Qwen-Image ComfyUI Workflows

If you are evaluating Qwen-Image comfyui workflows, the appeal is an open text-to-image model that handles both pictures and the words inside them. Qwen-Image is Alibaba's open image model from the Qwen family, a 20B-parameter MMDiT released under Apache 2.0, so the weights are open and you can run it on your own hardware rather than renting generations. The workflows here are the ready-made graphs for that: load the model, write a prompt, and generate, with the node graph already wired so you are not building it from scratch.

The model is a solid generalist with a few specialties. It renders text in images more cleanly than most open models, which suits posters and graphics, and it supports structural conditioning through ControlNet so you can drive a composition from an edge map, a pose, or a depth map rather than a prompt alone. It also works with LoRA adapters, so you can pull in a trained style or subject, and the Qwen-Image 2512 refresh sharpens human realism and text layout over the original. There is also a layer-decompose path for separating an image into editable layers, and a Lightning LoRA that cuts a generation down to roughly four steps.

Because these are open workflows, you control the resolution, the step count, the conditioning, and any LoRA you load, and you can re-run until the composition and any text are right. That openness is the difference between a fixed one-shot tool and a graph you can tune for the exact look you are after.

Everything here is open and editable. Open a Qwen-Image workflow on Comfy Cloud with nothing to install, or download a GGUF build and run it locally from around 12GB of VRAM. You can swap conditioning, add a LoRA, adjust the parameters, and there is no watermark on what you generate.