Type | |
Stats | 7,346 |
Reviews | |
Published | Feb 26, 2025 |
Base Model | |
Hash | AutoV2 996DBAD030 |
RedCraft uncensored 系列模型嚴禁發佈至NSFW非許可地區
非盈利模型 请勿以任何形式转发 传播禁止 [ Prohibition Against Dissemination ]
Laws and regulations in the location of the non-profit model composite publishing platform
NOW Comfy-Org/Wan_2.1_ComfyUI_repackaged
【例图页面蓝色Nodes或下载webp文件-可复现视频工作流】
Gallery sample images/videos (WEBP format) including the ComfyUI native workflow
This is a concise and clear GGUF model loading and tiled sampling workflow:
Wan 2.1 Low vram Comfy UI Workflow (GGUF) 4gb Vram - v1.1 | Wan Video Workflows | Civitai
节点:(或使用 comfyui manager 安装自定义节点)
https://github.com/city96/ComfyUI-GGUF
https://github.com/kijai/ComfyUI-WanVideoWrapper
https://github.com/BlenderNeko/ComfyUI_TiledKSampler
* 注意需要更新到最新版本的 comfyui-KJNodes GitHub - kijai/ComfyUI-KJNodes: Various custom nodes for ComfyUI update to the latest version of Comfyui KJNodes
Kijai ComfyUI wrapper nodes for WanVideo
WORK IN PROGRESS
@kijaidesign 's works
Huggingface - Kijai/WanVideo_comfy
GitHub - kijai/ComfyUI-WanVideoWrapper
主图视频来自 AiWood
https://www.bilibili.com/video/BV1TKP3eVEue
Text encoders to ComfyUI/models/text_encoders
Transformer to ComfyUI/models/diffusion_models
Vae to ComfyUI/models/vae
Right now I have only ran the I2V model succesfully.
Can't get frame counts under 81 to work, this was 512x512x81
~16GB used with 20/40 blocks offloaded
DiffSynth-Studio Inference GUI
Wan-Video LoRA & Finetune training.
DiffSynth-Studio/examples/wanvideo at main · modelscope/DiffSynth-Studio · GitHub
💜 Wan | 🖥️ GitHub | 🤗 Hugging Face | 🤖 ModelScope | 📑 Paper (Coming soon) | 📑 Blog | 💬 WeChat Group | 📖 Discord
Wan: Open and Advanced Large-Scale Video Generative Models
通义万相Wan2.1视频模型开源!视频生成模型新标杆,支持中文字效+高质量视频生成
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.
This repository features our T2V-14B model, which establishes a new SOTA performance benchmark among both open-source and closed-source models. It demonstrates exceptional capabilities in generating high-quality visuals with significant motion dynamics. It is also the only video model capable of producing both Chinese and English text and supports video generation at both 480P and 720P resolutions.