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| #!/usr/bin/env python | |
| from __future__ import annotations | |
| import gradio as gr | |
| from constants import UploadTarget | |
| from inference import InferencePipeline | |
| from trainer import Trainer | |
| def create_training_demo(trainer: Trainer, | |
| pipe: InferencePipeline | None = None) -> gr.Blocks: | |
| with gr.Blocks() as demo: | |
| with gr.Row(): | |
| with gr.Column(): | |
| with gr.Box(): | |
| gr.Markdown('Training Data') | |
| instance_images = gr.Files(label='Instance images') | |
| instance_prompt = gr.Textbox(label='Instance prompt', | |
| max_lines=1) | |
| gr.Markdown(''' | |
| - Upload images of the style you are planning on training on. | |
| - For an instance prompt, use a unique, made up word to avoid collisions. | |
| ''') | |
| with gr.Box(): | |
| gr.Markdown('Output Model') | |
| output_model_name = gr.Text(label='Name of your model', | |
| max_lines=1) | |
| delete_existing_model = gr.Checkbox( | |
| label='Delete existing model of the same name', | |
| value=False) | |
| validation_prompt = gr.Text(label='Validation Prompt') | |
| with gr.Box(): | |
| gr.Markdown('Upload Settings') | |
| with gr.Row(): | |
| upload_to_hub = gr.Checkbox( | |
| label='Upload model to Hub', value=False) | |
| use_private_repo = gr.Checkbox(label='Private', | |
| value=False) | |
| delete_existing_repo = gr.Checkbox( | |
| label='Delete existing repo of the same name', | |
| value=False) | |
| upload_to = gr.Radio( | |
| label='Upload to', | |
| choices=[_.value for _ in UploadTarget], | |
| value=UploadTarget.PERSONAL_PROFILE.value) | |
| with gr.Box(): | |
| gr.Markdown('Training Parameters') | |
| with gr.Row(): | |
| base_model = gr.Text( | |
| label='Base Model', | |
| value='stabilityai/stable-diffusion-2-1-base', | |
| max_lines=1) | |
| resolution = gr.Dropdown(choices=['512', '768'], | |
| value='512', | |
| label='Resolution') | |
| num_training_steps = gr.Number( | |
| label='Number of Training Steps', value=1000, precision=0) | |
| learning_rate = gr.Number(label='Learning Rate', value=0.0001) | |
| gradient_accumulation = gr.Number( | |
| label='Number of Gradient Accumulation', | |
| value=1, | |
| precision=0) | |
| seed = gr.Slider(label='Seed', | |
| minimum=0, | |
| maximum=100000, | |
| step=1, | |
| value=0) | |
| fp16 = gr.Checkbox(label='FP16', value=True) | |
| use_8bit_adam = gr.Checkbox(label='Use 8bit Adam', value=True) | |
| checkpointing_steps = gr.Number(label='Checkpointing Steps', | |
| value=100, | |
| precision=0) | |
| use_wandb = gr.Checkbox(label='Use W&B', value=False) | |
| validation_epochs = gr.Number(label='Validation Epochs', | |
| value=100, | |
| precision=0) | |
| gr.Markdown(''' | |
| - It will take about 8 minutes to train for 1000 steps with a T4 GPU. | |
| - You may want to try a small number of steps first, like 1, to see if everything works fine in your environment. | |
| - You need to set the environment variable `WANDB_API_KEY` if you'd like to use W&B. See [W&B documentation](https://docs.wandb.ai/guides/track/advanced/environment-variables). | |
| - **Note:** Due to [this issue](https://github.com/huggingface/accelerate/issues/944), currently, training will not terminate properly if you use W&B. | |
| ''') | |
| # TODO currently disabled | |
| remove_gpu_after_training = gr.Checkbox( | |
| label='Remove GPU after training', value=False, interactive=False) | |
| run_button = gr.Button('Start Training') | |
| with gr.Box(): | |
| gr.Markdown('Output message') | |
| output_message = gr.Markdown() | |
| if pipe is not None: | |
| run_button.click(fn=pipe.clear) | |
| run_button.click(fn=trainer.run, | |
| inputs=[ | |
| instance_images, | |
| instance_prompt, | |
| output_model_name, | |
| delete_existing_model, | |
| validation_prompt, | |
| base_model, | |
| resolution, | |
| num_training_steps, | |
| learning_rate, | |
| gradient_accumulation, | |
| seed, | |
| fp16, | |
| use_8bit_adam, | |
| checkpointing_steps, | |
| use_wandb, | |
| validation_epochs, | |
| upload_to_hub, | |
| use_private_repo, | |
| delete_existing_repo, | |
| upload_to, | |
| ], | |
| outputs=output_message) | |
| return demo | |
| if __name__ == '__main__': | |
| trainer = Trainer() | |
| demo = create_training_demo(trainer) | |
| demo.queue(max_size=1).launch(share=False) | |