HunyuanImage 2.1 is Tencent's 17-billion-parameter text-to-image model for native 2K images. Tencent publishes weights and inference code, with a documented Linux and CUDA setup for running the model yourself. Repository
What are the key facts about HunyuanImage 2.1?
| Field | Verified information |
|---|---|
| Developer | Tencent Hunyuan team. Model card |
| Released | September 8, 2025, for inference code and weights. Repository |
| Type | Text-to-image diffusion transformer with a separate refinement stage. Model card |
| Size or parameters | 17 billion parameters for the image model. Repository |
| License and access | Downloadable weights under the Tencent Hunyuan Community License Agreement. License |
| Where it runs | Documented local setup uses Linux and an NVIDIA CUDA GPU. Repository |
What can HunyuanImage 2.1 generate at 2K?
HunyuanImage 2.1 targets native high-resolution generation. Tencent documents Chinese and English prompting, several aspect ratios, and a character-aware text encoder intended to improve lettering. Model card
That makes it relevant to briefs where layout and readable objects matter together: a shop entrance, an illustrated package concept or a room containing signs. Treat these as candidate applications to test with your own prompts.
The published system separates the base image generator from a refiner. Tencent also provides prompt enhancement, allowing a short request to be expanded before generation. Model card
For evaluation, save the expanded prompt when you use one. The output should be judged against the description actually sent to the generator, especially when added details affect products, people or written text.
Consider preparing a small set of briefs before installation. Include a portrait, a scene with several objects and a layout containing text, then decide which errors would make each result unusable for your project.
What hardware and licensing limits apply to HunyuanImage 2.1?
Tencent's published minimum is 24 GB of GPU memory for a square 2048-by-2048 output, measured with FP8 quantization and CPU offloading enabled. This is a conditional configuration. Model card
The repository warns that 1K generation produces artifacts. Use its supported 2K width and height pairs when establishing a baseline rather than reducing dimensions to imitate a smaller image model. Repository
The license explicitly excludes the European Union, United Kingdom and South Korea from its defined territory. Read the actual agreement when deciding whether its grant covers your planned use. License
The download guide also specifies external text-encoding components. It names Qwen2.5-VL-7B-Instruct as the community alternative for the multimodal encoder, alongside ByT5-related files. Checkpoint guide
Account for the whole pipeline when preparing disk space and an environment. A parameter count describes a model component; it is not a complete installation-size estimate or a guarantee that every optional stage fits together.
No fixed end-to-end latency is promised here. Record your own runtime with the model variant, precision, offloading setting, resolution and refinement choice so that the result is meaningful to someone reproducing your setup.
How do you install and run HunyuanImage 2.1?
Prepare the environment and checkpoints
- Review the Tencent license and select an eligible machine or deployment environment. Use the repository's Linux and CUDA requirements as the installation baseline.
- Clone the official repository and install its Python dependencies in an environment dedicated to this project.
- Follow the checkpoint guide for the image weights and each required text encoder. Keep the documented directory layout.
- Choose a base or distilled configuration, then use the matching example in the repository. Treat refinement and prompt enhancement as explicit settings.
These are the documented initial installation commands. Run them in your intended Python environment; they download code and dependencies, but do not yet configure every checkpoint. Repository Checkpoint guide
git clone https://github.com/Tencent-Hunyuan/HunyuanImage-2.1.git
cd HunyuanImage-2.1
pip install -r requirements.txt
pip install flash-attn==2.7.3 --no-build-isolation
pip install -U "huggingface_hub[cli]"
hf download tencent/HunyuanImage-2.1 --local-dir ./ckpts
Continue with the separate encoder downloads in Tencent's checkpoint instructions. The image-model download alone is not the full setup; the guide supplies the exact destinations for the additional files. Checkpoint guide
Before generating, check that the selected example can find each component. When a run fails, distinguish missing files from missing Python dependencies and GPU allocation failures; each calls for a different correction.
Establish a reproducible first image
Use the repository's square 2K example as the initial configuration. Change its prompt to describe a single subject, a setting and one visible action, keeping the other settings stable for the first successful run.
Then test a more demanding brief: an illustrated tea shop with a readable sign, a counter in the foreground and a window behind it. Decide the required text before generation so proofreading has a concrete target.
Inspect lettering at full size. Check that each object belongs to the right person or location and that the refinement stage has not altered a detail you need to retain. Save the image with its actual generation settings.
If you enable prompt enhancement, compare the rewritten request with your brief before accepting the output. Treat added descriptions as proposed creative choices that you can revise, especially for product attributes.
For graph-based work, Tencent links a ComfyUI workflow from its repository. Use that model-specific example alongside the ComfyUI complete guide. Repository
Keep later family releases separate when comparing configurations. The sibling HunyuanImage 3.0 page covers a different release; its requirements should not be substituted here.
How does HunyuanImage 2.1 compare with Qwen-Image?
| Model | Published distinction | Deployment decision |
|---|---|---|
| HunyuanImage 2.1 | Native 2K generation with a base and refiner pipeline. Model card | Plan for the documented components and Tencent license. |
| Qwen-Image | Open image model emphasizing Chinese and English text rendering. Qwen launch | Evaluate its separate workflow and Apache 2.0 weight license. Qwen card |
Compare outputs using the same visible requirements, not parameter counts alone. A candidate passes only if it satisfies the subject, layout and text constraints your application actually needs.
What else should you know before running HunyuanImage 2.1?
Is HunyuanImage 2.1 a 17B model?
Yes, Tencent identifies HunyuanImage 2.1 as having 17 billion parameters in its image model. The complete workflow additionally uses text encoders and may include refinement and prompt enhancement. Model card
Can I run it with 24 GB of VRAM?
Tencent documents HunyuanImage 2.1 generating a 2048-by-2048 image with 24 GB of GPU memory using FP8 quantization and CPU offloading. Follow the corresponding settings and account for optional stages rather than assuming every configuration has the same requirement. Repository
Can I generate smaller images to save memory?
Tencent warns that 1K generation causes artifacts in HunyuanImage 2.1. Establish a supported 2K configuration before experimenting with a different pipeline or output-processing strategy. Repository
Are the weights Apache-licensed?
HunyuanImage 2.1 weights use the Tencent Hunyuan Community License Agreement. It has specific conditions and territorial scope, so consult that document before deploying or redistributing the weights. License
Sources
- Tencent repository and inference instructions
- Tencent model card
- Tencent license agreement
- Tencent checkpoint download guide
- Qwen-Image release announcement
- Qwen-Image model card
Top comments (0)