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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 MODEL_LIBRARY_ORG_NAME, SAMPLE_MODEL_REPO, 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(trainer: Trainer, |
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pipe: InferencePipeline | None = None) -> gr.Blocks: |
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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( |
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label='Training prompt', |
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max_lines=1, |
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placeholder='A man is surfing') |
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gr.Markdown(''' |
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- Upload a video and write a prompt describing the video. |
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''') |
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with gr.Box(): |
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gr.Markdown('Output Model') |
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output_model_name = gr.Text(label='Name of your model', |
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max_lines=1) |
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delete_existing_model = gr.Checkbox( |
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label='Delete existing model of the same name', |
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value=False) |
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validation_prompt = gr.Text(label='Validation Prompt') |
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with gr.Box(): |
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gr.Markdown('Upload Settings') |
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with gr.Row(): |
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upload_to_hub = gr.Checkbox( |
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label='Upload model to Hub', value=True) |
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use_private_repo = gr.Checkbox(label='Private', |
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value=True) |
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delete_existing_repo = gr.Checkbox( |
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label='Delete existing repo of the same name', |
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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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gr.Markdown(f''' |
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- By default, trained models will be uploaded to [Tune-A-Video Library](https://huggingface.co/{MODEL_LIBRARY_ORG_NAME}) (see [this example model](https://huggingface.co/{SAMPLE_MODEL_REPO})). |
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- You can also choose "Personal Profile", in which case, the model will be uploaded to https://huggingface.co/{{your_username}}/{{model_name}}. |
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''') |
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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', |
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value='CompVis/stable-diffusion-v1-4', |
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max_lines=1) |
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resolution = gr.Dropdown(choices=['512', '768'], |
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value='512', |
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label='Resolution', |
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visible=False) |
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num_training_steps = gr.Number( |
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label='Number of Training Steps', value=300, precision=0) |
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learning_rate = gr.Number(label='Learning Rate', |
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value=0.000035) |
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gradient_accumulation = gr.Number( |
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label='Number of Gradient Accumulation', |
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value=1, |
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precision=0) |
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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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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', |
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value=1000, |
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precision=0) |
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validation_epochs = gr.Number(label='Validation Epochs', |
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value=100, |
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precision=0) |
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gr.Markdown(''' |
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- The base model must be a model that is compatible with [diffusers](https://github.com/huggingface/diffusers) library. |
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- It takes a few minutes to download the base model first. |
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- It will take about 4 minutes to train for 300 steps with an A100 GPU. |
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- It takes a few minutes to upload your trained model. |
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- You may want to try a small number of steps first, like 1, to see if everything works fine in your environment. |
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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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remove_gpu_after_training = gr.Checkbox( |
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label='Remove GPU 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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run_button = gr.Button('Start Training') |
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with gr.Box(): |
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gr.Markdown('Output message') |
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output_message = gr.Markdown() |
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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(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_model, |
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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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remove_gpu_after_training, |
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], |
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outputs=output_message) |
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return demo |
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if __name__ == '__main__': |
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hf_token = os.getenv('HF_TOKEN') |
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trainer = Trainer(hf_token) |
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demo = create_training_demo(trainer) |
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demo.queue(max_size=1).launch(share=False) |
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