cascada

handler.py

@@ -62,12 +62,10 @@ class EndpointHandler():
         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
-        # )
+        self.controlnet_identitynet = ControlNetModel.from_pretrained(
+            controlnet_path, torch_dtype=dtype
+        )
         
-        self.controlnet_identitynet = ControlNetModel.from_pretrained(controlnet_path, torch_dtype=torch.float16)
-
         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"
@@ -285,10 +283,12 @@ class EndpointHandler():
         control_mask[y1:y2, x1:x2] = 255
         control_mask = Image.fromarray(control_mask.astype(np.uint8))
         
-        control_images = [face_kps] + [
-            self.controlnet_map_fn[s](img_controlnet).resize((width, height))
-            for s in self.my_controlnet_selection
-        ]
+        # control_images = [face_kps] + [
+        #     self.controlnet_map_fn[s](img_controlnet).resize((width, height))
+        #     for s in self.my_controlnet_selection
+        # ]
+        face_kps_test = draw_kps(face_image, face_info['kps'])
+
 
         print("Start inference...")
 
@@ -299,7 +299,7 @@ class EndpointHandler():
             prompt=prompt,
             negative_prompt=negative_prompt,
             image_embeds=face_emb,
-            image=control_images,
+            image=face_kps_test,
             control_mask=control_mask,
             controlnet_conditioning_scale=self.control_scales,
             num_inference_steps=num_inference_steps,