reorder code

.insightface/models/1k3d68.onnx

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.insightface/models/2d106det.onnx

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.insightface/models/antelopev2.zip

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.insightface/models/antelopev2/1k3d68.onnx

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.insightface/models/antelopev2/2d106det.onnx

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.insightface/models/antelopev2/genderage.onnx

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.insightface/models/antelopev2/glintr100.onnx

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.insightface/models/antelopev2/scrfd_10g_bnkps.onnx

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.insightface/models/genderage.onnx

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.insightface/models/glintr100.onnx

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.insightface/models/scrfd_10g_bnkps.onnx

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antelopev2/1k3d68.onnx

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-oid sha256:df5c06b8a0c12e422b2ed8947b8869faa4105387f199c477af038aa01f9a45cc
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antelopev2/2d106det.onnx

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antelopev2/genderage.onnx

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antelopev2/glintr100.onnx

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antelopev2/scrfd_10g_bnkps.onnx

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handler.py

@@ -28,10 +28,14 @@ dtype = torch.float16 if str(device).__contains__("cuda") else torch.float32
 
 class EndpointHandler():
     def __init__(self, model_dir):
-        print("Loading model from", model_dir)
         
-        face_adapter = f"/repository/checkpoints/ip-adapter.bin"
-        controlnet_path = f"/repository/checkpoints/ControlNetModel"
+        print("Loading FaceAnalysis", model_dir)
+        self.app = FaceAnalysis(name="antelopev2", root="/repository/models/antelopev2", providers=["CPUExecutionProvider"])
+        self.app.prepare(ctx_id=0, det_size=(640, 640))
+
+
+        openpose = OpenposeDetector.from_pretrained("lllyasviel/ControlNet")
+        # depth_anything = DepthAnything.from_pretrained('LiheYoung/depth_anything_vitl14').to(device).eval()
 
         # transform = Compose([
         #     Resize(
@@ -47,39 +51,13 @@ class EndpointHandler():
         #     PrepareForNet(),
         # ])
 
+        face_adapter = f"/repository/checkpoints/ip-adapter.bin"
+        controlnet_path = f"/repository/checkpoints/ControlNetModel"
+
         self.controlnet_identitynet = ControlNetModel.from_pretrained(
             controlnet_path, torch_dtype=dtype
         )
 
-        pretrained_model_name_or_path = "wangqixun/YamerMIX_v8"
-
-        self.pipe = StableDiffusionXLInstantIDPipeline.from_pretrained(
-        pretrained_model_name_or_path,
-        controlnet=[self.controlnet_identitynet],
-        torch_dtype=dtype,
-        safety_checker=None,
-        feature_extractor=None,
-        ).to(device)
-        
-        self.pipe.scheduler = diffusers.EulerDiscreteScheduler.from_config(
-            self.pipe.scheduler.config
-        )
-
-        # load and disable LCM
-        self.pipe.load_lora_weights("latent-consistency/lcm-lora-sdxl")
-        self.pipe.disable_lora()
-        
-        self.pipe.cuda()
-        self.pipe.load_ip_adapter_instantid(face_adapter)
-        self.pipe.image_proj_model.to("cuda")
-        self.pipe.unet.to("cuda")
-        
-        # if we need more parameters
-        # scheduler_class_name = "EulerDiscreteScheduler"        
-        # add_kwargs = {}
-        # scheduler = getattr(diffusers, scheduler_class_name)
-        # self.pipe.scheduler = scheduler.from_config(self.pipe.scheduler.config, **add_kwargs)
-
         controlnet_pose_model = "thibaud/controlnet-openpose-sdxl-1.0"
         controlnet_canny_model = "diffusers/controlnet-canny-sdxl-1.0"
         # controlnet_depth_model = "diffusers/controlnet-depth-sdxl-1.0-small"
@@ -90,19 +68,10 @@ class EndpointHandler():
         controlnet_canny = ControlNetModel.from_pretrained(
             controlnet_canny_model, torch_dtype=dtype
         ).to(device)
-        
         # controlnet_depth = ControlNetModel.from_pretrained(
         #     controlnet_depth_model, torch_dtype=dtype
         # ).to(device)
 
-        openpose = OpenposeDetector.from_pretrained("lllyasviel/ControlNet")
-        # depth_anything = DepthAnything.from_pretrained('LiheYoung/depth_anything_vitl14').to(device).eval()
-
-        def get_canny_image(image, t1=100, t2=200):
-            image = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR)
-            edges = cv2.Canny(image, t1, t2)
-            return Image.fromarray(edges, "L")
-        
         # def get_depth_map(image):
         
         #     image = np.array(image) / 255.0
@@ -124,6 +93,11 @@ class EndpointHandler():
 
         #     return depth_image
         
+        def get_canny_image(image, t1=100, t2=200):
+            image = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR)
+            edges = cv2.Canny(image, t1, t2)
+            return Image.fromarray(edges, "L")
+        
         self.controlnet_map = {
             "pose": controlnet_pose,
             "canny": controlnet_canny,
@@ -135,11 +109,35 @@ class EndpointHandler():
             "canny": get_canny_image,
             # "depth": get_depth_map,
         }
+
+        pretrained_model_name_or_path = "wangqixun/YamerMIX_v8"
+
+        self.pipe = StableDiffusionXLInstantIDPipeline.from_pretrained(
+        pretrained_model_name_or_path,
+        controlnet=[self.controlnet_identitynet],
+        torch_dtype=dtype,
+        safety_checker=None,
+        feature_extractor=None,
+        ).to(device)
         
-        self.app = FaceAnalysis(name="antelopev2", root="/repository/models/antelopev2", providers=["CPUExecutionProvider"])
-        self.app.prepare(ctx_id=0, det_size=(640, 640))
+        self.pipe.scheduler = diffusers.EulerDiscreteScheduler.from_config(
+            self.pipe.scheduler.config
+        )
 
-        self.generator = torch.Generator(device=device).manual_seed(42)
+        # load and disable LCM
+        self.pipe.load_lora_weights("latent-consistency/lcm-lora-sdxl")
+        self.pipe.disable_lora()
+        
+        self.pipe.cuda()
+        self.pipe.load_ip_adapter_instantid(face_adapter)
+        self.pipe.image_proj_model.to("cuda")
+        self.pipe.unet.to("cuda")
+        
+        # if we need more parameters
+        # scheduler_class_name = "EulerDiscreteScheduler"        
+        # add_kwargs = {}
+        # scheduler = getattr(diffusers, scheduler_class_name)
+        # self.pipe.scheduler = scheduler.from_config(self.pipe.scheduler.config, **add_kwargs)
 
         identitynet_strength_ratio = 0.8
         
@@ -283,6 +281,8 @@ class EndpointHandler():
 
         print("Start inference...")
 
+        self.generator = torch.Generator(device=device).manual_seed(42)
+
         self.pipe.set_ip_adapter_scale(adapter_strength_ratio)
         images = self.pipe(
             prompt=prompt,