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What you can do with Awesome Qwen Image

A curated list of checkpoints, quantizations, prompt rewriters, LoRAs, and tooling for Qwen-Image 2.1, Alibaba's 7B unified text-to-image and image-editing model.

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wildminder/awesome-qwen-image on GitHub

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Authorization notice. This tool is dual-use. Only use it on software, sites and systems you own or have explicit permission to test, and follow the relevant licenses and terms of service.

What do you want to do?

5 use cases

  • Generate images with the official Qwen-Image-2.1 base model

    Run the reference BF16 Diffusers checkpoint for text-to-image and editing.

    View prompt
    Using the wildminder/awesome-qwen-image list, load the official Qwen/Qwen-Image-2.1 model with QwenImage21Pipeline in Diffusers and generate an image from the prompt <prompt>. Report the output file path and the step count used. Verify that the image matches the prompt and that the default 40 steps without CFG ran.

  • Run Qwen-Image 2.1 in ComfyUI on an 8 to 12 GB GPU

    Use the INT4 ConvRot pack or Comfy-Org int8 files in a recent ComfyUI build.

    View prompt
    Using wildminder/awesome-qwen-image, set up ComfyUI with the int8 ConvRot Image Model and Text Encoder from Comfy-Org/Qwen-Image-2.1 in the folders listed in the README, then generate one image from the prompt <prompt>. Report the file paths used and the output image path. Verify that the Image Model is in models/diffusion_models/ and the text encoder is in models/text_encoders/.

  • Generate images in 8 steps with the official Turbo model

    Use Qwen's own 8-step checkpoint with CFG 1.0 for faster generation.

    View prompt
    Using wildminder/awesome-qwen-image, load Qwen/Qwen-Image-2.1-Turbo in a diffusers build that supports pipeline-configured sampling and generate one image in 8 steps with CFG 1.0 from the prompt <prompt>. Report the output path and the step and CFG values. Verify that the sampler was configured by the pipeline and not set by hand.

  • Rewrite short requests into detailed image prompts

    Use the official PE-T2I rewriter to expand a brief idea and get an aspect ratio.

    View prompt
    Using wildminder/awesome-qwen-image, load the Qwen/Qwen-Image-2.1-PE-T2I rewriter with AutoModelForCausalLM and AutoTokenizer, rewrite the request <request> using its system_prompt.txt, and return the English prompt and wh_ratio as JSON. Verify that you split the output on the think-tag before parsing JSON.

  • Run Qwen-Image 2.1 locally with llama.cpp and GGUF quants

    Pick a GGUF quant from Q2_K to Q8_0 that fits your memory budget.

    View prompt
    Using wildminder/awesome-qwen-image, download one GGUF quant between Q2_K and Q8_0 from unsloth/Qwen-Image-2.1-GGUF that fits <memory budget> and run it with llama.cpp on the prompt <prompt>. Report the chosen quant and output path. Verify the file loaded without errors and that the output matches the prompt.

How to use Awesome Qwen Image

  1. Check you have what it needs: A runtime that matches the chosen entry point: Diffusers, ComfyUI, llama.cpp, or MLX on Apple Silicon; Enough disk space: the base text encoder alone is about 17.5 GB in BF16; A GPU with 8 to 12 GB VRAM for the INT4 ConvRot pack, or a Mac with Apple Silicon for MLX-4bit.
  2. Open wildminder/awesome-qwen-image on GitHub and read the README and license.
  3. Open the official Qwen-Image-2.1 model card on Hugging Face and read the license and aspect-ratio table.
  4. Download the whole Qwen/Qwen-Image-2.1 repo, not a single file, because the DiT and text encoder ship as numbered safetensors shards with an index.
  5. Load the model with the QwenImage21Pipeline class in Diffusers.
  6. Run a sample prompt using the default of 40 steps without classifier-free guidance, then try CFG if prompt adherence is weak.

These steps come from our top use case, Generate images with the official Qwen-Image-2.1 base model, which also has a copy-paste agent prompt.

Awesome Qwen Image alternatives · Best AI repos for Content

About this repo

Requirements

  • A runtime that matches the chosen entry point: Diffusers, ComfyUI, llama.cpp, or MLX on Apple Silicon
  • Enough disk space: the base text encoder alone is about 17.5 GB in BF16
  • A GPU with 8 to 12 GB VRAM for the INT4 ConvRot pack, or a Mac with Apple Silicon for MLX-4bit
  • Acceptance of the Qwen Research License that covers the base weights
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Questions about Awesome Qwen Image

What is Awesome Qwen Image?

A curated list of checkpoints, quantizations, prompt rewriters, LoRAs, and tooling for Qwen-Image 2.1, Alibaba's 7B unified text-to-image and image-editing model. It is an open-source project on GitHub: https://github.com/wildminder/awesome-qwen-image.

Who is Awesome Qwen Image for?

Creators, developers, and AI agents who need to pick the right Qwen-Image 2.1 file for their runtime, VRAM budget, or Mac.

What can I do with Awesome Qwen Image?

5 use cases so far, for example: Generate images with the official Qwen-Image-2.1 base model; Run Qwen-Image 2.1 in ComfyUI on an 8 to 12 GB GPU; Generate images in 8 steps with the official Turbo model. Each has steps and a copy-paste agent prompt.

What do I need to run Awesome Qwen Image?

A runtime that matches the chosen entry point: Diffusers, ComfyUI, llama.cpp, or MLX on Apple Silicon; Enough disk space: the base text encoder alone is about 17.5 GB in BF16; A GPU with 8 to 12 GB VRAM for the INT4 ConvRot pack, or a Mac with Apple Silicon for MLX-4bit; Acceptance of the Qwen Research License that covers the base weights.

What are alternatives to Awesome Qwen Image?

Other hot AI repos in Content: Voice Studio, Anatomy, Comfy UI, Hyperframes, Earshot.

Is Awesome Qwen Image safe to run?

Deduped does not own, audit or vouch for it. Read the README and license, run it on your own systems first and review every command before you run it.