VTP - An open-source visual generative model pre-training framework from the MiniMax video team.

What is VTP?

VTP (Visual Tokenizer Pre-training) is an open-source visual generative model pre-training framework from the MiniMax video team. The framework focuses on optimizing the visual tokenizer by integrating contrastive learning, self-supervised learning, and reconstruction learning methods to pre-train the tokenizer from scratch, making the generated latent features more structured and easier to learn. VTP overcomes the limitations of traditional tokenizers, exhibiting excellent scalability. As parameters, computing power, and data scale increase, it can significantly improve the performance of downstream generative models, providing new ideas and methods for the field of visual generation.

Main functions of VTP

• Improve generation qualityBy optimizing the structure of the latent space, generative models can learn and generate high-quality images or videos more efficiently.
• Enhance scalabilityVTP exhibits excellent scaling characteristics; as the parameters, computing power, and data scale invested in the pre-training stage increase, the performance of the downstream generative model will continue to improve.
• Accelerated convergenceIn generation tasks, using a tokenizer pre-trained with VTP can significantly accelerate the convergence speed of downstream models.
• Multi-tasking adaptabilityVTP pre-trained tokenizers perform well in tasks such as zero-shot classification and image reconstruction, and have wide applicability.

VTP Technical Principles

• Jointly optimize multiple learning methods:By comparing positive and negative sample pairs, the model learns to distinguish features of different images, thereby enhancing its ability to understand image semantics.Using the structural information of the image itself as a supervision signal, we can learn high-level semantic features of the image, such as learning global information by predicting partial regions of the image.Traditional VAEs (Variational Autoencoders) learn the latent space by reconstructing images. VTP builds upon this by combining other learning methods to optimize the latent structure.
• Optimize the learnability of latent:The goal of VTP is to generate a latent space that is more compatible with downstream generative models. By optimizing the structure of the latent space to more closely resemble the structured representation of human visual perception, the learning efficiency and generation quality of downstream models are improved.
• Decoupling pre-training from downstream tasks:VTP decouples the pre-training of the tokenizer from the training of the downstream generative model. The pre-training stage focuses on optimizing the representation ability of the tokenizer, while the downstream task serves as a "black box evaluation system" to verify the performance improvement of the tokenizer.
• Scalable design:VTP demonstrates excellent scalability through large-scale pre-training. As resources are invested in the pre-training stage, the performance of the tokenizer continues to improve, driving up the performance of downstream generative models.

VTP's project address

• GitHub repositoryhttps://github.com/MiniMax-AI/VTP
• HuggingFace model libraryhttps://huggingface.co/collections/MiniMaxAI/vtp
• arXiv technical paper: https://arxiv.org/pdf/2512.13687v1

VTP application scenarios

• Image and video generationVTP improves the quality of generated content by optimizing the visual tokenizer, making it suitable for creative design, advertising production, film and television special effects, and other fields, enabling the rapid generation of high-quality image and video content.
• Zero-shot learningVTP uses contrastive and self-supervised learning to obtain general visual representations, performs well in zero-shot classification and cross-modal tasks, and can achieve image recognition and understanding without labeled data.
• Industrial-grade production systemVTP can accelerate downstream model convergence and improve efficiency, meeting the needs of rapid iteration and deployment.
• Data distribution optimizationBy adjusting the distribution of VTP's training data, images or videos that meet specific needs can be generated, making it suitable for scenarios such as medical image generation and personalized content creation.
• Research and DevelopmentVTP provides researchers with open-source code and pre-trained weights, helping them explore generative model mechanisms and develop more efficient model architectures.