Repost From Huggingface <a target="_blank" rel="ugc" href="https://huggingface.co/Qwen/Qwen-Image-2.1">https://huggingface.co/Qwen/Qwen-Image-2.1</a><h2 id="introduction">Introduction</h2>We are excited to open-source 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.Four key improvements define this release:<ul><li>Compact and Efficient — A lightweight architecture with mixed-granularity attention and prefix KV cache reuse delivers strong image quality at low computational cost.</li><li>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.</li><li>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.</li><li>Realistic Textures and Refined Aesthetics — Improved typography, portrait lighting, and fine details for more visually compelling results.</li></ul>

Qwen Image 2.1

qwenImage21_21_txt_bf16.safetensors

qwenImage21_21_txt_int8.safetensors

qwenImage21_21_bf16.safetensors

qwenImage21_21_int8.safetensors

qwen_image_2.1_vae_bf16.safetensors

Originally released by <a rel="ugc" href="https://huggingface.co/Qwen/Qwen-Image-2.1">Qwen on Hugging Face</a>. All credit goes to the Qwen team.
Qwen Image 2.1 combines text-to-image generation and image editing in a single model. Its visual generation component has 7 billion parameters across 32 single-stream DiT layers, balancing image quality, efficiency and versatility.
The release introduces four main improvements:
<ul>
 <li>Efficient architecture: Mixed-granularity attention and reuse of cached prefixes reduce computation while maintaining image quality.</li>
 <li>Generation and editing with transparency: Create regular or transparent RGBA images, edit transparent layers and isolate subjects from photographs.</li>
 <li>Flexible editing: Work with up to ten reference images, guide local changes using circles, painted annotations or separate masks, and preserve the identity of people and products.</li>
 <li>Improved visual detail: Refined typography, portrait lighting, realistic textures and finer details.</li>
</ul>
Released under the <a rel="ugc" href="https://huggingface.co/Qwen/Qwen-Image-2.1/blob/main/LICENSE">Qwen Research License Agreement</a>. See the <a rel="ugc" href="https://huggingface.co/Qwen/Qwen-Image-2.1">original model card</a> for examples and usage instructions.

qwen_image_2.1_bf16.safetensors

qwen_image_2.1_int8_convrot.safetensors

qwen3.5_9b_qwen_image_2.1_pe_t2i.int8_convrot.safetensors

qwen3vl_8b_w4a8.safetensors

qwen3vl_8b_bf16.safetensors

qwen3vl_8b_int8_convrot.safetensors

qwen3.5_9b_qwen_image_2.1_pe_i2i.int8_convrot.safetensors

v2.1

v2.1 Turbo

<h2 id="introduction">Introduction</h2>We are excited to open-source 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.Four key improvements define this release:<ul><li>Compact and Efficient — A lightweight architecture with mixed-granularity attention and prefix KV cache reuse delivers strong image quality at low computational cost.</li><li>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.</li><li>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.</li><li>Realistic Textures and Refined Aesthetics — Improved typography, portrait lighting, and fine details for more visually compelling results.</li></ul><img src="https://qianwen-res.oss-accelerate.aliyuncs.com/Qwen-Image/image2.1/images/example-01.png" width="100%" /><h2 id="showcase">Showcase</h2><img src="https://qianwen-res.oss-accelerate.aliyuncs.com/Qwen-Image/image2.1/images/example-04.png" width="30%" /><img src="https://qianwen-res.oss-accelerate.aliyuncs.com/Qwen-Image/image2.1/images/example-05.png" width="30%" /><img src="https://qianwen-res.oss-accelerate.aliyuncs.com/Qwen-Image/image2.1/images/example-06.png" width="30%" />Native transparent image generation<img src="https://qianwen-res.oss-accelerate.aliyuncs.com/Qwen-Image/image2.1/images/example-15.png" width="100%" />Group photograph generated from six portrait references<img src="https://qianwen-res.oss-accelerate.aliyuncs.com/Qwen-Image/image2.1/images/example-43.png" width="48%" /><img src="https://qianwen-res.oss-accelerate.aliyuncs.com/Qwen-Image/image2.1/images/example-44.png" width="48%" />Text rendering

Qwen_Image_2.1

qwenImage21_bf16.safetensors

bf16

<h2 id="noct-q">Noct Q</h2>Qwen Image 2.1, uncensored out of the box, packaged for ComfyUI in two versions: Turbo (6 steps) and Base (full steps). Nudes and explicit scenes render without a LoRA.<h3 id="what's-new-in-v3">What's new in V3</h3><ul><li>Turbo rebuilt and sampled in 6 steps: sharper images and seeds as varied as the full model</li><li>In a blind test on the same 150 prompts and seeds as V2 Turbo, fewer defects with the same prompt adherence and explicit rendering</li><li>Base is a new model, stronger on explicit scenes</li></ul><h3 id="settings-v3">Settings V3</h3><ul><li>Turbo: 6 steps, euler, no CFG, 832x1248.</li><li>Base: 25–50 steps, euler, simple. V3 Base: cfg 3 with the negative prompt, both set in the workflow.</li></ul><h3 id="prompt-examples">Prompt examples</h3>The prompts of three V3 Turbo showcase images, written like captions of the finished picture: the medium first, then the woman, what she does, where, the light and the framing. All three use V3.0 Turbo int8 convrot in the V3 workflow (6 steps) at 832x1248, and the same seed should give the same picture.Attic room, seed 96060<blockquote>An amateur photograph of a nude woman in her twenties with long light-brown hair and full hips, kneeling on an unmade bed in a Parisian attic room with two round windows, stretching her arms above her head with her eyes closed. Morning light through the round windows, rooftops outside. Full body from the side, sharp focus, natural skin texture.</blockquote>Candlelit bath, seed 96120<blockquote>A phone photograph of a nude woman in her twenties with a blonde bob and a slim build, sitting on the edge of a deep stone bathtub surrounded by lit candles, testing the water with one hand and looking down at it. Warm candlelight, dark slate tiles. Full body from the side, sharp focus, natural skin texture.</blockquote>Oil painting, seed 96240<blockquote>An oil painting on canvas of a woman in her twenties with auburn hair pinned up loosely, wearing a long white linen dress, standing by a tall open window in a quiet Dutch interior and reading a folded letter, her face turned down toward it. Soft north light falls across her face and the dress, a blue-and-white jug stands on a wooden table and the floor has black and white tiles. Visible brush strokes on her skin, hair and dress, a muted ochre and blue palette. Full body</blockquote><h3 id="what-it-does-well">What it does well</h3><ul><li>Full-body nudes in daylight, flash and night scenes</li><li>Explicit scenes, oral most reliably</li><li>Readable text on signs and labels</li><li>Anime and painted illustration</li><li>Plain-caption prompts: who, what she does, where, the light</li><li>Speed: about 12 s for a 1024x1536 image on an 8 GB card with int8 convrot</li><li>Image editing with the same files, in ComfyUI's Qwen Image 2.1 edit template</li></ul><h3 id="which-file-by-vram">Which file by Vram</h3><ul><li>8–12 GB: int8 convrot (7.3 GB), the default, almost pixel-identical to bf16</li><li>6–8 GB: int4 mixed (4.6 GB), the fastest; compositions shift a little</li><li>GGUF Q8_0, Q6_K, Q5_K, Q4_K (7.6 to 4.1 GB), in v1.0</li><li>fp8 (7.3 GB): plain fp8 e4m3fn</li><li>bf16 and fp16 (14.2 GB): for merging and training; fp16 for GPUs without bf16</li></ul><h3 id="notes">Notes</h3><ul><li>Shibari is VALIDATED, may allow obscure position support don't hesitate to try</li></ul>中文：基于 Qwen Image 2.1，开箱即可生成裸体与成人场景，无需 LoRA。两个版本：Turbo（6 步，无 CFG）和 Base（完整模型，25–50 步，cfg 3）。8–12G 显存用 int8 convrot，6–8G 用 int4 mixed。仅限非商业用途。<h3 id="license">License</h3>Built with Qwen. Qwen is licensed under the Qwen RESEARCH LICENSE AGREEMENT, Copyright (c) 2026 Hangzhou Tongyi Laboratory Technology Co., Ltd. All Rights Reserved. Free, non-commercial use only. Full licence: <a target="_blank" rel="ugc" href="https://huggingface.co/Qwen/Qwen-Image-2.1/blob/main/LICENSE">https://huggingface.co/Qwen/Qwen-Image-2.1/blob/main/LICENSE</a>. LICENSE and NOTICE, with the list of changes, are in the zip.

Noct Q - Uncensored Realism (built on Qwen Image 2.1) | 夜曲 ✨

Qwen Image 2.1 Base in five formats: int8 convrot, int4 mixed, fp8, bf16, fp16. 25-50 steps, cfg 1, euler, simple. Uncensored out of the box. Built with Qwen.

Files with SHA256: 89f4158d066cc33906a199fca85634f766892dd78f49b6698dabf187ac86c4bc