Turn One Photo into a Talking Video: The Complete Stand-In Guide

For English readers who want identity-preserving video generation in plain language

What You Will Learn

• Why Stand-In needs only 1 % extra weights yet beats full-model fine-tuning
• How to create a 5-second, 720 p clip of you speaking—starting from a single selfie
• How to layer community LoRA styles (Studio Ghibli, cyber-punk, oil-paint, etc.) on the same clip
• Exact commands, file paths, and error-checklists that work on Linux, Windows, and macOS
• Road-map for future features that the authors have already promised

1. What Exactly Is Stand-In?

Stand-In is a light-weight, plug-and-play identity-control framework for text-to-video models.
In everyday terms: give the system one clear photo of a face (human, cartoon, or even a toy) and any text prompt you like, and it returns a short video where that exact face appears and behaves naturally—without retraining the entire 14-billion-parameter base model.

Key numbers from the official paper:

2. What Can You Make Today?

The GitHub README shows six use-cases. Below are concise English descriptions plus direct links to the sample files.

All clips above are reproducible with the commands later in this guide.

3. Quick-Start in Ten Minutes

The next four sections are written as copy-paste instructions. No prior knowledge of machine-learning is required.

3.1 Grab the code

git clone https://github.com/WeChatCV/Stand-In.git
cd Stand-In

3.2 Prepare the environment

# 1. Create a clean Python 3.11 environment
conda create -n standin python=3.11 -y
conda activate standin

# 2. Install dependencies
pip install -r requirements.txt

# 3. Optional: faster inference on NVIDIA GPUs
pip install flash-attn --no-build-isolation

If conda is not installed, use the Miniconda installer first.

3.3 Let the script download models

python download_models.py

The script places three folders under checkpoints/:

• wan2.1-T2V-14B – base text-to-video model
• antelopev2 – face recognition encoder
• Stand-In_Wan2.1-T2V-14B_153M_v1.0 – the official adapter weights

If you already host the base model elsewhere, edit download_models.py and comment the relevant lines; then move your local copy into checkpoints/wan2.1-T2V-14B.

3.4 Generate your first clip

1. Save a high-resolution, front-facing photo as me.jpg inside test/input/.
2. Run:

python infer.py \
  --prompt "A young man smiles at the camera while coding at his desk" \
  --ip_image test/input/me.jpg \
  --output test/output/first_clip.mp4

On an RTX 4090 the command finishes in roughly 90 s and returns a 5-second, 24 fps, 720 p mp4.

Prompt tip: keep the description simple—e.g., “a woman”, “a man” instead of detailed facial adjectives—so the adapter focuses on preserving identity.

4. Adding Styles with Community LoRA

LoRA files are small adapter weights created by hobbyists for artistic styles. One popular example is the Studio Ghibli LoRA (link in README).

Assume you have downloaded ghibli.safetensors into the project root.

python infer_with_lora.py \
  --prompt "A Ghibli-style girl gently waves under falling cherry petals" \
  --ip_image test/input/me.jpg \
  --lora_path ghibli.safetensors \
  --lora_scale 0.8 \
  --output test/output/ghibli_me.mp4

• lora_scale ranges from 0 (no style) to 1 (full style).
• You can pass multiple pairs of --lora_path and --lora_scale for stacking.

5. Understanding the Tech—Without the Jargon

Traditional fine-tuning retrains billions of weights to “memorize” a face.
Stand-In inserts a tiny adapter network (153 M parameters) between the frozen base model and the face encoder. Think of it as a translator: the base model still “directs the movie,” but the adapter tells it which actor is starring.

Benefits:

• No catastrophic forgetting: the base model keeps its general video skills.
• Fast iteration: swap adapters in seconds; no full retraining.
• Small files: 153 MB download vs. 14 GB+ for full fine-tunes.

6. Command Reference & Troubleshooting

6.1 Core scripts

All scripts support --help for full flag lists.

6.2 Common problems

7. File Structure After Installation

Stand-In/
├── checkpoints/
│   ├── wan2.1-T2V-14B/          # 14 B base model (~14 GB)
│   ├── antelopev2/              # face encoder
│   └── Stand-In_Wan2.1...v1.0/ # 153 M adapter
├── examples/                    # extra scripts (pose, face-swap)
├── test/
│   ├── input/                   # your photos
│   └── output/                  # generated mp4 files
├── infer.py                     # main script
├── infer_with_lora.py           # style script
├── download_models.py           # automatic downloader
└── requirements.txt             # exact Python packages

8. Road-Map: What’s Coming Next

The maintainers have publicly confirmed the following items:

• ✅ v1.0 weights for Wan2.1-T2V-14B (already released)
• 🔜 Weights compatible with Wan2.2-T2V-A14B
• 🔜 Complete training dataset + preprocessing scripts
• 🔜 Full training code for custom identity adapters

When the training code drops, you will be able to fine-tune a personal adapter using 100–200 short clips (15–30 s each) of the same face.

9. Community Resources

10. Frequently Asked Questions

Q1: Do I need a high-end GPU?
A GTX 1660 (6 GB) can run 512×288 output; an RTX 4090 (24 GB) handles 720 p comfortably.

Q2: Can I use non-square images?
Yes. The preprocessing pipeline crops and resizes automatically.

Q3: Are commercial usage rights granted?
Check the license file in the repo. Currently Apache-2.0 for code; model weights follow the base Wan2.1 license.

Q4: How do I batch-generate 100 clips?
Create a text file with one prompt per line and use examples/batch.sh.

Q5: Why does my cat’s face look slightly off?
Non-human subjects work, but extreme lighting or fur patterns can confuse the encoder. Use well-lit, frontal photos.

11. Mini-Glossary

12. Cite This Work

If you publish content created with Stand-In, please cite:

@article{xue2025standin,
  title={Stand-In: A Lightweight and Plug-and-Play Identity Control for Video Generation}, 
  author={Bowen Xue and Qixin Yan and Wenjing Wang and Hao Liu and Chen Li},
  journal={arXiv preprint arXiv:2508.07901},
  year={2025},
}

13. Next Steps for You

1. Run the quick-start command above and share your first clip.
2. Download one additional LoRA and experiment with --lora_scale.
3. Bookmark the GitHub repo and watch releases for the upcoming Wan2.2 weights.
4. Prepare 100 short clips of yourself so you are ready for custom training when the code drops.

Enjoy creating videos where the star is always you.