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Runtime error
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ddea0a0
1
Parent(s):
d1ca433
Upload 3 files
Browse files- app_inference.py +240 -0
- blora_utils.py +46 -0
- inf.py +121 -0
app_inference.py
ADDED
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1 |
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#!/usr/bin/env python
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from __future__ import annotations
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import os
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import random
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from typing import Tuple, Optional
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import gradio as gr
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from huggingface_hub import HfApi
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from inf import InferencePipeline
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SAMPLE_MODEL_IDS = [
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'lora-library/B-LoRA-teddybear',
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'lora-library/B-LoRA-bull',
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'lora-library/B-LoRA-wolf_plushie',
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'lora-library/B-LoRA-pen_sketch',
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'lora-library/B-LoRA-cartoon_line',
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'lora-library/B-LoRA-multi-dog2',
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]
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css = """
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body {
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font-size: 30px;
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}
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.gr-image {
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width: 512px;
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height: 512px;
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object-fit: contain;
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margin: auto;
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}
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.lora-column {
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display: flex;
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flex-direction: column;
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align-items: center; /* Center align content vertically in columns */
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justify-content: center; /* Center content horizontally in columns */
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}
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.gr-row {
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align-items: center;
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justify-content: center;
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margin-top: 5px;
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}
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"""
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def get_choices(hf_token):
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api = HfApi(token=hf_token)
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choices = [
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info.modelId for info in api.list_models(author='lora-library')
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]
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models_list = ['None'] + SAMPLE_MODEL_IDS + choices
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return models_list
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def get_image_from_card(card, model_id) -> Optional[str]:
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try:
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card_path = f"https://huggingface.co/{model_id}/resolve/main/"
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widget = card.data.get('widget')
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if widget is not None or len(widget) > 0:
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output = widget[0].get('output')
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if output is not None:
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url = output.get('url')
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if url is not None:
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return card_path + url
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return None
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except Exception:
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return None
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def demo_init():
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try:
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choices = get_choices(app.hf_token)
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content_blora = random.choice(SAMPLE_MODEL_IDS)
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style_blora = random.choice(SAMPLE_MODEL_IDS)
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content_blora_prompt, content_blora_image = app.load_model_info(content_blora)
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style_blora_prompt, style_blora_image = app.load_model_info(style_blora)
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content_lora_model_id = gr.update(choices=choices, value=content_blora)
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content_prompt = gr.update(value=content_blora_prompt)
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content_image = gr.update(value=content_blora_image)
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style_lora_model_id = gr.update(choices=choices, value=style_blora)
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style_prompt = gr.update(value=style_blora_prompt)
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style_image = gr.update(value=style_blora_image)
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prompt = gr.update(
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value=f'{content_blora_prompt} in {style_blora_prompt[0].lower() + style_blora_prompt[1:]} style')
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return content_lora_model_id, content_prompt, content_image, style_lora_model_id, style_prompt, style_image, prompt
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except Exception as e:
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raise type(e)(f'failed to demo_init, due to: {e}')
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def toggle_column(is_checked):
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try:
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return 'None' if is_checked else random.choice(SAMPLE_MODEL_IDS)
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except Exception as e:
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raise type(e)(f'failed to toggle_column, due to: {e}')
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class InferenceUtil:
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def __init__(self, hf_token: str | None):
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self.hf_token = hf_token
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def load_model_info(self, lora_model_id: str) -> Tuple[str, Optional[str]]:
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try:
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try:
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card = InferencePipeline.get_model_card(lora_model_id,
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self.hf_token)
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except Exception:
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return '', None
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instance_prompt = getattr(card.data, 'instance_prompt', '')
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image_url = get_image_from_card(card, lora_model_id)
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return instance_prompt, image_url
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except Exception as e:
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raise type(e)(f'failed to load_model_info, due to: {e}')
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def update_model_info(self, model_source: str):
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try:
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if model_source == 'None':
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return '', None
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else:
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model_info = self.load_model_info(model_source)
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new_prompt, new_image = model_info[0], model_info[1]
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return new_prompt, new_image
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except Exception as e:
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raise type(e)(f'failed to update_model_info, due to: {e}')
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def create_inference_demo(pipe, #: InferencePipeline,
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hf_token: str | None = None) -> gr.Blocks:
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with gr.Blocks(css=css) as demo:
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with gr.Row(elem_classes="gr-row"):
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with gr.Column():
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with gr.Group(elem_classes="lora-column"):
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gr.Markdown('## Content B-LoRA')
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content_checkbox = gr.Checkbox(label='Use Content Only', value=False)
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content_lora_model_id = gr.Dropdown(label='Model ID', choices=[])
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content_prompt = gr.Text(label='Content instance prompt', interactive=False, max_lines=1)
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content_image = gr.Image(label='Content Image', elem_classes="gr-image")
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with gr.Column():
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with gr.Group(elem_classes="lora-column"):
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gr.Markdown('## Style B-LoRA')
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style_checkbox = gr.Checkbox(label='Use Style Only', value=False)
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style_lora_model_id = gr.Dropdown(label='Model ID', choices=[])
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style_prompt = gr.Text(label='Style instance prompt', interactive=False, max_lines=1)
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style_image = gr.Image(label='Style Image', elem_classes="gr-image")
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with gr.Row(elem_classes="gr-row"):
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with gr.Column():
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with gr.Group():
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prompt = gr.Textbox(
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label='Prompt',
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max_lines=1,
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placeholder='Example: "A [c] in [s] style"'
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)
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result = gr.Image(label='Result')
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with gr.Accordion('Other Parameters', open=False, elem_classes="gr-accordion"):
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content_alpha = gr.Slider(label='Content B-LoRA alpha',
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minimum=0,
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maximum=2,
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step=0.05,
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value=1)
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style_alpha = gr.Slider(label='Style B-LoRA alpha',
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minimum=0,
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maximum=2,
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step=0.05,
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value=1)
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170 |
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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=8888)
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num_steps = gr.Slider(label='Number of Steps',
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minimum=0,
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maximum=100,
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step=1,
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value=50)
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180 |
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guidance_scale = gr.Slider(label='CFG Scale',
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minimum=0,
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182 |
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maximum=50,
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step=0.1,
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value=7.5)
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+
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run_button = gr.Button('Generate')
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187 |
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demo.load(demo_init, inputs=[],
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outputs=[content_lora_model_id, content_prompt, content_image, style_lora_model_id, style_prompt,
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style_image, prompt], queue=False, show_progress="hidden")
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content_lora_model_id.change(
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191 |
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fn=app.update_model_info,
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inputs=content_lora_model_id,
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outputs=[
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content_prompt,
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195 |
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content_image,
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196 |
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])
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style_lora_model_id.change(
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fn=app.update_model_info,
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inputs=style_lora_model_id,
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outputs=[
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style_prompt,
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style_image,
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])
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style_prompt.change(
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fn=lambda content_blora_prompt,
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style_blora_prompt: f'{content_blora_prompt} in {style_blora_prompt[0].lower() + style_blora_prompt[1:]} style' if style_blora_prompt else content_blora_prompt,
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inputs=[content_prompt, style_prompt],
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outputs=prompt,
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)
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content_prompt.change(
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fn=lambda content_blora_prompt,
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style_blora_prompt: f'{content_blora_prompt} in {style_blora_prompt[0].lower() + style_blora_prompt[1:]} style' if content_blora_prompt else style_blora_prompt,
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inputs=[content_prompt, style_prompt],
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outputs=prompt,
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)
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content_checkbox.change(toggle_column, inputs=[content_checkbox],
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outputs=[style_lora_model_id])
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style_checkbox.change(toggle_column, inputs=[style_checkbox],
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outputs=[content_lora_model_id])
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inputs = [
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content_lora_model_id,
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style_lora_model_id,
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prompt,
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content_alpha,
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style_alpha,
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seed,
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num_steps,
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guidance_scale,
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]
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prompt.submit(fn=pipe.run, inputs=inputs, outputs=result)
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231 |
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run_button.click(fn=pipe.run, inputs=inputs, outputs=result)
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return demo
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233 |
+
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+
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if __name__ == '__main__':
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+
hf_token = os.getenv('HF_TOKEN')
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pipe = InferencePipeline(hf_token)
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app = InferenceUtil(hf_token)
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239 |
+
demo = create_inference_demo(pipe, hf_token)
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demo.queue(max_size=10).launch(share=False)
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blora_utils.py
ADDED
@@ -0,0 +1,46 @@
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from typing import Optional
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BLOCKS = {
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'content': ['unet.up_blocks.0.attentions.0'],
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'style': ['unet.up_blocks.0.attentions.1'],
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}
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9 |
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def is_belong_to_blocks(key, blocks):
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try:
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for g in blocks:
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if g in key:
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return True
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return False
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except Exception as e:
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raise type(e)(f'failed to is_belong_to_block, due to: {e}')
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17 |
+
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18 |
+
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19 |
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def filter_lora(state_dict, blocks_):
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20 |
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try:
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return {k: v for k, v in state_dict.items() if is_belong_to_blocks(k, blocks_)}
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22 |
+
except Exception as e:
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23 |
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raise type(e)(f'failed to filter_lora, due to: {e}')
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24 |
+
|
25 |
+
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26 |
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def scale_lora(state_dict, alpha):
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27 |
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try:
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28 |
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return {k: v * alpha for k, v in state_dict.items()}
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29 |
+
except Exception as e:
|
30 |
+
raise type(e)(f'failed to scale_lora, due to: {e}')
|
31 |
+
|
32 |
+
|
33 |
+
def get_target_modules(unet, blocks=None):
|
34 |
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try:
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35 |
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if not blocks:
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36 |
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blocks = [('.').join(blk.split('.')[1:]) for blk in BLOCKS['content'] + BLOCKS['style']]
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37 |
+
|
38 |
+
attns = [attn_processor_name.rsplit('.', 1)[0] for attn_processor_name, _ in unet.attn_processors.items() if
|
39 |
+
is_belong_to_blocks(attn_processor_name, blocks)]
|
40 |
+
|
41 |
+
target_modules = [f'{attn}.{mat}' for mat in ["to_k", "to_q", "to_v", "to_out.0"] for attn in attns]
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42 |
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return target_modules
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43 |
+
except Exception as e:
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44 |
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raise type(e)(f'failed to get_target_modules, due to: {e}')
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45 |
+
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46 |
+
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inf.py
ADDED
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|
1 |
+
from __future__ import annotations
|
2 |
+
|
3 |
+
import gc
|
4 |
+
import pathlib
|
5 |
+
|
6 |
+
import gradio as gr
|
7 |
+
import PIL.Image
|
8 |
+
import torch
|
9 |
+
from diffusers import StableDiffusionXLPipeline
|
10 |
+
from huggingface_hub import ModelCard
|
11 |
+
|
12 |
+
from blora_utils import BLOCKS, filter_lora, scale_lora
|
13 |
+
|
14 |
+
|
15 |
+
class InferencePipeline:
|
16 |
+
def __init__(self, hf_token: str | None = None):
|
17 |
+
self.hf_token = hf_token
|
18 |
+
self.base_model_id = "stabilityai/stable-diffusion-xl-base-1.0"
|
19 |
+
self.device = torch.device(
|
20 |
+
'cuda:0' if torch.cuda.is_available() else 'cpu')
|
21 |
+
if self.device.type == 'cpu':
|
22 |
+
self.pipe = StableDiffusionXLPipeline.from_pretrained(
|
23 |
+
self.base_model_id, use_auth_token=self.hf_token, cache_dir='./cache')
|
24 |
+
else:
|
25 |
+
self.pipe = StableDiffusionXLPipeline.from_pretrained(
|
26 |
+
self.base_model_id,
|
27 |
+
torch_dtype=torch.float16,
|
28 |
+
use_auth_token=self.hf_token)
|
29 |
+
self.pipe = self.pipe.to(self.device)
|
30 |
+
self.content_lora_model_id = None
|
31 |
+
self.style_lora_model_id = None
|
32 |
+
|
33 |
+
def clear(self) -> None:
|
34 |
+
self.content_lora_model_id = None
|
35 |
+
self.style_lora_model_id = None
|
36 |
+
del self.pipe
|
37 |
+
self.pipe = None
|
38 |
+
torch.cuda.empty_cache()
|
39 |
+
gc.collect()
|
40 |
+
|
41 |
+
def load_b_lora_to_unet(self, content_lora_model_id: str, style_lora_model_id: str, content_alpha: float,
|
42 |
+
style_alpha: float) -> None:
|
43 |
+
try:
|
44 |
+
# Get Content B-LoRA SD
|
45 |
+
if content_lora_model_id:
|
46 |
+
content_B_LoRA_sd, _ = self.pipe.lora_state_dict(content_lora_model_id, use_auth_token=self.hf_token)
|
47 |
+
content_B_LoRA = filter_lora(content_B_LoRA_sd, BLOCKS['content'])
|
48 |
+
content_B_LoRA = scale_lora(content_B_LoRA, content_alpha)
|
49 |
+
else:
|
50 |
+
content_B_LoRA = {}
|
51 |
+
|
52 |
+
# Get Style B-LoRA SD
|
53 |
+
if style_lora_model_id:
|
54 |
+
style_B_LoRA_sd, _ = self.pipe.lora_state_dict(style_lora_model_id, use_auth_token=self.hf_token)
|
55 |
+
style_B_LoRA = filter_lora(style_B_LoRA_sd, BLOCKS['style'])
|
56 |
+
style_B_LoRA = scale_lora(style_B_LoRA, style_alpha)
|
57 |
+
else:
|
58 |
+
style_B_LoRA = {}
|
59 |
+
|
60 |
+
# Merge B-LoRAs SD
|
61 |
+
res_lora = {**content_B_LoRA, **style_B_LoRA}
|
62 |
+
|
63 |
+
# Load
|
64 |
+
self.pipe.load_lora_into_unet(res_lora, None, self.pipe.unet)
|
65 |
+
except Exception as e:
|
66 |
+
raise type(e)(f'failed to load_b_lora_to_unet, due to: {e}')
|
67 |
+
|
68 |
+
@staticmethod
|
69 |
+
def check_if_model_is_local(lora_model_id: str) -> bool:
|
70 |
+
return pathlib.Path(lora_model_id).exists()
|
71 |
+
|
72 |
+
@staticmethod
|
73 |
+
def get_model_card(model_id: str,
|
74 |
+
hf_token: str | None = None) -> ModelCard:
|
75 |
+
if InferencePipeline.check_if_model_is_local(model_id):
|
76 |
+
card_path = (pathlib.Path(model_id) / 'README.md').as_posix()
|
77 |
+
else:
|
78 |
+
card_path = model_id
|
79 |
+
return ModelCard.load(card_path, token=hf_token)
|
80 |
+
|
81 |
+
@staticmethod
|
82 |
+
def get_base_model_info(lora_model_id: str,
|
83 |
+
hf_token: str | None = None) -> str:
|
84 |
+
card = InferencePipeline.get_model_card(lora_model_id, hf_token)
|
85 |
+
return card.data.base_model
|
86 |
+
|
87 |
+
def load_pipe(self, content_lora_model_id: str, style_lora_model_id: str, content_alpha: float,
|
88 |
+
style_alpha: float) -> None:
|
89 |
+
if content_lora_model_id == self.content_lora_model_id and style_lora_model_id == self.style_lora_model_id:
|
90 |
+
return
|
91 |
+
self.pipe.unload_lora_weights()
|
92 |
+
|
93 |
+
self.load_b_lora_to_unet(content_lora_model_id, style_lora_model_id, content_alpha, style_alpha)
|
94 |
+
|
95 |
+
self.content_lora_model_id = content_lora_model_id
|
96 |
+
self.style_lora_model_id = style_lora_model_id
|
97 |
+
|
98 |
+
def run(
|
99 |
+
self,
|
100 |
+
content_lora_model_id: str,
|
101 |
+
style_lora_model_id: str,
|
102 |
+
prompt: str,
|
103 |
+
content_alpha: float,
|
104 |
+
style_alpha: float,
|
105 |
+
seed: int,
|
106 |
+
n_steps: int,
|
107 |
+
guidance_scale: float,
|
108 |
+
) -> PIL.Image.Image:
|
109 |
+
if not torch.cuda.is_available():
|
110 |
+
raise gr.Error('CUDA is not available.')
|
111 |
+
|
112 |
+
self.load_pipe(content_lora_model_id, style_lora_model_id, content_alpha, style_alpha)
|
113 |
+
|
114 |
+
generator = torch.Generator(device=self.device).manual_seed(seed)
|
115 |
+
out = self.pipe(
|
116 |
+
prompt,
|
117 |
+
num_inference_steps=n_steps,
|
118 |
+
guidance_scale=guidance_scale,
|
119 |
+
generator=generator,
|
120 |
+
) # type: ignore
|
121 |
+
return out.images[0]
|