Update app.py

app.py

@@ -55,7 +55,7 @@ def pil_to_tensor(images):
 args = Args()
 
 # Define the data type for model weights
-weight_dtype = torch.float16
+weight_dtype = torch.float32
 
 if args.seed is not None:
         set_seed(args.seed)
@@ -66,32 +66,32 @@ noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_pa
 vae = AutoencoderKL.from_pretrained(
         args.pretrained_model_name_or_path,
         subfolder="vae",
-        torch_dtype=torch.float16,
+        torch_dtype=torch.float32,
          )
 unet = UNet2DConditionModel.from_pretrained(
         args.pretrained_model_name_or_path,
         subfolder="unet",
-        torch_dtype=torch.float16,
+        torch_dtype=torch.float32,
         )
 image_encoder = CLIPVisionModelWithProjection.from_pretrained(
         args.pretrained_model_name_or_path,
         subfolder="image_encoder",
-        torch_dtype=torch.float16,
+        torch_dtype=torch.float32,
     )
 unet_encoder = UNet2DConditionModel_ref.from_pretrained(
         args.pretrained_model_name_or_path,
         subfolder="unet_encoder",
-        torch_dtype=torch.float16,
+        torch_dtype=torch.float32,
     )
 text_encoder_one = CLIPTextModel.from_pretrained(
         args.pretrained_model_name_or_path,
         subfolder="text_encoder",
-        torch_dtype=torch.float16,
+        torch_dtype=torch.float32,
         )
 text_encoder_two = CLIPTextModelWithProjection.from_pretrained(
         args.pretrained_model_name_or_path,
         subfolder="text_encoder_2",
-        torch_dtype=torch.float16,
+        torch_dtype=torch.float32,
     )
 tokenizer_one = AutoTokenizer.from_pretrained(
         args.pretrained_model_name_or_path,
@@ -128,7 +128,7 @@ pipe = TryonPipeline.from_pretrained(
             scheduler = noise_scheduler,
             image_encoder=image_encoder,
             unet_encoder = unet_encoder,
-            torch_dtype=torch.float16,
+            torch_dtype=torch.float32,
     ).to(device)
 # pipe.enable_sequential_cpu_offload()
 # pipe.enable_model_cpu_offload()