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Wan 2
Model Family Report
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Generate images and videos using a powerful low-level workflow graph builder - the fastest, most flexible, and most advanced visual generation UI.
Browse Models
Generate images and videos using a powerful low-level workflow graph builder - the fastest, most flexible, and most advanced visual generation UI.
The Wan 2.1 family represents a significant advancement in the field of AI-powered video generation, introducing a suite of open foundation models designed for various video creation tasks. Released in February 2025 by Wan-AI, these models have established new benchmarks in both text-to-video and image-to-video generation while maintaining accessibility for researchers and content creators with consumer-grade hardware. This comprehensive overview explores the architecture, capabilities, technological innovations, and practical applications of the Wan 2.1 model family.
The Wan 2.1 family emerged as a response to growing demand for high-quality, accessible video generation technologies. Released on February 25, 2025, the model family represents the culmination of extensive research into efficient video processing architectures and training methodologies. The family was developed by Wan-AI, a research organization focused on advancing multimodal AI capabilities with particular emphasis on video generation technologies.
The model family consists of four primary variants, each optimized for specific use cases and hardware configurations:
All models in the family are released under the Apache 2.0 License, with model weights and inference code made publicly available through platforms such as Hugging Face and GitHub.
The Wan 2.1 family is built on a consistent architectural foundation that incorporates several innovative components. At the core of each model is the diffusion transformer paradigm combined with a Flow Matching framework, enabling high-quality video generation with temporal coherence.
One of the most significant technical innovations shared across the family is the Wan-VAE, a novel 3D causal Variational Autoencoder specifically designed for video processing. This component enables efficient spatio-temporal compression while maintaining temporal causality, allowing the models to handle unlimited-length 1080P videos during encoding and decoding processes. The Wan-VAE architecture represents a substantial advancement over previous approaches to video latent space encoding, as documented in the technical specifications.
All models in the family incorporate a T5 Encoder for processing multilingual text input, with cross-attention mechanisms embedded throughout the transformer blocks to integrate text into the model structure. This approach enables robust support for both English and Chinese visual text generation, making the models versatile tools for multilingual content creation.
The diffusion process employed by the Wan 2.1 family is depicted below:
A unified data processing and training methodology was employed across the Wan 2.1 family, contributing significantly to the models' performance. The training data underwent a rigorous four-step cleaning process that focused on fundamental dimensions, visual quality evaluation, motion quality verification, and deduplication.
This comprehensive data curation process ensured that the models were trained on high-quality, diverse content that accurately represented the tasks they were designed to perform. The careful attention to data quality has been cited as a key factor in the models' ability to generate realistic and visually appealing video content, as detailed in the training methodology documentation.
While sharing a common architectural foundation, each model in the Wan 2.1 family is optimized for specific use cases and hardware environments. This section provides a detailed comparison of the variants and their respective capabilities.
The Wan 2.1 T2V 14B serves as the flagship model in the family, offering comprehensive text-to-video generation capabilities at both 480P and 720P resolutions. With 14 billion parameters, this model represents the most powerful offering in the family, demonstrating superior performance in visual quality, motion coherence, and text fidelity.
The model's key architectural dimensions include a base dimension of 5120, input/output dimensions of 16, a feedforward dimension of 13824, and 40 attention heads across 40 layers. These substantial parameters enable the model to capture complex relationships between textual descriptions and visual elements, resulting in highly detailed and accurate video outputs.
As shown in the performance metrics, the T2V 14B model demonstrates superior results compared to both open-source and commercial alternatives in human evaluations. However, this performance comes with significant computational requirements, with the model needing approximately 28GB of VRAM for inference at 480P resolution.
The Wan 2.1 I2V 14B 720P is specialized for high-definition image-to-video conversion, focusing on transforming still images into fluid, realistic 720P video content. This model maintains the 14 billion parameter scale of the flagship T2V model but optimizes the architecture specifically for the image-to-video task.
The I2V 720P model excels at preserving the visual details and style of source images while generating natural motion sequences. This capability makes it particularly valuable for applications in creative content production, advertising, and visual effects where high-resolution output is essential.
Computational efficiency tests show that the model requires approximately 28GB of VRAM for inference, with generation times varying based on hardware configurations:
The Wan 2.1 I2V 14B 480P variant represents a balance between quality and computational efficiency, focusing on standard definition image-to-video conversion. While maintaining the 14 billion parameter architecture, this model is optimized specifically for 480P resolution, resulting in faster generation times and reduced resource requirements.
The I2V 480P model requires approximately 27GB of VRAM, making it slightly more accessible than its 720P counterpart. This variant is particularly valuable for applications where generation speed is prioritized over resolution, such as rapid prototyping, preview generation, or deployment in environments with more constrained hardware resources.
The Wan 2.1 T2V 1.3B represents a significant architectural achievement, delivering compelling text-to-video generation capabilities with only 1.3 billion parameters. This lightweight variant is designed specifically for resource-constrained environments, requiring only 8.19GB of VRAM for inference, making it accessible to users with consumer-grade GPUs.
While primarily optimized for 480P resolution, the T2V 1.3B model offers experimental support for 720P generation, though with reduced stability compared to its larger counterparts. Despite its modest size, the model demonstrates impressive performance metrics, generating a 5-second 480P video in approximately 4 minutes on an RTX 4090 without requiring optimization techniques like quantization.
This variant represents an important contribution to democratizing access to video generation technology, making high-quality results available to researchers and creators with limited computational resources, as detailed in the model documentation.
When assessing the Wan 2.1 family as a whole, several patterns emerge in terms of performance characteristics and resource requirements. The following comparison illustrates the relative strengths and tradeoffs of each variant:
The 14B models consistently deliver superior visual quality and temporal coherence, with the T2V 14B offering the most versatile capabilities across both text-to-video and image-to-video tasks. The I2V variants provide more specialized performance for image animation, with the 720P version offering the highest resolution outputs in the family.
The T2V 1.3B model stands out for its exceptional efficiency, delivering compelling results despite using less than 10% of the parameters of its larger counterparts. This efficiency-focused design makes it an important option for broadening access to video generation technology.
Across all variants, the Wan 2.1 family demonstrates consistent strengths in several areas:
The Wan 2.1 model family supports a diverse range of applications across creative, commercial, and research domains. The most prominent applications include:
The models excel at transforming written descriptions or still images into dynamic video content, offering powerful tools for storytellers, filmmakers, and digital artists. The ability to generate high-quality video from textual prompts enables rapid prototyping of visual concepts and facilitates iterative creative processes. Entertainment studios have begun incorporating these models into concept development workflows, as highlighted in case studies on the project website.
The text-to-video capabilities offer significant value for educational content development, allowing instructors to quickly generate illustrative videos from lesson plans or concept descriptions. This application is particularly valuable for subjects that benefit from visual demonstration but may have limited existing video resources.
Commercial applications of the Wan 2.1 family include rapid generation of product demonstrations, conceptual advertisements, and marketing content. The image-to-video variants are particularly useful in this domain, allowing marketers to animate existing product photography or brand imagery with natural motion.
Beyond practical applications, the Wan 2.1 family serves as an important research platform for further advancements in video generation technology. The open-source nature of the models, combined with comprehensive documentation, enables researchers to build upon these foundations for specialized applications and technical improvements.
The Wan 2.1 family introduces several technical innovations that represent meaningful contributions to the field of AI-powered video generation:
The Wan-VAE architecture developed for this model family represents a significant advancement in video latent space encoding. By implementing a 3D causal VAE design, the models achieve efficient spatio-temporal compression while preserving temporal causality, enabling the processing of unlimited-length videos with consistent quality.
The development of the T2V 1.3B variant demonstrates that high-quality video generation is possible with substantially fewer parameters than previously believed necessary. This achievement in parameter efficiency opens new possibilities for deployment in resource-constrained environments and mobile applications.
The Wan 2.1 family's robust support for both English and Chinese text generation within videos addresses a significant challenge in multilingual content creation. This capability is particularly valuable for global applications and represents an important step toward more inclusive AI systems.
All models in the Wan 2.1 family are released with clear usage guidelines prohibiting generation of content that violates laws, causes harm, spreads misinformation, or targets vulnerable populations. These ethical guidelines are consistent with the broader responsible AI principles advocated by Wan-AI and the research community.
The model documentation explicitly addresses potential misuse concerns and encourages users to implement appropriate safeguards when deploying these technologies in production environments.
The Wan 2.1 family establishes a strong foundation for future research and development in several areas:
Researchers and developers interested in contributing to these advancements are encouraged to explore the GitHub repository and engage with the growing community of practitioners building upon the Wan 2.1 foundation.
The Wan 2.1 model family represents a significant milestone in the democratization of advanced video generation capabilities. By combining state-of-the-art performance with careful attention to computational efficiency across multiple variants, these models make sophisticated video creation accessible to a broader range of users and applications than previously possible.
The family's comprehensive approach—offering specialized variants for different tasks, resolutions, and resource constraints—demonstrates a thoughtful consideration of diverse user needs. From the heavyweight T2V 14B flagship to the remarkably efficient T2V 1.3B, each model in the family balances quality with practical usability considerations.
As open foundation models released under permissive licensing, the Wan 2.1 family is poised to accelerate innovation across numerous domains, from creative media production to educational content development. The technical innovations introduced by these models, particularly the Wan-VAE architecture and efficient parameter scaling, contribute valuable approaches that will likely influence the next generation of video generation technologies.
For developers, researchers, and content creators interested in exploring these capabilities, comprehensive documentation and implementation examples are available through the DiffSynth-Studio repository and other resources maintained by the Wan-AI team.