Aioli Nodes — ComfyUI Custom Node Suite

Three utility nodes for outpainting and inpainting in ComfyUI.

Installation

Via ComfyUI Registry (recommended)

Search "Aioli Nodes" directly in the ComfyUI Manager → Install Nodes.

Via Git URL

https://github.com/aiolicollective/aioli-nodes

Manual installation

1. Copy the aioli-nodes folder into ComfyUI/custom_nodes/
2. Restart ComfyUI
3. The nodes appear under the Aioli Nodes category

No extra dependencies — only math (Python stdlib), torch and Pillow (already bundled with ComfyUI).

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🖼️ Ratio Outpaint Calc

Prepares an image for outpainting to a standard aspect ratio.
Automatically computes padding and generates the mask.

Inputs

Parameter	Type	Description
image	IMAGE	Source image
ratio	dropdown	none · 1:1 · 4:5 · 5:4 · 3:4 · 4:3 · 16:9 · 9:16

Outputs

Output	Type	Description
image_padded	IMAGE	Image padded with neutral grey (0.5)
mask	MASK	Binary mask (0 = keep, 1 = generate)

Workflow

Load Image → 🖼️ Ratio Outpaint Calc → VAE Encode (Inpaint) → KSampler

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📐 BBox Multiple Fix

Plugs in right after Mask Bounding Box (ComfyUI Essentials).
Rounds the crop to the chosen multiple and handles scaling (up or down) to a Flux-friendly resolution.

The node ensures the inpainted region stitches back pixel-perfectly onto the base image — no border artefacts, no alignment drift, even when the mask zone is at the very edge of the image.

Example

The inpaint applied back onto the base image fits the original contours exactly — pixel-perfect edges, no alignment drift.

Inputs

Parameter	Type	Description
image	IMAGE	Full source image (before crop)
mask	MASK	Full source mask (before crop)
x	INT	x output from Mask Bounding Box
y	INT	y output from Mask Bounding Box
width	INT	width output from Mask Bounding Box
height	INT	height output from Mask Bounding Box
multiple	dropdown	8 (VAE minimum) · 16 (Flux) · 32 (SD1.5) · 64 (SDXL)
target	dropdown	none · 512 · 768 · 1024 · 1536 · 2048
force_square	BOOLEAN	Force crop to 1:1 ratio — side = max(width, height). Default: False
force_target_downscale	BOOLEAN	If bbox > target, downscale toward target (GCD). Default: False — fallback to 2048 cap

Outputs

Output	Type	Description
image_cropped	IMAGE	Cropped image (scaled if needed)
mask_cropped	MASK	Cropped mask (scaled if needed)
x	INT	x position for ImageCompositeMasked
y	INT	y position for ImageCompositeMasked
orig_width	INT	Crop width in source BEFORE scale — use for resize-back after VAE Decode
orig_height	INT	Crop height in source BEFORE scale
width	INT	Final width after scale
height	INT	Final height after scale
target_size	INT	Numeric value of target (0 if none) — connect directly to ImageResize+

Scaling behaviour

Situation	Behaviour
bbox ≤ target	Upscale crop toward target — exact ratio via GCD
bbox > target + force_target_downscale = True	Downscale crop toward target — exact ratio via GCD
bbox > target + force_target_downscale = False	Fallback: round to multiple + cap at 2048px
target = none + bbox > 2048px	Downscale crop to fit 2048px — ratio preserved via GCD
force_square = True	Crop is squared first (max side), then scaled

Anti-clamp guarantee: the crop is always constrained to the available space around the bbox center — even when the mask zone is at the image border, the ratio is preserved pixel-perfectly (0% drift).

Workflow without scale

BBox Fix → VAE Encode → KSampler → VAE Decode → ImageCompositeMasked ← x, y

Workflow with upscale / downscale

BBox Fix → VAE Encode → KSampler → VAE Decode
  │                                      │
  ├── orig_width, orig_height            │
  ├── x, y               ImageResize+ ←─┘
  │                       ↑
  └── target_size ────────┘
                          │
               ImageCompositeMasked ← x, y

☁️ ComfyCloud alternative (no install required)

If you don't want to install the custom node locally — or if you're running ComfyUI Cloud where custom nodes aren't available — there's a pure-subgraph workflow that reproduces the BBoxMultipleFix behaviour using only pre-installed nodes.

⬇ Download ComfyCloud workflow

It wires together MaskBoundingBox+, ComfyMathExpression, ImageCrop+, ImageResize+, GrowMaskWithBlur, SolidMask, ImpactSwitch, and CropMask to deliver the same features:

• force_square · inpaint_mode (zone / whole image) · use_mask_blur
• multiple alignment (with floor-after-clamp, no drift)
• target_size (0 = none with 2048 cap, or 512 / 768 / 1024 / 1536 / 2048)
• Anti-clamp guarantee
• Pixel-perfect recompose via ImageCompositeMasked

The example pipeline uses nano-banana (Gemini Image) as the inpainting model, but you can swap it for any image-to-image node.

Running locally? The example uses LoadImageOutput, which works out-of-the-box on ComfyUI Cloud but may not be available in some local setups. If you hit an error on load, just replace that node with a standard Load Image node — everything else stays the same.

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🎨 Inpaint Color Fix

Plugs in right after VAE Decode, before ImageResize+ / ImageCompositeMasked.

Corrects colorimetric drift introduced by the generation — selectively applies a LAB color match only on pixels that haven't significantly changed, leaving truly creative pixels untouched. No external dependencies (pure numpy + torch).

Inputs

Parameter	Type	Description
original_crop	IMAGE	image_cropped from BBoxMultipleFix (before KSampler)
inpainted_crop	IMAGE	IMAGE from VAE Decode
delta_e_threshold	FLOAT	Similarity threshold (-1 = auto). Below = corrected, above = creative/intact
blend_strength	FLOAT	Color match strength on similar zones (0 = none, 1 = full)
feather_radius	INT	Gaussian blur radius on the correction mask (0 = disabled)
mask (optional)	MASK	Override mode: bypasses Delta-E entirely, the mask drives correction directly

Outputs

Output	Type	Description
image_corrected	IMAGE	Color-corrected crop — connect to ImageResize+
correction_mask	MASK	Debug mask (white = corrected, black = creative/intact)

Modes

Mode	Behaviour
No mask connected	Delta-E auto-detects similar vs creative pixels
Mask connected	Delta-E is bypassed — the mask controls correction directly

Delta-E threshold guide

Value	Effect
-1 (auto)	Recommended starting point
5–10	Strict — corrects almost everything except highly creative pixels
15–20	Balanced — fixes subtle drift, preserves real changes
25–35	Loose — only corrects near-identical pixels
50+	Near-global color match

Position in workflow

BBoxMultipleFix
  └── image_cropped ──────────────────────────────────┐
                                                       ↓
  └── image_cropped → KSampler → VAEDecode → 🎨 InpaintColorFix → ImageResize+ → ImageCompositeMasked

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🔁 Example Workflow — Flux2Klein Inpaint

⬇ Download workflow JSON

A complete, ready-to-use inpaint workflow for Flux2Klein (9B), packaged as a ComfyUI subgraph (FLUX2KLEIN_INPAINT).

Required models

Role	File
UNet	flux2/flux-2-klein-9b-fp8.safetensors
VAE	flux2/flux2-vae.safetensors
Text encoder	qwen_3_8b_fp8mixed.safetensors

Required custom nodes

• Aioli Nodes (this repo) — BBoxMultipleFix, InpaintColorFix
• ComfyUI Essentials — MaskBoundingBox+, ImageResize+
• ComfyUI KJNodes — GrowMaskWithBlur
• rgthree-comfy — Image Comparer (optional, for before/after preview)

Subgraph inputs

Input	Description
IMAGE	Source image (with painted mask)
MASK	Inpaint mask
Resize_Megapixels	Working resolution in megapixels (default: 4)
PROMPT	Inpaint prompt
Resize_Inpaint_Target	BBox target size (none · 512 · 768 · 1024 · 1536 · 2048)

Internal pipeline

LoadImageOutput (with mask painter)
  └→ FLUX2KLEIN_INPAINT subgraph
        ├── ImageScaleToTotalPixels   (resize source to working MP)
        ├── MaskBoundingBox+          (detect mask region)
        ├── BBoxMultipleFix           (crop + scale for Flux, anti-clamp, cap 2048px)
        ├── GrowMaskWithBlur          (soften mask edges)
        ├── VAEEncode × 2 + SetLatentNoiseMask
        ├── CLIPTextEncode → ReferenceLatent → FluxGuidance
        ├── Flux2Scheduler + KSamplerSelect + RandomNoise
        ├── SamplerCustomAdvanced
        ├── VAEDecode
        ├── InpaintColorFix           (selective LAB color match)
        ├── ImageResize+              (resize back to orig_width × orig_height)
        └── ImageCompositeMasked     (stitch result onto source)
  └→ SaveImage + Image Comparer (before / after)

Usage

1. Download the JSON and drag it into ComfyUI
2. Point LoadImageOutput to your image and paint your mask
3. Set your prompt and target resolution in the subgraph inputs
4. Run — the result is composited pixel-perfectly back onto the source image