#!/usr/bin/env python """Unofficial demo app for https://github.com/cloneofsimo/lora. The code in this repo is partly adapted from the following repository: https://huggingface.co/spaces/multimodalart/dreambooth-training/tree/a00184917aa273c6d8adab08d5deb9b39b997938 The license of the original code is MIT, which is specified in the README.md. """ from __future__ import annotations import os import pathlib import gradio as gr import torch from inference import InferencePipeline from trainer import Trainer from uploader import upload TITLE = '# LoRA + StableDiffusion Training UI' DESCRIPTION = 'This is an unofficial demo for [https://github.com/cloneofsimo/lora](https://github.com/cloneofsimo/lora).' USAGE_INFO = '''You can train and download models in the "Training" tab, and test them in the "Test" tab. You can also test the pretrained models in the [original repo](https://github.com/cloneofsimo/lora). Models with names starting with "lora/" are the pretrained models and the ones with names starting with "results/" are your trained models. After training, you can press "Reload Weight List" button to load your trained model names. Note that your trained models will be deleted when the second training is started. ''' SPACE_ID = os.getenv('SPACE_ID', 'hysts/LoRA-SD-training') SHARED_UI_WARNING = f'''# Attention - This Space doesn't work in this shared UI. You can duplicate and use it with a paid private T4 GPU.
Duplicate Space
''' CUDA_NOT_AVAILABLE_WARNING = '''# Attention - CUDA is not available in this environment. You can assign a GPU from the Settings tab if you are running this on HF Spaces. T4 small is sufficient to run this demo. ''' def show_warning(warning_text: str) -> gr.Blocks: with gr.Blocks() as demo: with gr.Box(): gr.Markdown(warning_text) return demo def update_output_files() -> dict: paths = sorted(pathlib.Path('results').glob('*.pt')) paths = [path.as_posix() for path in paths] # type: ignore return gr.update(value=paths or None) def create_training_demo(trainer: Trainer, pipe: InferencePipeline) -> gr.Blocks: with gr.Blocks() as demo: base_model = gr.Dropdown( choices=['stabilityai/stable-diffusion-2-1-base'], value='stabilityai/stable-diffusion-2-1-base', label='Base Model', visible=False) resolution = gr.Dropdown(choices=['512'], value='512', label='Resolution', visible=False) with gr.Row(): with gr.Box(): gr.Markdown('Training Data') concept_images = gr.Files(label='Images for your concept') concept_prompt = gr.Textbox(label='Concept Prompt', max_lines=1) gr.Markdown(''' - Upload images of the style you are planning on training on. - For a concept prompt, use a unique, made up word to avoid collisions. ''') with gr.Box(): gr.Markdown('Training Parameters') num_training_steps = gr.Number( label='Number of Training Steps', value=1000, precision=0) learning_rate = gr.Number(label='Learning Rate', value=0.0001) train_text_encoder = gr.Checkbox(label='Train Text Encoder', value=True) learning_rate_text = gr.Number( label='Learning Rate for Text Encoder', value=0.00005) gradient_accumulation = gr.Number( label='Number of Gradient Accumulation', value=1, precision=0) fp16 = gr.Checkbox(label='FP16', value=True) use_8bit_adam = gr.Checkbox(label='Use 8bit Adam', value=True) 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. ''') run_button = gr.Button('Start Training') with gr.Box(): with gr.Row(): check_status_button = gr.Button('Check Training Status') with gr.Column(): with gr.Box(): gr.Markdown('Message') training_status = gr.Markdown() output_files = gr.Files(label='Trained Weight Files') run_button.click(fn=pipe.clear) run_button.click(fn=trainer.run, inputs=[ base_model, resolution, concept_images, concept_prompt, num_training_steps, learning_rate, train_text_encoder, learning_rate_text, gradient_accumulation, fp16, use_8bit_adam, ], outputs=[ training_status, output_files, ], queue=False) check_status_button.click(fn=trainer.check_if_running, inputs=None, outputs=training_status, queue=False) check_status_button.click(fn=update_output_files, inputs=None, outputs=output_files, queue=False) return demo def find_weight_files() -> list[str]: curr_dir = pathlib.Path(__file__).parent paths = sorted(curr_dir.rglob('*.pt')) paths = [path for path in paths if not path.stem.endswith('.text_encoder')] return [path.relative_to(curr_dir).as_posix() for path in paths] def reload_lora_weight_list() -> dict: return gr.update(choices=find_weight_files()) def create_inference_demo(pipe: InferencePipeline) -> gr.Blocks: with gr.Blocks() as demo: with gr.Row(): with gr.Column(): base_model = gr.Dropdown( choices=['stabilityai/stable-diffusion-2-1-base'], value='stabilityai/stable-diffusion-2-1-base', label='Base Model', visible=False) reload_button = gr.Button('Reload Weight List') lora_weight_name = gr.Dropdown(choices=find_weight_files(), value='lora/lora_disney.pt', label='LoRA Weight File') prompt = gr.Textbox( label='Prompt', max_lines=1, placeholder='Example: "style of sks, baby lion"') alpha = gr.Slider(label='Alpha', minimum=0, maximum=2, step=0.05, value=1) alpha_for_text = gr.Slider(label='Alpha for Text Encoder', minimum=0, maximum=2, step=0.05, value=1) seed = gr.Slider(label='Seed', minimum=0, maximum=100000, step=1, value=1) with gr.Accordion('Other Parameters', open=False): num_steps = gr.Slider(label='Number of Steps', minimum=0, maximum=100, step=1, value=50) guidance_scale = gr.Slider(label='CFG Scale', minimum=0, maximum=50, step=0.1, value=7) run_button = gr.Button('Generate') gr.Markdown(''' - The pretrained models for "disney", "illust" and "pop" are trained with the concept prompt "style of sks". - The pretrained model for "kiriko" is trained with the concept prompt "game character bnha". For this model, the text encoder is also trained. ''') with gr.Column(): result = gr.Image(label='Result') reload_button.click(fn=reload_lora_weight_list, inputs=None, outputs=lora_weight_name) prompt.submit(fn=pipe.run, inputs=[ base_model, lora_weight_name, prompt, alpha, alpha_for_text, seed, num_steps, guidance_scale, ], outputs=result, queue=False) run_button.click(fn=pipe.run, inputs=[ base_model, lora_weight_name, prompt, alpha, alpha_for_text, seed, num_steps, guidance_scale, ], outputs=result, queue=False) return demo def create_upload_demo() -> gr.Blocks: with gr.Blocks() as demo: model_name = gr.Textbox(label='Model Name') hf_token = gr.Textbox( label='Hugging Face Token (with write permission)') upload_button = gr.Button('Upload') with gr.Box(): gr.Markdown('Message') result = gr.Markdown() gr.Markdown(''' - You can upload your trained model to your private Model repo (i.e. https://huggingface.co/{your_username}/{model_name}). - You can find your Hugging Face token [here](https://huggingface.co/settings/tokens). ''') upload_button.click(fn=upload, inputs=[model_name, hf_token], outputs=result) return demo pipe = InferencePipeline() trainer = Trainer() with gr.Blocks(css='style.css') as demo: if os.getenv('IS_SHARED_UI'): show_warning(SHARED_UI_WARNING) if not torch.cuda.is_available(): show_warning(CUDA_NOT_AVAILABLE_WARNING) gr.Markdown(TITLE) gr.Markdown(DESCRIPTION) with gr.Tabs(): with gr.TabItem('Training'): create_training_demo(trainer, pipe) with gr.TabItem('Test'): create_inference_demo(pipe) with gr.TabItem('Upload'): create_upload_demo() with gr.Accordion('Usage', open=False): gr.Markdown(USAGE_INFO) demo.queue(default_enabled=False).launch(share=False)