init

.gitignore

@@ -1,133 +1 @@
-gradio_queue.db*
-pretrained/*
-icetk_models/*
-!*/.gitkeep
-# Byte-compiled / optimized / DLL files
-__pycache__/
-*.py[cod]
-*$py.class
-
-# C extensions
-*.so
-
-# Distribution / packaging
-.Python
-build/
-develop-eggs/
-dist/
-downloads/
-eggs/
-.eggs/
-lib/
-lib64/
-parts/
-sdist/
-var/
-wheels/
-pip-wheel-metadata/
-share/python-wheels/
-*.egg-info/
-.installed.cfg
-*.egg
-MANIFEST
-
-# PyInstaller
-#  Usually these files are written by a python script from a template
-#  before PyInstaller builds the exe, so as to inject date/other infos into it.
-*.manifest
-*.spec
-
-# Installer logs
-pip-log.txt
-pip-delete-this-directory.txt
-
-# Unit test / coverage reports
-htmlcov/
-.tox/
-.nox/
-.coverage
-.coverage.*
-.cache
-nosetests.xml
-coverage.xml
-*.cover
-*.py,cover
-.hypothesis/
-.pytest_cache/
-
-# Translations
-*.mo
-*.pot
-
-# Django stuff:
-*.log
-local_settings.py
-db.sqlite3
-db.sqlite3-journal
-
-# Flask stuff:
-instance/
-.webassets-cache
-
-# Scrapy stuff:
-.scrapy
-
-# Sphinx documentation
-docs/_build/
-
-# PyBuilder
-target/
-
-# Jupyter Notebook
-.ipynb_checkpoints
-
-# IPython
-profile_default/
-ipython_config.py
-
-# pyenv
-.python-version
-
-# pipenv
-#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
-#   However, in case of collaboration, if having platform-specific dependencies or dependencies
-#   having no cross-platform support, pipenv may install dependencies that don't work, or not
-#   install all needed dependencies.
-#Pipfile.lock
-
-# PEP 582; used by e.g. github.com/David-OConnor/pyflow
-__pypackages__/
-
-# Celery stuff
-celerybeat-schedule
-celerybeat.pid
-
-# SageMath parsed files
-*.sage.py
-
-# Environments
-.env
-.venv
-env/
-venv/
-ENV/
-env.bak/
-venv.bak/
-
-# Spyder project settings
-.spyderproject
-.spyproject
-
-# Rope project settings
-.ropeproject
-
-# mkdocs documentation
-/site
-
-# mypy
-.mypy_cache/
-.dmypy.json
-dmypy.json
-
-# Pyre type checker
-.pyre/
+.venv

.idea/.gitignore

@@ -0,0 +1,8 @@
+# Default ignored files
+/shelf/
+/workspace.xml
+# Editor-based HTTP Client requests
+/httpRequests/
+# Datasource local storage ignored files
+/dataSources/
+/dataSources.local.xml

.idea/CogVideo.iml

@@ -0,0 +1,12 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<module type="PYTHON_MODULE" version="4">
+  <component name="NewModuleRootManager">
+    <content url="file://$MODULE_DIR$" />
+    <orderEntry type="inheritedJdk" />
+    <orderEntry type="sourceFolder" forTests="false" />
+  </component>
+  <component name="PyDocumentationSettings">
+    <option name="format" value="PLAIN" />
+    <option name="myDocStringFormat" value="Plain" />
+  </component>
+</module>

.idea/inspectionProfiles/Project_Default.xml

@@ -0,0 +1,20 @@
+<component name="InspectionProjectProfileManager">
+  <profile version="1.0">
+    <option name="myName" value="Project Default" />
+    <inspection_tool class="PyPackageRequirementsInspection" enabled="true" level="WARNING" enabled_by_default="true">
+      <option name="ignoredPackages">
+        <value>
+          <list size="7">
+            <item index="0" class="java.lang.String" itemvalue="openai" />
+            <item index="1" class="java.lang.String" itemvalue="sse_starlette" />
+            <item index="2" class="java.lang.String" itemvalue="fastapi" />
+            <item index="3" class="java.lang.String" itemvalue="timm" />
+            <item index="4" class="java.lang.String" itemvalue="gradio" />
+            <item index="5" class="java.lang.String" itemvalue="uvicorn" />
+            <item index="6" class="java.lang.String" itemvalue="diffusers" />
+          </list>
+        </value>
+      </option>
+    </inspection_tool>
+  </profile>
+</component>

.idea/inspectionProfiles/profiles_settings.xml

@@ -0,0 +1,6 @@
+<component name="InspectionProjectProfileManager">
+  <settings>
+    <option name="USE_PROJECT_PROFILE" value="false" />
+    <version value="1.0" />
+  </settings>
+</component>

.idea/misc.xml

@@ -0,0 +1,7 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="Black">
+    <option name="sdkName" value="Remote Python 3.10.14 (sftp://[email protected]:22/share/home/zyx/.conda/envs/cogvideox/bin/python)" />
+  </component>
+  <component name="ProjectRootManager" version="2" project-jdk-name="Remote Python 3.10.14 (sftp://[email protected]:22/share/home/zyx/.conda/envs/cogvideox/bin/python)" project-jdk-type="Python SDK" />
+</project>

.idea/modules.xml

@@ -0,0 +1,8 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectModuleManager">
+    <modules>
+      <module fileurl="file://$PROJECT_DIR$/.idea/CogVideo.iml" filepath="$PROJECT_DIR$/.idea/CogVideo.iml" />
+    </modules>
+  </component>
+</project>

.idea/vcs.xml

@@ -0,0 +1,6 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="VcsDirectoryMappings">
+    <mapping directory="" vcs="Git" />
+  </component>
+</project>

.pre-commit-config.yaml

@@ -1,46 +0,0 @@
-exclude: ^patch
-repos:
-- repo: https://github.com/pre-commit/pre-commit-hooks
-  rev: v4.2.0
-  hooks:
-  - id: check-executables-have-shebangs
-  - id: check-json
-  - id: check-merge-conflict
-  - id: check-shebang-scripts-are-executable
-  - id: check-toml
-  - id: check-yaml
-  - id: double-quote-string-fixer
-  - id: end-of-file-fixer
-  - id: mixed-line-ending
-    args: ['--fix=lf']
-  - id: requirements-txt-fixer
-  - id: trailing-whitespace
-- repo: https://github.com/myint/docformatter
-  rev: v1.4
-  hooks:
-  - id: docformatter
-    args: ['--in-place']
-- repo: https://github.com/pycqa/isort
-  rev: 5.10.1
-  hooks:
-    - id: isort
-- repo: https://github.com/pre-commit/mirrors-mypy
-  rev: v0.812
-  hooks:
-    - id: mypy
-      args: ['--ignore-missing-imports']
-- repo: https://github.com/google/yapf
-  rev: v0.32.0
-  hooks:
-  - id: yapf
-    args: ['--parallel', '--in-place']
-- repo: https://github.com/kynan/nbstripout
-  rev: 0.5.0
-  hooks:
-    - id: nbstripout
-      args: ['--extra-keys', 'metadata.interpreter metadata.kernelspec cell.metadata.pycharm']
-- repo: https://github.com/nbQA-dev/nbQA
-  rev: 1.3.1
-  hooks:
-    - id: nbqa-isort
-    - id: nbqa-yapf

.style.yapf

@@ -1,5 +0,0 @@
-[style]
-based_on_style = pep8
-blank_line_before_nested_class_or_def = false
-spaces_before_comment = 2
-split_before_logical_operator = true

CogVideo

@@ -1 +0,0 @@
-Subproject commit ff423aa169978fb2f636f761e348631fa3178b03

LICENSE

@@ -1,21 +0,0 @@
-MIT License
-
-Copyright (c) 2022 hysts
-
-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.

LICENSE.CogVideo

@@ -1,201 +0,0 @@
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README.md

@@ -1,13 +0,0 @@
----
-title: CogVideo
-emoji: 🌍
-colorFrom: indigo
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.1.6
-python_version: 3.9.13
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -1,138 +1,188 @@
-#!/usr/bin/env python
-
-from __future__ import annotations
-
+import os
 import gradio as gr
+import torch
+from diffusers import CogVideoXPipeline
+from diffusers.utils import export_to_video
+from datetime import datetime
+from openai import OpenAI
+import spaces
+import moviepy.editor as mp
+
+dtype = torch.float16
+device = "cuda" if torch.cuda.is_available() else "cpu"
+pipe = CogVideoXPipeline.from_pretrained("THUDM/CogVideoX-2b", torch_dtype=dtype).to(device)
+
+sys_prompt = """You are part of a team of bots that creates videos. You work with an assistant bot that will draw anything you say in square brackets.
+
+For example , outputting " a beautiful morning in the woods with the sun peaking through the trees " will trigger your partner bot to output an video of a forest morning , as described. You will be prompted by people looking to create detailed , amazing videos. The way to accomplish this is to take their short prompts and make them extremely detailed and descriptive.
+There are a few rules to follow:
+
+You will only ever output a single video description per user request.
+
+When modifications are requested , you should not simply make the description longer . You should refactor the entire description to integrate the suggestions.
+Other times the user will not want modifications , but instead want a new image . In this case , you should ignore your previous conversation with the user.
+
+Video descriptions must have the same num of words as examples below. Extra words will be ignored.
+"""
+
+
+def convert_prompt(prompt: str, retry_times: int = 3) -> str:
+    if not os.environ.get("OPENAI_API_KEY"):
+        return prompt
+    client = OpenAI()
+    text = prompt.strip()
+
+    for i in range(retry_times):
+        response = client.chat.completions.create(
+            messages=[
+                {"role": "system", "content": sys_prompt},
+                {"role": "user",
+                 "content": 'Create an imaginative video descriptive caption or modify an earlier caption for the user input : "a girl is on the beach"'},
+                {"role": "assistant",
+                 "content": "A radiant woman stands on a deserted beach, arms outstretched, wearing a beige trench coat, white blouse, light blue jeans, and chic boots, against a backdrop of soft sky and sea. Moments later, she is seen mid-twirl, arms exuberant, with the lighting suggesting dawn or dusk. Then, she runs along the beach, her attire complemented by an off-white scarf and black ankle boots, the tranquil sea behind her. Finally, she holds a paper airplane, her pose reflecting joy and freedom, with the ocean's gentle waves and the sky's soft pastel hues enhancing the serene ambiance."},
+                {"role": "user",
+                 "content": 'Create an imaginative video descriptive caption or modify an earlier caption for the user input : "A man jogging on a football field"'},
+                {"role": "assistant",
+                 "content": "A determined man in athletic attire, including a blue long-sleeve shirt, black shorts, and blue socks, jogs around a snow-covered soccer field, showcasing his solitary exercise in a quiet, overcast setting. His long dreadlocks, focused expression, and the serene winter backdrop highlight his dedication to fitness. As he moves, his attire, consisting of a blue sports sweatshirt, black athletic pants, gloves, and sneakers, grips the snowy ground. He is seen running past a chain-link fence enclosing the playground area, with a basketball hoop and children's slide, suggesting a moment of solitary exercise amidst the empty field."},
+                {"role": "user",
+                 "content": 'Create an imaginative video descriptive caption or modify an earlier caption for the user input : " A woman is dancing, HD footage, close-up"'},
+                {"role": "assistant",
+                 "content": "A young woman with her hair in an updo and wearing a teal hoodie stands against a light backdrop, initially looking over her shoulder with a contemplative expression. She then confidently makes a subtle dance move, suggesting rhythm and movement. Next, she appears poised and focused, looking directly at the camera. Her expression shifts to one of introspection as she gazes downward slightly. Finally, she dances with confidence, her left hand over her heart, symbolizing a poignant moment, all while dressed in the same teal hoodie against a plain, light-colored background."},
+                {"role": "user",
+                 "content": f'Create an imaginative video descriptive caption or modify an earlier caption in ENGLISH for the user input: "{text}"'},
+            ],
+            model="glm-4-0520",
+            temperature=0.01,
+            top_p=0.7,
+            stream=False,
+            max_tokens=250,
+        )
+        if response.choices:
+            return response.choices[0].message.content
+    return prompt
+
+
+@spaces.GPU()
+def infer(
+        prompt: str,
+        num_inference_steps: int,
+        guidance_scale: float,
+        progress=gr.Progress(track_tqdm=True)
+):
+    torch.cuda.empty_cache()
+
+    prompt_embeds, _ = pipe.encode_prompt(
+        prompt=prompt,
+        negative_prompt=None,
+        do_classifier_free_guidance=True,
+        num_videos_per_prompt=1,
+        max_sequence_length=226,
+        device=device,
+        dtype=dtype,
+    )
+
+    video = pipe(
+        num_inference_steps=num_inference_steps,
+        guidance_scale=guidance_scale,
+        prompt_embeds=prompt_embeds,
+        negative_prompt_embeds=torch.zeros_like(prompt_embeds),
+    ).frames[0]
+
+    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+    video_path = f"./output/{timestamp}.mp4"
+    os.makedirs(os.path.dirname(video_path), exist_ok=True)
+    export_to_video(video, video_path)
+    return video_path
+
+
+def convert_to_gif(video_path):
+    clip = mp.VideoFileClip(video_path)
+    clip = clip.set_fps(8)
+    clip = clip.resize(height=240)
+    gif_path = video_path.replace('.mp4', '.gif')
+    clip.write_gif(gif_path, fps=8)
+    return gif_path
+
+
+with gr.Blocks() as demo:
+    gr.Markdown("""
+           <div style="text-align: center; font-size: 32px; font-weight: bold; margin-bottom: 20px;">
+               CogVideoX-2B Huggingface Space🤗
+           </div>
+           <div style="text-align: center;">
+               <a href="https://huggingface.co/THUDM/CogVideoX-2b">🤗 Model Hub</a> |
+               <a href="https://github.com/THUDM/CogVideo">🌐 Github</a> 
+           </div>
+
+           <div style="text-align: center; font-size: 15px; font-weight: bold; color: red; margin-bottom: 20px;">
+            ⚠️ This demo is for academic research and experiential use only. 
+            Users should strictly adhere to local laws and ethics.
+            </div>
+           """)
+    with gr.Row():
+        with gr.Column():
+            prompt = gr.Textbox(label="Prompt (Less than 200 Words)", placeholder="Enter your prompt here", lines=5)
+            with gr.Row():
+                gr.Markdown(
+                    "✨Upon pressing the enhanced prompt button, we will use [GLM-4 Model](https://github.com/THUDM/GLM-4) to polish the prompt and overwrite the original one.")
+                enhance_button = gr.Button("✨ Enhance Prompt(Optional)")
 
-# from model import AppModel
-
-MAINTENANCE_NOTICE='Sorry, due to computing resources issues, this space is under maintenance, and will be restored as soon as possible. '
-
-DESCRIPTION = '''# <a href="https://github.com/THUDM/CogVideo">CogVideo</a>
-Currently, this Space only supports the first stage of the CogVideo pipeline due to hardware limitations.
-The model accepts only Chinese as input.
-By checking the "Translate to Chinese" checkbox, the results of English to Chinese translation with [this Space](https://huggingface.co/spaces/chinhon/translation_eng2ch) will be used as input.
-Since the translation model may mistranslate, you may want to use the translation results from other translation services.
-'''
-NOTES = 'This app is adapted from <a href="https://github.com/hysts/CogVideo_demo">https://github.com/hysts/CogVideo_demo</a>. It would be recommended to use the repo if you want to run the app yourself.'
-FOOTER = '<img id="visitor-badge" alt="visitor badge" src="https://visitor-badge.glitch.me/badge?page_id=THUDM.CogVideo" />'
-
-import json
-import requests
-import numpy as np
-import imageio.v2 as iio
-import base64
-import urllib.request 
-
-def post(
-        text,
-        translate,
-        seed,
-        only_first_stage,
-        image_prompt
-        ):
-    url = 'https://tianqi.aminer.cn/cogvideo/api/generate'
-    headers = {
-            "Content-Type": "application/json; charset=UTF-8",
-            "User-Agent": "Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/67.0.3396.87 Safari/537.36",
-        }
-    if image_prompt:
-        with open(image_prompt, "rb") as image_file:
-            encoded_img = str(base64.b64encode(image_file.read()), encoding='utf-8')
-    else:
-        encoded_img = None
-    print('开始请求...')
-    data = json.dumps({'text': text,
-                    'translate': translate,
-                    'seed': seed,
-                    'only_first_stage': only_first_stage,
-                    'image_prompt': encoded_img
-                    })
-    r = requests.post(url, data, headers=headers)
-    print(r)
-    
-    print('请求完毕...')
-    # translated_text = r.json()['data']['translated_text']
-    frames = r.json()['data']['frames']
-
-    result_video = ["" for i in range(len(frames))]
-    result_video[0] = "./temp1.mp4"
-    result_video[1] = "./temp2.mp4"
-    for i in range(len(result_video)):
-        url = frames[i]
-        result_video[i] = "./temp" + str(i) + ".mp4"
-        urllib.request.urlretrieve(url, result_video[i])
-
-    print('finished')
-    return result_video[0], result_video[1]
-    # return result_video[0], result_video[1], result_video[2], result_video[3]
-
-def main():
-    only_first_stage = True
-    # model = AppModel(only_first_stage)
-
-    with gr.Blocks(css='style.css') as demo:
-        # gr.Markdown(MAINTENANCE_NOTICE)
-        
-        gr.Markdown(DESCRIPTION)
-
-        with gr.Row():
             with gr.Column():
-                with gr.Group():
-                    text = gr.Textbox(label='Input Text')
-                    translate = gr.Checkbox(label='Translate to Chinese',
-                                            value=False)
-                    seed = gr.Slider(0,
-                                     100000,
-                                     step=1,
-                                     value=1234,
-                                     label='Seed')
-                    only_first_stage = gr.Checkbox(
-                        label='Only First Stage',
-                        value=only_first_stage,
-                        visible=not only_first_stage)
-                    image_prompt = gr.Image(type="filepath",
-                                            label="Image Prompt",
-                                            value=None)
-                    run_button = gr.Button('Run')
+                gr.Markdown("**Optional Parameters** (default values are recommended)")
+                with gr.Row():
+                    num_inference_steps = gr.Number(label="Inference Steps", value=50)
+                    guidance_scale = gr.Number(label="Guidance Scale", value=6.0)
+                generate_button = gr.Button("🎬 Generate Video")
 
-            with gr.Column():
-                with gr.Group():
-                    #translated_text = gr.Textbox(label='Translated Text')
-                    with gr.Tabs():
-                        with gr.TabItem('Output (Video)'):
-                            result_video1 = gr.Video(show_label=False)
-                            result_video2 = gr.Video(show_label=False)
-                            # result_video3 = gr.Video(show_label=False)
-                            # result_video4 = gr.Video(show_label=False)
-
-
-
-        # examples = gr.Examples(
-        #     examples=[['骑滑板的皮卡丘', False, 1234, True,None],
-        #               ['a cat playing chess', True, 1253, True,None]],
-        #     fn=model.run_with_translation,
-        #     inputs=[text, translate, seed, only_first_stage,image_prompt],
-        #     outputs=[translated_text, result_video],
-        #     cache_examples=True)
-
-        gr.Markdown(NOTES)
-        gr.Markdown(FOOTER)
-        print(gr.__version__)
-        run_button.click(fn=post,
-                         inputs=[
-                             text,
-                             translate,
-                             seed,
-                             only_first_stage,
-                             image_prompt
-                         ],
-                         outputs=[result_video1, result_video2])
-                         # outputs=[result_video1, result_video2, result_video3, result_video4])
-        print(gr.__version__)
-    demo.queue(concurrency_count=6)
-    demo.launch()
-
-
-if __name__ == '__main__':
-    main()
+        with gr.Column():
+            video_output = gr.Video(label="CogVideoX Generate Video", width=720, height=480)
+            with gr.Row():
+                download_video_button = gr.File(label="📥 Download Video", visible=False)
+                download_gif_button = gr.File(label="📥 Download GIF", visible=False)
+
+
+    def generate(prompt, num_inference_steps, guidance_scale, progress=gr.Progress(track_tqdm=True)):
+        video_path = infer(prompt, num_inference_steps, guidance_scale, progress=progress)
+        video_update = gr.update(visible=True, value=video_path)
+
+        gif_path = convert_to_gif(video_path)
+        gif_update = gr.update(visible=True, value=gif_path)
+
+        return video_path, video_update, gif_update
+
+
+    def enhance_prompt_func(prompt):
+        return convert_prompt(prompt, retry_times=1)
+
+
+    generate_button.click(
+        generate,
+        inputs=[prompt, num_inference_steps, guidance_scale],
+        outputs=[video_output, download_video_button, download_gif_button]
+    )
+
+    enhance_button.click(
+        enhance_prompt_func,
+        inputs=[prompt],
+        outputs=[prompt]
+    )
+
+
+    def enhance_prompt_func(prompt):
+        return convert_prompt(prompt, retry_times=1)
+
+
+    generate_button.click(
+        generate,
+        inputs=[prompt, num_inference_steps, guidance_scale],
+        outputs=[video_output, download_video_button, download_gif_button]
+    )
+
+    enhance_button.click(
+        enhance_prompt_func,
+        inputs=[prompt],
+        outputs=[prompt]
+    )
+
+if __name__ == "__main__":
+    demo.launch(server_name="127.0.0.1", server_port=7870, share=True)

model.py

@@ -1,1243 +0,0 @@
-# This code is adapted from https://github.com/THUDM/CogVideo/blob/ff423aa169978fb2f636f761e348631fa3178b03/cogvideo_pipeline.py
-
-from __future__ import annotations
-
-import argparse
-import logging
-import os
-import pathlib
-import shutil
-import subprocess
-import sys
-import tempfile
-import time
-import zipfile
-from typing import Any
-
-if os.getenv('SYSTEM') == 'spaces':
-    subprocess.run('pip install icetk==0.0.4'.split())
-    subprocess.run('pip install SwissArmyTransformer==0.2.9'.split())
-    subprocess.run(
-        'pip install git+https://github.com/Sleepychord/Image-Local-Attention@43fee31'
-        .split())
-    #subprocess.run('git clone https://github.com/NVIDIA/apex'.split())
-    #subprocess.run('git checkout 1403c21'.split(), cwd='apex')
-    #with open('patch.apex') as f:
-    #    subprocess.run('patch -p1'.split(), cwd='apex', stdin=f)
-    #subprocess.run(
-    #    'pip install -v --disable-pip-version-check --no-cache-dir --global-option --cpp_ext --global-option --cuda_ext ./'
-    #    .split(),
-    #    cwd='apex')
-    #subprocess.run('rm -rf apex'.split())
-    with open('patch') as f:
-        subprocess.run('patch -p1'.split(), cwd='CogVideo', stdin=f)
-
-    from huggingface_hub import hf_hub_download
-
-    def download_and_extract_icetk_models() -> None:
-        icetk_model_dir = pathlib.Path('/home/user/.icetk_models')
-        icetk_model_dir.mkdir()
-        path = hf_hub_download('THUDM/icetk',
-                               'models.zip',
-                               use_auth_token=os.getenv('HF_TOKEN'))
-        with zipfile.ZipFile(path) as f:
-            f.extractall(path=icetk_model_dir.as_posix())
-
-    def download_and_extract_cogvideo_models(name: str) -> None:
-        path = hf_hub_download('THUDM/CogVideo',
-                               name,
-                               use_auth_token=os.getenv('HF_TOKEN'))
-        with zipfile.ZipFile(path) as f:
-            f.extractall('pretrained')
-        os.remove(path)
-
-    def download_and_extract_cogview2_models(name: str) -> None:
-        path = hf_hub_download('THUDM/CogView2', name)
-        with zipfile.ZipFile(path) as f:
-            f.extractall()
-        shutil.move('/home/user/app/sharefs/cogview-new/cogview2-dsr',
-                    'pretrained')
-        shutil.rmtree('/home/user/app/sharefs/')
-        os.remove(path)
-
-    download_and_extract_icetk_models()
-    download_and_extract_cogvideo_models('cogvideo-stage1.zip')
-    #download_and_extract_cogvideo_models('cogvideo-stage2.zip')
-    #download_and_extract_cogview2_models('cogview2-dsr.zip')
-
-    os.environ['SAT_HOME'] = '/home/user/app/pretrained'
-
-import gradio as gr
-import imageio.v2 as iio
-import numpy as np
-import torch
-from icetk import IceTokenizer
-from SwissArmyTransformer import get_args
-from SwissArmyTransformer.arguments import set_random_seed
-from SwissArmyTransformer.generation.sampling_strategies import BaseStrategy
-from SwissArmyTransformer.resources import auto_create
-
-app_dir = pathlib.Path(__file__).parent
-submodule_dir = app_dir / 'CogVideo'
-sys.path.insert(0, submodule_dir.as_posix())
-
-from coglm_strategy import CoglmStrategy
-from models.cogvideo_cache_model import CogVideoCacheModel
-from sr_pipeline import DirectSuperResolution
-
-formatter = logging.Formatter(
-    '[%(asctime)s] %(name)s %(levelname)s: %(message)s',
-    datefmt='%Y-%m-%d %H:%M:%S')
-stream_handler = logging.StreamHandler(stream=sys.stdout)
-stream_handler.setLevel(logging.INFO)
-stream_handler.setFormatter(formatter)
-logger = logging.getLogger(__name__)
-logger.setLevel(logging.INFO)
-logger.propagate = False
-logger.addHandler(stream_handler)
-
-ICETK_MODEL_DIR = app_dir / 'icetk_models'
-
-
-def get_masks_and_position_ids_stage1(data, textlen, framelen):
-    # Extract batch size and sequence length.
-    tokens = data
-    seq_length = len(data[0])
-    # Attention mask (lower triangular).
-    attention_mask = torch.ones((1, textlen + framelen, textlen + framelen),
-                                device=data.device)
-    attention_mask[:, :textlen, textlen:] = 0
-    attention_mask[:, textlen:, textlen:].tril_()
-    attention_mask.unsqueeze_(1)
-    # Unaligned version
-    position_ids = torch.zeros(seq_length,
-                               dtype=torch.long,
-                               device=data.device)
-    torch.arange(textlen,
-                 out=position_ids[:textlen],
-                 dtype=torch.long,
-                 device=data.device)
-    torch.arange(512,
-                 512 + seq_length - textlen,
-                 out=position_ids[textlen:],
-                 dtype=torch.long,
-                 device=data.device)
-    position_ids = position_ids.unsqueeze(0)
-
-    return tokens, attention_mask, position_ids
-
-
-def get_masks_and_position_ids_stage2(data, textlen, framelen):
-    # Extract batch size and sequence length.
-    tokens = data
-    seq_length = len(data[0])
-
-    # Attention mask (lower triangular).
-    attention_mask = torch.ones((1, textlen + framelen, textlen + framelen),
-                                device=data.device)
-    attention_mask[:, :textlen, textlen:] = 0
-    attention_mask[:, textlen:, textlen:].tril_()
-    attention_mask.unsqueeze_(1)
-
-    # Unaligned version
-    position_ids = torch.zeros(seq_length,
-                               dtype=torch.long,
-                               device=data.device)
-    torch.arange(textlen,
-                 out=position_ids[:textlen],
-                 dtype=torch.long,
-                 device=data.device)
-    frame_num = (seq_length - textlen) // framelen
-    assert frame_num == 5
-    torch.arange(512,
-                 512 + framelen,
-                 out=position_ids[textlen:textlen + framelen],
-                 dtype=torch.long,
-                 device=data.device)
-    torch.arange(512 + framelen * 2,
-                 512 + framelen * 3,
-                 out=position_ids[textlen + framelen:textlen + framelen * 2],
-                 dtype=torch.long,
-                 device=data.device)
-    torch.arange(512 + framelen * (frame_num - 1),
-                 512 + framelen * frame_num,
-                 out=position_ids[textlen + framelen * 2:textlen +
-                                  framelen * 3],
-                 dtype=torch.long,
-                 device=data.device)
-    torch.arange(512 + framelen * 1,
-                 512 + framelen * 2,
-                 out=position_ids[textlen + framelen * 3:textlen +
-                                  framelen * 4],
-                 dtype=torch.long,
-                 device=data.device)
-    torch.arange(512 + framelen * 3,
-                 512 + framelen * 4,
-                 out=position_ids[textlen + framelen * 4:textlen +
-                                  framelen * 5],
-                 dtype=torch.long,
-                 device=data.device)
-
-    position_ids = position_ids.unsqueeze(0)
-
-    return tokens, attention_mask, position_ids
-
-
-def my_update_mems(hiddens, mems_buffers, mems_indexs,
-                   limited_spatial_channel_mem, text_len, frame_len):
-    if hiddens is None:
-        return None, mems_indexs
-    mem_num = len(hiddens)
-    ret_mem = []
-    with torch.no_grad():
-        for id in range(mem_num):
-            if hiddens[id][0] is None:
-                ret_mem.append(None)
-            else:
-                if id == 0 and limited_spatial_channel_mem and mems_indexs[
-                        id] + hiddens[0][0].shape[1] >= text_len + frame_len:
-                    if mems_indexs[id] == 0:
-                        for layer, hidden in enumerate(hiddens[id]):
-                            mems_buffers[id][
-                                layer, :, :text_len] = hidden.expand(
-                                    mems_buffers[id].shape[1], -1,
-                                    -1)[:, :text_len]
-                    new_mem_len_part2 = (mems_indexs[id] +
-                                         hiddens[0][0].shape[1] -
-                                         text_len) % frame_len
-                    if new_mem_len_part2 > 0:
-                        for layer, hidden in enumerate(hiddens[id]):
-                            mems_buffers[id][
-                                layer, :, text_len:text_len +
-                                new_mem_len_part2] = hidden.expand(
-                                    mems_buffers[id].shape[1], -1,
-                                    -1)[:, -new_mem_len_part2:]
-                    mems_indexs[id] = text_len + new_mem_len_part2
-                else:
-                    for layer, hidden in enumerate(hiddens[id]):
-                        mems_buffers[id][layer, :,
-                                         mems_indexs[id]:mems_indexs[id] +
-                                         hidden.shape[1]] = hidden.expand(
-                                             mems_buffers[id].shape[1], -1, -1)
-                    mems_indexs[id] += hidden.shape[1]
-                ret_mem.append(mems_buffers[id][:, :, :mems_indexs[id]])
-    return ret_mem, mems_indexs
-
-
-def calc_next_tokens_frame_begin_id(text_len, frame_len, total_len):
-    # The fisrt token's position id of the frame that the next token belongs to;
-    if total_len < text_len:
-        return None
-    return (total_len - text_len) // frame_len * frame_len + text_len
-
-
-def my_filling_sequence(
-        model,
-        tokenizer,
-        args,
-        seq,
-        batch_size,
-        get_masks_and_position_ids,
-        text_len,
-        frame_len,
-        strategy=BaseStrategy(),
-        strategy2=BaseStrategy(),
-        mems=None,
-        log_text_attention_weights=0,  # default to 0: no artificial change
-        mode_stage1=True,
-        enforce_no_swin=False,
-        guider_seq=None,
-        guider_text_len=0,
-        guidance_alpha=1,
-        limited_spatial_channel_mem=False,  # 空间通道的存储限制在本帧内
-        **kw_args):
-    '''
-        seq: [2, 3, 5, ..., -1(to be generated), -1, ...]
-        mems: [num_layers, batch_size, len_mems(index), mem_hidden_size]
-            cache, should be first mems.shape[1] parts of context_tokens.
-            mems are the first-level citizens here, but we don't assume what is memorized.
-            input mems are used when multi-phase generation.
-    '''
-    if guider_seq is not None:
-        logger.debug('Using Guidance In Inference')
-    if limited_spatial_channel_mem:
-        logger.debug("Limit spatial-channel's mem to current frame")
-    assert len(seq.shape) == 2
-
-    # building the initial tokens, attention_mask, and position_ids
-    actual_context_length = 0
-
-    while seq[-1][
-            actual_context_length] >= 0:  # the last seq has least given tokens
-        actual_context_length += 1  # [0, context_length-1] are given
-    assert actual_context_length > 0
-    current_frame_num = (actual_context_length - text_len) // frame_len
-    assert current_frame_num >= 0
-    context_length = text_len + current_frame_num * frame_len
-
-    tokens, attention_mask, position_ids = get_masks_and_position_ids(
-        seq, text_len, frame_len)
-    tokens = tokens[..., :context_length]
-    input_tokens = tokens.clone()
-
-    if guider_seq is not None:
-        guider_index_delta = text_len - guider_text_len
-        guider_tokens, guider_attention_mask, guider_position_ids = get_masks_and_position_ids(
-            guider_seq, guider_text_len, frame_len)
-        guider_tokens = guider_tokens[..., :context_length -
-                                      guider_index_delta]
-        guider_input_tokens = guider_tokens.clone()
-
-    for fid in range(current_frame_num):
-        input_tokens[:, text_len + 400 * fid] = tokenizer['<start_of_image>']
-        if guider_seq is not None:
-            guider_input_tokens[:, guider_text_len +
-                                400 * fid] = tokenizer['<start_of_image>']
-
-    attention_mask = attention_mask.type_as(next(
-        model.parameters()))  # if fp16
-    # initialize generation
-    counter = context_length - 1  # Last fixed index is ``counter''
-    index = 0  # Next forward starting index, also the length of cache.
-    mems_buffers_on_GPU = False
-    mems_indexs = [0, 0]
-    mems_len = [(400 + 74) if limited_spatial_channel_mem else 5 * 400 + 74,
-                5 * 400 + 74]
-    mems_buffers = [
-        torch.zeros(args.num_layers,
-                    batch_size,
-                    mem_len,
-                    args.hidden_size * 2,
-                    dtype=next(model.parameters()).dtype)
-        for mem_len in mems_len
-    ]
-
-    if guider_seq is not None:
-        guider_attention_mask = guider_attention_mask.type_as(
-            next(model.parameters()))  # if fp16
-        guider_mems_buffers = [
-            torch.zeros(args.num_layers,
-                        batch_size,
-                        mem_len,
-                        args.hidden_size * 2,
-                        dtype=next(model.parameters()).dtype)
-            for mem_len in mems_len
-        ]
-        guider_mems_indexs = [0, 0]
-        guider_mems = None
-
-    torch.cuda.empty_cache()
-    # step-by-step generation
-    while counter < len(seq[0]) - 1:
-        # we have generated counter+1 tokens
-        # Now, we want to generate seq[counter + 1],
-        # token[:, index: counter+1] needs forwarding.
-        if index == 0:
-            group_size = 2 if (input_tokens.shape[0] == batch_size
-                               and not mode_stage1) else batch_size
-
-            logits_all = None
-            for batch_idx in range(0, input_tokens.shape[0], group_size):
-                logits, *output_per_layers = model(
-                    input_tokens[batch_idx:batch_idx + group_size, index:],
-                    position_ids[..., index:counter + 1],
-                    attention_mask,  # TODO memlen
-                    mems=mems,
-                    text_len=text_len,
-                    frame_len=frame_len,
-                    counter=counter,
-                    log_text_attention_weights=log_text_attention_weights,
-                    enforce_no_swin=enforce_no_swin,
-                    **kw_args)
-                logits_all = torch.cat(
-                    (logits_all,
-                     logits), dim=0) if logits_all is not None else logits
-                mem_kv01 = [[o['mem_kv'][0] for o in output_per_layers],
-                            [o['mem_kv'][1] for o in output_per_layers]]
-                next_tokens_frame_begin_id = calc_next_tokens_frame_begin_id(
-                    text_len, frame_len, mem_kv01[0][0].shape[1])
-                for id, mem_kv in enumerate(mem_kv01):
-                    for layer, mem_kv_perlayer in enumerate(mem_kv):
-                        if limited_spatial_channel_mem and id == 0:
-                            mems_buffers[id][
-                                layer, batch_idx:batch_idx + group_size, :
-                                text_len] = mem_kv_perlayer.expand(
-                                    min(group_size,
-                                        input_tokens.shape[0] - batch_idx), -1,
-                                    -1)[:, :text_len]
-                            mems_buffers[id][layer, batch_idx:batch_idx+group_size, text_len:text_len+mem_kv_perlayer.shape[1]-next_tokens_frame_begin_id] =\
-                                mem_kv_perlayer.expand(min(group_size, input_tokens.shape[0]-batch_idx), -1, -1)[:, next_tokens_frame_begin_id:]
-                        else:
-                            mems_buffers[id][
-                                layer, batch_idx:batch_idx +
-                                group_size, :mem_kv_perlayer.
-                                shape[1]] = mem_kv_perlayer.expand(
-                                    min(group_size,
-                                        input_tokens.shape[0] - batch_idx), -1,
-                                    -1)
-                mems_indexs[0], mems_indexs[1] = mem_kv01[0][0].shape[
-                    1], mem_kv01[1][0].shape[1]
-                if limited_spatial_channel_mem:
-                    mems_indexs[0] -= (next_tokens_frame_begin_id - text_len)
-
-            mems = [
-                mems_buffers[id][:, :, :mems_indexs[id]] for id in range(2)
-            ]
-            logits = logits_all
-
-            # Guider
-            if guider_seq is not None:
-                guider_logits_all = None
-                for batch_idx in range(0, guider_input_tokens.shape[0],
-                                       group_size):
-                    guider_logits, *guider_output_per_layers = model(
-                        guider_input_tokens[batch_idx:batch_idx + group_size,
-                                            max(index -
-                                                guider_index_delta, 0):],
-                        guider_position_ids[
-                            ...,
-                            max(index - guider_index_delta, 0):counter + 1 -
-                            guider_index_delta],
-                        guider_attention_mask,
-                        mems=guider_mems,
-                        text_len=guider_text_len,
-                        frame_len=frame_len,
-                        counter=counter - guider_index_delta,
-                        log_text_attention_weights=log_text_attention_weights,
-                        enforce_no_swin=enforce_no_swin,
-                        **kw_args)
-                    guider_logits_all = torch.cat(
-                        (guider_logits_all, guider_logits), dim=0
-                    ) if guider_logits_all is not None else guider_logits
-                    guider_mem_kv01 = [[
-                        o['mem_kv'][0] for o in guider_output_per_layers
-                    ], [o['mem_kv'][1] for o in guider_output_per_layers]]
-                    for id, guider_mem_kv in enumerate(guider_mem_kv01):
-                        for layer, guider_mem_kv_perlayer in enumerate(
-                                guider_mem_kv):
-                            if limited_spatial_channel_mem and id == 0:
-                                guider_mems_buffers[id][
-                                    layer, batch_idx:batch_idx + group_size, :
-                                    guider_text_len] = guider_mem_kv_perlayer.expand(
-                                        min(group_size,
-                                            input_tokens.shape[0] - batch_idx),
-                                        -1, -1)[:, :guider_text_len]
-                                guider_next_tokens_frame_begin_id = calc_next_tokens_frame_begin_id(
-                                    guider_text_len, frame_len,
-                                    guider_mem_kv_perlayer.shape[1])
-                                guider_mems_buffers[id][layer, batch_idx:batch_idx+group_size, guider_text_len:guider_text_len+guider_mem_kv_perlayer.shape[1]-guider_next_tokens_frame_begin_id] =\
-                                    guider_mem_kv_perlayer.expand(min(group_size, input_tokens.shape[0]-batch_idx), -1, -1)[:, guider_next_tokens_frame_begin_id:]
-                            else:
-                                guider_mems_buffers[id][
-                                    layer, batch_idx:batch_idx +
-                                    group_size, :guider_mem_kv_perlayer.
-                                    shape[1]] = guider_mem_kv_perlayer.expand(
-                                        min(group_size,
-                                            input_tokens.shape[0] - batch_idx),
-                                        -1, -1)
-                    guider_mems_indexs[0], guider_mems_indexs[
-                        1] = guider_mem_kv01[0][0].shape[1], guider_mem_kv01[
-                            1][0].shape[1]
-                    if limited_spatial_channel_mem:
-                        guider_mems_indexs[0] -= (
-                            guider_next_tokens_frame_begin_id -
-                            guider_text_len)
-                guider_mems = [
-                    guider_mems_buffers[id][:, :, :guider_mems_indexs[id]]
-                    for id in range(2)
-                ]
-                guider_logits = guider_logits_all
-        else:
-            if not mems_buffers_on_GPU:
-                if not mode_stage1:
-                    torch.cuda.empty_cache()
-                    for idx, mem in enumerate(mems):
-                        mems[idx] = mem.to(next(model.parameters()).device)
-                    if guider_seq is not None:
-                        for idx, mem in enumerate(guider_mems):
-                            guider_mems[idx] = mem.to(
-                                next(model.parameters()).device)
-                else:
-                    torch.cuda.empty_cache()
-                    for idx, mem_buffer in enumerate(mems_buffers):
-                        mems_buffers[idx] = mem_buffer.to(
-                            next(model.parameters()).device)
-                    mems = [
-                        mems_buffers[id][:, :, :mems_indexs[id]]
-                        for id in range(2)
-                    ]
-                    if guider_seq is not None:
-                        for idx, guider_mem_buffer in enumerate(
-                                guider_mems_buffers):
-                            guider_mems_buffers[idx] = guider_mem_buffer.to(
-                                next(model.parameters()).device)
-                        guider_mems = [
-                            guider_mems_buffers[id]
-                            [:, :, :guider_mems_indexs[id]] for id in range(2)
-                        ]
-                    mems_buffers_on_GPU = True
-
-            logits, *output_per_layers = model(
-                input_tokens[:, index:],
-                position_ids[..., index:counter + 1],
-                attention_mask,  # TODO memlen
-                mems=mems,
-                text_len=text_len,
-                frame_len=frame_len,
-                counter=counter,
-                log_text_attention_weights=log_text_attention_weights,
-                enforce_no_swin=enforce_no_swin,
-                limited_spatial_channel_mem=limited_spatial_channel_mem,
-                **kw_args)
-            mem_kv0, mem_kv1 = [o['mem_kv'][0] for o in output_per_layers
-                                ], [o['mem_kv'][1] for o in output_per_layers]
-
-            if guider_seq is not None:
-                guider_logits, *guider_output_per_layers = model(
-                    guider_input_tokens[:,
-                                        max(index - guider_index_delta, 0):],
-                    guider_position_ids[...,
-                                        max(index -
-                                            guider_index_delta, 0):counter +
-                                        1 - guider_index_delta],
-                    guider_attention_mask,
-                    mems=guider_mems,
-                    text_len=guider_text_len,
-                    frame_len=frame_len,
-                    counter=counter - guider_index_delta,
-                    log_text_attention_weights=0,
-                    enforce_no_swin=enforce_no_swin,
-                    limited_spatial_channel_mem=limited_spatial_channel_mem,
-                    **kw_args)
-                guider_mem_kv0, guider_mem_kv1 = [
-                    o['mem_kv'][0] for o in guider_output_per_layers
-                ], [o['mem_kv'][1] for o in guider_output_per_layers]
-
-            if not mems_buffers_on_GPU:
-                torch.cuda.empty_cache()
-                for idx, mem_buffer in enumerate(mems_buffers):
-                    mems_buffers[idx] = mem_buffer.to(
-                        next(model.parameters()).device)
-                if guider_seq is not None:
-                    for idx, guider_mem_buffer in enumerate(
-                            guider_mems_buffers):
-                        guider_mems_buffers[idx] = guider_mem_buffer.to(
-                            next(model.parameters()).device)
-                mems_buffers_on_GPU = True
-
-            mems, mems_indexs = my_update_mems([mem_kv0, mem_kv1],
-                                               mems_buffers, mems_indexs,
-                                               limited_spatial_channel_mem,
-                                               text_len, frame_len)
-            if guider_seq is not None:
-                guider_mems, guider_mems_indexs = my_update_mems(
-                    [guider_mem_kv0, guider_mem_kv1], guider_mems_buffers,
-                    guider_mems_indexs, limited_spatial_channel_mem,
-                    guider_text_len, frame_len)
-
-        counter += 1
-        index = counter
-
-        logits = logits[:, -1].expand(batch_size,
-                                      -1)  # [batch size, vocab size]
-        tokens = tokens.expand(batch_size, -1)
-        if guider_seq is not None:
-            guider_logits = guider_logits[:, -1].expand(batch_size, -1)
-            guider_tokens = guider_tokens.expand(batch_size, -1)
-
-        if seq[-1][counter].item() < 0:
-            # sampling
-            guided_logits = guider_logits + (
-                logits - guider_logits
-            ) * guidance_alpha if guider_seq is not None else logits
-            if mode_stage1 and counter < text_len + 400:
-                tokens, mems = strategy.forward(guided_logits, tokens, mems)
-            else:
-                tokens, mems = strategy2.forward(guided_logits, tokens, mems)
-            if guider_seq is not None:
-                guider_tokens = torch.cat((guider_tokens, tokens[:, -1:]),
-                                          dim=1)
-
-            if seq[0][counter].item() >= 0:
-                for si in range(seq.shape[0]):
-                    if seq[si][counter].item() >= 0:
-                        tokens[si, -1] = seq[si, counter]
-                        if guider_seq is not None:
-                            guider_tokens[si,
-                                          -1] = guider_seq[si, counter -
-                                                           guider_index_delta]
-
-        else:
-            tokens = torch.cat(
-                (tokens, seq[:, counter:counter + 1].clone().expand(
-                    tokens.shape[0], 1).to(device=tokens.device,
-                                           dtype=tokens.dtype)),
-                dim=1)
-            if guider_seq is not None:
-                guider_tokens = torch.cat(
-                    (guider_tokens,
-                     guider_seq[:, counter - guider_index_delta:counter + 1 -
-                                guider_index_delta].clone().expand(
-                                    guider_tokens.shape[0], 1).to(
-                                        device=guider_tokens.device,
-                                        dtype=guider_tokens.dtype)),
-                    dim=1)
-
-        input_tokens = tokens.clone()
-        if guider_seq is not None:
-            guider_input_tokens = guider_tokens.clone()
-        if (index - text_len - 1) // 400 < (input_tokens.shape[-1] - text_len -
-                                            1) // 400:
-            boi_idx = ((index - text_len - 1) // 400 + 1) * 400 + text_len
-            while boi_idx < input_tokens.shape[-1]:
-                input_tokens[:, boi_idx] = tokenizer['<start_of_image>']
-                if guider_seq is not None:
-                    guider_input_tokens[:, boi_idx -
-                                        guider_index_delta] = tokenizer[
-                                            '<start_of_image>']
-                boi_idx += 400
-
-        if strategy.is_done:
-            break
-    return strategy.finalize(tokens, mems)
-
-
-class InferenceModel_Sequential(CogVideoCacheModel):
-    def __init__(self, args, transformer=None, parallel_output=True):
-        super().__init__(args,
-                         transformer=transformer,
-                         parallel_output=parallel_output,
-                         window_size=-1,
-                         cogvideo_stage=1)
-
-    # TODO: check it
-
-    def final_forward(self, logits, **kwargs):
-        logits_parallel = logits
-        logits_parallel = torch.nn.functional.linear(
-            logits_parallel.float(),
-            self.transformer.word_embeddings.weight[:20000].float())
-        return logits_parallel
-
-
-class InferenceModel_Interpolate(CogVideoCacheModel):
-    def __init__(self, args, transformer=None, parallel_output=True):
-        super().__init__(args,
-                         transformer=transformer,
-                         parallel_output=parallel_output,
-                         window_size=10,
-                         cogvideo_stage=2)
-
-    # TODO: check it
-
-    def final_forward(self, logits, **kwargs):
-        logits_parallel = logits
-        logits_parallel = torch.nn.functional.linear(
-            logits_parallel.float(),
-            self.transformer.word_embeddings.weight[:20000].float())
-        return logits_parallel
-
-
-def get_default_args() -> argparse.Namespace:
-    known = argparse.Namespace(generate_frame_num=5,
-                               coglm_temperature2=0.89,
-                               use_guidance_stage1=True,
-                               use_guidance_stage2=False,
-                               guidance_alpha=3.0,
-                               stage_1=True,
-                               stage_2=False,
-                               both_stages=False,
-                               parallel_size=1,
-                               stage1_max_inference_batch_size=-1,
-                               multi_gpu=False,
-                               layout='64, 464, 2064',
-                               window_size=10,
-                               additional_seqlen=2000,
-                               cogvideo_stage=1)
-
-    args_list = [
-        '--tokenizer-type',
-        'fake',
-        '--mode',
-        'inference',
-        '--distributed-backend',
-        'nccl',
-        '--fp16',
-        '--model-parallel-size',
-        '1',
-        '--temperature',
-        '1.05',
-        '--top_k',
-        '12',
-        '--sandwich-ln',
-        '--seed',
-        '1234',
-        '--num-workers',
-        '0',
-        '--batch-size',
-        '1',
-        '--max-inference-batch-size',
-        '8',
-    ]
-    args = get_args(args_list)
-    args = argparse.Namespace(**vars(args), **vars(known))
-    args.layout = [int(x) for x in args.layout.split(',')]
-    args.do_train = False
-    return args
-
-
-class Model:
-    def __init__(self, only_first_stage: bool = False):
-        self.args = get_default_args()
-        if only_first_stage:
-            self.args.stage_1 = True
-            self.args.both_stages = False
-        else:
-            self.args.stage_1 = False
-            self.args.both_stages = True
-
-        self.tokenizer = self.load_tokenizer()
-
-        self.model_stage1, self.args = self.load_model_stage1()
-        self.model_stage2, self.args = self.load_model_stage2()
-
-        self.strategy_cogview2, self.strategy_cogvideo = self.load_strategies()
-        self.dsr = self.load_dsr()
-
-        self.device = torch.device(self.args.device)
-
-    def load_tokenizer(self) -> IceTokenizer:
-        logger.info('--- load_tokenizer ---')
-        start = time.perf_counter()
-
-        tokenizer = IceTokenizer(ICETK_MODEL_DIR.as_posix())
-        tokenizer.add_special_tokens(
-            ['<start_of_image>', '<start_of_english>', '<start_of_chinese>'])
-
-        elapsed = time.perf_counter() - start
-        logger.info(f'--- done ({elapsed=:.3f}) ---')
-        return tokenizer
-
-    def load_model_stage1(
-            self) -> tuple[CogVideoCacheModel, argparse.Namespace]:
-        logger.info('--- load_model_stage1 ---')
-        start = time.perf_counter()
-
-        args = self.args
-        model_stage1, args = InferenceModel_Sequential.from_pretrained(
-            args, 'cogvideo-stage1')
-        model_stage1.eval()
-        if args.both_stages:
-            model_stage1 = model_stage1.cpu()
-
-        elapsed = time.perf_counter() - start
-        logger.info(f'--- done ({elapsed=:.3f}) ---')
-        return model_stage1, args
-
-    def load_model_stage2(
-            self) -> tuple[CogVideoCacheModel | None, argparse.Namespace]:
-        logger.info('--- load_model_stage2 ---')
-        start = time.perf_counter()
-
-        args = self.args
-        if args.both_stages:
-            model_stage2, args = InferenceModel_Interpolate.from_pretrained(
-                args, 'cogvideo-stage2')
-            model_stage2.eval()
-            if args.both_stages:
-                model_stage2 = model_stage2.cpu()
-        else:
-            model_stage2 = None
-
-        elapsed = time.perf_counter() - start
-        logger.info(f'--- done ({elapsed=:.3f}) ---')
-        return model_stage2, args
-
-    def load_strategies(self) -> tuple[CoglmStrategy, CoglmStrategy]:
-        logger.info('--- load_strategies ---')
-        start = time.perf_counter()
-
-        invalid_slices = [slice(self.tokenizer.num_image_tokens, None)]
-        strategy_cogview2 = CoglmStrategy(invalid_slices,
-                                          temperature=1.0,
-                                          top_k=16)
-        strategy_cogvideo = CoglmStrategy(
-            invalid_slices,
-            temperature=self.args.temperature,
-            top_k=self.args.top_k,
-            temperature2=self.args.coglm_temperature2)
-
-        elapsed = time.perf_counter() - start
-        logger.info(f'--- done ({elapsed=:.3f}) ---')
-        return strategy_cogview2, strategy_cogvideo
-
-    def load_dsr(self) -> DirectSuperResolution | None:
-        logger.info('--- load_dsr ---')
-        start = time.perf_counter()
-
-        if self.args.both_stages:
-            path = auto_create('cogview2-dsr', path=None)
-            dsr = DirectSuperResolution(self.args,
-                                        path,
-                                        max_bz=12,
-                                        onCUDA=False)
-        else:
-            dsr = None
-
-        elapsed = time.perf_counter() - start
-        logger.info(f'--- done ({elapsed=:.3f}) ---')
-        return dsr
-
-    @torch.inference_mode()
-    def process_stage1(self,
-                       model,
-                       seq_text,
-                       duration,
-                       video_raw_text=None,
-                       video_guidance_text='视频',
-                       image_text_suffix='',
-                       batch_size=1,
-                       image_prompt=None):
-        process_start_time = time.perf_counter()
-
-        generate_frame_num = self.args.generate_frame_num
-        tokenizer = self.tokenizer
-        use_guide = self.args.use_guidance_stage1
-
-        if next(model.parameters()).device != self.device:
-            move_start_time = time.perf_counter()
-            logger.debug('moving stage 1 model to cuda')
-
-            model = model.to(self.device)
-
-            elapsed = time.perf_counter() - move_start_time
-            logger.debug(f'moving in model1 takes time: {elapsed:.2f}')
-
-        if video_raw_text is None:
-            video_raw_text = seq_text
-        mbz = self.args.stage1_max_inference_batch_size if self.args.stage1_max_inference_batch_size > 0 else self.args.max_inference_batch_size
-        assert batch_size < mbz or batch_size % mbz == 0
-        frame_len = 400
-
-        # generate the first frame:
-        enc_text = tokenizer.encode(seq_text + image_text_suffix)
-        seq_1st = enc_text + [tokenizer['<start_of_image>']] + [-1] * 400
-        logger.info(
-            f'[Generating First Frame with CogView2] Raw text: {tokenizer.decode(enc_text):s}'
-        )
-        text_len_1st = len(seq_1st) - frame_len * 1 - 1
-
-        seq_1st = torch.tensor(seq_1st, dtype=torch.long,
-                               device=self.device).unsqueeze(0)
-        if image_prompt is None:
-            output_list_1st = []
-            for tim in range(max(batch_size // mbz, 1)):
-                start_time = time.perf_counter()
-                output_list_1st.append(
-                    my_filling_sequence(
-                        model,
-                        tokenizer,
-                        self.args,
-                        seq_1st.clone(),
-                        batch_size=min(batch_size, mbz),
-                        get_masks_and_position_ids=
-                        get_masks_and_position_ids_stage1,
-                        text_len=text_len_1st,
-                        frame_len=frame_len,
-                        strategy=self.strategy_cogview2,
-                        strategy2=self.strategy_cogvideo,
-                        log_text_attention_weights=1.4,
-                        enforce_no_swin=True,
-                        mode_stage1=True,
-                    )[0])
-                elapsed = time.perf_counter() - start_time
-                logger.info(f'[First Frame] Elapsed: {elapsed:.2f}')
-            output_tokens_1st = torch.cat(output_list_1st, dim=0)
-            given_tokens = output_tokens_1st[:, text_len_1st + 1:text_len_1st +
-                                            401].unsqueeze(
-                                                1
-                                            )  # given_tokens.shape: [bs, frame_num, 400]
-        else:
-            given_tokens = tokenizer.encode(image_path=image_prompt, image_size=160).repeat(batch_size, 1).unsqueeze(1)
-
-        # generate subsequent frames:
-        total_frames = generate_frame_num
-        enc_duration = tokenizer.encode(f'{float(duration)}秒')
-        if use_guide:
-            video_raw_text = video_raw_text + ' 视频'
-        enc_text_video = tokenizer.encode(video_raw_text)
-        seq = enc_duration + [tokenizer['<n>']] + enc_text_video + [
-            tokenizer['<start_of_image>']
-        ] + [-1] * 400 * generate_frame_num
-        guider_seq = enc_duration + [tokenizer['<n>']] + tokenizer.encode(
-            video_guidance_text) + [tokenizer['<start_of_image>']
-                                    ] + [-1] * 400 * generate_frame_num
-        logger.info(
-            f'[Stage1: Generating Subsequent Frames, Frame Rate {4/duration:.1f}] raw text: {tokenizer.decode(enc_text_video):s}'
-        )
-
-        text_len = len(seq) - frame_len * generate_frame_num - 1
-        guider_text_len = len(guider_seq) - frame_len * generate_frame_num - 1
-        seq = torch.tensor(seq, dtype=torch.long,
-                           device=self.device).unsqueeze(0).repeat(
-                               batch_size, 1)
-        guider_seq = torch.tensor(guider_seq,
-                                  dtype=torch.long,
-                                  device=self.device).unsqueeze(0).repeat(
-                                      batch_size, 1)
-
-        for given_frame_id in range(given_tokens.shape[1]):
-            seq[:, text_len + 1 + given_frame_id * 400:text_len + 1 +
-                (given_frame_id + 1) * 400] = given_tokens[:, given_frame_id]
-            guider_seq[:, guider_text_len + 1 +
-                       given_frame_id * 400:guider_text_len + 1 +
-                       (given_frame_id + 1) *
-                       400] = given_tokens[:, given_frame_id]
-        output_list = []
-
-        if use_guide:
-            video_log_text_attention_weights = 0
-        else:
-            guider_seq = None
-            video_log_text_attention_weights = 1.4
-
-        for tim in range(max(batch_size // mbz, 1)):
-            input_seq = seq[:min(batch_size, mbz)].clone(
-            ) if tim == 0 else seq[mbz * tim:mbz * (tim + 1)].clone()
-            guider_seq2 = (guider_seq[:min(batch_size, mbz)].clone()
-                           if tim == 0 else guider_seq[mbz * tim:mbz *
-                                                       (tim + 1)].clone()
-                           ) if guider_seq is not None else None
-            output_list.append(
-                my_filling_sequence(
-                    model,
-                    tokenizer,
-                    self.args,
-                    input_seq,
-                    batch_size=min(batch_size, mbz),
-                    get_masks_and_position_ids=
-                    get_masks_and_position_ids_stage1,
-                    text_len=text_len,
-                    frame_len=frame_len,
-                    strategy=self.strategy_cogview2,
-                    strategy2=self.strategy_cogvideo,
-                    log_text_attention_weights=video_log_text_attention_weights,
-                    guider_seq=guider_seq2,
-                    guider_text_len=guider_text_len,
-                    guidance_alpha=self.args.guidance_alpha,
-                    limited_spatial_channel_mem=True,
-                    mode_stage1=True,
-                )[0])
-
-        output_tokens = torch.cat(output_list, dim=0)[:, 1 + text_len:]
-
-        if self.args.both_stages:
-            move_start_time = time.perf_counter()
-            logger.debug('moving stage 1 model to cpu')
-            model = model.cpu()
-            torch.cuda.empty_cache()
-            elapsed = time.perf_counter() - move_start_time
-            logger.debug(f'moving in model1 takes time: {elapsed:.2f}')
-
-        # decoding
-        res = []
-        for seq in output_tokens:
-            decoded_imgs = [
-                self.postprocess(
-                    torch.nn.functional.interpolate(tokenizer.decode(
-                        image_ids=seq.tolist()[i * 400:(i + 1) * 400]),
-                                                    size=(480, 480))[0])
-                for i in range(total_frames)
-            ]
-            res.append(decoded_imgs)  # only the last image (target)
-
-        assert len(res) == batch_size
-        tokens = output_tokens[:, :+total_frames * 400].reshape(
-            -1, total_frames, 400).cpu()
-
-        elapsed = time.perf_counter() - process_start_time
-        logger.info(f'--- done ({elapsed=:.3f}) ---')
-        return tokens, res[0]
-
-    @torch.inference_mode()
-    def process_stage2(self,
-                       model,
-                       seq_text,
-                       duration,
-                       parent_given_tokens,
-                       video_raw_text=None,
-                       video_guidance_text='视频',
-                       gpu_rank=0,
-                       gpu_parallel_size=1):
-        process_start_time = time.perf_counter()
-
-        generate_frame_num = self.args.generate_frame_num
-        tokenizer = self.tokenizer
-        use_guidance = self.args.use_guidance_stage2
-
-        stage2_start_time = time.perf_counter()
-
-        if next(model.parameters()).device != self.device:
-            move_start_time = time.perf_counter()
-            logger.debug('moving stage-2 model to cuda')
-
-            model = model.to(self.device)
-
-            elapsed = time.perf_counter() - move_start_time
-            logger.debug(f'moving in stage-2 model takes time: {elapsed:.2f}')
-
-        try:
-            sample_num_allgpu = parent_given_tokens.shape[0]
-            sample_num = sample_num_allgpu // gpu_parallel_size
-            assert sample_num * gpu_parallel_size == sample_num_allgpu
-            parent_given_tokens = parent_given_tokens[gpu_rank *
-                                                      sample_num:(gpu_rank +
-                                                                  1) *
-                                                      sample_num]
-        except:
-            logger.critical('No frame_tokens found in interpolation, skip')
-            return False, []
-
-        # CogVideo Stage2 Generation
-        while duration >= 0.5:  # TODO: You can change the boundary to change the frame rate
-            parent_given_tokens_num = parent_given_tokens.shape[1]
-            generate_batchsize_persample = (parent_given_tokens_num - 1) // 2
-            generate_batchsize_total = generate_batchsize_persample * sample_num
-            total_frames = generate_frame_num
-            frame_len = 400
-            enc_text = tokenizer.encode(seq_text)
-            enc_duration = tokenizer.encode(str(float(duration)) + '秒')
-            seq = enc_duration + [tokenizer['<n>']] + enc_text + [
-                tokenizer['<start_of_image>']
-            ] + [-1] * 400 * generate_frame_num
-            text_len = len(seq) - frame_len * generate_frame_num - 1
-
-            logger.info(
-                f'[Stage2: Generating Frames, Frame Rate {int(4/duration):d}] raw text: {tokenizer.decode(enc_text):s}'
-            )
-
-            # generation
-            seq = torch.tensor(seq, dtype=torch.long,
-                               device=self.device).unsqueeze(0).repeat(
-                                   generate_batchsize_total, 1)
-            for sample_i in range(sample_num):
-                for i in range(generate_batchsize_persample):
-                    seq[sample_i * generate_batchsize_persample +
-                        i][text_len + 1:text_len + 1 +
-                           400] = parent_given_tokens[sample_i][2 * i]
-                    seq[sample_i * generate_batchsize_persample +
-                        i][text_len + 1 + 400:text_len + 1 +
-                           800] = parent_given_tokens[sample_i][2 * i + 1]
-                    seq[sample_i * generate_batchsize_persample +
-                        i][text_len + 1 + 800:text_len + 1 +
-                           1200] = parent_given_tokens[sample_i][2 * i + 2]
-
-            if use_guidance:
-                guider_seq = enc_duration + [
-                    tokenizer['<n>']
-                ] + tokenizer.encode(video_guidance_text) + [
-                    tokenizer['<start_of_image>']
-                ] + [-1] * 400 * generate_frame_num
-                guider_text_len = len(
-                    guider_seq) - frame_len * generate_frame_num - 1
-                guider_seq = torch.tensor(
-                    guider_seq, dtype=torch.long,
-                    device=self.device).unsqueeze(0).repeat(
-                        generate_batchsize_total, 1)
-                for sample_i in range(sample_num):
-                    for i in range(generate_batchsize_persample):
-                        guider_seq[sample_i * generate_batchsize_persample +
-                                   i][text_len + 1:text_len + 1 +
-                                      400] = parent_given_tokens[sample_i][2 *
-                                                                           i]
-                        guider_seq[sample_i * generate_batchsize_persample +
-                                   i][text_len + 1 + 400:text_len + 1 +
-                                      800] = parent_given_tokens[sample_i][2 *
-                                                                           i +
-                                                                           1]
-                        guider_seq[sample_i * generate_batchsize_persample +
-                                   i][text_len + 1 + 800:text_len + 1 +
-                                      1200] = parent_given_tokens[sample_i][2 *
-                                                                            i +
-                                                                            2]
-                video_log_text_attention_weights = 0
-            else:
-                guider_seq = None
-                guider_text_len = 0
-                video_log_text_attention_weights = 1.4
-
-            mbz = self.args.max_inference_batch_size
-
-            assert generate_batchsize_total < mbz or generate_batchsize_total % mbz == 0
-            output_list = []
-            start_time = time.perf_counter()
-            for tim in range(max(generate_batchsize_total // mbz, 1)):
-                input_seq = seq[:min(generate_batchsize_total, mbz)].clone(
-                ) if tim == 0 else seq[mbz * tim:mbz * (tim + 1)].clone()
-                guider_seq2 = (
-                    guider_seq[:min(generate_batchsize_total, mbz)].clone()
-                    if tim == 0 else guider_seq[mbz * tim:mbz *
-                                                (tim + 1)].clone()
-                ) if guider_seq is not None else None
-                output_list.append(
-                    my_filling_sequence(
-                        model,
-                        tokenizer,
-                        self.args,
-                        input_seq,
-                        batch_size=min(generate_batchsize_total, mbz),
-                        get_masks_and_position_ids=
-                        get_masks_and_position_ids_stage2,
-                        text_len=text_len,
-                        frame_len=frame_len,
-                        strategy=self.strategy_cogview2,
-                        strategy2=self.strategy_cogvideo,
-                        log_text_attention_weights=
-                        video_log_text_attention_weights,
-                        mode_stage1=False,
-                        guider_seq=guider_seq2,
-                        guider_text_len=guider_text_len,
-                        guidance_alpha=self.args.guidance_alpha,
-                        limited_spatial_channel_mem=True,
-                    )[0])
-            elapsed = time.perf_counter() - start_time
-            logger.info(f'Duration {duration:.2f}, Elapsed: {elapsed:.2f}\n')
-
-            output_tokens = torch.cat(output_list, dim=0)
-            output_tokens = output_tokens[:, text_len + 1:text_len + 1 +
-                                          (total_frames) * 400].reshape(
-                                              sample_num, -1,
-                                              400 * total_frames)
-            output_tokens_merge = torch.cat(
-                (output_tokens[:, :, :1 * 400], output_tokens[:, :,
-                                                              400 * 3:4 * 400],
-                 output_tokens[:, :, 400 * 1:2 * 400],
-                 output_tokens[:, :, 400 * 4:(total_frames) * 400]),
-                dim=2).reshape(sample_num, -1, 400)
-
-            output_tokens_merge = torch.cat(
-                (output_tokens_merge, output_tokens[:, -1:, 400 * 2:3 * 400]),
-                dim=1)
-            duration /= 2
-            parent_given_tokens = output_tokens_merge
-
-        if self.args.both_stages:
-            move_start_time = time.perf_counter()
-            logger.debug('moving stage 2 model to cpu')
-            model = model.cpu()
-            torch.cuda.empty_cache()
-            elapsed = time.perf_counter() - move_start_time
-            logger.debug(f'moving out model2 takes time: {elapsed:.2f}')
-
-        elapsed = time.perf_counter() - stage2_start_time
-        logger.info(f'CogVideo Stage2 completed. Elapsed: {elapsed:.2f}\n')
-
-        # direct super-resolution by CogView2
-        logger.info('[Direct super-resolution]')
-        dsr_start_time = time.perf_counter()
-
-        enc_text = tokenizer.encode(seq_text)
-        frame_num_per_sample = parent_given_tokens.shape[1]
-        parent_given_tokens_2d = parent_given_tokens.reshape(-1, 400)
-        text_seq = torch.tensor(enc_text, dtype=torch.long,
-                                device=self.device).unsqueeze(0).repeat(
-                                    parent_given_tokens_2d.shape[0], 1)
-        sred_tokens = self.dsr(text_seq, parent_given_tokens_2d)
-
-        decoded_sr_videos = []
-        for sample_i in range(sample_num):
-            decoded_sr_imgs = []
-            for frame_i in range(frame_num_per_sample):
-                decoded_sr_img = tokenizer.decode(
-                    image_ids=sred_tokens[frame_i + sample_i *
-                                          frame_num_per_sample][-3600:])
-                decoded_sr_imgs.append(
-                    self.postprocess(
-                        torch.nn.functional.interpolate(decoded_sr_img,
-                                                        size=(480, 480))[0]))
-            decoded_sr_videos.append(decoded_sr_imgs)
-
-        elapsed = time.perf_counter() - dsr_start_time
-        logger.info(
-            f'Direct super-resolution completed. Elapsed: {elapsed:.2f}')
-
-        elapsed = time.perf_counter() - process_start_time
-        logger.info(f'--- done ({elapsed=:.3f}) ---')
-        return True, decoded_sr_videos[0]
-
-    @staticmethod
-    def postprocess(tensor: torch.Tensor) -> np.ndarray:
-        return tensor.cpu().mul(255).add_(0.5).clamp_(0, 255).permute(
-            1, 2, 0).to(torch.uint8).numpy()
-
-    def run(self, text: str, seed: int,
-            only_first_stage: bool,image_prompt: None) -> list[np.ndarray]:
-        logger.info('==================== run ====================')
-        start = time.perf_counter()
-
-        set_random_seed(seed)
-        self.args.seed = seed
-
-        if only_first_stage:
-            self.args.stage_1 = True
-            self.args.both_stages = False
-        else:
-            self.args.stage_1 = False
-            self.args.both_stages = True
-
-        parent_given_tokens, res = self.process_stage1(
-            self.model_stage1,
-            text,
-            duration=4.0,
-            video_raw_text=text,
-            video_guidance_text='视频',
-            image_text_suffix=' 高清摄影',
-            batch_size=self.args.batch_size,
-            image_prompt=image_prompt)
-        if not only_first_stage:
-            _, res = self.process_stage2(
-                self.model_stage2,
-                text,
-                duration=2.0,
-                parent_given_tokens=parent_given_tokens,
-                video_raw_text=text + ' 视频',
-                video_guidance_text='视频',
-                gpu_rank=0,
-                gpu_parallel_size=1)  # TODO: 修改
-
-        elapsed = time.perf_counter() - start
-        logger.info(f'Elapsed: {elapsed:.3f}')
-        logger.info('==================== done ====================')
-        return res
-
-
-class AppModel(Model):
-    def __init__(self, only_first_stage: bool):
-        super().__init__(only_first_stage)
-        self.translator = gr.Interface.load(
-            'spaces/chinhon/translation_eng2ch')
-
-    def to_video(self, frames: list[np.ndarray]) -> str:
-        out_file = tempfile.NamedTemporaryFile(suffix='.mp4', delete=False)
-        if self.args.stage_1:
-            fps = 4
-        else:
-            fps = 8
-        writer = iio.get_writer(out_file.name, fps=fps)
-        for frame in frames:
-            writer.append_data(frame)
-        writer.close()
-        return out_file.name
-
-    def run_with_translation(
-            self, text: str, translate: bool, seed: int,
-            only_first_stage: bool,image_prompt: None) -> tuple[str | None, str | None]:
-            
-        logger.info(f'{text=}, {translate=}, {seed=}, {only_first_stage=},{image_prompt=}')
-        if translate:
-            text = translated_text = self.translator(text)
-        else:
-            translated_text = None
-        frames = self.run(text, seed, only_first_stage,image_prompt)
-        video_path = self.to_video(frames)
-        return translated_text, video_path

patch

@@ -1,51 +0,0 @@
-diff --git a/coglm_strategy.py b/coglm_strategy.py
-index d485715..a9eab3b 100644
---- a/coglm_strategy.py
-+++ b/coglm_strategy.py
-@@ -8,6 +8,7 @@
- 
- # here put the import lib
- import os
-+import pathlib
- import sys
- import math
- import random
-@@ -58,7 +59,8 @@ class CoglmStrategy:
-         self._is_done = False
-         self.outlier_count_down = torch.zeros(16) 
-         self.vis_list = [[]for i in range(16)]
--        self.cluster_labels = torch.tensor(np.load('cluster_label2.npy'), device='cuda', dtype=torch.long)
-+        cluster_label_path = pathlib.Path(__file__).parent / 'cluster_label2.npy'
-+        self.cluster_labels = torch.tensor(np.load(cluster_label_path), device='cuda', dtype=torch.long)
-         self.start_pos = -1
-         self.white_cluster = []
-         # self.fout = open('tmp.txt', 'w')
-@@ -98,4 +100,4 @@ class CoglmStrategy:
- 
-     def finalize(self, tokens, mems):
-         self._is_done = False
--        return tokens, mems
-
-+        return tokens, mems
-diff --git a/sr_pipeline/dsr_sampling.py b/sr_pipeline/dsr_sampling.py
-index 5b8dded..07e97fd 100644
---- a/sr_pipeline/dsr_sampling.py
-+++ b/sr_pipeline/dsr_sampling.py
-@@ -8,6 +8,7 @@
- 
- # here put the import lib
- import os
-+import pathlib
- import sys
- import math
- import random
-@@ -28,7 +29,8 @@ class IterativeEntfilterStrategy:
-         self.invalid_slices = invalid_slices
-         self.temperature = temperature
-         self.topk = topk        
--        self.cluster_labels = torch.tensor(np.load('cluster_label2.npy'), device='cuda', dtype=torch.long)
-+        cluster_label_path = pathlib.Path(__file__).parents[1] / 'cluster_label2.npy'
-+        self.cluster_labels = torch.tensor(np.load(cluster_label_path), device='cuda', dtype=torch.long)
- 
- 
-     def forward(self, logits_, tokens, temperature=None, entfilter=None, filter_topk=5, temperature2=None):

requirements.txt

@@ -1,7 +1,4 @@
---extra-index-url https://download.pytorch.org/whl/cu113
-imageio==2.19.5
-imageio-ffmpeg==0.4.7
-numpy==1.22.4
-opencv-python-headless==4.6.0.66
-torch==1.12.0+cu113
-torchvision==0.13.0+cu113
+gradio>=4.40.0
+imageio-ffmpeg>=0.5.1
+
+

samples.txt

@@ -1,2 +0,0 @@
-骑滑板的皮卡丘
-a cat playing chess

style.css

@@ -1,7 +0,0 @@
-h1 {
-  text-align: center;
-}
-img#visitor-badge {
-  display: block;
-  margin: auto;
-}