add the ImageTransitionLeftToRight node.

Author: zhangp365

README.md

@@ -102,7 +102,10 @@ Enhanced the official UpscaleImageWithModel node by adding a judge. If the input
 This node is designed to composite a watermark into the destination image. It can select the position of the watermark, resize the watermark according to the input ratio, and add a margin to the watermark.
 
 ## ImageTransition
-This node is designed to generate a transition image between two images. It can generate a transition image between two images.
+This node is designed to generate a transition image between two images. The first image gradually fades out while the second image simultaneously appears, creating a smooth transition effect.
+
+## ImageTransitionLeftToRight
+This node is designed to generate a transition image between two images. The first image gradually slides to the right while the second image simultaneously appears from the left, creating a smooth transition effect.
 
 ## TorchCompileModelAdvanced
 This node enables model compilation using torch.compile. It extends ComfyUI's original torch compile node by adding compile mode options and a toggle switch.

py/nodes_video.py

@@ -55,10 +55,105 @@ def adjust_frames(self, images: torch.Tensor, duration: float, fps: float, remov
             new_images = torch.cat([new_images, images[-1].unsqueeze(0).repeat(repeat_count, 1, 1, 1)], dim=0)
         return (new_images, len(new_images), fps)
 
+class ImageTransitionLeftToRight:
+    def __init__(self):
+        pass
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {"required":{
+            "before_image": ("IMAGE",),
+            "after_image": ("IMAGE",),
+            "duration": ("FLOAT", {"default": 5.0, "min": 0.1, "max": 60.0, "step": 0.1}),
+            "fps": ("FLOAT", {"default": 24.0, "min": 1.0, "max": 120.0, "step": 1.0}),
+            },
+        }
+    
+    RETURN_TYPES = ("IMAGE", "FLOAT", "FLOAT")
+    RETURN_NAMES = ("images", "duration", "fps")
+    FUNCTION = "create_transition"
+    CATEGORY = "utils"
+
+    def create_transition(self, before_image: torch.Tensor, after_image: torch.Tensor, duration: float, fps: float):
+        print(f"before_image: {before_image.shape}, after_image: {after_image.shape}")
+        # 确保输入是单张图片，如果是批次则取第一张
+        if len(before_image.shape) == 4 and before_image.shape[0] > 1:
+            before_image = before_image[0:1]
+        if len(after_image.shape) == 4 and after_image.shape[0] > 1:
+            after_image = after_image[0:1]
+            
+        # 获取目标尺寸（前图的尺寸）
+        _, target_width = before_image.shape[1:3]
+        
+        
+        adjusted_after = self.check_and_resizee_size(before_image, after_image)
+        
+        # 计算总帧数
+        total_frames = int(duration * fps)
+        
+        # 创建过渡帧
+        frames = []
+        
+        for i in range(total_frames):
+            # 计算当前过渡位置 (0.0 到 1.0)
+            progress = i / (total_frames - 1) if total_frames > 1 else 1.0
+            
+            # 计算过渡线的x坐标
+            transition_x = int(target_width * progress)
+            
+            # 创建新帧
+            new_frame = torch.zeros_like(before_image)
+            
+            # 从左到右过渡：左侧显示后图，右侧显示前图
+            # 先填充整个前图
+            new_frame[0] = before_image[0]
+            
+            # 然后在左侧填充后图（覆盖前图）
+            if transition_x > 0:
+                new_frame[0, :, :transition_x,:] = adjusted_after[0, :,  :transition_x, :]
+            
+            frames.append(new_frame)
+        
+        # 合并所有帧
+        result = torch.cat(frames, dim=0)
+        
+        return (result, duration, fps)
+
+    def check_and_resizee_size(self, before_image, after_image):
+                # 获取目标尺寸（前图的尺寸）
+        target_height, target_width = before_image.shape[1:3]
+        
+        # 获取后图的原始尺寸
+        after_height, after_width = after_image.shape[1:3]
+        adjusted_after = after_image
+        if target_height != after_height or target_width != after_width:
+            # 保持比例调整后图尺寸
+            # 先计算缩放比例
+            scale = min(target_height / after_height, target_width / after_width)
+            new_height = int(after_height * scale)
+            new_width = int(after_width * scale)
+            
+            # 缩放后图
+            resized_after = torch.nn.functional.interpolate(
+                after_image.movedim(-1, 1), size=(new_height, new_width), mode='bicubic', align_corners=False
+            ).movedim(1, -1).clamp(0.0, 1.0)
+            # 创建空白画布（与前图尺寸相同）
+            adjusted_after = torch.zeros_like(before_image)
+            
+            # 计算居中位置
+            y_offset = (target_height - new_height) // 2
+            x_offset = (target_width - new_width) // 2
+            
+            # 将缩放后的图像放置在画布中央
+            adjusted_after[:,:,:,:] = resized_after[0, y_offset:y_offset+new_height, x_offset:x_offset+new_width,:] 
+        return adjusted_after
+
 NODE_CLASS_MAPPINGS = {
-    "FrameAdjuster": FrameAdjuster
+    "FrameAdjuster": FrameAdjuster,
+    "ImageTransitionLeftToRight": ImageTransitionLeftToRight
 }
 
 NODE_DISPLAY_NAME_MAPPINGS = {
-    "FrameAdjuster": "Frame Adjuster"
+    "FrameAdjuster": "Frame Adjuster",
+    "ImageTransitionLeftToRight": "Image Transition Left to Right"
 }

pyproject.toml

@@ -1,7 +1,7 @@
 [project]
 name = "comfyui-utils-nodes"
 description = "Nodes:LoadImageWithSwitch, ImageBatchOneOrMore, ModifyTextGender, GenderControlOutput, ImageCompositeMaskedWithSwitch, ImageCompositeMaskedOneByOne, ColorCorrectOfUtils, SplitMask, MaskFastGrow, CheckpointLoaderSimpleWithSwitch, ImageResizeTo8x, MatchImageRatioToPreset, UpscaleImageWithModelIfNeed, MaskFromFaceModel, MaskCoverFourCorners, DetectorForNSFW, DeepfaceAnalyzeFaceAttributes etc."
-version = "1.2.7"
+version = "1.2.8"
 license = { file = "LICENSE" }
 dependencies = []