Update app.py

app.py

@@ -16,9 +16,9 @@ pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
 
 # Explicitly convert submodules to float32 to prevent dtype mismatch
 pipe.to(device)
-pipe.text_encoder.to(dtype=torch.float32)
-pipe.vae.to(dtype=torch.float32)
-pipe.unet.to(dtype=torch.float32)
+pipe.text_encoder.to(device, dtype=torch.float32)
+pipe.vae.to(device, dtype=torch.float32)
+pipe.unet.to(device, dtype=torch.float32)
 
 MAX_SEED = np.iinfo(np.int32).max
 MAX_IMAGE_SIZE = 1024
@@ -38,7 +38,11 @@ def infer(
     if randomize_seed:
         seed = random.randint(0, MAX_SEED)
 
-    generator = torch.Generator().manual_seed(seed)
+    generator = torch.Generator(device).manual_seed(seed)
+
+    # Ensure text inputs are moved to the correct device and dtype
+    prompt = str(prompt) if prompt else ""
+    negative_prompt = str(negative_prompt) if negative_prompt else ""
 
     image = pipe(
         prompt=prompt,
@@ -152,4 +156,4 @@ with gr.Blocks(css=css) as demo:
     )
 
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch()