ongogo/LyCORIS

Lora beYond Conventional methods, Other Rank adaptation Implementations for Stable diffusion.

LyCORIS - Lora beYond Conventional methods, Other Rank adaptation Implementations for Stable diffusion.

(This image is generated by the model trained in Hadamard product representation)

A project for implementing different algorithm to do parameter-efficient finetuning on stable diffusion or more.

This project is started from LoCon(see archive branch).

What we have now

See Algo.md or Demo.md for more example and explanation

Conventional LoRA

• Include Conv layer implementation from LoCon
• recommended settings
  • dim <= 64
  • alpha = 1 (or lower, like 0.3)

LoRA with Hadamard Product representation (LoHa)

• Ref: FedPara Low-Rank Hadamard Product For Communication-Efficient Federated Learning
• designed for federated learning, but has some cool property like rank<=dim^2 so should be good for parameter-efficient finetuning.
  • Conventional LoRA is rank<=dim
• recommended settings
  • dim <= 32
  • alpha = 1 (or lower)

WARNING: You are not supposed to use dim>64 in LoHa, which is over sqrt(original_dim) for almost all layer in SD

High dim with LoHa may cause unstable loss or just goes to NaN. If you want to use high dim LoHa, please use lower lr

WARNING-AGAIN: Use parameter-efficient algorithim in parameter-unefficient way is not a good idea

---

usage

You can just use these training scripts.

• derrian-distro/LoRA_Easy_Training_Scripts
• Linaqruf/kohya-trainer
• bmaltais/kohya_ss
• New Hollowstrawberry Lora Trainer

For kohya script

Activate sd-scripts' venv and then install this package

source PATH_TO_SDSCRIPTS_VENV/Scripts/activate

or

PATH_TO_SDSCRIPTS_VENV\Scripts\Activate.ps1 # or .bat for cmd

And then you can install this package:

• through pip

pip install lycoris_lora

• from source

git clone https://github.com/KohakuBlueleaf/LyCORIS
cd LyCORIS
pip install .

Finally you can use this package's kohya module to run kohya's training script

python3 sd-scripts/train_network.py \
  --network_module lycoris.kohya \
  --network_dim "DIM_FOR_LINEAR" --network_alpha "ALPHA_FOR_LINEAR"\
  --network_args "conv_dim=DIM_FOR_CONV" "conv_alpha=ALPHA_FOR_CONV" \
  "dropout=DROPOUT_RATE" "algo=lora" \

to train lycoris module for SD model

• algo list:

  • lora: Conventional Methods
  • loha: Hadamard product representation introduced by FedPara

• Tips:

  • Use network_dim=0 or conv_dim=0 to disable linear/conv layer
  • LoHa doesn't support dropout yet.

For a1111's sd-webui

download Extension into sd-webui, and then use your model as how you use lora model. LoHa Model supported

Additional Networks

Once you install the extension. You can also use your model in addnet
just use it as LoRA model. LoHa Model not supported yet

Extract LoCon

You can extract LoCon from a dreambooth model with its base model.

python3 extract_locon.py <settings> <base_model> <db_model> <output>

Use --help to get more info

$ python3 extract_locon.py --help
usage: extract_locon.py [-h] [--is_v2] [--device DEVICE] [--mode MODE] [--safetensors] [--linear_dim LINEAR_DIM] [--conv_dim CONV_DIM]
                        [--linear_threshold LINEAR_THRESHOLD] [--conv_threshold CONV_THRESHOLD] [--linear_ratio LINEAR_RATIO] [--conv_ratio CONV_RATIO]
                        [--linear_percentile LINEAR_PERCENTILE] [--conv_percentile CONV_PERCENTILE]
                        base_model db_model output_name

Example and Comparing for different algo

see Demo.md and Algo.md

Change Log

For full log, please see Change.md

2023/03/12 Update to 0.1.0

• Add cp-decomposition implementation for convolution layer
  • Both LoRA(LoCon) and LoHa can use this more parameter-efficient decomposition
• Add sparse bias for extracted LoRA
  • Will add to training in the future (Maybe)
• Change weight initialization method in LoHa
  • Use lower std to avoid loss to go high or NaN when using normal lr (like 0.5 in Dadap)

Todo list

• Module and Document for using LyCORIS in any other model, Not only SD.
• Proposition3 in FedPara
  • also need custom backward to save the vram
• Low rank + sparse representation
  • For extraction
  • For training
• Support more operation, not only linear and conv2d.
• Configure varying ranks or dimensions for specific modules as needed.
• Automatically selecting an algorithm based on the specific rank requirement.
• Explore other low-rank representations or parameter-efficient methods to fine-tune either the entire model or specific parts of it.
• More experiments for different task, not only diffusion models.

Citation

@misc{LyCORIS,
  author       = "Shih-Ying Yeh (Kohaku-BlueLeaf), Yu-Guan Hsieh, Zhidong Gao",
  title        = "LyCORIS - Lora beYond Conventional methods, Other Rank adaptation Implementations for Stable diffusion",
  howpublished = "\url{https://github.com/KohakuBlueleaf/LyCORIS}",
  month        = "March",
  year         = "2023"
}