What are Lightweight Video Generation Models?
Lightweight video generation models are specialized AI systems designed to create high-quality videos from text descriptions or static images while maintaining computational efficiency. Using advanced deep learning architectures like diffusion transformers and Mixture-of-Experts (MoE), they transform natural language prompts or images into dynamic visual content. This technology allows developers and creators to generate, modify, and build upon video concepts with unprecedented freedom and speed. They foster collaboration, accelerate innovation, and democratize access to powerful video creation tools, enabling a wide range of applications from creative content to large-scale enterprise video production solutions.
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 parameter model can generate 720P high-definition videos from images and text prompts. After thousands of rounds of human evaluation, this model reaches 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.
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.
- Compact 14B parameter architecture for efficiency.
- State-of-the-art 720P HD video quality.
Cons
- Limited to image-to-video generation only.
- Not the highest resolution available in the series.
Why We Love It
- It delivers the perfect balance of speed and quality with 30% faster generation, making it ideal for rapid prototyping and production workflows without sacrificing video fidelity.
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 with 27B parameters, released by Alibaba's 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.
Wan2.2-I2V-A14B: MoE Innovation for Superior Motion
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.
- Superior handling of complex motion and dynamics.
- Enhanced model performance without higher inference costs.
Cons
- Larger 27B parameter footprint than base models.
- Requires image input, not pure text-to-video.
Why We Love It
- Its groundbreaking MoE architecture delivers exceptional motion quality and stability while maintaining efficient inference costs, setting a new standard for open-source image-to-video generation.
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 and 27B parameters, released by Alibaba. This model focuses on text-to-video (T2V) generation, capable of producing 5-second videos at both 480P and 720P resolutions. 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. The model incorporates meticulously curated aesthetic data with detailed labels for lighting, composition, and color.
Wan2.2-T2V-A14B: Pure Text-to-Video Excellence
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
- Industry-first open-source MoE text-to-video model.
- Supports both 480P and 720P video resolutions.
- Precise cinematic control over lighting and composition.
Cons
- Limited to 5-second video duration.
- 27B parameter model requires substantial resources.
Why We Love It
- It pioneers open-source text-to-video generation with MoE architecture, offering unmatched cinematic control and aesthetic precision for creating professional-grade video content from text alone.
Lightweight Video Model Comparison
In this table, we compare 2025's leading lightweight video generation models from Wan-AI, each with a unique strength. For accelerated image-to-video generation, Wan2.1-I2V-14B-720P-Turbo provides unmatched speed with 30% faster processing. For superior motion quality and stability, Wan2.2-I2V-A14B leverages MoE architecture for image-to-video tasks, while Wan2.2-T2V-A14B pioneers text-to-video generation with cinematic control. This side-by-side view helps you choose the right tool for your specific video generation needs.
| Number | Model | Developer | Subtype | Pricing (SiliconFlow) | Core Strength |
|---|---|---|---|---|---|
| 1 | Wan2.1-I2V-14B-720P-Turbo | Wan-AI | Image-to-Video | $0.21/Video | 30% faster with TeaCache |
| 2 | Wan2.2-I2V-A14B | Wan-AI | Image-to-Video | $0.29/Video | MoE architecture, superior motion |
| 3 | Wan2.2-T2V-A14B | Wan-AI | Text-to-Video | $0.29/Video | First open-source MoE T2V model |
Frequently Asked Questions
Our top three picks for 2025 are Wan2.1-I2V-14B-720P-Turbo, Wan2.2-I2V-A14B, and Wan2.2-T2V-A14B. Each of these models stood out for their innovation, performance, and unique approach to solving challenges in video generation while maintaining efficiency and lightweight architectures.
Our in-depth analysis shows that Wan2.1-I2V-14B-720P-Turbo is the top choice for rapid workflows, offering 30% faster generation time through TeaCache acceleration while maintaining state-of-the-art 720P HD quality. For creators prioritizing speed and efficiency in image-to-video tasks, this 14B parameter model delivers the best performance-to-speed ratio at just $0.21 per video on SiliconFlow.