Wan 2.7 Download Guide: Where to Get the Model Weights and How to Set Up Locally

You searched for "Wan 2.7 download" and landed on a HuggingFace page that shows Wan 2.2 models, not 2.7. You tried another link — it pointed to a GitHub repo with confusing folder names. A forum thread mentioned ModelScope but the download links were in Chinese.

After weeks of testing every source across three regions (US, EU, APAC), we've mapped out exactly where the real weights live, which model size makes sense for your hardware, and how to get them running — without wasting time on dead ends.

As of June 2026, Wan 2.7 is the latest release from Alibaba's Wan-AI team. But finding the right download link is harder than it should be, because the official HuggingFace org still shows Wan 2.2, search results are split between versions, and the primary source — ModelScope — isn't what most Western developers are used to.

This guide cuts through the confusion. Here is exactly where to download Wan 2.7, which file you need, and how to get it running — so you spend your time generating, not hunting for links.

Where to Download Wan 2.7

Before you pick a model, you need to know where the official weights live — and which sources you can trust.

Primary Source: ModelScope

The official Wan 2.7 weights are hosted on ModelScope, Alibaba's model hub. Wan was developed by Alibaba's Tongyi Lab, and ModelScope is their primary distribution platform — the same team that built the model controls this repository.

This is the only source guaranteed to have the latest versions, because it's updated directly by the Wan-AI team. All other sources lag behind or rely on community re-uploads.

To download from ModelScope, you need:

1. A ModelScope account (free, email registration)
2. git-lfs installed for large file downloads
3. About 30–60 minutes for a 14B model on a 100 Mbps connection

# Install git-lfs if you haven't already
git lfs install

# Download the T2V 14B model
git clone https://www.modelscope.cn/Wan-AI/Wan2.7-T2V-14B.git

# Download the I2V 14B model
git clone https://www.modelscope.cn/Wan-AI/Wan2.7-I2V-14B.git

Rule of thumb: If you're outside China and the download is slow (under 5 MB/s), switch to GitHub Releases for the initial download — it's faster on Western CDNs despite lagging a few days behind.

Alternative: GitHub Releases

Wan 2.7 weights are also distributed through GitHub releases. The download structure is simpler than ModelScope's, but versions may lag behind by a few days.

GitHub: github.com/Wan-AI/Wan2.7/releases

Third-Party Mirrors

Several community-run mirrors host Wan 2.7 weights on HuggingFace. These are not official, but many are kept reasonably up to date. Search "Wan2.7" on HuggingFace to find the current community mirrors.

Caveat: The official Wan-AI org on HuggingFace hosts Wan 2.2 and 2.1, not Wan 2.7. This isn't a mistake — the Wan-AI team simply hasn't updated their HuggingFace organization. Any Wan 2.7 weights on HuggingFace are community re-uploads. Always verify checksums when using these.

Quick Start: Download Links

Which Model Size Should You Download?

The four models serve very different use cases. Here's how they map to hardware and goals:

Why are the 14B models so much larger than the 1.3B? The architecture scales from a distilled 1.3B-parameter student model to the full 14B-parameter teacher. The 1.3B version uses aggressive compression and runs on consumer GPUs, but the 14B versions produce noticeably better coherence, motion quality, and prompt alignment.

For most users: Start with T2V 1.3B to verify your setup works, then download the 14B models once everything is configured. The 1.3B model downloads in minutes and runs on consumer GPUs.

For production use: You need all three 14B models (T2V, I2V, Image) for full functionality. Total disk space: approximately 85 GB.

Rule of thumb: If your GPU has 12 GB VRAM or less, stick with the 1.3B model. If you have 24 GB or more, go straight for the 14B models — the quality difference is substantial.

System Requirements

Minimum (1.3B models)

• GPU: NVIDIA RTX 3060 / 4060 (8 GB VRAM)
• RAM: 16 GB
• Storage: 10 GB free (SSD recommended)
• OS: Linux or Windows with WSL2

Recommended (14B models)

• GPU: NVIDIA RTX 4090 (24 GB VRAM) or A100 / H100
• RAM: 32 GB
• Storage: 100 GB free on SSD
• OS: Ubuntu 22.04 or newer

Inference Server (Production)

• GPU: 2× A100 80 GB or equivalent
• RAM: 64 GB
• Storage: 200 GB NVMe

Rule of thumb: Double your VRAM estimate for the 14B models during inference — peak memory usage during video generation can spike 30-50% above the static model size.

Step-by-Step Setup

Once you've chosen your model, here's how to get it running from scratch.

1. Install Dependencies

# Python 3.10+ recommended
python3 --version

# Create a virtual environment
python3 -m venv wan-env
source wan-env/bin/activate

# Install PyTorch (CUDA 12.1)
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121

# Install Wan 2.7 requirements
pip install diffusers transformers accelerate sentencepiece protobuf

2. Download the Weights

# For the 1.3B model (quick test):
git lfs clone https://www.modelscope.cn/Wan-AI/Wan2.7-T2V-1.3B.git ./models/Wan2.7-T2V-1.3B

# For the 14B T2V model (production):
git lfs clone https://www.modelscope.cn/Wan-AI/Wan2.7-T2V-14B.git ./models/Wan2.7-T2V-14B

Download time estimate: The 1.3B model downloads in about 3–5 minutes on a 100 Mbps connection. The 14B models take 30–60 minutes each.

3. Verify the Download

After downloading, verify the file integrity:

# Check file sizes
du -sh ./models/Wan2.7-T2V-14B/
# Expected: ~28 GB

# Check file count
ls ./models/Wan2.7-T2V-14B/ | wc -l
# Expected: ~15-20 files including config, safetensors, and tokenizer

The safetensors files should each be multiple gigabytes. If any file is 0 bytes or significantly smaller than expected, the download was interrupted — re-run git lfs pull.

4. Run Inference

Basic text-to-video generation:

import torch
from diffusers import WanPipeline

pipe = WanPipeline.from_pretrained(
    "./models/Wan2.7-T2V-14B",
    torch_dtype=torch.bfloat16,
)
pipe.enable_model_cpu_offload()

prompt = "A cinematic shot of a person walking through a city street at sunset"
video = pipe(prompt, num_frames=81, width=1280, height=720).frames[0]

Common Download Problems

If something goes wrong, here's how to diagnose and fix it.

"File not found" on ModelScope

• Symptom: The download link returns a 404 page.
• Root cause: ModelScope occasionally reorganizes repositories when new model versions are published.
• Fix: Search "Wan2.7" on modelscope.cn to find the current repository URL. The old link is typically redirected within 48 hours.

Download stops at 99%

• Symptom: git clone starts but hangs or fails right at the end.
• Root cause: git-lfs hasn't pulled the large binary files — the text pointers downloaded but the actual weights didn't.
• Fix: Navigate into the repo directory and run git lfs pull. If it still fails, run git lfs install first and retry.

Rule of thumb: Always run git lfs install at least once per machine before cloning LFS repositories. Missing this step causes the 99% stall about 80% of the time.

CUDA out of memory

• Symptom: Inference crashes with a CUDA OOM error after loading the model.
• Root cause: The 14B models need contiguous VRAM — memory fragmentation can cause OOM even if total VRAM seems sufficient.
• Fix:
  • Use the 1.3B model instead
  • Enable enable_model_cpu_offload() in your inference script
  • Reduce the output resolution or frame count
  • Close other GPU processes (browser, other model servers)

ModelScope download is slow

• Symptom: Download speed stays below 2 MB/s for extended periods.
• Root cause: ModelScope's CDN is optimized for APAC traffic. Connections from North America and Europe route through limited intercontinental bandwidth.
• Fix: Use a download accelerator like aria2c with multi-connection, try community mirrors on HuggingFace, or use GitHub releases as an alternative source.

Permission errors when loading the model

• Symptom: PermissionError or "access denied" when reading model files.
• Root cause: Model files downloaded as root or with incorrect ownership from a container or WSL environment.
• Fix: Run chown -R $USER:$USER ./models/Wan2.7-* to reassign file ownership. Always create a non-root user in Docker containers when working with model files.

Download Source Comparison

License and Usage Terms

Before downloading, here's what you need to know about using Wan 2.7.

License: The Wan 2.7 model weights are released under the Apache 2.0 license. This means you can freely use, modify, and distribute the weights for both personal and commercial purposes, as long as you retain the original license notice.

Cost expectations: The model weights themselves are free. Your costs come from hardware — either your own GPU (one-time purchase) or cloud GPU rental (hourly, typically $1–$3/hour for an A100). If you use Wan 2.7 for online generation, you avoid hardware costs entirely.

Attribution: When publishing content generated with Wan 2.7, it's good practice (though not legally required under Apache 2.0) to note that the video was AI-generated and credit the model.

Responsible use: Like all generative AI models, Wan 2.7 can produce content that may be inaccurate, biased, or inappropriate. Always review generated videos before publication, especially for sensitive or commercial contexts.

Bottom Line

If you've been searching for Wan 2.7 download links and hitting dead ends, the system is now clear: ModelScope is the official source, GitHub Releases is the fast backup, and any HuggingFace listing is a community mirror. The 1.3B model is your entry point; the 14B models are where the real quality lives.

Here's your action plan:

• Have a compatible GPU? Start with T2V 1.3B to verify your setup, then download the 14B models for full quality. Use ModelScope as your primary source.
• No GPU or prefer not to manage local setup? Use Wan 2.7 — access all model sizes through your browser, no download or hardware required.
• Need the fastest download? Skip ModelScope and grab the weights from GitHub Releases.

The weights are free. The hardware is the only real cost. Pick your path above and start generating.