From 192ea8bd547d0d2d12e14dba919a32006942845f Mon Sep 17 00:00:00 2001
From: Your Name <room3dev@gmail.com>
Date: Fri, 26 Dec 2025 22:23:45 +0700
Subject: [PATCH] add load_image_sequence.py

---
 load_image_sequence.py | 74 ++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 74 insertions(+)
 create mode 100644 load_image_sequence.py

diff --git a/load_image_sequence.py b/load_image_sequence.py
new file mode 100644
index 0000000..597ee9d
--- /dev/null
+++ b/load_image_sequence.py
@@ -0,0 +1,74 @@
+import os
+import torch
+from PIL import Image
+import numpy as np
+
+
+def load_image(path: str) -> torch.Tensor:
+    """
+    Load image from disk and convert to ComfyUI IMAGE tensor
+    Shape: [1, H, W, 3], float32 0..1
+    """
+    img = Image.open(path).convert("RGB")
+    arr = np.array(img).astype(np.float32) / 255.0
+    return torch.from_numpy(arr)[None,]
+
+
+class JWBatchLoadImageSequence:
+    CATEGORY = "FrameIO"
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "path_pattern": (
+                    "STRING",
+                    {"default": "./frame{:06d}.webp", "multiline": False},
+                ),
+                "start_index": ("INT", {"default": 0, "min": 0, "step": 1}),
+                "frame_count": ("INT", {"default": 16, "min": 1, "step": 1}),
+                "ignore_missing_images": (("false", "true"), {"default": "false"}),
+            }
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    RETURN_NAMES = ("images",)
+    FUNCTION = "execute"
+
+    def execute(
+        self,
+        path_pattern: str,
+        start_index: int,
+        frame_count: int,
+        ignore_missing_images: str,
+    ):
+        ignore_missing_images = ignore_missing_images == "true"
+
+        image_paths = []
+        for i in range(start_index, start_index + frame_count):
+            try:
+                image_paths.append(path_pattern.format(i))
+            except KeyError:
+                image_paths.append(path_pattern.format(i=i))
+
+        if ignore_missing_images:
+            image_paths = [p for p in image_paths if os.path.exists(p)]
+        else:
+            for path in image_paths:
+                if not os.path.exists(path):
+                    raise FileNotFoundError(f"Image does not exist: {path}")
+
+        if len(image_paths) == 0:
+            raise RuntimeError("Image sequence empty - no images to load")
+
+        imgs = [load_image(p) for p in image_paths]
+        return (torch.cat(imgs, dim=0),)
+
+
+NODE_CLASS_MAPPINGS = {
+    "BatchLoadImageSequence": JWBatchLoadImageSequence
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "BatchLoadImageSequence": "Batch Load Image Sequence"
+}