app.py

import gradio as gr
import numpy as np
import random

from diffusers import DiffusionPipeline
from peft import PeftModel, PeftConfig
import torch

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

# Model list including your LoRA model
MODEL_LIST = [
    "CompVis/stable-diffusion-v1-4",
    "stabilityai/sdxl-turbo",
    "runwayml/stable-diffusion-v1-5",
    "stabilityai/stable-diffusion-2-1",
    "YaArtemNosenko/dino_stickers",
]

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

# Cache to avoid re-initializing pipelines repeatedly
model_cache = {}

def load_pipeline(model_id: str):
    """
    Loads or retrieves a cached DiffusionPipeline.
    
    If the chosen model is your LoRA adapter, then load the base model 
    (CompVis/stable-diffusion-v1-4) and apply the LoRA weights.
    """
    if model_id in model_cache:
        return model_cache[model_id]
    
    if model_id == "YaArtemNosenko/dino_stickers":
        # Use the specified base model for your LoRA adapter.
        base_model = "CompVis/stable-diffusion-v1-4"
        pipe = DiffusionPipeline.from_pretrained(base_model, torch_dtype=torch_dtype)
        # Load the LoRA weights
        pipe.unet = PeftModel.from_pretrained(
            pipe.unet, 
            model_id, 
            subfolder="unet", 
            torch_dtype=torch_dtype
        )
        pipe.text_encoder = PeftModel.from_pretrained(
            pipe.text_encoder, 
            model_id, 
            subfolder="text_encoder", 
            torch_dtype=torch_dtype
        )
    else:
        pipe = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch_dtype)
    
    pipe.to(device)
    model_cache[model_id] = pipe
    return pipe

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

def infer(
    model_id,
    prompt,
    negative_prompt,
    seed,
    randomize_seed,
    width,
    height,
    guidance_scale,
    num_inference_steps,
    lora_scale,  # New parameter for adjusting LoRA scale
    progress=gr.Progress(track_tqdm=True),
):
    # Load the pipeline for the chosen model
    pipe = load_pipeline(model_id)

    if randomize_seed:
        seed = random.randint(0, MAX_SEED)

    generator = torch.Generator(device=device).manual_seed(seed)

    # If using the LoRA model, update the LoRA scale if supported.
    if model_id == "YaArtemNosenko/dino_stickers":
        # This assumes your pipeline's unet has a method to update the LoRA scale.
        if hasattr(pipe.unet, "set_lora_scale"):
            pipe.unet.set_lora_scale(lora_scale)
        else:
            print("Warning: LoRA scale adjustment method not found on UNet.")

    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 = [
    "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
    "An astronaut riding a green horse",
    "A delicious ceviche cheesecake slice",
]

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 Gradio Template")

        with gr.Row():
            # Dropdown to select the model from Hugging Face
            model_id = gr.Dropdown(
                label="Model",
                choices=MODEL_LIST,
                value=MODEL_LIST[0],  # Default model
            )

        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, variant="primary")

        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",
            )

            seed = gr.Slider(
                label="Seed",
                minimum=0,
                maximum=MAX_SEED,
                step=1,
                value=42,  # Default seed
            )

            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=20.0,
                    step=0.5,
                    value=7.0,
                )

                num_inference_steps = gr.Slider(
                    label="Number of inference steps",
                    minimum=1,
                    maximum=100,
                    step=1,
                    value=20,
                )

            # New slider for LoRA scale.
            lora_scale = gr.Slider(
                label="LoRA Scale",
                minimum=0.0,
                maximum=2.0,
                step=0.1,
                value=1.0,
                info="Adjust the influence of the LoRA weights",
            )

        gr.Examples(examples=examples, inputs=[prompt])
    gr.on(
        triggers=[run_button.click, prompt.submit],
        fn=infer,
        inputs=[
            model_id,
            prompt,
            negative_prompt,
            seed,
            randomize_seed,
            width,
            height,
            guidance_scale,
            num_inference_steps,
            lora_scale,  # Pass the new slider value
        ],
        outputs=[result, seed],
    )

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