app.py

import spaces
import gradio as gr
import os
import numpy as np
import random
from huggingface_hub import login, ModelCard
import torch
from diffusers import StableDiffusionXLPipeline, AutoencoderKL
from blora_utils import BLOCKS, filter_lora, scale_lora

hf_token = os.environ.get("YOUR_HF_TOKEN_WITH_READ_PERMISSION")
login(token=hf_token)

MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 2048

SAMPLE_MODEL_IDS = [
    'lora-library/B-LoRA-teddybear',
    'lora-library/B-LoRA-bull',
    'lora-library/B-LoRA-wolf_plushie',
    'lora-library/B-LoRA-pen_sketch',
    'lora-library/B-LoRA-cartoon_line',
    'lora-library/B-LoRA-child',
    'lora-library/B-LoRA-vase',
    'lora-library/B-LoRA-scary_mug',
    'lora-library/B-LoRA-statue',
    'lora-library/B-LoRA-colorful_teapot',
    'lora-library/B-LoRA-grey_sloth_plushie',
    'lora-library/B-LoRA-teapot',
    'lora-library/B-LoRA-backpack_dog',
    'lora-library/B-LoRA-buddha',
    'lora-library/B-LoRA-dog6',
    'lora-library/B-LoRA-poop_emoji',
    'lora-library/B-LoRA-pot',
    'lora-library/B-LoRA-fat_bird',
    'lora-library/B-LoRA-elephant',
    'lora-library/B-LoRA-metal_bird',
    'lora-library/B-LoRA-cat',
    'lora-library/B-LoRA-dog2',
    'lora-library/B-LoRA-drawing1',
    'lora-library/B-LoRA-village_oil',
    'lora-library/B-LoRA-watercolor',
    'lora-library/B-LoRA-house_3d',
    'lora-library/B-LoRA-ink_sketch',
    'lora-library/B-LoRA-drawing3',
    'lora-library/B-LoRA-crayon_drawing',
    'lora-library/B-LoRA-kiss',
    'lora-library/B-LoRA-drawing4',
    'lora-library/B-LoRA-working_cartoon',
    'lora-library/B-LoRA-painting',
    'lora-library/B-LoRA-drawing2'
    'lora-library/B-LoRA-multi-dog2',
]

vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipeline = StableDiffusionXLPipeline.from_pretrained(
        "stabilityai/stable-diffusion-xl-base-1.0",
        vae=vae,
        torch_dtype=torch.float16,
    ).to("cuda")

def load_b_lora_to_unet(pipe, content_lora_model_id: str = '', style_lora_model_id: str = '', content_alpha: float = 1.,
                            style_alpha: float = 1.) -> None:
        try:
            # Get Content B-LoRA SD
            if content_lora_model_id:
                content_B_LoRA_sd, _ = pipe.lora_state_dict(content_lora_model_id, use_auth_token=True)
                content_B_LoRA = filter_lora(content_B_LoRA_sd, BLOCKS['content'])
                content_B_LoRA = scale_lora(content_B_LoRA, content_alpha)
            else:
                content_B_LoRA = {}

            # Get Style B-LoRA SD
            if style_lora_model_id:
                style_B_LoRA_sd, _ = pipe.lora_state_dict(style_lora_model_id, use_auth_token=True)
                style_B_LoRA = filter_lora(style_B_LoRA_sd, BLOCKS['style'])
                style_B_LoRA = scale_lora(style_B_LoRA, style_alpha)
            else:
                style_B_LoRA = {}

            # Merge B-LoRAs SD
            res_lora = {**content_B_LoRA, **style_B_LoRA}

            # Load
            pipe.load_lora_into_unet(res_lora, None, pipe.unet)
        except Exception as e:
            raise type(e)(f'failed to load_b_lora_to_unet, due to: {e}')

def load_b_loras(content_b_lora, style_b_lora):
    
    if content_b_lora != "" and content_b_lora is not None:
        # Get instance_prompt a.k.a trigger word
        content_model_card = ModelCard.load(content_b_lora)
        content_model_repo_data = content_model_card.data.to_dict()
        content_model_instance_prompt = content_model_repo_data.get("instance_prompt")
    else:
        content_model_instance_prompt = ''

    if style_b_lora != "" and style_b_lora is not None:
        # Get instance_prompt a.k.a trigger word
        style_model_card = ModelCard.load(style_b_lora)
        style_model_repo_data = style_model_card.data.to_dict()
        style_model_instance_prompt = style_model_repo_data.get("instance_prompt")
        style_model_instance_prompt = f"in {style_model_instance_prompt} style"
    else:
        style_model_instance_prompt = ''

    prepared_prompt = f"{content_model_instance_prompt} {style_model_instance_prompt}"
    
    return prepared_prompt

@spaces.GPU()
def main(content_b_lora, style_b_lora, prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps, progress=gr.Progress(track_tqdm=True)):

    if randomize_seed:
        seed = random.randint(0, MAX_SEED)
        
    generator = torch.Generator().manual_seed(seed)

    if content_b_lora is None:
        content_B_LoRA_path = ''
    else:
        content_B_LoRA_path = content_b_lora

    if style_b_lora is None:
        style_B_LoRA_path = ''
    else:
        style_B_LoRA_path = style_b_lora
    
    content_alpha,style_alpha = 1,1.1

    load_b_lora_to_unet(pipeline, content_B_LoRA_path, style_B_LoRA_path, content_alpha, style_alpha)
    prompt = prompt
    image = pipeline(
        prompt,
        generator=generator, 
        num_images_per_prompt=1,
        width = width, 
        height = height,
    ).images[0]
    
    pipeline.unload_lora_weights()
    
    return image

css="""
#col-container {
    margin: 0 auto;
    max-width: 520px;
}
"""

if torch.cuda.is_available():
    power_device = "GPU"
else:
    power_device = "CPU"

with gr.Blocks(css=css) as demo:
    
    with gr.Column(elem_id="col-container"):
        gr.Markdown(f"""
        # B-LoRas Inference
        Currently running on {power_device}.
        """)

        with gr.Row():    
            content_b_lora = gr.Dropdown(
                label="B-LoRa for content", 
                allow_custom_value=True,
                choices=SAMPLE_MODEL_IDS
            )
            style_b_lora = gr.Dropdown(
                label="B-LoRa for style", 
                allow_custom_value=True,
                choices=SAMPLE_MODEL_IDS
            )
        
        with gr.Column():
            load_b_loras_btn = gr.Button("load models")
        
        with gr.Row():
            
            prompt = gr.Text(
                label="Prompt",
                show_label=False,
                max_lines=1,
                placeholder="Enter your prompt",
                container=False,
            )
            
            run_button = gr.Button("Run", scale=0)
        
        result = gr.Image(label="Result", show_label=False)

        with gr.Accordion("Advanced Settings", open=False):
            
            negative_prompt = gr.Text(
                label="Negative prompt",
                max_lines=1,
                placeholder="Enter a negative prompt",
                visible=False,
            )
            
            seed = gr.Slider(
                label="Seed",
                minimum=0,
                maximum=MAX_SEED,
                step=1,
                value=0,
            )
            
            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
            
            with gr.Row():
                
                width = gr.Slider(
                    label="Width",
                    minimum=256,
                    maximum=MAX_IMAGE_SIZE,
                    step=32,
                    value=1024,
                )
                
                height = gr.Slider(
                    label="Height",
                    minimum=256,
                    maximum=MAX_IMAGE_SIZE,
                    step=32,
                    value=1024,
                )
            
            with gr.Row():
                
                guidance_scale = gr.Slider(
                    label="Guidance scale",
                    minimum=0.0,
                    maximum=10.0,
                    step=0.1,
                    value=0.0,
                )
                
                num_inference_steps = gr.Slider(
                    label="Number of inference steps",
                    minimum=1,
                    maximum=50,
                    step=1,
                    value=50,
                )

    load_b_loras_btn.click(
        fn = load_b_loras,
        inputs = [content_b_lora, style_b_lora],
        outputs = [prompt]
    )
    
    run_button.click(
        fn = main,
        inputs = [content_b_lora, style_b_lora, prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
        outputs = [result]
    )

demo.queue().launch()