Model reference · open weights
Wan2.1-Fun-InP is an open-weight video model from Alibaba. Wan2.1-Fun-V1.1-1.3B-InP (BF16) weighs 19.8 GB; the smallest configuration that runs it is RTX 4060 Ti 16 GB.
Summary of the alibaba-pai/Wan2.1-Fun-V1.1-1.3B-InP model card, 2026-10-01
What it is
| Released by | Alibaba |
|---|---|
| Released | 2025-04-24 |
| VRAM | 19.8 GB for the weights |
What it runs on
| Card | Weights | Memory |
|---|---|---|
| RTX 3060 12 GB | does not fit | 11.6 GB |
| RTX 4060 Ti 16 GB | fits (encoders offloaded) | 15.4 GB |
| RTX 3090 24 GB | tight | 23.4 GB |
| RTX 4090 24 GB | tight | 23.4 GB |
| RTX 5090 32 GB | fits | 31.0 GB |
| L40S 48 GB | fits | 44.0 GB |
| A100 80 GB | fits | 78.2 GB |
| H100 80 GB | fits | 78.1 GB |
| RTX PRO 6000 Blackwell 96 GB | fits | 93.8 GB |
| DGX Spark (GB10) 128 GB unified | fits | 107 GB |
| H200 141 GB | fits | 138 GB |
| B200 180 GB | fits | 176 GB |
From the model card
😊 Welcome!
V1.1:
| 名称 | 存储空间 | Hugging Face | Model Scope | 描述 |
|---|---|---|---|---|
| Wan2.1-Fun-V1.1-1.3B-InP | 19.0 GB | 🤗Link | 😄Link | Wan2.1-Fun-V1.1-1.3B文图生视频权重，以多分辨率训练，支持首尾图预测。 |
| Wan2.1-Fun-V1.1-14B-InP | 47.0 GB | 🤗Link | 😄Link | Wan2.1-Fun-V1.1-14B文图生视频权重，以多分辨率训练，支持首尾图预测。 |
| Wan2.1-Fun-V1.1-1.3B-Control | 19.0 GB | 🤗Link | 😄Link | Wan2.1-Fun-V1.1-1.3B视频控制权重支持不同的控制条件，如Canny、Depth、Pose、MLSD等，支持参考图 + 控制条件进行控制，支持使用轨迹控制。支持多分辨率（512，768，1024）的视频预测，支持多分辨率（512，768，1024）的视频预测，以81帧、每秒16帧进行训练，支持多语言预测 |
| Wan2.1-Fun-V1.1-14B-Control | 47.0 GB | 🤗Link | 😄Link | Wan2.1-Fun-V1.1-14B视视频控制权重支持不同的控制条件，如Canny、Depth、Pose、MLSD等，支持参考图 + 控制条件进行控制，支持使用轨迹控制。支持多分辨率（512，768，1024）的视频预测，支持多分辨率（512，768，1024）的视频预测，以81帧、每秒16帧进行训练，支持多语言预测 |
| Wan2.1-Fun-V1.1-1.3B-Control-Camera | 19.0 GB | 🤗Link | 😄Link | Wan2.1-Fun-V1.1-1.3B相机镜头控制权重。支持多分辨率（512，768，1024）的视频预测，支持多分辨率（512，768，1024）的视频预测，以81帧、每秒16帧进行训练，支持多语言预测 |
| Wan2.1-Fun-V1.1-14B-Control | 47.0 GB | 🤗Link | 😄Link | Wan2.1-Fun-V1.1-14B相机镜头控制权重。支持多分辨率（512，768，1024）的视频预测，支持多分辨率（512，768，1024）的视频预测，以81帧、每秒16帧进行训练，支持多语言预测 |
V1.0:
| 名称 | 存储空间 | Hugging Face | Model Scope | 描述 |
|---|---|---|---|---|
| Wan2.1-Fun-1.3B-InP | 19.0 GB | 🤗Link | 😄Link | Wan2.1-Fun-1.3B文图生视频权重，以多分辨率训练，支持首尾图预测。 |
| Wan2.1-Fun-14B-InP | 47.0 GB | 🤗Link | 😄Link | Wan2.1-Fun-14B文图生视频权重，以多分辨率训练，支持首尾图预测。 |
| Wan2.1-Fun-1.3B-Control | 19.0 GB | 🤗Link | 😄Link | Wan2.1-Fun-1.3B视频控制权重，支持不同的控制条件，如Canny、Depth、Pose、MLSD等，同时支持使用轨迹控制。支持多分辨率（512，768，1024）的视频预测，支持多分辨率（512，768，1024）的视频预测，以81帧、每秒16帧进行训练，支持多语言预测 |
| Wan2.1-Fun-14B-Control | 47.0 GB | 🤗Link | 😄Link | Wan2.1-Fun-14B视频控制权重，支持不同的控制条件，如Canny、Depth、Pose、MLSD等，同时支持使用轨迹控制。支持多分辨率（512，768，1024）的视频预测，支持多分辨率（512，768，1024）的视频预测，以81帧、每秒16帧进行训练，支持多语言预测 |
Generic Control Video + Reference Image: Reference Image Control Video Wan2.1-Fun-V1.1-14B-Control Wan2.1-Fun-V1.1-1.3B-Control
Generic Control Video (Canny, Pose, Depth, etc.) and Trajectory Control:
Pan Up
Pan Left
Pan Right
Pan Down
Pan Up + Pan Left
Pan Up + Pan Right
DSW 有免费 GPU 时间，用户可申请一次，申请后3个月内有效。
阿里云在Freetier提供免费GPU时间，获取并在阿里云PAI-DSW中使用，5分钟内即可启动CogVideoX-Fun。
我们的ComfyUI界面如下，具体查看ComfyUI README。
使用docker的情况下，请保证机器中已经正确安装显卡驱动与CUDA环境，然后以此执行以下命令：
# pull image
docker pull mybigpai-public-registry.cn-beijing.cr.aliyuncs.com/easycv/torch_cuda:cogvideox_fun
# enter image
docker run -it -p 7860:7860 --network host --gpus all --security-opt seccomp:unconfined --shm-size 200g mybigpai-public-registry.cn-beijing.cr.aliyuncs.com/easycv/torch_cuda:cogvideox_fun
# clone code
git clone https://github.com/aigc-apps/VideoX-Fun.git
# enter VideoX-Fun's dir
cd VideoX-Fun
# download weights
mkdir models/Diffusion_Transformer
mkdir models/Personalized_Model
# Please use the hugginface link or modelscope link to download the model.
# CogVideoX-Fun
# https://huggingface.co/alibaba-pai/CogVideoX-Fun-V1.1-5b-InP
# https://modelscope.cn/models/PAI/CogVideoX-Fun-V1.1-5b-InP
# Wan
# https://huggingface.co/alibaba-pai/Wan2.1-Fun-V1.1-14B-InP
# https://modelscope.cn/models/PAI/Wan2.1-Fun-V1.1-14B-InP
我们已验证该库可在以下环境中执行：
Windows 的详细信息：
Linux 的详细信息：
我们需要大约 60GB 的可用磁盘空间，请检查！
我们最好将权重按照指定路径进行放置：
通过comfyui：
将模型放入Comfyui的权重文件夹ComfyUI/models/Fun_Models/：
📦 ComfyUI/
├── 📂 models/
│ └── 📂 Fun_Models/
│ ├── 📂 CogVideoX-Fun-V1.1-2b-InP/
│ ├── 📂 CogVideoX-Fun-V1.1-5b-InP/
│ ├── 📂 Wan2.1-Fun-V1.1-14B-InP
│ └── 📂 Wan2.1-Fun-V1.1-1.3B-InP/
``Quoted from the model card on Hugging Face. The full card is behind the Hugging Face link above.
Running it yourself
Rent a machine by the hour. ComfyUI is installed on it. Open ComfyUI through the tunnel, then Templates → Wan 2.1 Text to Video, and choose this model's file in its model loader.
# on your rented machine (the ssh line is on its page in the console)
# get REPO FILE FOLDER: one file into /workspace/models/FOLDER, where ComfyUI loads it from
get() { hf download "$1" "$2" --local-dir /workspace/hf-files && mkdir -p "/workspace/models/$3" && mv "/workspace/hf-files/$2" "/workspace/models/$3/$4"; }
# the model (2.9 GB)
get alibaba-pai/Wan2.1-Fun-V1.1-1.3B-InP diffusion_pytorch_model.safetensors diffusion_models
# what the template also loads “Wan 2.1 Text to Video” (6.5 GB)
get Comfy-Org/Wan_2.1_ComfyUI_repackaged split_files/text_encoders/umt5_xxl_fp8_e4m3fn_scaled.safetensors text_encoders
get Comfy-Org/Wan_2.1_ComfyUI_repackaged split_files/vae/wan_2.1_vae.safetensors vae
start-comfyui# on your computer, in a second terminal: ComfyUI in your browser at http://localhost:8188
# HOST and PORT are your machine's, from its page in the console
ssh -L 8188:localhost:8188 dev@HOST -p PORT