--- language: - en license: other license_name: quantfunc-model-license tags: - image-generation - text-to-image - diffusion - quantized - quantfunc --- # QuantFunc
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# Qwen-Image-Series Pre-quantized **Qwen-Image-2512** text-to-image model series by [QuantFunc](https://github.com/user/quantfunc), with Lighting backend inference support. ## Overview Qwen-Image-2512 is a text-to-image diffusion model distilled from Alibaba Qwen team's image generation model. With the latest QuantFunc ComfyUI plugin, inference achieves **2x–11x speedup** over mainstream frameworks. ## Hardware Requirements - Supports NVIDIA RTX 30 series and above - RTX 20 series does not support BF16, which causes significant precision loss in Qwen series model quantization scenarios. Therefore, the 20 series currently only supports Z-Image models. ## Compatibility - The base models in this repository are compatible with **any version** of Qwen-Image transformer weights - The QuantFunc code plugin and ComfyUI plugin are **100% compatible** with previous versions of Qwen-Image models ## Directory Structure ``` Qwen-Image-Series/ ├── qwen-image-series-50x-above-base-model/ # Base model, optimized for RTX 50 series and above │ ├── text_encoder/ # Qwen2.5-VL text encoder (pre-quantized) │ ├── vae/ # 3D VAE decoder (~242MB) │ ├── tokenizer/ # Tokenizer │ ├── scheduler/ # Scheduler config │ ├── model_index.json │ └── quantfunc_config.json ├── qwen-image-series-50x-below-base-model/ # Base model, optimized for RTX 50 series and below │ └── (same structure as above) ├── transformer/ │ ├── config.json │ ├── qwen-image-2512-50x-above-lighting-4steps.safetensors # RTX 50+ Lighting 4-step (~14GB) │ ├── qwen-image-2512-50x-above-lighting-4steps-prequant.safetensors # RTX 50+ Lighting pre-quantized (~11GB) │ ├── qwen-image-2512-50x-below-lighting-4steps.safetensors # RTX 30/40 Lighting 4-step (~14GB) │ └── qwen-image-2512-50x-below-lighting-4steps-prequant.safetensors # RTX 30/40 Lighting pre-quantized (~11GB) ├── prequant/ # Pre-quantized modulation weights │ ├── qwen-image-2512-50x-above.safetensors # RTX 50+ mod weights (2512) │ ├── qwen-image-2512-50x-below.safetensors # RTX 30/40 mod weights (2512) │ ├── qwen-image-50x-above.safetensors # RTX 50+ mod weights (legacy) │ └── qwen-image-50x-below.safetensors # RTX 30/40 mod weights (legacy) └── precision-config/ # Lighting precision config samples ├── 50x-above-fp4-sample.json # FP4 config for RTX 50+ └── 50x-below-int4-sample.json # INT4 config for RTX 30/40 ``` ## Model Variants | Variant | base-model | transformer | Total Size | Target GPU | |---------|-----------|-------------|------------|------------| | **50x-above** | `qwen-image-series-50x-above-base-model` | `qwen-image-2512-50x-above-lighting-4steps.safetensors` | ~14GB | RTX 50 series and above | | **50x-below** | `qwen-image-series-50x-below-base-model` | `qwen-image-2512-50x-below-lighting-4steps.safetensors` | ~14GB | RTX 30/40 series | - **50x-above**: Optimized for RTX 50 series (Blackwell) and above - **50x-below**: Optimized for RTX 30/40 series - **4steps**: Distilled accelerated version — only 4 steps needed to generate images > The base-model and transformer must use the **same variant** (both above or both below). ## Quick Start ### Download ```bash pip install modelscope ``` ```python from modelscope import snapshot_download model_dir = snapshot_download('QuantFunc/Qwen-Image-Series') ``` ### Inference ```bash # RTX 50 series quantfunc \ --model-dir Qwen-Image-Series/qwen-image-series-50x-above-base-model \ --transformer Qwen-Image-Series/transformer/qwen-image-2512-50x-above-lighting-4steps.safetensors \ --auto-optimize --model-backend lighting \ --prompt "a beautiful sunset over the ocean with dramatic clouds" \ --output output.png --steps 4 # RTX 30/40 series quantfunc \ --model-dir Qwen-Image-Series/qwen-image-series-50x-below-base-model \ --transformer Qwen-Image-Series/transformer/qwen-image-2512-50x-below-lighting-4steps.safetensors \ --auto-optimize --model-backend lighting \ --prompt "a beautiful sunset over the ocean with dramatic clouds" \ --output output.png --steps 4 ``` `--auto-optimize` automatically configures VRAM management, attention backend, and offload strategies based on your GPU. ## SVDQ && Lighting Backend This repository provides **Lighting** backend models. Differences between the two backends: | Feature | Lighting | SVDQ | |---------|----------|------| | **Quantization** | Per-layer mixed precision (FP4/INT4/FP8/INT8) | Nunchaku-based holistic pre-quantization | | **LoRA Integration** | Real-time quantization — build a custom model in 5 minutes with zero speed loss, integrating any number of LoRAs | Runtime low-rank pathway | | **Ecosystem** | QuantFunc native | Compatible with the widely-adopted Nunchaku ecosystem, enhanced with Rotation quantization and Auto Rank dynamic rank optimization | | **Flexibility** | Per-layer/sub-layer precision control | Precision fixed at export time | | **Use Cases** | Rapid personal model customization, batch LoRA integration | Leverage Nunchaku ecosystem, runtime dynamic LoRA | ## Pre-quantized Modulation Weights (prequant/) The `prequant/` directory contains **pre-quantized modulation weights** extracted from SVDQ models. Use them with the Lighting backend for high-quality modulation without runtime quantization overhead. ```bash # From FP16 with mod weights (first run quantizes on-the-fly) quantfunc \ --model-dir Qwen-Image-Series/qwen-image-series-50x-above-base-model \ --model-backend lighting \ --precision-config Qwen-Image-Series/precision-config/50x-above-fp4-sample.json \ --mod-weights Qwen-Image-Series/prequant/qwen-image-2512-50x-above.safetensors \ --rotation-block-size 256 \ --prompt "a beautiful sunset" --steps 4 --auto-optimize ``` Alternatively, use the **pre-quantized Lighting transformer** for instant loading (no runtime quantization): ```bash quantfunc \ --model-dir Qwen-Image-Series/qwen-image-series-50x-above-base-model \ --transformer Qwen-Image-Series/transformer/qwen-image-2512-50x-above-lighting-4steps-prequant.safetensors \ --model-backend lighting \ --prompt "a beautiful sunset" --steps 4 --auto-optimize ``` ## Precision Config (precision-config/) Sample per-layer precision configurations for the Lighting backend: | File | Target GPU | Precision | |------|-----------|-----------| | `50x-above-fp4-sample.json` | RTX 50+ | FP4 attention + AF8WF4 MLP fc2 + INT8 modulation | | `50x-below-int4-sample.json` | RTX 30/40 | INT4 all layers + INT8 modulation | ## Related Repositories - [QuantFunc/Z-Image-Series](https://modelscope.cn/models/QuantFunc/Z-Image-Series) — Z-Image-Turbo text-to-image (lightweight, fast) - [QuantFunc/Qwen-Image-Edit-Series](https://modelscope.cn/models/QuantFunc/Qwen-Image-Edit-Series) — Qwen-Image-Edit image editing ## License The pre-quantized model weights in this repository are derived from the original models. Users must comply with the original model's license agreement. The QuantFunc inference engine and its plugins (including the ComfyUI plugin) are licensed separately — see official QuantFunc channels for details. For models quantized from commercially licensed models, users are responsible for obtaining the necessary commercial licenses from the original model providers.