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

🧩 Community Contributions

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

🗂️ Contents

🔥🔥🔥 News
🧩 Community Contributions
📑 Open-source Plan
📖 Introduction
✨ Key Features
🛠️ Dependencies and Installation
💻 System Requirements
📦 Environment Setup
📥 Install Dependencies
Performance Optimizations
🚀 Usage
🔥 Quick Start with Transformers
🏠 Local Installation & Usage
🎨 Interactive Gradio Demo
🧱 Models Cards
📝 Prompt Guide
Manually Writing Prompts
System Prompt For Automatic Rewriting the Prompt
Advanced Tips
More Cases
📊 Evaluation
📚 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 module achieves performance comparable to or surpassing leading closed-source models.

HunyuanImage-3.0 Framework

✨ 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 World-Knowledge Reasoning: The unified multimodal architecture endows HunyuanImage-3.0 with powerful reasoning capabilities. It 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.

🛠️ Dependencies and Installation

💻 System Requirements

🖥️ Operating System: Linux
🎮 GPU: NVIDIA GPU with CUDA support
💾 Disk Space: 170GB for model weights
🧠 GPU Memory: ≥3×80GB (4×80GB recommended for better performance)

📦 Environment Setup

🐍 Python: 3.12+ (recommended and tested)
🔥 PyTorch: 2.7.1
⚡ CUDA: 12.8

🧱 Models Cards

Model	Params	Download	Recommended VRAM	Supported
HunyuanImage-3.0	80B total (13B active)	HuggingFace	≥ 3 × 80 GB	✅ Text-to-Image
HunyuanImage-3.0-Instruct	80B total (13B active)	HuggingFace	≥ 3 × 80 GB	✅ Text-to-Image ✅ Prompt Self-Rewrite ✅ CoT Think

Notes: - Install performance extras (FlashAttention, FlashInfer) for faster inference. - Multi‑GPU inference is recommended for the Base model.

📝 Prompt Guide

Manually Writing Prompts.

The Pretrain Checkpoint does not automatically rewrite or enhance input prompts, Instruct Checkpoint can rewrite or enhance input prompts with thinking . For optimal results currently, we recommend community partners consulting our official guide on how to write effective prompts.

Reference: HunyuanImage 3.0 Prompt Handbook

System Prompt For Automatic Rewriting the Prompt.

We’ve included two system prompts in the PE folder of this repository that leverage DeepSeek to automatically enhance user inputs:

system_prompt_universal: This system prompt converts photographic style, artistic prompts into a detailed one.
system_prompt_text_rendering: This system prompt converts UI/Poster/Text Rending prompts to a deailed on that suits the model.

Note that these system prompts are in Chinese because Deepseek works better with Chinese system prompts. If you want to use it for English oriented model, you may translate it into English or refer to the comments in the PE file as a guide.

We also create a Yuanqi workflow to implement the universal one, you can directly try it.

Advanced Tips

Content Priority: Focus on describing the main subject and action first, followed by details about the environment and style. A more general description framework is: Main subject and scene + Image quality and style + Composition and perspective + Lighting and atmosphere + Technical parameters. Keywords can be added both before and after this structure.
Image resolution: Our model not only supports multiple resolutions but also offers both automatic and specified resolution options. In auto mode, the model automatically predicts the image resolution based on the input prompt. In specified mode (like traditional DiT), the model outputs an image resolution that strictly aligns with the user’s chosen resolution.

More Cases

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

HunyuanImage 3.0 Demo

Our model can effectively process very long text inputs, enabling users to precisely control the finer details of generated images. Extended prompts allow for intricate elements to be accurately captured, making it ideal for complex projects requiring precision and creativity.

<details> <summary>Show prompt</summary> A cinematic medium shot captures a single Asian woman seated on a chair within a dimly lit room, creating an intimate and theatrical atmosphere. The composition is focused on the subject, rendered with rich colors and intricate textures that evoke a nostalgic and moody feeling. The primary subject is a young Asian woman with a thoughtful and expressive countenance, her gaze directed slightly away from the camera. She is seated in a relaxed yet elegant posture on an ornate, vintage armchair. The chair is upholstered in a deep red velvet, its fabric showing detailed, intricate textures and slight signs of wear. She wears a simple, elegant dress in a dark teal hue, the material catching the light in a way that reveals its fine-woven texture. Her skin has a soft, matte quality, and the light delicately models the contours of her face and arms. The surrounding room is characterized by its vintage decor, which contributes to the historic and evocative mood. In the immediate background, partially blurred due to a shallow depth of field consistent with a f/2.8 aperture, the wall is covered with wallpaper featuring a subtle, damask pattern. The overall color palette is a carefully balanced interplay of deep teal and rich red hues, creating a visually compelling and cohesive environment. The entire scene is detailed, from the fibers of the upholstery to the subtle patterns on the wall. The lighting is highly dramatic and artistic, defined by high contrast and pronounced shadow play. A single key light source, positioned off-camera, projects gobo lighting patterns onto the scene, casting intricate shapes of light and shadow across the woman and the back wall. These dramatic shadows create a strong sense of depth and a theatrical quality. While some shadows are deep and defined, others remain soft, gently wrapping around the subject and preventing the loss of detail in darker areas. The soft focus on the background enhances the intimate feeling, drawing all attention to the expressive subject. The overall image presents a cinematic, photorealistic photography style. </details>	<details> <summary>Show prompt</summary> A cinematic, photorealistic medium shot captures a high-contrast urban street corner, defined by the sharp intersection of light and shadow. The primary subject is the exterior corner of a building, rendered in a low-saturation, realistic style. The building wall, which occupies the majority of the frame, is painted a warm orange with a finely detailed, rough stucco texture. Horizontal white stripes run across its surface. The base of the building is constructed from large, rough-hewn stone blocks, showing visible particles and texture. On the left, illuminated side of the building, there is a single window with closed, dark-colored shutters. Adjacent to the window, a simple black pendant lamp hangs from a thin, taut rope, casting a distinct, sharp-edged shadow onto the sunlit orange wall. The composition is split diagonally, with the right side of the building enveloped in a deep brown shadow. At the bottom of the frame, a smooth concrete sidewalk is visible, upon which the dynamic silhouette of a person is captured mid-stride, walking from right to left. In the shallow background, the faint, out-of-focus outlines of another building and the bare, skeletal branches of trees are softly visible, contributing to the quiet urban atmosphere and adding a sense of depth to the scene. These elements are rendered with minimal detail to keep the focus on the foreground architecture. The scene is illuminated by strong, natural sunlight originating from the upper left, creating a dramatic chiaroscuro effect. This hard light source casts deep, well-defined shadows, producing a sharp contrast between the brightly lit warm orange surfaces and the deep brown shadow areas. The lighting highlights the fine details in the wall texture and stone particles, emphasizing the photorealistic quality. The overall presentation reflects a high-quality photorealistic photography style, infused with a cinematic film noir aesthetic. </details>

<details> <summary>Show prompt</summary> A cinematic medium shot captures a single Asian woman seated on a chair within a dimly lit room, creating an intimate and theatrical atmosphere. The composition is focused on the subject, rendered with rich colors and intricate textures that evoke a nostalgic and moody feeling. The primary subject is a young Asian woman with a thoughtful and expressive countenance, her gaze directed slightly away from the camera. She is seated in a relaxed yet elegant posture on an ornate, vintage armchair. The chair is upholstered in a deep red velvet, its fabric showing detailed, intricate textures and slight signs of wear. She wears a simple, elegant dress in a dark teal hue, the material catching the light in a way that reveals its fine-woven texture. Her skin has a soft, matte quality, and the light delicately models the contours of her face and arms. The surrounding room is characterized by its vintage decor, which contributes to the historic and evocative mood. In the immediate background, partially blurred due to a shallow depth of field consistent with a f/2.8 aperture, the wall is covered with wallpaper featuring a subtle, damask pattern. The overall color palette is a carefully balanced interplay of deep teal and rich red hues, creating a visually compelling and cohesive environment. The entire scene is detailed, from the fibers of the upholstery to the subtle patterns on the wall. The lighting is highly dramatic and artistic, defined by high contrast and pronounced shadow play. A single key light source, positioned off-camera, projects gobo lighting patterns onto the scene, casting intricate shapes of light and shadow across the woman and the back wall. These dramatic shadows create a strong sense of depth and a theatrical quality. While some shadows are deep and defined, others remain soft, gently wrapping around the subject and preventing the loss of detail in darker areas. The soft focus on the background enhances the intimate feeling, drawing all attention to the expressive subject. The overall image presents a cinematic, photorealistic photography style. </details>

<details> <summary>Show prompt</summary> A cinematic, photorealistic medium shot captures a high-contrast urban street corner, defined by the sharp intersection of light and shadow. The primary subject is the exterior corner of a building, rendered in a low-saturation, realistic style. The building wall, which occupies the majority of the frame, is painted a warm orange with a finely detailed, rough stucco texture. Horizontal white stripes run across its surface. The base of the building is constructed from large, rough-hewn stone blocks, showing visible particles and texture. On the left, illuminated side of the building, there is a single window with closed, dark-colored shutters. Adjacent to the window, a simple black pendant lamp hangs from a thin, taut rope, casting a distinct, sharp-edged shadow onto the sunlit orange wall. The composition is split diagonally, with the right side of the building enveloped in a deep brown shadow. At the bottom of the frame, a smooth concrete sidewalk is visible, upon which the dynamic silhouette of a person is captured mid-stride, walking from right to left. In the shallow background, the faint, out-of-focus outlines of another building and the bare, skeletal branches of trees are softly visible, contributing to the quiet urban atmosphere and adding a sense of depth to the scene. These elements are rendered with minimal detail to keep the focus on the foreground architecture. The scene is illuminated by strong, natural sunlight originating from the upper left, creating a dramatic chiaroscuro effect. This hard light source casts deep, well-defined shadows, producing a sharp contrast between the brightly lit warm orange surfaces and the deep brown shadow areas. The lighting highlights the fine details in the wall texture and stone particles, emphasizing the photorealistic quality. The overall presentation reflects a high-quality photorealistic photography style, infused with a cinematic film noir aesthetic. </details>

📊 Evaluation

🤖 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.

Human Evaluation with Other Models

👥 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.