Krea 2 Turbo — Activation-Aware SVDQuant W4A4 for ComfyUI

Activation-aware INT4 quantization + SVDQuant low-rank correction for Krea 2 Turbo — ~2.4x faster than BF16, about a third the file size of the usual FP8 setup, no calibration dataset needed. Built for GPUs without FP8 tensor cores (Turing/Ampere, RTX 20/30-series), where the standard FP8 advice actually runs slower than plain BF16.

Speed (RTX 3090, 1024x1024, 8 steps, warm):

• BF16 (reference): 18.80 s — 1.00x

• W4A4, no low-rank branch: 6.49 s — 2.90x

• SVDQuant rank 64: 7.16 s — 2.63x

• SVDQuant rank 256 / rank 256 actaware: 7.77 s — 2.42x

The low-rank branch (the accuracy correction) costs ~9-10% of step time and barely varies with rank — going from rank 16 to rank 256 is only ~4% slower.

Fidelity, measured, not guessed: LPIPS against a BF16 reference, 16 prompts x 2 seeds, paired stats. The activation-aware objective (branch fit against real per-channel activation energy instead of assuming it's uniform) is the biggest single accuracy win in the whole project and costs nothing at inference — same shapes, same kernels, only different numbers inside the branch.

Links: 🔧 Custom nodes (GitHub): https://github.com/alperktt/Krea-2-SVDQuant-ComfyUI 📦 Checkpoints + full benchmarks + example images (Hugging Face): https://huggingface.co/AlperKTS/Krea-2-SVDQuant-ComfyUI

Setup:

1. Clone the custom nodes: git clone https://github.com/alperktt/Krea-2-SVDQuant-ComfyUI custom_nodes/krea2-svdquant Restart ComfyUI.

2. Download a checkpoint from Hugging Face into ComfyUI/models/diffusion_models/. Start with rank256-actaware — closest to BF16 of anything in the sweep, same speed as plain rank 256. rank64 if you want the smaller file and never load a LoRA. W4A4-noLowRank for max speed/min size with no branch at all.

3. Grab the text encoder (qwen3vl_4b_fp8_scaled.safetensors) and VAE (qwen_image_vae.safetensors) — standard for any Krea 2 Turbo workflow.

4. Load this workflow, select your checkpoint in the loader node, generate.

Loader node by checkpoint:

• W4A4-noLowRank → stock UNETLoader

• SVDQuant-W4A4-rank* (16/64/128/256/actaware) → Krea2 SVDQuant W4A4 Loader (this repo)

Using LoRAs? Use Krea2 SVDQuant LoRA Loader. On a quantized checkpoint the stock loader would have to dequantize the weight, add the LoRA, and requantize it — losing the 4-bit format and requantizing the LoRA delta along with it. This repo's node applies the LoRA as a parallel branch instead, so the quantized weight is never touched. If you load LoRAs, use rank 256 — rank 64 loses most of its accuracy advantage under one.

Tested on: RTX 3090 (Ampere) — works on any GPU with INT8/W4A4 tensor cores (Turing and up). Experimental, from-scratch project on ComfyUI's native comfy_kitchen backend. Benchmark reports from other GPUs welcome — open a discussion on the repo.