Update app.py

app.py

@@ -5,16 +5,21 @@ from diffusers import DiffusionPipeline
 pipeline = None
 
 # Load the pipeline and LoRA weights
-def load_cust(modelsyu):
+def load_cust(modelsyu, progress=gr.Progress()):
+    progress(10.4, desc="loading your lora models...")
+    global pipeline
     pipeline = DiffusionPipeline.from_pretrained(modelsyu)
-    output_result = pipeline()
-    return output_result
-    
+    # Ensure the pipeline uses the CPU
+    pipeline.to("cpu")
+    return "Models loaded successfully"
+
 def generate_image(prompt, negative_prompt):
     global pipeline
     # Generate the image with the provided prompts
     if pipeline is None:
         return "Pipeline not loaded. Please load the models first."
+    # Make sure the pipeline is set to use the CPU
+    pipeline.to("cpu")
     image = pipeline(prompt, negative_prompt=negative_prompt).images[0]
     return image
 
@@ -27,7 +32,7 @@ with gr.Blocks() as demo:
     with gr.Accordion('Load your custom models first'):
         basem = gr.Textbox(label="Your Lora model")  
         exports = gr.Button("Load your models")
-        outputid = gr.Textbox(label="output", interactive=False)
+        outputid = gr.Textbox(label="Output", interactive=False)
         exports.click(load_cust, inputs=[basem], outputs=[outputid])
     output_image = gr.Image(label="Generated Image")
     submit_button.click(generate_image, inputs=[prompt, negative_prompt], outputs=output_image)