Update app.py

app.py

@@ -40,17 +40,17 @@ noise_scheduler = DDIMScheduler(
         steps_offset=1,
     )
 #  1.2 vae
-vae = AutoencoderKL.from_pretrained(vae_model_path).to(dtype=torch.float16)
+vae = AutoencoderKL.from_pretrained(vae_model_path).to(dtype=torch.float32)
 #  1.3 ControlNet
 ## 1.3.1 load controlnet_softEdge
-controlnet_softEdge = ControlNetModel.from_pretrained(controlnet_softEdge_model_path, torch_dtype=torch.float16)
+controlnet_softEdge = ControlNetModel.from_pretrained(controlnet_softEdge_model_path, torch_dtype=torch.float32)
 ## 1.3.2 load controlnet_depth
-controlnet_depth = ControlNetModel.from_pretrained(controlnet_depth_model_path, torch_dtype=torch.float16)
+controlnet_depth = ControlNetModel.from_pretrained(controlnet_depth_model_path, torch_dtype=torch.float32)
 # 1.4 load SD pipeline
 pipe_softEdge = StableDiffusionControlNetPipeline.from_pretrained(
         base_model_path,
         controlnet=controlnet_softEdge,
-        torch_dtype=torch.float16,
+        torch_dtype=torch.float32,
         scheduler=noise_scheduler,
         vae=vae,
         feature_extractor=None,
@@ -59,7 +59,7 @@ pipe_softEdge = StableDiffusionControlNetPipeline.from_pretrained(
 pipe_depth = StableDiffusionControlNetPipeline.from_pretrained(
         base_model_path,
         controlnet=controlnet_depth,
-        torch_dtype=torch.float16,
+        torch_dtype=torch.float32,
         scheduler=noise_scheduler,
         vae=vae,
         feature_extractor=None,
@@ -88,7 +88,7 @@ def task1_StyleTransfer(photo, blur_radius, sketch):
 
     Control_factor = 1.2
     IP_factor = 0.6
-    ip_model = IPAdapter(pipe_depth, image_encoder_path, ip_ckpt, device, Control_factor=Control_factor, IP_factor=IP_factor，dtype=torch.float32)
+    ip_model = IPAdapter(pipe_depth, image_encoder_path, ip_ckpt, device, Control_factor=Control_factor, IP_factor=IP_factor)
 
     depth_image= Image.open(sketch)
     img_array = np.array(depth_image)