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| # Stable Diffusion | |
| *Stable Diffusion was made possible thanks to a collaboration with [Stability AI](https://stability.ai/) and [Runway](https://runwayml.com/) and builds upon our previous work:* | |
| [**High-Resolution Image Synthesis with Latent Diffusion Models**](https://ommer-lab.com/research/latent-diffusion-models/)<br/> | |
| [Robin Rombach](https://github.com/rromb)\*, | |
| [Andreas Blattmann](https://github.com/ablattmann)\*, | |
| [Dominik Lorenz](https://github.com/qp-qp)\, | |
| [Patrick Esser](https://github.com/pesser), | |
| [Björn Ommer](https://hci.iwr.uni-heidelberg.de/Staff/bommer)<br/> | |
| _[CVPR '22 Oral](https://openaccess.thecvf.com/content/CVPR2022/html/Rombach_High-Resolution_Image_Synthesis_With_Latent_Diffusion_Models_CVPR_2022_paper.html) | | |
| [GitHub](https://github.com/CompVis/latent-diffusion) | [arXiv](https://arxiv.org/abs/2112.10752) | [Project page](https://ommer-lab.com/research/latent-diffusion-models/)_ | |
|  | |
| [Stable Diffusion](#stable-diffusion-v1) is a latent text-to-image diffusion | |
| model. | |
| Thanks to a generous compute donation from [Stability AI](https://stability.ai/) and support from [LAION](https://laion.ai/), we were able to train a Latent Diffusion Model on 512x512 images from a subset of the [LAION-5B](https://laion.ai/blog/laion-5b/) database. | |
| Similar to Google's [Imagen](https://arxiv.org/abs/2205.11487), | |
| this model uses a frozen CLIP ViT-L/14 text encoder to condition the model on text prompts. | |
| With its 860M UNet and 123M text encoder, the model is relatively lightweight and runs on a GPU with at least 10GB VRAM. | |
| See [this section](#stable-diffusion-v1) below and the [model card](https://huggingface.co/CompVis/stable-diffusion). | |
| ## Requirements | |
| A suitable [conda](https://conda.io/) environment named `ldm` can be created | |
| and activated with: | |
| ``` | |
| conda env create -f environment.yaml | |
| conda activate ldm | |
| ``` | |
| You can also update an existing [latent diffusion](https://github.com/CompVis/latent-diffusion) environment by running | |
| ``` | |
| conda install pytorch torchvision -c pytorch | |
| pip install transformers==4.19.2 diffusers invisible-watermark | |
| pip install -e . | |
| ``` | |
| ## Stable Diffusion v1 | |
| Stable Diffusion v1 refers to a specific configuration of the model | |
| architecture that uses a downsampling-factor 8 autoencoder with an 860M UNet | |
| and CLIP ViT-L/14 text encoder for the diffusion model. The model was pretrained on 256x256 images and | |
| then finetuned on 512x512 images. | |
| *Note: Stable Diffusion v1 is a general text-to-image diffusion model and therefore mirrors biases and (mis-)conceptions that are present | |
| in its training data. | |
| Details on the training procedure and data, as well as the intended use of the model can be found in the corresponding [model card](Stable_Diffusion_v1_Model_Card.md).* | |
| The weights are available via [the CompVis organization at Hugging Face](https://huggingface.co/CompVis) under [a license which contains specific use-based restrictions to prevent misuse and harm as informed by the model card, but otherwise remains permissive](LICENSE). While commercial use is permitted under the terms of the license, **we do not recommend using the provided weights for services or products without additional safety mechanisms and considerations**, since there are [known limitations and biases](Stable_Diffusion_v1_Model_Card.md#limitations-and-bias) of the weights, and research on safe and ethical deployment of general text-to-image models is an ongoing effort. **The weights are research artifacts and should be treated as such.** | |
| [The CreativeML OpenRAIL M license](LICENSE) is an [Open RAIL M license](https://www.licenses.ai/blog/2022/8/18/naming-convention-of-responsible-ai-licenses), adapted from the work that [BigScience](https://bigscience.huggingface.co/) and [the RAIL Initiative](https://www.licenses.ai/) are jointly carrying in the area of responsible AI licensing. See also [the article about the BLOOM Open RAIL license](https://bigscience.huggingface.co/blog/the-bigscience-rail-license) on which our license is based. | |
| ### Weights | |
| We currently provide the following checkpoints: | |
| - `sd-v1-1.ckpt`: 237k steps at resolution `256x256` on [laion2B-en](https://huggingface.co/datasets/laion/laion2B-en). | |
| 194k steps at resolution `512x512` on [laion-high-resolution](https://huggingface.co/datasets/laion/laion-high-resolution) (170M examples from LAION-5B with resolution `>= 1024x1024`). | |
| - `sd-v1-2.ckpt`: Resumed from `sd-v1-1.ckpt`. | |
| 515k steps at resolution `512x512` on [laion-aesthetics v2 5+](https://laion.ai/blog/laion-aesthetics/) (a subset of laion2B-en with estimated aesthetics score `> 5.0`, and additionally | |
| filtered to images with an original size `>= 512x512`, and an estimated watermark probability `< 0.5`. The watermark estimate is from the [LAION-5B](https://laion.ai/blog/laion-5b/) metadata, the aesthetics score is estimated using the [LAION-Aesthetics Predictor V2](https://github.com/christophschuhmann/improved-aesthetic-predictor)). | |
| - `sd-v1-3.ckpt`: Resumed from `sd-v1-2.ckpt`. 195k steps at resolution `512x512` on "laion-aesthetics v2 5+" and 10\% dropping of the text-conditioning to improve [classifier-free guidance sampling](https://arxiv.org/abs/2207.12598). | |
| - `sd-v1-4.ckpt`: Resumed from `sd-v1-2.ckpt`. 225k steps at resolution `512x512` on "laion-aesthetics v2 5+" and 10\% dropping of the text-conditioning to improve [classifier-free guidance sampling](https://arxiv.org/abs/2207.12598). | |
| Evaluations with different classifier-free guidance scales (1.5, 2.0, 3.0, 4.0, | |
| 5.0, 6.0, 7.0, 8.0) and 50 PLMS sampling | |
| steps show the relative improvements of the checkpoints: | |
|  | |
| ### Text-to-Image with Stable Diffusion | |
|  | |
|  | |
| Stable Diffusion is a latent diffusion model conditioned on the (non-pooled) text embeddings of a CLIP ViT-L/14 text encoder. | |
| We provide a [reference script for sampling](#reference-sampling-script), but | |
| there also exists a [diffusers integration](#diffusers-integration), which we | |
| expect to see more active community development. | |
| #### Reference Sampling Script | |
| We provide a reference sampling script, which incorporates | |
| - a [Safety Checker Module](https://github.com/CompVis/stable-diffusion/pull/36), | |
| to reduce the probability of explicit outputs, | |
| - an [invisible watermarking](https://github.com/ShieldMnt/invisible-watermark) | |
| of the outputs, to help viewers [identify the images as machine-generated](scripts/tests/test_watermark.py). | |
| After [obtaining the `stable-diffusion-v1-*-original` weights](#weights), link them | |
| ``` | |
| mkdir -p models/ldm/stable-diffusion-v1/ | |
| ln -s <path/to/model.ckpt> models/ldm/stable-diffusion-v1/model.ckpt | |
| ``` | |
| and sample with | |
| ``` | |
| python scripts/txt2img.py --prompt "a photograph of an astronaut riding a horse" --plms | |
| ``` | |
| By default, this uses a guidance scale of `--scale 7.5`, [Katherine Crowson's implementation](https://github.com/CompVis/latent-diffusion/pull/51) of the [PLMS](https://arxiv.org/abs/2202.09778) sampler, | |
| and renders images of size 512x512 (which it was trained on) in 50 steps. All supported arguments are listed below (type `python scripts/txt2img.py --help`). | |
| ```commandline | |
| usage: txt2img.py [-h] [--prompt [PROMPT]] [--outdir [OUTDIR]] [--skip_grid] [--skip_save] [--ddim_steps DDIM_STEPS] [--plms] [--laion400m] [--fixed_code] [--ddim_eta DDIM_ETA] | |
| [--n_iter N_ITER] [--H H] [--W W] [--C C] [--f F] [--n_samples N_SAMPLES] [--n_rows N_ROWS] [--scale SCALE] [--from-file FROM_FILE] [--config CONFIG] [--ckpt CKPT] | |
| [--seed SEED] [--precision {full,autocast}] | |
| optional arguments: | |
| -h, --help show this help message and exit | |
| --prompt [PROMPT] the prompt to render | |
| --outdir [OUTDIR] dir to write results to | |
| --skip_grid do not save a grid, only individual samples. Helpful when evaluating lots of samples | |
| --skip_save do not save individual samples. For speed measurements. | |
| --ddim_steps DDIM_STEPS | |
| number of ddim sampling steps | |
| --plms use plms sampling | |
| --laion400m uses the LAION400M model | |
| --fixed_code if enabled, uses the same starting code across samples | |
| --ddim_eta DDIM_ETA ddim eta (eta=0.0 corresponds to deterministic sampling | |
| --n_iter N_ITER sample this often | |
| --H H image height, in pixel space | |
| --W W image width, in pixel space | |
| --C C latent channels | |
| --f F downsampling factor | |
| --n_samples N_SAMPLES | |
| how many samples to produce for each given prompt. A.k.a. batch size | |
| --n_rows N_ROWS rows in the grid (default: n_samples) | |
| --scale SCALE unconditional guidance scale: eps = eps(x, empty) + scale * (eps(x, cond) - eps(x, empty)) | |
| --from-file FROM_FILE | |
| if specified, load prompts from this file | |
| --config CONFIG path to config which constructs model | |
| --ckpt CKPT path to checkpoint of model | |
| --seed SEED the seed (for reproducible sampling) | |
| --precision {full,autocast} | |
| evaluate at this precision | |
| ``` | |
| Note: The inference config for all v1 versions is designed to be used with EMA-only checkpoints. | |
| For this reason `use_ema=False` is set in the configuration, otherwise the code will try to switch from | |
| non-EMA to EMA weights. If you want to examine the effect of EMA vs no EMA, we provide "full" checkpoints | |
| which contain both types of weights. For these, `use_ema=False` will load and use the non-EMA weights. | |
| #### Diffusers Integration | |
| A simple way to download and sample Stable Diffusion is by using the [diffusers library](https://github.com/huggingface/diffusers/tree/main#new--stable-diffusion-is-now-fully-compatible-with-diffusers): | |
| ```py | |
| # make sure you're logged in with `huggingface-cli login` | |
| from torch import autocast | |
| from diffusers import StableDiffusionPipeline | |
| pipe = StableDiffusionPipeline.from_pretrained( | |
| "CompVis/stable-diffusion-v1-4", | |
| use_auth_token=True | |
| ).to("cuda") | |
| prompt = "a photo of an astronaut riding a horse on mars" | |
| with autocast("cuda"): | |
| image = pipe(prompt)["sample"][0] | |
| image.save("astronaut_rides_horse.png") | |
| ``` | |
| ### Image Modification with Stable Diffusion | |
| By using a diffusion-denoising mechanism as first proposed by [SDEdit](https://arxiv.org/abs/2108.01073), the model can be used for different | |
| tasks such as text-guided image-to-image translation and upscaling. Similar to the txt2img sampling script, | |
| we provide a script to perform image modification with Stable Diffusion. | |
| The following describes an example where a rough sketch made in [Pinta](https://www.pinta-project.com/) is converted into a detailed artwork. | |
| ``` | |
| python scripts/img2img.py --prompt "A fantasy landscape, trending on artstation" --init-img <path-to-img.jpg> --strength 0.8 | |
| ``` | |
| Here, strength is a value between 0.0 and 1.0, that controls the amount of noise that is added to the input image. | |
| Values that approach 1.0 allow for lots of variations but will also produce images that are not semantically consistent with the input. See the following example. | |
| **Input** | |
|  | |
| **Outputs** | |
|  | |
|  | |
| This procedure can, for example, also be used to upscale samples from the base model. | |
| ## Comments | |
| - Our codebase for the diffusion models builds heavily on [OpenAI's ADM codebase](https://github.com/openai/guided-diffusion) | |
| and [https://github.com/lucidrains/denoising-diffusion-pytorch](https://github.com/lucidrains/denoising-diffusion-pytorch). | |
| Thanks for open-sourcing! | |
| - The implementation of the transformer encoder is from [x-transformers](https://github.com/lucidrains/x-transformers) by [lucidrains](https://github.com/lucidrains?tab=repositories). | |
| ## BibTeX | |
| ``` | |
| @misc{rombach2021highresolution, | |
| title={High-Resolution Image Synthesis with Latent Diffusion Models}, | |
| author={Robin Rombach and Andreas Blattmann and Dominik Lorenz and Patrick Esser and Björn Ommer}, | |
| year={2021}, | |
| eprint={2112.10752}, | |
| archivePrefix={arXiv}, | |
| primaryClass={cs.CV} | |
| } | |
| ``` | |