Change model precision to float32 for CPU compatibility

app.py

@@ -1,5 +1,5 @@
 import sys
-sys.path.append('/content/ViTON')
+sys.path.append('/content/ViTON')  # Ensure the SegBody.py file is on the Python path
 
 import gradio as gr
 from PIL import Image
@@ -10,13 +10,13 @@ from SegBody import segment_body  # Import the segmentation function
 # Check if CUDA is available and set the device accordingly
 device = "cuda" if torch.cuda.is_available() else "cpu"
 
-# Load models with fp16 variant for GPU compatibility
-vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
+# Load models with fp32 for CPU compatibility
+vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float32)  # Use float32 for CPU
 pipeline = AutoPipelineForInpainting.from_pretrained(
     "diffusers/stable-diffusion-xl-1.0-inpainting-0.1", 
     vae=vae, 
-    torch_dtype=torch.float16,  # Use fp16 for GPU
-    variant="fp16",  # Ensure you are using fp16 for GPU
+    torch_dtype=torch.float32,  # Use float32 for CPU
+    variant="fp32",  # Use fp32 for CPU
     use_safetensors=True
 ).to(device)  # Ensure it uses the appropriate device (CPU or GPU)