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AI 모델 라이브러리/Wan-AI/Wan2.1-VACE-1.3B-diffusers
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Wan2.1-VACE-1.3B-diffusers

Wan-AI · Wan2.1-VACE-1.3B-diffusers

💜 Wan &nbsp&nbsp ｜ &nbsp&nbsp 🖥️ GitHub &nbsp&nbsp | &nbsp&nbsp🤗 Hugging Face &nbsp&nbsp | &nbsp&nbsp🤖 ModelScope &nbsp&nbsp | &nbsp&nbsp 📑 Technical Report &nbsp&nbsp | &nbsp&nbsp 📑 Blog &nbsp&nbsp | &nbsp&nbsp💬 WeChat Group &nbsp&nbsp | &nbsp&nbsp 📖 Discord &nbsp&nbsp 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. If your work has improved **Wan2.1** and you would like more people to see it, please inform us. - Phantom has developed a unified video generation framework for single and multi-subject references based on **Wan2.1-T2V-1.3B**. Please refer to their examples. - UniAnimate-DiT, based on **Wan2.1-14B-I2V**, has trained a Human image animation model and has open-sourced the inference and training code. Feel free to enjoy it! - 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 gi

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영상 생성license:apache-2.0
컨텍스트 창미공개
입력 → 출력이미지 → 영상
모델 웨이트미확인
정보 업데이트2026/10/30 01:50:03
OVERVIEW

모델 소개

💜 Wan &nbsp&nbsp ｜ &nbsp&nbsp 🖥️ GitHub &nbsp&nbsp | &nbsp&nbsp🤗 Hugging Face &nbsp&nbsp | &nbsp&nbsp🤖 ModelScope &nbsp&nbsp | &nbsp&nbsp 📑 Technical Report &nbsp&nbsp | &nbsp&nbsp 📑 Blog &nbsp&nbsp | &nbsp&nbsp💬 WeChat Group &nbsp&nbsp | &nbsp&nbsp 📖 Discord &nbsp&nbsp 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. If your work has improved **Wan2.1** and you would like more people to see it, please inform us. - Phantom has developed a unified video generation framework for single and multi-subject references based on **Wan2.1-T2V-1.3B**. Please refer to their examples. - UniAnimate-DiT, based on **Wan2.1-14B-I2V**, has trained a Human image animation model and has open-sourced the inference and training code. Feel free to enjoy it! - 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 gi

주요 특징

特点摘要待依据来源补齐，不凭模型名称推断能力。

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暂无已采集的可核验公开评分。模型未上榜不代表能力较低。

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출처 및 추적 기록

huggingface · Wan-AI/Wan2.1-VACE-1.3B-diffusers ↗2026/10/29 14:51:25
huggingface · enrich:5bbad74a-68fa-4090-9d4c-2c51145d2da9 ↗2026/10/30 01:50:03

许可证：apache-2.0。模型介绍与第三方评测均以原来源的具体版本和方法为准。