Qwen-Image-2.1-Turbo locally: 8 steps, which files to download, and the non-commercial license catch
Qwen-Image-2.1-Turbo is an 8-step checkpoint of Alibaba's 7B Qwen-Image-2.1 for text-to-image and editing. The full Diffusers download is about 32.4 GB; the smallest ComfyUI set is about 14.3 GB. Here are the setup steps, sampler rules and the Qwen Research License limits.
This weekend, r/StableDiffusion has been running side-by-side tests of Qwen-Image-2.1-Turbo against MOSS-Image, Krea 2 Turbo and FLUX 3 Image Klein. The Qwen team released Turbo on October 9, 2026, according to the Qwen-Image-2.1 GitHub repo. If you want to try it yourself, here is what to download, how to set it up and what the license allows.
What Turbo is
The model card calls Turbo an accelerated checkpoint of Qwen-Image-2.1 for text-to-image and image editing in 8 denoising steps. The base model’s default is 40. Turbo keeps the same 7B visual generation architecture. The repo lists it as a 32-layer single-stream DiT, with Qwen3-VL 8B as the text encoder and a 64-channel RGBA VAE, so both checkpoints can output transparent images. Generation uses CFG 1 by default. Both checkpoints target the 2048 resolution level: 2048×2048 at 1:1, 2752×1536 at 16:9 and 1696×2528 at 2:3.
Download sizes
From the Hugging Face file listings:
| Setup | Files | Size on disk |
|---|---|---|
| Diffusers (original repo) | bf16 transformer (2 shards) + bf16 text encoder + VAE | about 32.4 GB |
| ComfyUI, full precision | qwen_image_2.1_turbo_bf16 + qwen3vl_8b_bf16 + VAE | about 32.4 GB |
| ComfyUI, smallest | qwen_image_2.1_turbo_int8_convrot (7.26 GB) + qwen3vl_8b_w4a8 (6.31 GB) + VAE (0.68 GB) | about 14.3 GB |
The transformer alone is 14.23 GB in bf16, and the text encoder is 17.53 GB. Neither Qwen nor Comfy-Org publishes a VRAM requirement, so treat these sizes as a lower bound, not a guarantee. For smaller GPUs, the repo’s documented fallback is pipe.enable_model_cpu_offload().
Diffusers setup
The model card says to install Diffusers from source, plus transformers>=5.17.0, accelerate and pillow. Turbo needs pipeline-configured sampling sigmas from diffusers PR #14950. The v0.41.0 release notes list that PR, so v0.41.0 or newer should also work. Load the model with QwenImage21Pipeline.from_pretrained("Qwen/Qwen-Image-2.1-Turbo", dtype=torch.bfloat16) and pass use_kv_cache=True. The prefix KV cache encodes the text and reference images once and reuses them across steps, which mainly speeds up edits with several reference images.
One gotcha: the 8-step schedule ships with the checkpoint. Setting num_inference_steps alone does not override it. Only an explicit sigmas argument does, and Qwen says other schedules have not been evaluated.
ComfyUI setup
ComfyUI has supported Qwen-Image-2.1 natively since its September 20 release. The Comfy-Org/Qwen-Image-2.1 repo now includes the Turbo files. Put the diffusion model in models/diffusion_models/, the text encoder in models/text_encoders/ and qwen_image_2.1_vae_bf16.safetensors in models/vae/. Comfy-Org says to use 8 steps and CFG 1. Instead of the full Turbo model, Comfy-Org also offers a 0.91 GB Turbo LoRA extracted from the base model (qwen_image_2.1_turbo_lora_avg_rank_178_bf16), which goes in models/loras/. The official text-to-image and edit workflow templates are a good starting point.
Other ways to run it
On Apple Silicon, Qwen points to the LightX2V-Mac-App. For a hosted option, Pro and Turbo APIs went live on Alibaba Cloud Model Studio on October 9. vLLM-Omni and SGLang support Qwen-Image-2.1 too, but the repo’s serving examples are written for the 40-step base checkpoint.
The license catch
Unlike MOSS-Image’s Apache-2.0 tag, Turbo uses the Qwen Research License. It grants rights for non-commercial purposes only, defined as research or evaluation. Commercial use needs a separate license from Qwen. If you distribute a model trained on Qwen-Image outputs, you must show “Built with Qwen” or “Improved using Qwen”. For a smaller, permissively licensed local setup, see our Bonsai Image 4B guide.