HunyuanImage 2.1: An Efficient Diffusion Model for High-Resolution (2K) Text-to-Image Generation

Have you ever imagined being able to generate highly detailed, 2K resolution images simply by providing text descriptions? Today, we introduce HunyuanImage 2.1, a powerful text-to-image generation model that not only understands complex textual descriptions but also operates effectively in multilingual environments, supporting both Chinese and English prompts to deliver an unprecedented image generation experience.

What is HunyuanImage 2.1?

HunyuanImage 2.1 is an efficient diffusion model developed by Tencent’s Hunyuan team, specifically designed for generating high-resolution (2K) images. Based on an advanced Diffusion Transformer (DiT) architecture and incorporating multiple technological innovations, this model can generate images with high semantic alignment and visual aesthetics while maintaining efficient inference capabilities.

In simple terms, HunyuanImage 2.1 functions like a “digital artist” that understands your text descriptions and transforms them into high-quality images. Whether you want to generate landscape artwork, character portraits, or complex scene compositions, it can produce satisfactory results based on your requirements.

Core Features of the Model

HunyuanImage 2.1 possesses several advanced characteristics that make it stand out in the field of text-to-image generation:

1. High-Quality Image Generation: The model supports generating images with resolutions up to 2048×2048 pixels, featuring rich details and realistic visuals.
2. Multilingual Support: Native support for both Chinese and English prompts, meeting the needs of users across different languages.
3. Flexible Aspect Ratios: Supports multiple image aspect ratios including 1:1, 16:9, 9:16, 4:3, 3:4, 3:2, and 2:3, adapting to various application scenarios.
4. Glyph Awareness: By integrating the ByT5 text encoder, the model achieves greater accuracy in generating text content, avoiding common shortcomings in text rendering found in other models.
5. Prompt Enhancement Functionality: Automatically rewrites user-input prompts, adding more details and descriptions to further improve generated image quality.

Technical Architecture of HunyuanImage 2.1

The architecture of HunyuanImage 2.1 consists of two main stages: a base text-to-image model and a refiner model. Here’s how they work in detail:

1. Base Text-to-Image Model

This stage forms the core of the model, responsible for converting text descriptions into basic image structures and content. It includes the following key components:

• High-Compression VAE: The model uses a high compression-rate variational autoencoder (VAE) that can compress the spatial dimensions of images by 32 times, significantly reducing computational requirements. This means the computational resources required for generating 2K images with our model are comparable to what other models need for 1K images, dramatically improving efficiency.
• Dual Text Encoders:

  • Multimodal Large Language Model (MLLM) Encoder: Used to understand scene descriptions, character actions, and detailed requirements, enhancing image-text alignment capabilities.
  • Multilingual ByT5 Encoder: Focuses on text generation and multilingual expression, ensuring accurately generated text content.
• Diffusion Transformer: The model adopts a combined single and dual-stream DiT architecture with 17 billion parameters, capable of handling complex image generation tasks.

2. Refiner Model

After the base model generates an image, the refiner model further optimizes image quality, reducing artifacts and enhancing details. This stage ensures that the final output achieves optimal clarity and visual effects.

Training Data and Captioning

HunyuanImage 2.1’s training data employs a structured captioning strategy covering short, medium, long, and extra-long levels of semantic information. This hierarchical captioning approach significantly improves the model’s understanding of complex text. Additionally, the team introduced OCR expert models and IP RAG technology to address the shortcomings of general visual language models in dense text and world knowledge descriptions.

Reinforcement Learning from Human Feedback (RLHF)

To optimize the model’s aesthetic appeal and structural coherence, HunyuanImage 2.1 employs Reinforcement Learning from Human Feedback (RLHF). This process consists of two stages:

1. Supervised Fine-Tuning (SFT): Fine-tunes the model using high-quality human-annotated data.
2. Reinforcement Learning (RL): Uses a reward distribution alignment algorithm to further optimize the model’s generation results.

Prompt Enhancement Model

HunyuanImage 2.1 also includes a prompt enhancement model (PromptEnhancer) that can automatically rewrite user-input text prompts, adding more details and descriptive content. This functionality significantly improves the quality and richness of generated images.

Model Distillation

To further improve inference efficiency, HunyuanImage 2.1 employs a MeanFlow-based distillation method. This approach addresses the instability and inefficiency inherent in standard mean flow training, enabling the model to generate high-quality images with only a few sampling steps.

Performance Comparison

HunyuanImage 2.1 demonstrates excellent performance in multiple evaluations. Here’s how it compares against other mainstream models:

SSAE Evaluation

SSAE (Structured Semantic Alignment Evaluation) is an intelligent evaluation metric based on multimodal large language models, used to assess the alignment between images and text. HunyuanImage 2.1 achieved the following results in SSAE evaluation:

From the results, we can see that HunyuanImage 2.1 performs optimally among open-source models and is very close to the performance of closed-source commercial models (such as GPT-Image).

GSB Evaluation

GSB evaluation is a method to assess model performance from an overall image perception perspective. In GSB evaluation, HunyuanImage 2.1 achieved a relative win rate of -1.36% against Seedream3.0 (closed-source) and 2.89% against Qwen-Image (open-source). This indicates that HunyuanImage 2.1’s image generation quality has reached a level comparable to closed-source commercial models while maintaining clear advantages among open-source models.

Installation and Usage

System Requirements

Before using HunyuanImage 2.1, ensure your system meets the following requirements:

• Hardware: NVIDIA GPU with CUDA support, at least 59GB VRAM (for generating 2048×2048 images with batch size 1).
• Operating System: Linux.

Installation Steps

1. Clone the repository:

   git clone https://github.com/Tencent-Hunyuan/HunyuanImage-2.1.git
   cd HunyuanImage-2.1
   

2. Install dependencies:

   pip install -r requirements.txt
   pip install flash-attn==2.7.3 --no-build-isolation
   

Model Download

Model weight files can be obtained through the official download guide.

Usage Example

Here’s a simple code example demonstrating how to use HunyuanImage 2.1 to generate images:

import torch
from hyimage.diffusion.pipelines.hunyuanimage_pipeline import HunyuanImagePipeline

# Load model (supports hunyuanimage-v2.1 and hunyuanimage-v2.1-distilled)
model_name = "hunyuanimage-v2.1"
pipe = HunyuanImagePipeline.from_pretrained(model_name=model_name, torch_dtype='bf16')
pipe = pipe.to("cuda")

prompt = "A cute cartoon-style anthropomorphic penguin plush toy with fluffy fur, standing in a painting studio, wearing a red knitted scarf and a red beret with the word 'Tencent' on it, holding a paintbrush with a focused expression as it paints an oil painting of the Mona Lisa, rendered in a photorealistic photographic style."
image = pipe(
    prompt=prompt,
    width=2048,  # Image width
    height=2048, # Image height
    use_reprompt=True,  # Enable prompt enhancement
    use_refiner=True,   # Enable refiner model
    num_inference_steps=50,  # Inference steps (recommend 50 for non-distilled model)
    guidance_scale=3.5, # Guidance scale
    shift=5,            # Shift parameter
    seed=649151,        # Random seed
)

image.save("generated_image.png")

Important Notes

• HunyuanImage 2.1 only supports 2K resolution image generation. Using 1K resolution may result in quality degradation.
• It is recommended to enable prompt enhancement and refiner functions for optimal results.
• The distilled model (hunyuanimage-v2.1-distilled) offers faster inference speed and recommends using 8 sampling steps.

Frequently Asked Questions

1. Which languages does HunyuanImage 2.1 support?

HunyuanImage 2.1 natively supports both Chinese and English prompts, capable of handling text-to-image generation tasks in multilingual environments.

2. How much VRAM is needed to generate a 2K image?

Generating a single 2048×2048 image requires at least 59GB of VRAM (with batch size 1). If your GPU has insufficient memory, you can enable CPU offloading functionality, though this may reduce inference speed.

3. How can I further improve generated image quality?

It is recommended to enable prompt enhancement (use_reprompt=True) and the refiner model (use_refiner=True), and use a higher number of inference steps (such as 50 steps).

4. What’s the difference between the distilled and non-distilled models?

The distilled model (hunyuanimage-v2.1-distilled) optimizes inference efficiency through model distillation technology, requiring only 8 sampling steps to generate high-quality images, while the non-distilled model requires 50 sampling steps.

5. Which aspect ratios does the model support?

HunyuanImage 2.1 supports multiple aspect ratios including 1:1, 16:9, 9:16, 4:3, 3:4, 3:2, and 2:3. It is recommended to use the officially recommended width and height combinations for best results.

Conclusion

HunyuanImage 2.1 represents a groundbreaking open-source model in the field of text-to-image generation. Through multiple technological innovations, it achieves efficient, high-quality 2K image generation while reaching industry-leading levels in semantic alignment and visual aesthetics. For researchers, developers, and general users alike, HunyuanImage 2.1 provides a powerful and flexible tool to transform imagination into visual reality.

If you’re interested in HunyuanImage 2.1, you can visit its GitHub repository for more information, or experience the online demo through the Hugging Face space.

References:
If you use HunyuanImage 2.1 in your research or applications, please cite the following:

@misc{HunyuanImage-2.1,
  title={HunyuanImage 2.1: An Efficient Diffusion Model for High-Resolution (2K) Text-to-Image Generation},
  author={Tencent Hunyuan Team},
  year={2025},
  howpublished={\url{https://github.com/Tencent-Hunyuan/HunyuanImage-2.1}},
}