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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("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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) |
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with gr.Accordion(label="Model info (Base model and prompt used for training)", open=False): |
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with gr.Row(): |
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base_model_used_for_training = gr.Text(label="Base model", interactive=False) |
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prompt_used_for_training = gr.Text(label="Training prompt", interactive=False) |
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prompt = gr.Textbox(label="Prompt", max_lines=1, placeholder='Example: "A panda is surfing"') |
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video_length = gr.Slider(label="Video length", minimum=4, maximum=12, step=1, value=8) |
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fps = gr.Slider(label="FPS", minimum=1, maximum=12, step=1, value=1) |
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seed = gr.Slider(label="Seed", minimum=0, maximum=100000, step=1, 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", minimum=0, maximum=100, step=1, value=50) |
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guidance_scale = gr.Slider(label="CFG Scale", minimum=0, maximum=50, step=0.1, value=7.5) |
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run_button = gr.Button("Generate") |
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gr.Markdown( |
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""" |
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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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) |
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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( |
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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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) |
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model_id.change( |
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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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) |
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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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