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🚀 Try this Example

View the complete source code on GitHub. Steps to run:
  1. Install fal:
pip install fal
  1. Authenticate (if not already done):
fal auth login
  1. Clone the demos repo and install dependencies:
git clone https://github.com/fal-ai-community/fal-demos.git
cd fal-demos
pip install -e .
  1. Run the trainer:
fal run fal_demos/video/wan_trainer.py::WanLoRATrainerDemo
This demo uses a GPU-heavy training stack and can run for a while depending on your step count.

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Deploy to fal

To host the trainer as an API: 
fal deploy fal_demos/video/wan_trainer.py::WanLoRATrainerDemo

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How it works

  • Downloads WAN weights from Hugging Face and a pinned training repo.
  • Prepares a dataset from your ZIP file (images and/or short videos).
  • Runs LoRA training with DeepSpeed and returns the adapter weights.

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Input format

Your training_data_url should point to a ZIP containing:
  • Images or videos (.png, .jpg, .jpeg, .gif, .mp4)
  • Optional caption files (my_clip.txt next to my_clip.mp4)
If you set auto_scale_input=true, videos are fit to 81 frames at 16 fps for WAN training.

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Example request

import fal_client

result = fal_client.submit(
    "your-username/wan-lora-trainer-demo",
    arguments={
        "training_data_url": "https://your-bucket/train.zip",
        "rank": 16,
        "number_of_steps": 400,
        "learning_rate": 2e-4,
        "trigger_phrase": "your-style",
        "auto_scale_input": True,
        "video_clip_mode": "single_beginning",
    },
).get()

print(result["lora_file"]["url"])