Qwen-Image-2.1

Qwen-Image-2.1

Qwen-Image-2.1 is a 7B text-to-image model from the Qwen team with strong text rendering. It is a different model from Qwen Image (2512 and Edit 2511), not an update you can swap in: it encodes prompts with the Qwen3-VL 8B model, the same encoder Ideogram 4 uses, and decodes with its own 64-channel VAE, which can produce images with a transparent background.

Two variants are available, and the variant is detected on install:

• Qwen-Image-2.1 — the base model. 40 steps by default.
• Qwen-Image-2.1 Turbo — distilled for 8 steps on a fixed schedule of its own.

Text-to-image, image-to-image, inpainting and outpainting on the Canvas are supported. Reference images (image editing), LoRAs and regional guidance are not supported yet.

License

Qwen-Image-2.1 is released under the Qwen Research License, which allows use for research and evaluation only. Commercial use needs a separate license from Alibaba’s Tongyi Lab. The same license covers the Comfy-Org and community repackages of the weights.

This is a summary, not legal advice. Read the full license on the model’s HuggingFace page before using a model commercially.

Installing Qwen-Image-2.1

The starter models install the complete Diffusers folders, which carry the transformer, the VAE and the Qwen3-VL 8B encoder:

• Qwen/Qwen-Image-2.1 — the base model, ~31 GB.
• Qwen/Qwen-Image-2.1-Turbo — the Turbo variant, ~30 GB.

You can also paste either repo ID into the Model Manager’s HuggingFace / URL field.

GGUF and single files

A GGUF or single-file transformer carries only the transformer. Pick the other two parts in the Components section next to the model; until both are set, the Invoke button tells you which one is missing:

• VAE — the Qwen-Image-2.1 VAE, from Comfy-Org’s vae/qwen_image_2.1_vae_bf16.safetensors or the vae folder of a Diffusers download. The VAEs of Qwen Image, Wan and FLUX do not fit and are not offered.
• Qwen3-VL Encoder — the Qwen3-VL 8B encoder, for example the Qwen3-VL 8B Encoder starter model, which Ideogram 4 uses as well. The 4B encoder Krea-2 uses does not work and is not offered. A llama.cpp GGUF of the 8B encoder works too; see Qwen3-VL GGUF encoders.

The Qwen-Image-2.1 (Q4_K_M GGUF) starter model, from unsloth/Qwen-Image-2.1-GGUF, installs the VAE and the 8B encoder with it, ~15 GB in all. Other quantization levels from that repository load the same way.

Single-file transformers in bf16, scaled fp8 or int8_convrot (ComfyUI repackages) load as well. nvfp4 files, torchao-quantized files (such as unsloth’s FP8 build), and int8 files without ComfyUI’s int8_convrot marker, are refused at install. GGUF files with int8 layers (ComfyUI-GGUF’s Q8_CR) are refused too.

The Turbo variant is recognized from the filename: a single file or GGUF whose name contains turbo installs as Turbo, anything else as the base model. If a file was detected as the wrong variant, edit the model in the Model Manager and change its Variant. The default settings were chosen at install, so also set Steps under Default Settings: 8 for Turbo, 40 for the base model.

Memory and speed

Measured on an RTX 4090 at 1024×1024:

Transformer	Resident	Time per image
bf16 (Diffusers)	13.9 GB during denoising	0.62 s per step, about 25 s for 40 steps
GGUF Q4_K_M	4.0 GB	about 40 s for 40 steps

The text encoder runs first and is unloaded from the GPU when the transformer needs the room. At 2048×2048 the bf16 transformer needs 15.5 GB and 3.7 s per step.

The VAE needs about 8 GB of working memory to decode a 1024×1024 image, and is priced the same to encode one for image-to-image, inpainting or outpainting (twice that with precision: float32). When an image is too large for the card to handle in one piece, Invoke decodes and encodes it in tiles; see Large decodes. Invoke picks 1024-pixel tiles, and 768 or 512 only on cards with less memory: measured at 2048×2048, 1024-pixel tiles came close to an untiled decode (47.7 dB PSNR), while the 256-pixel tiles other VAEs default to left visible seams (35.6 dB).

Transparent images

The Qwen-Image-2.1 VAE decodes an alpha channel along with the colors, so a prompt such as a sticker of a red fox on a transparent background produces a cut-out image. An image is saved with transparency only when it has some: when more than 0.1% of its pixels are noticeably transparent. Opaque images are saved as ordinary RGB, so the faint edges tiled decoding leaves in the alpha channel never turn an opaque image transparent.

Image-to-image, inpainting and outpainting read the colors of the input image and treat it as fully opaque, whatever its alpha channel holds.

Generation settings

• Steps — 40 for the base model and 8 for Turbo. Turbo’s schedule is tuned for 8 steps; other step counts are spread over the same curve and work, but 8 is what it was distilled for.
• CFG — 1 by default, which runs one prompt per step. Above 1, the negative prompt is used and each step costs about twice as long.
• There is no scheduler setting: both variants use their own schedule.
• Denoising strength (image-to-image, inpainting) — on the base model a strength changes the image about as much as on Qwen-Image or Z-Image. Turbo’s 8-step schedule spends most of its steps at high noise, so there a strength sets the starting noise level directly (0.5 starts at noise level 0.5): the same value changes the image less on Turbo than on the base model.

Image size and the noise schedule

The base model puts more of its steps at high noise the larger the image, up to the area of about 1448×1448 (2.1 megapixels). Larger images keep that schedule. Without the limit, an image-to-image pass at strength 0.5 would start at 77% noise on a 2048×2048 image and at 95%, almost pure noise, on a 4096×4096 one, so it would replace the image instead of refining it; with it, both start at 70%. Text-to-image above that size uses the held schedule too, so it differs from diffusers’ QwenImage21Pipeline there, and base-model images made at those sizes with an earlier Invoke version don’t reproduce exactly with the same seed. Turbo’s fixed schedule does not depend on the image size.