app.py

import gradio as gr
import torch
from diffusers import StableDiffusionPipeline
from safetensors.torch import load_file
import requests
import os

def download_lora(url, save_path="lora.safetensors"):
    response = requests.get(url, stream=True)
    if response.status_code == 200:
        with open(save_path, "wb") as f:
            for chunk in response.iter_content(chunk_size=8192):
                f.write(chunk)
        return save_path
    return None

def load_lora(model, lora_path):
    lora_weights = load_file(lora_path)
    model.load_lora_weights(lora_weights)
    return model

def generate_image(lora_url, prompt):
    base_model = "runwayml/stable-diffusion-v1-5"
    pipe = StableDiffusionPipeline.from_pretrained(base_model).to("cpu")
    
    lora_path = download_lora(lora_url)
    if lora_path:
        pipe = load_lora(pipe, lora_path)
    
    image = pipe(prompt).images[0]
    return image

with gr.Blocks() as demo:
    gr.Markdown("# LoRA-Enhanced Image Generator")
    lora_url = gr.Textbox(label="LoRA Model URL")
    prompt = gr.Textbox(label="Prompt")
    generate_btn = gr.Button("Generate")
    output = gr.Image()
    
    generate_btn.click(generate_image, inputs=[lora_url, prompt], outputs=output)

demo.launch()