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# Python
__pycache__/
*.py[cod]
*$py.class
.env
.venv
env/
venv/
ENV/
dist/
build/
*.egg-info/
.DS_Store
*.so
.Python
develop-eggs/
downloads/
eggs/
.eggs/
lib/
lib64/
parts/
sdist/
var/
wheels/
*.egg
# IDE
.idea/
.vscode/
*.swp
*.swo
# OS
.DS_Store
Thumbs.db
LICENSE
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README.md
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# Quick Image Sequence Process (QuickSeq)
A ComfyUI plugin for efficient image sequence processing. Features frame insertion, duplication, and removal with intuitive controls.
## Features
1. **Frame Insertion Between Frames**:
- Set the number of frames to insert between existing frames
- Choose whether to copy from the previous or next frame
- Example: With frames_between=2, sequence [A,B] becomes [A,A,A,B] or [A,B,B,B]
2. **Quick Process First Frames**:
- Add or remove frames at the beginning of the sequence
- Positive values: Add duplicates of the first frame
- Negative values: Remove frames from the beginning
- Example: With quick_process_first_frames=2, [A,B,C] becomes [A,A,A,B,C]
- Example: With quick_process_first_frames=-1, [A,B,C] becomes [B,C]
3. **Quick Process Last Frames**:
- Add or remove frames at the end of the sequence
- Positive values: Add duplicates of the last frame
- Negative values: Remove frames from the end
- Example: With quick_process_last_frames=2, [A,B,C] becomes [A,B,C,C,C]
- Example: With quick_process_last_frames=-1, [A,B,C] becomes [A,B]
4. **Processing Order Control**:
- Choose whether to process first/last frames before or after the between-frame operations
- Affects the final result when combining multiple operations
- "Yes": Process first/last frames first
- "No": Process between-frames first
5. **Real-time Frame Count Display**:
- Shows the total number of frames after processing
- Helps track sequence length changes
## Installation
1. Clone this repository into your ComfyUI's `custom_nodes` directory:
```bash
cd custom_nodes
git clone https://github.com/yourusername/ComfyUI-QuickImageSequenceProcess.git
```
2. Restart ComfyUI
3. Find "Quick Image Sequence Process" in the node menu
## Usage Example
1. Load an image sequence into ComfyUI
2. Add the "Quick Image Sequence Process" node
3. Configure parameters:
- `frames_between`: Number of frames to insert between existing frames
- `copy_frame`: Choose "previous" or "next" for inserted frames
- `quick_process_first_frames`: Add/remove frames at start (negative to remove)
- `quick_process_last_frames`: Add/remove frames at end (negative to remove)
- `process_F_L_frames_first`: Choose processing order priority
## Output
The node outputs:
- `image`: The processed image sequence
- `width`: Frame width
- `height`: Frame height
- `count`: Total number of frames in the sequence
## License
Copyright 2024 kazeyori
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
__init__.py
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# __init__.py
from .quick_image_sequence_process import QuickImageSequenceProcessNode
NODE_CLASS_MAPPINGS = {
"QuickImageSequenceProcess": QuickImageSequenceProcessNode,
}
NODE_DISPLAY_NAME_MAPPINGS = {
"QuickImageSequenceProcess": "Quick Image Sequence Process",
}
__all__ = ["NODE_CLASS_MAPPINGS", "NODE_DISPLAY_NAME_MAPPINGS"]
examples/example.json
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quick_image_sequence_process.py
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"""
@author: kazeyori
@title: Quick Image Sequence Process
@nickname: QuickSeq
@description: A ComfyUI plugin for efficient image sequence processing. Features frame insertion, duplication, and removal with intuitive controls.
"""
# quick_image_sequence_process.py
import numpy as np
import torch
class QuickImageSequenceProcessNode:
def __init__(self):
pass
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"images": ("IMAGE",), # Input image sequence
"frames_between": ("INT", {"default": 1}), # Number of frames to copy between each frame
"copy_frame": (["previous", "next"],), # Whether to copy the previous or next frame
"quick_process_first_frames": ("INT", {"default": 0}), # Number of frames to add/remove at the beginning (negative values for removal)
"quick_process_last_frames": ("INT", {"default": 0}), # Number of frames to add/remove at the end (negative values for removal)
"process_F_L_frames_first": (["yes", "no"],), # Whether to prioritize processing first and last frames
},
}
RETURN_TYPES = ("IMAGE", "INT", "INT", "INT") # Output: image, width, height, count
RETURN_NAMES = ("image", "width", "height", "count")
FUNCTION = "edit_image_sequence"
CATEGORY = "image/sequence"
def process_between_frames(self, images, frames_between, copy_frame):
"""Process middle frames (including frame insertion)"""
new_images = []
for i in range(len(images)):
# Add current frame
new_images.append(images[i])
# If not the last frame, insert copied frames between current and next
if i < len(images) - 1:
if copy_frame == "previous":
frame_to_copy = images[i]
else:
frame_to_copy = images[i + 1]
for _ in range(frames_between):
new_images.append(frame_to_copy)
return new_images
def process_end_frames(self, images, quick_process_first_frames, quick_process_last_frames):
"""Process first and last frames (adding or removing)"""
new_images = list(images) # Convert to list for modification
if quick_process_first_frames >= 0:
# Add frames at the beginning
first_frame = new_images[0]
new_images = [first_frame] * quick_process_first_frames + new_images
else:
# Remove frames from the beginning
new_images = new_images[-quick_process_first_frames:]
if quick_process_last_frames >= 0:
# Add frames at the end
last_frame = new_images[-1]
new_images.extend([last_frame] * quick_process_last_frames)
else:
# Remove frames from the end
new_images = new_images[:quick_process_last_frames]
return new_images
def edit_image_sequence(self, images, frames_between, copy_frame, quick_process_first_frames, quick_process_last_frames, process_F_L_frames_first):
"""
Edit image sequence:
1. Process order determined by process_F_L_frames_first
2. Insert specified number of copied frames between frames
3. Add or remove frames at the beginning and end
"""
# Ensure input is PyTorch tensor
if isinstance(images, np.ndarray):
images = torch.from_numpy(images)
if process_F_L_frames_first == "yes":
# Process first/last frames first, then middle frames
intermediate_images = self.process_end_frames(images, quick_process_first_frames, quick_process_last_frames)
new_images = self.process_between_frames(intermediate_images, frames_between, copy_frame)
else:
# Process middle frames first, then first/last frames
intermediate_images = self.process_between_frames(images, frames_between, copy_frame)
new_images = self.process_end_frames(intermediate_images, quick_process_first_frames, quick_process_last_frames)
# Ensure at least one frame remains
if not new_images:
raise ValueError("No frames remaining after processing. Please adjust parameters to ensure at least one frame.")
# Convert image sequence to PyTorch tensor
new_images = torch.stack(new_images)
# Get dimensions
width = new_images.shape[2]
height = new_images.shape[1]
frame_count = len(new_images)
# Display frame count
print(f"Processed sequence contains {frame_count} frames")
# Return results
return (new_images, width, height, frame_count)
requirements.txt
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numpy
torch