docs/LORAS.md

Loras Guide

Loras (Low-Rank Adaptations) allow you to customize video generation models by adding specific styles, characters, or effects to your videos.

Directory Structure

Loras are organized in different folders based on the model they're designed for:

Wan Text-to-Video Models

• loras/ - General t2v loras
• loras/1.3B/ - Loras specifically for 1.3B models
• loras/14B/ - Loras specifically for 14B models

Wan Image-to-Video Models

• loras_i2v/ - Image-to-video loras

Other Models

• loras_hunyuan/ - Hunyuan Video t2v loras
• loras_hunyuan_i2v/ - Hunyuan Video i2v loras
• loras_ltxv/ - LTX Video loras
• loras_flux/ - Flux loras

Custom Lora Directory

You can specify custom lora directories when launching the app:

# Use shared lora directory for both t2v and i2v
python wgp.py --lora-dir /path/to/shared/loras --lora-dir-i2v /path/to/shared/loras

# Specify different directories for different models
python wgp.py --lora-dir-hunyuan /path/to/hunyuan/loras --lora-dir-ltxv /path/to/ltx/loras

Using Loras

Basic Usage

1. Place your lora files in the appropriate directory
2. Launch WanGP
3. In the Advanced Tab, select the "Loras" section
4. Check the loras you want to activate
5. Set multipliers for each lora (default is 1.0)

Lora Multipliers

Multipliers control the strength of each lora's effect:

Simple Multipliers

1.2 0.8

• First lora: 1.2 strength
• Second lora: 0.8 strength

Time-based Multipliers

For dynamic effects over generation steps, use comma-separated values:

0.9,0.8,0.7
1.2,1.1,1.0

• For 30 steps: steps 0-9 use first value, 10-19 use second, 20-29 use third
• First lora: 0.9 → 0.8 → 0.7
• Second lora: 1.2 → 1.1 → 1.0

With models like Wan 2.2 that uses internally two diffusion models (High noise / Low Noise) you can specify which Loras you want to be applied for a specific phase by separating each phase with a ";".

For instance, if you want to disable a lora for phase High Noise and enablesit only for phase Low Noise:

0;1

As usual, you can use any float for of multiplier and have a multiplier varries throughout one phase for one Lora:

0.9,0.8;1.2,1.1,1

In this example multiplier 0.9 and 0.8 will be used during the High Noise phase and 1.2, 1.1 and 1 during the Low Noise phase.

Here is another example for two loras:

0.9,0.8;1.2,1.1,1
0.5;0,0.7

Note that the syntax for multipliers can also be used in a Finetune model definition file (except that each multiplier definition is a string in a json list)

Lora Presets

Lora Presets are combinations of loras with predefined multipliers and prompts.

Creating Presets

1. Configure your loras and multipliers
2. Write a prompt with comments (lines starting with #)
3. Save as a preset with .lset extension

Example Preset

# Use the keyword "ohnvx" to trigger the lora
A ohnvx character is driving a car through the city

Using Presets

# Load preset on startup
python wgp.py --lora-preset mypreset.lset

Managing Presets

• Edit, save, or delete presets directly from the web interface
• Presets include comments with usage instructions
• Share .lset files with other users

Supported Formats

WanGP supports multiple lora formats:

• Safetensors (.safetensors)
• Replicate format
• Standard PyTorch (.pt, .pth)

Loras Accelerators

Most Loras are used to apply a specific style or to alter the content of the output of the generated video. However some Loras have been designed to tranform a model into a distilled model which requires fewer steps to generate a video.

You will find most Loras Accelerators here: https://huggingface.co/DeepBeepMeep/Wan2.1/tree/main/loras_accelerators

Setup Instructions

1. Download the Lora
2. Place it in your loras/ directory if it is a t2v lora or in the loras_i2v/ directory if it isa i2v lora

FusioniX (or FusionX) Lora

If you need just one Lora accelerator use this one. It is a combination of multiple Loras acelerators (including Causvid below) and style loras. It will not only accelerate the video generation but it will also improve the quality. There are two versions of this lora whether you use it for t2v or i2v

Usage

1. Select a Wan t2v model (e.g., Wan 2.1 text2video 13B or Vace 13B)
2. Enable Advanced Mode
3. In Advanced Generation Tab:
   • Set Guidance Scale = 1
   • Set Shift Scale = 2
4. In Advanced Lora Tab:
   • Select CausVid Lora
   • Set multiplier to 1
5. Set generation steps from 8-10
6. Generate!

Safe-Forcing lightx2v Lora (Video Generation Accelerator)

Safeforcing Lora has been created by Kijai from the Safe-Forcing lightx2v distilled Wan model and can generate videos with only 2 steps and offers also a 2x speed improvement since it doesnt require classifier free guidance. It works on both t2v and i2v models You will find it under the name of Wan21_T2V_14B_lightx2v_cfg_step_distill_lora_rank32.safetensors

Usage

1. Select a Wan t2v or i2v model (e.g., Wan 2.1 text2video 13B or Vace 13B)
2. Enable Advanced Mode
3. In Advanced Generation Tab:
   • Set Guidance Scale = 1
   • Set Shift Scale = 5
4. In Advanced Lora Tab:
   • Select the Lora above
   • Set multiplier to 1
5. Set generation steps to 2-8
6. Generate!

CausVid Lora (Video Generation Accelerator)

CausVid is a distilled Wan model that generates videos in 4-12 steps with 2x speed improvement.

Usage

1. Select a Wan t2v model (e.g., Wan 2.1 text2video 13B or Vace 13B)
2. Enable Advanced Mode
3. In Advanced Generation Tab:
   • Set Guidance Scale = 1
   • Set Shift Scale = 7
4. In Advanced Lora Tab:
   • Select CausVid Lora
   • Set multiplier to 0.3
5. Set generation steps to 12
6. Generate!

CausVid Step/Multiplier Relationship

• 12 steps: 0.3 multiplier (recommended)
• 8 steps: 0.5-0.7 multiplier
• 4 steps: 0.8-1.0 multiplier

Note: Lower steps = lower quality (especially motion)

AccVid Lora (Video Generation Accelerator)

AccVid is a distilled Wan model that generates videos with a 2x speed improvement since classifier free guidance is no longer needed (that is cfg = 1).

Usage

1. Select a Wan t2v model (e.g., Wan 2.1 text2video 13B or Vace 13B) or Wan i2v model
2. Enable Advanced Mode
3. In Advanced Generation Tab:
   • Set Guidance Scale = 1
   • Set Shift Scale = 5
4. The number steps remain unchanged compared to what you would use with the original model but it will be two times faster since classifier free guidance is not needed

https://huggingface.co/Kijai/WanVideo_comfy/blob/main/Wan21_T2V_14B_lightx2v_cfg_step_distill_lora_rank32.safetensors

Performance Tips

Fast Loading/Unloading

• Loras can be added/removed without restarting the app
• Use the "Refresh" button to detect new loras
• Enable --check-loras to filter incompatible loras (slower startup)

Memory Management

• Loras are loaded on-demand to save VRAM
• Multiple loras can be used simultaneously
• Time-based multipliers don't use extra memory

Finding Loras

Sources

• Civitai - Large community collection
• HuggingFace - Official and community loras
• Discord Server - Community recommendations

Creating Loras

• Kohya - Popular training tool
• OneTrainer - Alternative training solution
• Custom datasets - Train on your own content

Macro System (Advanced)

Create multiple prompts from templates using macros. This allows you to generate variations of a sentence by defining lists of values for different variables.

Syntax Rule:

Define your variables on a single line starting with !. Each complete variable definition, including its name and values, must be separated by a colon (:).

Format:

! {Variable1}="valueA","valueB" : {Variable2}="valueC","valueD"
This is a template using {Variable1} and {Variable2}.

Example:

The following macro will generate three distinct prompts by cycling through the values for each variable.

Macro Definition:

! {Subject}="cat","woman","man" : {Location}="forest","lake","city" : {Possessive}="its","her","his"
In the video, a {Subject} is presented. The {Subject} is in a {Location} and looks at {Possessive} watch.

Generated Output:

In the video, a cat is presented. The cat is in a forest and looks at its watch.
In the video, a woman is presented. The woman is in a lake and looks at her watch.
In the video, a man is presented. The man is in a city and looks at his watch.

Troubleshooting

Lora Not Working

1. Check if lora is compatible with your model size (1.3B vs 14B)
2. Verify lora format is supported
3. Try different multiplier values
4. Check the lora was trained for your model type (t2v vs i2v)

Performance Issues

1. Reduce number of active loras
2. Lower multiplier values
3. Use --check-loras to filter incompatible files
4. Clear lora cache if issues persist

Memory Errors

1. Use fewer loras simultaneously
2. Reduce model size (use 1.3B instead of 14B)
3. Lower video resolution or frame count
4. Enable quantization if not already active

Command Line Options

# Lora-related command line options
--lora-dir path                    # Path to t2v loras directory
--lora-dir-i2v path               # Path to i2v loras directory  
--lora-dir-hunyuan path           # Path to Hunyuan t2v loras
--lora-dir-hunyuan-i2v path       # Path to Hunyuan i2v loras
--lora-dir-ltxv path              # Path to LTX Video loras
--lora-dir-flux path              # Path to Flux loras
--lora-preset preset              # Load preset on startup
--check-loras                     # Filter incompatible loras