AI๊ฐ ์์ง์ด๊ณ ๋ฏธ๋๊ฐ ์
๋ฐ์ดํธ๋ฉ๋๋ค.Wan2.1-FLF2V-14B-720P
๐ Wan    ๏ฝ    ๐ฅ๏ธ GitHub    |   ๐ค Hugging Face    |   ๐ค ModelScope    |    ๐ Technical Report    |    ๐ Blog    |   ๐ฌ WeChat Group    |    ๐ Discord    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: - ๐ **SOTA Performance**: **Wan2.1** consistently outperforms existing open-source models and state-of-the-art commercial solutions across multiple benchmarks. - ๐ **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. - ๐ **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. - ๐ **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. - ๐ **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. If your work has improved **Wan2.1** and you would like more people to see it, please inform us. - CFG-Zero enhances **Wan2.1** (covering both T2V and I2V models) from the perspective of CFG. - TeaCache now supports **Wan2.1** acceleration, capable of increasing speed by approximately 2x. Feel free to give it a try! - DiffSynth-Studio provides more support for **Wan2.1**, including video-to-video, FP8 quantization, VRAM optimization, LoRA training, and more. Please refer to their examples.
๋ฒ์ญ ์ค๋น ์ค์ ๋๋ค. ์๋ฌธ์ ํ์ํฉ๋๋ค.
๐ Wan    ๏ฝ    ๐ฅ๏ธ GitHub    |   ๐ค Hugging Face    |   ๐ค ModelScope    |    ๐ Technical Report    |    ๐ Blog    |   ๐ฌ WeChat Group    |    ๐ Discord    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: - ๐ **SOTA Performance**: **Wan2.1** consistently outperforms existing open-source models and state-of-the-art commercial solutions across multiple benchmarks. - ๐ **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. - ๐ **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. - ๐ **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. - ๐ **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. If your work has improved **Wan2.1** and you would like more people to see it, please inform us. - CFG-Zero enhances **Wan2.1** (covering both T2V and I2V models) from the perspective of CFG. - TeaCache now supports **Wan2.1** acceleration, capable of increasing speed by approximately 2x. Feel free to give it a try! - DiffSynth-Studio provides more support for **Wan2.1**, including video-to-video, FP8 quantization, VRAM optimization, LoRA training, and more. Please refer to their examples.