ComfyUI_UltimateSGUpscale

Tiled upscaling for ComfyUI using built-in nodes. Replicates the core features of UltimateSDUpscale as a transparent workflow you can inspect and modify.

Requirements

  • ComfyUI with SplitImageToTileList and ImageMergeTileList nodes (added in PR #12599)

Installation

Clone into your ComfyUI custom_nodes directory:

cd ComfyUI/custom_nodes
git clone https://github.com/ethanfel/ComfyUI_UltimateSGUpscale.git

This installs one custom node (Generate Seam Mask) and provides an example workflow.

What's Included

Example Workflow

example_workflows/tiled-upscale-builtin-nodes.json — a two-pass tiled upscaling workflow:

Pass 1 — Tiled Redraw: Upscales the image with a model (e.g. 4x-UltraSharp), splits it into overlapping tiles, runs each tile through KSampler, then merges them back with sine-based blending.

Pass 2 — Seam Fix (optional): Generates a mask targeting only the seam regions between tiles, then runs a second tiled denoise pass restricted to those seam bands via SetLatentNoiseMask. Mute or bypass the "Seam Fix" group to skip this pass.

Generate Seam Mask Node

A small helper node that creates a binary mask image with white bands at tile seam positions. It replicates SplitImageToTileList's tiling logic to place bands at the exact center of each overlap region.

Inputs:

Parameter Default Description
image_width 2048 Image width (connect from GetImageSize)
image_height 2048 Image height (connect from GetImageSize)
tile_width 1024 Tile width matching Pass 1
tile_height 1024 Tile height matching Pass 1
overlap 128 Overlap matching Pass 1
seam_width 64 Width of seam bands in pixels

Output: IMAGE — a mask with white bands at seam positions, black elsewhere.

How It Works

The workflow chains standard ComfyUI nodes together. SplitImageToTileList outputs a list, and ComfyUI's auto-iteration runs all downstream nodes (VAEEncode, KSampler, VAEDecode) once per tile automatically. Scalar inputs (model, conditioning, VAE) are reused across tiles. ImageMergeTileList reassembles tiles using sine-weighted blending for smooth overlap transitions.

The seam fix pass uses SetLatentNoiseMask to restrict denoising to only the masked seam regions, leaving the rest of the image untouched.

License

MIT

S
Description
No description provided
Readme 66 KiB
Languages
Python 100%