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| #!/usr/bin/env python | |
| """Demo app for https://github.com/adobe-research/custom-diffusion. | |
| The code in this repo is partly adapted from the following repository: | |
| https://huggingface.co/spaces/hysts/LoRA-SD-training | |
| """ | |
| from __future__ import annotations | |
| import sys | |
| import os | |
| import pathlib | |
| import gradio as gr | |
| import torch | |
| from inference import InferencePipeline | |
| from trainer import Trainer | |
| from uploader import upload | |
| TITLE = '# Custom Diffusion + StableDiffusion Training UI' | |
| DESCRIPTION = '''This is a demo for [https://github.com/adobe-research/custom-diffusion](https://github.com/adobe-research/custom-diffusion). | |
| It is recommended to upgrade to GPU in Settings after duplicating this space to use it. | |
| <a href="https://huggingface.co/spaces/nupurkmr9/custom-diffusion?duplicate=true"><img src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a> | |
| ''' | |
| DETAILDESCRIPTION=''' | |
| Custom Diffusion allows you to fine-tune text-to-image diffusion models, such as Stable Diffusion, given a few images of a new concept (~4-20). | |
| We fine-tune only a subset of model parameters, namely key and value projection matrices, in the cross-attention layers and the modifier token used to represent the object. | |
| This also reduces the extra storage for each additional concept to 75MB. | |
| Our method further allows you to use a combination of concepts. Demo for multiple concepts will be added soon. | |
| <center> | |
| <img src="https://huggingface.co/spaces/nupurkmr9/custom-diffusion/resolve/main/method.jpg" width="600" align="center" > | |
| </center> | |
| ''' | |
| ORIGINAL_SPACE_ID = 'nupurkmr9/custom-diffusion' | |
| SPACE_ID = os.getenv('SPACE_ID', ORIGINAL_SPACE_ID) | |
| 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. | |
| <center><a class="duplicate-button" style="display:inline-block" target="_blank" href="https://huggingface.co/spaces/{SPACE_ID}?duplicate=true"><img src="https://img.shields.io/badge/-Duplicate%20Space-blue?labelColor=white&style=flat&logo=data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAYAAAAf8/9hAAAAAXNSR0IArs4c6QAAAP5JREFUOE+lk7FqAkEURY+ltunEgFXS2sZGIbXfEPdLlnxJyDdYB62sbbUKpLbVNhyYFzbrrA74YJlh9r079973psed0cvUD4A+4HoCjsA85X0Dfn/RBLBgBDxnQPfAEJgBY+A9gALA4tcbamSzS4xq4FOQAJgCDwV2CPKV8tZAJcAjMMkUe1vX+U+SMhfAJEHasQIWmXNN3abzDwHUrgcRGmYcgKe0bxrblHEB4E/pndMazNpSZGcsZdBlYJcEL9Afo75molJyM2FxmPgmgPqlWNLGfwZGG6UiyEvLzHYDmoPkDDiNm9JR9uboiONcBXrpY1qmgs21x1QwyZcpvxt9NS09PlsPAAAAAElFTkSuQmCC&logoWidth=14" alt="Duplicate Space"></a></center> | |
| ''' | |
| if os.getenv('SYSTEM') == 'spaces' and SPACE_ID != ORIGINAL_SPACE_ID: | |
| SETTINGS = f'<a href="https://huggingface.co/spaces/{SPACE_ID}/settings">Settings</a>' | |
| else: | |
| SETTINGS = 'Settings' | |
| CUDA_NOT_AVAILABLE_WARNING = f'''# Attention - Running on CPU. | |
| <center> | |
| You can assign a GPU in the {SETTINGS} tab if you are running this on HF Spaces. | |
| "T4 small" is sufficient to run this demo. | |
| </center> | |
| ''' | |
| os.system("git clone https://github.com/adobe-research/custom-diffusion") | |
| sys.path.append("custom-diffusion") | |
| 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', 'CompVis/stable-diffusion-v1-4'], | |
| value='CompVis/stable-diffusion-v1-4', | |
| label='Base Model', | |
| visible=True) | |
| resolution = gr.Dropdown(choices=['512', '768'], | |
| value='512', | |
| label='Resolution', | |
| visible=True) | |
| with gr.Row(): | |
| with gr.Box(): | |
| gr.Markdown('Training Data') | |
| concept_images = gr.Files(label='Images for your concept') | |
| with gr.Row(): | |
| class_prompt = gr.Textbox(label='Class Prompt', | |
| max_lines=1, placeholder='Example: "cat"') | |
| with gr.Column(): | |
| modifier_token = gr.Checkbox(label='modifier token', | |
| value=True) | |
| train_text_encoder = gr.Checkbox(label='Train Text Encoder', | |
| value=False) | |
| concept_prompt = gr.Textbox(label='Concept Prompt', | |
| max_lines=1, placeholder='Example: "photo of a \<new1\> cat"') | |
| gr.Markdown(''' | |
| - We use "\<new1\>" modifier token in front of the concept, e.g., "\<new1\> cat". By default modifier_token is enabled. | |
| - If "Train Text Encoder", disable "modifier token" and use any unique text to describe the concept e.g. "ktn cat". | |
| - For a new concept an e.g. concept prompt is "photo of a \<new1\> cat" and "cat" for class prompt. | |
| - For a style concept, use "painting in the style of \<new1\> art" for concept prompt and "art" for class prompt. | |
| - Class prompt should be the object category. | |
| ''') | |
| 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=0.00001) | |
| batch_size = gr.Number( | |
| label='batch_size', value=1, precision=0) | |
| with gr.Row(): | |
| use_8bit_adam = gr.Checkbox(label='Use 8bit Adam', value=True) | |
| gradient_checkpointing = gr.Checkbox(label='Enable gradient checkpointing', value=False) | |
| with gr.Accordion('Other Parameters', open=False): | |
| gradient_accumulation = gr.Number( | |
| label='Number of Gradient Accumulation', | |
| value=1, | |
| precision=0) | |
| gen_images = gr.Checkbox(label='Generated images as regularization', | |
| value=False) | |
| gr.Markdown(''' | |
| - It will take about ~10 minutes to train for 1000 steps and ~21GB on a 3090 GPU. | |
| - Our results in the paper are trained with batch-size 4 (8 including class regularization samples). | |
| - Enable gradient checkpointing for lower memory requirements (~14GB) at the expense of slower backward pass. | |
| - Note that your trained models will be deleted when the second training is started. You can upload your trained model in the "Upload" tab. | |
| ''') | |
| 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, | |
| inputs=None, | |
| outputs=None,) | |
| run_button.click(fn=trainer.run, | |
| inputs=[ | |
| base_model, | |
| resolution, | |
| concept_images, | |
| concept_prompt, | |
| class_prompt, | |
| num_training_steps, | |
| learning_rate, | |
| train_text_encoder, | |
| modifier_token, | |
| gradient_accumulation, | |
| batch_size, | |
| use_8bit_adam, | |
| gradient_checkpointing, | |
| gen_images | |
| ], | |
| 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('*.bin')) | |
| paths = [path for path in paths if '.lfs' not in path.name] | |
| return [path.relative_to(curr_dir).as_posix() for path in paths] | |
| def reload_custom_diffusion_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', 'CompVis/stable-diffusion-v1-4'], | |
| value='CompVis/stable-diffusion-v1-4', | |
| label='Base Model', | |
| visible=True) | |
| resolution = gr.Dropdown(choices=[512, 768], | |
| value=512, | |
| label='Resolution', | |
| visible=True) | |
| reload_button = gr.Button('Reload Weight List') | |
| weight_name = gr.Dropdown(choices=find_weight_files(), | |
| value='custom-diffusion-models/cat.bin', | |
| label='Custom Diffusion Weight File') | |
| prompt = gr.Textbox( | |
| label='Prompt', | |
| max_lines=1, | |
| placeholder='Example: "\<new1\> cat in outer space"') | |
| seed = gr.Slider(label='Seed', | |
| minimum=0, | |
| maximum=100000, | |
| step=1, | |
| value=42) | |
| with gr.Accordion('Other Parameters', open=False): | |
| num_steps = gr.Slider(label='Number of Steps', | |
| minimum=0, | |
| maximum=500, | |
| step=1, | |
| value=200) | |
| guidance_scale = gr.Slider(label='CFG Scale', | |
| minimum=0, | |
| maximum=50, | |
| step=0.1, | |
| value=6) | |
| eta = gr.Slider(label='DDIM eta', | |
| minimum=0, | |
| maximum=1., | |
| step=0.1, | |
| value=1.) | |
| batch_size = gr.Slider(label='Batch Size', | |
| minimum=0, | |
| maximum=10., | |
| step=1, | |
| value=2) | |
| run_button = gr.Button('Generate') | |
| gr.Markdown(''' | |
| - Models with names starting with "custom-diffusion-models/" are the pretrained models provided in the [original repo](https://github.com/adobe-research/custom-diffusion), and the ones with names starting with "results/delta.bin" are your trained models. | |
| - After training, you can press "Reload Weight List" button to load your trained model names. | |
| - Change default batch-size and steps for faster sampling. | |
| ''') | |
| with gr.Column(): | |
| result = gr.Image(label='Result') | |
| reload_button.click(fn=reload_custom_diffusion_weight_list, | |
| inputs=None, | |
| outputs=weight_name) | |
| prompt.submit(fn=pipe.run, | |
| inputs=[ | |
| base_model, | |
| weight_name, | |
| prompt, | |
| seed, | |
| num_steps, | |
| guidance_scale, | |
| eta, | |
| batch_size, | |
| resolution | |
| ], | |
| outputs=result, | |
| queue=False) | |
| run_button.click(fn=pipe.run, | |
| inputs=[ | |
| base_model, | |
| weight_name, | |
| prompt, | |
| seed, | |
| num_steps, | |
| guidance_scale, | |
| eta, | |
| batch_size, | |
| resolution | |
| ], | |
| 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) | |
| gr.Markdown(DETAILDESCRIPTION) | |
| with gr.Tabs(): | |
| with gr.TabItem('Train'): | |
| create_training_demo(trainer, pipe) | |
| with gr.TabItem('Test'): | |
| create_inference_demo(pipe) | |
| with gr.TabItem('Upload'): | |
| create_upload_demo() | |
| demo.queue(default_enabled=False).launch(share=False) | |