HunyuanImage-3.0: A Powerful Native Multimodal Model for Image Generation (github.com)

Tencent’s HunyuanImage-3.0 has been open‑sourced: the technical report, inference code and model weights are available on GitHub and HuggingFace. The model is presented as a native multimodal autoregressive system (moving beyond DiT-style pipelines) that unifies text–image understanding and generation. Tencent claims text-to-image quality on par with or better than leading closed‑source models, enabled by dataset curation and reinforcement‑learning post‑training. The release includes base and “Instruct” checkpoints (the latter adds prompt‑rewriting and chain‑of‑thought style reasoning), distilled checkpoints, image‑to‑image and multi‑turn interfaces, plus VLLM support and a prompt handbook. Key technical details and implications: HunyuanImage‑3.0 is the largest open‑source image‑generation MoE to date — 80B total parameters with 64 experts and ~13B activated per token — designed for high capacity and selective routing. It supports auto or specified resolutions, very long prompts, and intelligent world‑knowledge elaboration that can expand sparse prompts into richer scenes. Practical requirements are substantial: ~170 GB disk, recommended ≥3×80GB GPUs (4×80GB suggested), Python 3.12+, PyTorch 2.7.1 + CUDA 12.8; optional FlashAttention and FlashInfer speedups (one‑time kernel compile) and multi‑GPU inference are recommended. Evaluation used SSAE (automated MLLM‑based alignment) and GSB human studies. For researchers and practitioners this release offers a scalable, open alternative for high‑fidelity, reasoning‑aware image generation — albeit with heavy hardware demands.