairs[ratios.index(closest_ratio)] # closest pair of divisors to aspect_ratio + return closest_pair + + +@cache +def find_hw_candidates(hw:int, aspect_ratio:float) -> tuple[int, int]: + """ + Finds h and w such that h*w = hw and h/w = aspect_ratio + """ + h, w = round(math.sqrt(hw * aspect_ratio)), round(math.sqrt(hw / aspect_ratio)) + # find h and w such that h*w = hw and h/w = aspect_ratio + if h * w != hw: + w_candidate = hw / h + # check if w is an integer + if not w_candidate.is_integer(): + h_candidate = hw / w + # check if h is an integer + if not h_candidate.is_integer(): + return iterative_closest_divisors(hw, aspect_ratio) + else: + h = int(h_candidate) + else: + w = int(w_candidate) + return h, w + + +def self_attn_forward(params: HypertileParams, scale_depth=True) -> Callable: + + @wraps(params.forward) + def wrapper(*args, **kwargs): + if not params.enabled: + return params.forward(*args, **kwargs) + + latent_tile_size = max(128, params.tile_size) // 8 + x = args[0] + + # VAE + if x.ndim == 4: + b, c, h, w = x.shape + + nh = random_divisor(h, latent_tile_size, params.swap_size) + nw = random_divisor(w, latent_tile_size, params.swap_size) + + if nh * nw > 1: + x = rearrange(x, "b c (nh h) (nw w) -> (b nh nw) c h w", nh=nh, nw=nw) # split into nh * nw tiles + + out = params.forward(x, *args[1:], **kwargs) + + if nh * nw > 1: + out = rearrange(out, "(b nh nw) c h w -> b c (nh h) (nw w)", nh=nh, nw=nw) + + # U-Net + else: + hw: int = x.size(1) + h, w = find_hw_candidates(hw, params.aspect_ratio) + assert h * w == hw, f"Invalid aspect ratio {params.aspect_ratio} for input of shape {x.shape}, hw={hw}, h={h}, w={w}" + + factor = 2 ** params.depth if scale_depth else 1 + nh = random_divisor(h, latent_tile_size * factor, params.swap_size) + nw = random_divisor(w, latent_tile_size * factor, params.swap_size) + + if nh * nw > 1: + x = rearrange(x, "b (nh h nw w) c -> (b nh nw) (h w) c", h=h // nh, w=w // nw, nh=nh, nw=nw) + + out = params.forward(x, *args[1:], **kwargs) + + if nh * nw > 1: + out = rearrange(out, "(b nh nw) hw c -> b nh nw hw c", nh=nh, nw=nw) + out = rearrange(out, "b nh nw (h w) c -> b (nh h nw w) c", h=h // nh, w=w // nw) + + return out + + return wrapper + + +def hypertile_hook_model(model: nn.Module, width, height, *, enable=False, tile_size_max=128, swap_size=1, max_depth=3, is_sdxl=False): + hypertile_layers = getattr(model, "__webui_hypertile_layers", None) + if hypertile_layers is None: + if not enable: + return + + hypertile_layers = {} + layers = DEPTH_LAYERS_XL if is_sdxl else DEPTH_LAYERS + + for depth in range(4): + for layer_name, module in model.named_modules(): + if any(layer_name.endswith(try_name) for try_name in layers[depth]): + params = HypertileParams() + module.__webui_hypertile_params = params + params.forward = module.forward + params.depth = depth + params.layer_name = layer_name + module.forward = self_attn_forward(params) + + hypertile_layers[layer_name] = 1 + + model.__webui_hypertile_layers = hypertile_layers + + aspect_ratio = width / height + tile_size = min(largest_tile_size_available(width, height), tile_size_max) + + for layer_name, module in model.named_modules(): + if layer_name in hypertile_layers: + params = module.__webui_hypertile_params + + params.tile_size = tile_size + params.swap_size = swap_size + params.aspect_ratio = aspect_ratio + params.enabled = enable and params.depth <= max_depth diff --git a/extensions-builtin/hypertile/scripts/hypertile_script.py b/extensions-builtin/hypertile/scripts/hypertile_script.py new file mode 100644 index 0000000000000000000000000000000000000000..52ba655fe37381fefad74b0bde26052d16a62116 --- /dev/null +++ b/extensions-builtin/hypertile/scripts/hypertile_script.py @@ -0,0 +1,109 @@ +import hypertile +from modules import scripts, script_callbacks, shared +from scripts.hypertile_xyz import add_axis_options + + +class ScriptHypertile(scripts.Script): + name = "Hypertile" + + def title(self): + return self.name + + def show(self, is_img2img): + return scripts.AlwaysVisible + + def process(self, p, *args): + hypertile.set_hypertile_seed(p.all_seeds[0]) + + configure_hypertile(p.width, p.height, enable_unet=shared.opts.hypertile_enable_unet) + + self.add_infotext(p) + + def before_hr(self, p, *args): + + enable = shared.opts.hypertile_enable_unet_secondpass or shared.opts.hypertile_enable_unet + + # exclusive hypertile seed for the second pass + if enable: + hypertile.set_hypertile_seed(p.all_seeds[0]) + + configure_hypertile(p.hr_upscale_to_x, p.hr_upscale_to_y, enable_unet=enable) + + if enable and not shared.opts.hypertile_enable_unet: + p.extra_generation_params["Hypertile U-Net second pass"] = True + + self.add_infotext(p, add_unet_params=True) + + def add_infotext(self, p, add_unet_params=False): + def option(name): + value = getattr(shared.opts, name) + default_value = shared.opts.get_default(name) + return None if value == default_value else value + + if shared.opts.hypertile_enable_unet: + p.extra_generation_params["Hypertile U-Net"] = True + + if shared.opts.hypertile_enable_unet or add_unet_params: + p.extra_generation_params["Hypertile U-Net max depth"] = option('hypertile_max_depth_unet') + p.extra_generation_params["Hypertile U-Net max tile size"] = option('hypertile_max_tile_unet') + p.extra_generation_params["Hypertile U-Net swap size"] = option('hypertile_swap_size_unet') + + if shared.opts.hypertile_enable_vae: + p.extra_generation_params["Hypertile VAE"] = True + p.extra_generation_params["Hypertile VAE max depth"] = option('hypertile_max_depth_vae') + p.extra_generation_params["Hypertile VAE max tile size"] = option('hypertile_max_tile_vae') + p.extra_generation_params["Hypertile VAE swap size"] = option('hypertile_swap_size_vae') + + +def configure_hypertile(width, height, enable_unet=True): + hypertile.hypertile_hook_model( + shared.sd_model.first_stage_model, + width, + height, + swap_size=shared.opts.hypertile_swap_size_vae, + max_depth=shared.opts.hypertile_max_depth_vae, + tile_size_max=shared.opts.hypertile_max_tile_vae, + enable=shared.opts.hypertile_enable_vae, + ) + + hypertile.hypertile_hook_model( + shared.sd_model.model, + width, + height, + swap_size=shared.opts.hypertile_swap_size_unet, + max_depth=shared.opts.hypertile_max_depth_unet, + tile_size_max=shared.opts.hypertile_max_tile_unet, + enable=enable_unet, + is_sdxl=shared.sd_model.is_sdxl + ) + + +def on_ui_settings(): + import gradio as gr + + options = { + "hypertile_explanation": shared.OptionHTML(""" + Hypertile optimizes the self-attention layer within U-Net and VAE models, + resulting in a reduction in computation time ranging from 1 to 4 times. The larger the generated image is, the greater the + benefit. + """), + + "hypertile_enable_unet": shared.OptionInfo(False, "Enable Hypertile U-Net", infotext="Hypertile U-Net").info("enables hypertile for all modes, including hires fix second pass; noticeable change in details of the generated picture"), + "hypertile_enable_unet_secondpass": shared.OptionInfo(False, "Enable Hypertile U-Net for hires fix second pass", infotext="Hypertile U-Net second pass").info("enables hypertile just for hires fix second pass - regardless of whether the above setting is enabled"), + "hypertile_max_depth_unet": shared.OptionInfo(3, "Hypertile U-Net max depth", gr.Slider, {"minimum": 0, "maximum": 3, "step": 1}, infotext="Hypertile U-Net max depth").info("larger = more neural network layers affected; minor effect on performance"), + "hypertile_max_tile_unet": shared.OptionInfo(256, "Hypertile U-Net max tile size", gr.Slider, {"minimum": 0, "maximum": 512, "step": 16}, infotext="Hypertile U-Net max tile size").info("larger = worse performance"), + "hypertile_swap_size_unet": shared.OptionInfo(3, "Hypertile U-Net swap size", gr.Slider, {"minimum": 0, "maximum": 64, "step": 1}, infotext="Hypertile U-Net swap size"), + + "hypertile_enable_vae": shared.OptionInfo(False, "Enable Hypertile VAE", infotext="Hypertile VAE").info("minimal change in the generated picture"), + "hypertile_max_depth_vae": shared.OptionInfo(3, "Hypertile VAE max depth", gr.Slider, {"minimum": 0, "maximum": 3, "step": 1}, infotext="Hypertile VAE max depth"), + "hypertile_max_tile_vae": shared.OptionInfo(128, "Hypertile VAE max tile size", gr.Slider, {"minimum": 0, "maximum": 512, "step": 16}, infotext="Hypertile VAE max tile size"), + "hypertile_swap_size_vae": shared.OptionInfo(3, "Hypertile VAE swap size ", gr.Slider, {"minimum": 0, "maximum": 64, "step": 1}, infotext="Hypertile VAE swap size"), + } + + for name, opt in options.items(): + opt.section = ('hypertile', "Hypertile") + shared.opts.add_option(name, opt) + + +script_callbacks.on_ui_settings(on_ui_settings) +script_callbacks.on_before_ui(add_axis_options) diff --git a/extensions-builtin/hypertile/scripts/hypertile_xyz.py b/extensions-builtin/hypertile/scripts/hypertile_xyz.py new file mode 100644 index 0000000000000000000000000000000000000000..9e96ae3c52781ef5e7148e3259bccca4de15a6b8 --- /dev/null +++ b/extensions-builtin/hypertile/scripts/hypertile_xyz.py @@ -0,0 +1,51 @@ +from modules import scripts +from modules.shared import opts + +xyz_grid = [x for x in scripts.scripts_data if x.script_class.__module__ == "xyz_grid.py"][0].module + +def int_applier(value_name:str, min_range:int = -1, max_range:int = -1): + """ + Returns a function that applies the given value to the given value_name in opts.data. + """ + def validate(value_name:str, value:str): + value = int(value) + # validate value + if not min_range == -1: + assert value >= min_range, f"Value {value} for {value_name} must be greater than or equal to {min_range}" + if not max_range == -1: + assert value <= max_range, f"Value {value} for {value_name} must be less than or equal to {max_range}" + def apply_int(p, x, xs): + validate(value_name, x) + opts.data[value_name] = int(x) + return apply_int + +def bool_applier(value_name:str): + """ + Returns a function that applies the given value to the given value_name in opts.data. + """ + def validate(value_name:str, value:str): + assert value.lower() in ["true", "false"], f"Value {value} for {value_name} must be either true or false" + def apply_bool(p, x, xs): + validate(value_name, x) + value_boolean = x.lower() == "true" + opts.data[value_name] = value_boolean + return apply_bool + +def add_axis_options(): + extra_axis_options = [ + xyz_grid.AxisOption("[Hypertile] Unet First pass Enabled", str, bool_applier("hypertile_enable_unet"), choices=xyz_grid.boolean_choice(reverse=True)), + xyz_grid.AxisOption("[Hypertile] Unet Second pass Enabled", str, bool_applier("hypertile_enable_unet_secondpass"), choices=xyz_grid.boolean_choice(reverse=True)), + xyz_grid.AxisOption("[Hypertile] Unet Max Depth", int, int_applier("hypertile_max_depth_unet", 0, 3), choices=lambda: [str(x) for x in range(4)]), + xyz_grid.AxisOption("[Hypertile] Unet Max Tile Size", int, int_applier("hypertile_max_tile_unet", 0, 512)), + xyz_grid.AxisOption("[Hypertile] Unet Swap Size", int, int_applier("hypertile_swap_size_unet", 0, 64)), + xyz_grid.AxisOption("[Hypertile] VAE Enabled", str, bool_applier("hypertile_enable_vae"), choices=xyz_grid.boolean_choice(reverse=True)), + xyz_grid.AxisOption("[Hypertile] VAE Max Depth", int, int_applier("hypertile_max_depth_vae", 0, 3), choices=lambda: [str(x) for x in range(4)]), + xyz_grid.AxisOption("[Hypertile] VAE Max Tile Size", int, int_applier("hypertile_max_tile_vae", 0, 512)), + xyz_grid.AxisOption("[Hypertile] VAE Swap Size", int, int_applier("hypertile_swap_size_vae", 0, 64)), + ] + set_a = {opt.label for opt in xyz_grid.axis_options} + set_b = {opt.label for opt in extra_axis_options} + if set_a.intersection(set_b): + return + + xyz_grid.axis_options.extend(extra_axis_options) diff --git a/extensions-builtin/mobile/javascript/mobile.js b/extensions-builtin/mobile/javascript/mobile.js new file mode 100644 index 0000000000000000000000000000000000000000..bff1acedff37d3683a01eb2bd6be76a8d33c0296 --- /dev/null +++ b/extensions-builtin/mobile/javascript/mobile.js @@ -0,0 +1,34 @@ +var isSetupForMobile = false; + +function isMobile() { + for (var tab of ["txt2img", "img2img"]) { + var imageTab = gradioApp().getElementById(tab + '_results'); + if (imageTab && imageTab.offsetParent && imageTab.offsetLeft == 0) { + return true; + } + } + + return false; +} + +function reportWindowSize() { + if (gradioApp().querySelector('.toprow-compact-tools')) return; // not applicable for compact prompt layout + + var currentlyMobile = isMobile(); + if (currentlyMobile == isSetupForMobile) return; + isSetupForMobile = currentlyMobile; + + for (var tab of ["txt2img", "img2img"]) { + var button = gradioApp().getElementById(tab + '_generate_box'); + var target = gradioApp().getElementById(currentlyMobile ? tab + '_results' : tab + '_actions_column'); + target.insertBefore(button, target.firstElementChild); + + gradioApp().getElementById(tab + '_results').classList.toggle('mobile', currentlyMobile); + } +} + +window.addEventListener("resize", reportWindowSize); + +onUiLoaded(function() { + reportWindowSize(); +}); diff --git a/extensions-builtin/prompt-bracket-checker/javascript/prompt-bracket-checker.js b/extensions-builtin/prompt-bracket-checker/javascript/prompt-bracket-checker.js new file mode 100644 index 0000000000000000000000000000000000000000..114cf94ccbf69b473757f2fc46443a39723a9269 --- /dev/null +++ b/extensions-builtin/prompt-bracket-checker/javascript/prompt-bracket-checker.js @@ -0,0 +1,42 @@ +// Stable Diffusion WebUI - Bracket checker +// By Hingashi no Florin/Bwin4L & @akx +// Counts open and closed brackets (round, square, curly) in the prompt and negative prompt text boxes in the txt2img and img2img tabs. +// If there's a mismatch, the keyword counter turns red and if you hover on it, a tooltip tells you what's wrong. + +function checkBrackets(textArea, counterElt) { + var counts = {}; + (textArea.value.match(/[(){}[\]]/g) || []).forEach(bracket => { + counts[bracket] = (counts[bracket] || 0) + 1; + }); + var errors = []; + + function checkPair(open, close, kind) { + if (counts[open] !== counts[close]) { + errors.push( + `${open}...${close} - Detected ${counts[open] || 0} opening and ${counts[close] || 0} closing ${kind}.` + ); + } + } + + checkPair('(', ')', 'round brackets'); + checkPair('[', ']', 'square brackets'); + checkPair('{', '}', 'curly brackets'); + counterElt.title = errors.join('\n'); + counterElt.classList.toggle('error', errors.length !== 0); +} + +function setupBracketChecking(id_prompt, id_counter) { + var textarea = gradioApp().querySelector("#" + id_prompt + " > label > textarea"); + var counter = gradioApp().getElementById(id_counter); + + if (textarea && counter) { + textarea.addEventListener("input", () => checkBrackets(textarea, counter)); + } +} + +onUiLoaded(function() { + setupBracketChecking('txt2img_prompt', 'txt2img_token_counter'); + setupBracketChecking('txt2img_neg_prompt', 'txt2img_negative_token_counter'); + setupBracketChecking('img2img_prompt', 'img2img_token_counter'); + setupBracketChecking('img2img_neg_prompt', 'img2img_negative_token_counter'); +}); diff --git a/extensions/put extensions here.txt b/extensions/put extensions here.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/html/card-no-preview.png b/html/card-no-preview.png new file mode 100644 index 0000000000000000000000000000000000000000..f135fc4ec1e9a9f0850a310fbfde4b8811232437 --- /dev/null +++ b/html/card-no-preview.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ab5740a5ae4494cd483daa1c5ba577b62260acd19cf94198c527ae05650f32dd +size 84440 diff --git a/html/extra-networks-card.html b/html/extra-networks-card.html new file mode 100644 index 0000000000000000000000000000000000000000..39674666f1e336d9bf61d2a6986721cf8591eeee --- /dev/null +++ b/html/extra-networks-card.html @@ -0,0 +1,14 @@ +
+ {background_image} +
+ {metadata_button} + {edit_button} +
+
+
+ +
+ {name} + {description} +
+
diff --git a/html/extra-networks-no-cards.html b/html/extra-networks-no-cards.html new file mode 100644 index 0000000000000000000000000000000000000000..389358d6c4b383fdc3c5686e029e7b3b1ae9a493 --- /dev/null +++ b/html/extra-networks-no-cards.html @@ -0,0 +1,8 @@ +
+

Nothing here. Add some content to the following directories:

+ +
    +{dirs} +
+
+ diff --git a/html/footer.html b/html/footer.html new file mode 100644 index 0000000000000000000000000000000000000000..8739a0f4752fd00b941d888d9a676158a3ba31a2 --- /dev/null +++ b/html/footer.html @@ -0,0 +1,15 @@ +
+ API +  •  + Github +  •  + Gradio +  •  + Startup profile +  •  + Reload UI +
+
+
+{versions} +
diff --git a/html/licenses.html b/html/licenses.html new file mode 100644 index 0000000000000000000000000000000000000000..ca44deddd3663514962493c06a42a38d608c1229 --- /dev/null +++ b/html/licenses.html @@ -0,0 +1,690 @@ + + +

CodeFormer

+Parts of CodeFormer code had to be copied to be compatible with GFPGAN. +
+S-Lab License 1.0
+
+Copyright 2022 S-Lab
+
+Redistribution and use for non-commercial purpose in source and
+binary forms, with or without modification, are permitted provided
+that the following conditions are met:
+
+1. Redistributions of source code must retain the above copyright
+   notice, this list of conditions and the following disclaimer.
+
+2. Redistributions in binary form must reproduce the above copyright
+   notice, this list of conditions and the following disclaimer in
+   the documentation and/or other materials provided with the
+   distribution.
+
+3. Neither the name of the copyright holder nor the names of its
+   contributors may be used to endorse or promote products derived
+   from this software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+In the event that redistribution and/or use for commercial purpose in
+source or binary forms, with or without modification is required,
+please contact the contributor(s) of the work.
+
+ + +

ESRGAN

+Code for architecture and reading models copied. +
+MIT License
+
+Copyright (c) 2021 victorca25
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
+
+ +

Real-ESRGAN

+Some code is copied to support ESRGAN models. +
+BSD 3-Clause License
+
+Copyright (c) 2021, Xintao Wang
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are met:
+
+1. Redistributions of source code must retain the above copyright notice, this
+   list of conditions and the following disclaimer.
+
+2. Redistributions in binary form must reproduce the above copyright notice,
+   this list of conditions and the following disclaimer in the documentation
+   and/or other materials provided with the distribution.
+
+3. Neither the name of the copyright holder nor the names of its
+   contributors may be used to endorse or promote products derived from
+   this software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+ +

InvokeAI

+Some code for compatibility with OSX is taken from lstein's repository. +
+MIT License
+
+Copyright (c) 2022 InvokeAI Team
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
+
+ +

LDSR

+Code added by contirubtors, most likely copied from this repository. +
+MIT License
+
+Copyright (c) 2022 Machine Vision and Learning Group, LMU Munich
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
+
+ +

CLIP Interrogator

+Some small amounts of code borrowed and reworked. +
+MIT License
+
+Copyright (c) 2022 pharmapsychotic
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
+
+ +

SwinIR

+Code added by contributors, most likely copied from this repository. + +
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+      comment syntax for the file format. We also recommend that a
+      file or class name and description of purpose be included on the
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+
+   Copyright [2021] [SwinIR Authors]
+
+   Licensed under the Apache License, Version 2.0 (the "License");
+   you may not use this file except in compliance with the License.
+   You may obtain a copy of the License at
+
+       http://www.apache.org/licenses/LICENSE-2.0
+
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+   See the License for the specific language governing permissions and
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+
+ +

Memory Efficient Attention

+The sub-quadratic cross attention optimization uses modified code from the Memory Efficient Attention package that Alex Birch optimized for 3D tensors. This license is updated to reflect that. +
+MIT License
+
+Copyright (c) 2023 Alex Birch
+Copyright (c) 2023 Amin Rezaei
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
+
+ +

Scaled Dot Product Attention

+Some small amounts of code borrowed and reworked. +
+   Copyright 2023 The HuggingFace Team. All rights reserved.
+
+   Licensed under the Apache License, Version 2.0 (the "License");
+   you may not use this file except in compliance with the License.
+   You may obtain a copy of the License at
+
+      http://www.apache.org/licenses/LICENSE-2.0
+
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+   distributed under the License is distributed on an "AS IS" BASIS,
+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
+   limitations under the License.
+
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+ +

Curated transformers

+The MPS workaround for nn.Linear on macOS 13.2.X is based on the MPS workaround for nn.Linear created by danieldk for Curated transformers +
+The MIT License (MIT)
+
+Copyright (C) 2021 ExplosionAI GmbH
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
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+in the Software without restriction, including without limitation the rights
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+all copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
+THE SOFTWARE.
+
+ +

TAESD

+Tiny AutoEncoder for Stable Diffusion option for live previews +
+MIT License
+
+Copyright (c) 2023 Ollin Boer Bohan
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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+furnished to do so, subject to the following conditions:
+
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+copies or substantial portions of the Software.
+
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+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
+
\ No newline at end of file diff --git a/javascript/aspectRatioOverlay.js b/javascript/aspectRatioOverlay.js new file mode 100644 index 0000000000000000000000000000000000000000..2cf2d571fc02a026b6cdedcf589a217ef0d65d27 --- /dev/null +++ b/javascript/aspectRatioOverlay.js @@ -0,0 +1,113 @@ + +let currentWidth = null; +let currentHeight = null; +let arFrameTimeout = setTimeout(function() {}, 0); + +function dimensionChange(e, is_width, is_height) { + + if (is_width) { + currentWidth = e.target.value * 1.0; + } + if (is_height) { + currentHeight = e.target.value * 1.0; + } + + var inImg2img = gradioApp().querySelector("#tab_img2img").style.display == "block"; + + if (!inImg2img) { + return; + } + + var targetElement = null; + + var tabIndex = get_tab_index('mode_img2img'); + if (tabIndex == 0) { // img2img + targetElement = gradioApp().querySelector('#img2img_image div[data-testid=image] img'); + } else if (tabIndex == 1) { //Sketch + targetElement = gradioApp().querySelector('#img2img_sketch div[data-testid=image] img'); + } else if (tabIndex == 2) { // Inpaint + targetElement = gradioApp().querySelector('#img2maskimg div[data-testid=image] img'); + } else if (tabIndex == 3) { // Inpaint sketch + targetElement = gradioApp().querySelector('#inpaint_sketch div[data-testid=image] img'); + } + + + if (targetElement) { + + var arPreviewRect = gradioApp().querySelector('#imageARPreview'); + if (!arPreviewRect) { + arPreviewRect = document.createElement('div'); + arPreviewRect.id = "imageARPreview"; + gradioApp().appendChild(arPreviewRect); + } + + + + var viewportOffset = targetElement.getBoundingClientRect(); + + var viewportscale = Math.min(targetElement.clientWidth / targetElement.naturalWidth, targetElement.clientHeight / targetElement.naturalHeight); + + var scaledx = targetElement.naturalWidth * viewportscale; + var scaledy = targetElement.naturalHeight * viewportscale; + + var cleintRectTop = (viewportOffset.top + window.scrollY); + var cleintRectLeft = (viewportOffset.left + window.scrollX); + var cleintRectCentreY = cleintRectTop + (targetElement.clientHeight / 2); + var cleintRectCentreX = cleintRectLeft + (targetElement.clientWidth / 2); + + var arscale = Math.min(scaledx / currentWidth, scaledy / currentHeight); + var arscaledx = currentWidth * arscale; + var arscaledy = currentHeight * arscale; + + var arRectTop = cleintRectCentreY - (arscaledy / 2); + var arRectLeft = cleintRectCentreX - (arscaledx / 2); + var arRectWidth = arscaledx; + var arRectHeight = arscaledy; + + arPreviewRect.style.top = arRectTop + 'px'; + arPreviewRect.style.left = arRectLeft + 'px'; + arPreviewRect.style.width = arRectWidth + 'px'; + arPreviewRect.style.height = arRectHeight + 'px'; + + clearTimeout(arFrameTimeout); + arFrameTimeout = setTimeout(function() { + arPreviewRect.style.display = 'none'; + }, 2000); + + arPreviewRect.style.display = 'block'; + + } + +} + + +onAfterUiUpdate(function() { + var arPreviewRect = gradioApp().querySelector('#imageARPreview'); + if (arPreviewRect) { + arPreviewRect.style.display = 'none'; + } + var tabImg2img = gradioApp().querySelector("#tab_img2img"); + if (tabImg2img) { + var inImg2img = tabImg2img.style.display == "block"; + if (inImg2img) { + let inputs = gradioApp().querySelectorAll('input'); + inputs.forEach(function(e) { + var is_width = e.parentElement.id == "img2img_width"; + var is_height = e.parentElement.id == "img2img_height"; + + if ((is_width || is_height) && !e.classList.contains('scrollwatch')) { + e.addEventListener('input', function(e) { + dimensionChange(e, is_width, is_height); + }); + e.classList.add('scrollwatch'); + } + if (is_width) { + currentWidth = e.value * 1.0; + } + if (is_height) { + currentHeight = e.value * 1.0; + } + }); + } + } +}); diff --git a/javascript/contextMenus.js b/javascript/contextMenus.js new file mode 100644 index 0000000000000000000000000000000000000000..ccae242f2b6a731e89d8752814aae6b78e143482 --- /dev/null +++ b/javascript/contextMenus.js @@ -0,0 +1,176 @@ + +var contextMenuInit = function() { + let eventListenerApplied = false; + let menuSpecs = new Map(); + + const uid = function() { + return Date.now().toString(36) + Math.random().toString(36).substring(2); + }; + + function showContextMenu(event, element, menuEntries) { + let posx = event.clientX + document.body.scrollLeft + document.documentElement.scrollLeft; + let posy = event.clientY + document.body.scrollTop + document.documentElement.scrollTop; + + let oldMenu = gradioApp().querySelector('#context-menu'); + if (oldMenu) { + oldMenu.remove(); + } + + let baseStyle = window.getComputedStyle(uiCurrentTab); + + const contextMenu = document.createElement('nav'); + contextMenu.id = "context-menu"; + contextMenu.style.background = baseStyle.background; + contextMenu.style.color = baseStyle.color; + contextMenu.style.fontFamily = baseStyle.fontFamily; + contextMenu.style.top = posy + 'px'; + contextMenu.style.left = posx + 'px'; + + + + const contextMenuList = document.createElement('ul'); + contextMenuList.className = 'context-menu-items'; + contextMenu.append(contextMenuList); + + menuEntries.forEach(function(entry) { + let contextMenuEntry = document.createElement('a'); + contextMenuEntry.innerHTML = entry['name']; + contextMenuEntry.addEventListener("click", function() { + entry['func'](); + }); + contextMenuList.append(contextMenuEntry); + + }); + + gradioApp().appendChild(contextMenu); + + let menuWidth = contextMenu.offsetWidth + 4; + let menuHeight = contextMenu.offsetHeight + 4; + + let windowWidth = window.innerWidth; + let windowHeight = window.innerHeight; + + if ((windowWidth - posx) < menuWidth) { + contextMenu.style.left = windowWidth - menuWidth + "px"; + } + + if ((windowHeight - posy) < menuHeight) { + contextMenu.style.top = windowHeight - menuHeight + "px"; + } + + } + + function appendContextMenuOption(targetElementSelector, entryName, entryFunction) { + + var currentItems = menuSpecs.get(targetElementSelector); + + if (!currentItems) { + currentItems = []; + menuSpecs.set(targetElementSelector, currentItems); + } + let newItem = { + id: targetElementSelector + '_' + uid(), + name: entryName, + func: entryFunction, + isNew: true + }; + + currentItems.push(newItem); + return newItem['id']; + } + + function removeContextMenuOption(uid) { + menuSpecs.forEach(function(v) { + let index = -1; + v.forEach(function(e, ei) { + if (e['id'] == uid) { + index = ei; + } + }); + if (index >= 0) { + v.splice(index, 1); + } + }); + } + + function addContextMenuEventListener() { + if (eventListenerApplied) { + return; + } + gradioApp().addEventListener("click", function(e) { + if (!e.isTrusted) { + return; + } + + let oldMenu = gradioApp().querySelector('#context-menu'); + if (oldMenu) { + oldMenu.remove(); + } + }); + gradioApp().addEventListener("contextmenu", function(e) { + let oldMenu = gradioApp().querySelector('#context-menu'); + if (oldMenu) { + oldMenu.remove(); + } + menuSpecs.forEach(function(v, k) { + if (e.composedPath()[0].matches(k)) { + showContextMenu(e, e.composedPath()[0], v); + e.preventDefault(); + } + }); + }); + eventListenerApplied = true; + + } + + return [appendContextMenuOption, removeContextMenuOption, addContextMenuEventListener]; +}; + +var initResponse = contextMenuInit(); +var appendContextMenuOption = initResponse[0]; +var removeContextMenuOption = initResponse[1]; +var addContextMenuEventListener = initResponse[2]; + +(function() { + //Start example Context Menu Items + let generateOnRepeat = function(genbuttonid, interruptbuttonid) { + let genbutton = gradioApp().querySelector(genbuttonid); + let interruptbutton = gradioApp().querySelector(interruptbuttonid); + if (!interruptbutton.offsetParent) { + genbutton.click(); + } + clearInterval(window.generateOnRepeatInterval); + window.generateOnRepeatInterval = setInterval(function() { + if (!interruptbutton.offsetParent) { + genbutton.click(); + } + }, + 500); + }; + + let generateOnRepeat_txt2img = function() { + generateOnRepeat('#txt2img_generate', '#txt2img_interrupt'); + }; + + let generateOnRepeat_img2img = function() { + generateOnRepeat('#img2img_generate', '#img2img_interrupt'); + }; + + appendContextMenuOption('#txt2img_generate', 'Generate forever', generateOnRepeat_txt2img); + appendContextMenuOption('#txt2img_interrupt', 'Generate forever', generateOnRepeat_txt2img); + appendContextMenuOption('#img2img_generate', 'Generate forever', generateOnRepeat_img2img); + appendContextMenuOption('#img2img_interrupt', 'Generate forever', generateOnRepeat_img2img); + + let cancelGenerateForever = function() { + clearInterval(window.generateOnRepeatInterval); + }; + + appendContextMenuOption('#txt2img_interrupt', 'Cancel generate forever', cancelGenerateForever); + appendContextMenuOption('#txt2img_generate', 'Cancel generate forever', cancelGenerateForever); + appendContextMenuOption('#img2img_interrupt', 'Cancel generate forever', cancelGenerateForever); + appendContextMenuOption('#img2img_generate', 'Cancel generate forever', cancelGenerateForever); + +})(); +//End example Context Menu Items + +onAfterUiUpdate(addContextMenuEventListener); diff --git a/javascript/dragdrop.js b/javascript/dragdrop.js new file mode 100644 index 0000000000000000000000000000000000000000..d680daf52f28c8ace0a99706d60e5ea756fb258e --- /dev/null +++ b/javascript/dragdrop.js @@ -0,0 +1,130 @@ +// allows drag-dropping files into gradio image elements, and also pasting images from clipboard + +function isValidImageList(files) { + return files && files?.length === 1 && ['image/png', 'image/gif', 'image/jpeg'].includes(files[0].type); +} + +function dropReplaceImage(imgWrap, files) { + if (!isValidImageList(files)) { + return; + } + + const tmpFile = files[0]; + + imgWrap.querySelector('.modify-upload button + button, .touch-none + div button + button')?.click(); + const callback = () => { + const fileInput = imgWrap.querySelector('input[type="file"]'); + if (fileInput) { + if (files.length === 0) { + files = new DataTransfer(); + files.items.add(tmpFile); + fileInput.files = files.files; + } else { + fileInput.files = files; + } + fileInput.dispatchEvent(new Event('change')); + } + }; + + if (imgWrap.closest('#pnginfo_image')) { + // special treatment for PNG Info tab, wait for fetch request to finish + const oldFetch = window.fetch; + window.fetch = async(input, options) => { + const response = await oldFetch(input, options); + if ('api/predict/' === input) { + const content = await response.text(); + window.fetch = oldFetch; + window.requestAnimationFrame(() => callback()); + return new Response(content, { + status: response.status, + statusText: response.statusText, + headers: response.headers + }); + } + return response; + }; + } else { + window.requestAnimationFrame(() => callback()); + } +} + +function eventHasFiles(e) { + if (!e.dataTransfer || !e.dataTransfer.files) return false; + if (e.dataTransfer.files.length > 0) return true; + if (e.dataTransfer.items.length > 0 && e.dataTransfer.items[0].kind == "file") return true; + + return false; +} + +function dragDropTargetIsPrompt(target) { + if (target?.placeholder && target?.placeholder.indexOf("Prompt") >= 0) return true; + if (target?.parentNode?.parentNode?.className?.indexOf("prompt") > 0) return true; + return false; +} + +window.document.addEventListener('dragover', e => { + const target = e.composedPath()[0]; + if (!eventHasFiles(e)) return; + + var targetImage = target.closest('[data-testid="image"]'); + if (!dragDropTargetIsPrompt(target) && !targetImage) return; + + e.stopPropagation(); + e.preventDefault(); + e.dataTransfer.dropEffect = 'copy'; +}); + +window.document.addEventListener('drop', e => { + const target = e.composedPath()[0]; + if (!eventHasFiles(e)) return; + + if (dragDropTargetIsPrompt(target)) { + e.stopPropagation(); + e.preventDefault(); + + let prompt_target = get_tab_index('tabs') == 1 ? "img2img_prompt_image" : "txt2img_prompt_image"; + + const imgParent = gradioApp().getElementById(prompt_target); + const files = e.dataTransfer.files; + const fileInput = imgParent.querySelector('input[type="file"]'); + if (fileInput) { + fileInput.files = files; + fileInput.dispatchEvent(new Event('change')); + } + } + + var targetImage = target.closest('[data-testid="image"]'); + if (targetImage) { + e.stopPropagation(); + e.preventDefault(); + const files = e.dataTransfer.files; + dropReplaceImage(targetImage, files); + return; + } +}); + +window.addEventListener('paste', e => { + const files = e.clipboardData.files; + if (!isValidImageList(files)) { + return; + } + + const visibleImageFields = [...gradioApp().querySelectorAll('[data-testid="image"]')] + .filter(el => uiElementIsVisible(el)) + .sort((a, b) => uiElementInSight(b) - uiElementInSight(a)); + + + if (!visibleImageFields.length) { + return; + } + + const firstFreeImageField = visibleImageFields + .filter(el => !el.querySelector('img'))?.[0]; + + dropReplaceImage( + firstFreeImageField ? + firstFreeImageField : + visibleImageFields[visibleImageFields.length - 1] + , files + ); +}); diff --git a/javascript/edit-attention.js b/javascript/edit-attention.js new file mode 100644 index 0000000000000000000000000000000000000000..688c2f112d6161877c947d8d17428fac77aa1df6 --- /dev/null +++ b/javascript/edit-attention.js @@ -0,0 +1,148 @@ +function keyupEditAttention(event) { + let target = event.originalTarget || event.composedPath()[0]; + if (!target.matches("*:is([id*='_toprow'] [id*='_prompt'], .prompt) textarea")) return; + if (!(event.metaKey || event.ctrlKey)) return; + + let isPlus = event.key == "ArrowUp"; + let isMinus = event.key == "ArrowDown"; + if (!isPlus && !isMinus) return; + + let selectionStart = target.selectionStart; + let selectionEnd = target.selectionEnd; + let text = target.value; + + function selectCurrentParenthesisBlock(OPEN, CLOSE) { + if (selectionStart !== selectionEnd) return false; + + // Find opening parenthesis around current cursor + const before = text.substring(0, selectionStart); + let beforeParen = before.lastIndexOf(OPEN); + if (beforeParen == -1) return false; + + let beforeClosingParen = before.lastIndexOf(CLOSE); + if (beforeClosingParen != -1 && beforeClosingParen > beforeParen) return false; + + // Find closing parenthesis around current cursor + const after = text.substring(selectionStart); + let afterParen = after.indexOf(CLOSE); + if (afterParen == -1) return false; + + let afterOpeningParen = after.indexOf(OPEN); + if (afterOpeningParen != -1 && afterOpeningParen < afterParen) return false; + + // Set the selection to the text between the parenthesis + const parenContent = text.substring(beforeParen + 1, selectionStart + afterParen); + if (/.*:-?[\d.]+/s.test(parenContent)) { + const lastColon = parenContent.lastIndexOf(":"); + selectionStart = beforeParen + 1; + selectionEnd = selectionStart + lastColon; + } else { + selectionStart = beforeParen + 1; + selectionEnd = selectionStart + parenContent.length; + } + + target.setSelectionRange(selectionStart, selectionEnd); + return true; + } + + function selectCurrentWord() { + if (selectionStart !== selectionEnd) return false; + const whitespace_delimiters = {"Tab": "\t", "Carriage Return": "\r", "Line Feed": "\n"}; + let delimiters = opts.keyedit_delimiters; + + for (let i of opts.keyedit_delimiters_whitespace) { + delimiters += whitespace_delimiters[i]; + } + + // seek backward to find beginning + while (!delimiters.includes(text[selectionStart - 1]) && selectionStart > 0) { + selectionStart--; + } + + // seek forward to find end + while (!delimiters.includes(text[selectionEnd]) && selectionEnd < text.length) { + selectionEnd++; + } + + target.setSelectionRange(selectionStart, selectionEnd); + return true; + } + + // If the user hasn't selected anything, let's select their current parenthesis block or word + if (!selectCurrentParenthesisBlock('<', '>') && !selectCurrentParenthesisBlock('(', ')') && !selectCurrentParenthesisBlock('[', ']')) { + selectCurrentWord(); + } + + event.preventDefault(); + + var closeCharacter = ')'; + var delta = opts.keyedit_precision_attention; + var start = selectionStart > 0 ? text[selectionStart - 1] : ""; + var end = text[selectionEnd]; + + if (start == '<') { + closeCharacter = '>'; + delta = opts.keyedit_precision_extra; + } else if (start == '(' && end == ')' || start == '[' && end == ']') { // convert old-style (((emphasis))) + let numParen = 0; + + while (text[selectionStart - numParen - 1] == start && text[selectionEnd + numParen] == end) { + numParen++; + } + + if (start == "[") { + weight = (1 / 1.1) ** numParen; + } else { + weight = 1.1 ** numParen; + } + + weight = Math.round(weight / opts.keyedit_precision_attention) * opts.keyedit_precision_attention; + + text = text.slice(0, selectionStart - numParen) + "(" + text.slice(selectionStart, selectionEnd) + ":" + weight + ")" + text.slice(selectionEnd + numParen); + selectionStart -= numParen - 1; + selectionEnd -= numParen - 1; + } else if (start != '(') { + // do not include spaces at the end + while (selectionEnd > selectionStart && text[selectionEnd - 1] == ' ') { + selectionEnd--; + } + + if (selectionStart == selectionEnd) { + return; + } + + text = text.slice(0, selectionStart) + "(" + text.slice(selectionStart, selectionEnd) + ":1.0)" + text.slice(selectionEnd); + + selectionStart++; + selectionEnd++; + } + + if (text[selectionEnd] != ':') return; + var weightLength = text.slice(selectionEnd + 1).indexOf(closeCharacter) + 1; + var weight = parseFloat(text.slice(selectionEnd + 1, selectionEnd + weightLength)); + if (isNaN(weight)) return; + + weight += isPlus ? delta : -delta; + weight = parseFloat(weight.toPrecision(12)); + if (Number.isInteger(weight)) weight += ".0"; + + if (closeCharacter == ')' && weight == 1) { + var endParenPos = text.substring(selectionEnd).indexOf(')'); + text = text.slice(0, selectionStart - 1) + text.slice(selectionStart, selectionEnd) + text.slice(selectionEnd + endParenPos + 1); + selectionStart--; + selectionEnd--; + } else { + text = text.slice(0, selectionEnd + 1) + weight + text.slice(selectionEnd + weightLength); + } + + target.focus(); + target.value = text; + target.selectionStart = selectionStart; + target.selectionEnd = selectionEnd; + + updateInput(target); +} + +addEventListener('keydown', (event) => { + keyupEditAttention(event); +}); diff --git a/javascript/edit-order.js b/javascript/edit-order.js new file mode 100644 index 0000000000000000000000000000000000000000..ed4ef9ac399a6d0bd83435958dc4d46837760c6a --- /dev/null +++ b/javascript/edit-order.js @@ -0,0 +1,41 @@ +/* alt+left/right moves text in prompt */ + +function keyupEditOrder(event) { + if (!opts.keyedit_move) return; + + let target = event.originalTarget || event.composedPath()[0]; + if (!target.matches("*:is([id*='_toprow'] [id*='_prompt'], .prompt) textarea")) return; + if (!event.altKey) return; + + let isLeft = event.key == "ArrowLeft"; + let isRight = event.key == "ArrowRight"; + if (!isLeft && !isRight) return; + event.preventDefault(); + + let selectionStart = target.selectionStart; + let selectionEnd = target.selectionEnd; + let text = target.value; + let items = text.split(","); + let indexStart = (text.slice(0, selectionStart).match(/,/g) || []).length; + let indexEnd = (text.slice(0, selectionEnd).match(/,/g) || []).length; + let range = indexEnd - indexStart + 1; + + if (isLeft && indexStart > 0) { + items.splice(indexStart - 1, 0, ...items.splice(indexStart, range)); + target.value = items.join(); + target.selectionStart = items.slice(0, indexStart - 1).join().length + (indexStart == 1 ? 0 : 1); + target.selectionEnd = items.slice(0, indexEnd).join().length; + } else if (isRight && indexEnd < items.length - 1) { + items.splice(indexStart + 1, 0, ...items.splice(indexStart, range)); + target.value = items.join(); + target.selectionStart = items.slice(0, indexStart + 1).join().length + 1; + target.selectionEnd = items.slice(0, indexEnd + 2).join().length; + } + + event.preventDefault(); + updateInput(target); +} + +addEventListener('keydown', (event) => { + keyupEditOrder(event); +}); diff --git a/javascript/extensions.js b/javascript/extensions.js new file mode 100644 index 0000000000000000000000000000000000000000..312131b76ebc2eea200698b81d024d98e8af9ea4 --- /dev/null +++ b/javascript/extensions.js @@ -0,0 +1,92 @@ + +function extensions_apply(_disabled_list, _update_list, disable_all) { + var disable = []; + var update = []; + + gradioApp().querySelectorAll('#extensions input[type="checkbox"]').forEach(function(x) { + if (x.name.startsWith("enable_") && !x.checked) { + disable.push(x.name.substring(7)); + } + + if (x.name.startsWith("update_") && x.checked) { + update.push(x.name.substring(7)); + } + }); + + restart_reload(); + + return [JSON.stringify(disable), JSON.stringify(update), disable_all]; +} + +function extensions_check() { + var disable = []; + + gradioApp().querySelectorAll('#extensions input[type="checkbox"]').forEach(function(x) { + if (x.name.startsWith("enable_") && !x.checked) { + disable.push(x.name.substring(7)); + } + }); + + gradioApp().querySelectorAll('#extensions .extension_status').forEach(function(x) { + x.innerHTML = "Loading..."; + }); + + + var id = randomId(); + requestProgress(id, gradioApp().getElementById('extensions_installed_html'), null, function() { + + }); + + return [id, JSON.stringify(disable)]; +} + +function install_extension_from_index(button, url) { + button.disabled = "disabled"; + button.value = "Installing..."; + + var textarea = gradioApp().querySelector('#extension_to_install textarea'); + textarea.value = url; + updateInput(textarea); + + gradioApp().querySelector('#install_extension_button').click(); +} + +function config_state_confirm_restore(_, config_state_name, config_restore_type) { + if (config_state_name == "Current") { + return [false, config_state_name, config_restore_type]; + } + let restored = ""; + if (config_restore_type == "extensions") { + restored = "all saved extension versions"; + } else if (config_restore_type == "webui") { + restored = "the webui version"; + } else { + restored = "the webui version and all saved extension versions"; + } + let confirmed = confirm("Are you sure you want to restore from this state?\nThis will reset " + restored + "."); + if (confirmed) { + restart_reload(); + gradioApp().querySelectorAll('#extensions .extension_status').forEach(function(x) { + x.innerHTML = "Loading..."; + }); + } + return [confirmed, config_state_name, config_restore_type]; +} + +function toggle_all_extensions(event) { + gradioApp().querySelectorAll('#extensions .extension_toggle').forEach(function(checkbox_el) { + checkbox_el.checked = event.target.checked; + }); +} + +function toggle_extension() { + let all_extensions_toggled = true; + for (const checkbox_el of gradioApp().querySelectorAll('#extensions .extension_toggle')) { + if (!checkbox_el.checked) { + all_extensions_toggled = false; + break; + } + } + + gradioApp().querySelector('#extensions .all_extensions_toggle').checked = all_extensions_toggled; +} diff --git a/javascript/extraNetworks.js b/javascript/extraNetworks.js new file mode 100644 index 0000000000000000000000000000000000000000..98a7abb745c2baf0f131ee881ac221396657c51d --- /dev/null +++ b/javascript/extraNetworks.js @@ -0,0 +1,400 @@ +function toggleCss(key, css, enable) { + var style = document.getElementById(key); + if (enable && !style) { + style = document.createElement('style'); + style.id = key; + style.type = 'text/css'; + document.head.appendChild(style); + } + if (style && !enable) { + document.head.removeChild(style); + } + if (style) { + style.innerHTML == ''; + style.appendChild(document.createTextNode(css)); + } +} + +function setupExtraNetworksForTab(tabname) { + gradioApp().querySelector('#' + tabname + '_extra_tabs').classList.add('extra-networks'); + + var tabs = gradioApp().querySelector('#' + tabname + '_extra_tabs > div'); + var searchDiv = gradioApp().getElementById(tabname + '_extra_search'); + var search = searchDiv.querySelector('textarea'); + var sort = gradioApp().getElementById(tabname + '_extra_sort'); + var sortOrder = gradioApp().getElementById(tabname + '_extra_sortorder'); + var refresh = gradioApp().getElementById(tabname + '_extra_refresh'); + var showDirsDiv = gradioApp().getElementById(tabname + '_extra_show_dirs'); + var showDirs = gradioApp().querySelector('#' + tabname + '_extra_show_dirs input'); + var promptContainer = gradioApp().querySelector('.prompt-container-compact#' + tabname + '_prompt_container'); + var negativePrompt = gradioApp().querySelector('#' + tabname + '_neg_prompt'); + + tabs.appendChild(searchDiv); + tabs.appendChild(sort); + tabs.appendChild(sortOrder); + tabs.appendChild(refresh); + tabs.appendChild(showDirsDiv); + + var applyFilter = function() { + var searchTerm = search.value.toLowerCase(); + + gradioApp().querySelectorAll('#' + tabname + '_extra_tabs div.card').forEach(function(elem) { + var searchOnly = elem.querySelector('.search_only'); + var text = elem.querySelector('.name').textContent.toLowerCase() + " " + elem.querySelector('.search_term').textContent.toLowerCase(); + + var visible = text.indexOf(searchTerm) != -1; + + if (searchOnly && searchTerm.length < 4) { + visible = false; + } + + elem.style.display = visible ? "" : "none"; + }); + + applySort(); + }; + + var applySort = function() { + var cards = gradioApp().querySelectorAll('#' + tabname + '_extra_tabs div.card'); + + var reverse = sortOrder.classList.contains("sortReverse"); + var sortKey = sort.querySelector("input").value.toLowerCase().replace("sort", "").replaceAll(" ", "_").replace(/_+$/, "").trim() || "name"; + sortKey = "sort" + sortKey.charAt(0).toUpperCase() + sortKey.slice(1); + var sortKeyStore = sortKey + "-" + (reverse ? "Descending" : "Ascending") + "-" + cards.length; + + if (sortKeyStore == sort.dataset.sortkey) { + return; + } + sort.dataset.sortkey = sortKeyStore; + + cards.forEach(function(card) { + card.originalParentElement = card.parentElement; + }); + var sortedCards = Array.from(cards); + sortedCards.sort(function(cardA, cardB) { + var a = cardA.dataset[sortKey]; + var b = cardB.dataset[sortKey]; + if (!isNaN(a) && !isNaN(b)) { + return parseInt(a) - parseInt(b); + } + + return (a < b ? -1 : (a > b ? 1 : 0)); + }); + if (reverse) { + sortedCards.reverse(); + } + cards.forEach(function(card) { + card.remove(); + }); + sortedCards.forEach(function(card) { + card.originalParentElement.appendChild(card); + }); + }; + + search.addEventListener("input", applyFilter); + sortOrder.addEventListener("click", function() { + sortOrder.classList.toggle("sortReverse"); + applySort(); + }); + applyFilter(); + + extraNetworksApplySort[tabname] = applySort; + extraNetworksApplyFilter[tabname] = applyFilter; + + var showDirsUpdate = function() { + var css = '#' + tabname + '_extra_tabs .extra-network-subdirs { display: none; }'; + toggleCss(tabname + '_extra_show_dirs_style', css, !showDirs.checked); + localSet('extra-networks-show-dirs', showDirs.checked ? 1 : 0); + }; + showDirs.checked = localGet('extra-networks-show-dirs', 1) == 1; + showDirs.addEventListener("change", showDirsUpdate); + showDirsUpdate(); +} + +function extraNetworksMovePromptToTab(tabname, id, showPrompt, showNegativePrompt) { + if (!gradioApp().querySelector('.toprow-compact-tools')) return; // only applicable for compact prompt layout + + var promptContainer = gradioApp().getElementById(tabname + '_prompt_container'); + var prompt = gradioApp().getElementById(tabname + '_prompt_row'); + var negPrompt = gradioApp().getElementById(tabname + '_neg_prompt_row'); + var elem = id ? gradioApp().getElementById(id) : null; + + if (showNegativePrompt && elem) { + elem.insertBefore(negPrompt, elem.firstChild); + } else { + promptContainer.insertBefore(negPrompt, promptContainer.firstChild); + } + + if (showPrompt && elem) { + elem.insertBefore(prompt, elem.firstChild); + } else { + promptContainer.insertBefore(prompt, promptContainer.firstChild); + } + + if (elem) { + elem.classList.toggle('extra-page-prompts-active', showNegativePrompt || showPrompt); + } +} + + +function extraNetworksUrelatedTabSelected(tabname) { // called from python when user selects an unrelated tab (generate) + extraNetworksMovePromptToTab(tabname, '', false, false); +} + +function extraNetworksTabSelected(tabname, id, showPrompt, showNegativePrompt) { // called from python when user selects an extra networks tab + extraNetworksMovePromptToTab(tabname, id, showPrompt, showNegativePrompt); + +} + +function applyExtraNetworkFilter(tabname) { + setTimeout(extraNetworksApplyFilter[tabname], 1); +} + +function applyExtraNetworkSort(tabname) { + setTimeout(extraNetworksApplySort[tabname], 1); +} + +var extraNetworksApplyFilter = {}; +var extraNetworksApplySort = {}; +var activePromptTextarea = {}; + +function setupExtraNetworks() { + setupExtraNetworksForTab('txt2img'); + setupExtraNetworksForTab('img2img'); + + function registerPrompt(tabname, id) { + var textarea = gradioApp().querySelector("#" + id + " > label > textarea"); + + if (!activePromptTextarea[tabname]) { + activePromptTextarea[tabname] = textarea; + } + + textarea.addEventListener("focus", function() { + activePromptTextarea[tabname] = textarea; + }); + } + + registerPrompt('txt2img', 'txt2img_prompt'); + registerPrompt('txt2img', 'txt2img_neg_prompt'); + registerPrompt('img2img', 'img2img_prompt'); + registerPrompt('img2img', 'img2img_neg_prompt'); +} + +onUiLoaded(setupExtraNetworks); + +var re_extranet = /<([^:^>]+:[^:]+):[\d.]+>(.*)/; +var re_extranet_g = /<([^:^>]+:[^:]+):[\d.]+>/g; + +function tryToRemoveExtraNetworkFromPrompt(textarea, text) { + var m = text.match(re_extranet); + var replaced = false; + var newTextareaText; + if (m) { + var extraTextBeforeNet = opts.extra_networks_add_text_separator; + var extraTextAfterNet = m[2]; + var partToSearch = m[1]; + var foundAtPosition = -1; + newTextareaText = textarea.value.replaceAll(re_extranet_g, function(found, net, pos) { + m = found.match(re_extranet); + if (m[1] == partToSearch) { + replaced = true; + foundAtPosition = pos; + return ""; + } + return found; + }); + + if (foundAtPosition >= 0) { + if (newTextareaText.substr(foundAtPosition, extraTextAfterNet.length) == extraTextAfterNet) { + newTextareaText = newTextareaText.substr(0, foundAtPosition) + newTextareaText.substr(foundAtPosition + extraTextAfterNet.length); + } + if (newTextareaText.substr(foundAtPosition - extraTextBeforeNet.length, extraTextBeforeNet.length) == extraTextBeforeNet) { + newTextareaText = newTextareaText.substr(0, foundAtPosition - extraTextBeforeNet.length) + newTextareaText.substr(foundAtPosition); + } + } + } else { + newTextareaText = textarea.value.replaceAll(new RegExp(text, "g"), function(found) { + if (found == text) { + replaced = true; + return ""; + } + return found; + }); + } + + if (replaced) { + textarea.value = newTextareaText; + return true; + } + + return false; +} + +function cardClicked(tabname, textToAdd, allowNegativePrompt) { + var textarea = allowNegativePrompt ? activePromptTextarea[tabname] : gradioApp().querySelector("#" + tabname + "_prompt > label > textarea"); + + if (!tryToRemoveExtraNetworkFromPrompt(textarea, textToAdd)) { + textarea.value = textarea.value + opts.extra_networks_add_text_separator + textToAdd; + } + + updateInput(textarea); +} + +function saveCardPreview(event, tabname, filename) { + var textarea = gradioApp().querySelector("#" + tabname + '_preview_filename > label > textarea'); + var button = gradioApp().getElementById(tabname + '_save_preview'); + + textarea.value = filename; + updateInput(textarea); + + button.click(); + + event.stopPropagation(); + event.preventDefault(); +} + +function extraNetworksSearchButton(tabs_id, event) { + var searchTextarea = gradioApp().querySelector("#" + tabs_id + ' > label > textarea'); + var button = event.target; + var text = button.classList.contains("search-all") ? "" : button.textContent.trim(); + + searchTextarea.value = text; + updateInput(searchTextarea); +} + +var globalPopup = null; +var globalPopupInner = null; + +function closePopup() { + if (!globalPopup) return; + globalPopup.style.display = "none"; +} + +function popup(contents) { + if (!globalPopup) { + globalPopup = document.createElement('div'); + globalPopup.classList.add('global-popup'); + + var close = document.createElement('div'); + close.classList.add('global-popup-close'); + close.addEventListener("click", closePopup); + close.title = "Close"; + globalPopup.appendChild(close); + + globalPopupInner = document.createElement('div'); + globalPopupInner.classList.add('global-popup-inner'); + globalPopup.appendChild(globalPopupInner); + + gradioApp().querySelector('.main').appendChild(globalPopup); + } + + globalPopupInner.innerHTML = ''; + globalPopupInner.appendChild(contents); + + globalPopup.style.display = "flex"; +} + +var storedPopupIds = {}; +function popupId(id) { + if (!storedPopupIds[id]) { + storedPopupIds[id] = gradioApp().getElementById(id); + } + + popup(storedPopupIds[id]); +} + +function extraNetworksShowMetadata(text) { + var elem = document.createElement('pre'); + elem.classList.add('popup-metadata'); + elem.textContent = text; + + popup(elem); +} + +function requestGet(url, data, handler, errorHandler) { + var xhr = new XMLHttpRequest(); + var args = Object.keys(data).map(function(k) { + return encodeURIComponent(k) + '=' + encodeURIComponent(data[k]); + }).join('&'); + xhr.open("GET", url + "?" + args, true); + + xhr.onreadystatechange = function() { + if (xhr.readyState === 4) { + if (xhr.status === 200) { + try { + var js = JSON.parse(xhr.responseText); + handler(js); + } catch (error) { + console.error(error); + errorHandler(); + } + } else { + errorHandler(); + } + } + }; + var js = JSON.stringify(data); + xhr.send(js); +} + +function extraNetworksRequestMetadata(event, extraPage, cardName) { + var showError = function() { + extraNetworksShowMetadata("there was an error getting metadata"); + }; + + requestGet("./sd_extra_networks/metadata", {page: extraPage, item: cardName}, function(data) { + if (data && data.metadata) { + extraNetworksShowMetadata(data.metadata); + } else { + showError(); + } + }, showError); + + event.stopPropagation(); +} + +var extraPageUserMetadataEditors = {}; + +function extraNetworksEditUserMetadata(event, tabname, extraPage, cardName) { + var id = tabname + '_' + extraPage + '_edit_user_metadata'; + + var editor = extraPageUserMetadataEditors[id]; + if (!editor) { + editor = {}; + editor.page = gradioApp().getElementById(id); + editor.nameTextarea = gradioApp().querySelector("#" + id + "_name" + ' textarea'); + editor.button = gradioApp().querySelector("#" + id + "_button"); + extraPageUserMetadataEditors[id] = editor; + } + + editor.nameTextarea.value = cardName; + updateInput(editor.nameTextarea); + + editor.button.click(); + + popup(editor.page); + + event.stopPropagation(); +} + +function extraNetworksRefreshSingleCard(page, tabname, name) { + requestGet("./sd_extra_networks/get-single-card", {page: page, tabname: tabname, name: name}, function(data) { + if (data && data.html) { + var card = gradioApp().querySelector(`#${tabname}_${page.replace(" ", "_")}_cards > .card[data-name="${name}"]`); + + var newDiv = document.createElement('DIV'); + newDiv.innerHTML = data.html; + var newCard = newDiv.firstElementChild; + + newCard.style.display = ''; + card.parentElement.insertBefore(newCard, card); + card.parentElement.removeChild(card); + } + }); +} + +window.addEventListener("keydown", function(event) { + if (event.key == "Escape") { + closePopup(); + } +}); diff --git a/javascript/generationParams.js b/javascript/generationParams.js new file mode 100644 index 0000000000000000000000000000000000000000..7c0fd221d63313ab063f545570eb0da780b9da3a --- /dev/null +++ b/javascript/generationParams.js @@ -0,0 +1,35 @@ +// attaches listeners to the txt2img and img2img galleries to update displayed generation param text when the image changes + +let txt2img_gallery, img2img_gallery, modal = undefined; +onAfterUiUpdate(function() { + if (!txt2img_gallery) { + txt2img_gallery = attachGalleryListeners("txt2img"); + } + if (!img2img_gallery) { + img2img_gallery = attachGalleryListeners("img2img"); + } + if (!modal) { + modal = gradioApp().getElementById('lightboxModal'); + modalObserver.observe(modal, {attributes: true, attributeFilter: ['style']}); + } +}); + +let modalObserver = new MutationObserver(function(mutations) { + mutations.forEach(function(mutationRecord) { + let selectedTab = gradioApp().querySelector('#tabs div button.selected')?.innerText; + if (mutationRecord.target.style.display === 'none' && (selectedTab === 'txt2img' || selectedTab === 'img2img')) { + gradioApp().getElementById(selectedTab + "_generation_info_button")?.click(); + } + }); +}); + +function attachGalleryListeners(tab_name) { + var gallery = gradioApp().querySelector('#' + tab_name + '_gallery'); + gallery?.addEventListener('click', () => gradioApp().getElementById(tab_name + "_generation_info_button").click()); + gallery?.addEventListener('keydown', (e) => { + if (e.keyCode == 37 || e.keyCode == 39) { // left or right arrow + gradioApp().getElementById(tab_name + "_generation_info_button").click(); + } + }); + return gallery; +} diff --git a/javascript/hints.js b/javascript/hints.js new file mode 100644 index 0000000000000000000000000000000000000000..6de9372e8ea8c9fb032351e241d0f9c265995290 --- /dev/null +++ b/javascript/hints.js @@ -0,0 +1,203 @@ +// mouseover tooltips for various UI elements + +var titles = { + "Sampling steps": "How many times to improve the generated image iteratively; higher values take longer; very low values can produce bad results", + "Sampling method": "Which algorithm to use to produce the image", + "GFPGAN": "Restore low quality faces using GFPGAN neural network", + "Euler a": "Euler Ancestral - very creative, each can get a completely different picture depending on step count, setting steps higher than 30-40 does not help", + "DDIM": "Denoising Diffusion Implicit Models - best at inpainting", + "UniPC": "Unified Predictor-Corrector Framework for Fast Sampling of Diffusion Models", + "DPM adaptive": "Ignores step count - uses a number of steps determined by the CFG and resolution", + + "\u{1F4D0}": "Auto detect size from img2img", + "Batch count": "How many batches of images to create (has no impact on generation performance or VRAM usage)", + "Batch size": "How many image to create in a single batch (increases generation performance at cost of higher VRAM usage)", + "CFG Scale": "Classifier Free Guidance Scale - how strongly the image should conform to prompt - lower values produce more creative results", + "Seed": "A value that determines the output of random number generator - if you create an image with same parameters and seed as another image, you'll get the same result", + "\u{1f3b2}\ufe0f": "Set seed to -1, which will cause a new random number to be used every time", + "\u267b\ufe0f": "Reuse seed from last generation, mostly useful if it was randomized", + "\u2199\ufe0f": "Read generation parameters from prompt or last generation if prompt is empty into user interface.", + "\u{1f4c2}": "Open images output directory", + "\u{1f4be}": "Save style", + "\u{1f5d1}\ufe0f": "Clear prompt", + "\u{1f4cb}": "Apply selected styles to current prompt", + "\u{1f4d2}": "Paste available values into the field", + "\u{1f3b4}": "Show/hide extra networks", + "\u{1f300}": "Restore progress", + + "Inpaint a part of image": "Draw a mask over an image, and the script will regenerate the masked area with content according to prompt", + "SD upscale": "Upscale image normally, split result into tiles, improve each tile using img2img, merge whole image back", + + "Just resize": "Resize image to target resolution. Unless height and width match, you will get incorrect aspect ratio.", + "Crop and resize": "Resize the image so that entirety of target resolution is filled with the image. Crop parts that stick out.", + "Resize and fill": "Resize the image so that entirety of image is inside target resolution. Fill empty space with image's colors.", + + "Mask blur": "How much to blur the mask before processing, in pixels.", + "Masked content": "What to put inside the masked area before processing it with Stable Diffusion.", + "fill": "fill it with colors of the image", + "original": "keep whatever was there originally", + "latent noise": "fill it with latent space noise", + "latent nothing": "fill it with latent space zeroes", + "Inpaint at full resolution": "Upscale masked region to target resolution, do inpainting, downscale back and paste into original image", + + "Denoising strength": "Determines how little respect the algorithm should have for image's content. At 0, nothing will change, and at 1 you'll get an unrelated image. With values below 1.0, processing will take less steps than the Sampling Steps slider specifies.", + + "Skip": "Stop processing current image and continue processing.", + "Interrupt": "Stop processing images and return any results accumulated so far.", + "Save": "Write image to a directory (default - log/images) and generation parameters into csv file.", + + "X values": "Separate values for X axis using commas.", + "Y values": "Separate values for Y axis using commas.", + + "None": "Do not do anything special", + "Prompt matrix": "Separate prompts into parts using vertical pipe character (|) and the script will create a picture for every combination of them (except for the first part, which will be present in all combinations)", + "X/Y/Z plot": "Create grid(s) where images will have different parameters. Use inputs below to specify which parameters will be shared by columns and rows", + "Custom code": "Run Python code. Advanced user only. Must run program with --allow-code for this to work", + + "Prompt S/R": "Separate a list of words with commas, and the first word will be used as a keyword: script will search for this word in the prompt, and replace it with others", + "Prompt order": "Separate a list of words with commas, and the script will make a variation of prompt with those words for their every possible order", + + "Tiling": "Produce an image that can be tiled.", + "Tile overlap": "For SD upscale, how much overlap in pixels should there be between tiles. Tiles overlap so that when they are merged back into one picture, there is no clearly visible seam.", + + "Variation seed": "Seed of a different picture to be mixed into the generation.", + "Variation strength": "How strong of a variation to produce. At 0, there will be no effect. At 1, you will get the complete picture with variation seed (except for ancestral samplers, where you will just get something).", + "Resize seed from height": "Make an attempt to produce a picture similar to what would have been produced with same seed at specified resolution", + "Resize seed from width": "Make an attempt to produce a picture similar to what would have been produced with same seed at specified resolution", + + "Interrogate": "Reconstruct prompt from existing image and put it into the prompt field.", + + "Images filename pattern": "Use tags like [seed] and [date] to define how filenames for images are chosen. Leave empty for default.", + "Directory name pattern": "Use tags like [seed] and [date] to define how subdirectories for images and grids are chosen. Leave empty for default.", + "Max prompt words": "Set the maximum number of words to be used in the [prompt_words] option; ATTENTION: If the words are too long, they may exceed the maximum length of the file path that the system can handle", + + "Loopback": "Performs img2img processing multiple times. Output images are used as input for the next loop.", + "Loops": "How many times to process an image. Each output is used as the input of the next loop. If set to 1, behavior will be as if this script were not used.", + "Final denoising strength": "The denoising strength for the final loop of each image in the batch.", + "Denoising strength curve": "The denoising curve controls the rate of denoising strength change each loop. Aggressive: Most of the change will happen towards the start of the loops. Linear: Change will be constant through all loops. Lazy: Most of the change will happen towards the end of the loops.", + + "Style 1": "Style to apply; styles have components for both positive and negative prompts and apply to both", + "Style 2": "Style to apply; styles have components for both positive and negative prompts and apply to both", + "Apply style": "Insert selected styles into prompt fields", + "Create style": "Save current prompts as a style. If you add the token {prompt} to the text, the style uses that as a placeholder for your prompt when you use the style in the future.", + + "Checkpoint name": "Loads weights from checkpoint before making images. You can either use hash or a part of filename (as seen in settings) for checkpoint name. Recommended to use with Y axis for less switching.", + "Inpainting conditioning mask strength": "Only applies to inpainting models. Determines how strongly to mask off the original image for inpainting and img2img. 1.0 means fully masked, which is the default behaviour. 0.0 means a fully unmasked conditioning. Lower values will help preserve the overall composition of the image, but will struggle with large changes.", + + "Eta noise seed delta": "If this values is non-zero, it will be added to seed and used to initialize RNG for noises when using samplers with Eta. You can use this to produce even more variation of images, or you can use this to match images of other software if you know what you are doing.", + + "Filename word regex": "This regular expression will be used extract words from filename, and they will be joined using the option below into label text used for training. Leave empty to keep filename text as it is.", + "Filename join string": "This string will be used to join split words into a single line if the option above is enabled.", + + "Quicksettings list": "List of setting names, separated by commas, for settings that should go to the quick access bar at the top, rather than the usual setting tab. See modules/shared.py for setting names. Requires restarting to apply.", + + "Weighted sum": "Result = A * (1 - M) + B * M", + "Add difference": "Result = A + (B - C) * M", + "No interpolation": "Result = A", + + "Initialization text": "If the number of tokens is more than the number of vectors, some may be skipped.\nLeave the textbox empty to start with zeroed out vectors", + "Learning rate": "How fast should training go. Low values will take longer to train, high values may fail to converge (not generate accurate results) and/or may break the embedding (This has happened if you see Loss: nan in the training info textbox. If this happens, you need to manually restore your embedding from an older not-broken backup).\n\nYou can set a single numeric value, or multiple learning rates using the syntax:\n\n rate_1:max_steps_1, rate_2:max_steps_2, ...\n\nEG: 0.005:100, 1e-3:1000, 1e-5\n\nWill train with rate of 0.005 for first 100 steps, then 1e-3 until 1000 steps, then 1e-5 for all remaining steps.", + + "Clip skip": "Early stopping parameter for CLIP model; 1 is stop at last layer as usual, 2 is stop at penultimate layer, etc.", + + "Approx NN": "Cheap neural network approximation. Very fast compared to VAE, but produces pictures with 4 times smaller horizontal/vertical resolution and lower quality.", + "Approx cheap": "Very cheap approximation. Very fast compared to VAE, but produces pictures with 8 times smaller horizontal/vertical resolution and extremely low quality.", + + "Hires. fix": "Use a two step process to partially create an image at smaller resolution, upscale, and then improve details in it without changing composition", + "Hires steps": "Number of sampling steps for upscaled picture. If 0, uses same as for original.", + "Upscale by": "Adjusts the size of the image by multiplying the original width and height by the selected value. Ignored if either Resize width to or Resize height to are non-zero.", + "Resize width to": "Resizes image to this width. If 0, width is inferred from either of two nearby sliders.", + "Resize height to": "Resizes image to this height. If 0, height is inferred from either of two nearby sliders.", + "Discard weights with matching name": "Regular expression; if weights's name matches it, the weights is not written to the resulting checkpoint. Use ^model_ema to discard EMA weights.", + "Extra networks tab order": "Comma-separated list of tab names; tabs listed here will appear in the extra networks UI first and in order listed.", + "Negative Guidance minimum sigma": "Skip negative prompt for steps where image is already mostly denoised; the higher this value, the more skips there will be; provides increased performance in exchange for minor quality reduction." +}; + +function updateTooltip(element) { + if (element.title) return; // already has a title + + let text = element.textContent; + let tooltip = localization[titles[text]] || titles[text]; + + if (!tooltip) { + let value = element.value; + if (value) tooltip = localization[titles[value]] || titles[value]; + } + + if (!tooltip) { + // Gradio dropdown options have `data-value`. + let dataValue = element.dataset.value; + if (dataValue) tooltip = localization[titles[dataValue]] || titles[dataValue]; + } + + if (!tooltip) { + for (const c of element.classList) { + if (c in titles) { + tooltip = localization[titles[c]] || titles[c]; + break; + } + } + } + + if (tooltip) { + element.title = tooltip; + } +} + +// Nodes to check for adding tooltips. +const tooltipCheckNodes = new Set(); +// Timer for debouncing tooltip check. +let tooltipCheckTimer = null; + +function processTooltipCheckNodes() { + for (const node of tooltipCheckNodes) { + updateTooltip(node); + } + tooltipCheckNodes.clear(); +} + +onUiUpdate(function(mutationRecords) { + for (const record of mutationRecords) { + if (record.type === "childList" && record.target.classList.contains("options")) { + // This smells like a Gradio dropdown menu having changed, + // so let's enqueue an update for the input element that shows the current value. + let wrap = record.target.parentNode; + let input = wrap?.querySelector("input"); + if (input) { + input.title = ""; // So we'll even have a chance to update it. + tooltipCheckNodes.add(input); + } + } + for (const node of record.addedNodes) { + if (node.nodeType === Node.ELEMENT_NODE && !node.classList.contains("hide")) { + if (!node.title) { + if ( + node.tagName === "SPAN" || + node.tagName === "BUTTON" || + node.tagName === "P" || + node.tagName === "INPUT" || + (node.tagName === "LI" && node.classList.contains("item")) // Gradio dropdown item + ) { + tooltipCheckNodes.add(node); + } + } + node.querySelectorAll('span, button, p').forEach(n => tooltipCheckNodes.add(n)); + } + } + } + if (tooltipCheckNodes.size) { + clearTimeout(tooltipCheckTimer); + tooltipCheckTimer = setTimeout(processTooltipCheckNodes, 1000); + } +}); + +onUiLoaded(function() { + for (var comp of window.gradio_config.components) { + if (comp.props.webui_tooltip && comp.props.elem_id) { + var elem = gradioApp().getElementById(comp.props.elem_id); + if (elem) { + elem.title = comp.props.webui_tooltip; + } + } + } +}); diff --git a/javascript/hires_fix.js b/javascript/hires_fix.js new file mode 100644 index 0000000000000000000000000000000000000000..0d04ab3b424338634af3e71a2f9d8796a5f00224 --- /dev/null +++ b/javascript/hires_fix.js @@ -0,0 +1,18 @@ + +function onCalcResolutionHires(enable, width, height, hr_scale, hr_resize_x, hr_resize_y) { + function setInactive(elem, inactive) { + elem.classList.toggle('inactive', !!inactive); + } + + var hrUpscaleBy = gradioApp().getElementById('txt2img_hr_scale'); + var hrResizeX = gradioApp().getElementById('txt2img_hr_resize_x'); + var hrResizeY = gradioApp().getElementById('txt2img_hr_resize_y'); + + gradioApp().getElementById('txt2img_hires_fix_row2').style.display = opts.use_old_hires_fix_width_height ? "none" : ""; + + setInactive(hrUpscaleBy, opts.use_old_hires_fix_width_height || hr_resize_x > 0 || hr_resize_y > 0); + setInactive(hrResizeX, opts.use_old_hires_fix_width_height || hr_resize_x == 0); + setInactive(hrResizeY, opts.use_old_hires_fix_width_height || hr_resize_y == 0); + + return [enable, width, height, hr_scale, hr_resize_x, hr_resize_y]; +} diff --git a/javascript/imageMaskFix.js b/javascript/imageMaskFix.js new file mode 100644 index 0000000000000000000000000000000000000000..900c56f32fdf7128f0433621df25a0fbd14c4e42 --- /dev/null +++ b/javascript/imageMaskFix.js @@ -0,0 +1,43 @@ +/** + * temporary fix for https://github.com/AUTOMATIC1111/stable-diffusion-webui/issues/668 + * @see https://github.com/gradio-app/gradio/issues/1721 + */ +function imageMaskResize() { + const canvases = gradioApp().querySelectorAll('#img2maskimg .touch-none canvas'); + if (!canvases.length) { + window.removeEventListener('resize', imageMaskResize); + return; + } + + const wrapper = canvases[0].closest('.touch-none'); + const previewImage = wrapper.previousElementSibling; + + if (!previewImage.complete) { + previewImage.addEventListener('load', imageMaskResize); + return; + } + + const w = previewImage.width; + const h = previewImage.height; + const nw = previewImage.naturalWidth; + const nh = previewImage.naturalHeight; + const portrait = nh > nw; + + const wW = Math.min(w, portrait ? h / nh * nw : w / nw * nw); + const wH = Math.min(h, portrait ? h / nh * nh : w / nw * nh); + + wrapper.style.width = `${wW}px`; + wrapper.style.height = `${wH}px`; + wrapper.style.left = `0px`; + wrapper.style.top = `0px`; + + canvases.forEach(c => { + c.style.width = c.style.height = ''; + c.style.maxWidth = '100%'; + c.style.maxHeight = '100%'; + c.style.objectFit = 'contain'; + }); +} + +onAfterUiUpdate(imageMaskResize); +window.addEventListener('resize', imageMaskResize); diff --git a/javascript/imageviewer.js b/javascript/imageviewer.js new file mode 100644 index 0000000000000000000000000000000000000000..625c5d148df27f33de315b9db0a446176b7ab8cb --- /dev/null +++ b/javascript/imageviewer.js @@ -0,0 +1,262 @@ +// A full size 'lightbox' preview modal shown when left clicking on gallery previews +function closeModal() { + gradioApp().getElementById("lightboxModal").style.display = "none"; +} + +function showModal(event) { + const source = event.target || event.srcElement; + const modalImage = gradioApp().getElementById("modalImage"); + const lb = gradioApp().getElementById("lightboxModal"); + modalImage.src = source.src; + if (modalImage.style.display === 'none') { + lb.style.setProperty('background-image', 'url(' + source.src + ')'); + } + lb.style.display = "flex"; + lb.focus(); + + const tabTxt2Img = gradioApp().getElementById("tab_txt2img"); + const tabImg2Img = gradioApp().getElementById("tab_img2img"); + // show the save button in modal only on txt2img or img2img tabs + if (tabTxt2Img.style.display != "none" || tabImg2Img.style.display != "none") { + gradioApp().getElementById("modal_save").style.display = "inline"; + } else { + gradioApp().getElementById("modal_save").style.display = "none"; + } + event.stopPropagation(); +} + +function negmod(n, m) { + return ((n % m) + m) % m; +} + +function updateOnBackgroundChange() { + const modalImage = gradioApp().getElementById("modalImage"); + if (modalImage && modalImage.offsetParent) { + let currentButton = selected_gallery_button(); + let preview = gradioApp().querySelectorAll('.livePreview > img'); + if (opts.js_live_preview_in_modal_lightbox && preview.length > 0) { + // show preview image if available + modalImage.src = preview[preview.length - 1].src; + } else if (currentButton?.children?.length > 0 && modalImage.src != currentButton.children[0].src) { + modalImage.src = currentButton.children[0].src; + if (modalImage.style.display === 'none') { + const modal = gradioApp().getElementById("lightboxModal"); + modal.style.setProperty('background-image', `url(${modalImage.src})`); + } + } + } +} + +function modalImageSwitch(offset) { + var galleryButtons = all_gallery_buttons(); + + if (galleryButtons.length > 1) { + var currentButton = selected_gallery_button(); + + var result = -1; + galleryButtons.forEach(function(v, i) { + if (v == currentButton) { + result = i; + } + }); + + if (result != -1) { + var nextButton = galleryButtons[negmod((result + offset), galleryButtons.length)]; + nextButton.click(); + const modalImage = gradioApp().getElementById("modalImage"); + const modal = gradioApp().getElementById("lightboxModal"); + modalImage.src = nextButton.children[0].src; + if (modalImage.style.display === 'none') { + modal.style.setProperty('background-image', `url(${modalImage.src})`); + } + setTimeout(function() { + modal.focus(); + }, 10); + } + } +} + +function saveImage() { + const tabTxt2Img = gradioApp().getElementById("tab_txt2img"); + const tabImg2Img = gradioApp().getElementById("tab_img2img"); + const saveTxt2Img = "save_txt2img"; + const saveImg2Img = "save_img2img"; + if (tabTxt2Img.style.display != "none") { + gradioApp().getElementById(saveTxt2Img).click(); + } else if (tabImg2Img.style.display != "none") { + gradioApp().getElementById(saveImg2Img).click(); + } else { + console.error("missing implementation for saving modal of this type"); + } +} + +function modalSaveImage(event) { + saveImage(); + event.stopPropagation(); +} + +function modalNextImage(event) { + modalImageSwitch(1); + event.stopPropagation(); +} + +function modalPrevImage(event) { + modalImageSwitch(-1); + event.stopPropagation(); +} + +function modalKeyHandler(event) { + switch (event.key) { + case "s": + saveImage(); + break; + case "ArrowLeft": + modalPrevImage(event); + break; + case "ArrowRight": + modalNextImage(event); + break; + case "Escape": + closeModal(); + break; + } +} + +function setupImageForLightbox(e) { + if (e.dataset.modded) { + return; + } + + e.dataset.modded = true; + e.style.cursor = 'pointer'; + e.style.userSelect = 'none'; + + var isFirefox = navigator.userAgent.toLowerCase().indexOf('firefox') > -1; + + // For Firefox, listening on click first switched to next image then shows the lightbox. + // If you know how to fix this without switching to mousedown event, please. + // For other browsers the event is click to make it possiblr to drag picture. + var event = isFirefox ? 'mousedown' : 'click'; + + e.addEventListener(event, function(evt) { + if (evt.button == 1) { + open(evt.target.src); + evt.preventDefault(); + return; + } + if (!opts.js_modal_lightbox || evt.button != 0) return; + + modalZoomSet(gradioApp().getElementById('modalImage'), opts.js_modal_lightbox_initially_zoomed); + evt.preventDefault(); + showModal(evt); + }, true); + +} + +function modalZoomSet(modalImage, enable) { + if (modalImage) modalImage.classList.toggle('modalImageFullscreen', !!enable); +} + +function modalZoomToggle(event) { + var modalImage = gradioApp().getElementById("modalImage"); + modalZoomSet(modalImage, !modalImage.classList.contains('modalImageFullscreen')); + event.stopPropagation(); +} + +function modalTileImageToggle(event) { + const modalImage = gradioApp().getElementById("modalImage"); + const modal = gradioApp().getElementById("lightboxModal"); + const isTiling = modalImage.style.display === 'none'; + if (isTiling) { + modalImage.style.display = 'block'; + modal.style.setProperty('background-image', 'none'); + } else { + modalImage.style.display = 'none'; + modal.style.setProperty('background-image', `url(${modalImage.src})`); + } + + event.stopPropagation(); +} + +onAfterUiUpdate(function() { + var fullImg_preview = gradioApp().querySelectorAll('.gradio-gallery > div > img'); + if (fullImg_preview != null) { + fullImg_preview.forEach(setupImageForLightbox); + } + updateOnBackgroundChange(); +}); + +document.addEventListener("DOMContentLoaded", function() { + //const modalFragment = document.createDocumentFragment(); + const modal = document.createElement('div'); + modal.onclick = closeModal; + modal.id = "lightboxModal"; + modal.tabIndex = 0; + modal.addEventListener('keydown', modalKeyHandler, true); + + const modalControls = document.createElement('div'); + modalControls.className = 'modalControls gradio-container'; + modal.append(modalControls); + + const modalZoom = document.createElement('span'); + modalZoom.className = 'modalZoom cursor'; + modalZoom.innerHTML = '⤡'; + modalZoom.addEventListener('click', modalZoomToggle, true); + modalZoom.title = "Toggle zoomed view"; + modalControls.appendChild(modalZoom); + + const modalTileImage = document.createElement('span'); + modalTileImage.className = 'modalTileImage cursor'; + modalTileImage.innerHTML = '⊞'; + modalTileImage.addEventListener('click', modalTileImageToggle, true); + modalTileImage.title = "Preview tiling"; + modalControls.appendChild(modalTileImage); + + const modalSave = document.createElement("span"); + modalSave.className = "modalSave cursor"; + modalSave.id = "modal_save"; + modalSave.innerHTML = "🖫"; + modalSave.addEventListener("click", modalSaveImage, true); + modalSave.title = "Save Image(s)"; + modalControls.appendChild(modalSave); + + const modalClose = document.createElement('span'); + modalClose.className = 'modalClose cursor'; + modalClose.innerHTML = '×'; + modalClose.onclick = closeModal; + modalClose.title = "Close image viewer"; + modalControls.appendChild(modalClose); + + const modalImage = document.createElement('img'); + modalImage.id = 'modalImage'; + modalImage.onclick = closeModal; + modalImage.tabIndex = 0; + modalImage.addEventListener('keydown', modalKeyHandler, true); + modal.appendChild(modalImage); + + const modalPrev = document.createElement('a'); + modalPrev.className = 'modalPrev'; + modalPrev.innerHTML = '❮'; + modalPrev.tabIndex = 0; + modalPrev.addEventListener('click', modalPrevImage, true); + modalPrev.addEventListener('keydown', modalKeyHandler, true); + modal.appendChild(modalPrev); + + const modalNext = document.createElement('a'); + modalNext.className = 'modalNext'; + modalNext.innerHTML = '❯'; + modalNext.tabIndex = 0; + modalNext.addEventListener('click', modalNextImage, true); + modalNext.addEventListener('keydown', modalKeyHandler, true); + + modal.appendChild(modalNext); + + try { + gradioApp().appendChild(modal); + } catch (e) { + gradioApp().body.appendChild(modal); + } + + document.body.appendChild(modal); + +}); diff --git a/javascript/imageviewerGamepad.js b/javascript/imageviewerGamepad.js new file mode 100644 index 0000000000000000000000000000000000000000..a22c7e6e6435f677c7a86dbbae5da86af8fdc9eb --- /dev/null +++ b/javascript/imageviewerGamepad.js @@ -0,0 +1,63 @@ +let gamepads = []; + +window.addEventListener('gamepadconnected', (e) => { + const index = e.gamepad.index; + let isWaiting = false; + gamepads[index] = setInterval(async() => { + if (!opts.js_modal_lightbox_gamepad || isWaiting) return; + const gamepad = navigator.getGamepads()[index]; + const xValue = gamepad.axes[0]; + if (xValue <= -0.3) { + modalPrevImage(e); + isWaiting = true; + } else if (xValue >= 0.3) { + modalNextImage(e); + isWaiting = true; + } + if (isWaiting) { + await sleepUntil(() => { + const xValue = navigator.getGamepads()[index].axes[0]; + if (xValue < 0.3 && xValue > -0.3) { + return true; + } + }, opts.js_modal_lightbox_gamepad_repeat); + isWaiting = false; + } + }, 10); +}); + +window.addEventListener('gamepaddisconnected', (e) => { + clearInterval(gamepads[e.gamepad.index]); +}); + +/* +Primarily for vr controller type pointer devices. +I use the wheel event because there's currently no way to do it properly with web xr. + */ +let isScrolling = false; +window.addEventListener('wheel', (e) => { + if (!opts.js_modal_lightbox_gamepad || isScrolling) return; + isScrolling = true; + + if (e.deltaX <= -0.6) { + modalPrevImage(e); + } else if (e.deltaX >= 0.6) { + modalNextImage(e); + } + + setTimeout(() => { + isScrolling = false; + }, opts.js_modal_lightbox_gamepad_repeat); +}); + +function sleepUntil(f, timeout) { + return new Promise((resolve) => { + const timeStart = new Date(); + const wait = setInterval(function() { + if (f() || new Date() - timeStart > timeout) { + clearInterval(wait); + resolve(); + } + }, 20); + }); +} diff --git a/javascript/inputAccordion.js b/javascript/inputAccordion.js new file mode 100644 index 0000000000000000000000000000000000000000..7570309aa73fe051b41481db0da46dca94e57ab9 --- /dev/null +++ b/javascript/inputAccordion.js @@ -0,0 +1,68 @@ +function inputAccordionChecked(id, checked) { + var accordion = gradioApp().getElementById(id); + accordion.visibleCheckbox.checked = checked; + accordion.onVisibleCheckboxChange(); +} + +function setupAccordion(accordion) { + var labelWrap = accordion.querySelector('.label-wrap'); + var gradioCheckbox = gradioApp().querySelector('#' + accordion.id + "-checkbox input"); + var extra = gradioApp().querySelector('#' + accordion.id + "-extra"); + var span = labelWrap.querySelector('span'); + var linked = true; + + var isOpen = function() { + return labelWrap.classList.contains('open'); + }; + + var observerAccordionOpen = new MutationObserver(function(mutations) { + mutations.forEach(function(mutationRecord) { + accordion.classList.toggle('input-accordion-open', isOpen()); + + if (linked) { + accordion.visibleCheckbox.checked = isOpen(); + accordion.onVisibleCheckboxChange(); + } + }); + }); + observerAccordionOpen.observe(labelWrap, {attributes: true, attributeFilter: ['class']}); + + if (extra) { + labelWrap.insertBefore(extra, labelWrap.lastElementChild); + } + + accordion.onChecked = function(checked) { + if (isOpen() != checked) { + labelWrap.click(); + } + }; + + var visibleCheckbox = document.createElement('INPUT'); + visibleCheckbox.type = 'checkbox'; + visibleCheckbox.checked = isOpen(); + visibleCheckbox.id = accordion.id + "-visible-checkbox"; + visibleCheckbox.className = gradioCheckbox.className + " input-accordion-checkbox"; + span.insertBefore(visibleCheckbox, span.firstChild); + + accordion.visibleCheckbox = visibleCheckbox; + accordion.onVisibleCheckboxChange = function() { + if (linked && isOpen() != visibleCheckbox.checked) { + labelWrap.click(); + } + + gradioCheckbox.checked = visibleCheckbox.checked; + updateInput(gradioCheckbox); + }; + + visibleCheckbox.addEventListener('click', function(event) { + linked = false; + event.stopPropagation(); + }); + visibleCheckbox.addEventListener('input', accordion.onVisibleCheckboxChange); +} + +onUiLoaded(function() { + for (var accordion of gradioApp().querySelectorAll('.input-accordion')) { + setupAccordion(accordion); + } +}); diff --git a/javascript/localStorage.js b/javascript/localStorage.js new file mode 100644 index 0000000000000000000000000000000000000000..dc1a36c328799ea3df1843001d397aa638935952 --- /dev/null +++ b/javascript/localStorage.js @@ -0,0 +1,26 @@ + +function localSet(k, v) { + try { + localStorage.setItem(k, v); + } catch (e) { + console.warn(`Failed to save ${k} to localStorage: ${e}`); + } +} + +function localGet(k, def) { + try { + return localStorage.getItem(k); + } catch (e) { + console.warn(`Failed to load ${k} from localStorage: ${e}`); + } + + return def; +} + +function localRemove(k) { + try { + return localStorage.removeItem(k); + } catch (e) { + console.warn(`Failed to remove ${k} from localStorage: ${e}`); + } +} diff --git a/javascript/localization.js b/javascript/localization.js new file mode 100644 index 0000000000000000000000000000000000000000..8f00c18686057e3e12154f657170b014b13320a5 --- /dev/null +++ b/javascript/localization.js @@ -0,0 +1,205 @@ + +// localization = {} -- the dict with translations is created by the backend + +var ignore_ids_for_localization = { + setting_sd_hypernetwork: 'OPTION', + setting_sd_model_checkpoint: 'OPTION', + modelmerger_primary_model_name: 'OPTION', + modelmerger_secondary_model_name: 'OPTION', + modelmerger_tertiary_model_name: 'OPTION', + train_embedding: 'OPTION', + train_hypernetwork: 'OPTION', + txt2img_styles: 'OPTION', + img2img_styles: 'OPTION', + setting_random_artist_categories: 'OPTION', + setting_face_restoration_model: 'OPTION', + setting_realesrgan_enabled_models: 'OPTION', + extras_upscaler_1: 'OPTION', + extras_upscaler_2: 'OPTION', +}; + +var re_num = /^[.\d]+$/; +var re_emoji = /[\p{Extended_Pictographic}\u{1F3FB}-\u{1F3FF}\u{1F9B0}-\u{1F9B3}]/u; + +var original_lines = {}; +var translated_lines = {}; + +function hasLocalization() { + return window.localization && Object.keys(window.localization).length > 0; +} + +function textNodesUnder(el) { + var n, a = [], walk = document.createTreeWalker(el, NodeFilter.SHOW_TEXT, null, false); + while ((n = walk.nextNode())) a.push(n); + return a; +} + +function canBeTranslated(node, text) { + if (!text) return false; + if (!node.parentElement) return false; + + var parentType = node.parentElement.nodeName; + if (parentType == 'SCRIPT' || parentType == 'STYLE' || parentType == 'TEXTAREA') return false; + + if (parentType == 'OPTION' || parentType == 'SPAN') { + var pnode = node; + for (var level = 0; level < 4; level++) { + pnode = pnode.parentElement; + if (!pnode) break; + + if (ignore_ids_for_localization[pnode.id] == parentType) return false; + } + } + + if (re_num.test(text)) return false; + if (re_emoji.test(text)) return false; + return true; +} + +function getTranslation(text) { + if (!text) return undefined; + + if (translated_lines[text] === undefined) { + original_lines[text] = 1; + } + + var tl = localization[text]; + if (tl !== undefined) { + translated_lines[tl] = 1; + } + + return tl; +} + +function processTextNode(node) { + var text = node.textContent.trim(); + + if (!canBeTranslated(node, text)) return; + + var tl = getTranslation(text); + if (tl !== undefined) { + node.textContent = tl; + } +} + +function processNode(node) { + if (node.nodeType == 3) { + processTextNode(node); + return; + } + + if (node.title) { + let tl = getTranslation(node.title); + if (tl !== undefined) { + node.title = tl; + } + } + + if (node.placeholder) { + let tl = getTranslation(node.placeholder); + if (tl !== undefined) { + node.placeholder = tl; + } + } + + textNodesUnder(node).forEach(function(node) { + processTextNode(node); + }); +} + +function localizeWholePage() { + processNode(gradioApp()); + + function elem(comp) { + var elem_id = comp.props.elem_id ? comp.props.elem_id : "component-" + comp.id; + return gradioApp().getElementById(elem_id); + } + + for (var comp of window.gradio_config.components) { + if (comp.props.webui_tooltip) { + let e = elem(comp); + + let tl = e ? getTranslation(e.title) : undefined; + if (tl !== undefined) { + e.title = tl; + } + } + if (comp.props.placeholder) { + let e = elem(comp); + let textbox = e ? e.querySelector('[placeholder]') : null; + + let tl = textbox ? getTranslation(textbox.placeholder) : undefined; + if (tl !== undefined) { + textbox.placeholder = tl; + } + } + } +} + +function dumpTranslations() { + if (!hasLocalization()) { + // If we don't have any localization, + // we will not have traversed the app to find + // original_lines, so do that now. + localizeWholePage(); + } + var dumped = {}; + if (localization.rtl) { + dumped.rtl = true; + } + + for (const text in original_lines) { + if (dumped[text] !== undefined) continue; + dumped[text] = localization[text] || text; + } + + return dumped; +} + +function download_localization() { + var text = JSON.stringify(dumpTranslations(), null, 4); + + var element = document.createElement('a'); + element.setAttribute('href', 'data:text/plain;charset=utf-8,' + encodeURIComponent(text)); + element.setAttribute('download', "localization.json"); + element.style.display = 'none'; + document.body.appendChild(element); + + element.click(); + + document.body.removeChild(element); +} + +document.addEventListener("DOMContentLoaded", function() { + if (!hasLocalization()) { + return; + } + + onUiUpdate(function(m) { + m.forEach(function(mutation) { + mutation.addedNodes.forEach(function(node) { + processNode(node); + }); + }); + }); + + localizeWholePage(); + + if (localization.rtl) { // if the language is from right to left, + (new MutationObserver((mutations, observer) => { // wait for the style to load + mutations.forEach(mutation => { + mutation.addedNodes.forEach(node => { + if (node.tagName === 'STYLE') { + observer.disconnect(); + + for (const x of node.sheet.rules) { // find all rtl media rules + if (Array.from(x.media || []).includes('rtl')) { + x.media.appendMedium('all'); // enable them + } + } + } + }); + }); + })).observe(gradioApp(), {childList: true}); + } +}); diff --git a/javascript/notification.js b/javascript/notification.js new file mode 100644 index 0000000000000000000000000000000000000000..3ee972ae1661b62066171e01af69b84b854aeef7 --- /dev/null +++ b/javascript/notification.js @@ -0,0 +1,53 @@ +// Monitors the gallery and sends a browser notification when the leading image is new. + +let lastHeadImg = null; + +let notificationButton = null; + +onAfterUiUpdate(function() { + if (notificationButton == null) { + notificationButton = gradioApp().getElementById('request_notifications'); + + if (notificationButton != null) { + notificationButton.addEventListener('click', () => { + void Notification.requestPermission(); + }, true); + } + } + + const galleryPreviews = gradioApp().querySelectorAll('div[id^="tab_"] div[id$="_results"] .thumbnail-item > img'); + + if (galleryPreviews == null) return; + + const headImg = galleryPreviews[0]?.src; + + if (headImg == null || headImg == lastHeadImg) return; + + lastHeadImg = headImg; + + // play notification sound if available + const notificationAudio = gradioApp().querySelector('#audio_notification audio'); + if (notificationAudio) { + notificationAudio.volume = opts.notification_volume / 100.0 || 1.0; + notificationAudio.play(); + } + + if (document.hasFocus()) return; + + // Multiple copies of the images are in the DOM when one is selected. Dedup with a Set to get the real number generated. + const imgs = new Set(Array.from(galleryPreviews).map(img => img.src)); + + const notification = new Notification( + 'Stable Diffusion', + { + body: `Generated ${imgs.size > 1 ? imgs.size - opts.return_grid : 1} image${imgs.size > 1 ? 's' : ''}`, + icon: headImg, + image: headImg, + } + ); + + notification.onclick = function(_) { + parent.focus(); + this.close(); + }; +}); diff --git a/javascript/profilerVisualization.js b/javascript/profilerVisualization.js new file mode 100644 index 0000000000000000000000000000000000000000..9d8e5f42f327f93db42773ebf0b97ee1e9671806 --- /dev/null +++ b/javascript/profilerVisualization.js @@ -0,0 +1,153 @@ + +function createRow(table, cellName, items) { + var tr = document.createElement('tr'); + var res = []; + + items.forEach(function(x, i) { + if (x === undefined) { + res.push(null); + return; + } + + var td = document.createElement(cellName); + td.textContent = x; + tr.appendChild(td); + res.push(td); + + var colspan = 1; + for (var n = i + 1; n < items.length; n++) { + if (items[n] !== undefined) { + break; + } + + colspan += 1; + } + + if (colspan > 1) { + td.colSpan = colspan; + } + }); + + table.appendChild(tr); + + return res; +} + +function showProfile(path, cutoff = 0.05) { + requestGet(path, {}, function(data) { + var table = document.createElement('table'); + table.className = 'popup-table'; + + data.records['total'] = data.total; + var keys = Object.keys(data.records).sort(function(a, b) { + return data.records[b] - data.records[a]; + }); + var items = keys.map(function(x) { + return {key: x, parts: x.split('/'), time: data.records[x]}; + }); + var maxLength = items.reduce(function(a, b) { + return Math.max(a, b.parts.length); + }, 0); + + var cols = createRow(table, 'th', ['record', 'seconds']); + cols[0].colSpan = maxLength; + + function arraysEqual(a, b) { + return !(a < b || b < a); + } + + var addLevel = function(level, parent, hide) { + var matching = items.filter(function(x) { + return x.parts[level] && !x.parts[level + 1] && arraysEqual(x.parts.slice(0, level), parent); + }); + var sorted = matching.sort(function(a, b) { + return b.time - a.time; + }); + var othersTime = 0; + var othersList = []; + var othersRows = []; + var childrenRows = []; + sorted.forEach(function(x) { + var visible = x.time >= cutoff && !hide; + + var cells = []; + for (var i = 0; i < maxLength; i++) { + cells.push(x.parts[i]); + } + cells.push(x.time.toFixed(3)); + var cols = createRow(table, 'td', cells); + for (i = 0; i < level; i++) { + cols[i].className = 'muted'; + } + + var tr = cols[0].parentNode; + if (!visible) { + tr.classList.add("hidden"); + } + + if (x.time >= cutoff) { + childrenRows.push(tr); + } else { + othersTime += x.time; + othersList.push(x.parts[level]); + othersRows.push(tr); + } + + var children = addLevel(level + 1, parent.concat([x.parts[level]]), true); + if (children.length > 0) { + var cell = cols[level]; + var onclick = function() { + cell.classList.remove("link"); + cell.removeEventListener("click", onclick); + children.forEach(function(x) { + x.classList.remove("hidden"); + }); + }; + cell.classList.add("link"); + cell.addEventListener("click", onclick); + } + }); + + if (othersTime > 0) { + var cells = []; + for (var i = 0; i < maxLength; i++) { + cells.push(parent[i]); + } + cells.push(othersTime.toFixed(3)); + cells[level] = 'others'; + var cols = createRow(table, 'td', cells); + for (i = 0; i < level; i++) { + cols[i].className = 'muted'; + } + + var cell = cols[level]; + var tr = cell.parentNode; + var onclick = function() { + tr.classList.add("hidden"); + cell.classList.remove("link"); + cell.removeEventListener("click", onclick); + othersRows.forEach(function(x) { + x.classList.remove("hidden"); + }); + }; + + cell.title = othersList.join(", "); + cell.classList.add("link"); + cell.addEventListener("click", onclick); + + if (hide) { + tr.classList.add("hidden"); + } + + childrenRows.push(tr); + } + + return childrenRows; + }; + + addLevel(0, []); + + popup(table); + }); +} + diff --git a/javascript/progressbar.js b/javascript/progressbar.js new file mode 100644 index 0000000000000000000000000000000000000000..777614954b2d489df32813fb27911dd9bbcd9c9a --- /dev/null +++ b/javascript/progressbar.js @@ -0,0 +1,186 @@ +// code related to showing and updating progressbar shown as the image is being made + +function rememberGallerySelection() { + +} + +function getGallerySelectedIndex() { + +} + +function request(url, data, handler, errorHandler) { + var xhr = new XMLHttpRequest(); + xhr.open("POST", url, true); + xhr.setRequestHeader("Content-Type", "application/json"); + xhr.onreadystatechange = function() { + if (xhr.readyState === 4) { + if (xhr.status === 200) { + try { + var js = JSON.parse(xhr.responseText); + handler(js); + } catch (error) { + console.error(error); + errorHandler(); + } + } else { + errorHandler(); + } + } + }; + var js = JSON.stringify(data); + xhr.send(js); +} + +function pad2(x) { + return x < 10 ? '0' + x : x; +} + +function formatTime(secs) { + if (secs > 3600) { + return pad2(Math.floor(secs / 60 / 60)) + ":" + pad2(Math.floor(secs / 60) % 60) + ":" + pad2(Math.floor(secs) % 60); + } else if (secs > 60) { + return pad2(Math.floor(secs / 60)) + ":" + pad2(Math.floor(secs) % 60); + } else { + return Math.floor(secs) + "s"; + } +} + +function setTitle(progress) { + var title = 'Stable Diffusion'; + + if (opts.show_progress_in_title && progress) { + title = '[' + progress.trim() + '] ' + title; + } + + if (document.title != title) { + document.title = title; + } +} + + +function randomId() { + return "task(" + Math.random().toString(36).slice(2, 7) + Math.random().toString(36).slice(2, 7) + Math.random().toString(36).slice(2, 7) + ")"; +} + +// starts sending progress requests to "/internal/progress" uri, creating progressbar above progressbarContainer element and +// preview inside gallery element. Cleans up all created stuff when the task is over and calls atEnd. +// calls onProgress every time there is a progress update +function requestProgress(id_task, progressbarContainer, gallery, atEnd, onProgress, inactivityTimeout = 40) { + var dateStart = new Date(); + var wasEverActive = false; + var parentProgressbar = progressbarContainer.parentNode; + + var divProgress = document.createElement('div'); + divProgress.className = 'progressDiv'; + divProgress.style.display = opts.show_progressbar ? "block" : "none"; + var divInner = document.createElement('div'); + divInner.className = 'progress'; + + divProgress.appendChild(divInner); + parentProgressbar.insertBefore(divProgress, progressbarContainer); + + var livePreview = null; + + var removeProgressBar = function() { + if (!divProgress) return; + + setTitle(""); + parentProgressbar.removeChild(divProgress); + if (gallery && livePreview) gallery.removeChild(livePreview); + atEnd(); + + divProgress = null; + }; + + var funProgress = function(id_task) { + request("./internal/progress", {id_task: id_task, live_preview: false}, function(res) { + if (res.completed) { + removeProgressBar(); + return; + } + + let progressText = ""; + + divInner.style.width = ((res.progress || 0) * 100.0) + '%'; + divInner.style.background = res.progress ? "" : "transparent"; + + if (res.progress > 0) { + progressText = ((res.progress || 0) * 100.0).toFixed(0) + '%'; + } + + if (res.eta) { + progressText += " ETA: " + formatTime(res.eta); + } + + setTitle(progressText); + + if (res.textinfo && res.textinfo.indexOf("\n") == -1) { + progressText = res.textinfo + " " + progressText; + } + + divInner.textContent = progressText; + + var elapsedFromStart = (new Date() - dateStart) / 1000; + + if (res.active) wasEverActive = true; + + if (!res.active && wasEverActive) { + removeProgressBar(); + return; + } + + if (elapsedFromStart > inactivityTimeout && !res.queued && !res.active) { + removeProgressBar(); + return; + } + + if (onProgress) { + onProgress(res); + } + + setTimeout(() => { + funProgress(id_task, res.id_live_preview); + }, opts.live_preview_refresh_period || 500); + }, function() { + removeProgressBar(); + }); + }; + + var funLivePreview = function(id_task, id_live_preview) { + request("./internal/progress", {id_task: id_task, id_live_preview: id_live_preview}, function(res) { + if (!divProgress) { + return; + } + + if (res.live_preview && gallery) { + var img = new Image(); + img.onload = function() { + if (!livePreview) { + livePreview = document.createElement('div'); + livePreview.className = 'livePreview'; + gallery.insertBefore(livePreview, gallery.firstElementChild); + } + + livePreview.appendChild(img); + if (livePreview.childElementCount > 2) { + livePreview.removeChild(livePreview.firstElementChild); + } + }; + img.src = res.live_preview; + } + + setTimeout(() => { + funLivePreview(id_task, res.id_live_preview); + }, opts.live_preview_refresh_period || 500); + }, function() { + removeProgressBar(); + }); + }; + + funProgress(id_task, 0); + + if (gallery) { + funLivePreview(id_task, 0); + } + +} diff --git a/javascript/resizeHandle.js b/javascript/resizeHandle.js new file mode 100644 index 0000000000000000000000000000000000000000..8c5c5169210603ea229b96b746f9eb16ee4bfe56 --- /dev/null +++ b/javascript/resizeHandle.js @@ -0,0 +1,141 @@ +(function() { + const GRADIO_MIN_WIDTH = 320; + const GRID_TEMPLATE_COLUMNS = '1fr 16px 1fr'; + const PAD = 16; + const DEBOUNCE_TIME = 100; + + const R = { + tracking: false, + parent: null, + parentWidth: null, + leftCol: null, + leftColStartWidth: null, + screenX: null, + }; + + let resizeTimer; + let parents = []; + + function setLeftColGridTemplate(el, width) { + el.style.gridTemplateColumns = `${width}px 16px 1fr`; + } + + function displayResizeHandle(parent) { + if (window.innerWidth < GRADIO_MIN_WIDTH * 2 + PAD * 4) { + parent.style.display = 'flex'; + if (R.handle != null) { + R.handle.style.opacity = '0'; + } + return false; + } else { + parent.style.display = 'grid'; + if (R.handle != null) { + R.handle.style.opacity = '100'; + } + return true; + } + } + + function afterResize(parent) { + if (displayResizeHandle(parent) && parent.style.gridTemplateColumns != GRID_TEMPLATE_COLUMNS) { + const oldParentWidth = R.parentWidth; + const newParentWidth = parent.offsetWidth; + const widthL = parseInt(parent.style.gridTemplateColumns.split(' ')[0]); + + const ratio = newParentWidth / oldParentWidth; + + const newWidthL = Math.max(Math.floor(ratio * widthL), GRADIO_MIN_WIDTH); + setLeftColGridTemplate(parent, newWidthL); + + R.parentWidth = newParentWidth; + } + } + + function setup(parent) { + const leftCol = parent.firstElementChild; + const rightCol = parent.lastElementChild; + + parents.push(parent); + + parent.style.display = 'grid'; + parent.style.gap = '0'; + parent.style.gridTemplateColumns = GRID_TEMPLATE_COLUMNS; + + const resizeHandle = document.createElement('div'); + resizeHandle.classList.add('resize-handle'); + parent.insertBefore(resizeHandle, rightCol); + + resizeHandle.addEventListener('mousedown', (evt) => { + if (evt.button !== 0) return; + + evt.preventDefault(); + evt.stopPropagation(); + + document.body.classList.add('resizing'); + + R.tracking = true; + R.parent = parent; + R.parentWidth = parent.offsetWidth; + R.handle = resizeHandle; + R.leftCol = leftCol; + R.leftColStartWidth = leftCol.offsetWidth; + R.screenX = evt.screenX; + }); + + resizeHandle.addEventListener('dblclick', (evt) => { + evt.preventDefault(); + evt.stopPropagation(); + + parent.style.gridTemplateColumns = GRID_TEMPLATE_COLUMNS; + }); + + afterResize(parent); + } + + window.addEventListener('mousemove', (evt) => { + if (evt.button !== 0) return; + + if (R.tracking) { + evt.preventDefault(); + evt.stopPropagation(); + + const delta = R.screenX - evt.screenX; + const leftColWidth = Math.max(Math.min(R.leftColStartWidth - delta, R.parent.offsetWidth - GRADIO_MIN_WIDTH - PAD), GRADIO_MIN_WIDTH); + setLeftColGridTemplate(R.parent, leftColWidth); + } + }); + + window.addEventListener('mouseup', (evt) => { + if (evt.button !== 0) return; + + if (R.tracking) { + evt.preventDefault(); + evt.stopPropagation(); + + R.tracking = false; + + document.body.classList.remove('resizing'); + } + }); + + + window.addEventListener('resize', () => { + clearTimeout(resizeTimer); + + resizeTimer = setTimeout(function() { + for (const parent of parents) { + afterResize(parent); + } + }, DEBOUNCE_TIME); + }); + + setupResizeHandle = setup; +})(); + +onUiLoaded(function() { + for (var elem of gradioApp().querySelectorAll('.resize-handle-row')) { + if (!elem.querySelector('.resize-handle')) { + setupResizeHandle(elem); + } + } +}); diff --git a/javascript/settings.js b/javascript/settings.js new file mode 100644 index 0000000000000000000000000000000000000000..e6009290ab37472e49cc74c7fe37c714fa5f40db --- /dev/null +++ b/javascript/settings.js @@ -0,0 +1,71 @@ +let settingsExcludeTabsFromShowAll = { + settings_tab_defaults: 1, + settings_tab_sysinfo: 1, + settings_tab_actions: 1, + settings_tab_licenses: 1, +}; + +function settingsShowAllTabs() { + gradioApp().querySelectorAll('#settings > div').forEach(function(elem) { + if (settingsExcludeTabsFromShowAll[elem.id]) return; + + elem.style.display = "block"; + }); +} + +function settingsShowOneTab() { + gradioApp().querySelector('#settings_show_one_page').click(); +} + +onUiLoaded(function() { + var edit = gradioApp().querySelector('#settings_search'); + var editTextarea = gradioApp().querySelector('#settings_search > label > input'); + var buttonShowAllPages = gradioApp().getElementById('settings_show_all_pages'); + var settings_tabs = gradioApp().querySelector('#settings div'); + + onEdit('settingsSearch', editTextarea, 250, function() { + var searchText = (editTextarea.value || "").trim().toLowerCase(); + + gradioApp().querySelectorAll('#settings > div[id^=settings_] div[id^=column_settings_] > *').forEach(function(elem) { + var visible = elem.textContent.trim().toLowerCase().indexOf(searchText) != -1; + elem.style.display = visible ? "" : "none"; + }); + + if (searchText != "") { + settingsShowAllTabs(); + } else { + settingsShowOneTab(); + } + }); + + settings_tabs.insertBefore(edit, settings_tabs.firstChild); + settings_tabs.appendChild(buttonShowAllPages); + + + buttonShowAllPages.addEventListener("click", settingsShowAllTabs); +}); + + +onOptionsChanged(function() { + if (gradioApp().querySelector('#settings .settings-category')) return; + + var sectionMap = {}; + gradioApp().querySelectorAll('#settings > div > button').forEach(function(x) { + sectionMap[x.textContent.trim()] = x; + }); + + opts._categories.forEach(function(x) { + var section = x[0]; + var category = x[1]; + + var span = document.createElement('SPAN'); + span.textContent = category; + span.className = 'settings-category'; + + var sectionElem = sectionMap[section]; + if (!sectionElem) return; + + sectionElem.parentElement.insertBefore(span, sectionElem); + }); +}); + diff --git a/javascript/textualInversion.js b/javascript/textualInversion.js new file mode 100644 index 0000000000000000000000000000000000000000..20443fcca01bbba6712e40136c57dbcdb78ca945 --- /dev/null +++ b/javascript/textualInversion.js @@ -0,0 +1,17 @@ + + + +function start_training_textual_inversion() { + gradioApp().querySelector('#ti_error').innerHTML = ''; + + var id = randomId(); + requestProgress(id, gradioApp().getElementById('ti_output'), gradioApp().getElementById('ti_gallery'), function() {}, function(progress) { + gradioApp().getElementById('ti_progress').innerHTML = progress.textinfo; + }); + + var res = Array.from(arguments); + + res[0] = id; + + return res; +} diff --git a/javascript/token-counters.js b/javascript/token-counters.js new file mode 100644 index 0000000000000000000000000000000000000000..2ecc7d91010eb7977aae064da97c239414404f49 --- /dev/null +++ b/javascript/token-counters.js @@ -0,0 +1,75 @@ +let promptTokenCountUpdateFunctions = {}; + +function update_txt2img_tokens(...args) { + // Called from Gradio + update_token_counter("txt2img_token_button"); + update_token_counter("txt2img_negative_token_button"); + if (args.length == 2) { + return args[0]; + } + return args; +} + +function update_img2img_tokens(...args) { + // Called from Gradio + update_token_counter("img2img_token_button"); + update_token_counter("img2img_negative_token_button"); + if (args.length == 2) { + return args[0]; + } + return args; +} + +function update_token_counter(button_id) { + promptTokenCountUpdateFunctions[button_id]?.(); +} + + +function recalculatePromptTokens(name) { + promptTokenCountUpdateFunctions[name]?.(); +} + +function recalculate_prompts_txt2img() { + // Called from Gradio + recalculatePromptTokens('txt2img_prompt'); + recalculatePromptTokens('txt2img_neg_prompt'); + return Array.from(arguments); +} + +function recalculate_prompts_img2img() { + // Called from Gradio + recalculatePromptTokens('img2img_prompt'); + recalculatePromptTokens('img2img_neg_prompt'); + return Array.from(arguments); +} + +function setupTokenCounting(id, id_counter, id_button) { + var prompt = gradioApp().getElementById(id); + var counter = gradioApp().getElementById(id_counter); + var textarea = gradioApp().querySelector(`#${id} > label > textarea`); + + if (opts.disable_token_counters) { + counter.style.display = "none"; + return; + } + + if (counter.parentElement == prompt.parentElement) { + return; + } + + prompt.parentElement.insertBefore(counter, prompt); + prompt.parentElement.style.position = "relative"; + + var func = onEdit(id, textarea, 800, function() { + gradioApp().getElementById(id_button)?.click(); + }); + promptTokenCountUpdateFunctions[id] = func; + promptTokenCountUpdateFunctions[id_button] = func; +} + +function setupTokenCounters() { + setupTokenCounting('txt2img_prompt', 'txt2img_token_counter', 'txt2img_token_button'); + setupTokenCounting('txt2img_neg_prompt', 'txt2img_negative_token_counter', 'txt2img_negative_token_button'); + setupTokenCounting('img2img_prompt', 'img2img_token_counter', 'img2img_token_button'); + setupTokenCounting('img2img_neg_prompt', 'img2img_negative_token_counter', 'img2img_negative_token_button'); +} diff --git a/javascript/ui.js b/javascript/ui.js new file mode 100644 index 0000000000000000000000000000000000000000..18c9f891afc1746f8a919ea6989cea099f7e62a6 --- /dev/null +++ b/javascript/ui.js @@ -0,0 +1,411 @@ +// various functions for interaction with ui.py not large enough to warrant putting them in separate files + +function set_theme(theme) { + var gradioURL = window.location.href; + if (!gradioURL.includes('?__theme=')) { + window.location.replace(gradioURL + '?__theme=' + theme); + } +} + +function all_gallery_buttons() { + var allGalleryButtons = gradioApp().querySelectorAll('[style="display: block;"].tabitem div[id$=_gallery].gradio-gallery .thumbnails > .thumbnail-item.thumbnail-small'); + var visibleGalleryButtons = []; + allGalleryButtons.forEach(function(elem) { + if (elem.parentElement.offsetParent) { + visibleGalleryButtons.push(elem); + } + }); + return visibleGalleryButtons; +} + +function selected_gallery_button() { + return all_gallery_buttons().find(elem => elem.classList.contains('selected')) ?? null; +} + +function selected_gallery_index() { + return all_gallery_buttons().findIndex(elem => elem.classList.contains('selected')); +} + +function extract_image_from_gallery(gallery) { + if (gallery.length == 0) { + return [null]; + } + if (gallery.length == 1) { + return [gallery[0]]; + } + + var index = selected_gallery_index(); + + if (index < 0 || index >= gallery.length) { + // Use the first image in the gallery as the default + index = 0; + } + + return [gallery[index]]; +} + +window.args_to_array = Array.from; // Compatibility with e.g. extensions that may expect this to be around + +function switch_to_txt2img() { + gradioApp().querySelector('#tabs').querySelectorAll('button')[0].click(); + + return Array.from(arguments); +} + +function switch_to_img2img_tab(no) { + gradioApp().querySelector('#tabs').querySelectorAll('button')[1].click(); + gradioApp().getElementById('mode_img2img').querySelectorAll('button')[no].click(); +} +function switch_to_img2img() { + switch_to_img2img_tab(0); + return Array.from(arguments); +} + +function switch_to_sketch() { + switch_to_img2img_tab(1); + return Array.from(arguments); +} + +function switch_to_inpaint() { + switch_to_img2img_tab(2); + return Array.from(arguments); +} + +function switch_to_inpaint_sketch() { + switch_to_img2img_tab(3); + return Array.from(arguments); +} + +function switch_to_extras() { + gradioApp().querySelector('#tabs').querySelectorAll('button')[2].click(); + + return Array.from(arguments); +} + +function get_tab_index(tabId) { + let buttons = gradioApp().getElementById(tabId).querySelector('div').querySelectorAll('button'); + for (let i = 0; i < buttons.length; i++) { + if (buttons[i].classList.contains('selected')) { + return i; + } + } + return 0; +} + +function create_tab_index_args(tabId, args) { + var res = Array.from(args); + res[0] = get_tab_index(tabId); + return res; +} + +function get_img2img_tab_index() { + let res = Array.from(arguments); + res.splice(-2); + res[0] = get_tab_index('mode_img2img'); + return res; +} + +function create_submit_args(args) { + var res = Array.from(args); + + // As it is currently, txt2img and img2img send back the previous output args (txt2img_gallery, generation_info, html_info) whenever you generate a new image. + // This can lead to uploading a huge gallery of previously generated images, which leads to an unnecessary delay between submitting and beginning to generate. + // I don't know why gradio is sending outputs along with inputs, but we can prevent sending the image gallery here, which seems to be an issue for some. + // If gradio at some point stops sending outputs, this may break something + if (Array.isArray(res[res.length - 3])) { + res[res.length - 3] = null; + } + + return res; +} + +function showSubmitButtons(tabname, show) { + gradioApp().getElementById(tabname + '_interrupt').style.display = show ? "none" : "block"; + gradioApp().getElementById(tabname + '_skip').style.display = show ? "none" : "block"; +} + +function showRestoreProgressButton(tabname, show) { + var button = gradioApp().getElementById(tabname + "_restore_progress"); + if (!button) return; + + button.style.display = show ? "flex" : "none"; +} + +function submit() { + showSubmitButtons('txt2img', false); + + var id = randomId(); + localSet("txt2img_task_id", id); + + requestProgress(id, gradioApp().getElementById('txt2img_gallery_container'), gradioApp().getElementById('txt2img_gallery'), function() { + showSubmitButtons('txt2img', true); + localRemove("txt2img_task_id"); + showRestoreProgressButton('txt2img', false); + }); + + var res = create_submit_args(arguments); + + res[0] = id; + + return res; +} + +function submit_img2img() { + showSubmitButtons('img2img', false); + + var id = randomId(); + localSet("img2img_task_id", id); + + requestProgress(id, gradioApp().getElementById('img2img_gallery_container'), gradioApp().getElementById('img2img_gallery'), function() { + showSubmitButtons('img2img', true); + localRemove("img2img_task_id"); + showRestoreProgressButton('img2img', false); + }); + + var res = create_submit_args(arguments); + + res[0] = id; + res[1] = get_tab_index('mode_img2img'); + + return res; +} + +function submit_extras() { + showSubmitButtons('extras', false); + + var id = randomId(); + + requestProgress(id, gradioApp().getElementById('extras_gallery_container'), gradioApp().getElementById('extras_gallery'), function() { + showSubmitButtons('extras', true); + }); + + var res = create_submit_args(arguments); + + res[0] = id; + + console.log(res); + return res; +} + +function restoreProgressTxt2img() { + showRestoreProgressButton("txt2img", false); + var id = localGet("txt2img_task_id"); + + if (id) { + requestProgress(id, gradioApp().getElementById('txt2img_gallery_container'), gradioApp().getElementById('txt2img_gallery'), function() { + showSubmitButtons('txt2img', true); + }, null, 0); + } + + return id; +} + +function restoreProgressImg2img() { + showRestoreProgressButton("img2img", false); + + var id = localGet("img2img_task_id"); + + if (id) { + requestProgress(id, gradioApp().getElementById('img2img_gallery_container'), gradioApp().getElementById('img2img_gallery'), function() { + showSubmitButtons('img2img', true); + }, null, 0); + } + + return id; +} + + +/** + * Configure the width and height elements on `tabname` to accept + * pasting of resolutions in the form of "width x height". + */ +function setupResolutionPasting(tabname) { + var width = gradioApp().querySelector(`#${tabname}_width input[type=number]`); + var height = gradioApp().querySelector(`#${tabname}_height input[type=number]`); + for (const el of [width, height]) { + el.addEventListener('paste', function(event) { + var pasteData = event.clipboardData.getData('text/plain'); + var parsed = pasteData.match(/^\s*(\d+)\D+(\d+)\s*$/); + if (parsed) { + width.value = parsed[1]; + height.value = parsed[2]; + updateInput(width); + updateInput(height); + event.preventDefault(); + } + }); + } +} + +onUiLoaded(function() { + showRestoreProgressButton('txt2img', localGet("txt2img_task_id")); + showRestoreProgressButton('img2img', localGet("img2img_task_id")); + setupResolutionPasting('txt2img'); + setupResolutionPasting('img2img'); +}); + + +function modelmerger() { + var id = randomId(); + requestProgress(id, gradioApp().getElementById('modelmerger_results_panel'), null, function() {}); + + var res = create_submit_args(arguments); + res[0] = id; + return res; +} + + +function ask_for_style_name(_, prompt_text, negative_prompt_text) { + var name_ = prompt('Style name:'); + return [name_, prompt_text, negative_prompt_text]; +} + +function confirm_clear_prompt(prompt, negative_prompt) { + if (confirm("Delete prompt?")) { + prompt = ""; + negative_prompt = ""; + } + + return [prompt, negative_prompt]; +} + + +var opts = {}; +onAfterUiUpdate(function() { + if (Object.keys(opts).length != 0) return; + + var json_elem = gradioApp().getElementById('settings_json'); + if (json_elem == null) return; + + var textarea = json_elem.querySelector('textarea'); + var jsdata = textarea.value; + opts = JSON.parse(jsdata); + + executeCallbacks(optionsChangedCallbacks); /*global optionsChangedCallbacks*/ + + Object.defineProperty(textarea, 'value', { + set: function(newValue) { + var valueProp = Object.getOwnPropertyDescriptor(HTMLTextAreaElement.prototype, 'value'); + var oldValue = valueProp.get.call(textarea); + valueProp.set.call(textarea, newValue); + + if (oldValue != newValue) { + opts = JSON.parse(textarea.value); + } + + executeCallbacks(optionsChangedCallbacks); + }, + get: function() { + var valueProp = Object.getOwnPropertyDescriptor(HTMLTextAreaElement.prototype, 'value'); + return valueProp.get.call(textarea); + } + }); + + json_elem.parentElement.style.display = "none"; + + setupTokenCounters(); +}); + +onOptionsChanged(function() { + var elem = gradioApp().getElementById('sd_checkpoint_hash'); + var sd_checkpoint_hash = opts.sd_checkpoint_hash || ""; + var shorthash = sd_checkpoint_hash.substring(0, 10); + + if (elem && elem.textContent != shorthash) { + elem.textContent = shorthash; + elem.title = sd_checkpoint_hash; + elem.href = "https://google.com/search?q=" + sd_checkpoint_hash; + } +}); + +let txt2img_textarea, img2img_textarea = undefined; + +function restart_reload() { + document.body.innerHTML = '

Reloading...

'; + + var requestPing = function() { + requestGet("./internal/ping", {}, function(data) { + location.reload(); + }, function() { + setTimeout(requestPing, 500); + }); + }; + + setTimeout(requestPing, 2000); + + return []; +} + +// Simulate an `input` DOM event for Gradio Textbox component. Needed after you edit its contents in javascript, otherwise your edits +// will only visible on web page and not sent to python. +function updateInput(target) { + let e = new Event("input", {bubbles: true}); + Object.defineProperty(e, "target", {value: target}); + target.dispatchEvent(e); +} + + +var desiredCheckpointName = null; +function selectCheckpoint(name) { + desiredCheckpointName = name; + gradioApp().getElementById('change_checkpoint').click(); +} + +function currentImg2imgSourceResolution(w, h, scaleBy) { + var img = gradioApp().querySelector('#mode_img2img > div[style="display: block;"] img'); + return img ? [img.naturalWidth, img.naturalHeight, scaleBy] : [0, 0, scaleBy]; +} + +function updateImg2imgResizeToTextAfterChangingImage() { + // At the time this is called from gradio, the image has no yet been replaced. + // There may be a better solution, but this is simple and straightforward so I'm going with it. + + setTimeout(function() { + gradioApp().getElementById('img2img_update_resize_to').click(); + }, 500); + + return []; + +} + + + +function setRandomSeed(elem_id) { + var input = gradioApp().querySelector("#" + elem_id + " input"); + if (!input) return []; + + input.value = "-1"; + updateInput(input); + return []; +} + +function switchWidthHeight(tabname) { + var width = gradioApp().querySelector("#" + tabname + "_width input[type=number]"); + var height = gradioApp().querySelector("#" + tabname + "_height input[type=number]"); + if (!width || !height) return []; + + var tmp = width.value; + width.value = height.value; + height.value = tmp; + + updateInput(width); + updateInput(height); + return []; +} + + +var onEditTimers = {}; + +// calls func after afterMs milliseconds has passed since the input elem has beed enited by user +function onEdit(editId, elem, afterMs, func) { + var edited = function() { + var existingTimer = onEditTimers[editId]; + if (existingTimer) clearTimeout(existingTimer); + + onEditTimers[editId] = setTimeout(func, afterMs); + }; + + elem.addEventListener("input", edited); + + return edited; +} diff --git a/javascript/ui_settings_hints.js b/javascript/ui_settings_hints.js new file mode 100644 index 0000000000000000000000000000000000000000..d088f9494f826d9534dc105ac2f99bda702d22c0 --- /dev/null +++ b/javascript/ui_settings_hints.js @@ -0,0 +1,62 @@ +// various hints and extra info for the settings tab + +var settingsHintsSetup = false; + +onOptionsChanged(function() { + if (settingsHintsSetup) return; + settingsHintsSetup = true; + + gradioApp().querySelectorAll('#settings [id^=setting_]').forEach(function(div) { + var name = div.id.substr(8); + var commentBefore = opts._comments_before[name]; + var commentAfter = opts._comments_after[name]; + + if (!commentBefore && !commentAfter) return; + + var span = null; + if (div.classList.contains('gradio-checkbox')) span = div.querySelector('label span'); + else if (div.classList.contains('gradio-checkboxgroup')) span = div.querySelector('span').firstChild; + else if (div.classList.contains('gradio-radio')) span = div.querySelector('span').firstChild; + else span = div.querySelector('label span').firstChild; + + if (!span) return; + + if (commentBefore) { + var comment = document.createElement('DIV'); + comment.className = 'settings-comment'; + comment.innerHTML = commentBefore; + span.parentElement.insertBefore(document.createTextNode('\xa0'), span); + span.parentElement.insertBefore(comment, span); + span.parentElement.insertBefore(document.createTextNode('\xa0'), span); + } + if (commentAfter) { + comment = document.createElement('DIV'); + comment.className = 'settings-comment'; + comment.innerHTML = commentAfter; + span.parentElement.insertBefore(comment, span.nextSibling); + span.parentElement.insertBefore(document.createTextNode('\xa0'), span.nextSibling); + } + }); +}); + +function settingsHintsShowQuicksettings() { + requestGet("./internal/quicksettings-hint", {}, function(data) { + var table = document.createElement('table'); + table.className = 'popup-table'; + + data.forEach(function(obj) { + var tr = document.createElement('tr'); + var td = document.createElement('td'); + td.textContent = obj.name; + tr.appendChild(td); + + td = document.createElement('td'); + td.textContent = obj.label; + tr.appendChild(td); + + table.appendChild(tr); + }); + + popup(table); + }); +} diff --git a/launch.py b/launch.py new file mode 100644 index 0000000000000000000000000000000000000000..cafab78060f727848ac47bd041b1c51d69875c33 --- /dev/null +++ b/launch.py @@ -0,0 +1,48 @@ +from modules import launch_utils + +args = launch_utils.args +python = launch_utils.python +git = launch_utils.git +index_url = launch_utils.index_url +dir_repos = launch_utils.dir_repos + +commit_hash = launch_utils.commit_hash +git_tag = launch_utils.git_tag + +run = launch_utils.run +is_installed = launch_utils.is_installed +repo_dir = launch_utils.repo_dir + +run_pip = launch_utils.run_pip +check_run_python = launch_utils.check_run_python +git_clone = launch_utils.git_clone +git_pull_recursive = launch_utils.git_pull_recursive +list_extensions = launch_utils.list_extensions +run_extension_installer = launch_utils.run_extension_installer +prepare_environment = launch_utils.prepare_environment +configure_for_tests = launch_utils.configure_for_tests +start = launch_utils.start + + +def main(): + if args.dump_sysinfo: + filename = launch_utils.dump_sysinfo() + + print(f"Sysinfo saved as {filename}. Exiting...") + + exit(0) + + launch_utils.startup_timer.record("initial startup") + + with launch_utils.startup_timer.subcategory("prepare environment"): + if not args.skip_prepare_environment: + prepare_environment() + + if args.test_server: + configure_for_tests() + + start() + + +if __name__ == "__main__": + main() diff --git a/localizations/Put localization files here.txt b/localizations/Put localization files here.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/models/Stable-diffusion/Put Stable Diffusion checkpoints here.txt b/models/Stable-diffusion/Put Stable Diffusion checkpoints here.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/models/VAE-approx/model.pt b/models/VAE-approx/model.pt new file mode 100644 index 0000000000000000000000000000000000000000..09c6b8f7fda5e15495c6203ca323d6573745d0af --- /dev/null +++ b/models/VAE-approx/model.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4f88c9078bb2238cdd0d8864671dd33e3f42e091e41f08903f3c15e4a54a9b39 +size 213777 diff --git a/models/VAE/Put VAE here.txt b/models/VAE/Put VAE here.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/models/deepbooru/Put your deepbooru release project folder here.txt b/models/deepbooru/Put your deepbooru release project folder here.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/models/karlo/ViT-L-14_stats.th b/models/karlo/ViT-L-14_stats.th new file mode 100644 index 0000000000000000000000000000000000000000..a6a06e94ecaa4f2977972ff991f75db6c90403ea Binary files /dev/null and b/models/karlo/ViT-L-14_stats.th differ diff --git a/modules/Roboto-Regular.ttf b/modules/Roboto-Regular.ttf new file mode 100644 index 0000000000000000000000000000000000000000..500b1045b0c94d83d2e6798aaf1faa55a2dab6fc Binary files /dev/null and b/modules/Roboto-Regular.ttf differ diff --git a/modules/cache.py b/modules/cache.py new file mode 100644 index 0000000000000000000000000000000000000000..16f39df2c6d47ab3e094348efebbb588f1af80bc --- /dev/null +++ b/modules/cache.py @@ -0,0 +1,124 @@ +import json +import os +import os.path +import threading +import time + +from modules.paths import data_path, script_path + +cache_filename = os.environ.get('SD_WEBUI_CACHE_FILE', os.path.join(data_path, "cache.json")) +cache_data = None +cache_lock = threading.Lock() + +dump_cache_after = None +dump_cache_thread = None + + +def dump_cache(): + """ + Marks cache for writing to disk. 5 seconds after no one else flags the cache for writing, it is written. + """ + + global dump_cache_after + global dump_cache_thread + + def thread_func(): + global dump_cache_after + global dump_cache_thread + + while dump_cache_after is not None and time.time() < dump_cache_after: + time.sleep(1) + + with cache_lock: + cache_filename_tmp = cache_filename + "-" + with open(cache_filename_tmp, "w", encoding="utf8") as file: + json.dump(cache_data, file, indent=4, ensure_ascii=False) + + os.replace(cache_filename_tmp, cache_filename) + + dump_cache_after = None + dump_cache_thread = None + + with cache_lock: + dump_cache_after = time.time() + 5 + if dump_cache_thread is None: + dump_cache_thread = threading.Thread(name='cache-writer', target=thread_func) + dump_cache_thread.start() + + +def cache(subsection): + """ + Retrieves or initializes a cache for a specific subsection. + + Parameters: + subsection (str): The subsection identifier for the cache. + + Returns: + dict: The cache data for the specified subsection. + """ + + global cache_data + + if cache_data is None: + with cache_lock: + if cache_data is None: + if not os.path.isfile(cache_filename): + cache_data = {} + else: + try: + with open(cache_filename, "r", encoding="utf8") as file: + cache_data = json.load(file) + except Exception: + os.replace(cache_filename, os.path.join(script_path, "tmp", "cache.json")) + print('[ERROR] issue occurred while trying to read cache.json, move current cache to tmp/cache.json and create new cache') + cache_data = {} + + s = cache_data.get(subsection, {}) + cache_data[subsection] = s + + return s + + +def cached_data_for_file(subsection, title, filename, func): + """ + Retrieves or generates data for a specific file, using a caching mechanism. + + Parameters: + subsection (str): The subsection of the cache to use. + title (str): The title of the data entry in the subsection of the cache. + filename (str): The path to the file to be checked for modifications. + func (callable): A function that generates the data if it is not available in the cache. + + Returns: + dict or None: The cached or generated data, or None if data generation fails. + + The `cached_data_for_file` function implements a caching mechanism for data stored in files. + It checks if the data associated with the given `title` is present in the cache and compares the + modification time of the file with the cached modification time. If the file has been modified, + the cache is considered invalid and the data is regenerated using the provided `func`. + Otherwise, the cached data is returned. + + If the data generation fails, None is returned to indicate the failure. Otherwise, the generated + or cached data is returned as a dictionary. + """ + + existing_cache = cache(subsection) + ondisk_mtime = os.path.getmtime(filename) + + entry = existing_cache.get(title) + if entry: + cached_mtime = entry.get("mtime", 0) + if ondisk_mtime > cached_mtime: + entry = None + + if not entry or 'value' not in entry: + value = func() + if value is None: + return None + + entry = {'mtime': ondisk_mtime, 'value': value} + existing_cache[title] = entry + + dump_cache() + + return entry['value'] diff --git a/modules/call_queue.py b/modules/call_queue.py new file mode 100644 index 0000000000000000000000000000000000000000..396501918884db3afd352eb4dca4febd5682f4c4 --- /dev/null +++ b/modules/call_queue.py @@ -0,0 +1,118 @@ +from functools import wraps +import html +import time + +from modules import shared, progress, errors, devices, fifo_lock + +queue_lock = fifo_lock.FIFOLock() + + +def wrap_queued_call(func): + def f(*args, **kwargs): + with queue_lock: + res = func(*args, **kwargs) + + return res + + return f + + +def wrap_gradio_gpu_call(func, extra_outputs=None): + @wraps(func) + def f(*args, **kwargs): + + # if the first argument is a string that says "task(...)", it is treated as a job id + if args and type(args[0]) == str and args[0].startswith("task(") and args[0].endswith(")"): + id_task = args[0] + progress.add_task_to_queue(id_task) + else: + id_task = None + + with queue_lock: + shared.state.begin(job=id_task) + progress.start_task(id_task) + + try: + res = func(*args, **kwargs) + progress.record_results(id_task, res) + finally: + progress.finish_task(id_task) + + shared.state.end() + + return res + + return wrap_gradio_call(f, extra_outputs=extra_outputs, add_stats=True) + + +def wrap_gradio_call(func, extra_outputs=None, add_stats=False): + @wraps(func) + def f(*args, extra_outputs_array=extra_outputs, **kwargs): + run_memmon = shared.opts.memmon_poll_rate > 0 and not shared.mem_mon.disabled and add_stats + if run_memmon: + shared.mem_mon.monitor() + t = time.perf_counter() + + try: + res = list(func(*args, **kwargs)) + except Exception as e: + # When printing out our debug argument list, + # do not print out more than a 100 KB of text + max_debug_str_len = 131072 + message = "Error completing request" + arg_str = f"Arguments: {args} {kwargs}"[:max_debug_str_len] + if len(arg_str) > max_debug_str_len: + arg_str += f" (Argument list truncated at {max_debug_str_len}/{len(arg_str)} characters)" + errors.report(f"{message}\n{arg_str}", exc_info=True) + + shared.state.job = "" + shared.state.job_count = 0 + + if extra_outputs_array is None: + extra_outputs_array = [None, ''] + + error_message = f'{type(e).__name__}: {e}' + res = extra_outputs_array + [f"
{html.escape(error_message)}
"] + + devices.torch_gc() + + shared.state.skipped = False + shared.state.interrupted = False + shared.state.job_count = 0 + + if not add_stats: + return tuple(res) + + elapsed = time.perf_counter() - t + elapsed_m = int(elapsed // 60) + elapsed_s = elapsed % 60 + elapsed_text = f"{elapsed_s:.1f} sec." + if elapsed_m > 0: + elapsed_text = f"{elapsed_m} min. "+elapsed_text + + if run_memmon: + mem_stats = {k: -(v//-(1024*1024)) for k, v in shared.mem_mon.stop().items()} + active_peak = mem_stats['active_peak'] + reserved_peak = mem_stats['reserved_peak'] + sys_peak = mem_stats['system_peak'] + sys_total = mem_stats['total'] + sys_pct = sys_peak/max(sys_total, 1) * 100 + + toltip_a = "Active: peak amount of video memory used during generation (excluding cached data)" + toltip_r = "Reserved: total amout of video memory allocated by the Torch library " + toltip_sys = "System: peak amout of video memory allocated by all running programs, out of total capacity" + + text_a = f"A: {active_peak/1024:.2f} GB" + text_r = f"R: {reserved_peak/1024:.2f} GB" + text_sys = f"Sys: {sys_peak/1024:.1f}/{sys_total/1024:g} GB ({sys_pct:.1f}%)" + + vram_html = f"

{text_a}, {text_r}, {text_sys}

" + else: + vram_html = '' + + # last item is always HTML + res[-1] += f"

Time taken: {elapsed_text}

{vram_html}
" + + return tuple(res) + + return f diff --git a/modules/cmd_args.py b/modules/cmd_args.py new file mode 100644 index 0000000000000000000000000000000000000000..8073ed6b7b0b74f5e02535383d09ce5ea77933e7 --- /dev/null +++ b/modules/cmd_args.py @@ -0,0 +1,121 @@ +import argparse +import json +import os +from modules.paths_internal import models_path, script_path, data_path, extensions_dir, extensions_builtin_dir, sd_default_config, sd_model_file # noqa: F401 + +parser = argparse.ArgumentParser() + +parser.add_argument("-f", action='store_true', help=argparse.SUPPRESS) # allows running as root; implemented outside of webui +parser.add_argument("--update-all-extensions", action='store_true', help="launch.py argument: download updates for all extensions when starting the program") +parser.add_argument("--skip-python-version-check", action='store_true', help="launch.py argument: do not check python version") +parser.add_argument("--skip-torch-cuda-test", action='store_true', help="launch.py argument: do not check if CUDA is able to work properly") +parser.add_argument("--reinstall-xformers", action='store_true', help="launch.py argument: install the appropriate version of xformers even if you have some version already installed") +parser.add_argument("--reinstall-torch", action='store_true', help="launch.py argument: install the appropriate version of torch even if you have some version already installed") +parser.add_argument("--update-check", action='store_true', help="launch.py argument: check for updates at startup") +parser.add_argument("--test-server", action='store_true', help="launch.py argument: configure server for testing") +parser.add_argument("--log-startup", action='store_true', help="launch.py argument: print a detailed log of what's happening at startup") +parser.add_argument("--skip-prepare-environment", action='store_true', help="launch.py argument: skip all environment preparation") +parser.add_argument("--skip-install", action='store_true', help="launch.py argument: skip installation of packages") +parser.add_argument("--dump-sysinfo", action='store_true', help="launch.py argument: dump limited sysinfo file (without information about extensions, options) to disk and quit") +parser.add_argument("--loglevel", type=str, help="log level; one of: CRITICAL, ERROR, WARNING, INFO, DEBUG", default=None) +parser.add_argument("--do-not-download-clip", action='store_true', help="do not download CLIP model even if it's not included in the checkpoint") +parser.add_argument("--data-dir", type=str, default=os.path.dirname(os.path.dirname(os.path.realpath(__file__))), help="base path where all user data is stored") +parser.add_argument("--config", type=str, default=sd_default_config, help="path to config which constructs model",) +parser.add_argument("--ckpt", type=str, default=sd_model_file, help="path to checkpoint of stable diffusion model; if specified, this checkpoint will be added to the list of checkpoints and loaded",) +parser.add_argument("--ckpt-dir", type=str, default=None, help="Path to directory with stable diffusion checkpoints") +parser.add_argument("--vae-dir", type=str, default=None, help="Path to directory with VAE files") +parser.add_argument("--gfpgan-dir", type=str, help="GFPGAN directory", default=('./src/gfpgan' if os.path.exists('./src/gfpgan') else './GFPGAN')) +parser.add_argument("--gfpgan-model", type=str, help="GFPGAN model file name", default=None) +parser.add_argument("--no-half", action='store_true', help="do not switch the model to 16-bit floats") +parser.add_argument("--no-half-vae", action='store_true', help="do not switch the VAE model to 16-bit floats") +parser.add_argument("--no-progressbar-hiding", action='store_true', help="do not hide progressbar in gradio UI (we hide it because it slows down ML if you have hardware acceleration in browser)") +parser.add_argument("--max-batch-count", type=int, default=16, help="maximum batch count value for the UI") +parser.add_argument("--embeddings-dir", type=str, default=os.path.join(data_path, 'embeddings'), help="embeddings directory for textual inversion (default: embeddings)") +parser.add_argument("--textual-inversion-templates-dir", type=str, default=os.path.join(script_path, 'textual_inversion_templates'), help="directory with textual inversion templates") +parser.add_argument("--hypernetwork-dir", type=str, default=os.path.join(models_path, 'hypernetworks'), help="hypernetwork directory") +parser.add_argument("--localizations-dir", type=str, default=os.path.join(script_path, 'localizations'), help="localizations directory") +parser.add_argument("--allow-code", action='store_true', help="allow custom script execution from webui") +parser.add_argument("--medvram", action='store_true', help="enable stable diffusion model optimizations for sacrificing a little speed for low VRM usage") +parser.add_argument("--medvram-sdxl", action='store_true', help="enable --medvram optimization just for SDXL models") +parser.add_argument("--lowvram", action='store_true', help="enable stable diffusion model optimizations for sacrificing a lot of speed for very low VRM usage") +parser.add_argument("--lowram", action='store_true', help="load stable diffusion checkpoint weights to VRAM instead of RAM") +parser.add_argument("--always-batch-cond-uncond", action='store_true', help="does not do anything") +parser.add_argument("--unload-gfpgan", action='store_true', help="does not do anything.") +parser.add_argument("--precision", type=str, help="evaluate at this precision", choices=["full", "autocast"], default="autocast") +parser.add_argument("--upcast-sampling", action='store_true', help="upcast sampling. No effect with --no-half. Usually produces similar results to --no-half with better performance while using less memory.") +parser.add_argument("--share", action='store_true', help="use share=True for gradio and make the UI accessible through their site") +parser.add_argument("--ngrok", type=str, help="ngrok authtoken, alternative to gradio --share", default=None) +parser.add_argument("--ngrok-region", type=str, help="does not do anything.", default="") +parser.add_argument("--ngrok-options", type=json.loads, help='The options to pass to ngrok in JSON format, e.g.: \'{"authtoken_from_env":true, "basic_auth":"user:password", "oauth_provider":"google", "oauth_allow_emails":"user@asdf.com"}\'', default=dict()) +parser.add_argument("--enable-insecure-extension-access", action='store_true', help="enable extensions tab regardless of other options") +parser.add_argument("--codeformer-models-path", type=str, help="Path to directory with codeformer model file(s).", default=os.path.join(models_path, 'Codeformer')) +parser.add_argument("--gfpgan-models-path", type=str, help="Path to directory with GFPGAN model file(s).", default=os.path.join(models_path, 'GFPGAN')) +parser.add_argument("--esrgan-models-path", type=str, help="Path to directory with ESRGAN model file(s).", default=os.path.join(models_path, 'ESRGAN')) +parser.add_argument("--bsrgan-models-path", type=str, help="Path to directory with BSRGAN model file(s).", default=os.path.join(models_path, 'BSRGAN')) +parser.add_argument("--realesrgan-models-path", type=str, help="Path to directory with RealESRGAN model file(s).", default=os.path.join(models_path, 'RealESRGAN')) +parser.add_argument("--clip-models-path", type=str, help="Path to directory with CLIP model file(s).", default=None) +parser.add_argument("--xformers", action='store_true', help="enable xformers for cross attention layers") +parser.add_argument("--force-enable-xformers", action='store_true', help="enable xformers for cross attention layers regardless of whether the checking code thinks you can run it; do not make bug reports if this fails to work") +parser.add_argument("--xformers-flash-attention", action='store_true', help="enable xformers with Flash Attention to improve reproducibility (supported for SD2.x or variant only)") +parser.add_argument("--deepdanbooru", action='store_true', help="does not do anything") +parser.add_argument("--opt-split-attention", action='store_true', help="prefer Doggettx's cross-attention layer optimization for automatic choice of optimization") +parser.add_argument("--opt-sub-quad-attention", action='store_true', help="prefer memory efficient sub-quadratic cross-attention layer optimization for automatic choice of optimization") +parser.add_argument("--sub-quad-q-chunk-size", type=int, help="query chunk size for the sub-quadratic cross-attention layer optimization to use", default=1024) +parser.add_argument("--sub-quad-kv-chunk-size", type=int, help="kv chunk size for the sub-quadratic cross-attention layer optimization to use", default=None) +parser.add_argument("--sub-quad-chunk-threshold", type=int, help="the percentage of VRAM threshold for the sub-quadratic cross-attention layer optimization to use chunking", default=None) +parser.add_argument("--opt-split-attention-invokeai", action='store_true', help="prefer InvokeAI's cross-attention layer optimization for automatic choice of optimization") +parser.add_argument("--opt-split-attention-v1", action='store_true', help="prefer older version of split attention optimization for automatic choice of optimization") +parser.add_argument("--opt-sdp-attention", action='store_true', help="prefer scaled dot product cross-attention layer optimization for automatic choice of optimization; requires PyTorch 2.*") +parser.add_argument("--opt-sdp-no-mem-attention", action='store_true', help="prefer scaled dot product cross-attention layer optimization without memory efficient attention for automatic choice of optimization, makes image generation deterministic; requires PyTorch 2.*") +parser.add_argument("--disable-opt-split-attention", action='store_true', help="prefer no cross-attention layer optimization for automatic choice of optimization") +parser.add_argument("--disable-nan-check", action='store_true', help="do not check if produced images/latent spaces have nans; useful for running without a checkpoint in CI") +parser.add_argument("--use-cpu", nargs='+', help="use CPU as torch device for specified modules", default=[], type=str.lower) +parser.add_argument("--use-ipex", action="store_true", help="use Intel XPU as torch device") +parser.add_argument("--disable-model-loading-ram-optimization", action='store_true', help="disable an optimization that reduces RAM use when loading a model") +parser.add_argument("--listen", action='store_true', help="launch gradio with 0.0.0.0 as server name, allowing to respond to network requests") +parser.add_argument("--port", type=int, help="launch gradio with given server port, you need root/admin rights for ports < 1024, defaults to 7860 if available", default=None) +parser.add_argument("--show-negative-prompt", action='store_true', help="does not do anything", default=False) +parser.add_argument("--ui-config-file", type=str, help="filename to use for ui configuration", default=os.path.join(data_path, 'ui-config.json')) +parser.add_argument("--hide-ui-dir-config", action='store_true', help="hide directory configuration from webui", default=False) +parser.add_argument("--freeze-settings", action='store_true', help="disable editing settings", default=False) +parser.add_argument("--ui-settings-file", type=str, help="filename to use for ui settings", default=os.path.join(data_path, 'config.json')) +parser.add_argument("--gradio-debug", action='store_true', help="launch gradio with --debug option") +parser.add_argument("--gradio-auth", type=str, help='set gradio authentication like "username:password"; or comma-delimit multiple like "u1:p1,u2:p2,u3:p3"', default=None) +parser.add_argument("--gradio-auth-path", type=str, help='set gradio authentication file path ex. "/path/to/auth/file" same auth format as --gradio-auth', default=None) +parser.add_argument("--gradio-img2img-tool", type=str, help='does not do anything') +parser.add_argument("--gradio-inpaint-tool", type=str, help="does not do anything") +parser.add_argument("--gradio-allowed-path", action='append', help="add path to gradio's allowed_paths, make it possible to serve files from it", default=[data_path]) +parser.add_argument("--opt-channelslast", action='store_true', help="change memory type for stable diffusion to channels last") +parser.add_argument("--styles-file", type=str, help="filename to use for styles", default=os.path.join(data_path, 'styles.csv')) +parser.add_argument("--autolaunch", action='store_true', help="open the webui URL in the system's default browser upon launch", default=False) +parser.add_argument("--theme", type=str, help="launches the UI with light or dark theme", default=None) +parser.add_argument("--use-textbox-seed", action='store_true', help="use textbox for seeds in UI (no up/down, but possible to input long seeds)", default=False) +parser.add_argument("--disable-console-progressbars", action='store_true', help="do not output progressbars to console", default=False) +parser.add_argument("--enable-console-prompts", action='store_true', help="does not do anything", default=False) # Legacy compatibility, use as default value shared.opts.enable_console_prompts +parser.add_argument('--vae-path', type=str, help='Checkpoint to use as VAE; setting this argument disables all settings related to VAE', default=None) +parser.add_argument("--disable-safe-unpickle", action='store_true', help="disable checking pytorch models for malicious code", default=False) +parser.add_argument("--api", action='store_true', help="use api=True to launch the API together with the webui (use --nowebui instead for only the API)") +parser.add_argument("--api-auth", type=str, help='Set authentication for API like "username:password"; or comma-delimit multiple like "u1:p1,u2:p2,u3:p3"', default=None) +parser.add_argument("--api-log", action='store_true', help="use api-log=True to enable logging of all API requests") +parser.add_argument("--nowebui", action='store_true', help="use api=True to launch the API instead of the webui") +parser.add_argument("--ui-debug-mode", action='store_true', help="Don't load model to quickly launch UI") +parser.add_argument("--device-id", type=str, help="Select the default CUDA device to use (export CUDA_VISIBLE_DEVICES=0,1,etc might be needed before)", default=None) +parser.add_argument("--administrator", action='store_true', help="Administrator rights", default=False) +parser.add_argument("--cors-allow-origins", type=str, help="Allowed CORS origin(s) in the form of a comma-separated list (no spaces)", default=None) +parser.add_argument("--cors-allow-origins-regex", type=str, help="Allowed CORS origin(s) in the form of a single regular expression", default=None) +parser.add_argument("--tls-keyfile", type=str, help="Partially enables TLS, requires --tls-certfile to fully function", default=None) +parser.add_argument("--tls-certfile", type=str, help="Partially enables TLS, requires --tls-keyfile to fully function", default=None) +parser.add_argument("--disable-tls-verify", action="store_false", help="When passed, enables the use of self-signed certificates.", default=None) +parser.add_argument("--server-name", type=str, help="Sets hostname of server", default=None) +parser.add_argument("--gradio-queue", action='store_true', help="does not do anything", default=True) +parser.add_argument("--no-gradio-queue", action='store_true', help="Disables gradio queue; causes the webpage to use http requests instead of websockets; was the default in earlier versions") +parser.add_argument("--skip-version-check", action='store_true', help="Do not check versions of torch and xformers") +parser.add_argument("--no-hashing", action='store_true', help="disable sha256 hashing of checkpoints to help loading performance", default=False) +parser.add_argument("--no-download-sd-model", action='store_true', help="don't download SD1.5 model even if no model is found in --ckpt-dir", default=False) +parser.add_argument('--subpath', type=str, help='customize the subpath for gradio, use with reverse proxy') +parser.add_argument('--add-stop-route', action='store_true', help='does not do anything') +parser.add_argument('--api-server-stop', action='store_true', help='enable server stop/restart/kill via api') +parser.add_argument('--timeout-keep-alive', type=int, default=30, help='set timeout_keep_alive for uvicorn') +parser.add_argument("--disable-all-extensions", action='store_true', help="prevent all extensions from running regardless of any other settings", default=False) +parser.add_argument("--disable-extra-extensions", action='store_true', help="prevent all extensions except built-in from running regardless of any other settings", default=False) +parser.add_argument("--skip-load-model-at-start", action='store_true', help="if load a model at web start, only take effect when --nowebui", ) diff --git a/modules/codeformer_model.py b/modules/codeformer_model.py new file mode 100644 index 0000000000000000000000000000000000000000..3ad8a9db806d3406610d81534a6d7c85301cceb0 --- /dev/null +++ b/modules/codeformer_model.py @@ -0,0 +1,132 @@ +import os + +import cv2 +import torch + +import modules.face_restoration +import modules.shared +from modules import shared, devices, modelloader, errors +from modules.paths import models_path + +# codeformer people made a choice to include modified basicsr library to their project which makes +# it utterly impossible to use it alongside with other libraries that also use basicsr, like GFPGAN. +# I am making a choice to include some files from codeformer to work around this issue. +model_dir = "Codeformer" +model_path = os.path.join(models_path, model_dir) +model_url = 'https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/codeformer.pth' + +codeformer = None + + +def setup_model(dirname): + os.makedirs(model_path, exist_ok=True) + + path = modules.paths.paths.get("CodeFormer", None) + if path is None: + return + + try: + from torchvision.transforms.functional import normalize + from modules.codeformer.codeformer_arch import CodeFormer + from basicsr.utils import img2tensor, tensor2img + from facelib.utils.face_restoration_helper import FaceRestoreHelper + from facelib.detection.retinaface import retinaface + + net_class = CodeFormer + + class FaceRestorerCodeFormer(modules.face_restoration.FaceRestoration): + def name(self): + return "CodeFormer" + + def __init__(self, dirname): + self.net = None + self.face_helper = None + self.cmd_dir = dirname + + def create_models(self): + + if self.net is not None and self.face_helper is not None: + self.net.to(devices.device_codeformer) + return self.net, self.face_helper + model_paths = modelloader.load_models(model_path, model_url, self.cmd_dir, download_name='codeformer-v0.1.0.pth', ext_filter=['.pth']) + if len(model_paths) != 0: + ckpt_path = model_paths[0] + else: + print("Unable to load codeformer model.") + return None, None + net = net_class(dim_embd=512, codebook_size=1024, n_head=8, n_layers=9, connect_list=['32', '64', '128', '256']).to(devices.device_codeformer) + checkpoint = torch.load(ckpt_path)['params_ema'] + net.load_state_dict(checkpoint) + net.eval() + + if hasattr(retinaface, 'device'): + retinaface.device = devices.device_codeformer + face_helper = FaceRestoreHelper(1, face_size=512, crop_ratio=(1, 1), det_model='retinaface_resnet50', save_ext='png', use_parse=True, device=devices.device_codeformer) + + self.net = net + self.face_helper = face_helper + + return net, face_helper + + def send_model_to(self, device): + self.net.to(device) + self.face_helper.face_det.to(device) + self.face_helper.face_parse.to(device) + + def restore(self, np_image, w=None): + np_image = np_image[:, :, ::-1] + + original_resolution = np_image.shape[0:2] + + self.create_models() + if self.net is None or self.face_helper is None: + return np_image + + self.send_model_to(devices.device_codeformer) + + self.face_helper.clean_all() + self.face_helper.read_image(np_image) + self.face_helper.get_face_landmarks_5(only_center_face=False, resize=640, eye_dist_threshold=5) + self.face_helper.align_warp_face() + + for cropped_face in self.face_helper.cropped_faces: + cropped_face_t = img2tensor(cropped_face / 255., bgr2rgb=True, float32=True) + normalize(cropped_face_t, (0.5, 0.5, 0.5), (0.5, 0.5, 0.5), inplace=True) + cropped_face_t = cropped_face_t.unsqueeze(0).to(devices.device_codeformer) + + try: + with torch.no_grad(): + output = self.net(cropped_face_t, w=w if w is not None else shared.opts.code_former_weight, adain=True)[0] + restored_face = tensor2img(output, rgb2bgr=True, min_max=(-1, 1)) + del output + devices.torch_gc() + except Exception: + errors.report('Failed inference for CodeFormer', exc_info=True) + restored_face = tensor2img(cropped_face_t, rgb2bgr=True, min_max=(-1, 1)) + + restored_face = restored_face.astype('uint8') + self.face_helper.add_restored_face(restored_face) + + self.face_helper.get_inverse_affine(None) + + restored_img = self.face_helper.paste_faces_to_input_image() + restored_img = restored_img[:, :, ::-1] + + if original_resolution != restored_img.shape[0:2]: + restored_img = cv2.resize(restored_img, (0, 0), fx=original_resolution[1]/restored_img.shape[1], fy=original_resolution[0]/restored_img.shape[0], interpolation=cv2.INTER_LINEAR) + + self.face_helper.clean_all() + + if shared.opts.face_restoration_unload: + self.send_model_to(devices.cpu) + + return restored_img + + global codeformer + codeformer = FaceRestorerCodeFormer(dirname) + shared.face_restorers.append(codeformer) + + except Exception: + errors.report("Error setting up CodeFormer", exc_info=True) + + # sys.path = stored_sys_path diff --git a/modules/config_states.py b/modules/config_states.py new file mode 100644 index 0000000000000000000000000000000000000000..651793c7f6f659f8751e35f894a8f588f276d9ac --- /dev/null +++ b/modules/config_states.py @@ -0,0 +1,198 @@ +""" +Supports saving and restoring webui and extensions from a known working set of commits +""" + +import os +import json +import tqdm + +from datetime import datetime +import git + +from modules import shared, extensions, errors +from modules.paths_internal import script_path, config_states_dir + +all_config_states = {} + + +def list_config_states(): + global all_config_states + + all_config_states.clear() + os.makedirs(config_states_dir, exist_ok=True) + + config_states = [] + for filename in os.listdir(config_states_dir): + if filename.endswith(".json"): + path = os.path.join(config_states_dir, filename) + try: + with open(path, "r", encoding="utf-8") as f: + j = json.load(f) + assert "created_at" in j, '"created_at" does not exist' + j["filepath"] = path + config_states.append(j) + except Exception as e: + print(f'[ERROR]: Config states {path}, {e}') + + config_states = sorted(config_states, key=lambda cs: cs["created_at"], reverse=True) + + for cs in config_states: + timestamp = datetime.fromtimestamp(cs["created_at"]).strftime('%Y-%m-%d %H:%M:%S') + name = cs.get("name", "Config") + full_name = f"{name}: {timestamp}" + all_config_states[full_name] = cs + + return all_config_states + + +def get_webui_config(): + webui_repo = None + + try: + if os.path.exists(os.path.join(script_path, ".git")): + webui_repo = git.Repo(script_path) + except Exception: + errors.report(f"Error reading webui git info from {script_path}", exc_info=True) + + webui_remote = None + webui_commit_hash = None + webui_commit_date = None + webui_branch = None + if webui_repo and not webui_repo.bare: + try: + webui_remote = next(webui_repo.remote().urls, None) + head = webui_repo.head.commit + webui_commit_date = webui_repo.head.commit.committed_date + webui_commit_hash = head.hexsha + webui_branch = webui_repo.active_branch.name + + except Exception: + webui_remote = None + + return { + "remote": webui_remote, + "commit_hash": webui_commit_hash, + "commit_date": webui_commit_date, + "branch": webui_branch, + } + + +def get_extension_config(): + ext_config = {} + + for ext in extensions.extensions: + ext.read_info_from_repo() + + entry = { + "name": ext.name, + "path": ext.path, + "enabled": ext.enabled, + "is_builtin": ext.is_builtin, + "remote": ext.remote, + "commit_hash": ext.commit_hash, + "commit_date": ext.commit_date, + "branch": ext.branch, + "have_info_from_repo": ext.have_info_from_repo + } + + ext_config[ext.name] = entry + + return ext_config + + +def get_config(): + creation_time = datetime.now().timestamp() + webui_config = get_webui_config() + ext_config = get_extension_config() + + return { + "created_at": creation_time, + "webui": webui_config, + "extensions": ext_config + } + + +def restore_webui_config(config): + print("* Restoring webui state...") + + if "webui" not in config: + print("Error: No webui data saved to config") + return + + webui_config = config["webui"] + + if "commit_hash" not in webui_config: + print("Error: No commit saved to webui config") + return + + webui_commit_hash = webui_config.get("commit_hash", None) + webui_repo = None + + try: + if os.path.exists(os.path.join(script_path, ".git")): + webui_repo = git.Repo(script_path) + except Exception: + errors.report(f"Error reading webui git info from {script_path}", exc_info=True) + return + + try: + webui_repo.git.fetch(all=True) + webui_repo.git.reset(webui_commit_hash, hard=True) + print(f"* Restored webui to commit {webui_commit_hash}.") + except Exception: + errors.report(f"Error restoring webui to commit{webui_commit_hash}") + + +def restore_extension_config(config): + print("* Restoring extension state...") + + if "extensions" not in config: + print("Error: No extension data saved to config") + return + + ext_config = config["extensions"] + + results = [] + disabled = [] + + for ext in tqdm.tqdm(extensions.extensions): + if ext.is_builtin: + continue + + ext.read_info_from_repo() + current_commit = ext.commit_hash + + if ext.name not in ext_config: + ext.disabled = True + disabled.append(ext.name) + results.append((ext, current_commit[:8], False, "Saved extension state not found in config, marking as disabled")) + continue + + entry = ext_config[ext.name] + + if "commit_hash" in entry and entry["commit_hash"]: + try: + ext.fetch_and_reset_hard(entry["commit_hash"]) + ext.read_info_from_repo() + if current_commit != entry["commit_hash"]: + results.append((ext, current_commit[:8], True, entry["commit_hash"][:8])) + except Exception as ex: + results.append((ext, current_commit[:8], False, ex)) + else: + results.append((ext, current_commit[:8], False, "No commit hash found in config")) + + if not entry.get("enabled", False): + ext.disabled = True + disabled.append(ext.name) + else: + ext.disabled = False + + shared.opts.disabled_extensions = disabled + shared.opts.save(shared.config_filename) + + print("* Finished restoring extensions. Results:") + for ext, prev_commit, success, result in results: + if success: + print(f" + {ext.name}: {prev_commit} -> {result}") + else: + print(f" ! {ext.name}: FAILURE ({result})") diff --git a/modules/deepbooru.py b/modules/deepbooru.py new file mode 100644 index 0000000000000000000000000000000000000000..547e1b4c67aeb75a06c9991f957f51b0ef6fdd0f --- /dev/null +++ b/modules/deepbooru.py @@ -0,0 +1,98 @@ +import os +import re + +import torch +import numpy as np + +from modules import modelloader, paths, deepbooru_model, devices, images, shared + +re_special = re.compile(r'([\\()])') + + +class DeepDanbooru: + def __init__(self): + self.model = None + + def load(self): + if self.model is not None: + return + + files = modelloader.load_models( + model_path=os.path.join(paths.models_path, "torch_deepdanbooru"), + model_url='https://github.com/AUTOMATIC1111/TorchDeepDanbooru/releases/download/v1/model-resnet_custom_v3.pt', + ext_filter=[".pt"], + download_name='model-resnet_custom_v3.pt', + ) + + self.model = deepbooru_model.DeepDanbooruModel() + self.model.load_state_dict(torch.load(files[0], map_location="cpu")) + + self.model.eval() + self.model.to(devices.cpu, devices.dtype) + + def start(self): + self.load() + self.model.to(devices.device) + + def stop(self): + if not shared.opts.interrogate_keep_models_in_memory: + self.model.to(devices.cpu) + devices.torch_gc() + + def tag(self, pil_image): + self.start() + res = self.tag_multi(pil_image) + self.stop() + + return res + + def tag_multi(self, pil_image, force_disable_ranks=False): + threshold = shared.opts.interrogate_deepbooru_score_threshold + use_spaces = shared.opts.deepbooru_use_spaces + use_escape = shared.opts.deepbooru_escape + alpha_sort = shared.opts.deepbooru_sort_alpha + include_ranks = shared.opts.interrogate_return_ranks and not force_disable_ranks + + pic = images.resize_image(2, pil_image.convert("RGB"), 512, 512) + a = np.expand_dims(np.array(pic, dtype=np.float32), 0) / 255 + + with torch.no_grad(), devices.autocast(): + x = torch.from_numpy(a).to(devices.device) + y = self.model(x)[0].detach().cpu().numpy() + + probability_dict = {} + + for tag, probability in zip(self.model.tags, y): + if probability < threshold: + continue + + if tag.startswith("rating:"): + continue + + probability_dict[tag] = probability + + if alpha_sort: + tags = sorted(probability_dict) + else: + tags = [tag for tag, _ in sorted(probability_dict.items(), key=lambda x: -x[1])] + + res = [] + + filtertags = {x.strip().replace(' ', '_') for x in shared.opts.deepbooru_filter_tags.split(",")} + + for tag in [x for x in tags if x not in filtertags]: + probability = probability_dict[tag] + tag_outformat = tag + if use_spaces: + tag_outformat = tag_outformat.replace('_', ' ') + if use_escape: + tag_outformat = re.sub(re_special, r'\\\1', tag_outformat) + if include_ranks: + tag_outformat = f"({tag_outformat}:{probability:.3f})" + + res.append(tag_outformat) + + return ", ".join(res) + + +model = DeepDanbooru() diff --git a/modules/deepbooru_model.py b/modules/deepbooru_model.py new file mode 100644 index 0000000000000000000000000000000000000000..7a53884624e96284c35214ce02b8a2891d92c3e8 --- /dev/null +++ b/modules/deepbooru_model.py @@ -0,0 +1,678 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F + +from modules import devices + +# see https://github.com/AUTOMATIC1111/TorchDeepDanbooru for more + + +class DeepDanbooruModel(nn.Module): + def __init__(self): + super(DeepDanbooruModel, self).__init__() + + self.tags = [] + + self.n_Conv_0 = nn.Conv2d(kernel_size=(7, 7), in_channels=3, out_channels=64, stride=(2, 2)) + self.n_MaxPool_0 = nn.MaxPool2d(kernel_size=(3, 3), stride=(2, 2)) + self.n_Conv_1 = nn.Conv2d(kernel_size=(1, 1), in_channels=64, out_channels=256) + self.n_Conv_2 = nn.Conv2d(kernel_size=(1, 1), in_channels=64, out_channels=64) + self.n_Conv_3 = nn.Conv2d(kernel_size=(3, 3), in_channels=64, out_channels=64) + self.n_Conv_4 = nn.Conv2d(kernel_size=(1, 1), in_channels=64, out_channels=256) + self.n_Conv_5 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=64) + self.n_Conv_6 = nn.Conv2d(kernel_size=(3, 3), in_channels=64, out_channels=64) + self.n_Conv_7 = nn.Conv2d(kernel_size=(1, 1), in_channels=64, out_channels=256) + self.n_Conv_8 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=64) + self.n_Conv_9 = nn.Conv2d(kernel_size=(3, 3), in_channels=64, out_channels=64) + self.n_Conv_10 = nn.Conv2d(kernel_size=(1, 1), in_channels=64, out_channels=256) + self.n_Conv_11 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=512, stride=(2, 2)) + self.n_Conv_12 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=128) + self.n_Conv_13 = nn.Conv2d(kernel_size=(3, 3), in_channels=128, out_channels=128, stride=(2, 2)) + self.n_Conv_14 = nn.Conv2d(kernel_size=(1, 1), in_channels=128, out_channels=512) + self.n_Conv_15 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=128) + self.n_Conv_16 = nn.Conv2d(kernel_size=(3, 3), in_channels=128, out_channels=128) + self.n_Conv_17 = nn.Conv2d(kernel_size=(1, 1), in_channels=128, out_channels=512) + self.n_Conv_18 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=128) + self.n_Conv_19 = nn.Conv2d(kernel_size=(3, 3), in_channels=128, out_channels=128) + self.n_Conv_20 = nn.Conv2d(kernel_size=(1, 1), in_channels=128, out_channels=512) + self.n_Conv_21 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=128) + self.n_Conv_22 = nn.Conv2d(kernel_size=(3, 3), in_channels=128, out_channels=128) + self.n_Conv_23 = nn.Conv2d(kernel_size=(1, 1), in_channels=128, out_channels=512) + self.n_Conv_24 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=128) + self.n_Conv_25 = nn.Conv2d(kernel_size=(3, 3), in_channels=128, out_channels=128) + self.n_Conv_26 = nn.Conv2d(kernel_size=(1, 1), in_channels=128, out_channels=512) + self.n_Conv_27 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=128) + self.n_Conv_28 = nn.Conv2d(kernel_size=(3, 3), in_channels=128, out_channels=128) + self.n_Conv_29 = nn.Conv2d(kernel_size=(1, 1), in_channels=128, out_channels=512) + self.n_Conv_30 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=128) + self.n_Conv_31 = nn.Conv2d(kernel_size=(3, 3), in_channels=128, out_channels=128) + self.n_Conv_32 = nn.Conv2d(kernel_size=(1, 1), in_channels=128, out_channels=512) + self.n_Conv_33 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=128) + self.n_Conv_34 = nn.Conv2d(kernel_size=(3, 3), in_channels=128, out_channels=128) + self.n_Conv_35 = nn.Conv2d(kernel_size=(1, 1), in_channels=128, out_channels=512) + self.n_Conv_36 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=1024, stride=(2, 2)) + self.n_Conv_37 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=256) + self.n_Conv_38 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256, stride=(2, 2)) + self.n_Conv_39 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024) + self.n_Conv_40 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256) + self.n_Conv_41 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256) + self.n_Conv_42 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024) + self.n_Conv_43 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256) + self.n_Conv_44 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256) + self.n_Conv_45 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024) + self.n_Conv_46 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256) + self.n_Conv_47 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256) + self.n_Conv_48 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024) + self.n_Conv_49 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256) + self.n_Conv_50 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256) + self.n_Conv_51 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024) + self.n_Conv_52 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256) + self.n_Conv_53 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256) + self.n_Conv_54 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024) + self.n_Conv_55 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256) + self.n_Conv_56 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256) + self.n_Conv_57 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024) + self.n_Conv_58 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256) + self.n_Conv_59 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256) + self.n_Conv_60 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024) + self.n_Conv_61 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256) + self.n_Conv_62 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256) + self.n_Conv_63 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024) + self.n_Conv_64 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256) + self.n_Conv_65 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256) + self.n_Conv_66 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024) + self.n_Conv_67 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256) + self.n_Conv_68 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256) + self.n_Conv_69 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024) + self.n_Conv_70 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256) + self.n_Conv_71 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256) + self.n_Conv_72 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024) + self.n_Conv_73 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256) + self.n_Conv_74 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256) + self.n_Conv_75 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024) + self.n_Conv_76 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256) + self.n_Conv_77 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256) + self.n_Conv_78 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024) + self.n_Conv_79 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256) + self.n_Conv_80 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256) + self.n_Conv_81 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024) + self.n_Conv_82 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256) + self.n_Conv_83 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256) + self.n_Conv_84 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024) + self.n_Conv_85 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256) + self.n_Conv_86 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256) + self.n_Conv_87 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024) + self.n_Conv_88 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256) + self.n_Conv_89 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256) + self.n_Conv_90 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024) + self.n_Conv_91 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256) + self.n_Conv_92 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256) + self.n_Conv_93 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024) + self.n_Conv_94 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256) + self.n_Conv_95 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256) + self.n_Conv_96 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024) + self.n_Conv_97 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256) + self.n_Conv_98 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256, stride=(2, 2)) + self.n_Conv_99 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024) + self.n_Conv_100 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=1024, stride=(2, 2)) + self.n_Conv_101 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256) + self.n_Conv_102 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256) + self.n_Conv_103 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024) + self.n_Conv_104 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256) + self.n_Conv_105 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256) + self.n_Conv_106 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024) + self.n_Conv_107 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256) + self.n_Conv_108 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256) + self.n_Conv_109 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024) + self.n_Conv_110 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256) + self.n_Conv_111 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256) + self.n_Conv_112 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024) + self.n_Conv_113 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256) + self.n_Conv_114 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256) + self.n_Conv_115 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024) + self.n_Conv_116 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256) + self.n_Conv_117 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256) + self.n_Conv_118 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024) + self.n_Conv_119 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256) + self.n_Conv_120 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256) + self.n_Conv_121 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024) + self.n_Conv_122 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256) + self.n_Conv_123 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256) + self.n_Conv_124 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024) + self.n_Conv_125 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256) + self.n_Conv_126 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256) + self.n_Conv_127 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024) + self.n_Conv_128 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256) + self.n_Conv_129 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256) + self.n_Conv_130 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024) + self.n_Conv_131 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256) + self.n_Conv_132 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256) + self.n_Conv_133 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024) + self.n_Conv_134 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256) + self.n_Conv_135 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256) + self.n_Conv_136 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024) + self.n_Conv_137 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256) + self.n_Conv_138 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256) + self.n_Conv_139 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024) + self.n_Conv_140 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256) + self.n_Conv_141 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256) + self.n_Conv_142 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024) + self.n_Conv_143 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256) + self.n_Conv_144 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256) + self.n_Conv_145 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024) + self.n_Conv_146 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256) + self.n_Conv_147 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256) + self.n_Conv_148 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024) + self.n_Conv_149 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256) + self.n_Conv_150 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256) + self.n_Conv_151 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024) + self.n_Conv_152 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256) + self.n_Conv_153 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256) + self.n_Conv_154 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024) + self.n_Conv_155 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256) + self.n_Conv_156 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256) + self.n_Conv_157 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024) + self.n_Conv_158 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=2048, stride=(2, 2)) + self.n_Conv_159 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=512) + self.n_Conv_160 = nn.Conv2d(kernel_size=(3, 3), in_channels=512, out_channels=512, stride=(2, 2)) + self.n_Conv_161 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=2048) + self.n_Conv_162 = nn.Conv2d(kernel_size=(1, 1), in_channels=2048, out_channels=512) + self.n_Conv_163 = nn.Conv2d(kernel_size=(3, 3), in_channels=512, out_channels=512) + self.n_Conv_164 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=2048) + self.n_Conv_165 = nn.Conv2d(kernel_size=(1, 1), in_channels=2048, out_channels=512) + self.n_Conv_166 = nn.Conv2d(kernel_size=(3, 3), in_channels=512, out_channels=512) + self.n_Conv_167 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=2048) + self.n_Conv_168 = nn.Conv2d(kernel_size=(1, 1), in_channels=2048, out_channels=4096, stride=(2, 2)) + self.n_Conv_169 = nn.Conv2d(kernel_size=(1, 1), in_channels=2048, out_channels=1024) + self.n_Conv_170 = nn.Conv2d(kernel_size=(3, 3), in_channels=1024, out_channels=1024, stride=(2, 2)) + self.n_Conv_171 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=4096) + self.n_Conv_172 = nn.Conv2d(kernel_size=(1, 1), in_channels=4096, out_channels=1024) + self.n_Conv_173 = nn.Conv2d(kernel_size=(3, 3), in_channels=1024, out_channels=1024) + self.n_Conv_174 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=4096) + self.n_Conv_175 = nn.Conv2d(kernel_size=(1, 1), in_channels=4096, out_channels=1024) + self.n_Conv_176 = nn.Conv2d(kernel_size=(3, 3), in_channels=1024, out_channels=1024) + self.n_Conv_177 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=4096) + self.n_Conv_178 = nn.Conv2d(kernel_size=(1, 1), in_channels=4096, out_channels=9176, bias=False) + + def forward(self, *inputs): + t_358, = inputs + t_359 = t_358.permute(*[0, 3, 1, 2]) + t_359_padded = F.pad(t_359, [2, 3, 2, 3], value=0) + t_360 = self.n_Conv_0(t_359_padded.to(self.n_Conv_0.bias.dtype) if devices.unet_needs_upcast else t_359_padded) + t_361 = F.relu(t_360) + t_361 = F.pad(t_361, [0, 1, 0, 1], value=float('-inf')) + t_362 = self.n_MaxPool_0(t_361) + t_363 = self.n_Conv_1(t_362) + t_364 = self.n_Conv_2(t_362) + t_365 = F.relu(t_364) + t_365_padded = F.pad(t_365, [1, 1, 1, 1], value=0) + t_366 = self.n_Conv_3(t_365_padded) + t_367 = F.relu(t_366) + t_368 = self.n_Conv_4(t_367) + t_369 = torch.add(t_368, t_363) + t_370 = F.relu(t_369) + t_371 = self.n_Conv_5(t_370) + t_372 = F.relu(t_371) + t_372_padded = F.pad(t_372, [1, 1, 1, 1], value=0) + t_373 = self.n_Conv_6(t_372_padded) + t_374 = F.relu(t_373) + t_375 = self.n_Conv_7(t_374) + t_376 = torch.add(t_375, t_370) + t_377 = F.relu(t_376) + t_378 = self.n_Conv_8(t_377) + t_379 = F.relu(t_378) + t_379_padded = F.pad(t_379, [1, 1, 1, 1], value=0) + t_380 = self.n_Conv_9(t_379_padded) + t_381 = F.relu(t_380) + t_382 = self.n_Conv_10(t_381) + t_383 = torch.add(t_382, t_377) + t_384 = F.relu(t_383) + t_385 = self.n_Conv_11(t_384) + t_386 = self.n_Conv_12(t_384) + t_387 = F.relu(t_386) + t_387_padded = F.pad(t_387, [0, 1, 0, 1], value=0) + t_388 = self.n_Conv_13(t_387_padded) + t_389 = F.relu(t_388) + t_390 = self.n_Conv_14(t_389) + t_391 = torch.add(t_390, t_385) + t_392 = F.relu(t_391) + t_393 = self.n_Conv_15(t_392) + t_394 = F.relu(t_393) + t_394_padded = F.pad(t_394, [1, 1, 1, 1], value=0) + t_395 = self.n_Conv_16(t_394_padded) + t_396 = F.relu(t_395) + t_397 = self.n_Conv_17(t_396) + t_398 = torch.add(t_397, t_392) + t_399 = F.relu(t_398) + t_400 = self.n_Conv_18(t_399) + t_401 = F.relu(t_400) + t_401_padded = F.pad(t_401, [1, 1, 1, 1], value=0) + t_402 = self.n_Conv_19(t_401_padded) + t_403 = F.relu(t_402) + t_404 = self.n_Conv_20(t_403) + t_405 = torch.add(t_404, t_399) + t_406 = F.relu(t_405) + t_407 = self.n_Conv_21(t_406) + t_408 = F.relu(t_407) + t_408_padded = F.pad(t_408, [1, 1, 1, 1], value=0) + t_409 = self.n_Conv_22(t_408_padded) + t_410 = F.relu(t_409) + t_411 = self.n_Conv_23(t_410) + t_412 = torch.add(t_411, t_406) + t_413 = F.relu(t_412) + t_414 = self.n_Conv_24(t_413) + t_415 = F.relu(t_414) + t_415_padded = F.pad(t_415, [1, 1, 1, 1], value=0) + t_416 = self.n_Conv_25(t_415_padded) + t_417 = F.relu(t_416) + t_418 = self.n_Conv_26(t_417) + t_419 = torch.add(t_418, t_413) + t_420 = F.relu(t_419) + t_421 = self.n_Conv_27(t_420) + t_422 = F.relu(t_421) + t_422_padded = F.pad(t_422, [1, 1, 1, 1], value=0) + t_423 = self.n_Conv_28(t_422_padded) + t_424 = F.relu(t_423) + t_425 = self.n_Conv_29(t_424) + t_426 = torch.add(t_425, t_420) + t_427 = F.relu(t_426) + t_428 = self.n_Conv_30(t_427) + t_429 = F.relu(t_428) + t_429_padded = F.pad(t_429, [1, 1, 1, 1], value=0) + t_430 = self.n_Conv_31(t_429_padded) + t_431 = F.relu(t_430) + t_432 = self.n_Conv_32(t_431) + t_433 = torch.add(t_432, t_427) + t_434 = F.relu(t_433) + t_435 = self.n_Conv_33(t_434) + t_436 = F.relu(t_435) + t_436_padded = F.pad(t_436, [1, 1, 1, 1], value=0) + t_437 = self.n_Conv_34(t_436_padded) + t_438 = F.relu(t_437) + t_439 = self.n_Conv_35(t_438) + t_440 = torch.add(t_439, t_434) + t_441 = F.relu(t_440) + t_442 = self.n_Conv_36(t_441) + t_443 = self.n_Conv_37(t_441) + t_444 = F.relu(t_443) + t_444_padded = F.pad(t_444, [0, 1, 0, 1], value=0) + t_445 = self.n_Conv_38(t_444_padded) + t_446 = F.relu(t_445) + t_447 = self.n_Conv_39(t_446) + t_448 = torch.add(t_447, t_442) + t_449 = F.relu(t_448) + t_450 = self.n_Conv_40(t_449) + t_451 = F.relu(t_450) + t_451_padded = F.pad(t_451, [1, 1, 1, 1], value=0) + t_452 = self.n_Conv_41(t_451_padded) + t_453 = F.relu(t_452) + t_454 = self.n_Conv_42(t_453) + t_455 = torch.add(t_454, t_449) + t_456 = F.relu(t_455) + t_457 = self.n_Conv_43(t_456) + t_458 = F.relu(t_457) + t_458_padded = F.pad(t_458, [1, 1, 1, 1], value=0) + t_459 = self.n_Conv_44(t_458_padded) + t_460 = F.relu(t_459) + t_461 = self.n_Conv_45(t_460) + t_462 = torch.add(t_461, t_456) + t_463 = F.relu(t_462) + t_464 = self.n_Conv_46(t_463) + t_465 = F.relu(t_464) + t_465_padded = F.pad(t_465, [1, 1, 1, 1], value=0) + t_466 = self.n_Conv_47(t_465_padded) + t_467 = F.relu(t_466) + t_468 = self.n_Conv_48(t_467) + t_469 = torch.add(t_468, t_463) + t_470 = F.relu(t_469) + t_471 = self.n_Conv_49(t_470) + t_472 = F.relu(t_471) + t_472_padded = F.pad(t_472, [1, 1, 1, 1], value=0) + t_473 = self.n_Conv_50(t_472_padded) + t_474 = F.relu(t_473) + t_475 = self.n_Conv_51(t_474) + t_476 = torch.add(t_475, t_470) + t_477 = F.relu(t_476) + t_478 = self.n_Conv_52(t_477) + t_479 = F.relu(t_478) + t_479_padded = F.pad(t_479, [1, 1, 1, 1], value=0) + t_480 = self.n_Conv_53(t_479_padded) + t_481 = F.relu(t_480) + t_482 = self.n_Conv_54(t_481) + t_483 = torch.add(t_482, t_477) + t_484 = F.relu(t_483) + t_485 = self.n_Conv_55(t_484) + t_486 = F.relu(t_485) + t_486_padded = F.pad(t_486, [1, 1, 1, 1], value=0) + t_487 = self.n_Conv_56(t_486_padded) + t_488 = F.relu(t_487) + t_489 = self.n_Conv_57(t_488) + t_490 = torch.add(t_489, t_484) + t_491 = F.relu(t_490) + t_492 = self.n_Conv_58(t_491) + t_493 = F.relu(t_492) + t_493_padded = F.pad(t_493, [1, 1, 1, 1], value=0) + t_494 = self.n_Conv_59(t_493_padded) + t_495 = F.relu(t_494) + t_496 = self.n_Conv_60(t_495) + t_497 = torch.add(t_496, t_491) + t_498 = F.relu(t_497) + t_499 = self.n_Conv_61(t_498) + t_500 = F.relu(t_499) + t_500_padded = F.pad(t_500, [1, 1, 1, 1], value=0) + t_501 = self.n_Conv_62(t_500_padded) + t_502 = F.relu(t_501) + t_503 = self.n_Conv_63(t_502) + t_504 = torch.add(t_503, t_498) + t_505 = F.relu(t_504) + t_506 = self.n_Conv_64(t_505) + t_507 = F.relu(t_506) + t_507_padded = F.pad(t_507, [1, 1, 1, 1], value=0) + t_508 = self.n_Conv_65(t_507_padded) + t_509 = F.relu(t_508) + t_510 = self.n_Conv_66(t_509) + t_511 = torch.add(t_510, t_505) + t_512 = F.relu(t_511) + t_513 = self.n_Conv_67(t_512) + t_514 = F.relu(t_513) + t_514_padded = F.pad(t_514, [1, 1, 1, 1], value=0) + t_515 = self.n_Conv_68(t_514_padded) + t_516 = F.relu(t_515) + t_517 = self.n_Conv_69(t_516) + t_518 = torch.add(t_517, t_512) + t_519 = F.relu(t_518) + t_520 = self.n_Conv_70(t_519) + t_521 = F.relu(t_520) + t_521_padded = F.pad(t_521, [1, 1, 1, 1], value=0) + t_522 = self.n_Conv_71(t_521_padded) + t_523 = F.relu(t_522) + t_524 = self.n_Conv_72(t_523) + t_525 = torch.add(t_524, t_519) + t_526 = F.relu(t_525) + t_527 = self.n_Conv_73(t_526) + t_528 = F.relu(t_527) + t_528_padded = F.pad(t_528, [1, 1, 1, 1], value=0) + t_529 = self.n_Conv_74(t_528_padded) + t_530 = F.relu(t_529) + t_531 = self.n_Conv_75(t_530) + t_532 = torch.add(t_531, t_526) + t_533 = F.relu(t_532) + t_534 = self.n_Conv_76(t_533) + t_535 = F.relu(t_534) + t_535_padded = F.pad(t_535, [1, 1, 1, 1], value=0) + t_536 = self.n_Conv_77(t_535_padded) + t_537 = F.relu(t_536) + t_538 = self.n_Conv_78(t_537) + t_539 = torch.add(t_538, t_533) + t_540 = F.relu(t_539) + t_541 = self.n_Conv_79(t_540) + t_542 = F.relu(t_541) + t_542_padded = F.pad(t_542, [1, 1, 1, 1], value=0) + t_543 = self.n_Conv_80(t_542_padded) + t_544 = F.relu(t_543) + t_545 = self.n_Conv_81(t_544) + t_546 = torch.add(t_545, t_540) + t_547 = F.relu(t_546) + t_548 = self.n_Conv_82(t_547) + t_549 = F.relu(t_548) + t_549_padded = F.pad(t_549, [1, 1, 1, 1], value=0) + t_550 = self.n_Conv_83(t_549_padded) + t_551 = F.relu(t_550) + t_552 = self.n_Conv_84(t_551) + t_553 = torch.add(t_552, t_547) + t_554 = F.relu(t_553) + t_555 = self.n_Conv_85(t_554) + t_556 = F.relu(t_555) + t_556_padded = F.pad(t_556, [1, 1, 1, 1], value=0) + t_557 = self.n_Conv_86(t_556_padded) + t_558 = F.relu(t_557) + t_559 = self.n_Conv_87(t_558) + t_560 = torch.add(t_559, t_554) + t_561 = F.relu(t_560) + t_562 = self.n_Conv_88(t_561) + t_563 = F.relu(t_562) + t_563_padded = F.pad(t_563, [1, 1, 1, 1], value=0) + t_564 = self.n_Conv_89(t_563_padded) + t_565 = F.relu(t_564) + t_566 = self.n_Conv_90(t_565) + t_567 = torch.add(t_566, t_561) + t_568 = F.relu(t_567) + t_569 = self.n_Conv_91(t_568) + t_570 = F.relu(t_569) + t_570_padded = F.pad(t_570, [1, 1, 1, 1], value=0) + t_571 = self.n_Conv_92(t_570_padded) + t_572 = F.relu(t_571) + t_573 = self.n_Conv_93(t_572) + t_574 = torch.add(t_573, t_568) + t_575 = F.relu(t_574) + t_576 = self.n_Conv_94(t_575) + t_577 = F.relu(t_576) + t_577_padded = F.pad(t_577, [1, 1, 1, 1], value=0) + t_578 = self.n_Conv_95(t_577_padded) + t_579 = F.relu(t_578) + t_580 = self.n_Conv_96(t_579) + t_581 = torch.add(t_580, t_575) + t_582 = F.relu(t_581) + t_583 = self.n_Conv_97(t_582) + t_584 = F.relu(t_583) + t_584_padded = F.pad(t_584, [0, 1, 0, 1], value=0) + t_585 = self.n_Conv_98(t_584_padded) + t_586 = F.relu(t_585) + t_587 = self.n_Conv_99(t_586) + t_588 = self.n_Conv_100(t_582) + t_589 = torch.add(t_587, t_588) + t_590 = F.relu(t_589) + t_591 = self.n_Conv_101(t_590) + t_592 = F.relu(t_591) + t_592_padded = F.pad(t_592, [1, 1, 1, 1], value=0) + t_593 = self.n_Conv_102(t_592_padded) + t_594 = F.relu(t_593) + t_595 = self.n_Conv_103(t_594) + t_596 = torch.add(t_595, t_590) + t_597 = F.relu(t_596) + t_598 = self.n_Conv_104(t_597) + t_599 = F.relu(t_598) + t_599_padded = F.pad(t_599, [1, 1, 1, 1], value=0) + t_600 = self.n_Conv_105(t_599_padded) + t_601 = F.relu(t_600) + t_602 = self.n_Conv_106(t_601) + t_603 = torch.add(t_602, t_597) + t_604 = F.relu(t_603) + t_605 = self.n_Conv_107(t_604) + t_606 = F.relu(t_605) + t_606_padded = F.pad(t_606, [1, 1, 1, 1], value=0) + t_607 = self.n_Conv_108(t_606_padded) + t_608 = F.relu(t_607) + t_609 = self.n_Conv_109(t_608) + t_610 = torch.add(t_609, t_604) + t_611 = F.relu(t_610) + t_612 = self.n_Conv_110(t_611) + t_613 = F.relu(t_612) + t_613_padded = F.pad(t_613, [1, 1, 1, 1], value=0) + t_614 = self.n_Conv_111(t_613_padded) + t_615 = F.relu(t_614) + t_616 = self.n_Conv_112(t_615) + t_617 = torch.add(t_616, t_611) + t_618 = F.relu(t_617) + t_619 = self.n_Conv_113(t_618) + t_620 = F.relu(t_619) + t_620_padded = F.pad(t_620, [1, 1, 1, 1], value=0) + t_621 = self.n_Conv_114(t_620_padded) + t_622 = F.relu(t_621) + t_623 = self.n_Conv_115(t_622) + t_624 = torch.add(t_623, t_618) + t_625 = F.relu(t_624) + t_626 = self.n_Conv_116(t_625) + t_627 = F.relu(t_626) + t_627_padded = F.pad(t_627, [1, 1, 1, 1], value=0) + t_628 = self.n_Conv_117(t_627_padded) + t_629 = F.relu(t_628) + t_630 = self.n_Conv_118(t_629) + t_631 = torch.add(t_630, t_625) + t_632 = F.relu(t_631) + t_633 = self.n_Conv_119(t_632) + t_634 = F.relu(t_633) + t_634_padded = F.pad(t_634, [1, 1, 1, 1], value=0) + t_635 = self.n_Conv_120(t_634_padded) + t_636 = F.relu(t_635) + t_637 = self.n_Conv_121(t_636) + t_638 = torch.add(t_637, t_632) + t_639 = F.relu(t_638) + t_640 = self.n_Conv_122(t_639) + t_641 = F.relu(t_640) + t_641_padded = F.pad(t_641, [1, 1, 1, 1], value=0) + t_642 = self.n_Conv_123(t_641_padded) + t_643 = F.relu(t_642) + t_644 = self.n_Conv_124(t_643) + t_645 = torch.add(t_644, t_639) + t_646 = F.relu(t_645) + t_647 = self.n_Conv_125(t_646) + t_648 = F.relu(t_647) + t_648_padded = F.pad(t_648, [1, 1, 1, 1], value=0) + t_649 = self.n_Conv_126(t_648_padded) + t_650 = F.relu(t_649) + t_651 = self.n_Conv_127(t_650) + t_652 = torch.add(t_651, t_646) + t_653 = F.relu(t_652) + t_654 = self.n_Conv_128(t_653) + t_655 = F.relu(t_654) + t_655_padded = F.pad(t_655, [1, 1, 1, 1], value=0) + t_656 = self.n_Conv_129(t_655_padded) + t_657 = F.relu(t_656) + t_658 = self.n_Conv_130(t_657) + t_659 = torch.add(t_658, t_653) + t_660 = F.relu(t_659) + t_661 = self.n_Conv_131(t_660) + t_662 = F.relu(t_661) + t_662_padded = F.pad(t_662, [1, 1, 1, 1], value=0) + t_663 = self.n_Conv_132(t_662_padded) + t_664 = F.relu(t_663) + t_665 = self.n_Conv_133(t_664) + t_666 = torch.add(t_665, t_660) + t_667 = F.relu(t_666) + t_668 = self.n_Conv_134(t_667) + t_669 = F.relu(t_668) + t_669_padded = F.pad(t_669, [1, 1, 1, 1], value=0) + t_670 = self.n_Conv_135(t_669_padded) + t_671 = F.relu(t_670) + t_672 = self.n_Conv_136(t_671) + t_673 = torch.add(t_672, t_667) + t_674 = F.relu(t_673) + t_675 = self.n_Conv_137(t_674) + t_676 = F.relu(t_675) + t_676_padded = F.pad(t_676, [1, 1, 1, 1], value=0) + t_677 = self.n_Conv_138(t_676_padded) + t_678 = F.relu(t_677) + t_679 = self.n_Conv_139(t_678) + t_680 = torch.add(t_679, t_674) + t_681 = F.relu(t_680) + t_682 = self.n_Conv_140(t_681) + t_683 = F.relu(t_682) + t_683_padded = F.pad(t_683, [1, 1, 1, 1], value=0) + t_684 = self.n_Conv_141(t_683_padded) + t_685 = F.relu(t_684) + t_686 = self.n_Conv_142(t_685) + t_687 = torch.add(t_686, t_681) + t_688 = F.relu(t_687) + t_689 = self.n_Conv_143(t_688) + t_690 = F.relu(t_689) + t_690_padded = F.pad(t_690, [1, 1, 1, 1], value=0) + t_691 = self.n_Conv_144(t_690_padded) + t_692 = F.relu(t_691) + t_693 = self.n_Conv_145(t_692) + t_694 = torch.add(t_693, t_688) + t_695 = F.relu(t_694) + t_696 = self.n_Conv_146(t_695) + t_697 = F.relu(t_696) + t_697_padded = F.pad(t_697, [1, 1, 1, 1], value=0) + t_698 = self.n_Conv_147(t_697_padded) + t_699 = F.relu(t_698) + t_700 = self.n_Conv_148(t_699) + t_701 = torch.add(t_700, t_695) + t_702 = F.relu(t_701) + t_703 = self.n_Conv_149(t_702) + t_704 = F.relu(t_703) + t_704_padded = F.pad(t_704, [1, 1, 1, 1], value=0) + t_705 = self.n_Conv_150(t_704_padded) + t_706 = F.relu(t_705) + t_707 = self.n_Conv_151(t_706) + t_708 = torch.add(t_707, t_702) + t_709 = F.relu(t_708) + t_710 = self.n_Conv_152(t_709) + t_711 = F.relu(t_710) + t_711_padded = F.pad(t_711, [1, 1, 1, 1], value=0) + t_712 = self.n_Conv_153(t_711_padded) + t_713 = F.relu(t_712) + t_714 = self.n_Conv_154(t_713) + t_715 = torch.add(t_714, t_709) + t_716 = F.relu(t_715) + t_717 = self.n_Conv_155(t_716) + t_718 = F.relu(t_717) + t_718_padded = F.pad(t_718, [1, 1, 1, 1], value=0) + t_719 = self.n_Conv_156(t_718_padded) + t_720 = F.relu(t_719) + t_721 = self.n_Conv_157(t_720) + t_722 = torch.add(t_721, t_716) + t_723 = F.relu(t_722) + t_724 = self.n_Conv_158(t_723) + t_725 = self.n_Conv_159(t_723) + t_726 = F.relu(t_725) + t_726_padded = F.pad(t_726, [0, 1, 0, 1], value=0) + t_727 = self.n_Conv_160(t_726_padded) + t_728 = F.relu(t_727) + t_729 = self.n_Conv_161(t_728) + t_730 = torch.add(t_729, t_724) + t_731 = F.relu(t_730) + t_732 = self.n_Conv_162(t_731) + t_733 = F.relu(t_732) + t_733_padded = F.pad(t_733, [1, 1, 1, 1], value=0) + t_734 = self.n_Conv_163(t_733_padded) + t_735 = F.relu(t_734) + t_736 = self.n_Conv_164(t_735) + t_737 = torch.add(t_736, t_731) + t_738 = F.relu(t_737) + t_739 = self.n_Conv_165(t_738) + t_740 = F.relu(t_739) + t_740_padded = F.pad(t_740, [1, 1, 1, 1], value=0) + t_741 = self.n_Conv_166(t_740_padded) + t_742 = F.relu(t_741) + t_743 = self.n_Conv_167(t_742) + t_744 = torch.add(t_743, t_738) + t_745 = F.relu(t_744) + t_746 = self.n_Conv_168(t_745) + t_747 = self.n_Conv_169(t_745) + t_748 = F.relu(t_747) + t_748_padded = F.pad(t_748, [0, 1, 0, 1], value=0) + t_749 = self.n_Conv_170(t_748_padded) + t_750 = F.relu(t_749) + t_751 = self.n_Conv_171(t_750) + t_752 = torch.add(t_751, t_746) + t_753 = F.relu(t_752) + t_754 = self.n_Conv_172(t_753) + t_755 = F.relu(t_754) + t_755_padded = F.pad(t_755, [1, 1, 1, 1], value=0) + t_756 = self.n_Conv_173(t_755_padded) + t_757 = F.relu(t_756) + t_758 = self.n_Conv_174(t_757) + t_759 = torch.add(t_758, t_753) + t_760 = F.relu(t_759) + t_761 = self.n_Conv_175(t_760) + t_762 = F.relu(t_761) + t_762_padded = F.pad(t_762, [1, 1, 1, 1], value=0) + t_763 = self.n_Conv_176(t_762_padded) + t_764 = F.relu(t_763) + t_765 = self.n_Conv_177(t_764) + t_766 = torch.add(t_765, t_760) + t_767 = F.relu(t_766) + t_768 = self.n_Conv_178(t_767) + t_769 = F.avg_pool2d(t_768, kernel_size=t_768.shape[-2:]) + t_770 = torch.squeeze(t_769, 3) + t_770 = torch.squeeze(t_770, 2) + t_771 = torch.sigmoid(t_770) + return t_771 + + def load_state_dict(self, state_dict, **kwargs): + self.tags = state_dict.get('tags', []) + + super(DeepDanbooruModel, self).load_state_dict({k: v for k, v in state_dict.items() if k != 'tags'}) + diff --git a/modules/devices.py b/modules/devices.py new file mode 100644 index 0000000000000000000000000000000000000000..ea1f712f95040d3fc4205ea7cd3aef134724ccfc --- /dev/null +++ b/modules/devices.py @@ -0,0 +1,167 @@ +import sys +import contextlib +from functools import lru_cache + +import torch +from modules import errors, shared + +if sys.platform == "darwin": + from modules import mac_specific + +if shared.cmd_opts.use_ipex: + from modules import xpu_specific + + +def has_xpu() -> bool: + return shared.cmd_opts.use_ipex and xpu_specific.has_xpu + + +def has_mps() -> bool: + if sys.platform != "darwin": + return False + else: + return mac_specific.has_mps + + +def get_cuda_device_string(): + if shared.cmd_opts.device_id is not None: + return f"cuda:{shared.cmd_opts.device_id}" + + return "cuda" + + +def get_optimal_device_name(): + if torch.cuda.is_available(): + return get_cuda_device_string() + + if has_mps(): + return "mps" + + if has_xpu(): + return xpu_specific.get_xpu_device_string() + + return "cpu" + + +def get_optimal_device(): + return torch.device(get_optimal_device_name()) + + +def get_device_for(task): + if task in shared.cmd_opts.use_cpu or "all" in shared.cmd_opts.use_cpu: + return cpu + + return get_optimal_device() + + +def torch_gc(): + + if torch.cuda.is_available(): + with torch.cuda.device(get_cuda_device_string()): + torch.cuda.empty_cache() + torch.cuda.ipc_collect() + + if has_mps(): + mac_specific.torch_mps_gc() + + if has_xpu(): + xpu_specific.torch_xpu_gc() + + +def enable_tf32(): + if torch.cuda.is_available(): + + # enabling benchmark option seems to enable a range of cards to do fp16 when they otherwise can't + # see https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/4407 + device_id = (int(shared.cmd_opts.device_id) if shared.cmd_opts.device_id is not None and shared.cmd_opts.device_id.isdigit() else 0) or torch.cuda.current_device() + if torch.cuda.get_device_capability(device_id) == (7, 5) and torch.cuda.get_device_name(device_id).startswith("NVIDIA GeForce GTX 16"): + torch.backends.cudnn.benchmark = True + + torch.backends.cuda.matmul.allow_tf32 = True + torch.backends.cudnn.allow_tf32 = True + + +errors.run(enable_tf32, "Enabling TF32") + +cpu: torch.device = torch.device("cpu") +device: torch.device = None +device_interrogate: torch.device = None +device_gfpgan: torch.device = None +device_esrgan: torch.device = None +device_codeformer: torch.device = None +dtype: torch.dtype = torch.float16 +dtype_vae: torch.dtype = torch.float16 +dtype_unet: torch.dtype = torch.float16 +unet_needs_upcast = False + + +def cond_cast_unet(input): + return input.to(dtype_unet) if unet_needs_upcast else input + + +def cond_cast_float(input): + return input.float() if unet_needs_upcast else input + + +nv_rng = None + + +def autocast(disable=False): + if disable: + return contextlib.nullcontext() + + if dtype == torch.float32 or shared.cmd_opts.precision == "full": + return contextlib.nullcontext() + + return torch.autocast("cuda") + + +def without_autocast(disable=False): + return torch.autocast("cuda", enabled=False) if torch.is_autocast_enabled() and not disable else contextlib.nullcontext() + + +class NansException(Exception): + pass + + +def test_for_nans(x, where): + if shared.cmd_opts.disable_nan_check: + return + + if not torch.all(torch.isnan(x)).item(): + return + + if where == "unet": + message = "A tensor with all NaNs was produced in Unet." + + if not shared.cmd_opts.no_half: + message += " This could be either because there's not enough precision to represent the picture, or because your video card does not support half type. Try setting the \"Upcast cross attention layer to float32\" option in Settings > Stable Diffusion or using the --no-half commandline argument to fix this." + + elif where == "vae": + message = "A tensor with all NaNs was produced in VAE." + + if not shared.cmd_opts.no_half and not shared.cmd_opts.no_half_vae: + message += " This could be because there's not enough precision to represent the picture. Try adding --no-half-vae commandline argument to fix this." + else: + message = "A tensor with all NaNs was produced." + + message += " Use --disable-nan-check commandline argument to disable this check." + + raise NansException(message) + + +@lru_cache +def first_time_calculation(): + """ + just do any calculation with pytorch layers - the first time this is done it allocaltes about 700MB of memory and + spends about 2.7 seconds doing that, at least wih NVidia. + """ + + x = torch.zeros((1, 1)).to(device, dtype) + linear = torch.nn.Linear(1, 1).to(device, dtype) + linear(x) + + x = torch.zeros((1, 1, 3, 3)).to(device, dtype) + conv2d = torch.nn.Conv2d(1, 1, (3, 3)).to(device, dtype) + conv2d(x) + diff --git a/modules/errors.py b/modules/errors.py new file mode 100644 index 0000000000000000000000000000000000000000..f788c8d52729fb945fc155ad7d766ba848c9d21b --- /dev/null +++ b/modules/errors.py @@ -0,0 +1,150 @@ +import sys +import textwrap +import traceback + + +exception_records = [] + + +def format_traceback(tb): + return [[f"{x.filename}, line {x.lineno}, {x.name}", x.line] for x in traceback.extract_tb(tb)] + + +def format_exception(e, tb): + return {"exception": str(e), "traceback": format_traceback(tb)} + + +def get_exceptions(): + try: + return list(reversed(exception_records)) + except Exception as e: + return str(e) + + +def record_exception(): + _, e, tb = sys.exc_info() + if e is None: + return + + if exception_records and exception_records[-1] == e: + return + + exception_records.append(format_exception(e, tb)) + + if len(exception_records) > 5: + exception_records.pop(0) + + +def report(message: str, *, exc_info: bool = False) -> None: + """ + Print an error message to stderr, with optional traceback. + """ + + record_exception() + + for line in message.splitlines(): + print("***", line, file=sys.stderr) + if exc_info: + print(textwrap.indent(traceback.format_exc(), " "), file=sys.stderr) + print("---", file=sys.stderr) + + +def print_error_explanation(message): + record_exception() + + lines = message.strip().split("\n") + max_len = max([len(x) for x in lines]) + + print('=' * max_len, file=sys.stderr) + for line in lines: + print(line, file=sys.stderr) + print('=' * max_len, file=sys.stderr) + + +def display(e: Exception, task, *, full_traceback=False): + record_exception() + + print(f"{task or 'error'}: {type(e).__name__}", file=sys.stderr) + te = traceback.TracebackException.from_exception(e) + if full_traceback: + # include frames leading up to the try-catch block + te.stack = traceback.StackSummary(traceback.extract_stack()[:-2] + te.stack) + print(*te.format(), sep="", file=sys.stderr) + + message = str(e) + if "copying a param with shape torch.Size([640, 1024]) from checkpoint, the shape in current model is torch.Size([640, 768])" in message: + print_error_explanation(""" +The most likely cause of this is you are trying to load Stable Diffusion 2.0 model without specifying its config file. +See https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features#stable-diffusion-20 for how to solve this. + """) + + +already_displayed = {} + + +def display_once(e: Exception, task): + record_exception() + + if task in already_displayed: + return + + display(e, task) + + already_displayed[task] = 1 + + +def run(code, task): + try: + code() + except Exception as e: + display(task, e) + + +def check_versions(): + from packaging import version + from modules import shared + + import torch + import gradio + + expected_torch_version = "2.0.0" + expected_xformers_version = "0.0.20" + expected_gradio_version = "3.41.2" + + if version.parse(torch.__version__) < version.parse(expected_torch_version): + print_error_explanation(f""" +You are running torch {torch.__version__}. +The program is tested to work with torch {expected_torch_version}. +To reinstall the desired version, run with commandline flag --reinstall-torch. +Beware that this will cause a lot of large files to be downloaded, as well as +there are reports of issues with training tab on the latest version. + +Use --skip-version-check commandline argument to disable this check. + """.strip()) + + if shared.xformers_available: + import xformers + + if version.parse(xformers.__version__) < version.parse(expected_xformers_version): + print_error_explanation(f""" +You are running xformers {xformers.__version__}. +The program is tested to work with xformers {expected_xformers_version}. +To reinstall the desired version, run with commandline flag --reinstall-xformers. + +Use --skip-version-check commandline argument to disable this check. + """.strip()) + + if gradio.__version__ != expected_gradio_version: + print_error_explanation(f""" +You are running gradio {gradio.__version__}. +The program is designed to work with gradio {expected_gradio_version}. +Using a different version of gradio is extremely likely to break the program. + +Reasons why you have the mismatched gradio version can be: + - you use --skip-install flag. + - you use webui.py to start the program instead of launch.py. + - an extension installs the incompatible gradio version. + +Use --skip-version-check commandline argument to disable this check. + """.strip()) + diff --git a/modules/esrgan_model.py b/modules/esrgan_model.py new file mode 100644 index 0000000000000000000000000000000000000000..1e4260e2c62dbb14387e90e369dc109f435867b0 --- /dev/null +++ b/modules/esrgan_model.py @@ -0,0 +1,229 @@ +import sys + +import numpy as np +import torch +from PIL import Image + +import modules.esrgan_model_arch as arch +from modules import modelloader, images, devices +from modules.shared import opts +from modules.upscaler import Upscaler, UpscalerData + + +def mod2normal(state_dict): + # this code is copied from https://github.com/victorca25/iNNfer + if 'conv_first.weight' in state_dict: + crt_net = {} + items = list(state_dict) + + crt_net['model.0.weight'] = state_dict['conv_first.weight'] + crt_net['model.0.bias'] = state_dict['conv_first.bias'] + + for k in items.copy(): + if 'RDB' in k: + ori_k = k.replace('RRDB_trunk.', 'model.1.sub.') + if '.weight' in k: + ori_k = ori_k.replace('.weight', '.0.weight') + elif '.bias' in k: + ori_k = ori_k.replace('.bias', '.0.bias') + crt_net[ori_k] = state_dict[k] + items.remove(k) + + crt_net['model.1.sub.23.weight'] = state_dict['trunk_conv.weight'] + crt_net['model.1.sub.23.bias'] = state_dict['trunk_conv.bias'] + crt_net['model.3.weight'] = state_dict['upconv1.weight'] + crt_net['model.3.bias'] = state_dict['upconv1.bias'] + crt_net['model.6.weight'] = state_dict['upconv2.weight'] + crt_net['model.6.bias'] = state_dict['upconv2.bias'] + crt_net['model.8.weight'] = state_dict['HRconv.weight'] + crt_net['model.8.bias'] = state_dict['HRconv.bias'] + crt_net['model.10.weight'] = state_dict['conv_last.weight'] + crt_net['model.10.bias'] = state_dict['conv_last.bias'] + state_dict = crt_net + return state_dict + + +def resrgan2normal(state_dict, nb=23): + # this code is copied from https://github.com/victorca25/iNNfer + if "conv_first.weight" in state_dict and "body.0.rdb1.conv1.weight" in state_dict: + re8x = 0 + crt_net = {} + items = list(state_dict) + + crt_net['model.0.weight'] = state_dict['conv_first.weight'] + crt_net['model.0.bias'] = state_dict['conv_first.bias'] + + for k in items.copy(): + if "rdb" in k: + ori_k = k.replace('body.', 'model.1.sub.') + ori_k = ori_k.replace('.rdb', '.RDB') + if '.weight' in k: + ori_k = ori_k.replace('.weight', '.0.weight') + elif '.bias' in k: + ori_k = ori_k.replace('.bias', '.0.bias') + crt_net[ori_k] = state_dict[k] + items.remove(k) + + crt_net[f'model.1.sub.{nb}.weight'] = state_dict['conv_body.weight'] + crt_net[f'model.1.sub.{nb}.bias'] = state_dict['conv_body.bias'] + crt_net['model.3.weight'] = state_dict['conv_up1.weight'] + crt_net['model.3.bias'] = state_dict['conv_up1.bias'] + crt_net['model.6.weight'] = state_dict['conv_up2.weight'] + crt_net['model.6.bias'] = state_dict['conv_up2.bias'] + + if 'conv_up3.weight' in state_dict: + # modification supporting: https://github.com/ai-forever/Real-ESRGAN/blob/main/RealESRGAN/rrdbnet_arch.py + re8x = 3 + crt_net['model.9.weight'] = state_dict['conv_up3.weight'] + crt_net['model.9.bias'] = state_dict['conv_up3.bias'] + + crt_net[f'model.{8+re8x}.weight'] = state_dict['conv_hr.weight'] + crt_net[f'model.{8+re8x}.bias'] = state_dict['conv_hr.bias'] + crt_net[f'model.{10+re8x}.weight'] = state_dict['conv_last.weight'] + crt_net[f'model.{10+re8x}.bias'] = state_dict['conv_last.bias'] + + state_dict = crt_net + return state_dict + + +def infer_params(state_dict): + # this code is copied from https://github.com/victorca25/iNNfer + scale2x = 0 + scalemin = 6 + n_uplayer = 0 + plus = False + + for block in list(state_dict): + parts = block.split(".") + n_parts = len(parts) + if n_parts == 5 and parts[2] == "sub": + nb = int(parts[3]) + elif n_parts == 3: + part_num = int(parts[1]) + if (part_num > scalemin + and parts[0] == "model" + and parts[2] == "weight"): + scale2x += 1 + if part_num > n_uplayer: + n_uplayer = part_num + out_nc = state_dict[block].shape[0] + if not plus and "conv1x1" in block: + plus = True + + nf = state_dict["model.0.weight"].shape[0] + in_nc = state_dict["model.0.weight"].shape[1] + out_nc = out_nc + scale = 2 ** scale2x + + return in_nc, out_nc, nf, nb, plus, scale + + +class UpscalerESRGAN(Upscaler): + def __init__(self, dirname): + self.name = "ESRGAN" + self.model_url = "https://github.com/cszn/KAIR/releases/download/v1.0/ESRGAN.pth" + self.model_name = "ESRGAN_4x" + self.scalers = [] + self.user_path = dirname + super().__init__() + model_paths = self.find_models(ext_filter=[".pt", ".pth"]) + scalers = [] + if len(model_paths) == 0: + scaler_data = UpscalerData(self.model_name, self.model_url, self, 4) + scalers.append(scaler_data) + for file in model_paths: + if file.startswith("http"): + name = self.model_name + else: + name = modelloader.friendly_name(file) + + scaler_data = UpscalerData(name, file, self, 4) + self.scalers.append(scaler_data) + + def do_upscale(self, img, selected_model): + try: + model = self.load_model(selected_model) + except Exception as e: + print(f"Unable to load ESRGAN model {selected_model}: {e}", file=sys.stderr) + return img + model.to(devices.device_esrgan) + img = esrgan_upscale(model, img) + return img + + def load_model(self, path: str): + if path.startswith("http"): + # TODO: this doesn't use `path` at all? + filename = modelloader.load_file_from_url( + url=self.model_url, + model_dir=self.model_download_path, + file_name=f"{self.model_name}.pth", + ) + else: + filename = path + + state_dict = torch.load(filename, map_location='cpu' if devices.device_esrgan.type == 'mps' else None) + + if "params_ema" in state_dict: + state_dict = state_dict["params_ema"] + elif "params" in state_dict: + state_dict = state_dict["params"] + num_conv = 16 if "realesr-animevideov3" in filename else 32 + model = arch.SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=num_conv, upscale=4, act_type='prelu') + model.load_state_dict(state_dict) + model.eval() + return model + + if "body.0.rdb1.conv1.weight" in state_dict and "conv_first.weight" in state_dict: + nb = 6 if "RealESRGAN_x4plus_anime_6B" in filename else 23 + state_dict = resrgan2normal(state_dict, nb) + elif "conv_first.weight" in state_dict: + state_dict = mod2normal(state_dict) + elif "model.0.weight" not in state_dict: + raise Exception("The file is not a recognized ESRGAN model.") + + in_nc, out_nc, nf, nb, plus, mscale = infer_params(state_dict) + + model = arch.RRDBNet(in_nc=in_nc, out_nc=out_nc, nf=nf, nb=nb, upscale=mscale, plus=plus) + model.load_state_dict(state_dict) + model.eval() + + return model + + +def upscale_without_tiling(model, img): + img = np.array(img) + img = img[:, :, ::-1] + img = np.ascontiguousarray(np.transpose(img, (2, 0, 1))) / 255 + img = torch.from_numpy(img).float() + img = img.unsqueeze(0).to(devices.device_esrgan) + with torch.no_grad(): + output = model(img) + output = output.squeeze().float().cpu().clamp_(0, 1).numpy() + output = 255. * np.moveaxis(output, 0, 2) + output = output.astype(np.uint8) + output = output[:, :, ::-1] + return Image.fromarray(output, 'RGB') + + +def esrgan_upscale(model, img): + if opts.ESRGAN_tile == 0: + return upscale_without_tiling(model, img) + + grid = images.split_grid(img, opts.ESRGAN_tile, opts.ESRGAN_tile, opts.ESRGAN_tile_overlap) + newtiles = [] + scale_factor = 1 + + for y, h, row in grid.tiles: + newrow = [] + for tiledata in row: + x, w, tile = tiledata + + output = upscale_without_tiling(model, tile) + scale_factor = output.width // tile.width + + newrow.append([x * scale_factor, w * scale_factor, output]) + newtiles.append([y * scale_factor, h * scale_factor, newrow]) + + newgrid = images.Grid(newtiles, grid.tile_w * scale_factor, grid.tile_h * scale_factor, grid.image_w * scale_factor, grid.image_h * scale_factor, grid.overlap * scale_factor) + output = images.combine_grid(newgrid) + return output diff --git a/modules/esrgan_model_arch.py b/modules/esrgan_model_arch.py new file mode 100644 index 0000000000000000000000000000000000000000..353c70dd867cb894a0ac208f39394280175e4e14 --- /dev/null +++ b/modules/esrgan_model_arch.py @@ -0,0 +1,465 @@ +# this file is adapted from https://github.com/victorca25/iNNfer + +from collections import OrderedDict +import math +import torch +import torch.nn as nn +import torch.nn.functional as F + + +#################### +# RRDBNet Generator +#################### + +class RRDBNet(nn.Module): + def __init__(self, in_nc, out_nc, nf, nb, nr=3, gc=32, upscale=4, norm_type=None, + act_type='leakyrelu', mode='CNA', upsample_mode='upconv', convtype='Conv2D', + finalact=None, gaussian_noise=False, plus=False): + super(RRDBNet, self).__init__() + n_upscale = int(math.log(upscale, 2)) + if upscale == 3: + n_upscale = 1 + + self.resrgan_scale = 0 + if in_nc % 16 == 0: + self.resrgan_scale = 1 + elif in_nc != 4 and in_nc % 4 == 0: + self.resrgan_scale = 2 + + fea_conv = conv_block(in_nc, nf, kernel_size=3, norm_type=None, act_type=None, convtype=convtype) + rb_blocks = [RRDB(nf, nr, kernel_size=3, gc=32, stride=1, bias=1, pad_type='zero', + norm_type=norm_type, act_type=act_type, mode='CNA', convtype=convtype, + gaussian_noise=gaussian_noise, plus=plus) for _ in range(nb)] + LR_conv = conv_block(nf, nf, kernel_size=3, norm_type=norm_type, act_type=None, mode=mode, convtype=convtype) + + if upsample_mode == 'upconv': + upsample_block = upconv_block + elif upsample_mode == 'pixelshuffle': + upsample_block = pixelshuffle_block + else: + raise NotImplementedError(f'upsample mode [{upsample_mode}] is not found') + if upscale == 3: + upsampler = upsample_block(nf, nf, 3, act_type=act_type, convtype=convtype) + else: + upsampler = [upsample_block(nf, nf, act_type=act_type, convtype=convtype) for _ in range(n_upscale)] + HR_conv0 = conv_block(nf, nf, kernel_size=3, norm_type=None, act_type=act_type, convtype=convtype) + HR_conv1 = conv_block(nf, out_nc, kernel_size=3, norm_type=None, act_type=None, convtype=convtype) + + outact = act(finalact) if finalact else None + + self.model = sequential(fea_conv, ShortcutBlock(sequential(*rb_blocks, LR_conv)), + *upsampler, HR_conv0, HR_conv1, outact) + + def forward(self, x, outm=None): + if self.resrgan_scale == 1: + feat = pixel_unshuffle(x, scale=4) + elif self.resrgan_scale == 2: + feat = pixel_unshuffle(x, scale=2) + else: + feat = x + + return self.model(feat) + + +class RRDB(nn.Module): + """ + Residual in Residual Dense Block + (ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks) + """ + + def __init__(self, nf, nr=3, kernel_size=3, gc=32, stride=1, bias=1, pad_type='zero', + norm_type=None, act_type='leakyrelu', mode='CNA', convtype='Conv2D', + spectral_norm=False, gaussian_noise=False, plus=False): + super(RRDB, self).__init__() + # This is for backwards compatibility with existing models + if nr == 3: + self.RDB1 = ResidualDenseBlock_5C(nf, kernel_size, gc, stride, bias, pad_type, + norm_type, act_type, mode, convtype, spectral_norm=spectral_norm, + gaussian_noise=gaussian_noise, plus=plus) + self.RDB2 = ResidualDenseBlock_5C(nf, kernel_size, gc, stride, bias, pad_type, + norm_type, act_type, mode, convtype, spectral_norm=spectral_norm, + gaussian_noise=gaussian_noise, plus=plus) + self.RDB3 = ResidualDenseBlock_5C(nf, kernel_size, gc, stride, bias, pad_type, + norm_type, act_type, mode, convtype, spectral_norm=spectral_norm, + gaussian_noise=gaussian_noise, plus=plus) + else: + RDB_list = [ResidualDenseBlock_5C(nf, kernel_size, gc, stride, bias, pad_type, + norm_type, act_type, mode, convtype, spectral_norm=spectral_norm, + gaussian_noise=gaussian_noise, plus=plus) for _ in range(nr)] + self.RDBs = nn.Sequential(*RDB_list) + + def forward(self, x): + if hasattr(self, 'RDB1'): + out = self.RDB1(x) + out = self.RDB2(out) + out = self.RDB3(out) + else: + out = self.RDBs(x) + return out * 0.2 + x + + +class ResidualDenseBlock_5C(nn.Module): + """ + Residual Dense Block + The core module of paper: (Residual Dense Network for Image Super-Resolution, CVPR 18) + Modified options that can be used: + - "Partial Convolution based Padding" arXiv:1811.11718 + - "Spectral normalization" arXiv:1802.05957 + - "ICASSP 2020 - ESRGAN+ : Further Improving ESRGAN" N. C. + {Rakotonirina} and A. {Rasoanaivo} + """ + + def __init__(self, nf=64, kernel_size=3, gc=32, stride=1, bias=1, pad_type='zero', + norm_type=None, act_type='leakyrelu', mode='CNA', convtype='Conv2D', + spectral_norm=False, gaussian_noise=False, plus=False): + super(ResidualDenseBlock_5C, self).__init__() + + self.noise = GaussianNoise() if gaussian_noise else None + self.conv1x1 = conv1x1(nf, gc) if plus else None + + self.conv1 = conv_block(nf, gc, kernel_size, stride, bias=bias, pad_type=pad_type, + norm_type=norm_type, act_type=act_type, mode=mode, convtype=convtype, + spectral_norm=spectral_norm) + self.conv2 = conv_block(nf+gc, gc, kernel_size, stride, bias=bias, pad_type=pad_type, + norm_type=norm_type, act_type=act_type, mode=mode, convtype=convtype, + spectral_norm=spectral_norm) + self.conv3 = conv_block(nf+2*gc, gc, kernel_size, stride, bias=bias, pad_type=pad_type, + norm_type=norm_type, act_type=act_type, mode=mode, convtype=convtype, + spectral_norm=spectral_norm) + self.conv4 = conv_block(nf+3*gc, gc, kernel_size, stride, bias=bias, pad_type=pad_type, + norm_type=norm_type, act_type=act_type, mode=mode, convtype=convtype, + spectral_norm=spectral_norm) + if mode == 'CNA': + last_act = None + else: + last_act = act_type + self.conv5 = conv_block(nf+4*gc, nf, 3, stride, bias=bias, pad_type=pad_type, + norm_type=norm_type, act_type=last_act, mode=mode, convtype=convtype, + spectral_norm=spectral_norm) + + def forward(self, x): + x1 = self.conv1(x) + x2 = self.conv2(torch.cat((x, x1), 1)) + if self.conv1x1: + x2 = x2 + self.conv1x1(x) + x3 = self.conv3(torch.cat((x, x1, x2), 1)) + x4 = self.conv4(torch.cat((x, x1, x2, x3), 1)) + if self.conv1x1: + x4 = x4 + x2 + x5 = self.conv5(torch.cat((x, x1, x2, x3, x4), 1)) + if self.noise: + return self.noise(x5.mul(0.2) + x) + else: + return x5 * 0.2 + x + + +#################### +# ESRGANplus +#################### + +class GaussianNoise(nn.Module): + def __init__(self, sigma=0.1, is_relative_detach=False): + super().__init__() + self.sigma = sigma + self.is_relative_detach = is_relative_detach + self.noise = torch.tensor(0, dtype=torch.float) + + def forward(self, x): + if self.training and self.sigma != 0: + self.noise = self.noise.to(x.device) + scale = self.sigma * x.detach() if self.is_relative_detach else self.sigma * x + sampled_noise = self.noise.repeat(*x.size()).normal_() * scale + x = x + sampled_noise + return x + +def conv1x1(in_planes, out_planes, stride=1): + return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False) + + +#################### +# SRVGGNetCompact +#################### + +class SRVGGNetCompact(nn.Module): + """A compact VGG-style network structure for super-resolution. + This class is copied from https://github.com/xinntao/Real-ESRGAN + """ + + def __init__(self, num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=16, upscale=4, act_type='prelu'): + super(SRVGGNetCompact, self).__init__() + self.num_in_ch = num_in_ch + self.num_out_ch = num_out_ch + self.num_feat = num_feat + self.num_conv = num_conv + self.upscale = upscale + self.act_type = act_type + + self.body = nn.ModuleList() + # the first conv + self.body.append(nn.Conv2d(num_in_ch, num_feat, 3, 1, 1)) + # the first activation + if act_type == 'relu': + activation = nn.ReLU(inplace=True) + elif act_type == 'prelu': + activation = nn.PReLU(num_parameters=num_feat) + elif act_type == 'leakyrelu': + activation = nn.LeakyReLU(negative_slope=0.1, inplace=True) + self.body.append(activation) + + # the body structure + for _ in range(num_conv): + self.body.append(nn.Conv2d(num_feat, num_feat, 3, 1, 1)) + # activation + if act_type == 'relu': + activation = nn.ReLU(inplace=True) + elif act_type == 'prelu': + activation = nn.PReLU(num_parameters=num_feat) + elif act_type == 'leakyrelu': + activation = nn.LeakyReLU(negative_slope=0.1, inplace=True) + self.body.append(activation) + + # the last conv + self.body.append(nn.Conv2d(num_feat, num_out_ch * upscale * upscale, 3, 1, 1)) + # upsample + self.upsampler = nn.PixelShuffle(upscale) + + def forward(self, x): + out = x + for i in range(0, len(self.body)): + out = self.body[i](out) + + out = self.upsampler(out) + # add the nearest upsampled image, so that the network learns the residual + base = F.interpolate(x, scale_factor=self.upscale, mode='nearest') + out += base + return out + + +#################### +# Upsampler +#################### + +class Upsample(nn.Module): + r"""Upsamples a given multi-channel 1D (temporal), 2D (spatial) or 3D (volumetric) data. + The input data is assumed to be of the form + `minibatch x channels x [optional depth] x [optional height] x width`. + """ + + def __init__(self, size=None, scale_factor=None, mode="nearest", align_corners=None): + super(Upsample, self).__init__() + if isinstance(scale_factor, tuple): + self.scale_factor = tuple(float(factor) for factor in scale_factor) + else: + self.scale_factor = float(scale_factor) if scale_factor else None + self.mode = mode + self.size = size + self.align_corners = align_corners + + def forward(self, x): + return nn.functional.interpolate(x, size=self.size, scale_factor=self.scale_factor, mode=self.mode, align_corners=self.align_corners) + + def extra_repr(self): + if self.scale_factor is not None: + info = f'scale_factor={self.scale_factor}' + else: + info = f'size={self.size}' + info += f', mode={self.mode}' + return info + + +def pixel_unshuffle(x, scale): + """ Pixel unshuffle. + Args: + x (Tensor): Input feature with shape (b, c, hh, hw). + scale (int): Downsample ratio. + Returns: + Tensor: the pixel unshuffled feature. + """ + b, c, hh, hw = x.size() + out_channel = c * (scale**2) + assert hh % scale == 0 and hw % scale == 0 + h = hh // scale + w = hw // scale + x_view = x.view(b, c, h, scale, w, scale) + return x_view.permute(0, 1, 3, 5, 2, 4).reshape(b, out_channel, h, w) + + +def pixelshuffle_block(in_nc, out_nc, upscale_factor=2, kernel_size=3, stride=1, bias=True, + pad_type='zero', norm_type=None, act_type='relu', convtype='Conv2D'): + """ + Pixel shuffle layer + (Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional + Neural Network, CVPR17) + """ + conv = conv_block(in_nc, out_nc * (upscale_factor ** 2), kernel_size, stride, bias=bias, + pad_type=pad_type, norm_type=None, act_type=None, convtype=convtype) + pixel_shuffle = nn.PixelShuffle(upscale_factor) + + n = norm(norm_type, out_nc) if norm_type else None + a = act(act_type) if act_type else None + return sequential(conv, pixel_shuffle, n, a) + + +def upconv_block(in_nc, out_nc, upscale_factor=2, kernel_size=3, stride=1, bias=True, + pad_type='zero', norm_type=None, act_type='relu', mode='nearest', convtype='Conv2D'): + """ Upconv layer """ + upscale_factor = (1, upscale_factor, upscale_factor) if convtype == 'Conv3D' else upscale_factor + upsample = Upsample(scale_factor=upscale_factor, mode=mode) + conv = conv_block(in_nc, out_nc, kernel_size, stride, bias=bias, + pad_type=pad_type, norm_type=norm_type, act_type=act_type, convtype=convtype) + return sequential(upsample, conv) + + + + + + + + +#################### +# Basic blocks +#################### + + +def make_layer(basic_block, num_basic_block, **kwarg): + """Make layers by stacking the same blocks. + Args: + basic_block (nn.module): nn.module class for basic block. (block) + num_basic_block (int): number of blocks. (n_layers) + Returns: + nn.Sequential: Stacked blocks in nn.Sequential. + """ + layers = [] + for _ in range(num_basic_block): + layers.append(basic_block(**kwarg)) + return nn.Sequential(*layers) + + +def act(act_type, inplace=True, neg_slope=0.2, n_prelu=1, beta=1.0): + """ activation helper """ + act_type = act_type.lower() + if act_type == 'relu': + layer = nn.ReLU(inplace) + elif act_type in ('leakyrelu', 'lrelu'): + layer = nn.LeakyReLU(neg_slope, inplace) + elif act_type == 'prelu': + layer = nn.PReLU(num_parameters=n_prelu, init=neg_slope) + elif act_type == 'tanh': # [-1, 1] range output + layer = nn.Tanh() + elif act_type == 'sigmoid': # [0, 1] range output + layer = nn.Sigmoid() + else: + raise NotImplementedError(f'activation layer [{act_type}] is not found') + return layer + + +class Identity(nn.Module): + def __init__(self, *kwargs): + super(Identity, self).__init__() + + def forward(self, x, *kwargs): + return x + + +def norm(norm_type, nc): + """ Return a normalization layer """ + norm_type = norm_type.lower() + if norm_type == 'batch': + layer = nn.BatchNorm2d(nc, affine=True) + elif norm_type == 'instance': + layer = nn.InstanceNorm2d(nc, affine=False) + elif norm_type == 'none': + def norm_layer(x): return Identity() + else: + raise NotImplementedError(f'normalization layer [{norm_type}] is not found') + return layer + + +def pad(pad_type, padding): + """ padding layer helper """ + pad_type = pad_type.lower() + if padding == 0: + return None + if pad_type == 'reflect': + layer = nn.ReflectionPad2d(padding) + elif pad_type == 'replicate': + layer = nn.ReplicationPad2d(padding) + elif pad_type == 'zero': + layer = nn.ZeroPad2d(padding) + else: + raise NotImplementedError(f'padding layer [{pad_type}] is not implemented') + return layer + + +def get_valid_padding(kernel_size, dilation): + kernel_size = kernel_size + (kernel_size - 1) * (dilation - 1) + padding = (kernel_size - 1) // 2 + return padding + + +class ShortcutBlock(nn.Module): + """ Elementwise sum the output of a submodule to its input """ + def __init__(self, submodule): + super(ShortcutBlock, self).__init__() + self.sub = submodule + + def forward(self, x): + output = x + self.sub(x) + return output + + def __repr__(self): + return 'Identity + \n|' + self.sub.__repr__().replace('\n', '\n|') + + +def sequential(*args): + """ Flatten Sequential. It unwraps nn.Sequential. """ + if len(args) == 1: + if isinstance(args[0], OrderedDict): + raise NotImplementedError('sequential does not support OrderedDict input.') + return args[0] # No sequential is needed. + modules = [] + for module in args: + if isinstance(module, nn.Sequential): + for submodule in module.children(): + modules.append(submodule) + elif isinstance(module, nn.Module): + modules.append(module) + return nn.Sequential(*modules) + + +def conv_block(in_nc, out_nc, kernel_size, stride=1, dilation=1, groups=1, bias=True, + pad_type='zero', norm_type=None, act_type='relu', mode='CNA', convtype='Conv2D', + spectral_norm=False): + """ Conv layer with padding, normalization, activation """ + assert mode in ['CNA', 'NAC', 'CNAC'], f'Wrong conv mode [{mode}]' + padding = get_valid_padding(kernel_size, dilation) + p = pad(pad_type, padding) if pad_type and pad_type != 'zero' else None + padding = padding if pad_type == 'zero' else 0 + + if convtype=='PartialConv2D': + from torchvision.ops import PartialConv2d # this is definitely not going to work, but PartialConv2d doesn't work anyway and this shuts up static analyzer + c = PartialConv2d(in_nc, out_nc, kernel_size=kernel_size, stride=stride, padding=padding, + dilation=dilation, bias=bias, groups=groups) + elif convtype=='DeformConv2D': + from torchvision.ops import DeformConv2d # not tested + c = DeformConv2d(in_nc, out_nc, kernel_size=kernel_size, stride=stride, padding=padding, + dilation=dilation, bias=bias, groups=groups) + elif convtype=='Conv3D': + c = nn.Conv3d(in_nc, out_nc, kernel_size=kernel_size, stride=stride, padding=padding, + dilation=dilation, bias=bias, groups=groups) + else: + c = nn.Conv2d(in_nc, out_nc, kernel_size=kernel_size, stride=stride, padding=padding, + dilation=dilation, bias=bias, groups=groups) + + if spectral_norm: + c = nn.utils.spectral_norm(c) + + a = act(act_type) if act_type else None + if 'CNA' in mode: + n = norm(norm_type, out_nc) if norm_type else None + return sequential(p, c, n, a) + elif mode == 'NAC': + if norm_type is None and act_type is not None: + a = act(act_type, inplace=False) + n = norm(norm_type, in_nc) if norm_type else None + return sequential(n, a, p, c) diff --git a/modules/extensions.py b/modules/extensions.py new file mode 100644 index 0000000000000000000000000000000000000000..8adf306b6be0663927a4296d37867f2439584461 --- /dev/null +++ b/modules/extensions.py @@ -0,0 +1,237 @@ +from __future__ import annotations + +import configparser +import os +import threading +import re + +from modules import shared, errors, cache, scripts +from modules.gitpython_hack import Repo +from modules.paths_internal import extensions_dir, extensions_builtin_dir, script_path # noqa: F401 + + +os.makedirs(extensions_dir, exist_ok=True) + + +def active(): + if shared.cmd_opts.disable_all_extensions or shared.opts.disable_all_extensions == "all": + return [] + elif shared.cmd_opts.disable_extra_extensions or shared.opts.disable_all_extensions == "extra": + return [x for x in extensions if x.enabled and x.is_builtin] + else: + return [x for x in extensions if x.enabled] + + +class ExtensionMetadata: + filename = "metadata.ini" + config: configparser.ConfigParser + canonical_name: str + requires: list + + def __init__(self, path, canonical_name): + self.config = configparser.ConfigParser() + + filepath = os.path.join(path, self.filename) + if os.path.isfile(filepath): + try: + self.config.read(filepath) + except Exception: + errors.report(f"Error reading {self.filename} for extension {canonical_name}.", exc_info=True) + + self.canonical_name = self.config.get("Extension", "Name", fallback=canonical_name) + self.canonical_name = canonical_name.lower().strip() + + self.requires = self.get_script_requirements("Requires", "Extension") + + def get_script_requirements(self, field, section, extra_section=None): + """reads a list of requirements from the config; field is the name of the field in the ini file, + like Requires or Before, and section is the name of the [section] in the ini file; additionally, + reads more requirements from [extra_section] if specified.""" + + x = self.config.get(section, field, fallback='') + + if extra_section: + x = x + ', ' + self.config.get(extra_section, field, fallback='') + + return self.parse_list(x.lower()) + + def parse_list(self, text): + """converts a line from config ("ext1 ext2, ext3 ") into a python list (["ext1", "ext2", "ext3"])""" + + if not text: + return [] + + # both "," and " " are accepted as separator + return [x for x in re.split(r"[,\s]+", text.strip()) if x] + + +class Extension: + lock = threading.Lock() + cached_fields = ['remote', 'commit_date', 'branch', 'commit_hash', 'version'] + metadata: ExtensionMetadata + + def __init__(self, name, path, enabled=True, is_builtin=False, metadata=None): + self.name = name + self.path = path + self.enabled = enabled + self.status = '' + self.can_update = False + self.is_builtin = is_builtin + self.commit_hash = '' + self.commit_date = None + self.version = '' + self.branch = None + self.remote = None + self.have_info_from_repo = False + self.metadata = metadata if metadata else ExtensionMetadata(self.path, name.lower()) + self.canonical_name = metadata.canonical_name + + def to_dict(self): + return {x: getattr(self, x) for x in self.cached_fields} + + def from_dict(self, d): + for field in self.cached_fields: + setattr(self, field, d[field]) + + def read_info_from_repo(self): + if self.is_builtin or self.have_info_from_repo: + return + + def read_from_repo(): + with self.lock: + if self.have_info_from_repo: + return + + self.do_read_info_from_repo() + + return self.to_dict() + + try: + d = cache.cached_data_for_file('extensions-git', self.name, os.path.join(self.path, ".git"), read_from_repo) + self.from_dict(d) + except FileNotFoundError: + pass + self.status = 'unknown' if self.status == '' else self.status + + def do_read_info_from_repo(self): + repo = None + try: + if os.path.exists(os.path.join(self.path, ".git")): + repo = Repo(self.path) + except Exception: + errors.report(f"Error reading github repository info from {self.path}", exc_info=True) + + if repo is None or repo.bare: + self.remote = None + else: + try: + self.remote = next(repo.remote().urls, None) + commit = repo.head.commit + self.commit_date = commit.committed_date + if repo.active_branch: + self.branch = repo.active_branch.name + self.commit_hash = commit.hexsha + self.version = self.commit_hash[:8] + + except Exception: + errors.report(f"Failed reading extension data from Git repository ({self.name})", exc_info=True) + self.remote = None + + self.have_info_from_repo = True + + def list_files(self, subdir, extension): + dirpath = os.path.join(self.path, subdir) + if not os.path.isdir(dirpath): + return [] + + res = [] + for filename in sorted(os.listdir(dirpath)): + res.append(scripts.ScriptFile(self.path, filename, os.path.join(dirpath, filename))) + + res = [x for x in res if os.path.splitext(x.path)[1].lower() == extension and os.path.isfile(x.path)] + + return res + + def check_updates(self): + repo = Repo(self.path) + for fetch in repo.remote().fetch(dry_run=True): + if fetch.flags != fetch.HEAD_UPTODATE: + self.can_update = True + self.status = "new commits" + return + + try: + origin = repo.rev_parse('origin') + if repo.head.commit != origin: + self.can_update = True + self.status = "behind HEAD" + return + except Exception: + self.can_update = False + self.status = "unknown (remote error)" + return + + self.can_update = False + self.status = "latest" + + def fetch_and_reset_hard(self, commit='origin'): + repo = Repo(self.path) + # Fix: `error: Your local changes to the following files would be overwritten by merge`, + # because WSL2 Docker set 755 file permissions instead of 644, this results to the error. + repo.git.fetch(all=True) + repo.git.reset(commit, hard=True) + self.have_info_from_repo = False + + +def list_extensions(): + extensions.clear() + + if shared.cmd_opts.disable_all_extensions: + print("*** \"--disable-all-extensions\" arg was used, will not load any extensions ***") + elif shared.opts.disable_all_extensions == "all": + print("*** \"Disable all extensions\" option was set, will not load any extensions ***") + elif shared.cmd_opts.disable_extra_extensions: + print("*** \"--disable-extra-extensions\" arg was used, will only load built-in extensions ***") + elif shared.opts.disable_all_extensions == "extra": + print("*** \"Disable all extensions\" option was set, will only load built-in extensions ***") + + loaded_extensions = {} + + # scan through extensions directory and load metadata + for dirname in [extensions_builtin_dir, extensions_dir]: + if not os.path.isdir(dirname): + continue + + for extension_dirname in sorted(os.listdir(dirname)): + path = os.path.join(dirname, extension_dirname) + if not os.path.isdir(path): + continue + + canonical_name = extension_dirname + metadata = ExtensionMetadata(path, canonical_name) + + # check for duplicated canonical names + already_loaded_extension = loaded_extensions.get(metadata.canonical_name) + if already_loaded_extension is not None: + errors.report(f'Duplicate canonical name "{canonical_name}" found in extensions "{extension_dirname}" and "{already_loaded_extension.name}". Former will be discarded.', exc_info=False) + continue + + is_builtin = dirname == extensions_builtin_dir + extension = Extension(name=extension_dirname, path=path, enabled=extension_dirname not in shared.opts.disabled_extensions, is_builtin=is_builtin, metadata=metadata) + extensions.append(extension) + loaded_extensions[canonical_name] = extension + + # check for requirements + for extension in extensions: + for req in extension.metadata.requires: + required_extension = loaded_extensions.get(req) + if required_extension is None: + errors.report(f'Extension "{extension.name}" requires "{req}" which is not installed.', exc_info=False) + continue + + if not extension.enabled: + errors.report(f'Extension "{extension.name}" requires "{required_extension.name}" which is disabled.', exc_info=False) + continue + + +extensions: list[Extension] = [] diff --git a/modules/extra_networks.py b/modules/extra_networks.py new file mode 100644 index 0000000000000000000000000000000000000000..5ebe72260488f31b30532b94d0f6fff1ea2e1660 --- /dev/null +++ b/modules/extra_networks.py @@ -0,0 +1,224 @@ +import json +import os +import re +import logging +from collections import defaultdict + +from modules import errors + +extra_network_registry = {} +extra_network_aliases = {} + + +def initialize(): + extra_network_registry.clear() + extra_network_aliases.clear() + + +def register_extra_network(extra_network): + extra_network_registry[extra_network.name] = extra_network + + +def register_extra_network_alias(extra_network, alias): + extra_network_aliases[alias] = extra_network + + +def register_default_extra_networks(): + from modules.extra_networks_hypernet import ExtraNetworkHypernet + register_extra_network(ExtraNetworkHypernet()) + + +class ExtraNetworkParams: + def __init__(self, items=None): + self.items = items or [] + self.positional = [] + self.named = {} + + for item in self.items: + parts = item.split('=', 2) if isinstance(item, str) else [item] + if len(parts) == 2: + self.named[parts[0]] = parts[1] + else: + self.positional.append(item) + + def __eq__(self, other): + return self.items == other.items + + +class ExtraNetwork: + def __init__(self, name): + self.name = name + + def activate(self, p, params_list): + """ + Called by processing on every run. Whatever the extra network is meant to do should be activated here. + Passes arguments related to this extra network in params_list. + User passes arguments by specifying this in his prompt: + + + + Where name matches the name of this ExtraNetwork object, and arg1:arg2:arg3 are any natural number of text arguments + separated by colon. + + Even if the user does not mention this ExtraNetwork in his prompt, the call will stil be made, with empty params_list - + in this case, all effects of this extra networks should be disabled. + + Can be called multiple times before deactivate() - each new call should override the previous call completely. + + For example, if this ExtraNetwork's name is 'hypernet' and user's prompt is: + + > "1girl, " + + params_list will be: + + [ + ExtraNetworkParams(items=["agm", "1.1"]), + ExtraNetworkParams(items=["ray"]) + ] + + """ + raise NotImplementedError + + def deactivate(self, p): + """ + Called at the end of processing for housekeeping. No need to do anything here. + """ + + raise NotImplementedError + + +def lookup_extra_networks(extra_network_data): + """returns a dict mapping ExtraNetwork objects to lists of arguments for those extra networks. + + Example input: + { + 'lora': [], + 'lyco': [], + 'hypernet': [] + } + + Example output: + + { + : [, ], + : [] + } + """ + + res = {} + + for extra_network_name, extra_network_args in list(extra_network_data.items()): + extra_network = extra_network_registry.get(extra_network_name, None) + alias = extra_network_aliases.get(extra_network_name, None) + + if alias is not None and extra_network is None: + extra_network = alias + + if extra_network is None: + logging.info(f"Skipping unknown extra network: {extra_network_name}") + continue + + res.setdefault(extra_network, []).extend(extra_network_args) + + return res + + +def activate(p, extra_network_data): + """call activate for extra networks in extra_network_data in specified order, then call + activate for all remaining registered networks with an empty argument list""" + + activated = [] + + for extra_network, extra_network_args in lookup_extra_networks(extra_network_data).items(): + + try: + extra_network.activate(p, extra_network_args) + activated.append(extra_network) + except Exception as e: + errors.display(e, f"activating extra network {extra_network.name} with arguments {extra_network_args}") + + for extra_network_name, extra_network in extra_network_registry.items(): + if extra_network in activated: + continue + + try: + extra_network.activate(p, []) + except Exception as e: + errors.display(e, f"activating extra network {extra_network_name}") + + if p.scripts is not None: + p.scripts.after_extra_networks_activate(p, batch_number=p.iteration, prompts=p.prompts, seeds=p.seeds, subseeds=p.subseeds, extra_network_data=extra_network_data) + + +def deactivate(p, extra_network_data): + """call deactivate for extra networks in extra_network_data in specified order, then call + deactivate for all remaining registered networks""" + + data = lookup_extra_networks(extra_network_data) + + for extra_network in data: + try: + extra_network.deactivate(p) + except Exception as e: + errors.display(e, f"deactivating extra network {extra_network.name}") + + for extra_network_name, extra_network in extra_network_registry.items(): + if extra_network in data: + continue + + try: + extra_network.deactivate(p) + except Exception as e: + errors.display(e, f"deactivating unmentioned extra network {extra_network_name}") + + +re_extra_net = re.compile(r"<(\w+):([^>]+)>") + + +def parse_prompt(prompt): + res = defaultdict(list) + + def found(m): + name = m.group(1) + args = m.group(2) + + res[name].append(ExtraNetworkParams(items=args.split(":"))) + + return "" + + prompt = re.sub(re_extra_net, found, prompt) + + return prompt, res + + +def parse_prompts(prompts): + res = [] + extra_data = None + + for prompt in prompts: + updated_prompt, parsed_extra_data = parse_prompt(prompt) + + if extra_data is None: + extra_data = parsed_extra_data + + res.append(updated_prompt) + + return res, extra_data + + +def get_user_metadata(filename): + if filename is None: + return {} + + basename, ext = os.path.splitext(filename) + metadata_filename = basename + '.json' + + metadata = {} + try: + if os.path.isfile(metadata_filename): + with open(metadata_filename, "r", encoding="utf8") as file: + metadata = json.load(file) + except Exception as e: + errors.display(e, f"reading extra network user metadata from {metadata_filename}") + + return metadata diff --git a/modules/extra_networks_hypernet.py b/modules/extra_networks_hypernet.py new file mode 100644 index 0000000000000000000000000000000000000000..192f11b9cbd88447a0f80dbd2f0ace26d74f18b2 --- /dev/null +++ b/modules/extra_networks_hypernet.py @@ -0,0 +1,28 @@ +from modules import extra_networks, shared +from modules.hypernetworks import hypernetwork + + +class ExtraNetworkHypernet(extra_networks.ExtraNetwork): + def __init__(self): + super().__init__('hypernet') + + def activate(self, p, params_list): + additional = shared.opts.sd_hypernetwork + + if additional != "None" and additional in shared.hypernetworks and not any(x for x in params_list if x.items[0] == additional): + hypernet_prompt_text = f"" + p.all_prompts = [f"{prompt}{hypernet_prompt_text}" for prompt in p.all_prompts] + params_list.append(extra_networks.ExtraNetworkParams(items=[additional, shared.opts.extra_networks_default_multiplier])) + + names = [] + multipliers = [] + for params in params_list: + assert params.items + + names.append(params.items[0]) + multipliers.append(float(params.items[1]) if len(params.items) > 1 else 1.0) + + hypernetwork.load_hypernetworks(names, multipliers) + + def deactivate(self, p): + pass diff --git a/modules/extras.py b/modules/extras.py new file mode 100644 index 0000000000000000000000000000000000000000..4653c3f6edacc067eb935c81d7e71c71790ce449 --- /dev/null +++ b/modules/extras.py @@ -0,0 +1,330 @@ +import os +import re +import shutil +import json + + +import torch +import tqdm + +from modules import shared, images, sd_models, sd_vae, sd_models_config, errors +from modules.ui_common import plaintext_to_html +import gradio as gr +import safetensors.torch + + +def run_pnginfo(image): + if image is None: + return '', '', '' + + geninfo, items = images.read_info_from_image(image) + items = {**{'parameters': geninfo}, **items} + + info = '' + for key, text in items.items(): + info += f""" +
+

{plaintext_to_html(str(key))}

+

{plaintext_to_html(str(text))}

+
+""".strip()+"\n" + + if len(info) == 0: + message = "Nothing found in the image." + info = f"

{message}

" + + return '', geninfo, info + + +def create_config(ckpt_result, config_source, a, b, c): + def config(x): + res = sd_models_config.find_checkpoint_config_near_filename(x) if x else None + return res if res != shared.sd_default_config else None + + if config_source == 0: + cfg = config(a) or config(b) or config(c) + elif config_source == 1: + cfg = config(b) + elif config_source == 2: + cfg = config(c) + else: + cfg = None + + if cfg is None: + return + + filename, _ = os.path.splitext(ckpt_result) + checkpoint_filename = filename + ".yaml" + + print("Copying config:") + print(" from:", cfg) + print(" to:", checkpoint_filename) + shutil.copyfile(cfg, checkpoint_filename) + + +checkpoint_dict_skip_on_merge = ["cond_stage_model.transformer.text_model.embeddings.position_ids"] + + +def to_half(tensor, enable): + if enable and tensor.dtype == torch.float: + return tensor.half() + + return tensor + + +def read_metadata(primary_model_name, secondary_model_name, tertiary_model_name): + metadata = {} + + for checkpoint_name in [primary_model_name, secondary_model_name, tertiary_model_name]: + checkpoint_info = sd_models.checkpoints_list.get(checkpoint_name, None) + if checkpoint_info is None: + continue + + metadata.update(checkpoint_info.metadata) + + return json.dumps(metadata, indent=4, ensure_ascii=False) + + +def run_modelmerger(id_task, primary_model_name, secondary_model_name, tertiary_model_name, interp_method, multiplier, save_as_half, custom_name, checkpoint_format, config_source, bake_in_vae, discard_weights, save_metadata, add_merge_recipe, copy_metadata_fields, metadata_json): + shared.state.begin(job="model-merge") + + def fail(message): + shared.state.textinfo = message + shared.state.end() + return [*[gr.update() for _ in range(4)], message] + + def weighted_sum(theta0, theta1, alpha): + return ((1 - alpha) * theta0) + (alpha * theta1) + + def get_difference(theta1, theta2): + return theta1 - theta2 + + def add_difference(theta0, theta1_2_diff, alpha): + return theta0 + (alpha * theta1_2_diff) + + def filename_weighted_sum(): + a = primary_model_info.model_name + b = secondary_model_info.model_name + Ma = round(1 - multiplier, 2) + Mb = round(multiplier, 2) + + return f"{Ma}({a}) + {Mb}({b})" + + def filename_add_difference(): + a = primary_model_info.model_name + b = secondary_model_info.model_name + c = tertiary_model_info.model_name + M = round(multiplier, 2) + + return f"{a} + {M}({b} - {c})" + + def filename_nothing(): + return primary_model_info.model_name + + theta_funcs = { + "Weighted sum": (filename_weighted_sum, None, weighted_sum), + "Add difference": (filename_add_difference, get_difference, add_difference), + "No interpolation": (filename_nothing, None, None), + } + filename_generator, theta_func1, theta_func2 = theta_funcs[interp_method] + shared.state.job_count = (1 if theta_func1 else 0) + (1 if theta_func2 else 0) + + if not primary_model_name: + return fail("Failed: Merging requires a primary model.") + + primary_model_info = sd_models.checkpoints_list[primary_model_name] + + if theta_func2 and not secondary_model_name: + return fail("Failed: Merging requires a secondary model.") + + secondary_model_info = sd_models.checkpoints_list[secondary_model_name] if theta_func2 else None + + if theta_func1 and not tertiary_model_name: + return fail(f"Failed: Interpolation method ({interp_method}) requires a tertiary model.") + + tertiary_model_info = sd_models.checkpoints_list[tertiary_model_name] if theta_func1 else None + + result_is_inpainting_model = False + result_is_instruct_pix2pix_model = False + + if theta_func2: + shared.state.textinfo = "Loading B" + print(f"Loading {secondary_model_info.filename}...") + theta_1 = sd_models.read_state_dict(secondary_model_info.filename, map_location='cpu') + else: + theta_1 = None + + if theta_func1: + shared.state.textinfo = "Loading C" + print(f"Loading {tertiary_model_info.filename}...") + theta_2 = sd_models.read_state_dict(tertiary_model_info.filename, map_location='cpu') + + shared.state.textinfo = 'Merging B and C' + shared.state.sampling_steps = len(theta_1.keys()) + for key in tqdm.tqdm(theta_1.keys()): + if key in checkpoint_dict_skip_on_merge: + continue + + if 'model' in key: + if key in theta_2: + t2 = theta_2.get(key, torch.zeros_like(theta_1[key])) + theta_1[key] = theta_func1(theta_1[key], t2) + else: + theta_1[key] = torch.zeros_like(theta_1[key]) + + shared.state.sampling_step += 1 + del theta_2 + + shared.state.nextjob() + + shared.state.textinfo = f"Loading {primary_model_info.filename}..." + print(f"Loading {primary_model_info.filename}...") + theta_0 = sd_models.read_state_dict(primary_model_info.filename, map_location='cpu') + + print("Merging...") + shared.state.textinfo = 'Merging A and B' + shared.state.sampling_steps = len(theta_0.keys()) + for key in tqdm.tqdm(theta_0.keys()): + if theta_1 and 'model' in key and key in theta_1: + + if key in checkpoint_dict_skip_on_merge: + continue + + a = theta_0[key] + b = theta_1[key] + + # this enables merging an inpainting model (A) with another one (B); + # where normal model would have 4 channels, for latenst space, inpainting model would + # have another 4 channels for unmasked picture's latent space, plus one channel for mask, for a total of 9 + if a.shape != b.shape and a.shape[0:1] + a.shape[2:] == b.shape[0:1] + b.shape[2:]: + if a.shape[1] == 4 and b.shape[1] == 9: + raise RuntimeError("When merging inpainting model with a normal one, A must be the inpainting model.") + if a.shape[1] == 4 and b.shape[1] == 8: + raise RuntimeError("When merging instruct-pix2pix model with a normal one, A must be the instruct-pix2pix model.") + + if a.shape[1] == 8 and b.shape[1] == 4:#If we have an Instruct-Pix2Pix model... + theta_0[key][:, 0:4, :, :] = theta_func2(a[:, 0:4, :, :], b, multiplier)#Merge only the vectors the models have in common. Otherwise we get an error due to dimension mismatch. + result_is_instruct_pix2pix_model = True + else: + assert a.shape[1] == 9 and b.shape[1] == 4, f"Bad dimensions for merged layer {key}: A={a.shape}, B={b.shape}" + theta_0[key][:, 0:4, :, :] = theta_func2(a[:, 0:4, :, :], b, multiplier) + result_is_inpainting_model = True + else: + theta_0[key] = theta_func2(a, b, multiplier) + + theta_0[key] = to_half(theta_0[key], save_as_half) + + shared.state.sampling_step += 1 + + del theta_1 + + bake_in_vae_filename = sd_vae.vae_dict.get(bake_in_vae, None) + if bake_in_vae_filename is not None: + print(f"Baking in VAE from {bake_in_vae_filename}") + shared.state.textinfo = 'Baking in VAE' + vae_dict = sd_vae.load_vae_dict(bake_in_vae_filename, map_location='cpu') + + for key in vae_dict.keys(): + theta_0_key = 'first_stage_model.' + key + if theta_0_key in theta_0: + theta_0[theta_0_key] = to_half(vae_dict[key], save_as_half) + + del vae_dict + + if save_as_half and not theta_func2: + for key in theta_0.keys(): + theta_0[key] = to_half(theta_0[key], save_as_half) + + if discard_weights: + regex = re.compile(discard_weights) + for key in list(theta_0): + if re.search(regex, key): + theta_0.pop(key, None) + + ckpt_dir = shared.cmd_opts.ckpt_dir or sd_models.model_path + + filename = filename_generator() if custom_name == '' else custom_name + filename += ".inpainting" if result_is_inpainting_model else "" + filename += ".instruct-pix2pix" if result_is_instruct_pix2pix_model else "" + filename += "." + checkpoint_format + + output_modelname = os.path.join(ckpt_dir, filename) + + shared.state.nextjob() + shared.state.textinfo = "Saving" + print(f"Saving to {output_modelname}...") + + metadata = {} + + if save_metadata and copy_metadata_fields: + if primary_model_info: + metadata.update(primary_model_info.metadata) + if secondary_model_info: + metadata.update(secondary_model_info.metadata) + if tertiary_model_info: + metadata.update(tertiary_model_info.metadata) + + if save_metadata: + try: + metadata.update(json.loads(metadata_json)) + except Exception as e: + errors.display(e, "readin metadata from json") + + metadata["format"] = "pt" + + if save_metadata and add_merge_recipe: + merge_recipe = { + "type": "webui", # indicate this model was merged with webui's built-in merger + "primary_model_hash": primary_model_info.sha256, + "secondary_model_hash": secondary_model_info.sha256 if secondary_model_info else None, + "tertiary_model_hash": tertiary_model_info.sha256 if tertiary_model_info else None, + "interp_method": interp_method, + "multiplier": multiplier, + "save_as_half": save_as_half, + "custom_name": custom_name, + "config_source": config_source, + "bake_in_vae": bake_in_vae, + "discard_weights": discard_weights, + "is_inpainting": result_is_inpainting_model, + "is_instruct_pix2pix": result_is_instruct_pix2pix_model + } + + sd_merge_models = {} + + def add_model_metadata(checkpoint_info): + checkpoint_info.calculate_shorthash() + sd_merge_models[checkpoint_info.sha256] = { + "name": checkpoint_info.name, + "legacy_hash": checkpoint_info.hash, + "sd_merge_recipe": checkpoint_info.metadata.get("sd_merge_recipe", None) + } + + sd_merge_models.update(checkpoint_info.metadata.get("sd_merge_models", {})) + + add_model_metadata(primary_model_info) + if secondary_model_info: + add_model_metadata(secondary_model_info) + if tertiary_model_info: + add_model_metadata(tertiary_model_info) + + metadata["sd_merge_recipe"] = json.dumps(merge_recipe) + metadata["sd_merge_models"] = json.dumps(sd_merge_models) + + _, extension = os.path.splitext(output_modelname) + if extension.lower() == ".safetensors": + safetensors.torch.save_file(theta_0, output_modelname, metadata=metadata if len(metadata)>0 else None) + else: + torch.save(theta_0, output_modelname) + + sd_models.list_models() + created_model = next((ckpt for ckpt in sd_models.checkpoints_list.values() if ckpt.name == filename), None) + if created_model: + created_model.calculate_shorthash() + + create_config(output_modelname, config_source, primary_model_info, secondary_model_info, tertiary_model_info) + + print(f"Checkpoint saved to {output_modelname}.") + shared.state.textinfo = "Checkpoint saved" + shared.state.end() + + return [*[gr.Dropdown.update(choices=sd_models.checkpoint_tiles()) for _ in range(4)], "Checkpoint saved to " + output_modelname] diff --git a/modules/face_restoration.py b/modules/face_restoration.py new file mode 100644 index 0000000000000000000000000000000000000000..2c86c6ccce338a1411f4367a0bc6e4046ad67cae --- /dev/null +++ b/modules/face_restoration.py @@ -0,0 +1,19 @@ +from modules import shared + + +class FaceRestoration: + def name(self): + return "None" + + def restore(self, np_image): + return np_image + + +def restore_faces(np_image): + face_restorers = [x for x in shared.face_restorers if x.name() == shared.opts.face_restoration_model or shared.opts.face_restoration_model is None] + if len(face_restorers) == 0: + return np_image + + face_restorer = face_restorers[0] + + return face_restorer.restore(np_image) diff --git a/modules/fifo_lock.py b/modules/fifo_lock.py new file mode 100644 index 0000000000000000000000000000000000000000..c35b3ae25a3cf383c8beae04db3e0a3d66785135 --- /dev/null +++ b/modules/fifo_lock.py @@ -0,0 +1,37 @@ +import threading +import collections + + +# reference: https://gist.github.com/vitaliyp/6d54dd76ca2c3cdfc1149d33007dc34a +class FIFOLock(object): + def __init__(self): + self._lock = threading.Lock() + self._inner_lock = threading.Lock() + self._pending_threads = collections.deque() + + def acquire(self, blocking=True): + with self._inner_lock: + lock_acquired = self._lock.acquire(False) + if lock_acquired: + return True + elif not blocking: + return False + + release_event = threading.Event() + self._pending_threads.append(release_event) + + release_event.wait() + return self._lock.acquire() + + def release(self): + with self._inner_lock: + if self._pending_threads: + release_event = self._pending_threads.popleft() + release_event.set() + + self._lock.release() + + __enter__ = acquire + + def __exit__(self, t, v, tb): + self.release() diff --git a/modules/generation_parameters_copypaste.py b/modules/generation_parameters_copypaste.py new file mode 100644 index 0000000000000000000000000000000000000000..5baf31d3eb182302d987ee43df0d62908f8ff436 --- /dev/null +++ b/modules/generation_parameters_copypaste.py @@ -0,0 +1,450 @@ +from __future__ import annotations +import base64 +import io +import json +import os +import re + +import gradio as gr +from modules.paths import data_path +from modules import shared, ui_tempdir, script_callbacks, processing +from PIL import Image + +re_param_code = r'\s*(\w[\w \-/]+):\s*("(?:\\.|[^\\"])+"|[^,]*)(?:,|$)' +re_param = re.compile(re_param_code) +re_imagesize = re.compile(r"^(\d+)x(\d+)$") +re_hypernet_hash = re.compile("\(([0-9a-f]+)\)$") +type_of_gr_update = type(gr.update()) + + +class ParamBinding: + def __init__(self, paste_button, tabname, source_text_component=None, source_image_component=None, source_tabname=None, override_settings_component=None, paste_field_names=None): + self.paste_button = paste_button + self.tabname = tabname + self.source_text_component = source_text_component + self.source_image_component = source_image_component + self.source_tabname = source_tabname + self.override_settings_component = override_settings_component + self.paste_field_names = paste_field_names or [] + + +paste_fields: dict[str, dict] = {} +registered_param_bindings: list[ParamBinding] = [] + + +def reset(): + paste_fields.clear() + registered_param_bindings.clear() + + +def quote(text): + if ',' not in str(text) and '\n' not in str(text) and ':' not in str(text): + return text + + return json.dumps(text, ensure_ascii=False) + + +def unquote(text): + if len(text) == 0 or text[0] != '"' or text[-1] != '"': + return text + + try: + return json.loads(text) + except Exception: + return text + + +def image_from_url_text(filedata): + if filedata is None: + return None + + if type(filedata) == list and filedata and type(filedata[0]) == dict and filedata[0].get("is_file", False): + filedata = filedata[0] + + if type(filedata) == dict and filedata.get("is_file", False): + filename = filedata["name"] + is_in_right_dir = ui_tempdir.check_tmp_file(shared.demo, filename) + assert is_in_right_dir, 'trying to open image file outside of allowed directories' + + filename = filename.rsplit('?', 1)[0] + return Image.open(filename) + + if type(filedata) == list: + if len(filedata) == 0: + return None + + filedata = filedata[0] + + if filedata.startswith("data:image/png;base64,"): + filedata = filedata[len("data:image/png;base64,"):] + + filedata = base64.decodebytes(filedata.encode('utf-8')) + image = Image.open(io.BytesIO(filedata)) + return image + + +def add_paste_fields(tabname, init_img, fields, override_settings_component=None): + paste_fields[tabname] = {"init_img": init_img, "fields": fields, "override_settings_component": override_settings_component} + + # backwards compatibility for existing extensions + import modules.ui + if tabname == 'txt2img': + modules.ui.txt2img_paste_fields = fields + elif tabname == 'img2img': + modules.ui.img2img_paste_fields = fields + + +def create_buttons(tabs_list): + buttons = {} + for tab in tabs_list: + buttons[tab] = gr.Button(f"Send to {tab}", elem_id=f"{tab}_tab") + return buttons + + +def bind_buttons(buttons, send_image, send_generate_info): + """old function for backwards compatibility; do not use this, use register_paste_params_button""" + for tabname, button in buttons.items(): + source_text_component = send_generate_info if isinstance(send_generate_info, gr.components.Component) else None + source_tabname = send_generate_info if isinstance(send_generate_info, str) else None + + register_paste_params_button(ParamBinding(paste_button=button, tabname=tabname, source_text_component=source_text_component, source_image_component=send_image, source_tabname=source_tabname)) + + +def register_paste_params_button(binding: ParamBinding): + registered_param_bindings.append(binding) + + +def connect_paste_params_buttons(): + for binding in registered_param_bindings: + destination_image_component = paste_fields[binding.tabname]["init_img"] + fields = paste_fields[binding.tabname]["fields"] + override_settings_component = binding.override_settings_component or paste_fields[binding.tabname]["override_settings_component"] + + destination_width_component = next(iter([field for field, name in fields if name == "Size-1"] if fields else []), None) + destination_height_component = next(iter([field for field, name in fields if name == "Size-2"] if fields else []), None) + + if binding.source_image_component and destination_image_component: + if isinstance(binding.source_image_component, gr.Gallery): + func = send_image_and_dimensions if destination_width_component else image_from_url_text + jsfunc = "extract_image_from_gallery" + else: + func = send_image_and_dimensions if destination_width_component else lambda x: x + jsfunc = None + + binding.paste_button.click( + fn=func, + _js=jsfunc, + inputs=[binding.source_image_component], + outputs=[destination_image_component, destination_width_component, destination_height_component] if destination_width_component else [destination_image_component], + show_progress=False, + ) + + if binding.source_text_component is not None and fields is not None: + connect_paste(binding.paste_button, fields, binding.source_text_component, override_settings_component, binding.tabname) + + if binding.source_tabname is not None and fields is not None: + paste_field_names = ['Prompt', 'Negative prompt', 'Steps', 'Face restoration'] + (["Seed"] if shared.opts.send_seed else []) + binding.paste_field_names + binding.paste_button.click( + fn=lambda *x: x, + inputs=[field for field, name in paste_fields[binding.source_tabname]["fields"] if name in paste_field_names], + outputs=[field for field, name in fields if name in paste_field_names], + show_progress=False, + ) + + binding.paste_button.click( + fn=None, + _js=f"switch_to_{binding.tabname}", + inputs=None, + outputs=None, + show_progress=False, + ) + + +def send_image_and_dimensions(x): + if isinstance(x, Image.Image): + img = x + else: + img = image_from_url_text(x) + + if shared.opts.send_size and isinstance(img, Image.Image): + w = img.width + h = img.height + else: + w = gr.update() + h = gr.update() + + return img, w, h + + +def restore_old_hires_fix_params(res): + """for infotexts that specify old First pass size parameter, convert it into + width, height, and hr scale""" + + firstpass_width = res.get('First pass size-1', None) + firstpass_height = res.get('First pass size-2', None) + + if shared.opts.use_old_hires_fix_width_height: + hires_width = int(res.get("Hires resize-1", 0)) + hires_height = int(res.get("Hires resize-2", 0)) + + if hires_width and hires_height: + res['Size-1'] = hires_width + res['Size-2'] = hires_height + return + + if firstpass_width is None or firstpass_height is None: + return + + firstpass_width, firstpass_height = int(firstpass_width), int(firstpass_height) + width = int(res.get("Size-1", 512)) + height = int(res.get("Size-2", 512)) + + if firstpass_width == 0 or firstpass_height == 0: + firstpass_width, firstpass_height = processing.old_hires_fix_first_pass_dimensions(width, height) + + res['Size-1'] = firstpass_width + res['Size-2'] = firstpass_height + res['Hires resize-1'] = width + res['Hires resize-2'] = height + + +def parse_generation_parameters(x: str): + """parses generation parameters string, the one you see in text field under the picture in UI: +``` +girl with an artist's beret, determined, blue eyes, desert scene, computer monitors, heavy makeup, by Alphonse Mucha and Charlie Bowater, ((eyeshadow)), (coquettish), detailed, intricate +Negative prompt: ugly, fat, obese, chubby, (((deformed))), [blurry], bad anatomy, disfigured, poorly drawn face, mutation, mutated, (extra_limb), (ugly), (poorly drawn hands), messy drawing +Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 965400086, Size: 512x512, Model hash: 45dee52b +``` + + returns a dict with field values + """ + + res = {} + + prompt = "" + negative_prompt = "" + + done_with_prompt = False + + *lines, lastline = x.strip().split("\n") + if len(re_param.findall(lastline)) < 3: + lines.append(lastline) + lastline = '' + + for line in lines: + line = line.strip() + if line.startswith("Negative prompt:"): + done_with_prompt = True + line = line[16:].strip() + if done_with_prompt: + negative_prompt += ("" if negative_prompt == "" else "\n") + line + else: + prompt += ("" if prompt == "" else "\n") + line + + if shared.opts.infotext_styles != "Ignore": + found_styles, prompt, negative_prompt = shared.prompt_styles.extract_styles_from_prompt(prompt, negative_prompt) + + if shared.opts.infotext_styles == "Apply": + res["Styles array"] = found_styles + elif shared.opts.infotext_styles == "Apply if any" and found_styles: + res["Styles array"] = found_styles + + res["Prompt"] = prompt + res["Negative prompt"] = negative_prompt + + for k, v in re_param.findall(lastline): + try: + if v[0] == '"' and v[-1] == '"': + v = unquote(v) + + m = re_imagesize.match(v) + if m is not None: + res[f"{k}-1"] = m.group(1) + res[f"{k}-2"] = m.group(2) + else: + res[k] = v + except Exception: + print(f"Error parsing \"{k}: {v}\"") + + # Missing CLIP skip means it was set to 1 (the default) + if "Clip skip" not in res: + res["Clip skip"] = "1" + + hypernet = res.get("Hypernet", None) + if hypernet is not None: + res["Prompt"] += f"""""" + + if "Hires resize-1" not in res: + res["Hires resize-1"] = 0 + res["Hires resize-2"] = 0 + + if "Hires sampler" not in res: + res["Hires sampler"] = "Use same sampler" + + if "Hires checkpoint" not in res: + res["Hires checkpoint"] = "Use same checkpoint" + + if "Hires prompt" not in res: + res["Hires prompt"] = "" + + if "Hires negative prompt" not in res: + res["Hires negative prompt"] = "" + + restore_old_hires_fix_params(res) + + # Missing RNG means the default was set, which is GPU RNG + if "RNG" not in res: + res["RNG"] = "GPU" + + if "Schedule type" not in res: + res["Schedule type"] = "Automatic" + + if "Schedule max sigma" not in res: + res["Schedule max sigma"] = 0 + + if "Schedule min sigma" not in res: + res["Schedule min sigma"] = 0 + + if "Schedule rho" not in res: + res["Schedule rho"] = 0 + + if "VAE Encoder" not in res: + res["VAE Encoder"] = "Full" + + if "VAE Decoder" not in res: + res["VAE Decoder"] = "Full" + + skip = set(shared.opts.infotext_skip_pasting) + res = {k: v for k, v in res.items() if k not in skip} + + return res + + +infotext_to_setting_name_mapping = [ + +] +"""Mapping of infotext labels to setting names. Only left for backwards compatibility - use OptionInfo(..., infotext='...') instead. +Example content: + +infotext_to_setting_name_mapping = [ + ('Conditional mask weight', 'inpainting_mask_weight'), + ('Model hash', 'sd_model_checkpoint'), + ('ENSD', 'eta_noise_seed_delta'), + ('Schedule type', 'k_sched_type'), +] +""" + + +def create_override_settings_dict(text_pairs): + """creates processing's override_settings parameters from gradio's multiselect + + Example input: + ['Clip skip: 2', 'Model hash: e6e99610c4', 'ENSD: 31337'] + + Example output: + {'CLIP_stop_at_last_layers': 2, 'sd_model_checkpoint': 'e6e99610c4', 'eta_noise_seed_delta': 31337} + """ + + res = {} + + params = {} + for pair in text_pairs: + k, v = pair.split(":", maxsplit=1) + + params[k] = v.strip() + + mapping = [(info.infotext, k) for k, info in shared.opts.data_labels.items() if info.infotext] + for param_name, setting_name in mapping + infotext_to_setting_name_mapping: + value = params.get(param_name, None) + + if value is None: + continue + + res[setting_name] = shared.opts.cast_value(setting_name, value) + + return res + + +def connect_paste(button, paste_fields, input_comp, override_settings_component, tabname): + def paste_func(prompt): + if not prompt and not shared.cmd_opts.hide_ui_dir_config: + filename = os.path.join(data_path, "params.txt") + if os.path.exists(filename): + with open(filename, "r", encoding="utf8") as file: + prompt = file.read() + + params = parse_generation_parameters(prompt) + script_callbacks.infotext_pasted_callback(prompt, params) + res = [] + + for output, key in paste_fields: + if callable(key): + v = key(params) + else: + v = params.get(key, None) + + if v is None: + res.append(gr.update()) + elif isinstance(v, type_of_gr_update): + res.append(v) + else: + try: + valtype = type(output.value) + + if valtype == bool and v == "False": + val = False + else: + val = valtype(v) + + res.append(gr.update(value=val)) + except Exception: + res.append(gr.update()) + + return res + + if override_settings_component is not None: + already_handled_fields = {key: 1 for _, key in paste_fields} + + def paste_settings(params): + vals = {} + + mapping = [(info.infotext, k) for k, info in shared.opts.data_labels.items() if info.infotext] + for param_name, setting_name in mapping + infotext_to_setting_name_mapping: + if param_name in already_handled_fields: + continue + + v = params.get(param_name, None) + if v is None: + continue + + if setting_name == "sd_model_checkpoint" and shared.opts.disable_weights_auto_swap: + continue + + v = shared.opts.cast_value(setting_name, v) + current_value = getattr(shared.opts, setting_name, None) + + if v == current_value: + continue + + vals[param_name] = v + + vals_pairs = [f"{k}: {v}" for k, v in vals.items()] + + return gr.Dropdown.update(value=vals_pairs, choices=vals_pairs, visible=bool(vals_pairs)) + + paste_fields = paste_fields + [(override_settings_component, paste_settings)] + + button.click( + fn=paste_func, + inputs=[input_comp], + outputs=[x[0] for x in paste_fields], + show_progress=False, + ) + button.click( + fn=None, + _js=f"recalculate_prompts_{tabname}", + inputs=[], + outputs=[], + show_progress=False, + ) + diff --git a/modules/gfpgan_model.py b/modules/gfpgan_model.py new file mode 100644 index 0000000000000000000000000000000000000000..6c3ce0bdea8265982fd51fa8d3be60b8f204970a --- /dev/null +++ b/modules/gfpgan_model.py @@ -0,0 +1,125 @@ +import os + +import facexlib +import gfpgan + +import modules.face_restoration +from modules import paths, shared, devices, modelloader, errors + +model_dir = "GFPGAN" +user_path = None +model_path = os.path.join(paths.models_path, model_dir) +model_file_path = None +model_url = "https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth" +have_gfpgan = False +loaded_gfpgan_model = None + + +def gfpgann(): + global loaded_gfpgan_model + global model_path + global model_file_path + if loaded_gfpgan_model is not None: + loaded_gfpgan_model.gfpgan.to(devices.device_gfpgan) + return loaded_gfpgan_model + + if gfpgan_constructor is None: + return None + + models = modelloader.load_models(model_path, model_url, user_path, ext_filter=['.pth']) + + if len(models) == 1 and models[0].startswith("http"): + model_file = models[0] + elif len(models) != 0: + gfp_models = [] + for item in models: + if 'GFPGAN' in os.path.basename(item): + gfp_models.append(item) + latest_file = max(gfp_models, key=os.path.getctime) + model_file = latest_file + else: + print("Unable to load gfpgan model!") + return None + + if hasattr(facexlib.detection.retinaface, 'device'): + facexlib.detection.retinaface.device = devices.device_gfpgan + model_file_path = model_file + model = gfpgan_constructor(model_path=model_file, upscale=1, arch='clean', channel_multiplier=2, bg_upsampler=None, device=devices.device_gfpgan) + loaded_gfpgan_model = model + + return model + + +def send_model_to(model, device): + model.gfpgan.to(device) + model.face_helper.face_det.to(device) + model.face_helper.face_parse.to(device) + + +def gfpgan_fix_faces(np_image): + model = gfpgann() + if model is None: + return np_image + + send_model_to(model, devices.device_gfpgan) + + np_image_bgr = np_image[:, :, ::-1] + cropped_faces, restored_faces, gfpgan_output_bgr = model.enhance(np_image_bgr, has_aligned=False, only_center_face=False, paste_back=True) + np_image = gfpgan_output_bgr[:, :, ::-1] + + model.face_helper.clean_all() + + if shared.opts.face_restoration_unload: + send_model_to(model, devices.cpu) + + return np_image + + +gfpgan_constructor = None + + +def setup_model(dirname): + try: + os.makedirs(model_path, exist_ok=True) + from gfpgan import GFPGANer + from facexlib import detection, parsing # noqa: F401 + global user_path + global have_gfpgan + global gfpgan_constructor + global model_file_path + + facexlib_path = model_path + + if dirname is not None: + facexlib_path = dirname + + load_file_from_url_orig = gfpgan.utils.load_file_from_url + facex_load_file_from_url_orig = facexlib.detection.load_file_from_url + facex_load_file_from_url_orig2 = facexlib.parsing.load_file_from_url + + def my_load_file_from_url(**kwargs): + return load_file_from_url_orig(**dict(kwargs, model_dir=model_file_path)) + + def facex_load_file_from_url(**kwargs): + return facex_load_file_from_url_orig(**dict(kwargs, save_dir=facexlib_path, model_dir=None)) + + def facex_load_file_from_url2(**kwargs): + return facex_load_file_from_url_orig2(**dict(kwargs, save_dir=facexlib_path, model_dir=None)) + + gfpgan.utils.load_file_from_url = my_load_file_from_url + facexlib.detection.load_file_from_url = facex_load_file_from_url + facexlib.parsing.load_file_from_url = facex_load_file_from_url2 + user_path = dirname + have_gfpgan = True + gfpgan_constructor = GFPGANer + + class FaceRestorerGFPGAN(modules.face_restoration.FaceRestoration): + def name(self): + return "GFPGAN" + + def restore(self, np_image): + return gfpgan_fix_faces(np_image) + + shared.face_restorers.append(FaceRestorerGFPGAN()) + except Exception: + errors.report("Error setting up GFPGAN", exc_info=True) diff --git a/modules/gitpython_hack.py b/modules/gitpython_hack.py new file mode 100644 index 0000000000000000000000000000000000000000..b55f0640e5ecb945ec72e9aeccd525c6dd9d7cb8 --- /dev/null +++ b/modules/gitpython_hack.py @@ -0,0 +1,42 @@ +from __future__ import annotations + +import io +import subprocess + +import git + + +class Git(git.Git): + """ + Git subclassed to never use persistent processes. + """ + + def _get_persistent_cmd(self, attr_name, cmd_name, *args, **kwargs): + raise NotImplementedError(f"Refusing to use persistent process: {attr_name} ({cmd_name} {args} {kwargs})") + + def get_object_header(self, ref: str | bytes) -> tuple[str, str, int]: + ret = subprocess.check_output( + [self.GIT_PYTHON_GIT_EXECUTABLE, "cat-file", "--batch-check"], + input=self._prepare_ref(ref), + cwd=self._working_dir, + timeout=2, + ) + return self._parse_object_header(ret) + + def stream_object_data(self, ref: str) -> tuple[str, str, int, Git.CatFileContentStream]: + # Not really streaming, per se; this buffers the entire object in memory. + # Shouldn't be a problem for our use case, since we're only using this for + # object headers (commit objects). + ret = subprocess.check_output( + [self.GIT_PYTHON_GIT_EXECUTABLE, "cat-file", "--batch"], + input=self._prepare_ref(ref), + cwd=self._working_dir, + timeout=30, + ) + bio = io.BytesIO(ret) + hexsha, typename, size = self._parse_object_header(bio.readline()) + return (hexsha, typename, size, self.CatFileContentStream(size, bio)) + + +class Repo(git.Repo): + GitCommandWrapperType = Git diff --git a/modules/gradio_extensons.py b/modules/gradio_extensons.py new file mode 100644 index 0000000000000000000000000000000000000000..34e38700a62fbb512c9b5867829737fd6d17a610 --- /dev/null +++ b/modules/gradio_extensons.py @@ -0,0 +1,83 @@ +import gradio as gr + +from modules import scripts, ui_tempdir, patches + + +def add_classes_to_gradio_component(comp): + """ + this adds gradio-* to the component for css styling (ie gradio-button to gr.Button), as well as some others + """ + + comp.elem_classes = [f"gradio-{comp.get_block_name()}", *(comp.elem_classes or [])] + + if getattr(comp, 'multiselect', False): + comp.elem_classes.append('multiselect') + + +def IOComponent_init(self, *args, **kwargs): + self.webui_tooltip = kwargs.pop('tooltip', None) + + if scripts.scripts_current is not None: + scripts.scripts_current.before_component(self, **kwargs) + + scripts.script_callbacks.before_component_callback(self, **kwargs) + + res = original_IOComponent_init(self, *args, **kwargs) + + add_classes_to_gradio_component(self) + + scripts.script_callbacks.after_component_callback(self, **kwargs) + + if scripts.scripts_current is not None: + scripts.scripts_current.after_component(self, **kwargs) + + return res + + +def Block_get_config(self): + config = original_Block_get_config(self) + + webui_tooltip = getattr(self, 'webui_tooltip', None) + if webui_tooltip: + config["webui_tooltip"] = webui_tooltip + + config.pop('example_inputs', None) + + return config + + +def BlockContext_init(self, *args, **kwargs): + if scripts.scripts_current is not None: + scripts.scripts_current.before_component(self, **kwargs) + + scripts.script_callbacks.before_component_callback(self, **kwargs) + + res = original_BlockContext_init(self, *args, **kwargs) + + add_classes_to_gradio_component(self) + + scripts.script_callbacks.after_component_callback(self, **kwargs) + + if scripts.scripts_current is not None: + scripts.scripts_current.after_component(self, **kwargs) + + return res + + +def Blocks_get_config_file(self, *args, **kwargs): + config = original_Blocks_get_config_file(self, *args, **kwargs) + + for comp_config in config["components"]: + if "example_inputs" in comp_config: + comp_config["example_inputs"] = {"serialized": []} + + return config + + +original_IOComponent_init = patches.patch(__name__, obj=gr.components.IOComponent, field="__init__", replacement=IOComponent_init) +original_Block_get_config = patches.patch(__name__, obj=gr.blocks.Block, field="get_config", replacement=Block_get_config) +original_BlockContext_init = patches.patch(__name__, obj=gr.blocks.BlockContext, field="__init__", replacement=BlockContext_init) +original_Blocks_get_config_file = patches.patch(__name__, obj=gr.blocks.Blocks, field="get_config_file", replacement=Blocks_get_config_file) + + +ui_tempdir.install_ui_tempdir_override() diff --git a/modules/hashes.py b/modules/hashes.py new file mode 100644 index 0000000000000000000000000000000000000000..59a81eaabc91567a1a3a3caa12f1f9944f487806 --- /dev/null +++ b/modules/hashes.py @@ -0,0 +1,81 @@ +import hashlib +import os.path + +from modules import shared +import modules.cache + +dump_cache = modules.cache.dump_cache +cache = modules.cache.cache + + +def calculate_sha256(filename): + hash_sha256 = hashlib.sha256() + blksize = 1024 * 1024 + + with open(filename, "rb") as f: + for chunk in iter(lambda: f.read(blksize), b""): + hash_sha256.update(chunk) + + return hash_sha256.hexdigest() + + +def sha256_from_cache(filename, title, use_addnet_hash=False): + hashes = cache("hashes-addnet") if use_addnet_hash else cache("hashes") + ondisk_mtime = os.path.getmtime(filename) + + if title not in hashes: + return None + + cached_sha256 = hashes[title].get("sha256", None) + cached_mtime = hashes[title].get("mtime", 0) + + if ondisk_mtime > cached_mtime or cached_sha256 is None: + return None + + return cached_sha256 + + +def sha256(filename, title, use_addnet_hash=False): + hashes = cache("hashes-addnet") if use_addnet_hash else cache("hashes") + + sha256_value = sha256_from_cache(filename, title, use_addnet_hash) + if sha256_value is not None: + return sha256_value + + if shared.cmd_opts.no_hashing: + return None + + print(f"Calculating sha256 for {filename}: ", end='') + if use_addnet_hash: + with open(filename, "rb") as file: + sha256_value = addnet_hash_safetensors(file) + else: + sha256_value = calculate_sha256(filename) + print(f"{sha256_value}") + + hashes[title] = { + "mtime": os.path.getmtime(filename), + "sha256": sha256_value, + } + + dump_cache() + + return sha256_value + + +def addnet_hash_safetensors(b): + """kohya-ss hash for safetensors from https://github.com/kohya-ss/sd-scripts/blob/main/library/train_util.py""" + hash_sha256 = hashlib.sha256() + blksize = 1024 * 1024 + + b.seek(0) + header = b.read(8) + n = int.from_bytes(header, "little") + + offset = n + 8 + b.seek(offset) + for chunk in iter(lambda: b.read(blksize), b""): + hash_sha256.update(chunk) + + return hash_sha256.hexdigest() + diff --git a/modules/images.py b/modules/images.py new file mode 100644 index 0000000000000000000000000000000000000000..b1860969e2be83f6f58a18ceca2d7c84664ab918 --- /dev/null +++ b/modules/images.py @@ -0,0 +1,793 @@ +from __future__ import annotations + +import datetime + +import pytz +import io +import math +import os +from collections import namedtuple +import re + +import numpy as np +import piexif +import piexif.helper +from PIL import Image, ImageFont, ImageDraw, ImageColor, PngImagePlugin +import string +import json +import hashlib + +from modules import sd_samplers, shared, script_callbacks, errors +from modules.paths_internal import roboto_ttf_file +from modules.shared import opts + +LANCZOS = (Image.Resampling.LANCZOS if hasattr(Image, 'Resampling') else Image.LANCZOS) + + +def get_font(fontsize: int): + try: + return ImageFont.truetype(opts.font or roboto_ttf_file, fontsize) + except Exception: + return ImageFont.truetype(roboto_ttf_file, fontsize) + + +def image_grid(imgs, batch_size=1, rows=None): + if rows is None: + if opts.n_rows > 0: + rows = opts.n_rows + elif opts.n_rows == 0: + rows = batch_size + elif opts.grid_prevent_empty_spots: + rows = math.floor(math.sqrt(len(imgs))) + while len(imgs) % rows != 0: + rows -= 1 + else: + rows = math.sqrt(len(imgs)) + rows = round(rows) + if rows > len(imgs): + rows = len(imgs) + + cols = math.ceil(len(imgs) / rows) + + params = script_callbacks.ImageGridLoopParams(imgs, cols, rows) + script_callbacks.image_grid_callback(params) + + w, h = imgs[0].size + grid = Image.new('RGB', size=(params.cols * w, params.rows * h), color='black') + + for i, img in enumerate(params.imgs): + grid.paste(img, box=(i % params.cols * w, i // params.cols * h)) + + return grid + + +Grid = namedtuple("Grid", ["tiles", "tile_w", "tile_h", "image_w", "image_h", "overlap"]) + + +def split_grid(image, tile_w=512, tile_h=512, overlap=64): + w = image.width + h = image.height + + non_overlap_width = tile_w - overlap + non_overlap_height = tile_h - overlap + + cols = math.ceil((w - overlap) / non_overlap_width) + rows = math.ceil((h - overlap) / non_overlap_height) + + dx = (w - tile_w) / (cols - 1) if cols > 1 else 0 + dy = (h - tile_h) / (rows - 1) if rows > 1 else 0 + + grid = Grid([], tile_w, tile_h, w, h, overlap) + for row in range(rows): + row_images = [] + + y = int(row * dy) + + if y + tile_h >= h: + y = h - tile_h + + for col in range(cols): + x = int(col * dx) + + if x + tile_w >= w: + x = w - tile_w + + tile = image.crop((x, y, x + tile_w, y + tile_h)) + + row_images.append([x, tile_w, tile]) + + grid.tiles.append([y, tile_h, row_images]) + + return grid + + +def combine_grid(grid): + def make_mask_image(r): + r = r * 255 / grid.overlap + r = r.astype(np.uint8) + return Image.fromarray(r, 'L') + + mask_w = make_mask_image(np.arange(grid.overlap, dtype=np.float32).reshape((1, grid.overlap)).repeat(grid.tile_h, axis=0)) + mask_h = make_mask_image(np.arange(grid.overlap, dtype=np.float32).reshape((grid.overlap, 1)).repeat(grid.image_w, axis=1)) + + combined_image = Image.new("RGB", (grid.image_w, grid.image_h)) + for y, h, row in grid.tiles: + combined_row = Image.new("RGB", (grid.image_w, h)) + for x, w, tile in row: + if x == 0: + combined_row.paste(tile, (0, 0)) + continue + + combined_row.paste(tile.crop((0, 0, grid.overlap, h)), (x, 0), mask=mask_w) + combined_row.paste(tile.crop((grid.overlap, 0, w, h)), (x + grid.overlap, 0)) + + if y == 0: + combined_image.paste(combined_row, (0, 0)) + continue + + combined_image.paste(combined_row.crop((0, 0, combined_row.width, grid.overlap)), (0, y), mask=mask_h) + combined_image.paste(combined_row.crop((0, grid.overlap, combined_row.width, h)), (0, y + grid.overlap)) + + return combined_image + + +class GridAnnotation: + def __init__(self, text='', is_active=True): + self.text = text + self.is_active = is_active + self.size = None + + +def draw_grid_annotations(im, width, height, hor_texts, ver_texts, margin=0): + + color_active = ImageColor.getcolor(opts.grid_text_active_color, 'RGB') + color_inactive = ImageColor.getcolor(opts.grid_text_inactive_color, 'RGB') + color_background = ImageColor.getcolor(opts.grid_background_color, 'RGB') + + def wrap(drawing, text, font, line_length): + lines = [''] + for word in text.split(): + line = f'{lines[-1]} {word}'.strip() + if drawing.textlength(line, font=font) <= line_length: + lines[-1] = line + else: + lines.append(word) + return lines + + def draw_texts(drawing, draw_x, draw_y, lines, initial_fnt, initial_fontsize): + for line in lines: + fnt = initial_fnt + fontsize = initial_fontsize + while drawing.multiline_textsize(line.text, font=fnt)[0] > line.allowed_width and fontsize > 0: + fontsize -= 1 + fnt = get_font(fontsize) + drawing.multiline_text((draw_x, draw_y + line.size[1] / 2), line.text, font=fnt, fill=color_active if line.is_active else color_inactive, anchor="mm", align="center") + + if not line.is_active: + drawing.line((draw_x - line.size[0] // 2, draw_y + line.size[1] // 2, draw_x + line.size[0] // 2, draw_y + line.size[1] // 2), fill=color_inactive, width=4) + + draw_y += line.size[1] + line_spacing + + fontsize = (width + height) // 25 + line_spacing = fontsize // 2 + + fnt = get_font(fontsize) + + pad_left = 0 if sum([sum([len(line.text) for line in lines]) for lines in ver_texts]) == 0 else width * 3 // 4 + + cols = im.width // width + rows = im.height // height + + assert cols == len(hor_texts), f'bad number of horizontal texts: {len(hor_texts)}; must be {cols}' + assert rows == len(ver_texts), f'bad number of vertical texts: {len(ver_texts)}; must be {rows}' + + calc_img = Image.new("RGB", (1, 1), color_background) + calc_d = ImageDraw.Draw(calc_img) + + for texts, allowed_width in zip(hor_texts + ver_texts, [width] * len(hor_texts) + [pad_left] * len(ver_texts)): + items = [] + texts + texts.clear() + + for line in items: + wrapped = wrap(calc_d, line.text, fnt, allowed_width) + texts += [GridAnnotation(x, line.is_active) for x in wrapped] + + for line in texts: + bbox = calc_d.multiline_textbbox((0, 0), line.text, font=fnt) + line.size = (bbox[2] - bbox[0], bbox[3] - bbox[1]) + line.allowed_width = allowed_width + + hor_text_heights = [sum([line.size[1] + line_spacing for line in lines]) - line_spacing for lines in hor_texts] + ver_text_heights = [sum([line.size[1] + line_spacing for line in lines]) - line_spacing * len(lines) for lines in ver_texts] + + pad_top = 0 if sum(hor_text_heights) == 0 else max(hor_text_heights) + line_spacing * 2 + + result = Image.new("RGB", (im.width + pad_left + margin * (cols-1), im.height + pad_top + margin * (rows-1)), color_background) + + for row in range(rows): + for col in range(cols): + cell = im.crop((width * col, height * row, width * (col+1), height * (row+1))) + result.paste(cell, (pad_left + (width + margin) * col, pad_top + (height + margin) * row)) + + d = ImageDraw.Draw(result) + + for col in range(cols): + x = pad_left + (width + margin) * col + width / 2 + y = pad_top / 2 - hor_text_heights[col] / 2 + + draw_texts(d, x, y, hor_texts[col], fnt, fontsize) + + for row in range(rows): + x = pad_left / 2 + y = pad_top + (height + margin) * row + height / 2 - ver_text_heights[row] / 2 + + draw_texts(d, x, y, ver_texts[row], fnt, fontsize) + + return result + + +def draw_prompt_matrix(im, width, height, all_prompts, margin=0): + prompts = all_prompts[1:] + boundary = math.ceil(len(prompts) / 2) + + prompts_horiz = prompts[:boundary] + prompts_vert = prompts[boundary:] + + hor_texts = [[GridAnnotation(x, is_active=pos & (1 << i) != 0) for i, x in enumerate(prompts_horiz)] for pos in range(1 << len(prompts_horiz))] + ver_texts = [[GridAnnotation(x, is_active=pos & (1 << i) != 0) for i, x in enumerate(prompts_vert)] for pos in range(1 << len(prompts_vert))] + + return draw_grid_annotations(im, width, height, hor_texts, ver_texts, margin) + + +def resize_image(resize_mode, im, width, height, upscaler_name=None): + """ + Resizes an image with the specified resize_mode, width, and height. + + Args: + resize_mode: The mode to use when resizing the image. + 0: Resize the image to the specified width and height. + 1: Resize the image to fill the specified width and height, maintaining the aspect ratio, and then center the image within the dimensions, cropping the excess. + 2: Resize the image to fit within the specified width and height, maintaining the aspect ratio, and then center the image within the dimensions, filling empty with data from image. + im: The image to resize. + width: The width to resize the image to. + height: The height to resize the image to. + upscaler_name: The name of the upscaler to use. If not provided, defaults to opts.upscaler_for_img2img. + """ + + upscaler_name = upscaler_name or opts.upscaler_for_img2img + + def resize(im, w, h): + if upscaler_name is None or upscaler_name == "None" or im.mode == 'L': + return im.resize((w, h), resample=LANCZOS) + + scale = max(w / im.width, h / im.height) + + if scale > 1.0: + upscalers = [x for x in shared.sd_upscalers if x.name == upscaler_name] + if len(upscalers) == 0: + upscaler = shared.sd_upscalers[0] + print(f"could not find upscaler named {upscaler_name or ''}, using {upscaler.name} as a fallback") + else: + upscaler = upscalers[0] + + im = upscaler.scaler.upscale(im, scale, upscaler.data_path) + + if im.width != w or im.height != h: + im = im.resize((w, h), resample=LANCZOS) + + return im + + if resize_mode == 0: + res = resize(im, width, height) + + elif resize_mode == 1: + ratio = width / height + src_ratio = im.width / im.height + + src_w = width if ratio > src_ratio else im.width * height // im.height + src_h = height if ratio <= src_ratio else im.height * width // im.width + + resized = resize(im, src_w, src_h) + res = Image.new("RGB", (width, height)) + res.paste(resized, box=(width // 2 - src_w // 2, height // 2 - src_h // 2)) + + else: + ratio = width / height + src_ratio = im.width / im.height + + src_w = width if ratio < src_ratio else im.width * height // im.height + src_h = height if ratio >= src_ratio else im.height * width // im.width + + resized = resize(im, src_w, src_h) + res = Image.new("RGB", (width, height)) + res.paste(resized, box=(width // 2 - src_w // 2, height // 2 - src_h // 2)) + + if ratio < src_ratio: + fill_height = height // 2 - src_h // 2 + if fill_height > 0: + res.paste(resized.resize((width, fill_height), box=(0, 0, width, 0)), box=(0, 0)) + res.paste(resized.resize((width, fill_height), box=(0, resized.height, width, resized.height)), box=(0, fill_height + src_h)) + elif ratio > src_ratio: + fill_width = width // 2 - src_w // 2 + if fill_width > 0: + res.paste(resized.resize((fill_width, height), box=(0, 0, 0, height)), box=(0, 0)) + res.paste(resized.resize((fill_width, height), box=(resized.width, 0, resized.width, height)), box=(fill_width + src_w, 0)) + + return res + + +invalid_filename_chars = '<>:"/\\|?*\n\r\t' +invalid_filename_prefix = ' ' +invalid_filename_postfix = ' .' +re_nonletters = re.compile(r'[\s' + string.punctuation + ']+') +re_pattern = re.compile(r"(.*?)(?:\[([^\[\]]+)\]|$)") +re_pattern_arg = re.compile(r"(.*)<([^>]*)>$") +max_filename_part_length = 128 +NOTHING_AND_SKIP_PREVIOUS_TEXT = object() + + +def sanitize_filename_part(text, replace_spaces=True): + if text is None: + return None + + if replace_spaces: + text = text.replace(' ', '_') + + text = text.translate({ord(x): '_' for x in invalid_filename_chars}) + text = text.lstrip(invalid_filename_prefix)[:max_filename_part_length] + text = text.rstrip(invalid_filename_postfix) + return text + + +class FilenameGenerator: + replacements = { + 'seed': lambda self: self.seed if self.seed is not None else '', + 'seed_first': lambda self: self.seed if self.p.batch_size == 1 else self.p.all_seeds[0], + 'seed_last': lambda self: NOTHING_AND_SKIP_PREVIOUS_TEXT if self.p.batch_size == 1 else self.p.all_seeds[-1], + 'steps': lambda self: self.p and self.p.steps, + 'cfg': lambda self: self.p and self.p.cfg_scale, + 'width': lambda self: self.image.width, + 'height': lambda self: self.image.height, + 'styles': lambda self: self.p and sanitize_filename_part(", ".join([style for style in self.p.styles if not style == "None"]) or "None", replace_spaces=False), + 'sampler': lambda self: self.p and sanitize_filename_part(self.p.sampler_name, replace_spaces=False), + 'model_hash': lambda self: getattr(self.p, "sd_model_hash", shared.sd_model.sd_model_hash), + 'model_name': lambda self: sanitize_filename_part(shared.sd_model.sd_checkpoint_info.name_for_extra, replace_spaces=False), + 'date': lambda self: datetime.datetime.now().strftime('%Y-%m-%d'), + 'datetime': lambda self, *args: self.datetime(*args), # accepts formats: [datetime], [datetime], [datetime