56 lines
2.8 KiB
Markdown
56 lines
2.8 KiB
Markdown
# ComfyUI_UltimateSGUpscale
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Tiled upscaling for ComfyUI using built-in nodes. Replicates the core features of [UltimateSDUpscale](https://github.com/ssitu/ComfyUI_UltimateSDUpscale) as a transparent workflow you can inspect and modify.
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## Requirements
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- ComfyUI with `SplitImageToTileList` and `ImageMergeTileList` nodes (added in [PR #12599](https://github.com/comfyanonymous/ComfyUI/pull/12599))
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## Installation
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Clone into your ComfyUI `custom_nodes` directory:
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```bash
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cd ComfyUI/custom_nodes
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git clone https://github.com/ethanfel/ComfyUI_UltimateSGUpscale.git
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```
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This installs one custom node (`Generate Seam Mask`) and provides an example workflow.
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## What's Included
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### Example Workflow
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`example_workflows/tiled-upscale-builtin-nodes.json` — a two-pass tiled upscaling workflow:
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**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.
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**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.
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### Generate Seam Mask Node
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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.
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**Inputs:**
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| Parameter | Default | Description |
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|-----------|---------|-------------|
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| image_width | 2048 | Image width (connect from GetImageSize) |
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| image_height | 2048 | Image height (connect from GetImageSize) |
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| tile_width | 1024 | Tile width matching Pass 1 |
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| tile_height | 1024 | Tile height matching Pass 1 |
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| overlap | 128 | Overlap matching Pass 1 |
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| seam_width | 64 | Width of seam bands in pixels |
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| mode | binary | `binary`: hard 0/1 mask. `gradient`: linear falloff for use with Differential Diffusion. |
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**Output:** `IMAGE` — a mask with bands at seam positions, black elsewhere.
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## How It Works
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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.
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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.
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## License
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MIT
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