diff --git a/uniAnimate_Inference.py b/uniAnimate_Inference.py
deleted file mode 100644
index 6595185..0000000
--- a/uniAnimate_Inference.py
+++ /dev/null
@@ -1,168 +0,0 @@
-<<<<<<< HEAD
-import torch
-
-from .utils.config import Config
-from .tools.inferences import inference_unianimate_entrance
-from . import run_align_pose
-
-# from tools import *
-
-
-
-class UniAnimateImage:
-    @classmethod
-    def INPUT_TYPES(cls):
-        return {
-            "required": {
-                "steps": ("INT", {"default": 30, "min": 25, "max": 50, "step": 1}),
-                "useFirstFrame": ("BOOLEAN", { "default": False }),
-                "reference_image": ("IMAGE",),  # single image
-                "ref_pose": ("IMAGE",),  # single image
-                "pose_sequence": ("IMAGE",),   # Batch of pose images
-                "frame_interval": ("INT", {"default": 1, "min": 1, "max": 8, "step": 1}),
-                "max_frames": ("INT", {"default": 32, "min": 1, "max": 64, "step": 1}),
-                "resolution_x": ("INT", {"default": 512, "min": 512, "max": 768, "step": 256}),
-            }
-        }
-
-    RETURN_TYPES = ("IMAGE", "MASK")
-    FUNCTION = "process"
-    CATEGORY = "image"
-
-    def process(self, steps, useFirstFrame, reference_image, ref_pose, pose_sequence, frame_interval, max_frames, resolution_x):
-        cfg_update = Config(load=True)
-        resolution_y = 768
-        if resolution_x == 768:
-            resolution_y = 1216
-        resolution = [resolution_x, resolution_y]
-        print("Ready for inference.")
-        
-        # print(f"image is: {reference_image}")
-        
-        frames = inference_unianimate_entrance(steps, useFirstFrame, reference_image, ref_pose, pose_sequence, frame_interval, max_frames, resolution, cfg_update=cfg_update.cfg_dict)
-        mask_template = torch.zeros((1, resolution_y, resolution_x), dtype=torch.float32)
-        masks = [mask_template.clone() for _ in range(len(pose_sequence))]
-        masks = torch.cat(masks, dim=0)
-        return (frames, masks)
-
-class Gen_align_pose:
-    @classmethod
-    def INPUT_TYPES(cls):
-        return {
-            "required": {
-                "reference_image": ("IMAGE",),  # single image
-                "video": ("IMAGE",),   # video
-            }
-        }
-
-    RETURN_TYPES = ("IMAGE", "IMAGE")
-    FUNCTION = "process"
-    CATEGORY = "image"
-
-    def process(self, reference_image, video):    
-        if torch.cuda.is_available():
-            print(f"CUDA version: {torch.version.cuda}")
-            print(f"CUDNN version: {torch.backends.cudnn.version()}")
-            print(f"Device name: {torch.cuda.get_device_name(0)}")
-        else:
-            print("CUDA is not available")
-        poses, refPose = run_align_pose.mp_main(reference_image, video)
-        return (refPose, poses)
-
-
-
-NODE_CLASS_MAPPINGS = {
-    "UniAnimateImage" : UniAnimateImage,
-    "Gen_align_pose" : Gen_align_pose,
-    
-}
-
-NODE_DISPLAY_NAME_MAPPINGS = {
-    "UniAnimateImage" :"Animate image with UniAnimate",
-    "Gen_align_pose" :"Align & Generate poses for UniAnimate",
-    
-=======
-import torch
-
-from .utils.config import Config
-from .tools.inferences import inference_unianimate_entrance
-from . import run_align_pose
-
-# from tools import *
-
-
-
-class UniAnimateImage:
-    @classmethod
-    def INPUT_TYPES(cls):
-        return {
-            "required": {
-                "steps": ("INT", {"default": 30, "min": 25, "max": 50, "step": 1}),
-                "useFirstFrame": ("BOOLEAN", { "default": False }),
-                "reference_image": ("IMAGE",),  # single image
-                "ref_pose": ("IMAGE",),  # single image
-                "pose_sequence": ("IMAGE",),   # Batch of pose images
-                "frame_interval": ("INT", {"default": 1, "min": 1, "max": 8, "step": 1}),
-                "max_frames": ("INT", {"default": 32, "min": 1, "max": 64, "step": 1}),
-                "resolution_x": ("INT", {"default": 512, "min": 512, "max": 768, "step": 256}),
-            }
-        }
-
-    RETURN_TYPES = ("IMAGE", "MASK")
-    FUNCTION = "process"
-    CATEGORY = "image"
-
-    def process(self, steps, useFirstFrame, reference_image, ref_pose, pose_sequence, frame_interval, max_frames, resolution_x):
-        cfg_update = Config(load=True)
-        resolution_y = 768
-        if resolution_x == 768:
-            resolution_y = 1216
-        resolution = [resolution_x, resolution_y]
-        print("Ready for inference.")
-        
-        # print(f"image is: {reference_image}")
-        
-        frames = inference_unianimate_entrance(steps, useFirstFrame, reference_image, ref_pose, pose_sequence, frame_interval, max_frames, resolution, cfg_update=cfg_update.cfg_dict)
-        mask_template = torch.zeros((1, resolution_y, resolution_x), dtype=torch.float32)
-        masks = [mask_template.clone() for _ in range(len(pose_sequence))]
-        masks = torch.cat(masks, dim=0)
-        return (frames, masks)
-
-class Gen_align_pose:
-    @classmethod
-    def INPUT_TYPES(cls):
-        return {
-            "required": {
-                "reference_image": ("IMAGE",),  # single image
-                "video": ("IMAGE",),   # video
-            }
-        }
-
-    RETURN_TYPES = ("IMAGE", "IMAGE")
-    FUNCTION = "process"
-    CATEGORY = "image"
-
-    def process(self, reference_image, video):    
-        if torch.cuda.is_available():
-            print(f"CUDA version: {torch.version.cuda}")
-            print(f"CUDNN version: {torch.backends.cudnn.version()}")
-            print(f"Device name: {torch.cuda.get_device_name(0)}")
-        else:
-            print("CUDA is not available")
-        poses, refPose = run_align_pose.mp_main(reference_image, video)
-        return (refPose, poses)
-
-
-
-NODE_CLASS_MAPPINGS = {
-    "UniAnimateImage" : UniAnimateImage,
-    "Gen_align_pose" : Gen_align_pose,
-    
-}
-
-NODE_DISPLAY_NAME_MAPPINGS = {
-    "UniAnimateImage" :"Animate image with UniAnimate",
-    "Gen_align_pose" :"Align & Generate poses for UniAnimate",
-    
->>>>>>> 626e7afc02230297b6f553675ea1c32c29971314
-}
\ No newline at end of file