Update app.py

app.py

@@ -3,7 +3,6 @@ from diffusers import StableDiffusion3Pipeline
 from huggingface_hub import login
 import os
 import gradio as gr
-from transformers import pipeline as transformers_pipeline
 
 # Retrieve the token from the environment variable
 token = os.getenv("HF_TOKEN")  # Hugging Face token from the secret
@@ -12,32 +11,11 @@ if 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 with quantization enabled for CPU
+# 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)  # Use float16 for less memory usage
+pipe = StableDiffusion3Pipeline.from_pretrained(model_id)  # No LoRA integration
 
-# Perform quantization on the model to reduce the memory footprint
-pipe.unet = torch.quantization.quantize_dynamic(pipe.unet, {torch.nn.Linear}, dtype=torch.qint8)
-
-pipe.to("cpu")  # Ensure it runs on CPU
-
-# Define the path to the LoRA model
-lora_model_path = "./lora_model.pth"  # Assuming the file is saved locally
-
-# 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
-    lora_weights = torch.load(lora_model_path, map_location="cpu")
-    
-    # Apply weights to the UNet submodule
-    for name, param in pipe.unet.named_parameters():  # Accessing unet parameters
-        if name in lora_weights:
-            param.data += lora_weights[name]
-
-    return pipe
-
-# Load and apply the LoRA model weights
-pipe = load_lora_model(pipe, lora_model_path)
+pipe.to("cpu")  # Ensuring it runs on CPU
 
 # Function to generate an image from a text prompt
 def generate_image(prompt):