Ethanfel
06b42a610b
Replace per-pixel Python loop with vectorized torch.arange + slice operations. Fix DifferentialDiffusion node position to avoid visual overlap with SplitImageToTileList node 14 on the canvas. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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
SplitImageToTileListandImageMergeTileListnodes (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 helper node that creates a mask image with bands at tile seam positions. It replicates SplitImageToTileList's tiling logic to place bands at the exact center of each overlap region. Supports binary (hard) and gradient (linear falloff) modes.
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 |
| mode | binary | binary: hard 0/1 mask. gradient: linear falloff for use with Differential Diffusion. |
Output: IMAGE — a mask with 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. The example workflow uses gradient mode with a DifferentialDiffusion node so that seam centers receive full denoising while edges blend smoothly into the surrounding image.
License
MIT