QuantFunc/Qwen-Image-Layered-Series

QuantFunc

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Qwen-Image-Layered-Series

⚠️ Config-only repository — no model weights. This repo contains only QuantFunc per-layer precision configs for Qwen-Image-Layered (RGBA layer decomposition). It does not contain, mirror, or redistribute any model weights. You bring your own official Qwen/Qwen-Image-Layered; these configs only tell the QuantFunc engine how to quantize it at load time, on your own machine.

Powered by the QuantFunc ComfyUI plugin — the fastest diffusion inference engine:

• 🚀 2x–11x speedup over standard BF16/FP16 Python pipelines.
• ⚙️ Native C++/CUDA (libquantfunc.so / quantfunc.dll), zero Python model dependencies.
• 🧩 Universal format adapter — loads diffusers / BFL / HF layouts directly, no manual conversion.
• 🟢 Full GPU coverage — RTX 20/30/40/50 · A100/H100/H200/B100/B200 (CUDA 12 & 13); native FP4 on Blackwell.

👉 Install the plugin: https://github.com/RealJonathanYip/ComfyUI-QuantFunc

What this repository provides

Just the precision configs — no weights:

Qwen-Image-Layered-Series/
├── config.json                        # = 50x-below INT4 map (HF download-counter query file)
└── precision-config/
    ├── 50x-above-fp4-sample.json      # NVFP4 (FP4 weights, af8wf4 MLP) — RTX 50 / SM120+
    └── 50x-below-int4-sample.json     # INT4 per-group-128 — all SMs (robust fallback)

We deliberately do not host Qwen-Image-Layered weights. The QuantFunc Lighting backend does runtime quantization: you load the official weights and they are quantized in-memory at load, so no pre-quantized checkpoint is ever distributed.

How to use

1. Obtain the official model yourself — Qwen/Qwen-Image-Layered (diffusers layout). Follow Qwen's distribution channels and license.
2. Install the QuantFunc ComfyUI plugin: https://github.com/RealJonathanYip/ComfyUI-QuantFunc
3. Load the official model through the Build Pipeline node (universal format adapter).
4. Precision config — leave the node on auto detect (it recognizes Qwen-Image-Layered and applies the right map automatically: NVFP4 on RTX 50 / SM120+, INT4 otherwise), or point it at a file manually.

Precision configs

Two GPU tiers (the auto-detect picks by SM):

File	Target GPU	Scheme
50x-above-fp4-sample.json	RTX 50 / SM120+	NVFP4 (FP4 e2m1 weights); FP8 activations on the MLP only (af8wf4), attention stays W4A4
50x-below-int4-sample.json	RTX 20/30/40 + datacenter	INT4 per-group-128 (AUTO_4 → INT4 on all SMs); robust, fully coherent at any SM

Why the MLP is af8wf4 on the NVFP4 map: use_additional_t_cond + layer3d modulation make the MLP input activations large enough to saturate the FP4-activation per-16 FP8 (e4m3 max 448) microscale → green-noise background. FP8 activation (per-token FP16 act-scale) on the MLP removes it; attention tolerates FP4 activation and stays on the fast W4A4 path. This differs from the base Qwen-Image NVFP4 map by exactly one layer (the MLP up-projection net.0.proj). In both maps the img_mod/txt_mod modulation GEMMs stay INT8.

⚠️ Companion settings REQUIRED for coherence (not part of the precision map)

• base scheduler (configs/qwen-image-base-scheduler.json)
• num_inference_steps = 50
• true_cfg_scale = 4.0
• non-empty negative_prompt
• a real RGBA composite input image
• resolution 640

NVFP4 (50x-above) is SM120+ only (FP4 is native sm_120a, never PTX-JIT). On older GPUs use the INT4 map.

Legal / Attribution

• This repository distributes only the QuantFunc precision-config JSON — our own work, Apache-2.0.
• It contains no Qwen weights and is not affiliated with, nor endorsed by, the Qwen team.
• You are solely responsible for obtaining the official model and complying with its license and terms of use.

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