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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()
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