What are Open Source Models for Animation Video?
Open source models for animation video are specialized AI systems that transform static images or text descriptions into dynamic video sequences. Using advanced deep learning architectures like diffusion transformers and Mixture-of-Experts (MoE) systems, they generate smooth, natural video animations from various inputs. This technology allows developers and creators to produce professional-quality animated content with unprecedented freedom. They foster collaboration, accelerate innovation, and democratize access to powerful video generation tools, enabling applications from digital storytelling to large-scale enterprise video production.
Wan-AI/Wan2.2-I2V-A14B
Wan2.2-I2V-A14B is one of the industry's first open-source image-to-video generation models featuring a Mixture-of-Experts (MoE) architecture, released by Alibaba's AI initiative, Wan-AI. The model specializes in transforming a static image into a smooth, natural video sequence based on a text prompt. Its key innovation is the MoE architecture, which employs a high-noise expert for the initial video layout and a low-noise expert to refine details in later stages, enhancing model performance without increasing inference costs.
Wan-AI/Wan2.2-I2V-A14B: Pioneering MoE Architecture for Video
Wan2.2-I2V-A14B is one of the industry's first open-source image-to-video generation models featuring a Mixture-of-Experts (MoE) architecture, released by Alibaba's AI initiative, Wan-AI. The model specializes in transforming a static image into a smooth, natural video sequence based on a text prompt. Its key innovation is the MoE architecture, which employs a high-noise expert for the initial video layout and a low-noise expert to refine details in later stages, enhancing model performance without increasing inference costs. Compared to its predecessors, Wan2.2 was trained on a significantly larger dataset, which notably improves its ability to handle complex motion, aesthetics, and semantics, resulting in more stable videos with reduced unrealistic camera movements.
Pros
- Industry-first open-source MoE architecture for video generation.
- Enhanced performance without increasing inference costs.
- Trained on significantly larger datasets for better quality.
Cons
- Requires static image input to generate video sequences.
- May require technical expertise for optimal prompt engineering.
Why We Love It
- It pioneered the MoE architecture in open-source video generation, delivering professional-quality animations with improved motion handling and semantic understanding.
Wan-AI/Wan2.2-T2V-A14B
Wan2.2-T2V-A14B is the industry's first open-source video generation model with a Mixture-of-Experts (MoE) architecture, released by Alibaba. This model focuses on text-to-video (T2V) generation, capable of producing 5-second videos at both 480P and 720P resolutions. By introducing an MoE architecture, it expands the total model capacity while keeping inference costs nearly unchanged.
Wan-AI/Wan2.2-T2V-A14B: Revolutionary Text-to-Video Generation
Wan2.2-T2V-A14B is the industry's first open-source video generation model with a Mixture-of-Experts (MoE) architecture, released by Alibaba. This model focuses on text-to-video (T2V) generation, capable of producing 5-second videos at both 480P and 720P resolutions. By introducing an MoE architecture, it expands the total model capacity while keeping inference costs nearly unchanged; it features a high-noise expert for the early stages to handle the overall layout and a low-noise expert for later stages to refine video details. Furthermore, Wan2.2 incorporates meticulously curated aesthetic data with detailed labels for lighting, composition, and color, allowing for more precise and controllable generation of cinematic styles. Compared to its predecessor, the model was trained on significantly larger datasets, which notably enhances its generalization across motion, semantics, and aesthetics, enabling better handling of complex dynamic effects.
Pros
- First open-source T2V model with MoE architecture.
- Supports both 480P and 720P video generation.
- Incorporates curated aesthetic data for cinematic styles.
Cons
- Limited to 5-second video duration.
- Requires well-crafted text prompts for optimal results.
Why We Love It
- It revolutionizes text-to-video generation with industry-first MoE architecture, enabling precise cinematic control and complex dynamic effects from simple text descriptions.
Wan-AI/Wan2.1-I2V-14B-720P-Turbo
Wan2.1-I2V-14B-720P-Turbo is the TeaCache accelerated version of the Wan2.1-I2V-14B-720P model, reducing single video generation time by 30%. This 14B model can generate 720P high-definition videos and utilizes a diffusion transformer architecture with innovative spatiotemporal variational autoencoders (VAE), scalable training strategies, and large-scale data construction.
Wan-AI/Wan2.1-I2V-14B-720P-Turbo: Speed Meets Quality
Wan2.1-I2V-14B-720P-Turbo is the TeaCache accelerated version of the Wan2.1-I2V-14B-720P model, reducing single video generation time by 30%. Wan2.1-I2V-14B-720P is an open-source advanced image-to-video generation model, part of the Wan2.1 video foundation model suite. This 14B model can generate 720P high-definition videos. And after thousands of rounds of human evaluation, this model is reaching state-of-the-art performance levels. It utilizes a diffusion transformer architecture and enhances generation capabilities through innovative spatiotemporal variational autoencoders (VAE), scalable training strategies, and large-scale data construction. The model also understands and processes both Chinese and English text, providing powerful support for video generation tasks.
Pros
- 30% faster generation time with TeaCache acceleration.
- State-of-the-art performance validated by human evaluation.
- Generates 720P high-definition videos.
Cons
- Higher computational requirements due to 14B parameters.
- Requires initial image input for video generation.
Why We Love It
- It delivers the perfect balance of speed and quality, offering 30% faster generation while maintaining state-of-the-art performance in 720P video creation.
AI Video Model Comparison
In this table, we compare 2025's leading open source animation video models, each with a unique strength. For image-to-video with cutting-edge MoE architecture, Wan2.2-I2V-A14B leads innovation. For text-to-video generation, Wan2.2-T2V-A14B offers revolutionary capabilities, while Wan2.1-I2V-14B-720P-Turbo prioritizes speed and HD quality. This side-by-side view helps you choose the right tool for your specific animation video creation needs.
| Number | Model | Developer | Subtype | Pricing (SiliconFlow) | Core Strength |
|---|---|---|---|---|---|
| 1 | Wan-AI/Wan2.2-I2V-A14B | Wan | Image-to-Video | $0.29/Video | MoE architecture pioneer |
| 2 | Wan-AI/Wan2.2-T2V-A14B | Wan | Text-to-Video | $0.29/Video | Cinematic style control |
| 3 | Wan-AI/Wan2.1-I2V-14B-720P-Turbo | Wan | Image-to-Video | $0.21/Video | 30% faster HD generation |
Frequently Asked Questions
Our top three picks for 2025 are Wan-AI/Wan2.2-I2V-A14B, Wan-AI/Wan2.2-T2V-A14B, and Wan-AI/Wan2.1-I2V-14B-720P-Turbo. Each of these models stood out for their innovation, performance, and unique approach to solving challenges in video generation, from pioneering MoE architecture to achieving state-of-the-art animation quality.
Our analysis shows different leaders for specific needs. Wan2.2-T2V-A14B excels for text-to-video generation with cinematic control. For image-to-video with cutting-edge architecture, Wan2.2-I2V-A14B leads with its MoE innovation. For fast, high-quality HD video generation, Wan2.1-I2V-14B-720P-Turbo offers the best speed-to-quality ratio.