This repository provides a tiled sampling tool for ComfyUI, enabling the denoising of larger images by breaking them into smaller tiles. It aims to minimize visible seams in the final output by gradually denoising each tile in a randomized sequence.
- Allows for precise control over tile dimensions and tiling strategies.
- Offers multiple strategies for denoising that cater to different needs and preferences.
- Compatible with various samplers, enhancing flexibility in image processing workflows.
Context
This tool is a specialized extension for ComfyUI designed to enhance the image denoising process by utilizing a tiled sampling approach. Its primary purpose is to facilitate the handling of larger images, ensuring that the denoising process is efficient and results in high-quality images with minimal seam visibility.
Key Features & Benefits
The tiled sampler includes adjustable settings for tile width and height, allowing users to customize their workflow based on the specific requirements of their images. The inclusion of different tiling strategies—such as random, random strict, padded, and simple—provides users with options to balance between seam reduction and processing efficiency, catering to diverse artistic needs.
Advanced Functionalities
The random tiling strategy minimizes seams by randomizing tile positions during the denoising process, while the random strict strategy ensures that border tiles are not cropped, though it may not work well with certain samplers. The padded strategy enhances context for each tile during processing, though it may require denoising more tiles, thereby impacting efficiency. Each of these strategies allows users to select the most suitable method for their specific image characteristics.
Practical Benefits
By implementing this tiled sampling tool, users can expect improved control over the denoising process, leading to higher quality images with reduced seam visibility. This tool streamlines the workflow in ComfyUI, making it more efficient for artists and developers working with large-scale images.
Credits/Acknowledgments
This tool is developed by BlenderNeko and is available under an open-source license, encouraging community contributions and enhancements.