unsloth/Qwen-Image-2.1-GGUF
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.
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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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