How to Set Up Qwen-Image-2.1 in ComfyUI

Create a RunPod account

1. 1

   Sign up at RunPod

   Go to runpod.io and create an account (this is a referral link).

   Open runpod.io↗

1. 2

   Add funds to your account

   $10 covers a solid testing session with the default int8 setup. The int8 diffusion model and text encoder together are about 13.6GB, so most of your pod time goes to generation, not downloading.

Configure and deploy the pod

Pick your GPU based on the weight tier from the callout above: 12GB+ for the int8 default, 24GB+ for --full, 8-10GB+ for --gguf.

1. 3

   Pick a GPU that clears your chosen tier's VRAM floor

   From the dashboard, click Pods in the left nav, then Deploy Pod. Sort by VRAM and price.

1. 4

   Set the pod template to a PyTorch build

   Runpod Pytorch is the current default template; it ships CUDA and PyTorch pre-installed. If a different template is selected, hit Change template and pick it from the list.

2. 5

   Expose the ports ComfyUI needs

   Under Expose HTTP ports, set 8888,4000,8188,8889. 8188 is ComfyUI's default port, 8888 is Jupyter, and 8889 is where the optional Gradio UI further down this guide runs if you use it.

3. 6

   Set container disk to at least 40GB (60GB if using --full)

   The int8 and GGUF tiers fit comfortably in 40GB alongside ComfyUI and its Python environment; the bf16 tier needs more headroom.

4. 7

   Attach a network volume instead of relying on container disk alone

   A network volume survives stopping the pod; container disk doesn't. The model download (13.6GB+ depending on tier) is one you don't want to repeat every time you redeploy onto fresh container disk. Attach a 40GB+ network volume, mount it at /workspace, and the download happens once.

5. 8

   Deploy On-Demand

   On-Demand bills per second of actual usage, no upfront commitment, right for testing before you know how much you'll use this.

Run the setup script in Jupyter

1. 9

   Open Jupyter Lab

   On the pod's Connect tab, port 8888 shows "Jupyter Lab, Ready." Click it.

2. 10

   Download the setup script

   Grab setup_qwen_image_2_1_comfyui.py below and save it locally.

   Download the setup script (.py)↓
3. 11

   Upload the setup script

   In Jupyter, upload the file you just downloaded into /workspace.

4. 12

   Open a terminal and run the script

   python setup_qwen_image_2_1_comfyui.py for the int8 default. Add --full for bf16, or --gguf for the Q4_K_M quantized model. It clones ComfyUI and downloads the diffusion model, text encoder, and VAE for your chosen tier.

Open ComfyUI and load a workflow

1. 13

   Go back to the pod's Connect tab in RunPod

   Same screen you used to open Jupyter.

2. 14

   Open port 8188

   That's ComfyUI's default port, already exposed from the pod-template step above. Click it once the setup script finishes.

3. 15

   Close the pop-ups

   ComfyUI opens with a couple of dialogs on top. Close them to get to the empty canvas.

4. 16

   Open the Templates sidebar and search "qwen image 2.1"

   Three official Comfy-Org templates appear: Qwen Image 2.1: Text to Image, Qwen Image 2.1: Image Edit, and Remove Background: Qwen Image 2.1. Pick the one matching what you want to do.

5. 17

   Point each model loader at your downloaded files

   If a node shows a red missing-model error, press R to refresh the models list, then select your diffusion model, text encoder, and qwen_image_2.1_vae_bf16.safetensors in their respective loader nodes.

Text-to-image: the parameters that matter

Weight tiers and hardware

Pick one tier before running the setup script; VRAM figures are the practical floor for that tier, not a hard vendor-published minimum.

Background removal and transparency

Adding a LoRA

1. 18

   Place LoRA files in the loras folder

   The setup script creates ComfyUI/models/loras/ during install. Drop any Qwen-Image-2.1-compatible .safetensors LoRA there.

2. 19

   Add a Load LoRA node between the model loader and the sampler

   Expand the workflow's internal sub-graph (the node-group expansion button, top-right of the compact group), double-click the canvas, search Load LoRA, and wire its MODEL and CLIP outputs into the path the diffusion model and text encoder originally fed directly.

3. 20

   Set the LoRA strength

   Start around 0.8-1.0 and adjust from there; too high a strength on an image LoRA tends to distort anatomy and composition before it improves style fidelity.

Skip the node graph: a simple Gradio UI

If you don't want to touch ComfyUI's node graph at all, the setup script can start a small Gradio app alongside ComfyUI automatically. It talks to ComfyUI's API in the background and gives you three plain tabs: a prompt box for text-to-image, an image-upload box plus prompt for editing, and a one-click background remover. On the Image Edit tab, the file picker takes multiple images in one selection, and selecting again adds more rather than replacing what's already there - use Clear all reference images to start over.

1. 21

   Download the Gradio app before you run --serve

   Grab qwen_image_2_1_gradio.py below and upload it into /workspace, right next to setup_qwen_image_2_1_comfyui.py. The setup script looks for it in its own folder and launches it for you if it's there.

   Download the Gradio app (.py)↓
2. 22

   Run the setup script's --install step as normal

   python setup_qwen_image_2_1_comfyui.py --install. If the Gradio file is present, this also installs gradio, requests, and pillow alongside ComfyUI's own dependencies.

3. 23

   Run --serve as normal

   python setup_qwen_image_2_1_comfyui.py --serve. It starts ComfyUI, waits for it to answer, then starts the Gradio UI on top of it automatically on port 8889, already in the Expose HTTP ports list from the pod-template step above, so there's nothing extra to expose. Open port 8889 from the pod's Connect tab for the plain UI, or 8188 for the ComfyUI node graph - both are running at once.

What we tested

Real numbers from our own run, not vendor-quoted figures: RunPod RTX PRO 4000 Blackwell (24GB usable VRAM), EU-RO-1, secure cloud, $0.57/hr, int8 default, Text to Image template at the framework's 1024x1280 portrait resolution.

Troubleshooting