Update app.py

app.py

@@ -9,27 +9,27 @@ from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
 import cv2
 import torch
 
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+# Force CPU usage
+device = torch.device("cpu")
 
+# Load the model with float32 for CPU compatibility
 pipe = StableDiffusionXLPipeline.from_pretrained(
     "votepurchase/animagine-xl-4.0",
-    torch_dtype=torch.float16,
+    torch_dtype=torch.float32,  # Use float32 for CPU
 )
 
 pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
-pipe.to(device)
+pipe.to(device)  # Move the model to CPU
 
 MAX_SEED = np.iinfo(np.int32).max
 MAX_IMAGE_SIZE = 1216
 
-    
 @spaces.GPU
 def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps):
-
     if randomize_seed:
         seed = random.randint(0, MAX_SEED)
 
-    generator = torch.Generator().manual_seed(seed)
+    generator = torch.Generator(device=device).manual_seed(seed)  # Use CPU generator
 
     output_image = pipe(
         prompt=prompt,
@@ -43,7 +43,6 @@ def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance
 
     return output_image
 
-
 css = """
 #col-container {
     margin: 0 auto;
@@ -52,9 +51,7 @@ css = """
 """
 
 with gr.Blocks(css=css) as demo:
-
     with gr.Column(elem_id="col-container"):
-
         with gr.Row():
             prompt = gr.Text(
                 label="Prompt",
@@ -63,13 +60,11 @@ with gr.Blocks(css=css) as demo:
                 placeholder="Enter your prompt",
                 container=False,
             )
-
             run_button = gr.Button("Run", scale=0)
 
         result = gr.Image(label="Result", show_label=False)
-        
-        with gr.Accordion("Advanced Settings", open=False):
 
+        with gr.Accordion("Advanced Settings", open=False):
             negative_prompt = gr.Text(
                 label="Negative prompt",
                 max_lines=1,
@@ -93,7 +88,7 @@ with gr.Blocks(css=css) as demo:
                     minimum=256,
                     maximum=MAX_IMAGE_SIZE,
                     step=32,
-                    value=1024,#832,
+                    value=1024,  # Default width
                 )
 
                 height = gr.Slider(
@@ -101,7 +96,7 @@ with gr.Blocks(css=css) as demo:
                     minimum=256,
                     maximum=MAX_IMAGE_SIZE,
                     step=32,
-                    value=1024,#1216,
+                    value=1024,  # Default height
                 )
 
             with gr.Row():
@@ -121,7 +116,7 @@ with gr.Blocks(css=css) as demo:
                     value=28,
                 )
 
-    run_button.click(#lambda x: None, inputs=None, outputs=result).then(
+    run_button.click(
         fn=infer,
         inputs=[prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
         outputs=[result]