Hunyuan Image v3 (also referred to as HunyuanImage 3.0) is an advanced open-source text-to-image generation model developed by Tencent. It is designed to produce highly realistic, detailed, and stylistically diverse images from natural language prompts, supporting both Chinese and English input. The model leverages a large-scale mixture-of-experts (MoE) architecture, integrating a powerful large language model (LLM) with diffusion-based image generation techniques. This combination enables Hunyuan Image v3 to deliver state-of-the-art image quality, strong text-image alignment, and flexible aspect ratio support.

A key innovation of Hunyuan Image v3 is its unified multimodal framework, which allows for both image understanding and generation within a single autoregressive architecture. The model is fully open source, with code, weights, and a commercial license available for free use by individuals and enterprises. Hunyuan Image v3 stands out for its ability to handle complex, long-form prompts, generate accurate text within images, and support a wide range of creative and professional applications. Its performance has been benchmarked to rival or surpass leading closed-source models, making it a significant milestone in open-source AI image generation.

• Architecture: Mixture-of-Experts (MoE) large language model (LLM) integrated with diffusion-based image generation; incorporates a vision encoder and variational autoencoder (VAE) with projection layers
• Parameters: 80 billion total parameters (13 billion activated per token during inference)
• Resolution: Supports flexible aspect ratios and high resolutions (specific maximum resolution not always stated, but supports professional-grade outputs)
• Input/Output formats: Text prompts (Chinese and English) as input; image outputs in standard formats such as PNG and JPEG
• Performance metrics: Achieves a 14.1% relative win rate over HunyuanImage 2.1 in professional human evaluation; outperforms or matches Seedream 4.0, Nano Banana, and GPT-Image in text-image alignment and visual quality using GSB (Good/Same/Bad) evaluation method

• The model excels with both Chinese and English prompts, making it suitable for multilingual applications
• For best results, use detailed and context-rich prompts to leverage the model’s semantic understanding capabilities
• Prompt adherence and text-image alignment are strong, but overly ambiguous or contradictory prompts may reduce output quality
• The model’s MoE architecture activates only a subset of experts per token, balancing high capacity with computational efficiency
• Image generation speed may vary depending on prompt complexity and output resolution; higher quality settings may increase inference time
• Iterative prompt refinement can significantly improve output quality, especially for complex scenes or specific artistic styles
• Avoid extremely short or vague prompts, as these may yield generic or less relevant images

• Use descriptive, multi-sentence prompts to guide the model toward desired compositions, styles, and details
• Specify aspect ratio and resolution in the prompt if a particular format is required
• To generate images with embedded text (e.g., posters, annotations), clearly indicate the desired text and its placement within the prompt
• For stylistic control, include references to specific art styles, lighting conditions, or color palettes in the prompt
• If initial outputs are not satisfactory, iteratively adjust the prompt by clarifying intent or adding constraints (e.g., “in the style of watercolor, with soft lighting”)
• Leverage the model’s ability to handle long-form prompts for complex scenes or multi-object compositions
• For professional use, review and curate outputs, as even state-of-the-art models may occasionally produce artifacts or minor inconsistencies

• Generates hyper-realistic, high-fidelity images from natural language prompts
• Supports both Chinese and English input with strong semantic understanding in both languages
• Excels at text-image alignment, producing images that closely match prompt descriptions
• Capable of generating accurate and legible text within images (e.g., posters, labels)
• Handles complex, multi-object scenes and long-form prompts effectively
• Offers flexible aspect ratio and resolution support for diverse creative and professional needs
• Open-source with commercial licensing, enabling broad adoption and customization

• Professional graphic design, including posters, advertisements, and marketing materials with embedded text
• Creative illustration and concept art for entertainment, gaming, and publishing industries
• Automated content generation for social media, blogs, and digital marketing
• Rapid prototyping and visualization for product design and architecture
• Educational content creation, such as visual aids and infographics
• Personal creative projects, including digital art, storyboarding, and visual storytelling
• Industry-specific applications such as e-commerce product imagery, fashion design, and branding

• Some users report that the model’s ability to generate text within images is notably strong, outperforming many competitors in poster and annotation tasks
• The MoE architecture provides efficiency, but resource requirements remain significant for high-resolution outputs
• Community feedback highlights the model’s versatility and prompt adherence, especially for complex or multilingual prompts
• Occasional artifacts or minor inconsistencies may appear, particularly in highly detailed or crowded scenes
• Human evaluation benchmarks show a clear improvement over previous versions and competitive models, but subjective preferences may vary
• Positive reviews emphasize the model’s open-source nature, commercial usability, and strong performance in both artistic and photorealistic tasks
• Some users note that prompt engineering is crucial; vague or contradictory prompts can reduce output quality
• Advanced users appreciate the ability to fine-tune or customize the model for domain-specific applications

• High computational requirements for inference, especially at large resolutions or batch sizes
• May not always perfectly render extremely complex scenes or highly specialized artistic styles without prompt refinement
• Occasional minor artifacts or inconsistencies, particularly in edge cases or with ambiguous prompts