update

app.py

@@ -24,8 +24,10 @@ import gradio as gr
 # global variable
 MAX_SEED = np.iinfo(np.int32).max
 #device = "cuda" if torch.cuda.is_available() else "cpu"
+torch_dtype = torch.float16
 if torch.backends.mps.is_available():
   device = "mps"
+  torch_dtype = torch.float32
 elif torch.cuda.is_available():
   device = "cuda"
 else:
@@ -48,27 +50,30 @@ face_adapter = f'./checkpoints/ip-adapter.bin'
 controlnet_path = f'./checkpoints/ControlNetModel'
 
 # Load pipeline
-controlnet = ControlNetModel.from_pretrained(controlnet_path, torch_dtype=torch.float16)
+#controlnet = ControlNetModel.from_pretrained(controlnet_path, torch_dtype=torch.float16)
+controlnet = ControlNetModel.from_pretrained(controlnet_path, torch_dtype=torch_dtype)
 
 base_model_path = 'wangqixun/YamerMIX_v8'
 
 pipe = StableDiffusionXLInstantIDPipeline.from_pretrained(
     base_model_path,
     controlnet=controlnet,
-    torch_dtype=torch.float16,
+    #torch_dtype=torch.float16,
+    torch_dtype=torch_dtype,
     safety_checker=None,
     feature_extractor=None,
 )
 if device == 'mps':
-  pipe.to("mps")
+    pipe.to("mps", torch_dtype)
+    pipe.enable_attention_slicing()
 elif device == 'cuda':
-  pipe.cuda()
+    pipe.cuda()
 pipe.load_ip_adapter_instantid(face_adapter)
 #pipe.image_proj_model.to('cuda')
 #pipe.unet.to('cuda')
 if device == 'mps' or device == 'cuda':
-  pipe.image_proj_model.to(device)
-  pipe.unet.to(device)
+    pipe.image_proj_model.to(device)
+    pipe.unet.to(device)
 
 def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
     if randomize_seed: