Initial commit

.gitignore

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+__pycache__/
+*.pyc

README.md

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+# ComfyUI-FrameIO
+
+High-performance frame input/output nodes for AI video pipelines.
+
+## Features
+- Save batch IMAGE tensors to WebP
+- Lossy or lossless WebP
+- Skip identical frames via hashing
+- STRING_LIST output (list-native)
+- Optimized for long AI video generation
+
+## Included Nodes
+- Batch Save Image Sequence (WebP)
+
+## Installation
+```bash
+cd ComfyUI/custom_nodes
+git clone https://github.com/yourname/ComfyUI-FrameIO.git
+```
+
+## Recommended Settings
+- Quality: 90–95
+- Skip Identical Frames: true
+
+## License
+MIT

__init__.py

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+from .save_webp_sequence import NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS
+
+__all__ = ['NODE_CLASS_MAPPINGS', 'NODE_DISPLAY_NAME_MAPPINGS']

requirements.txt

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+pillow
+numpy

save_webp_sequence.py

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+import os
+import torch
+import numpy as np
+import hashlib
+from PIL import Image
+
+from comfy.utils import ProgressBar
+
+def image_tensor_hash(img: torch.Tensor) -> str:
+    img = img.clamp(0, 1)
+    arr = img.cpu().numpy()
+    return hashlib.sha1(arr.tobytes()).hexdigest()
+
+
+class BatchSaveImageSequenceWebP:
+    CATEGORY = "FrameIO"
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "images": ("IMAGE",),
+                "path_pattern": (
+                    "STRING",
+                    {"default": "./frame{:06d}.webp", "multiline": False},
+                ),
+                "start_index": ("INT", {"default": 0, "min": 0, "step": 1}),
+                "lossless": (("false", "true"), {"default": "false"}),
+                "quality": ("INT", {"default": 95, "min": 1, "max": 100}),
+                "skip_identical": (("false", "true"), {"default": "true"}),
+                "overwrite": (("true", "false"), {"default": "true"}),
+            }
+        }
+
+    RETURN_TYPES = ("STRING_LIST",)
+    RETURN_NAMES = ("paths",)
+    FUNCTION = "execute"
+
+    def execute(
+        self,
+        images,
+        path_pattern,
+        start_index,
+        lossless,
+        quality,
+        skip_identical,
+        overwrite,
+    ):
+        overwrite = overwrite == "true"
+        lossless = lossless == "true"
+        skip_identical = skip_identical == "true"
+
+        batch_size = images.shape[0]
+        saved_paths = []
+        seen_hashes = {}
+
+        pbar = ProgressBar(batch_size)
+
+        for i in range(batch_size):
+            index = start_index + i
+
+            try:
+                path = path_pattern.format(index)
+            except KeyError:
+                path = path_pattern.format(i=index)
+
+            os.makedirs(os.path.dirname(path) or ".", exist_ok=True)
+
+            img = images[i]
+
+            if skip_identical:
+                h = image_tensor_hash(img)
+                if h in seen_hashes:
+                    saved_paths.append(seen_hashes[h])
+                    pbar.update(1)
+                    continue
+
+            if os.path.exists(path) and not overwrite:
+                saved_paths.append(path)
+                pbar.update(1)
+                continue
+
+            img = img.clamp(0, 1)
+            img = (img.cpu().numpy() * 255).astype(np.uint8)
+            pil_img = Image.fromarray(img, mode="RGB")
+
+            kwargs = {"format": "WEBP", "method": 6}
+            if lossless:
+                kwargs["lossless"] = True
+            else:
+                kwargs["lossless"] = False
+                kwargs["quality"] = quality
+
+            pil_img.save(path, **kwargs)
+
+            saved_paths.append(path)
+            if skip_identical:
+                seen_hashes[h] = path
+
+            pbar.update(1)
+
+        return (saved_paths,)
+
+
+NODE_CLASS_MAPPINGS = {
+    "BatchSaveImageSequenceWebP": BatchSaveImageSequenceWebP
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "BatchSaveImageSequenceWebP": "Batch Save Image Sequence (WebP)"
+}