🟢 Beginner Friendly (Web) / 🔴 Advanced (Local) 🎬 Type: AI Talking Avatar / Video Generator 💸 Free & Open Source (MIT License) ⭐ 2.3k GitHub Stars
What is LongCat-Video-Avatar 1.5?
LongCat-Video-Avatar 1.5 is a free, open-source AI tool made by Meituan (one of China’s largest tech companies) that turns a single photo into a realistic talking video. You upload a portrait image, give it an audio clip of speech, and it produces a video where the person in the photo appears to speak — with accurate lip movements, natural eye blinking, head movements, and even body gestures.
Think of it like this: you have a still photo of a person, and you want to bring it to life so their mouth moves in sync with any audio you choose. That’s exactly what LongCat-Video-Avatar 1.5 does — and it does it at a quality level that was previously only available in expensive commercial software.
The Hugging Face Space at huggingface.co/spaces/victor/LongCat-Video-Avatar-1.5 gives you a free, ready-to-use version in your browser — no installation, no GPU required on your side. For power users, the full model and code are also freely available on GitHub and Hugging Face.
It’s built on a massive 13.6 billion parameter AI model and supports generating videos up to 5 minutes long without the face drifting or losing quality — something most free tools can’t do.
Who is it for?
- Content creators and YouTubers who want to create talking-head videos without appearing on camera themselves
- E-commerce sellers who want to generate product marketing videos with a speaking digital presenter
- Educators and course creators who want to convert audio lectures into virtual lecturer videos
- Social media managers who need fast, engaging video content for TikTok, Instagram Reels, or YouTube Shorts
- Businesses and startups wanting a custom digital human spokesperson or virtual customer service agent
- Developers and AI researchers who want to experiment with or build on a state-of-the-art open-source talking avatar model
- Animators and artists who want to animate illustrated characters, anime figures, or stylised portraits
- Anyone who wants to turn a photo into a realistic speaking video for free, without a subscription
What makes it special?
- Completely free and open source — MIT licensed, meaning you can use it for personal and commercial work without paying anything
- Try it instantly in your browser — the Hugging Face Space lets you test it right now with no sign-up, no download, no GPU needed on your end
- Powered by 13.6 billion parameters — the same scale as large language models like GPT; most free avatar tools use far smaller models
- Full-body synchronisation — doesn’t just sync the lips; controls eye movements, facial expressions, and body gestures all at once for truly natural results
- Natural micro-movements — even during silent pauses, the avatar keeps blinking, breathing, and making subtle posture shifts so it never looks frozen or fake
- Stable long video generation — can produce up to 5 minutes of video (~5,000 frames) without the face drifting or degrading in quality over time
- Works on photos, illustrations, and anime — not just real people; also animates drawn characters, anime faces, and stylised portraits
- Multi-person support — can animate two people in the same scene having a conversation, each driven by their own separate audio track
- Very fast inference — uses an 8-step distillation method (DMD) making it roughly 15x faster than the original model at generating frames
- Commercial-grade quality — benchmarks show it leading on EvalTalker, the industry standard evaluation for talking avatar video quality
Requirements before you start
Option A — Use the Hugging Face Space (no setup needed)
This is the easiest way. You only need:
- A web browser (Chrome, Firefox, Edge, Safari all work)
- A portrait photo (JPG or PNG, face clearly visible, good lighting)
- An audio clip of speech (WAV or MP3 format recommended)
- A free Hugging Face account — sign up here (takes 30 seconds)
💡 Note: The free Hugging Face Space runs on shared community GPUs. During busy times you may have to wait in a queue. If it says “sleeping,” click the space and wait a moment for it to wake up — this is normal for free community spaces.
Option B — Run it locally on your own machine (for advanced users)
Running it locally gives you faster speeds, no queue times, and full control — but requires a powerful computer:
- Operating system: Linux (recommended) or Windows with WSL2
- GPU: NVIDIA GPU with at least 24 GB VRAM (e.g. RTX 3090, RTX 4090, or A100). Consumer GPUs with 8–12 GB VRAM will not be enough for the full model
- RAM: 32 GB system RAM minimum (64 GB recommended)
- Storage: At least 60 GB free disk space for the model weights and dependencies
- Python 3.10 or higher — download here
- CUDA 12.1+ — NVIDIA’s GPU toolkit. Download here
- Git — to clone the repository. Download here
- A Hugging Face account — needed to download the model weights
- ffmpeg — for audio/video processing. Download here
⚠️ No GPU? Use the web version. If you don’t have a powerful NVIDIA GPU, stick with the Hugging Face Space — it’s free and does the heavy lifting on Hugging Face’s servers.
Step-by-step setup
🌐 Part 1 — Using the Hugging Face Space (easiest, recommended for beginners)
Step 1 — Create a free Hugging Face account
Go to huggingface.co/join, enter your email and a password, and verify your email. It’s free and takes under a minute.
Step 2 — Open the LongCat-Video-Avatar 1.5 Space
Go to: huggingface.co/spaces/victor/LongCat-Video-Avatar-1.5
If the space shows “Sleeping,” click the page and wait 30–60 seconds for it to start up. This is normal — free community spaces go to sleep when not in use to save resources.
Step 3 — Prepare your portrait image
Before uploading, make sure your photo:
- Shows a clear, well-lit face (front-facing works best)
- Is in JPG or PNG format
- Is not blurry or heavily filtered
- Has the face taking up most of the frame — avoid wide shots where the face is tiny
You can use a photo of a real person, an illustrated character, or an anime portrait — the model works on all of these.
Step 4 — Prepare your audio clip
You need an audio file of speech. This can be:
- A recording of your own voice
- Text-to-speech audio generated with a free tool like ElevenLabs or TTSMaker
- Any spoken-word audio file in WAV or MP3 format
Keep the audio under 30 seconds for your first test to keep generation time manageable.
Step 5 — Upload your image and audio
In the Space interface, you will see upload boxes for:
- Portrait image — click to upload your photo
- Audio clip — click to upload your speech file
- Text prompt — type a short description of what you want, for example: “A woman speaking naturally, looking at the camera, with subtle head movements”
Step 6 — Click Generate and wait
Click the Generate button. The Space will process your inputs. Depending on queue length, this takes anywhere from 30 seconds to a few minutes on the free tier. You’ll see a progress bar or status message while it works.
Step 7 — Download your video
When generation is complete, the video will appear in the output panel. Click the download button (usually a small arrow or three-dot menu next to the video) to save the MP4 file to your computer. That’s it — you now have a talking avatar video, completely free.
🖥️ Part 2 — Running LongCat-Video-Avatar 1.5 locally (for advanced users)
Step 1 — Clone the GitHub repository
git clone --single-branch --branch main https://github.com/meituan-longcat/LongCat-Video
cd LongCat-VideoStep 2 — Create a Python virtual environment
python -m venv venv
source venv/bin/activateOn Windows, the second line is instead:
venv\Scripts\activateStep 3 — Install the required packages
pip install -r requirements_avatar.txtThis may take several minutes as it downloads PyTorch and other AI libraries.
Step 4 — Log in to Hugging Face to download model weights
Get your access token from huggingface.co/settings/tokens, then run:
pip install huggingface_hub
huggingface-cli loginPaste your token when prompted.
Step 5 — Download the model weights
Download the base video model:
huggingface-cli download meituan-longcat/LongCat-Video --local-dir ./weights/LongCat-VideoThen download the Avatar 1.5 model weights:
huggingface-cli download meituan-longcat/LongCat-Video-Avatar-1.5 --local-dir ./weights/LongCat-Video-Avatar-1.5These files are large (30–40 GB combined). Make sure you have enough disk space and a stable internet connection. The download may take 30–90 minutes.
Step 6 — Run the demo script
To generate a single-person talking avatar video from an image and audio file:
python run_demo_avatar_single_audio_to_video.py \
--image_path /path/to/your/portrait.jpg \
--audio_path /path/to/your/audio.wav \
--output_path ./output_video.mp4Replace the paths with the actual locations of your image and audio files. The script will save the final MP4 to the path you specified.
Step 7 — (Optional) Generate a two-person conversation video
python run_demo_avatar_multi_audio_to_video.py \
--image_path /path/to/portrait.jpg \
--audio_path_1 /path/to/person1_audio.wav \
--audio_path_2 /path/to/person2_audio.wav \
--output_path ./output_conversation.mp4Step 8 — Tips for better results
- Lip sync accuracy: If lip movements don’t match the audio well, increase the
audio_cfgparameter between 3 and 5. Example:--audio_cfg 4 - Resolution: The model supports 480P and 720P output. Use
--resolution 720pfor higher quality (requires more VRAM) - Long videos: For videos longer than 30 seconds, the model uses Cross-Chunk Latent Stitching automatically to keep quality stable throughout
Common errors and fixes
| Error | What it means | How to fix it |
|---|---|---|
| Space shows “Sleeping” or won’t load | The free community space has gone idle to save resources | Click anywhere on the space page and wait 30–60 seconds for it to wake up. Refresh if needed |
| Long wait time / stuck in queue | Many users are using the free space at the same time | Try during off-peak hours (early morning in your timezone). Or duplicate the space to your own Hugging Face account and run it there with your own GPU |
| Lips don’t match the audio | Audio CFG value is too low, or the audio quality is poor | Locally: increase --audio_cfg to a value between 3 and 5. Also ensure your audio is clean without background noise or music |
| Face looks blurry or distorted | Input portrait is low resolution or not front-facing | Use a higher-resolution, well-lit, clearly focused photo. The face should be front-facing and take up most of the frame |
CUDA out of memory | Your GPU doesn’t have enough VRAM to run the model locally | Try enabling INT8 quantization by adding --use_int8 to your command. If that still fails, use the Hugging Face Space web version instead |
ModuleNotFoundError on startup | A required Python package is missing | Make sure you activated your virtual environment (source venv/bin/activate) and ran pip install -r requirements_avatar.txt fully |
| Model download fails or is very slow | Large files and slow or unstable internet connection | The model weights are 30–40 GB. Use a wired connection if possible. The huggingface-cli download command will resume where it left off if interrupted — just run it again |
| Video output is choppy or has no audio | ffmpeg is not installed or not found in your system PATH | Install ffmpeg from ffmpeg.org and verify it works by running ffmpeg -version in your terminal |
Free vs Paid comparison
LongCat-Video-Avatar 1.5 itself is completely free and open source. The table below compares the free Hugging Face Space against running it locally, and against typical paid commercial avatar tools.
| Feature | Free (HF Space) | Free (Self-hosted) | Paid Commercial Tools |
|---|---|---|---|
| Cost | $0 | $0 (need own GPU) | $20–$100+/month |
| Setup required | None — browser only | Significant (GPU, Python, 40 GB download) | None — browser only |
| Generation speed | Slow (shared GPU, may queue) | Fast (your own GPU) | Fast (dedicated cloud) |
| Watermark on output | None | None | Often on lower tiers |
| Commercial use allowed | ✅ MIT License | ✅ MIT License | Varies — check terms |
| Privacy (your data) | Processed on HF servers | ✅ Stays on your machine | Sent to company servers |
| Max video length | ~5 seconds (demo limit) | 5+ minutes | Varies by plan |
| Multi-person conversation | ⚠️ Limited in Space demo | ✅ Yes (dual audio streams) | Rarely available |
| Works on anime / illustrations | ✅ Yes | ✅ Yes | Rarely |
| Requires powerful GPU | No | Yes (24 GB+ VRAM) | No |
Bottom line: For casual use and testing, the Hugging Face Space is the perfect starting point — free, instant, no GPU. For creators doing this at volume or needing longer videos and full privacy, running it locally on a capable machine (or renting a cloud GPU) unlocks the full power of the model at zero ongoing cost.
Alternatives — 3 similar tools
1. SadTalker
One of the most popular open-source talking head tools. You upload a face image and an audio file and it produces a lip-synced video. Lighter than LongCat (runs on consumer GPUs with less VRAM), but not as realistic for full-body motion or long videos. A great starting point if your GPU has less than 24 GB VRAM.
🔗 github.com/OpenTalker/SadTalker
2. MuseTalk
A real-time lip-sync tool from Tencent’s Muse team. Very fast, open-source, and optimised for streaming or live applications. Focuses specifically on lip movement rather than full-body dynamics, making it lighter and quicker. A good choice for live streaming or chatbot avatar applications.
🔗 github.com/TMElyralab/MuseTalk
3. HeyGen (paid)
The leading commercial talking avatar platform. No technical setup, excellent quality, and a polished interface for creating professional digital human videos. Has a free trial but requires a paid plan for regular use. Best for businesses that want a done-for-you solution without any technical work.
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