SeFi-Image – Open-Source Text-to-Image Model Based on Semantic-Priority Diffusion

1. What is SeFi-Image

SeFi-Image is an open-source text-to-image generation model based on a semantic-priority diffusion architecture, offering three parameter configurations: 1B, 2B, and 5B. This model separates high-level semantic structures from texture details and introduces a temporal offset mechanism, allowing the semantic stream to begin denoising one time step earlier, thereby providing a clear structural anchor for texture generation. The 5B model requires only 125K A800 GPU hours for training and demonstrates strong performance on multiple benchmarks such as GenEval, LongTextBench, CVTG-2K, and OneIG. It supports a variety of tasks including scene synthesis, rich text layout, anime character generation, style diversity, and portrait creation.

Image source: Official article
Image source: official article

Technical Positioning and Domain: SeFi-Image belongs to the field of text-to-image generation models at the intersection of computer vision and natural language processing. Its uniqueness lies in the use of a semantic-priority diffusion architecture, which splits the traditional single diffusion process into two independent streams: semantic and texture. By implementing temporal offset, it achieves asynchronous denoising, thereby showing clear advantages in tasks that require precise structural control, such as complex scene synthesis and text rendering. This model aims to fill the gap in existing diffusion models regarding high-level semantic guidance, providing a more controllable structured generation solution for generative AI.

Development Background: This model was developed by a research team (including project authors) with the goal of overcoming systematic shortcomings in current mainstream diffusion models concerning semantic structure control. Existing models often encounter issues such as semantic confusion, object misplacement, or text blurriness when handling complex combinations, multi-object layouts, and embedded long texts. SeFi-Image addresses these pain points with an end-to-end solution by explicitly separating high-level semantics from low-level textures. The development motivation stems from simulating the human creative logic of "structure first, then details," aiming to replicate a hierarchical construction process in machine-generated images.

Core Value: The core value of SeFi-Image lies in solving the long-standing challenge of imprecise structural control in text-to-image generation, especially in complex scenes requiring the simultaneous coordination of multiple objects, spatial relationships, or embedded readable text. Its semantic-priority mechanism significantly improves the accuracy of geometric layouts, object occlusion relationships, and text arrangements in generated images, without compromising the richness of texture details. Additionally, the extremely low training cost (only 125K GPU hours for the 5B model) greatly reduces the development threshold for high-quality image generation models, enabling more research teams and small-to-medium enterprises to perform secondary development based on this architecture.

Technical Features: Key technologies include the semantic-texture decoupling architecture, temporal offset mechanism, and a three-stage generation process. The semantic and texture streams each have independent denoising trajectories, and the temporal offset ensures that semantic structures form before textures, providing a stable structural anchor for subsequent texture generation. The three-stage process includes semantic initialization, asynchronous joint denoising, and texture refinement and completion, improving the fidelity of texture reconstruction while balancing the trade-off between reconstruction and generation through asynchronous mechanisms. This architecture enables a qualitative leap in performance for tasks involving complex semantic combinations and long-text rendering.

2. Key Features

• Text-to-Image Generation: Users can input natural language descriptions, and the model can generate corresponding high-quality images. It supports complex scene descriptions, object relationships, style attributes, and text embedding, achieving industry-leading levels in both structural accuracy and visual quality. This feature is based on semantic-priority diffusion, ensuring accurate semantic understanding is mapped to image layout.

• Multi-Scale Model Selection: Offers three parameter scales: 1B, 2B, and 5B, suitable for different computational resources and quality requirements. The 1B model is ideal for rapid prototyping and low VRAM environments (such as consumer-grade GPUs), while the 5B model focuses on optimal generation quality and structural control. Users can switch flexibly based on task complexity to achieve a balance between efficiency and effectiveness.

• Semantic-Priority Diffusion Mechanism: A core innovative feature that separates semantic structure from texture details during generation. By using temporal offset, the semantic stream begins denoising one time step earlier, providing clear structural anchors for the texture stream. This enables the model to excel in complex spatial relationships, multi-object compositions, and text layout, reducing semantic confusion and structural chaos.

• Long Text Rendering Capability: Supports long text prompts in both Chinese and English, achieving a score of 0.978 on the LongTextBench benchmark, significantly outperforming similar products. It can generate commercial materials such as posters, menus, and labels with a large amount of text, ensuring readability and reasonable layout. This feature has direct practical value for advertising design and brand material creation.

• Multi-Domain Generation Support: Covers various domains including scene synthesis, rich text layout, anime characters, stylized art, and portrait photography. The model demonstrates outstanding performance in style diversity, capable of generating artistic styles such as illustrations, ink wash, plush toys, stickers, and sketches, meeting creators' needs across different visual scenarios.

• Efficient Training and Inference: The 5B model was trained using only 125K A800 GPU hours, significantly reducing computational costs compared to similar models. During inference, the semantic-priority architecture reduces unnecessary computations through structured guidance, improving sampling speed while maintaining generation quality. This characteristic allows the model to operate efficiently even in resource-constrained environments.

• Precise Structural Control: The semantic-priority mechanism enables the model to precisely control the spatial relationships of objects, occlusion order, and text embedding positions. It achieves an accuracy of 0.895 in the CVTG-2K character-level visual text generation test, producing posters and labels with high readability, significantly outperforming conventional diffusion models.

• Leading Performance Across Multiple Benchmarks: Ranks first in several authoritative benchmarks such as GenEval (0.88) and OneIG-EN (0.5606), demonstrating comprehensive generation capabilities. This feature validates the model's overall strength in general image generation, text rendering, and style diversity, providing users with reliable performance guarantees.

3. How to Use

1. Environment Requirements: It is recommended to use an NVIDIA A100 or a GPU of comparable performance. The 5B model requires at least 40GB of GPU memory; the 2B model is recommended for an RTX 4090 (24GB); the 1B model can run on an RTX 3060 (12GB). The operating system should be Linux (Ubuntu 20.04+), and Python 3.8 or higher, PyTorch 2.0+, CUDA 11.8+, and related deep learning dependencies must be installed.

2. Acquiring Resources: Visit the project's official website (sefi-web) to obtain links to the paper and code, or directly access the GitHub repository to download the inference source code. Model weight files must be downloaded from the HuggingFace model library (SeFi-Image). Choose the appropriate checkpoint file based on your needs (1B, 2B, or 5B), and it is recommended to verify the integrity of the files.

3. Setting Up the Environment: Create a Python virtual environment and use pip to install the required packages, including diffusers, transformers, accelerate, safetensors, and others. Adjust the configuration file according to the model specification, setting the sampling steps (default 50 steps), guidance scale (recommended 7.5), and output resolution. The first run may require downloading cached components.

4. Loading the Model: Use the official loading script (e.g., load_model.py) to initialize the model, specifying the model path and specification parameters. It supports loading from a local path or automatically downloading from HuggingFace. After loading, you can confirm that the weights were correctly loaded by printing the model structure.

5. Input Prompts: Write text prompts that can include scene descriptions, style instructions, and textual content. It supports mixed Chinese and English input, and for long text prompts, it is recommended to separate semantic units with commas or periods. Example: "A cat wearing a suit, sitting at an office desk, with a cup of coffee on the desk, city night view in the background, poster style."

6. Executing Generation: Run the inference script (e.g., python inference.py --prompt "..." --model_path ./sefi-5b). The model automatically performs a semantics-priority diffusion process, first initializing the semantics, then asynchronously denoising in conjunction, and finally texture refinement and completion. You can adjust the --num_inference_steps parameter to control the generation quality and speed.

7. Output Results: The generated images are saved by default in PNG format, and you can customize the output directory and filename. You can set the output resolution (e.g., 1024x1024) using parameters, and batch generation of multiple prompts is supported. It is recommended to use the --seed parameter to fix the random seed for reproducibility of results.

8. Notes: When using the 5B model, ensure that there is sufficient GPU memory; long text prompts should not exceed 512 characters; the first inference may take a long time to load the model; it is recommended to use the officially suggested sampling parameter combination (guidance scale 7.5, steps 50) to achieve the best results. If GPU memory is insufficient, you can enable CPU offloading or reduce the resolution.

4. Pros and Cons Analysis

5. Comparative Analysis with Similar Tools

Selection Recommendations: For users requiring precise structural control and long-text rendering, SeFi-Image is the optimal choice. Its leading performance on multiple benchmarks such as GenEval and LongTextBench demonstrates its unique advantages in complex scene synthesis and text embedding. It is particularly suitable for application scenarios with high layout and text readability requirements, such as advertising design and animation creation. However, note that its hardware requirements are relatively high, and its community ecosystem is still in the growth phase, making it more suitable for technically capable teams aiming for deep integration.

For users seeking a mature ecosystem and broad community support, Stable Diffusion 3 Medium is a more reliable option. Its large community provides a wealth of extension resources such as LoRA and ControlNet, and it offers better compatibility with consumer-grade GPUs. Although it does not outperform SeFi-Image on certain benchmarks, it has advantages in general generation tasks and ease of use. For users prioritizing Chinese scenarios, Qwen-Image slightly outperforms on Chinese benchmarks and benefits from the Alibaba Cloud ecosystem, offering higher convenience in deployment and integration. It is well-suited for domestic enterprises and developers.

6. Editor's Summary

SeFi-Image demonstrates distinctive innovation in technical advancements. The semantic-texture decoupling architecture and temporal offset mechanism are not merely incremental improvements, but a rethinking of the generation process in diffusion models. By prioritizing high-level semantic guidance, the model achieves quantifiable and significant improvements in complex composition and long-text rendering tasks, providing a new paradigm for future research on controllable structural generation. In terms of practical value, SeFi-Image has direct application potential in scenarios such as advertising posters, anime characters, and stylized art. Its low training cost makes it accessible for more teams to customize and develop based on this architecture, thereby reducing the barriers to research and development of high-quality image generation models. This model is primarily aimed at researchers with a foundation in deep learning, AI application developers, and design teams requiring high-quality visual assets. For academic institutions and technologically advanced enterprises seeking the latest technical achievements, SeFi-Image is a promising foundational model worth exploring in depth. Looking ahead, as the community ecosystem matures and more downstream extensions emerge, SeFi-Image is expected to hold a significant position in the text-to-image generation field, particularly in vertical scenarios requiring precise structural control, where its advantages will be further amplified. However, current challenges such as hardware requirements and documentation completeness still need to be addressed to expand its user base.

7. Application Scenarios

• Advertising Poster Design: In the field of advertising and poster design, SeFi-Image can generate brand posters, product menus, and product labels containing clear and readable text. Its long-text rendering capability ensures accurate text embedding into images, while its semantic-priority architecture guarantees a well-organized layout, significantly improving design efficiency and reducing post-production text correction work.

• Anime Character Creation: For anime and character creation, the model supports generating anime characters, fantasy scenes, and various compositions such as full-body or close-up shots. The semantic structure control makes character poses, multi-person interactions, and scene combinations more harmonious. Creators can quickly iterate on character designs using text prompts, accelerating the concept art creation process.

• Natural Scene Generation: In the context of natural scene and landscape generation, SeFi-Image can create images of weather, cities, animals, and scenic views with flexible aspect ratios. The model's ability to compose complex scenes surpasses that of conventional diffusion models, enabling it to handle multiple objects and spatial relationships simultaneously, generating realistic natural environment images.

• Artistic Style Creation: The model supports various artistic styles such as illustrations, ink wash, plush toys, stickers, and sketches, making it suitable for stylized visual content creation. Users can easily switch between styles using text prompts, without requiring additional training, making it ideal for social media content, product packaging, and artistic experimentation.

• Portrait Photography Assistance: In portrait and photography assistance scenarios, SeFi-Image can generate portraits and environmental shots with diverse lighting, poses, and materials. The semantic-priority mechanism ensures accurate human structure, natural facial features, and body proportions, which can be used for model sample image generation, photographic composition references, and other commercial applications.

8. FAQ

Q: What are the core advantages of SeFi-Image compared to Stable Diffusion?
A: The core advantage of SeFi-Image lies in its semantics-priority diffusion architecture, which allows for more precise control over spatial relationships between objects and text layout, particularly excelling in long-text rendering and complex scene synthesis. While Stable Diffusion has a more mature community ecosystem and a richer set of plugins, it falls short of SeFi-Image in terms of structural control precision.

Q: What hardware configuration is required to run SeFi-Image?
A: The 1B model can run on consumer-grade GPUs (e.g., RTX 3060 12GB), the 2B model is recommended to be run on RTX 4090 24GB, and the 5B model requires NVIDIA A100 40GB or higher. It is recommended to use a Linux system for optimal performance. If GPU memory is insufficient, CPU offloading can be enabled or the resolution can be reduced.

Q: Does SeFi-Image support Chinese input?
A: Yes, it supports both Chinese and English input. The model performs well in rendering long Chinese text, but it slightly underperforms compared to Qwen-Image on the OneIG-ZH benchmark, indicating there is still room for optimization in Chinese scenarios. It is recommended to use a mix of Chinese and English prompts for more stable results.

Q: Are the model weights open-sourced? Can they be used for commercial purposes?
A: The model weights and code have been open-sourced on GitHub and HuggingFace. Please check the LICENSE file in the project repository for specific licensing information. The project does not currently explicitly restrict commercial use, but users are advised to carefully review the license terms before commercial deployment to ensure compliance with usage requirements.

Q: How can I quickly get started with SeFi-Image?
A: First, download the code and model weights from GitHub. After installing the dependencies, run the official example scripts. The official repository provides simple inference code, and users can generate images by modifying the prompts. For detailed documentation, refer to the project website and the arXiv paper. Community discussions can be found in the GitHub Issues section.

Q: What output resolutions does SeFi-Image support?
A: The model supports common square and rectangular resolutions such as 512x512, 1024x1024, and 1024x768. It is recommended to use 1024x1024 for the best quality. Output resolution can be adjusted via the configuration file, but note that GPU memory usage increases with higher resolutions.

9. Project Links

• Project Website: https://jmliu206.github.io/sefi-web/
• GitHub Repository: https://github.com/jmliu206/SeFi-Image
• HuggingFace Model Library: https://huggingface.co/SeFi-Image
• arXiv Technical Paper: https://arxiv.org/pdf/2606.22568