darksidewalker-ComfyUI-DaSiWa-Nodes/docs/tiled_diffusion.md

Tiled Diffusion (Image)

Tiled Diffusion (Image) is a MODEL patch for ordinary image checkpoints (SD 1.x/SDXL/Flux-family and other 4-D latent models). Every sampler step, the patched model denoises the latent in overlapping tiles and blends the per-tile predictions with positive pyramid weights, so a model can sample a canvas far larger than its native resolution (hires-fix / upscale passes) without tile seams and without the full-canvas attention cost.

Registered node type DaSiWaTiledDiffusion, category DaSiWa/Image. Written from this pack's own MiniMaxH3 per-step tiled sampler; no code from other tiled-diffusion packs.

Wiring

Load Checkpoint → Tiled Diffusion (Image) → KSampler (model). Everything else (conditioning, latent, VAE) stays as it is. Any sampler and scheduler works, because tiling happens inside the model call.

Inputs

Input	Default	Meaning
model	—	Image diffusion model to patch. The clone is patched; the original stays untouched.
tile_width / tile_height	1024	Tile size in pixels. Use the model's native resolution (SD 1.5: 512, SDXL/Flux: 1024). Must be a multiple of 8 and of the model's latent scale.
overlap	128	Overlap of neighbouring tiles in pixels; 64 px or more blends seams away. Must be smaller than both tile sizes.

Latents no larger than one tile are passed through with a single model call, so the patch costs nothing on small images.

Limits

• ControlNet is not supported: the wrapper raises a clear error. Remove either the ControlNet or the tiled MODEL patch.
• denoise_mask (inpaint/mask sampling) and inpaint-model c_concat latents are sliced per tile; all other conditioning passes through unchanged.
• Image latents only (B,C,H,W). Video models raise an error; use MiniMaxH3 Enhanced Upscale for H3.
• Per-step tiling multiplies the number of model forwards by the tile count; VRAM stays near one tile's cost.