add Easy batch

__init__.py

@@ -3,6 +3,7 @@ from .k_easy_version import Easy_Version
 from .k_easy_wan22_prompt import EasyWan22Prompt
 from .k_easy_resize import EasyResize
 from .k_ai_pipeline import EasyAIPipeline
+from .k_easy_batch import MultiKeyframeImageBatch  # Added for new VFX batch node
 
 # Aggregate mappings from each node file
 # Assuming each node file defines its own NODE_CLASS_MAPPINGS and NODE_DISPLAY_NAME_MAPPINGS
@@ -19,19 +20,24 @@ from .k_easy_resize import NODE_DISPLAY_NAME_MAPPINGS as resize_display
 from .k_ai_pipeline import NODE_CLASS_MAPPINGS as pipeline_mappings
 from .k_ai_pipeline import NODE_DISPLAY_NAME_MAPPINGS as pipeline_display
 
+from .k_easy_batch import NODE_CLASS_MAPPINGS as batch_mappings  # Added for new VFX batch node
+from .k_easy_batch import NODE_DISPLAY_NAME_MAPPINGS as batch_display  # Added for new VFX batch node
+
 # Merge all mappings
 NODE_CLASS_MAPPINGS = {
     **version_mappings,
     **wan_mappings,
     **resize_mappings,
-    **pipeline_mappings
+    **pipeline_mappings,
+    **batch_mappings  # Added for new VFX batch node
 }
 
 NODE_DISPLAY_NAME_MAPPINGS = {
     **version_display,
     **wan_display,
     **resize_display,
-    **pipeline_display
+    **pipeline_display,
+    **batch_display  # Added for new VFX batch node
 }
 
 WEB_DIRECTORY = "./web"

k_easy_batch.py

@@ -0,0 +1,108 @@
+import torch
+
+class MultiKeyframeImageBatch:
+    """
+    A custom ComfyUI node for VFX workflows that takes 1-4 keyframe images with specified frame positions
+    and creates an IMAGE batch sequence of a given total length, filling undefined frames with black images.
+    Useful for preparing sparse control sequences for video models like Wan 2.2, where black frames indicate
+    no latent update. Assumes all input images have identical dimensions.
+    """
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "total_frames": ("INT", {
+                    "default": 16,
+                    "min": 1,
+                    "max": 1024,
+                    "step": 1,
+                    "display": "number"
+                }),
+                "image1": ("IMAGE", ),
+                "frame1": ("INT", {
+                    "default": 0,
+                    "min": 0,
+                    "max": 1023,
+                    "step": 1,
+                    "display": "number"
+                }),
+            },
+            "optional": {
+                "image2": ("IMAGE", ),
+                "frame2": ("INT", {
+                    "default": 4,
+                    "min": 0,
+                    "max": 1023,
+                    "step": 1,
+                    "display": "number"
+                }),
+                "image3": ("IMAGE", ),
+                "frame3": ("INT", {
+                    "default": 8,
+                    "min": 0,
+                    "max": 1023,
+                    "step": 1,
+                    "display": "number"
+                }),
+                "image4": ("IMAGE", ),
+                "frame4": ("INT", {
+                    "default": 12,
+                    "min": 0,
+                    "max": 1023,
+                    "step": 1,
+                    "display": "number"
+                }),
+            }
+        }
+
+    RETURN_TYPES = ("IMAGE", )
+    RETURN_NAMES = ("image_batch", )
+    FUNCTION = "create_batch"
+    CATEGORY = "Koolook/VFX"
+    OUTPUT_NODE = False  # Not necessarily an output node, but can be chained
+
+    def create_batch(self, total_frames, image1, frame1, image2=None, frame2=None, image3=None, frame3=None, image4=None, frame4=None):
+        if image1.shape[0] != 1:
+            raise ValueError("Each input should be a single IMAGE (batch size 1), not a pre-batched tensor.")
+
+        h, w, c = image1.shape[1:]
+        black = torch.zeros(h, w, c)  # [H, W, C] zero tensor for black frame
+
+        # Initialize list of frames with blacks
+        frame_images = [black.clone() for _ in range(total_frames)]
+
+        # Collect connected keyframes
+        keyframes = [(image1[0], frame1)]
+        if image2 is not None and frame2 is not None:
+            if image2.shape[1:] != (h, w, c):
+                raise ValueError("All images must have the same height, width, and channels.")
+            keyframes.append((image2[0], frame2))
+        if image3 is not None and frame3 is not None:
+            if image3.shape[1:] != (h, w, c):
+                raise ValueError("All images must have the same height, width, and channels.")
+            keyframes.append((image3[0], frame3))
+        if image4 is not None and frame4 is not None:
+            if image4.shape[1:] != (h, w, c):
+                raise ValueError("All images must have the same height, width, and channels.")
+            keyframes.append((image4[0], frame4))
+
+        # Place keyframes in the sequence
+        for img, f in keyframes:
+            if 0 <= f < total_frames:
+                frame_images[f] = img
+            else:
+                print(f"Warning: Frame {f} is out of range (0 to {total_frames-1}); ignoring this keyframe.")
+
+        # Stack into batch tensor [total_frames, H, W, C]
+        batch = torch.stack(frame_images, dim=0)
+        return (batch, )
+
+# Node mappings for ComfyUI registration
+NODE_CLASS_MAPPINGS = {
+    "MultiKeyframeImageBatch": MultiKeyframeImageBatch
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "MultiKeyframeImageBatch": "Multi Keyframe Image Batch (VFX)"
+}