Qwen-Image-2.1-GGUF

Read our How to Run Qwen-Image-2.1 Guide! 💜

This is a GGUF quantized version of Qwen-Image-2.1. <br> unsloth/Qwen-Image-2.1-GGUF uses Unsloth Dynamic 2.0 methodology for SOTA performance.

• Important layers are upcasted to higher precision, per tensor, from a measured sensitivity scan.
• Run these with Unsloth Desktop, stable-diffusion.cpp and more. A GGUF is the denoiser only, so it needs the VAE and the Qwen3-VL text encoder alongside it.
• VAE: unsloth/Qwen-Image-2.1-FP8 vae/qwen_image_2.1_vae_bf16.safetensors. Text encoder: unsloth/Qwen3-VL-8B-Instruct-GGUF Qwen3-VL-8B-Instruct-UD-Q4_K_XL.gguf, the Dynamic 2.0 4-bit rung rather than the uniform Q4_K_M. Measured against the Q4_K_M encoder at a shared seed, with the denoiser and VAE held fixed: LPIPS 0.029, SSIM 0.959, 5.15 GB vs 5.03 GB, 36.5 s vs 39.0 s.

<div> <div style="display: flex; gap: 5px; align-items: center; "> <a href="https://github.com/unslothai/unsloth/"> <img src="https://github.com/unslothai/unsloth/raw/main/images/unsloth%20new%20logo.png" width="133"> </a> <a href="https://discord.gg/unsloth"> <img src="https://github.com/unslothai/unsloth/raw/main/images/Discord%20button.png" width="173"> </a> <a href="https://unsloth.ai/docs/models/qwen-image-2.1"> <img src="https://raw.githubusercontent.com/unslothai/unsloth/refs/heads/main/images/documentation%20green%20button.png" width="143"> </a> </div> </div> See below for image editing operating inside of Unsloth Desktop: <img width="600" alt="qwen-image-2.1 unsloth desktop" src="https://unsloth.ai/docs/~gitbook/image?url=https%3A%2F%2F3215535692-files.gitbook.io%2F%7E%2Ffiles%2Fv0%2Fb%2Fgitbook-x-prod.appspot.com%2Fo%2Fspaces%252FxhOjnexMCB3dmuQFQ2Zq%252Fuploads%252F8E4zaQZmeYBhYIXkvveM%252F01-2edit-multi-image.webp%3Falt%3Dmedia%26token%3Dad7d102c-b304-4c75-b66d-4a68cd518a37&width=768&dpr=3&quality=100&sign=a5a37f593c66ddc27c847bab0d138eda&sv=3" />

sd-cli --diffusion-model qwen-image-2.1-Q4_K_M.gguf \
  --vae qwen_image_2.1_vae_bf16.safetensors \
  --llm Qwen3-VL-8B-Instruct-UD-Q4_K_XL.gguf \
  -p "a cartoon sloth mascot waving, flat vector illustration, bright colours" \
  --steps 20 --cfg-scale 6.0 --sampling-method euler -W 1024 -H 1024 --diffusion-fa \
  -o out.png

Samples

Rendered with the Q4_K_M denoiser and the Q4_K_M text encoder, 1024x1024, 20 steps, cfg 6.0, euler.

<table> <tr> <td><img src="assets/cafe.png" width="200"></td> <td><img src="assets/spaces.png" width="200"></td> </tr> <tr> <td><img src="assets/cinema.png" width="200"></td> <td><img src="assets/cardesert.png" width="200"></td> </tr> </table>

---

<p align="center"> <img src="https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-Image/image2.1/logo.png" width="400"/> </p> <p align="center"> 🤖 <a href="https://modelscope.cn/models/Qwen/Qwen-Image-2.1">ModelScope</a>&nbsp;&nbsp;| &nbsp;&nbsp;🤗 <a href="https://huggingface.co/Qwen/Qwen-Image-2.1">HuggingFace</a>&nbsp;&nbsp;| &nbsp;&nbsp;📑 <a href="https://qwen.ai/blog?id=qwen-image-2.1">Blog</a>&nbsp;&nbsp;| &nbsp;&nbsp;🖥️ <a href="https://huggingface.co/spaces/Qwen/Qwen-Image-2.1">Demo</a>&nbsp;&nbsp;| &nbsp;&nbsp;🫨 <a href="https://discord.gg/BEYSk3pkSu">Discord</a>&nbsp;&nbsp;| &nbsp;&nbsp;💬 <a href="https://huggingface.co/Qwen/Qwen-Image-2.1/blob/main/assets/qr.png">WeChat</a> </p>

Introduction

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:

• Compact and Efficient: a 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.

<p align="center"> <img src="https://qianwen-res.oss-accelerate.aliyuncs.com/Qwen-Image/image2.1/images/example-01.png" width="100%"/> </p>

For more details, see the GitHub repo and Blog.

Quick Start

Installation

pip install torch>=2.4.0
pip install transformers>=5.17
pip install git+https://github.com/huggingface/diffusers
pip install accelerate pillow

Text-to-Image

import torch
from diffusers import QwenImage21Pipeline

pipe = QwenImage21Pipeline.from_pretrained(
    "Qwen/Qwen-Image-2.1", torch_dtype=torch.bfloat16
).to("cuda")

image = pipe(
    prompt="A neon shop sign that reads \"QWEN IMAGE 2.1\", rainy night, reflections on wet pavement",
    width=2048, height=2048,
    num_inference_steps=40,
    generator=torch.Generator("cuda").manual_seed(42),
).images[0]

image.save("t2i_example.png")

Image Editing

import torch
from PIL import Image
from diffusers import QwenImage21Pipeline

pipe = QwenImage21Pipeline.from_pretrained(
    "Qwen/Qwen-Image-2.1", torch_dtype=torch.bfloat16
).to("cuda")

input_image = Image.open("input.png")

image = pipe(
    prompt="Change the background to a sunset beach",
    image=input_image,
    num_inference_steps=40,
    generator=torch.Generator("cuda").manual_seed(42),
).images[0]

image.save("edit_example.png")

Transparent Image Generation (RGBA)

Use the recommended prompt format for transparent images:

image = pipe(
    prompt="This is an RGBA image with transparency. A cute cartoon dragon sticker. The image has alpha channel and the background is transparent.",
    width=2048, height=2048,
    num_inference_steps=40,
    generator=torch.Generator("cuda").manual_seed(42),
).images[0]

image.save("transparent_example.png")

Supported Aspect Ratios

aspect_ratios = {
    "1:1":  (2048, 2048),
    "4:3":  (2400, 1792),
    "3:4":  (1792, 2400),
    "3:2":  (2528, 1696),
    "2:3":  (1696, 2528),
    "16:9": (2752, 1536),
    "9:16": (1536, 2752),
}

Memory Optimization

pipe = QwenImage21Pipeline.from_pretrained(
    "Qwen/Qwen-Image-2.1", torch_dtype=torch.bfloat16
)
pipe.enable_model_cpu_offload()

Showcase

<p align="center"> <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%"/> </p> <p align="center"><em>Native transparent image generation</em></p> <p align="center"> <img src="https://qianwen-res.oss-accelerate.aliyuncs.com/Qwen-Image/image2.1/images/example-15.png" width="100%"/> </p> <p align="center"><em>Group photograph generated from six portrait references</em></p> <p align="center"> <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%"/> </p> <p align="center"><em>Text rendering</em></p>

License

This model is licensed under the Qwen Research License Agreement.