WAN 2.1 Lora Trainer - ComfyUI VSCode Full - T1.0

Easy to use Lora Trainer(:3000) with ComfyUI(:8000) for testing and VsCode(:8888).

https://runpod.io/console/deploy?template=9j2tjwyxys&ref=0eayrc3z

Has diffusion-pipe pre-installed on the container

File Management UI needs some more work to streamline the process but can be run by going to

1. cd /workspace/file-manager
2. source /workspace/bcomfy/bin/activate
3. npm install
4. node app.js

visit the https://xxxxxxxxxxx-3000.proxy.runpod.net/

Current functionality

Current functionality of file-manager

1. Select a name from the Wan models on Hungging Face paste it to the file manager and it will download to the file (Needs better loading UI)

2. Uploads traning data = image and text files

3. Settings can modify the tomal files. Ensure whatever model you add is also added to the ckpt_path = 'Wan2.1-14b'

4. Manually run traning but hope to create a full ui that can do everything

Use this method for now:

Highly recommed to follow this Youtube tutorial on how to setup traning data. This template is fully setup with everything you need, so just skip to the traning part.

📁 File Manager - Quick Start Guide

1. Download Wan Models

   • Select a model name from Hugging Face (e.g., Wan-AI/Wan2.1-T2V-14B)

   • Paste it into the file manager download field

   • Click download (Note: UI loading indicators will be improved in future updates)

2. Upload Training Data

   • Use the file manager to upload your training images and text files

   • Files will be automatically placed in the correct input directories

3. Configure Settings

   • Use the Settings tab to modify the TOML configuration files

   • Ensure your model path is correctly set in the configuration

   • Example: Verify ckpt_path = '/workspace/diffusion-pipe/models/wan2.1-14b' matches your model

4. Start Training Manually

   • Currently, training must be initiated via command line

   • Navigate to the correct directory and run the training command:

   cd /workspace/diffusion-pipe
   NCCL_P2P_DISABLE="1" NCCL_IB_DISABLE="1" deepspeed --num_gpus=1 train.py --deepspeed --config examples/wan_video.toml
   

Future updates will include a complete UI for all training functions.