app.py

import gradio as gr
import torch
from diffusers import AutoPipelineForText2Image

# List of available models
MODEL_OPTIONS = {
    "Stable Diffusion 1.5": "runwayml/stable-diffusion-v1-5",
    "Stable Diffusion 2.1": "stabilityai/stable-diffusion-2-1", 
    "Stable Diffusion XL": "stabilityai/stable-diffusion-xl-base-1.0"
}

def generate_image(
    model_choice,
    lora_url,
    prompt,
    negative_prompt,
    steps,
    width,
    height,
    guidance_scale,
    seed
):
    # Get the selected model ID
    model_id = MODEL_OPTIONS[model_choice]
    
    # Initialize the pipeline
    pipe = AutoPipelineForText2Image.from_pretrained(
        model_id,
        torch_dtype=torch.float16,
        use_safetensors=True
    ).to("cuda")

    # Load LoRA weights if provided
    if lora_url:
        pipe.load_lora_weights(lora_url)
        pipe.fuse_lora()

    # Set up generator with seed if provided
    generator = None
    if seed:
        generator = torch.Generator(device="cuda").manual_seed(int(seed))

    # Generate image
    image = pipe(
        prompt=prompt,
        negative_prompt=negative_prompt,
        num_inference_steps=int(steps),
        width=int(width),
        height=int(height),
        guidance_scale=guidance_scale,
        generator=generator
    ).images[0]

    return image

# Gradio UI components
with gr.Blocks() as demo:
    gr.Markdown("## 🎨 Text-to-Image Generation with LoRA")
    
    with gr.Row():
        with gr.Column():
            model_choice = gr.Dropdown(
                label="Select Base Model",
                choices=list(MODEL_OPTIONS.keys()),
                value="Stable Diffusion XL"
            )
            lora_url = gr.Textbox(
                label="LoRA Repository ID (e.g., 'username/lora-name')",
                placeholder="Optional Hugging Face repository ID"
            )
            prompt = gr.Textbox(
                label="Prompt",
                placeholder="Enter your prompt here..."
            )
            negative_prompt = gr.Textbox(
                label="Negative Prompt",
                placeholder="Enter what to exclude from the image..."
            )
            
            with gr.Row():
                steps = gr.Number(
                    label="Inference Steps",
                    value=25,
                    precision=0
                )
                guidance_scale = gr.Slider(
                    label="Guidance Scale",
                    minimum=1.0,
                    maximum=20.0,
                    value=7.5
                )
                
            with gr.Row():
                width = gr.Number(
                    label="Width",
                    value=1024,
                    precision=0
                )
                height = gr.Number(
                    label="Height", 
                    value=1024,
                    precision=0
                )
                seed = gr.Number(
                    label="Seed (optional)",
                    precision=0
                )
            
            generate_btn = gr.Button("Generate Image", variant="primary")
        
        with gr.Column():
            output_image = gr.Image(label="Generated Image", height=600)

    generate_btn.click(
        fn=generate_image,
        inputs=[
            model_choice,
            lora_url,
            prompt,
            negative_prompt,
            steps,
            width,
            height,
            guidance_scale,
            seed
        ],
        outputs=output_image
    )

demo.launch()