unsloth/Qwen-Image-2.1-GGUF

Hugging Face Models Trending Models

Summary

Qwen-Image-2.1 is a unified text-to-image generation and image editing model with 7B parameters, featuring improvements in efficiency, transparency, versatility, and realism. This GGUF quantized version from unsloth enables efficient local inference.

Task: text-to-image Tags: gguf, quantized, unsloth, qwen, image-generation, text-to-image, en, zh, base_model:Qwen/Qwen-Image-2.1, base_model:quantized:Qwen/Qwen-Image-2.1, license:other, region:us
Original Article
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Cached at: 09/25/26, 09:13 PM

unsloth/Qwen-Image-2.1-GGUF · Hugging Face

Source: https://huggingface.co/unsloth/Qwen-Image-2.1-GGUF

https://huggingface.co/unsloth/Qwen-Image-2.1-GGUF#read-our-how-to-run-qwen-image-21-guide-%F0%9F%92%9CRead our How toRun Qwen-Image-2.1 Guide!💜

This is a GGUF quantized version ofQwen-Image-2.1. unsloth/Qwen-Image-2.1-GGUF usesUnsloth Dynamic 2.0methodology for SOTA performance.

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

https://huggingface.co/unsloth/Qwen-Image-2.1-GGUF#samplesSamples

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


🤖ModelScope| 🤗HuggingFace| 📑Blog| 🖥️Demo| 🫨Discord| 💬WeChat

https://huggingface.co/unsloth/Qwen-Image-2.1-GGUF#introductionIntroduction

We are excited to open-sourceQwen-Image-2.1, a unified text-to-image generation and image editing model in the Qwen family. With just7B 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 to10 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 theGitHub repoandBlog.

https://huggingface.co/unsloth/Qwen-Image-2.1-GGUF#quick-startQuick Start

https://huggingface.co/unsloth/Qwen-Image-2.1-GGUF#installationInstallation

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

https://huggingface.co/unsloth/Qwen-Image-2.1-GGUF#text-to-imageText-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")

https://huggingface.co/unsloth/Qwen-Image-2.1-GGUF#image-editingImage 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")

https://huggingface.co/unsloth/Qwen-Image-2.1-GGUF#transparent-image-generation-rgbaTransparent 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")

https://huggingface.co/unsloth/Qwen-Image-2.1-GGUF#supported-aspect-ratiosSupported 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),
}

https://huggingface.co/unsloth/Qwen-Image-2.1-GGUF#memory-optimizationMemory Optimization

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

https://huggingface.co/unsloth/Qwen-Image-2.1-GGUF#showcaseShowcase

Native transparent image generation

Group photograph generated from six portrait references

Text rendering

https://huggingface.co/unsloth/Qwen-Image-2.1-GGUF#licenseLicense

This model is licensed under theQwen Research License Agreement.

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