app.py

import gradio as gr
import numpy as np
import random

# import spaces #[uncomment to use ZeroGPU]
from diffusers import DiffusionPipeline, StableDiffusionPipeline
import torch
from peft import PeftModel

device = "cuda" if torch.cuda.is_available() else "cpu"

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


if torch.cuda.is_available():
    torch_dtype = torch.float16
else:
    torch_dtype = torch.float32

pipelines = {}
lora_pipelines = {}

def get_base_pipeline(model_repo_id):
    """
    Базовая модель
    """
    if model_repo_id not in pipelines:
        pipe = DiffusionPipeline.from_pretrained(
            model_repo_id, 
            torch_dtype=torch_dtype,
            safety_checker=None,
            requires_safety_checker=False
        )
        pipe = pipe.to(device)
        pipelines[model_repo_id] = pipe
    return pipelines[model_repo_id]

def get_lora_pipeline(base_model_id, lora_model_id, lora_scale=0.8):
    """
    Базовая модель + LoRA
    """
    cache_key = f"{base_model_id}_{lora_model_id}_{lora_scale}"
    
    if cache_key not in lora_pipelines:
        # базовая модель
        pipe = StableDiffusionPipeline.from_pretrained(
            base_model_id,
            torch_dtype=torch_dtype,
            safety_checker=None,
            requires_safety_checker=False
        )

        pipe.unet = PeftModel.from_pretrained(
            pipe.unet,
            subfolder="unet",
            model_id=lora_model_id,
            adapter_name="default",
            repo_type="model"
        )
        
        pipe.text_encoder = PeftModel.from_pretrained(
            pipe.text_encoder,
            subfolder="text_encoder",
            model_id=lora_model_id,
            adapter_name="default",
            repo_type="model"
        )
        
        pipe = pipe.to(device)
        lora_pipelines[cache_key] = pipe
    
    return lora_pipelines[cache_key]

# @spaces.GPU #[uncomment to use ZeroGPU]
def infer(
    prompt,
    chosen_model,
    lora_model,
    lora_scale,
    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)
    
    use_lora = lora_model != "none"
    
    if use_lora:

        base_model = "runwayml/stable-diffusion-v1-5"  
        pipe = get_lora_pipeline(base_model, lora_model, lora_scale)
        
        # с LoRA scale
        image = pipe(
            prompt=prompt,
            negative_prompt=negative_prompt,
            guidance_scale=guidance_scale,
            num_inference_steps=num_inference_steps,
            width=width,
            height=height,
            generator=generator,
            cross_attention_kwargs={"scale": lora_scale},
        ).images[0]
    else:
        pipe = get_base_pipeline(chosen_model)
        
        image = pipe(
            prompt=prompt,
            negative_prompt=negative_prompt,
            guidance_scale=guidance_scale,
            num_inference_steps=num_inference_steps,
            width=width,
            height=height,
            generator=generator,
        ).images[0]

    return image, seed


examples = [
    "A blue Blobby dancing in the rain",
    "A pink Blobby wearing a sombrero hat and laughing",
]

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(" # Text-to-Image with LoRA Support")
        
        with gr.Row():
            chosen_model = gr.Dropdown(
                ["stabilityai/sdxl-turbo", 
                 "runwayml/stable-diffusion-v1-5",
                 "PrunaAI/runwayml-stable-diffusion-v1-5-turbo-tiny-green-smashed"],
                label="Base Model",
                value="runwayml/stable-diffusion-v1-5",
                info="Choose base model for inference",
            )
            
            lora_model = gr.Dropdown(
                ["none", "turnipseason/blobbies_SD_v1.5_lora"],
                label="LoRA",
                value="none",
                info="Choose a LoRA adapter",
            )
        
        lora_scale = gr.Slider(
            label="LoRA scale",
            minimum=0.0,
            maximum=1.5,
            step=0.1,
            value=0.8,
            info="Strength of LoRA application",
        )
            
        with gr.Row():
            prompt = gr.Textbox(
                label="Prompt",
                info="Enter your prompt",
                lines=5,
                value="An orange Blobby having fun with an apple.",
            )

            run_button = gr.Button("Run", scale=0, variant="primary")

        result = gr.Image(label="Result", show_label=False)

        with gr.Accordion("Advanced Settings", open=True):
            negative_prompt = gr.Text(
                label="Negative prompt",
                max_lines=1,
                placeholder="Enter a negative prompt",
                visible=True,
            )

            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=512,  
                )

                height = gr.Slider(
                    label="Height",
                    minimum=256,
                    maximum=MAX_IMAGE_SIZE,
                    step=32,
                    value=512,  
                )

            with gr.Row():
                guidance_scale = gr.Slider(
                    label="Guidance scale",
                    minimum=0.0,
                    maximum=10.0,
                    step=0.1,
                    value=7.5,  
                )

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

        gr.Examples(examples=examples, inputs=[prompt])
    
    gr.on(
        triggers=[run_button.click, prompt.submit],
        fn=infer,
        inputs=[
            prompt,
            chosen_model,
            lora_model,
            lora_scale,
            negative_prompt,
            seed,
            randomize_seed,
            width,
            height,
            guidance_scale,
            num_inference_steps,
        ],
        outputs=[result, seed],
    )

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