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Parent(s):
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Update for HF Space
Browse files- README.md +2 -1
- app.py +16 -24
- model.py +60 -3
- requirements.txt +3 -6
- prompt.txt β samples.txt +0 -0
README.md
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@@ -4,7 +4,8 @@ emoji: π
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colorFrom: indigo
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colorTo: yellow
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sdk: gradio
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sdk_version: 3.0
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app_file: app.py
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pinned: false
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---
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colorFrom: indigo
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colorTo: yellow
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sdk: gradio
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sdk_version: 3.1.0
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python_version: 3.9.13
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app_file: app.py
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pinned: false
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---
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app.py
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from __future__ import annotations
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import argparse
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import gradio as gr
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from model import AppModel
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DESCRIPTION = '''# <a href="https://github.com/THUDM/CogVideo">CogVideo</a>
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If you check the "Translate to Chinese" checkbox, the app will use the English to Chinese translation results with [this Space](https://huggingface.co/spaces/chinhon/translation_eng2ch) as input.
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But the translation model may mistranslate and the results could be poor.
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So, it is also a good idea to input the translation results from other translation services.
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'''
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def set_example_text(example: list) -> dict:
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def main():
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model = AppModel(
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with gr.Blocks(css='style.css') as demo:
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gr.Markdown(DESCRIPTION)
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label='Seed')
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only_first_stage = gr.Checkbox(
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label='Only First Stage',
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value=
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visible=not
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run_button = gr.Button('Run')
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with open('samples.txt') as f:
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samples = [
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line.strip().split('\t') for line in f.readlines()
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]
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examples = gr.Dataset(components=[text], samples=samples)
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with gr.Column():
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with gr.TabItem('Output (Gallery)'):
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result_gallery = gr.Gallery(show_label=False)
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run_button.click(fn=model.run_with_translation,
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inputs=[
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text,
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inputs=examples,
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outputs=examples.components)
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demo.launch(
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enable_queue=True,
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share=args.share,
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)
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if __name__ == '__main__':
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from __future__ import annotations
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import gradio as gr
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from model import AppModel
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DESCRIPTION = '''# <a href="https://github.com/THUDM/CogVideo">CogVideo</a>
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Currently, this Space only supports the first stage of the CogVideo pipeline due to hardware limitations.
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The model accepts only Chinese as input.
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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.
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Since the translation model may mistranslate, you may want to use the translation results from other translation services.
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'''
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NOTES = 'This app is adapted from https://github.com/hysts/CogVideo_demo. It would be recommended to use the repo if you want to run the app yourself.'
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FOOTER = '<img id="visitor-badge" alt="visitor badge" src="https://visitor-badge.glitch.me/badge?page_id=THUDM.CogVideo" />'
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def set_example_text(example: list) -> dict:
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def main():
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only_first_stage = True
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model = AppModel(only_first_stage)
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with gr.Blocks(css='style.css') as demo:
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gr.Markdown(DESCRIPTION)
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label='Seed')
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only_first_stage = gr.Checkbox(
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label='Only First Stage',
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value=only_first_stage,
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visible=not only_first_stage)
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run_button = gr.Button('Run')
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with open('samples.txt') as f:
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samples = [[line.strip()] for line in f.readlines()]
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examples = gr.Dataset(components=[text], samples=samples)
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with gr.Column():
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with gr.TabItem('Output (Gallery)'):
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result_gallery = gr.Gallery(show_label=False)
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gr.Markdown(NOTES)
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gr.Markdown(FOOTER)
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run_button.click(fn=model.run_with_translation,
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inputs=[
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text,
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inputs=examples,
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outputs=examples.components)
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demo.launch(enable_queue=True, share=False)
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if __name__ == '__main__':
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model.py
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# This code is adapted from https://github.com/THUDM/
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from __future__ import annotations
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import argparse
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import functools
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import logging
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import pathlib
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import sys
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import tempfile
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import time
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from typing import Any
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import gradio as gr
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import imageio.v2 as iio
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import numpy as np
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start = time.perf_counter()
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set_random_seed(seed)
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if only_first_stage:
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self.args.stage_1 = True
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def run_with_translation(
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self, text: str, translate: bool, seed: int, only_first_stage: bool
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) -> tuple[str | None,
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logger.info(f'{text=}, {translate=}, {seed=}, {only_first_stage=}')
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if translate:
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text = translated_text = self.translator(text)
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# This code is adapted from https://github.com/THUDM/CogVideo/blob/ff423aa169978fb2f636f761e348631fa3178b03/cogvideo_pipeline.py
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from __future__ import annotations
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import argparse
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import logging
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import os
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import pathlib
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import shutil
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import subprocess
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import sys
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import tempfile
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import time
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import zipfile
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from typing import Any
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if os.getenv('SYSTEM') == 'spaces':
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subprocess.run('pip install icetk==0.0.4'.split())
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subprocess.run('pip install SwissArmyTransformer==0.2.9'.split())
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subprocess.run(
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'pip install git+https://github.com/Sleepychord/Image-Local-Attention@43fee31'
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.split())
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#subprocess.run('git clone https://github.com/NVIDIA/apex'.split())
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#subprocess.run('git checkout 1403c21'.split(), cwd='apex')
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#with open('patch.apex') as f:
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# subprocess.run('patch -p1'.split(), cwd='apex', stdin=f)
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#subprocess.run(
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# 'pip install -v --disable-pip-version-check --no-cache-dir --global-option --cpp_ext --global-option --cuda_ext ./'
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# .split(),
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# cwd='apex')
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#subprocess.run('rm -rf apex'.split())
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with open('patch') as f:
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subprocess.run('patch -p1'.split(), cwd='CogVideo', stdin=f)
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from huggingface_hub import hf_hub_download
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def download_and_extract_icetk_models() -> None:
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icetk_model_dir = pathlib.Path('/home/user/.icetk_models')
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icetk_model_dir.mkdir()
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path = hf_hub_download('THUDM/icetk',
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'models.zip',
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use_auth_token=os.getenv('HF_TOKEN'))
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with zipfile.ZipFile(path) as f:
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f.extractall(path=icetk_model_dir.as_posix())
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def download_and_extract_cogvideo_models(name: str) -> None:
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path = hf_hub_download('THUDM/CogVideo',
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name,
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use_auth_token=os.getenv('HF_TOKEN'))
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with zipfile.ZipFile(path) as f:
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f.extractall('pretrained')
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os.remove(path)
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def download_and_extract_cogview2_models(name: str) -> None:
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path = hf_hub_download('THUDM/CogView2', name)
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with zipfile.ZipFile(path) as f:
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f.extractall()
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shutil.move('/home/user/app/sharefs/cogview-new/cogview2-dsr',
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'pretrained')
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shutil.rmtree('/home/user/app/sharefs/')
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os.remove(path)
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download_and_extract_icetk_models()
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download_and_extract_cogvideo_models('cogvideo-stage1.zip')
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#download_and_extract_cogvideo_models('cogvideo-stage2.zip')
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#download_and_extract_cogview2_models('cogview2-dsr.zip')
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os.environ['SAT_HOME'] = '/home/user/app/pretrained'
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import gradio as gr
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import imageio.v2 as iio
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import numpy as np
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start = time.perf_counter()
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set_random_seed(seed)
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self.args.seed = seed
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if only_first_stage:
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self.args.stage_1 = True
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def run_with_translation(
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self, text: str, translate: bool, seed: int, only_first_stage: bool
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) -> tuple[str | None, str | None, list[np.ndarray] | None]:
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logger.info(f'{text=}, {translate=}, {seed=}, {only_first_stage=}')
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if translate:
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text = translated_text = self.translator(text)
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requirements.txt
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gradio==3.1.0
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icetk==0.0.4
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imageio==2.19.5
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imageio-ffmpeg==0.4.7
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numpy==1.22.4
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opencv-python-headless==4.6.0.66
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torchvision==0.13.0
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--extra-index-url https://download.pytorch.org/whl/cu113
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imageio==2.19.5
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imageio-ffmpeg==0.4.7
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numpy==1.22.4
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opencv-python-headless==4.6.0.66
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torch==1.12.0+cu113
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torchvision==0.13.0+cu113
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prompt.txt β samples.txt
RENAMED
File without changes
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