Wan 2.2 A14B Text-to-Video is an advanced AI model developed by Wan-AI that transforms text descriptions into high-quality video content. This model represents a significant advancement in text-to-video generation technology, capable of producing 720P resolution videos at 24 frames per second directly from textual prompts. The model is part of the Wan 2.2 series, which includes both 14B parameter and 5B parameter variants designed for different computational requirements and use cases.

The model utilizes a sophisticated architecture that combines diffusion transformers with advanced compression techniques through the Wan2.2-VAE system. What makes this model particularly notable is its ability to run on consumer-grade hardware, including RTX 4090 graphics cards, making high-quality video generation accessible to a broader range of users. The model supports both text-to-video and image-to-video generation tasks within a unified framework, offering flexibility for various creative and professional applications.

The underlying technology employs a high-compression design with a compression ratio of 4×16×16 for the VAE component, achieving an overall compression rate of 64 while maintaining video reconstruction quality. The model incorporates advanced optimization techniques including PyTorch FSDP and DeepSpeed Ulysses for multi-GPU acceleration, and FlashAttention3 for improved efficiency on compatible hardware architectures.

Architecture

Diffusion Transformer with Wan2.2-VAE

Parameters

14B (A14B variant) and 5B (TI2V-5B variant)

Resolution

720P (1280×720) and 480P support

Frame Rate

24 fps

Video Length

Up to 5 seconds

Input/Output formats

Text prompts to MP4 video output

Compression Ratio

4×32×32 total compression for TI2V-5B

Memory Requirements

Minimum 80GB VRAM for single-GPU inference without optimization

Multi-GPU Support

FSDP + DeepSpeed Ulysses acceleration

Hardware Compatibility

Consumer-grade GPUs including RTX 4090

• Memory optimization is crucial for consumer hardware deployment, requiring careful use of model offloading and dtype conversion options
• The model performs best with detailed, descriptive prompts that specify visual elements, motion, and scene composition
• Generation time varies significantly based on hardware configuration, with single consumer GPUs requiring approximately 9 minutes for 5-second 720P videos
• Multi-GPU setups can dramatically reduce inference time through distributed processing techniques
• Prompt extension features are available but may be disabled for faster inference when not needed
• The model benefits from warm-up phases before achieving optimal performance metrics
• FlashAttention3 optimization is specifically available for Hopper architecture GPUs

• Use the --offloadmodel True flag combined with --convertmodeldtype to reduce GPU memory usage on consumer hardware
• For optimal memory efficiency on single GPUs, enable --t5cpu to offload the text encoder to CPU memory
• Structure prompts with specific visual details, lighting conditions, and motion descriptions for better results
• Implement multi-GPU inference using torchrun with appropriate ulysses_size settings for your hardware configuration
• Start with basic inference without prompt extension to establish baseline performance before adding advanced features
• Monitor peak GPU memory usage and adjust batch sizes accordingly to prevent out-of-memory errors
• Use distributed testing configurations for production deployments requiring consistent performance
• Consider the 5B parameter variant for applications requiring faster inference times with acceptable quality trade-offs

• Generates high-definition 720P videos at professional 24fps frame rates
• Supports both text-to-video and image-to-video generation in a unified framework
• Produces realistic motion and cinematic visuals from textual descriptions
• Handles complex scene compositions with multiple objects and characters
• Maintains temporal consistency across video frames
• Supports various aspect ratios and resolution configurations
• Achieves superior performance compared to leading commercial models on benchmark evaluations
• Enables efficient deployment on consumer-grade hardware through optimization techniques
• Provides flexible inference options for different computational budgets
• Supports distributed processing for enterprise-scale applications

• Creating promotional videos and marketing content from script descriptions
• Generating concept visualizations for film and animation pre-production
• Producing educational content with dynamic visual explanations
• Developing prototype animations for game development and interactive media
• Creating social media content with engaging visual narratives
• Generating training materials and instructional videos from text-based curricula
• Producing artistic and creative video content for digital art projects
• Developing proof-of-concept videos for product demonstrations
• Creating animated sequences for presentations and business communications
• Generating visual content for research and academic publications

• The model requires significant computational resources, with 80GB VRAM recommended for optimal single-GPU performance
• Generation times can be substantial on consumer hardware, requiring patience for high-quality outputs
• Memory optimization techniques may impact generation quality and should be tested for specific use cases
• The model performs best with well-structured, detailed prompts rather than simple or vague descriptions
• Multi-GPU setups require careful configuration of distributed processing parameters
• Performance varies significantly across different GPU architectures and memory configurations
• The model may exhibit inconsistencies in complex scenes with multiple moving elements
• Users report excellent results for cinematic and artistic content generation
• Community feedback indicates strong performance for creative applications
• Some users note learning curve requirements for optimal prompt engineering

• Requires substantial computational resources with minimum 80GB VRAM for optimal performance without memory optimization techniques
• Limited to 5-second video duration, which may not be sufficient for longer-form content applications
• Generation times on consumer hardware can be prohibitively long for real-time or interactive applications