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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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from constants import UploadTarget |
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from inference import InferencePipeline |
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from trainer import Trainer |
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def create_training_demo( |
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trainer: Trainer, pipe: InferencePipeline | None = None, disable_run_button: bool = False |
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) -> gr.Blocks: |
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def read_log() -> str: |
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with open(trainer.log_file) as f: |
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lines = f.readlines() |
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return "".join(lines[-10:]) |
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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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gr.Markdown("Training Data") |
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training_video = gr.File(label="Training video") |
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training_prompt = gr.Textbox(label="Training prompt", max_lines=1, placeholder="A man is surfing") |
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gr.Markdown( |
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""" |
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- Upload a video and write a `Training Prompt` that describes the video. |
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""" |
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) |
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with gr.Column(): |
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with gr.Box(): |
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gr.Markdown("Training Parameters") |
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with gr.Row(): |
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base_model = gr.Text(label="Base Model", value="CompVis/stable-diffusion-v1-4", max_lines=1) |
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resolution = gr.Dropdown( |
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choices=["512", "768"], value="512", label="Resolution", visible=False |
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) |
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hf_token = gr.Text( |
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label="Hugging Face Write Token", type="password", visible=os.getenv("HF_TOKEN") is None |
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) |
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with gr.Accordion(label="Advanced options", open=False): |
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num_training_steps = gr.Number(label="Number of Training Steps", value=300, precision=0) |
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learning_rate = gr.Number(label="Learning Rate", value=0.000035) |
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gradient_accumulation = gr.Number( |
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label="Number of Gradient Accumulation", value=1, precision=0 |
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) |
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seed = gr.Slider(label="Seed", minimum=0, maximum=100000, step=1, randomize=True, value=0) |
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fp16 = gr.Checkbox(label="FP16", value=True) |
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use_8bit_adam = gr.Checkbox(label="Use 8bit Adam", value=False) |
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checkpointing_steps = gr.Number(label="Checkpointing Steps", value=1000, precision=0) |
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validation_epochs = gr.Number(label="Validation Epochs", value=100, precision=0) |
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gr.Markdown( |
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""" |
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- The base model must be a Stable Diffusion model compatible with [diffusers](https://github.com/huggingface/diffusers) library. |
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- Expected time to train a model for 300 steps: ~20 minutes with T4 |
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- You can check the training status by pressing the "Open logs" button if you are running this on your Space. |
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""" |
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) |
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with gr.Row(): |
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with gr.Column(): |
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gr.Markdown("Output Model") |
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output_model_name = gr.Text(label="Name of your model", placeholder="The surfer man", max_lines=1) |
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validation_prompt = gr.Text( |
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label="Validation Prompt", placeholder="prompt to test the model, e.g: a dog is surfing" |
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) |
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with gr.Column(): |
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gr.Markdown("Upload Settings") |
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with gr.Row(): |
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upload_to_hub = gr.Checkbox(label="Upload model to Hub", value=True) |
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use_private_repo = gr.Checkbox(label="Private", value=True) |
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delete_existing_repo = gr.Checkbox(label="Delete existing repo of the same name", value=False) |
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upload_to = gr.Radio( |
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label="Upload to", |
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choices=[_.value for _ in UploadTarget], |
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value=UploadTarget.MODEL_LIBRARY.value, |
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) |
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pause_space_after_training = gr.Checkbox( |
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label="Pause this Space after training", |
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value=False, |
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interactive=bool(os.getenv("SPACE_ID")), |
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visible=False, |
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) |
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run_button = gr.Button("Start Training", interactive=not disable_run_button) |
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with gr.Box(): |
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gr.Text(label="Log", value=read_log, lines=10, max_lines=10, every=1) |
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if pipe is not None: |
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run_button.click(fn=pipe.clear) |
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run_button.click( |
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fn=trainer.run, |
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inputs=[ |
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training_video, |
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training_prompt, |
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output_model_name, |
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delete_existing_repo, |
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validation_prompt, |
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base_model, |
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resolution, |
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num_training_steps, |
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learning_rate, |
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gradient_accumulation, |
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seed, |
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fp16, |
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use_8bit_adam, |
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checkpointing_steps, |
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validation_epochs, |
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upload_to_hub, |
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use_private_repo, |
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delete_existing_repo, |
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upload_to, |
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pause_space_after_training, |
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hf_token, |
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], |
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) |
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return demo |
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if __name__ == "__main__": |
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trainer = Trainer() |
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demo = create_training_demo(trainer) |
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demo.queue(api_open=False, max_size=1).launch() |
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