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from __future__ import annotations |
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import os |
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import gradio as gr |
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import torch |
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from inference import InferencePipeline |
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class InferenceUtil: |
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def __init__(self, hf_token: str | None): |
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self.hf_token = hf_token |
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def load_model_info(self, model_id: str) -> tuple[str, str]: |
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try: |
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card = InferencePipeline.get_model_card(model_id, self.hf_token) |
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except Exception: |
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return '', '' |
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base_model = getattr(card.data, 'base_model', '') |
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training_prompt = getattr(card.data, 'training_prompt', '') |
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return base_model, training_prompt |
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DESCRIPTION = '# [Tune-A-Video](https://tuneavideo.github.io/)' |
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if not torch.cuda.is_available(): |
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DESCRIPTION += '\n<p>Running on CPU 🥶 This demo does not work on CPU.</p>' |
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CACHE_EXAMPLES = torch.cuda.is_available() and os.getenv( |
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'CACHE_EXAMPLES') == '1' |
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HF_TOKEN = os.getenv('HF_TOKEN') |
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pipe = InferencePipeline(HF_TOKEN) |
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app = InferenceUtil(HF_TOKEN) |
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with gr.Blocks(css='style.css') as demo: |
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gr.Markdown(DESCRIPTION) |
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with gr.Row(): |
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with gr.Column(): |
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with gr.Box(): |
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model_id = gr.Dropdown( |
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label='Model ID', |
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choices=[ |
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'Tune-A-Video-library/a-man-is-surfing', |
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'Tune-A-Video-library/mo-di-bear-guitar', |
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'Tune-A-Video-library/redshift-man-skiing', |
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], |
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value='Tune-A-Video-library/a-man-is-surfing') |
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with gr.Accordion( |
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label= |
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'Model info (Base model and prompt used for training)', |
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open=False): |
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with gr.Row(): |
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base_model_used_for_training = gr.Text( |
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label='Base model', interactive=False) |
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prompt_used_for_training = gr.Text( |
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label='Training prompt', interactive=False) |
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prompt = gr.Textbox(label='Prompt', |
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max_lines=1, |
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placeholder='Example: "A panda is surfing"') |
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video_length = gr.Slider(label='Video length', |
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minimum=4, |
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maximum=12, |
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step=1, |
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value=8) |
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fps = gr.Slider(label='FPS', |
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minimum=1, |
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maximum=12, |
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step=1, |
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value=1) |
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seed = gr.Slider(label='Seed', |
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minimum=0, |
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maximum=100000, |
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step=1, |
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value=0) |
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with gr.Accordion('Other Parameters', open=False): |
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num_steps = gr.Slider(label='Number of Steps', |
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minimum=0, |
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maximum=100, |
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step=1, |
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value=50) |
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guidance_scale = gr.Slider(label='CFG Scale', |
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minimum=0, |
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maximum=50, |
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step=0.1, |
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value=7.5) |
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run_button = gr.Button('Generate') |
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gr.Markdown(''' |
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- It takes a few minutes to download model first. |
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- Expected time to generate an 8-frame video: 70 seconds with T4, 24 seconds with A10G, (10 seconds with A100) |
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''') |
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with gr.Column(): |
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result = gr.Video(label='Result') |
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with gr.Row(): |
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examples = [ |
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[ |
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'Tune-A-Video-library/a-man-is-surfing', |
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'A panda is surfing.', |
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8, |
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1, |
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3, |
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50, |
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7.5, |
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], |
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[ |
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'Tune-A-Video-library/a-man-is-surfing', |
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'A racoon is surfing, cartoon style.', |
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8, |
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1, |
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3, |
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50, |
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7.5, |
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], |
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[ |
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'Tune-A-Video-library/mo-di-bear-guitar', |
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'a handsome prince is playing guitar, modern disney style.', |
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8, |
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1, |
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123, |
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50, |
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7.5, |
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], |
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[ |
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'Tune-A-Video-library/mo-di-bear-guitar', |
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'a magical princess is playing guitar, modern disney style.', |
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8, |
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1, |
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123, |
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50, |
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7.5, |
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], |
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[ |
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'Tune-A-Video-library/mo-di-bear-guitar', |
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'a rabbit is playing guitar, modern disney style.', |
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8, |
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1, |
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123, |
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50, |
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7.5, |
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], |
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[ |
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'Tune-A-Video-library/mo-di-bear-guitar', |
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'a baby is playing guitar, modern disney style.', |
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8, |
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1, |
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123, |
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50, |
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7.5, |
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], |
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[ |
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'Tune-A-Video-library/redshift-man-skiing', |
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'(redshift style) spider man is skiing.', |
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8, |
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1, |
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123, |
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50, |
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7.5, |
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], |
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[ |
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'Tune-A-Video-library/redshift-man-skiing', |
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'(redshift style) black widow is skiing.', |
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8, |
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1, |
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123, |
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50, |
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7.5, |
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], |
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[ |
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'Tune-A-Video-library/redshift-man-skiing', |
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'(redshift style) batman is skiing.', |
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8, |
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1, |
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123, |
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50, |
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7.5, |
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], |
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[ |
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'Tune-A-Video-library/redshift-man-skiing', |
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'(redshift style) hulk is skiing.', |
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8, |
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1, |
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123, |
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50, |
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7.5, |
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], |
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] |
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gr.Examples(examples=examples, |
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inputs=[ |
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model_id, |
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prompt, |
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video_length, |
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fps, |
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seed, |
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num_steps, |
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guidance_scale, |
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], |
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outputs=result, |
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fn=pipe.run, |
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cache_examples=CACHE_EXAMPLES) |
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model_id.change(fn=app.load_model_info, |
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inputs=model_id, |
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outputs=[ |
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base_model_used_for_training, |
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prompt_used_for_training, |
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]) |
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inputs = [ |
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model_id, |
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prompt, |
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video_length, |
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fps, |
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seed, |
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num_steps, |
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guidance_scale, |
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] |
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prompt.submit(fn=pipe.run, inputs=inputs, outputs=result) |
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run_button.click(fn=pipe.run, inputs=inputs, outputs=result) |
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demo.queue().launch() |
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