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

This is a GGUF quantized version of Qwen-Image-2.1.

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.

See below for image editing operating inside of Unsloth Desktop: qwen-image-2.1 unsloth desktop

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.

![](assets/cafe.png)
![](assets/spaces.png)




![](assets/cinema.png)
![](assets/cardesert.png)

![](https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-Image/image2.1/logo.png)




🤖 [ModelScope](https://modelscope.cn/models/Qwen/Qwen-Image-2.1)  |
  🤗 [HuggingFace](https://huggingface.co/Qwen/Qwen-Image-2.1)  |
  📑 [Blog](https://qwen.ai/blog?id=qwen-image-2.1)  |
  🖥️ [Demo](https://huggingface.co/spaces/Qwen/Qwen-Image-2.1)  |
  🫨 [Discord](https://discord.gg/BEYSk3pkSu)  |
  💬 [WeChat](https://huggingface.co/Qwen/Qwen-Image-2.1/blob/main/assets/qr.png)

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.

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

Native transparent image generation

Group photograph generated from six portrait references

Text rendering

License

This model is licensed under the Qwen Research License Agreement.