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How to run Qwen-Image 2.1 in ComfyUI on an 8 to 12 GB GPU with Awesome Qwen Image

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

  • Content
  • Intermediate
  • 1 hour
  • From the README
  • Any agent

Authorization notice. Awesome Qwen Image is dual-use. Only use it on software, sites and systems you own or have explicit permission to test.

Who it is forA ComfyUI user with a consumer GPU of 8 to 12 GB VRAM who wants a local image workflow.

The problemThe full BF16 model and text encoder are too large for a small GPU, and files must go in the right folders.

The outcomeA ComfyUI workflow that loads quantized Qwen-Image 2.1 weights and generates images on modest VRAM.

What you need first

  • 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

Steps

  1. Pick the Comfy-Org int8 ConvRot files if you want the official ComfyUI repackage, or the chfm INT4ConvRot-ComfyUI pack for 8 to 12 GB VRAM.
  2. Put the Image Model file in models/diffusion_models/, the text encoder in models/text_encoders/, and the VAE in models/vae/.
  3. Use a recent ComfyUI build, which loads ConvRot files with the standard diffusion-model and text-encoder loaders.
  4. Load the official ComfyUI native workflow example from the docs.comfy.org Qwen-Image 2.1 tutorial, or build a workflow with those loaders.
  5. Generate a test image and compare quality against the BF16 base if you have the VRAM.

Prompt for your agent

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/.

Caveat. ConvRot loading depends on a recent ComfyUI build, and the README does not name a specific version.

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