Update app.py

app.py

@@ -1,7 +1,8 @@
-from diffusers import StableDiffusionPipeline
 import torch
-import os
+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
@@ -12,20 +13,33 @@ else:
 
 # Load the Stable Diffusion 3.5 model
 model_id = "stabilityai/stable-diffusion-3.5-large"
-pipe = StableDiffusion3Pipeline.from_pretrained(model_id, torch_dtype=torch.float16)
+pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
 pipe.to("cuda")
 
-# Define the path to the LoRA model (since it's in the main directory)
-lora_model_path = "lora_model.pth"  # Path to the uploaded LoRA model
+# Define the path to the LoRA model
+lora_model_path = "https://huggingface.co/spaces/DonImages/Testing2/resolve/main/lora_model.pth"  # LoRA model path
 
-# Load the LoRA model weights into the pipeline
-pipe.load_lora_model(lora_model_path)  # Integrate the LoRA weights
+# 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
 
-import gradio as gr
+# Gradio interface
 iface = gr.Interface(fn=generate_image, inputs="text", outputs="image")
-iface.launch()
+iface.launch()