All in One, Wan for All
We are excited to introduce our latest model to our talented community creators:
Wan2.1-VACE, All-in-One Video Creation and Editing model.
Model size: 1.3B, 14B License: Apache-2.0
If we are in Wan Day, what will it be like? 如果我们在万相世界，会是什么样子？
模型支持两种文本到视频模型（1.3B 和 14B）和两种分辨率（480P 和 720P）。
WAN-VACE is not a T2V model per se, but rather R(reference)2V, Can be understood as Video ControlNet for WAN , so there is no way to provide a T2V workflow. The CausVid accelerator is a distillation accelerator technology that can be used on WAN-VACE to provide 4-8 steps of accelerated generation.
WAN-VACE本身不是T2V模型，而是R（参考）2V，可以理解为WAN的视频CN，因此无法提供T2V工作流程。CausVid加速器是一种蒸馏加速技术，可用于WAN-VACE，提供4-8步加速生成。
Introduction
VACE is an all-in-one model designed for video creation and editing. It encompasses various tasks, including reference-to-video generation (R2V), video-to-video editing (V2V), and masked video-to-video editing (MV2V), allowing users to compose these tasks freely. This functionality enables users to explore diverse possibilities and streamlines their workflows effectively, offering a range of capabilities, such as Move-Anything, Swap-Anything, Reference-Anything, Expand-Anything, Animate-Anything, and more.
VACE是一款专为视频创建和编辑而设计的一体化模型。它包括各种任务，包括视频生成（R2V）、视频到视频编辑（V2V）和屏蔽视频到视频剪辑（MV2V），允许用户自由组合这些任务。此功能使用户能够探索各种可能性，并有效地简化他们的工作流程，提供一系列功能，如移动任何内容、交换任何内容、引用任何内容、扩展任何内容、为任何内容设置动画等。
About CausVid-Wan2-1:
5-16 The PERFECT solution to CausVid from Kijai （Best practices）
Wan21_CausVid_14B_T2V_lora_rank32.safetensors · Kijai/WanVideo_comfy
Through weight extraction and block separation,
KJ give us a universal CausVid LoRA in rank32 for Any 14B WAN model,
EVEN including FT models and I2V model！
Although this may not have been CausVid's initial intention, by flexibly adjusting the LoRA parameters (0.3~0.5), we have achieved unprecedented availability on home grade graphics cards！
KJ-Godlike also provides a 1.3B bidirectional inference version of LoRA export file
Wan21_CausVid_bidirect2_T2V_1_3B_lora_rank32.safetensors
same time, we also noticed that xunhuang1995 uploaded the Warp-4Step_cfg2 autoregressive version 1.3B CausVid model from: tianweiy/CausVid
相与为壹，全部在万
Best Adaptation for WAN-VACE full Models
5/15 REDCausVid-Wan2-1-14B-DMD2-FP8 Uploaded 8-15 steps CFG 1
本页面右侧下载列表，Safetensors 格式，workflow 在 Trainning data 压缩包内
The download list on the right side of this page is in Safetensors format, and the workflow is included in the Training data compressed file. The example images and videos also include workflows (yes, you can directly throw the original video files into ComfyUI and try to capture the workflow)
5/15 Aiwood WAN-ACE Fully functional workflow Uploaded
5/15 ComfyUI KJ-WanVideoWrapper have been updated
5/14 autoregressive_checkpoint.pt 1.3b Uploaded , PT UNET Loader
5/14 bidirectional_checkpoint2.pt 1.3b Uploaded , PT UNET Loader
NEW Sampler Flowmatch_causvid in KJ-WanVideoWrapper
Releases from：
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[ The adaptability test results of WAN1.2 LoRAs for VACE show that about 75% of I2V/T2V LoRA weights can take effect, but the sensitivity is reduced ( try to increase the LoRA weight ,more than 100% Sometimes it can be helpful ) ]
Fullview of Aiwood WAN-ACE Fully functional workflow：
source: https://www.bilibili.com/video/BV1FGE6zGEDK ⭐ leave a star⭐
CausVid 加速器项目页 https://causvid.github.io/
WAN-VACE 模型的参数和配置如下：
📌 Wan2.1-VACE provides solutions for various tasks, including reference-to-video generation (R2V), video-to-video editing (V2V), and masked video-to-video editing (MV2V), allowing creators to freely combine these capabilities to achieve complex tasks.
👉 Multimodal inputs enhancing the controllability of video generation.
👉 Unified single model for consistent solutions across tasks.
👉 Free combination of capabilities unlocking deeper creative
📌 Wan2.1-VACE为各种任务提供解决方案，包括参考视频生成（R2V）、视频到视频编辑（V2V）和屏蔽视频到视频剪辑（MV2V），允许创作者自由组合这些功能来实现复杂的任务。
👉 多模态输入增强了视频生成的可控性。
👉 统一的单一模型，实现跨任务的一致解决方案。
👉 自由组合功能，释放更深层次的创造力
WAN实时生成来了！Hybrid AI model crafts smooth, high-quality videos in seconds
The CausVid generative AI tool uses a diffusion model to teach an autoregressive (frame-by-frame) system to rapidly produce stable, high-resolution videos.
Wan2.1based 混合AI模型在几秒钟内（9帧/秒）制作出流畅、高质量的视频
CausVid生成AI工具使用扩散模型来指导自回归（逐帧）系统快速生成稳定的高分辨率视频。
From Slow Bidirectional to
Fast Autoregressive Video Diffusion Models
CausVid https://causvid.github.io/
tianweiy (Tianwei Yin)
RedCaus/REDCausVid-Wan2-1-14B-DMD2-FP8 Uploaded / WAN-VACE14B 最佳适配
CausVid/autoregressive_checkpoint uploaded / 自回归模型基于 WAN1.3B 已收录
CausVid/bidirectional_checkpoint2 uploaded / 双向推导模型基于 WAN1.3B 已收录
Kijai/Wan2_1-T2V-14B_CausVid_fp8_e4m3fn.safetensors / HF仓库 WanVideo_comfy
⭐ leave a star⭐
licensed by Creative Commons Attribution Non Commercial 4.0
Thank you for this friend's additional comment. I was too excited last night and didn't sleep, so I stopped updating before finishing：
We’ll need to use the official Python-based inference codes
1) Clone https://github.com/tianweiy/CausVid and follow instructions to install requirements
2) Clone https://huggingface.co/Wan-AI/Wan2.1-T2V-1.3B into wan_models/Wan2.1-T2V-1.3B
3) Put the pt file inside checkpoint_folder/model.pt
4) Run inference code, python minimal_inference/autoregressive_inference.py --config_path configs/wan_causal_dmd.yaml --checkpoint_folder XXX --output_folder XXX --prompt_file_path XXX
Reddit posts about CausVid: https://www.reddit.com/r/StableDiffusion/comments/1khjy4o/causvid_generate_videos_in_seconds_not_minutes/
https://www.reddit.com/r/StableDiffusion/comments/1k0gxer/causvid_from_slow_bidirectional_to_fast/
We have tested the CausVid based on Wan1.3b version, which has incredible speed, and are currently testing the 14B version produced by lightx2v.
LightX2V: Light Video Generation Inference Framework
Supported Model List
How to Run
Please refer to the documentation in lightx2v.
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通义实验室 WAN 2.1 Model Zoo
Institute for Intelligent Computing专注于各领域大模型技术研发与创新应用。实验室研究方向涵盖自然语言处理、多模态、视觉AIGC、语音等多个领域。我们并积极推进研究成果的产业化落地。实验室同时积极参与开源社区建设，全方位拥抱开源社区，共同探索AI模型的开源开放。
Developer / Models Name / Kijai`s ComfyUI Model
RedCaus/REDCausVid-Wan2-1-14B-DMD2-FP8 Uploaded / WAN-VACE14B 最佳适配
CausVid/autoregressive_checkpoint included / 自回归模型基于 WAN1.3B 已收录
CausVid/bidirectional_checkpoint2 included / 双向推导模型基于 WAN1.3B 已收录
CausVid/wan_causal_ode_checkpoint_model testing / 自回归因果推导 测试中
CausVid/wan_i2v_causal_ode_checkpoint_model testing / 文生图模型 测试中
lightx2v/Wan2.1-T2V-14B-CausVid unqualify / 自回归模型14B AiWood实测不达标
lightx2v/Wan2.1-T2V-14B-CausVid quant unqualify / 自回归模型14B量化版 实测不达标
Wan Team/1.3B text-to-video included / 文生视频1.3B 已收录
Wan Team/14B text-to-video included / 文生视频14B 已收录
Wan Team/14B image-to-video 480P included / 图生视频14B 已收录
Wan Team/14B image-to-video 720P included / 图生视频14B 已收录
Wan Team/14B first-last-frame-to-video 720P included / 视频首尾帧 已收录
Wan Team/Wan2_1_VAE included / KiJai‘s WAN视频VAE 已收录
ComfyORG/Wan2.1_VAE included / Comfy‘s WAN视频VAE 已收录
google/umt5-xxl umt5-xxl-enc safetensors included / TE编码器 已收录
mlf/open-clip-xlm-roberta-large-vit-huge-14 safetensors included / CLIP编码器 已收录
DiffSynth-Studio Team/1.3B aesthetics LoRA 美学蒸馏-通义万相2.1-1.3B-LoRA-v1
DiffSynth-Studio Team/1.3B Highres-fix LoRA 高分辨率修复-通义万相2.1-1.3B-LoRA-v1
DiffSynth-Studio Team/1.3B ExVideo LoRA 长度扩展-通义万相2.1-1.3B-LoRA-v1
DiffSynth-Studio Team/1.3B Speed Control adapter 速度控制-通义万相2.1-1.3B-适配器-v1
PAI Team/ WAN2.1 Fun 1.3B InP 支持首尾帧 / Kijai/WanVideo_comfy
PAI Team/ WAN2.1 Fun 14B InP 支持首尾帧 / Kijai/WanVideo_comfy
PAI Team/ WAN2.1 Fun 1.3B Control 控制器 / Kijai/WanVideo_comfy
PAI Team/ WAN2.1 Fun 14B Control 控制器 / Kijai/WanVideo_comfy
PAI Team/ WAN2.1 Fun 14B Control 控制器 / Kijai/WanVideo_comfy
PAI Team/ WAN2.1-Fun-V1_1-14B-Control-Camera / Kijai/WanVideo_comfy
IIC Team/ VACE-通义万相2.1-1.3B-Preview / Kijai/WanVideo_comfy
IC ( In-Context ) Controler 多模态控制器 :
ali-vilab/ VACE: All-in-One Video Creation and Editing / Kijai/WanVideo_comfy
Phantom-video/Phantom Subject-Consistent via Cross-Modal Alignment
KwaiVGI/ ReCamMaster Camera-Controlled 镜头多角度 / Kijai/WanVideo_comfy
Digital Character 数字人 via Wan2.1 :
ali-vilab/ UniAnimate-DiT 长序列骨骼角色视频 / Kijai/WanVideo_comfy
Fantasy-AMAP/ 音频驱动数字人 FantasyTalking / Kijai/WanVideo_comfy
Fantasy-AMAP/ 角色一致性身份保留 FantasyID / Fantasy-AMAP/fantasy-id
Uncensored NSFW 解锁版本：
REDCraft AIGC / WAN2.1 720P NSFW Unlocked / forPrivate use【非公开】
CubeyAI / WAN General NSFW model (FIXED) / The Best Universal LoRA
昆仑万维发布 SkyReels based on Wan2.1
Skywork / SkyReels-V2-I2V-14B-720P / Image-to-Video / Kijai/WanVideo_comfy
Skywork / SkyReels-V2-I2V-14B-540P / Image-to-Video / Kijai/WanVideo_comfy
Skywork / SkyReels-V2-T2V-14B-540P / Text-to-Video / Kijai/WanVideo_comfy
Skywork / SkyReels-V2-T2V-14B-720P /Text-to-Video / Kijai/WanVideo_comfy
Skywork / SkyReels-V2-I2V-1.3B-540P / Image-to-Video / Kijai/WanVideo_comfy
AutoRegressive Diffusion-Forcing 无限长度生成架构
Skywork / SkyReels-V2-DF-14B-720P / Text-to-Video / Kijai/WanVideo_comfy
Skywork / SkyReels-V2-DF-14B-540P / Text-to-Video / Kijai/WanVideo_comfy
Skywork / SkyReels-V2-DF-1.3B-540P / Text-to-Video / Kijai/WanVideo_comfy
昆仑万维发布 SkyReels 视频标注模型：
Skywork / SkyCaptioner-V1 Skywork (Skywork) / Skywork/SkyCaptioner-V1
Tiny AutoEncoder / taew2_1 safetensors / Kijai/WanVideo_comfy
A tiny distilled VAE model for encoding images into latents and decoding latent representations into images
WAN 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.
Description
FLF2V：最新Wan2.1首尾帧视频生成！
破狼介绍页：[ComfyUI]FLF2V：最新Wan2.1官方首尾帧视频生成！万相可控性生态加速 - 知乎
FLF2V： Wan2.1 first and last frame video generation!
---
ComfyUI
ComfyUI-WanVideoWrapper example_workflows:
https://github.com/kijai/ComfyUI-WanVideoWrapper/tree/main/example_workflows
---
WAN Official Model Repository：
Wan-AI/Wan2.1-FLF2V-14B-720P · Hugging Face
---
Kijai‘s WAN_Comfy Model Repository：
Kijai/WanVideo_comfy · Hugging Face
---
City96's GGUF Model Repository：
FAQ
Comments (47)
is it possible to train lora for this?
Yes, this is the Comfy community version
asking for a friend
@METAFILM_Ai sorry little confused. i am trying to train a lora for this. Using diffusion pipe, but safetensor alone is not enough. i need first a config.json to run it. Where do i find it or i am on a wrong path? thx
How much VRAM does this model need?
If the block swap is setting to 30, 24GB is more suitable. If 16GB, you can try opening it to 40-50 blocks swap
@METAFILM_Ai In my other workflows 30 is the max for the smaller model and 40 for the larger. I'm not sure if these models are different but the smallest wan itv quant with max offloading can just barely fit into 12GB. It's less tweaking to get it ot run on a 16GB card. The guff models will probably run on 8GB. I've heard of 4GBworkflows but that might be the t2v which is very small
想请问，载点提供的wanAIWan21VideoModelSafetensors_kijaiWan21I2V14B480P.safetensors这个模型是否是Kijai的huggingface中的Wan2_1-I2V-14B-480P_fp8_e4m3fn.safetensors这个？我怕我搞混生成出来的动画有问题...
是的
請問我該從哪裡複製範例的Workflow?
我從C站儲存的範例都是mp4, Comfy無法讀取
另外, 我的原圖是正常的圖
是否透過本貼的模型(屬於unet?)就可以I2V生成NSFW的video?
你可以蒐索找到一個WAN的Workflow
@METAFILM_Ai
有從
https://blog.comfy.org/p/wan21-video-model-native-support
的連結找到workflow與相關必要檔案並有成功產生"一般"480P的i2v
但不知道為甚麼, 我的input(原圖)是普通圖片(SFW), 但無論如何都無法產生NSFW的內容(每當重點部位要出現時, 都會自動生成色塊或遮蔽物擋住重點部位)
即使套上C站上的那些NSFW的Lora, 也無法生成NSFW內容
我一開始以為是上面那個blog提供的unet有檔NSFW, 但後來改用本篇提供的unet也無法產生
不知道是我哪邊弄錯了
Kijai-Wan2.1-I2V-14B-720P = Wan2_1-I2V-14B-720P_fp8_e4m3fn.safetensors
If anyone else was wondering. I don't know if it was mentioned anywhere
Yes the same model
Incredible, I can make a 2 second 256x256 video with my GTX 920 with 2GB of VRAM, with this model: wanAIWan21VideoModelSafetensors_comfyorgI2v480pGGUF (but it took almost two hours.)
wow the card should be almost 8-10 years old .. and wait 2 hours .. what if it was a fail .. the frustration would kill me 😰
GTX920 Can install the latest CUDA? incredible
bruv just rent a 48gb vram on Runpod.. 3 hours, costs like a bag of chips
提示下列错误应该如何解决？（How to solve the following error?）
Prompt outputs failed validation: UpscaleModelLoader: - Value not in list: model_name: '4x_foolhardy_Remacri.pth' not in ['umt5_xxl_fp8_e4m3fn_scaled.safetensors'] LoadImage: - Custom validation failed for node: image - Invalid image file: ComfyUI_00058_.png CLIPLoader: - Value not in list: clip_name: 'None' not in [] LoaderGGUF: - Value not in list: gguf_name: 'None' not in [] VAELoaderKJ: - Value not in list: vae_name: 'Wan2_1_VAE_bf16.safetensors' not in ['wan_2.1_vae.safetensors', 'taesd', 'taesdxl', 'taesd3', 'taef1'
缺失一系列模型文件：4x_foolhardy_Remacri.pth 分辨率提升模型，Clip和T5的文本编码器缺失，VAE和模型都没找到。
@METAFILM_Ai感谢回答，请问 模型是放在diffusion_models吗，为什么读取不到
@wgz1852094542150 upscale的模型有专门放置的地方，你直接用manager去model那边搜寻下载就可以直接用了
@madnessdevilpurg8570感谢回答，目前下载好所有节点后，发现出图一直卡在43%wan图像到视频那里，请问是什么问题。下面是后台显示： [ComfyUI-Manager] default cache updated: https://api.comfy.org/nodes
FETCH DATA from: https://raw.githubusercontent.com/ltdrdata/ComfyUI-Manager/main/custom-node-list.json [DONE]
[ComfyUI-Manager] All startup tasks have been completed.
@wgz1852094542150 你說的這條訊息應該不是啥錯誤訊息，就是單純的進度報告
@madnessdevilpurg8570 问题在于它一直卡在这，wan图片转视频这个节点，是因为我没下载工作流上指定的gguf模型吗？
where is the workflow ? and i missing that everyone finding this workflow and i can't find it anywhere ?
I've tried this on several WAN workflows but cannot get it or other WAN models to work--only the small "Fun" WAN checkpoint seems to work. I'm wondering if my rig just can't handle it? I feel like it should--I have a brand new 5070 Blackwell with 16gb, 12gb vram.
Upgrade that ram bottleneck. 16 < 32+
In the case of video generation, 16g and 12g are problematic. Even with a blackwell gpu, that's almost underpowered. I recommend offloading the models to CPU to free up some of the vram on your blackwell so that it'll be able to render. If you're using ComfyUI, there are nodes+workflows for that... the Kijai nodes (I think) have cpu offload options, and you can also re-quantize your models on the fly using those nodes. For example. I rent my gpus as needed and for images and testing prompts for video, I rent an RTX a6000 or L40. In that case, I use my fp32 utxxl clip, vae, and fp16 wan models, but I offload them to CPU and re-quantize them to fp8 using the nodes. However, when its time to take my prompts to a hardcore gpu, I rent an a100, h100, or an h200 and crank them out using the full floating point settings of the models and don't offload anything to CPU. And yes, it makes a HUGE difference in motion quality output, totally worth the bigger gpu, but to get it to "work" on your lower vram, offload to cpu and also quantize your larger models down to fp8 using the settings. OH, and almost forgot. The blackwells MUST HAVE the latest version of cuda 12.8+ to render video, no ifs, ands, or buts... so try that first. upgrade/update your cuda, THEN grab your offloadable nodes, because that will come in handy anyway. (a100 gpu rentals are usually just over a dollar an hour, and for the 3 hours a day you'd use it, totally worth it. An a100 is the price of a car.)
Also worth mentioning, installing triton, sageattention, etc along with your cuda update/upgrade is highly recommended, along with teacache. I let the video cook for 6 steps before letting cache start to help bake in the quality motion... and the output renders much faster and not much difference in quality, usually not any noticable difference.
@BreezyHeezy Thank you for all of this! I really appreciate it. I'll give it a go when I have the time. There is really so much to learn and it just doesn't stop. Thankfully I've been able to use 480p WAN models now, so that's some progress.
Doesn't work at all. Not loading to the A1111, no way to start using it
good job
Tell me, these are uncensored versions, right?
i cant get this work, it always says like some index error and closed automaticly
It may be that some model specifications do not match. You can download the video uploaded by the player ( yes, the video file can also be thrown into ComfyUI ) and read the workflow to analyze the reason
how do i get the workflow?
These models produce only black frames and don't seem to work as they should. The base Wan 2.1 I2V renders fine.
Thank you.
It may be that some model specifications do not match. You can download the video uploaded by the player ( yes, the video file can also be thrown into ComfyUI ) and read the workflow to analyze the reason
Everything was working great, until yesterday. Now all of a sudden the model stopped accepting prompts. It's doing its own thing. Nothing has been installed or updated. What could be the cause? I'm using GGUF.
Did ComfyUI update automatically? The current version has undergone significant changes, and I often feel confused =（
Does anyone know if the order in which you arrange the LORAs impacts the output? For example does Lora1 + Lora2 give a different result than Lora2+Lora1? Or if you should put the stronger or weaker strength Lora early or later Lora1(1)+Lora2(.5) vs the inverse? Thanks!
As far as I remember, the order doesn't affect anything.
In theory, it shouldn't affect. But in my own XP, and AFAIK tinkering with Pony and IL, I found that if I had lots of Lora with high strenght (lora>4 and strength>0.8), changing the order slightly changed the output.
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onTHEFLYWanAIWan21VideoModel_FLF2V14B720P.safetensors
wanAIWan21VideoModelSafetensors_FLF2V14B720P_1.safetensors
wanAIWan21VideoModelSafetensors_FLF2V14B720P.safetensors
Wan2_1-FLF2V-14B-720P_fp16.safetensors
Wan2_1-FLF2V-14B-720P_fp16.safetensors
Wan2_1-FLF2V-14B-720P_fp16.safetensors
Wan2_1-FLF2V-14B-720P_fp16.safetensors
Wan2_1-FLF2V-14B-720P_fp16.safetensors
Wan2_1-FLF2V-14B-720P_fp16.safetensors
Wan2_1-FLF2V-14B-720P_fp16.safetensors
Wan2_1-FLF2V-14B-720P_fp16.safetensors
Wan2_1-FLF2V-14B-720P_fp16.safetensors
Wan2_1-FLF2V-14B-720P_fp16.safetensors
onTHEFLYWanAIWan21VideoModel_FLF2V14B720P.safetensors
onTHEFLYWanAIWan21VideoModel_FLF2V14B720P.safetensors
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wanAIWan21VideoModelSafetensors_FLF2V14B720P.safetensors
wan2.1_flf2v_720p_14B_fp8_e4m3fn.safetensors
wan2.1_flf2v_720p_14B_fp8_e4m3fn.safetensors
wan2.1_flf2v_720p_14B_fp8_e4m3fn.safetensors
wan2.1_flf2v_720p_14B_fp8_e4m3fn.safetensors
wan2.1_flf2v_720p_14B_fp8_e4m3fn.safetensors
wan2.1_flf2v_720p_14B_fp8_e4m3fn.safetensors
wan2.1_flf2v_720p_14B_fp8_e4m3fn.safetensors
wan2.1_flf2v_720p_14B_fp8_e4m3fn.safetensors
wan2.1_flf2v_720p_14B_fp8_e4m3fn.safetensors
wanAIWan21VideoModelSafetensors_FLF2V14B720P.safetensors
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Wan2_1-FLF2V-14B-720P_fp8_e4m3fn.safetensors
Wan2_1-FLF2V-14B-720P_fp8_e4m3fn.safetensors
Wan2_1-FLF2V-14B-720P_fp8_e4m3fn.safetensors
Wan2_1-FLF2V-14B-720P_fp8_e4m3fn.safetensors
Wan2_1-FLF2V-14B-720P_fp8_e4m3fn.safetensors
Wan2_1-FLF2V-14B-720P_fp8_e4m3fn.safetensors
Wan2_1-FLF2V-14B-720P_fp8_e4m3fn.safetensors
Wan2_1-FLF2V-14B-720P_fp8_e4m3fn.safetensors
Wan2_1-FLF2V-14B-720P_fp8_e4m3fn.safetensors
Wan2_1-FLF2V-14B-720P_fp8_e4m3fn.safetensors
Wan2_1-FLF2V-14B-720P_fp8_e4m3fn.safetensors
Wan2_1-FLF2V-14B-720P_fp8_e4m3fn.safetensors
Wan2_1-FLF2V-14B-720P_fp8_e4m3fn.safetensors