Изображения
Bagel
ByteDance's open-source unified multimodal model for understanding, generation and image editing.
★ 6,2 тыс. звёздApache-2.0
Смотреть руководство →HunyuanImage-3.0 — проект с открытым исходным кодом из категории «Изображения» от Tencent-Hunyuan (Tencent-Hunyuan/HunyuanImage-3.0 на GitHub). У него 3 284 звезды на GitHub и 191 форк, он распространяется по лицензии NOASSERTION и последний раз обновлялся на GitHub 23 июня 2026.
Tencent's HunyuanImage-3.0 — a powerful native multimodal model for image generation.
Decide the first concrete outcome you want from it, so you can pick the right example, template or mode instead of exploring everything at once.
Clone the repository (or install the published package) and skim its README quick start for the requirements for your platform.
Run the documented install step and set any required configuration — such as provider keys, data sources or local services — before the first run.
Execute the smallest documented example end to end, confirm the output is what you expected, then adapt it to your real data or workflow.
A single model architecture designed natively for text-to-image generation.
HunyuanImage-3.0-Instruct enhances prompts intelligently and supports image-to-image editing and multi-image fusion.
The Instruct-Distil checkpoint is recommended at 8 sampling steps for efficient deployment.
Load weights with Transformers (renaming the folder to avoid dots, as the README notes) and generate in a few lines.
A documented vLLM path significantly speeds up inference serving.
An interactive Gradio demo is included for local experimentation.
Get the source so you can inspect, configure and run it locally.
git clone https://github.com/Tencent-Hunyuan/HunyuanImage-3.0.git
cd HunyuanImage-3.0Install PyTorch (CUDA 12.8), the other requirements, and optionally FlashInfer for optimised MoE inference.
pip install torch==2.8.0 torchvision==0.23.0 torchaudio==2.8.0 --index-url https://download.pytorch.org/whl/cu128
pip install -r requirements.txt
pip install flashinfer-python==0.5.0 # optionalFetch the Instruct weights from Hugging Face, rename the directory (no dots), then run the repo demo scripts.
hf download tencent/HunyuanImage-3.0-Instruct --local-dir ./HunyuanImage-3-Instruct
# then run the demo — see README: run_demo_instruct.shIt is maintained by Tencent-Hunyuan on GitHub (Tencent-Hunyuan/HunyuanImage-3.0), where the source, issues and releases live.
No clear licence was identified from the repo metadata or README, so check the repository's licence file before relying on it commercially.
No API key for local inference — you download the open weights and run them on substantial GPU hardware; the optional Tencent Cloud prompt-enhancement uses that cloud account.
Compared with hosted image APIs such as Midjourney or DALL-E, HunyuanImage-3.0's distinguishing focus is: tencent's HunyuanImage-3.0 — a powerful native multimodal model for image generation. Pick it when that focus matches your need; the alternative may suit you better if its broader or different scope is the priority.
Clone https://github.com/Tencent-Hunyuan/HunyuanImage-3.0 (or install its published package), follow the README quick start to install and configure it, then run the smallest documented example — the Run section on this page mirrors those steps.
ByteDance's open-source unified multimodal model for understanding, generation and image editing.
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