I just finished an ambitious project building out a unique Krea2 tiled diffusion approach.

The 'secret sauce' is using the qwen3/krea2's vision encoding ability and apply it tile by tile rather than the whole image - giving extremely good cohesion and adherence with the right settings (denoise 0.1-0.2). No text prompt is needed, but another unique ability - you can provide PER TILE text prompt in addition to global - only rely on the image itself with no text - or rely on text only, global and/or per tile.

You'll need to install my custom nodes (git clone in your custom nodes folder:
https://github.com/RandyHaylor/krea2-tiled-diffusion-comfyui

=== Krea 2 Tiled Diffusion ===

Tiled high-res detail pass for Krea 2

- Two custom nodes

Excellent adherence to the original

- Each tile is shown the region it is about to draw

- Detail is added, composition is not reinvented

- vision_weight 1.0 keeps it close

* Up to 8.0 tightens it further

* Both usable

No ControlNet, no tile preprocessors, no seam repair

- No masking, no regional conditioning nodes

- No second model

- Load image, upscale, encode, sample

* Two of our nodes and ComfyUI's own

Tiles recombine inside each sampling step

- Others finish each tile, then blend

* Tiles already decided content independently

* Blending cannot reconcile them

- Here all tiles run the same step

* Recombined under a raised cosine weight before it returns

* Neighbours cannot diverge

Each tile is shown only its own region

- The encoder looks at that tile's crop, not the full image

- Show a tile the whole picture and it draws the whole picture

* Usual cause of duplicated subjects

Prompt need not describe the image

- Subject comes from what the encoder sees

- Complete working prompt for a figure, two katanas, footwear, text blocks

* a black and white image in a high quality drawing style

- Global prompt applies to EVERY tile

* Keep to medium, palette, style

- Nine optional per-tile fields

* Text in one field affects only that tile

Memory scales with the tile, not the canvas

- Model never given the full latent

- Bigger output = bigger grid, not bigger card

- 3x3 reaches high resolutions on cards that cannot hold the whole latent

Latent in, latent out

- Latent must already be at target resolution

- Neither node resizes, crops, or uses a VAE

- Inserts into a hires chain with no pixel round trip

=== Settings ===

Detail pass

- 2x2, 256px overlap, 4 steps, 0.10 denoise

Rebuild

- 2x2, 256px overlap, 8 steps, 0.75 denoise

Reduced VRAM

- 3x3

All three

- euler, simple, cfg 1

=== Models ===

Tested with

- krea2TurboRawINT8 — INT8, approx. 12 GB

- qwen3vl4bInt8W4a8_int8convrot

Also suitable

- Smaller quantised qwen3vl_4b text encoders

- Smallest v1.2 identity edit LoRA rank

=== Links ===

Required custom nodes

- https://github.com/RandyHaylor/krea2-tiled-diffusion-comfyui