app.py

import torch
from diffusers import StableDiffusionPipeline
from huggingface_hub import login
import os
import gradio as gr

# Retrieve the token from the environment variable
token = os.getenv("HF_TOKEN")  # Hugging Face token from the secret
if token:
    login(token=token)  # Log in with the retrieved token
else:
    raise ValueError("Hugging Face token not found. Please set it as a repository secret in the Space settings.")

# Load the Stable Diffusion 3.5 model
model_id = "stabilityai/stable-diffusion-3.5-large"
pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
pipe.to("cuda")

# Define the path to the LoRA model
lora_model_path = "https://huggingface.co/spaces/DonImages/Testing2/resolve/main/lora_model.pth"  # LoRA model path

# Custom method to load and apply LoRA weights to the Stable Diffusion pipeline
def load_lora_model(pipe, lora_model_path):
    # Load the LoRA weights (assuming it's a PyTorch .pth file)
    lora_weights = torch.load(lora_model_path, map_location="cuda")

    # Modify this section based on how LoRA is intended to interact with your Stable Diffusion model
    # Here, we just load the weights into the model's parameters (this is a conceptual approach)
    for name, param in pipe.named_parameters():
        if name in lora_weights:
            param.data += lora_weights[name]  # Apply LoRA weights to the parameters

    return pipe  # Return the updated model

# Load and apply the LoRA model weights
pipe = load_lora_model(pipe, lora_model_path)

# Function to generate an image from a text prompt
def generate_image(prompt):
    image = pipe(prompt).images[0]
    return image

# Gradio interface
iface = gr.Interface(fn=generate_image, inputs="text", outputs="image")
iface.launch()