vslinx-ComfyUI-vslinx-nodes/web/docs/vsLinx_MultiDiffusionTiledHiresFix.md

A model-agnostic MultiDiffusion tiled hires-fix / refiner that re-renders an image in overlapping tiles without producing tile seams. It works on any model (SD / SDXL / Flux / etc.) and needs no extra node packs.

It runs a single sampling pass over the whole image. Every denoising step the latent is split into overlapping tiles, the model is evaluated per tile, and the predictions are averaged in latent space (MultiDiffusion, Bar-Tal et al.). Because the tiles are re-synced every step they can't diverge, so:

• there are no tile seams and no "double-exposure" ghosting to blend away, and
• per-step UNet activations stay tile-sized — you get whole-image coherence at roughly tile-sized peak VRAM, unlike a single full-image pass.

This node does not upscale on its own. Upscale the image first (e.g. Upscale Image By or an upscale-model node), then feed it in here with a low denoise (~0.3–0.5) to refine the detail — the classic "hires fix", done in tiles.

Parameters:

Parameter	Type	Description
image	IMAGE	The (already-upscaled) image to refine in tiles.
model	MODEL	The diffusion model.
positive	CONDITIONING	Positive conditioning, shared across all tiles.
negative	CONDITIONING	Negative conditioning, shared across all tiles.
vae	VAE	VAE used to encode the image and decode the result.
seed	INT	Sampling seed.
steps	INT	KSampler steps.
cfg	FLOAT	Classifier-free guidance scale.
sampler_name	COMBO	KSampler sampler.
scheduler	COMBO	KSampler scheduler.
denoise	FLOAT	Denoise strength — for a tiled hires-fix keep this low (e.g. 0.3–0.5).
rows	INT	Number of tile rows (latent-space grid).
columns	INT	Number of tile columns (latent-space grid).
overlap	FLOAT	Overlap between tiles as a fraction of tile size (added on top of overlap_x/overlap_y).
overlap_x	INT	Extra horizontal overlap in pixels.
overlap_y	INT	Extra vertical overlap in pixels.
vae_decode_tiled	BOOLEAN	Decode the final full-image latent in tiles instead of one pass, so the decode can't spike VRAM (or, on Windows, spill into slow shared system memory).
vae_decode_tile_size	INT	Tile size in pixels for the tiled VAE decode.

Outputs:

Parameter	Type	Description
image	IMAGE	The refined image.

Notes:

• More rows/columns means smaller per-step tiles, which lowers the peak VRAM of each model evaluation. The total number of model evaluations per step is rows × columns, so a finer grid trades a little speed for lower VRAM.
• The final VAE decode is a single full-image pass and is independent of rows/columns (the grid only tiles the per-step model call, not the VAE). If that decode pins your VRAM — on Windows it may not raise a clean out-of-memory error and instead crawl by spilling into shared system memory — enable vae_decode_tiled with a bounded vae_decode_tile_size.
• This node delegates to any model_function_wrapper already installed upstream, so it can stack with other wrapper-based nodes instead of overwriting them.
• Upscale VAEs are supported. If the connected VAE is an upscale VAE from a pack like ComfyUI-VAE-Utils (e.g. the Wan2.1 upscale2x image VAE), its decoder outputs extra channels (3 × k²) that must be unshuffled into a k-times larger RGB image. The node auto-detects this and decodes the same way that pack's own decode node does, so you get a correct (and larger) image instead of a broken multi-channel result. Normal VAEs are unaffected. This detection also applies when vae_decode_tiled is enabled.