Spaces:
Sleeping
Sleeping
File size: 12,475 Bytes
b083a19 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 |
#!/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).'
ORIGINAL_SPACE_ID = 'hysts/LoRA-SD-training'
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>
'''
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.
- 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)
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('''
- Models with names starting with "lora/" are the pretrained models provided in the [original repo](https://github.com/cloneofsimo/lora), 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.
- 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('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)
|