Introduction to Diffusion Self-Distillation

The GitHub repository diffusion-self-distillation by primecai presents a groundbreaking approach in the field of image generation, specifically focusing on zero-shot customized image generation. This project is an official implementation of the paper “Diffusion Self-Distillation for Zero-Shot Customized Image Generation” intended for presentation at CVPR 2025. The repository, though currently under construction, promises significant advancements in subject-preserving generation and relighting models.

Overview of Key Features

At its core, this project leverages a pre-trained text-to-image diffusion model to generate its own dataset for text-conditioned image-to-image tasks, thereby overcoming the limitation of insufficient high-quality paired data. The method, termed Diffusion Self-Distillation, involves several key steps:

• Utilizing a text-to-image diffusion model’s capability to create grids of images.
• Curating a large paired dataset with the assistance of a Visual-Language Model.
• Fine-tuning the text-to-image model into a text+image-to-image model using the curated dataset.

This approach demonstrates impressive results, outperforming existing zero-shot methods and competing with per-instance tuning techniques on various identity-preservation generation tasks without requiring test-time optimization.

Technical Aspects and Implementation Details

The project is implemented in Python, necessitating a significant amount of GPU memory (>24GB). However, the developers are working on a quantized version to support devices with less than 24GB GPU memory. The codebase includes:

• Setup and Dependencies: Cloning the repository and installing dependencies via pip install -r requirements.txt.
• Pretrained Models: Downloading pretrained models from Hugging Face or Google Drive, which include necessary files like config.json, diffusion_pytorch_model.safetensors, and pytorch_lora_weights.safetensors.
• Inference: Generating subject-preserving images using the generate.py script, specifying paths to the model, Lora weights, and condition images.

Implementation Challenges

The implementation of Diffusion Self-Distillation faces several challenges, including:

• Computational Resources: The high demand for GPU memory poses a significant challenge, especially for users with limited computational resources.
• Model Complexity: Fine-tuning a pre-trained text-to-image model to adapt to new tasks while preserving its original capabilities is a complex task.
• Data Curation: Creating a large, high-quality paired dataset through the interaction of diffusion models and visual-language models requires careful curation and validation.

Potential Use Cases and Applications

The capabilities of Diffusion Self-Distillation have far-reaching implications for various applications:

• Artistic Control: Providing artists with fine-grained control over text-to-image generation, enabling them to produce images of specific instances in novel contexts.
• Identity-Preserving Generation: Useful for tasks like relighting, where the goal is to modify an image while preserving its essential subject or identity.
• Customized Image Generation: Enabling zero-shot customized image generation without the need for extensive paired datasets or test-time optimization.

Getting Started with Diffusion Self-Distillation

To embark on this project, follow these steps:

1. Clone the Repository: Start by cloning the diffusion-self-distillation repository from GitHub.
2. Install Dependencies: Run pip install -r requirements.txt to set up your environment.
3. Download Pretrained Models: Obtain the necessary models from Hugging Face or Google Drive.
4. Configure Paths: Ensure you have the correct paths for the model, Lora weights, and any condition images you wish to use.
5. Run Inference Script: Execute the generate.py script with appropriate arguments to generate subject-preserving images. For example:

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   CUDA_VISIBLE_DEVICES=0 python generate.py \ --model_path /PATH/TO/transformer \ --lora_path /PATH/TO/pytorch_lora_weights.safetensors \ --image_path /PATH/TO/conditioning_image.png \ --text "this character sitting on a chair" \ --output_path output.png \ --guidance 3.5 \ --i_guidance 1.0 \ --t_guidance 1.0

Additional Resources

For those interested in exploring this project further, several resources are available:

• Project Website
• Research Paper on arXiv
• Hugging Face Demo
• Hugging Face Model
• Dataset on Hugging Face

Conclusion

The diffusion-self-distillation repository presents a significant leap forward in the domain of image generation, particularly for tasks requiring fine-grained control and customization. By leveraging Diffusion Self-Distillation, developers and artists can achieve high-quality, identity-preserving image generation without the need for extensive datasets or complex optimization processes at test time. As this project continues to evolve, it is poised to make a substantial impact on both academic research and practical applications in computer vision and generative modeling.

Citation

If you use this work in your research or projects, please cite the original paper:

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@inproceedings{cai2024dsd,
    author={Cai, Shengqu and Chan, Eric Ryan and Zhang, Yunzhi and Guibas, Leonidas and Wu, Jiajun and Wetzstein, Gordon.},
    title={Diffusion Self-Distillation for Zero-Shot Customized Image Generation},
    booktitle={CVPR},
    year={2025}
}

• GitHub
• Open Source
• Python
• AI
• Computer Vision
• Image Generation