<h3 id="about-these-variants">About these variants</h3>Four precision levels are available for Qwen-Image-2.1, trading VRAM and speed against generation quality:- BF16 — full-precision reference (~14 GB). Highest quality, largest footprint. - INT8 (W8A8) — 8-bit weights + 8-bit activations (~7 GB). Near-lossless quality, ~2× smaller than BF16, runs on INT8 tensor cores (RTX 30-series and up). - INT6 (W6A8) — 6-bit weights + 8-bit activations (~5.5 GB). Middle-ground: smaller than INT8 with only a small quality trade-off. - INT4 (W4A8) — 4-bit weights + 8-bit activations (~4 GB). Smallest footprint; uses ConvRot with a per-tensor codebook that decodes to INT8 for compute, so it runs on the same INT8 hardware as W8A8. Larger quality trade-off than INT6. Qwen-Image-2.1 7B — INT4 (W4A8) &amp; INT6 (W6A8) ConvRot for ComfyUI INT4 and INT6 quantized weights of Qwen-Image-2.1 for fast, low-VRAM inference in ComfyUI. This is a modified (quantized) version of the Qwen-Image-2.1 model. It is not an official Qwen release and is not endorsed by the Qwen team. About Qwen-Image-2.1 A unified text-to-image generation and image editing model in the Qwen family. With just 7B parameters in its visual generation component (32 Single-Stream DiT layers), Qwen-Image-2.1 balances generation quality, inference efficiency, and versatility. Highlights - Efficient Image Generation — combines strong visual performance with fast inference and a compact design, making high-quality image creation accessible across a wide range of creative workflows. - Flexible Creative Control — supports diverse inputs, outputs, and localized edits, giving creators the flexibility to explore ideas and refine details within a unified workflow. Key improvements in 2.1 - Compact and Efficient — lightweight architecture with mixed-granularity attention and prefix KV cache reuse delivers strong image quality at low computational cost. - Native Transparency, Unified Creation and Editing — generate regular or transparent (RGBA) images from text, edit transparent layers, and extract subjects from photographs — all in one model. - Versatile Editing — support up to 10 reference images, specify local edits via circles, painted annotations, or separate masks, and preserve identity for people and products. - Realistic Textures and Refined Aesthetics — improved typography, portrait lighting, and fine details for more visually compelling results. License Qwen-Image-2.1 is licensed under the [<a target="_blank" rel="ugc" href="https://huggingface.co/Qwen/Qwen-Image-2.1/blob/main/LICENSE">Qwen Research License</a>]<a target="_blank" rel="ugc" href="https://huggingface.co/Qwen/Qwen-Image-2.1/blob/main/LICENSE"> </a>These files are a quantized derivative and are distributed under the same license — see the original repository for full terms, permitted uses, and any commercial-use restrictions.

Qwen Image 2.1/Turbo INT4(W4A8)/INT6(W6A8)/INT8

qwen_image_2.1_W6A8_g32.safetensors

Base INT6(W6A8)

Files with SHA256: 595bcc9dc3b19888da2e5d0b4b6a5f6262dc6c756374f94c0a0e05e76eb5b766