Conferences
28
September
28-01
September -
October
Estoril, Portugal
28
September
29
September
29
September
29
September
Lisbon, Portugal
30
September
30
September
Lisbon, Portugal
01
October
Lisbon, Portugal
01
October
01
October
Moscow, Russia
02
October
05-08
October
Moscow, Russia
07
October
14-16
October
14
October
Sofia, Bulgaria
19-20
October
19-21
October
Copenhagen, Denmark
22
October
22-23
October
Rome, Italy
02-05
November
Rome, Italy
02
November
02
November
04
November
04
November
05
November
09-10
November
09
November
Mexico City, Mexico
22
November
23-25
November
24-25
November
Colombo, Sri Lanka
28
November
Colombo, Sri Lanka
30-02
November -
December
01-02
December
Vienna, Austria
03-04
December
03-04
December
Vienna, Austria
04
December
07-11
December
08
December
Bangkok, Thailand
08
December
08
December
08
December
Bangkok, Thailand
09-10
December
10
December
Bangkok, Thailand
10
December
Koh Chang, Thailand
12-15
December
Gaborone, Botswana
15-18
February
17
February
18
February
Mexico City, Mexico
24-26
February
Rio de Janeiro, Brazil
02-04
March
09-11
March
Amsterdam, Netherlands
19-21
April
21-23
April
Ta’ Qali, Malta
03-05
May
Munich, Germany
13-14
May
Düsseldorf, Germany
18-19
May
Limassol, Cyprus
10-11
June
Ho Chi Minh City, Vietnam
14
June
Toronto, Canada
15-17
June
05-06
July

Qwen-Image-2.1: Installing and Testing a Free Local Image Generation Model


We tested Alibaba’s free local image generation model, Qwen-Image-2.1, which can create and edit images. 

Qwen-Image-2.1 is a compact model that can run locally. It can generate and edit images from text prompts, supports transparency, and can work with multiple references simultaneously.

A quantized model is a model whose weights are stored with lower numerical precision. It takes up less space and usually requires less memory, but may lose some quality.

We tested two quantized versions on two different hardware setups:

Qwen-Image-2.1 on DGX Spark in ComfyUI 0.37.0. The following components were installed:

  • a 7B generator in Q8_0 quantization;
  • Qwen3-VL 8B text encoder: INT8 ConvRot from Comfy-Org.
  • VAE in BF16;
  • two prompt enhancement models based on Qwen3.5-VL 9B: one for generation and one for editing.
  • About 36 GB total on disk.

Qwen-Image-2.1 on a Mac with an M3 Pro and 18 GB of memory via stable-diffusion.cpp, build master-920-2f88688, with Metal acceleration.

  • a 7B generator in Q4_K_M quantization;
  • Qwen3-VL 8B text encoder in UD-Q4_K_XL quantization;
  • a separate mmproj vision module in F16 for working with references;
  • VAE in BF16.
  • Prompt enhancement models were not installed.
  • About 11.2 GB of weights total on disk.

The models we tested had no censorship, all prompts were completed without refusals!

Generation Results

We used a proven prompt from the article https://cpa.rip/prompts/ai-prompts-igaming/.

Compressed Q4 on a Macbook Pro M3 pro 18Gb, 512×512 size, generation time: 5 minutes, 47 seconds:

Qwen-Image-2.1 Q8 on DGX Spark, 1092×1088 size, generation time: 10 seconds:

We uploaded the ad creative to an LLM and asked it to create a description in JSON format. Based on this description, Qwen-Image-2.1 generated a similar image:

Testing editing of a finished reference on Q8:

When generating images with a large amount of text, errors and artifacts may occur:

Available Qwen-Image-2.1 Modes

Qwen-Image-2.1 combines generation and editing in one model. The following modes are available:

  • Text-to-image generation – creating an image from scratch.
  • Image editing – modifying an uploaded image with a text command, such as changing the background, clothing color, or an object.
  • Working with multiple references – combining objects, characters, and details from different references. The model supports up to 10 images.
  • Generation and editing with transparency – creating objects on a transparent background and modifying existing RGBA images.
  • Object extraction – isolating a person or object from a photo while removing the surrounding background.
  • Local edits – modifying a selected area marked with a circle, a drawn annotation, or a separate mask.

Installation With Codex/Claude Code

The easiest way to install Qwen-Image-2.1 locally is to give an LLM a command, and it will install, configure, and test everything. Example command:

Install for me on Mac https://huggingface.co/Qwen/Qwen-Image-2.1?spm=a2ty_o06.30285417.0.0.5e12c921YNM0wk&file=Qwen-Image-2.1

Codex selected the compressed Q4_K_M version on its own, which will run on your hardware. If necessary, you can specify the exact model:


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