app.py

import gradio as gr
import numpy as np
import torch
from diffusers import StableDiffusionPipeline
from peft import PeftModel, LoraConfig
import os

def get_lora_sd_pipeline(
    ckpt_dir='./lora_logos', 
    base_model_name_or_path=None, 
    dtype=torch.float16, 
    adapter_name="default"
    ):
    unet_sub_dir = os.path.join(ckpt_dir, "unet")
    text_encoder_sub_dir = os.path.join(ckpt_dir, "text_encoder")
    
    if os.path.exists(text_encoder_sub_dir) and base_model_name_or_path is None:
        config = LoraConfig.from_pretrained(text_encoder_sub_dir)
        base_model_name_or_path = config.base_model_name_or_path
    
    if base_model_name_or_path is None:
        raise ValueError("Please specify the base model name or path")
    
    pipe = StableDiffusionPipeline.from_pretrained(base_model_name_or_path, torch_dtype=dtype)
    before_params = pipe.unet.parameters()
    pipe.unet = PeftModel.from_pretrained(pipe.unet, unet_sub_dir, adapter_name=adapter_name)
    pipe.fuse_lora(lora_scale=0.5)
    after_params = pipe.unet.parameters()
    print("Parameters changed:", any(torch.any(b != a) for b, a in zip(before_params, after_params)))
    
    if os.path.exists(text_encoder_sub_dir):
        pipe.text_encoder = PeftModel.from_pretrained(pipe.text_encoder, text_encoder_sub_dir, adapter_name=adapter_name)
    
    if dtype in (torch.float16, torch.bfloat16):
        pipe.unet.half()
        pipe.text_encoder.half()
    
    return pipe

def process_prompt(prompt, tokenizer, text_encoder, max_length=77):
    tokens = tokenizer(prompt, truncation=False, return_tensors="pt")["input_ids"]
    chunks = [tokens[:, i:i + max_length] for i in range(0, tokens.shape[1], max_length)]
    
    with torch.no_grad():
        embeds = [text_encoder(chunk.to(text_encoder.device))[0] for chunk in chunks]
    
    return torch.cat(embeds, dim=1)

def align_embeddings(prompt_embeds, negative_prompt_embeds):
    max_length = max(prompt_embeds.shape[1], negative_prompt_embeds.shape[1])
    return torch.nn.functional.pad(prompt_embeds, (0, 0, 0, max_length - prompt_embeds.shape[1])), \
           torch.nn.functional.pad(negative_prompt_embeds, (0, 0, 0, max_length - negative_prompt_embeds.shape[1]))

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model_id_default = "CompVis/stable-diffusion-v1-4"
torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32

pipe_default = get_lora_sd_pipeline(ckpt_dir='./lora_logos', base_model_name_or_path=model_id_default, dtype=torch_dtype).to(device)

MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 1024

def infer(
    prompt, 
    negative_prompt, 
    width=512, 
    height=512, 
    num_inference_steps=20, 
    model_id='CompVis/stable-diffusion-v1-4', 
    seed=42, 
    guidance_scale=7.0, 
    lora_scale=0.5
    ):
    generator = torch.Generator(device).manual_seed(seed)
    
    if model_id != model_id_default:
        pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch_dtype).to(device)
        prompt_embeds = process_prompt(prompt, pipe.tokenizer, pipe.text_encoder)
        negative_prompt_embeds = process_prompt(negative_prompt, pipe.tokenizer, pipe.text_encoder)
        prompt_embeds, negative_prompt_embeds = align_embeddings(prompt_embeds, negative_prompt_embeds)
    else:
        pipe = pipe_default
        prompt_embeds = process_prompt(prompt, pipe.tokenizer, pipe.text_encoder)
        negative_prompt_embeds = process_prompt(negative_prompt, pipe.tokenizer, pipe.text_encoder)
        prompt_embeds, negative_prompt_embeds = align_embeddings(prompt_embeds, negative_prompt_embeds)
        print(f"LoRA adapter loaded: {pipe.unet.active_adapters}")
        print(f"LoRA scale applied: {lora_scale}")
        pipe.fuse_lora(lora_scale=lora_scale)
    
    params = {
        'prompt_embeds': prompt_embeds,
        'negative_prompt_embeds': negative_prompt_embeds,
        'guidance_scale': guidance_scale,
        'num_inference_steps': num_inference_steps,
        'width': width,
        'height': height,
        'generator': generator,
    }
    
    return pipe(**params).images[0]

css = """
#col-container {
    margin: 0 auto;
    max-width: 640px;
}
"""

with gr.Blocks(css=css) as demo:
    with gr.Column(elem_id="col-container"):
        gr.Markdown(" # DEMO Text-to-Image")
        
        with gr.Row():
            model_id = gr.Textbox(
                label="Model ID",
                max_lines=1,
                placeholder="Enter model id like 'CompVis/stable-diffusion-v1-4'",
                value=model_id_default
            )

        prompt = gr.Textbox(
            label="Prompt",
            max_lines=1,
            placeholder="Enter your prompt",
        )

        negative_prompt = gr.Textbox(
            label="Negative prompt",
            max_lines=1,
            placeholder="Enter a negative prompt",
        )

        with gr.Row():
            seed = gr.Number(
                label="Seed",
                minimum=0,
                maximum=MAX_SEED,
                step=1,
                value=42,
            )

        with gr.Row():
            guidance_scale = gr.Slider(
                label="Guidance scale",
                minimum=0.0,
                maximum=10.0,
                step=0.1,
                value=7.0,
            )

        with gr.Row():
            lora_scale = gr.Slider(
                label="LoRA scale",
                minimum=0.0,
                maximum=1.0,
                step=0.1,
                value=0.5,
            )

        with gr.Row():
            num_inference_steps = gr.Slider(
                label="Number of inference steps",
                minimum=1,
                maximum=50,
                step=1,
                value=20,
            )

        with gr.Accordion("Optional Settings", open=False):
            with gr.Row():
                width = gr.Slider(
                    label="Width",
                    minimum=256,
                    maximum=MAX_IMAGE_SIZE,
                    step=32,
                    value=512,
                )
            
            with gr.Row():
                height = gr.Slider(
                    label="Height",
                    minimum=256,
                    maximum=MAX_IMAGE_SIZE,
                    step=32,
                    value=512,
                )

        run_button = gr.Button("Run", scale=1, variant="primary")
        result = gr.Image(label="Result", show_label=False)
    
    gr.on(
        triggers=[run_button.click, prompt.submit],
        fn=infer,
        inputs=[
            prompt,
            negative_prompt,
            width,
            height,
            num_inference_steps,
            model_id,
            seed,
            guidance_scale,
            lora_scale,
        ],
        outputs=[result],
    )

if __name__ == "__main__":
    demo.launch()