HunyuanImage-3.0

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🎨 HunyuanImage-3.0: A Powerful Native Multimodal Model for Image Generation

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💻 Official website(官网) Try our model!&nbsp&nbsp

🔥🔥🔥 News

• *January 26, 2026**: 🚀 **HunyuanImage-3.0-Instruct-Distil** - Distilled checkpoint for efficient deployment (8 steps sampling recommended).
• *January 26, 2026**: 🎉 **HunyuanImage-3.0-Instruct** - Release of **Instruct (with reasoning)** for intelligent prompt enhancement and **Image-to-Image** generation for creative editing.
• *October 30, 2025**: 🚀 **HunyuanImage-3.0 vLLM Acceleration** - Significantly faster inference with vLLM support.
• *September 28, 2025**: 📖 **HunyuanImage-3.0 Technical Report** - Comprehensive technical documentation now available.
• *September 28, 2025**: 🎉 **HunyuanImage-3.0 Open Source** - Inference code and model weights publicly available.

🧩 Community Contributions

If you develop/use HunyuanImage-3.0 in your projects, welcome to let us know.

📑 Open-source Plan

HunyuanImage-3.0 (Image Generation Model)

• [x] Inference
• [x] HunyuanImage-3.0 Checkpoints
• [x] HunyuanImage-3.0-Instruct Checkpoints (with reasoning)
• [x] vLLM Support
• [x] Distilled Checkpoints
• [x] Image-to-Image Generation
• [ ] Multi-turn Interaction

🗂️ Contents

🔥🔥🔥 News

🧩 Community Contributions

📑 Open-source Plan

📖 Introduction

✨ Key Features

🚀 Usage

• 📦 Environment Setup
• 📥 Install Dependencies
• HunyuanImage-3.0 (Text-to-image)
• 🔥 Quick Start with Transformers
• 1️⃣ Download model weights
• 2️⃣ Run with Transformers
• 🏠 Local Installation & Usage
• 1️⃣ Clone the Repository
• 2️⃣ Download Model Weights
• 3️⃣ Run the Demo
• 4️⃣ Command Line Arguments
• 🎨 Interactive Gradio Demo
• 1️⃣ Install Gradio
• 2️⃣ Configure Environment
• 3️⃣ Launch the Web Interface
• 4️⃣ Access the Interface
• HunyuanImage-3.0-Instruct
• 🔥 Quick Start with Transformers
• 1️⃣ Download model weights
• 2️⃣ Run with Transformers
• 🏠 Local Installation & Usage
• 1️⃣ Clone the Repository
• 2️⃣ Download Model Weights
• 3️⃣ Run the Demo
• 4️⃣ Command Line Arguments
• 5️⃣ For fewer Sampling Steps

🧱 Models Cards

📊 Evaluation

• Evaluation of HunyuanImage-3.0-Instruct
• Evaluation of HunyuanImage-3.0 (Text-to-Image)

🖼️ Showcase

• Showcases of HunyuanImage-3.0-Instruct

📚 Citation

🙏 Acknowledgements

🌟🚀 Github Star History

📖 Introduction

HunyuanImage-3.0** is a groundbreaking native multimodal model that unifies multimodal understanding and generation within an autoregressive framework. Our text-to-image and image-to-image model achieves performance **comparable to or surpassing** leading closed-source models.

✨ Key Features

🧠 **Unified Multimodal Architecture:** Moving beyond the prevalent DiT-based architectures, HunyuanImage-3.0 employs a unified autoregressive framework. This design enables a more direct and integrated modeling of text and image modalities, leading to surprisingly effective and contextually rich image generation.

🏆 **The Largest Image Generation MoE Model:** This is the largest open-source image generation Mixture of Experts (MoE) model to date. It features 64 experts and a total of 80 billion parameters, with 13 billion activated per token, significantly enhancing its capacity and performance.

🎨 **Superior Image Generation Performance:** Through rigorous dataset curation and advanced reinforcement learning post-training, we've achieved an optimal balance between semantic accuracy and visual excellence. The model demonstrates exceptional prompt adherence while delivering photorealistic imagery with stunning aesthetic quality and fine-grained details.

💭 **Intelligent Image Understanding and World-Knowledge Reasoning:** The unified multimodal architecture endows HunyuanImage-3.0 with powerful reasoning capabilities. It under stands user's input image, and leverages its extensive world knowledge to intelligently interpret user intent, automatically elaborating on sparse prompts with contextually appropriate details to produce superior, more complete visual outputs.

🚀 Usage

📦 Environment Setup

🐍 **Python:** 3.12+ (recommended and tested)

⚡ **CUDA:** 12.8

📥 Install Dependencies

For **up to 3x faster inference**, install these optimizations:

💡**Installation Tips:** It is critical that the CUDA version used by PyTorch matches the system's CUDA version.

FlashInfer relies on this compatibility when compiling kernels at runtime.

GCC version >=9 is recommended for compiling FlashAttention and FlashInfer.

⚡ **Performance Tips:** These optimizations can significantly speed up your inference!

💡**Notation:** When FlashInfer is enabled, the first inference may be slower (about 10 minutes) due to kernel compilation. Subsequent inferences on the same machine will be much faster.

HunyuanImage-3.0 (Text-to-image)

🔥 Quick Start with Transformers

1️⃣ Download model weights

2️⃣ Run with Transformers

🏠 Local Installation & Usage

1️⃣ Clone the Repository

2️⃣ Download Model Weights

3️⃣ Run the Demo

The Pretrain Checkpoint does not automatically rewrite or enhance input prompts, for optimal results currently, we recommend community partners to use deepseek to rewrite the prompts. You can go to Tencent Cloud to apply for an API Key.

4️⃣ Command Line Arguments

Arguments Description Recommended

• ---------------------- ------------------------------------------------------------ -----------
• -prompt Input prompt (Required)
• -model-id Model path (Required)
• -attn-impl Attention implementation. Either sdpa or flash attention 2 . sdpa
• -moe-impl MoE implementation. Either eager or flashinfer flashinfer
• -seed Random seed for image generation None
• -diff-infer-steps Diffusion infer steps 50
• -image-size Image resolution. Can be auto , like 1280x768 or 16:9 auto
• -save Image save path. image.png
• -verbose Verbose level. 0: No log; 1: log inference information. 0
• -rewrite Whether to enable rewriting 1

🎨 Interactive Gradio Demo

Launch an interactive web interface for easy text-to-image generation.

1️⃣ Install Gradio

2️⃣ Configure Environment

3️⃣ Launch the Web Interface

Basic Launch:**

With Performance Optimizations:**

4️⃣ Access the Interface

🌐 **Web Interface:** Open your browser and navigate to http://localhost:443 (or your configured port)

Latest Version (Image-to-image & Text-image-to-image)

HunyuanImage-3.0-Instruct (Instruction reasoning and Image-to-image generation, including editing and multi-image fusion)

🔥 Quick Start with Transformers

1️⃣ Download model weights

2️⃣ Run with Transformers

🏠 Local Installation & Usage

1️⃣ Clone the Repository

2️⃣ Download Model Weights

3️⃣ Run the Demo

More demos in run demo instruct.sh .

4️⃣ Command Line Arguments

Arguments Description Recommended

• ---------------------- ------------------------------------------------------------ -----------
• -prompt Input prompt (Required)
• -image Image to run. For multiple images, use comma-separated paths (e.g., 'img1.png,img2.png') (Required)
• -model-id Model path (Required)
• -attn-impl Attention implementation. Now only support 'sdpa' sdpa
• -moe-impl MoE implementation. Either eager or flashinfer flashinfer
• -seed Random seed for image generation. Use None for random seed None
• -diff-infer-steps Number of inference steps 50
• -image-size Image resolution. Can be auto , like 1280x768 or 16:9 auto
• -use-system-prompt System prompt type. Options: None , dynamic , en vanilla , en recaption , en think recaption , en unified , custom en unified
• -system-prompt Custom system prompt. Used when --use-system-prompt is custom None
• -bot-task Task type. image for direct generation; auto for text; recaption for re-write->image; think recaption for think->re-write->image think recaption
• -save Image save path image.png
• -verbose Verbose level 2
• -reproduce Whether to reproduce the results True
• -infer-align-image-size Whether to align the target image size to the src image size True
• -max new tokens Maximum number of new tokens to generate 2048
• -use-taylor-cache Use Taylor Cache when sampling False

5️⃣ For fewer Sampling Steps

We recommend using the model HunyuanImage-3.0-Instruct-Distil with --diff-infer-steps 8 , while keeping all other recommended parameter values **unchanged**.

🧱 Models Cards

📊 Evaluation

Evaluation of HunyuanImage-3.0-Instruct

👥 **GSB (Human Evaluation)**

We adopted the GSB (Good/Same/Bad) evaluation method commonly used to assess the relative performance between two models from an overall image perception perspective. In total, we utilized 1,000+ single- and multi-images editing cases, generating an equal number of image samples for all compared models in a single run. For a fair comparison, we conducted inference only once for each prompt, avoiding any cherry-picking of results. When comparing with the baseline methods, we maintained the default settings for all selected models. The evaluation was performed by more than 100 professional evaluators.

Evaluation of HunyuanImage-3.0 (Text-to-Image)

🤖 **SSAE (Machine Evaluation)**

SSAE (Structured Semantic Alignment Evaluation) is an intelligent evaluation metric for image-text alignment based on advanced multimodal large language models (MLLMs). We extracted 3500 key points across 12 categories, then used multimodal large language models to automatically evaluate and score by comparing the generated images with these key points based on the visual content of the images. Mean Image Accuracy represents the image-wise average score across all key points, while Global Accuracy directly calculates the average score across all key points.

👥 **GSB (Human Evaluation)**

We adopted the GSB (Good/Same/Bad) evaluation method commonly used to assess the relative performance between two models from an overall image perception perspective. In total, we utilized 1,000 text prompts, generating an equal number of image samples for all compared models in a single run. For a fair comparison, we conducted inference only once for each prompt, avoiding any cherry-picking of results. When comparing with the baseline methods, we maintained the default settings for all selected models. The evaluation was performed by more than 100 professional evaluators.

🖼️ Showcase

Our model can follow complex instructions to generate high‑quality, creative images.

For text-to-image showcases in HunyuanImage-3.0, click the following links:

HunyuanImage-3.0

Showcases of HunyuanImage-3.0-Instruct

HunyuanImage-3.0-Instruct demonstrates powerful capabilities in intelligent image generation and editing. The following showcases highlight its core features:

🧠 **Intelligent Visual Understanding and Reasoning (CoT Think)**: The model performs structured thinking to analyze user's input image and prompt, expand user's intent and editing tasks into a stucture, comprehnsive instructions, and leading to a better image generation and editing performance.

breaking down complex prompts and editing tasks into detailed visual components including subject, composition, lighting, color palette, and style.

✏️ **Prompt Self-Rewrite**: Automatically enhances sparse or vague prompts into professional-grade, detail-rich descriptions that capture the user's intent more accurately.

🎨 **Text-to-Image (T2I)**: Generates high-quality images from text prompts with exceptional prompt adherence and photorealistic quality.

🖼️ **Image-to-Image (TI2I)**: Supports creative image editing, including adding elements, removing objects, modifying styles, and seamless background replacement while preserving key visual elements.

🔀 **Multi-Image Fusion**: Intelligently combines multiple reference images (up to 3 inputs) to create coherent composite images that integrate visual elements from different sources.

Showcase 1: Detailed Thought and Reasoning Process**

Showcase 2: Creative T2I Generation with Complex Scene Understanding**

Prompt: 3D 毛绒质感拟人化马，暖棕浅棕肌理，穿藏蓝西装、白衬衫，戴深棕手套；疲惫带期待，坐于电脑前，旁置印 "HAPPY AGAIN" 的马克杯。橙红渐变背景，配超大号藏蓝粗体 "马上下班"，叠加米黄 "Happy New Year" 并标 "(2026)"。橙红为主，藏蓝米黄撞色，毛绒温暖柔和。

Showcase 3: Precise Image Editing with Element Preservation**

Showcase 4: Style Transformation with Thematic Enhancement**

Showcase 5: Advanced Style Transfer and Product Mockup Generation**

Showcase 6: Multi-Image Fusion and Creative Composition**

📚 Citation

If you find HunyuanImage-3.0 useful in your research, please cite our work:

🙏 Acknowledgements

We extend our heartfelt gratitude to the following open-source projects and communities for their invaluable contributions:

🤗 Transformers - State-of-the-art NLP library

🎨 Diffusers - Diffusion models library

🌐 HuggingFace - AI model hub and community

⚡ FlashAttention - Memory-efficient attention

🚀 FlashInfer - Optimized inference engine

🌟🚀 Github Star History

[](https://github.com/Tencent-Hunyuan/HunyuanImage-3.0)

[](https://github.com/Tencent-Hunyuan/HunyuanImage-3.0)

[](https://www.star-history.com/#Tencent-Hunyuan/HunyuanImage-3.0&Date)