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wavespeedai / wan-2.1-t2v-480p

Accelerated inference for Wan 2.1 14B text to video, a comprehensive and open suite of video foundation models that pushes the boundaries of video generation.

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Official model
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This model is priced by how many seconds of video are generated.

Check out our docs for more information about how per second of video pricing works on Replicate.

Readme

Accelerated Inference for Wan 2.1 14B

We are WaveSpeedAI, providing highly-optimized inference optimization for generative AI models.

We are excited to introduce our new product, a highly-optimized inference endpoint for Wan-2.1 14B model, a comprehensive and open suite of video foundation models that pushes the boundaries of video generation.

We utilize cutting-edge inference acceleration techniques to provide very fast inference for this model. And we are happy to bring this to you together with Replicate and DataCrunch.

Model Description ✨

Wan: Open and Advanced Large-Scale Video Generative Models

In this repository, we present Wan2.1, a comprehensive and open suite of video foundation models that pushes the boundaries of video generation. Wan2.1 offers these key features: - 👍 SOTA Performance: Wan2.1 consistently outperforms existing open-source models and state-of-the-art commercial solutions across multiple benchmarks. - 👍 Supports Consumer-grade GPUs: The T2V-1.3B model requires only 8.19 GB VRAM, making it compatible with almost all consumer-grade GPUs. It can generate a 5-second 480P video on an RTX 4090 in about 4 minutes (without optimization techniques like quantization). Its performance is even comparable to some closed-source models. - 👍 Multiple Tasks: Wan2.1 excels in Text-to-Video, Image-to-Video, Video Editing, Text-to-Image, and Video-to-Audio, advancing the field of video generation. - 👍 Visual Text Generation: Wan2.1 is the first video model capable of generating both Chinese and English text, featuring robust text generation that enhances its practical applications. - 👍 Powerful Video VAE: Wan-VAE delivers exceptional efficiency and performance, encoding and decoding 1080P videos of any length while preserving temporal information, making it an ideal foundation for video and image generation.