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from __future__ import annotations |
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import enum |
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import gradio as gr |
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from huggingface_hub import HfApi |
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from constants import MODEL_LIBRARY_ORG_NAME, UploadTarget |
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from inference import InferencePipeline |
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from utils import find_exp_dirs |
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class ModelSource(enum.Enum): |
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HUB_LIB = UploadTarget.MODEL_LIBRARY.value |
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LOCAL = 'Local' |
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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_hub_model_list(self) -> dict: |
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api = HfApi(token=self.hf_token) |
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choices = [ |
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info.modelId |
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for info in api.list_models(author=MODEL_LIBRARY_ORG_NAME) |
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] |
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return gr.update(choices=choices, |
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value=choices[0] if choices else None) |
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@staticmethod |
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def load_local_model_list() -> dict: |
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choices = find_exp_dirs() |
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return gr.update(choices=choices, |
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value=choices[0] if choices else None) |
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def reload_model_list(self, model_source: str) -> dict: |
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if model_source == ModelSource.HUB_LIB.value: |
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return self.load_hub_model_list() |
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elif model_source == ModelSource.LOCAL.value: |
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return self.load_local_model_list() |
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else: |
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raise ValueError |
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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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def reload_model_list_and_update_model_info( |
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self, model_source: str) -> tuple[dict, str, str]: |
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model_list_update = self.reload_model_list(model_source) |
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model_list = model_list_update['choices'] |
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model_info = self.load_model_info(model_list[0] if model_list else '') |
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return model_list_update, *model_info |
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def create_inference_demo(pipe: InferencePipeline, |
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hf_token: str | None = None) -> gr.Blocks: |
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app = InferenceUtil(hf_token) |
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with gr.Blocks() as demo: |
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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_source = gr.Radio( |
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label='Model Source', |
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choices=[_.value for _ in ModelSource], |
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value=ModelSource.HUB_LIB.value) |
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reload_button = gr.Button('Reload Model List') |
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model_id = gr.Dropdown(label='Model ID', |
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choices=None, |
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value=None) |
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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( |
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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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- After training, you can press "Reload Model List" button to load your trained model names. |
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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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model_source.change(fn=app.reload_model_list_and_update_model_info, |
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inputs=model_source, |
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outputs=[ |
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model_id, |
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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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reload_button.click(fn=app.reload_model_list_and_update_model_info, |
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inputs=model_source, |
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outputs=[ |
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model_id, |
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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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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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return demo |
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if __name__ == '__main__': |
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import os |
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hf_token = os.getenv('HF_TOKEN') |
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pipe = InferencePipeline(hf_token) |
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demo = create_inference_demo(pipe, hf_token) |
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demo.queue(api_open=False, max_size=10).launch() |
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