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Change Log

2024/10/02 update to 3.1.0

Highlights

  • Support all the quantized linear layer by automatic detecting method
  • Support Flux in Kohya-ss/sd-scripts
  • Support wildcard matching for select layers in preset

Full change log

New Features

  • Support Flux
  • Support any quantized linear layer such as torchao
  • Refined Functional API to support drop-in replacement between different algorithms
  • Support wildcard for name matching in preset

Bug fixes

  • fix bugs in loading function of BOFT/OFT
  • fix bugs in loading function of LoKr
  • fix wrong behaviour of weight-decomposition when multiplier != 1

Improvements

  • Improve the coverage of unit-test

2024/06/29 update to 3.0.0 - Brand New Functional API, Parametrize API and Module API

The reasons of 3.0.0

We reconstruct the whole library with new Class definition and brand new Functional API system.

We also removed lot of redundant/unused modules.

Since the whole library are changed significantly. We decide to call it 3.0.0 as a new major version.

Major Changes

  • New Module API
  • Add Parametrize API
  • Add Functional API
    • LoCon/LoHa/LoKr/Diag-OFT/BOFT only.
  • Remove optional deps from install_requires
  • Remove lot of redundant/deprecated modules
  • Better testing
  • HunYuan DiT Support (PR in kohya-ss/sd-scripts)

Full change log

New Features

  • LyCORIS now have consistent API for different algorithm like bypass_forward_diff or get_diff_weight method. Developers of other project can utilize these API to do more tricks or integrate LyCORIS into their framework more easily.
  • LyCORIS now have parametrize API which utilize torch.nn.utils.parametrize.register_parametrization to directly patch individual parameters. Which can be useful for MHA layer or other tricky modules.
    • Currently only support 2~5D tensors. And LyCORIS will pretend these weights are weight of Linear/Conv1,2,3D then send it into LyCORIS modules
    • More native implementation or more detailed control will be added in the future.
  • LyCORIS now have functional API. Developers who prefer functional more than Module things can utilize this feature.
    • Functional API also allow developers who don't want to introduce new dependencies. Just copy-paste the source code and utilizing it. (with Apache-2 License, directly copy-paste is totally allowed)
  • Add support for Conv1d and Conv3d module on LoCon/LoHa/LoKr/Full/OFT/BOFT/GLoRA (not All algo in LyCORIS support them, you may receive error when apply unsopported algo), support inherited module (for example: LoRACompatibleConv or LoRACompatibleLinear from huggingface/diffusers)
  • HunYuan DiT support.

Improvements, Fixes, Slight Changes

  • Drop dependencies related to kohya-ss/sd-scripts:
    • We now take kohya-ss/sd-scripts as optional dependency
    • Which means transformers, diffusers and anything related to kohya are all optional deps now.
  • The definition of dropout and rank_dropout in each algorithm are changed. Since some concept of original rank_dropout in the lora of kohya-ss/sd-script is hard to applied to other algorithm. We can only design the dropout for each module seperatedly.
  • apply_max_norm issue are all fixed.
  • DyLoRA, (IA)^3, GLoRA are all rewritten and support Linear/Conv1,2,3d.
  • (IA)^3, GLoRA, Diag-OFT, BOFT are supported in create_lycoris_from_weights
    • lycoris.kohya.create_network_from_weights also support them as well.
  • Fix wrong implementation of BOFT.
  • create_lycoris_from_weights and create_network_from_weights now have correct logging infos.
  • get_module and make_module are moved into modules' API.

Deprecation

  • HCP modules are dropped. We will wait until HCP have better wrapper API.
  • HyperNetwork-related modules like hypernet/, attention.py, lilora.py are removed.
  • Uncompleted GLoKr are removed.
  • code copied from kohya-ss/sd-scripts are removed. The original sd-scripts repo is now an optional dependency.

2024/03/15 update to 2.2.0 - QLyCORIS and DoRA

New Algo

New Features

  • Support "bypass" (a.k.a. adapter) mode for LoHa/LoKr/OFT/BOFT
    • LoHa will require 2xFLOPs since we rebuild full diff weight and then do one more forward.
    • LoKr, OFT, BOFT should be more efficient than LoHa in bypass mode.
  • Support bnb 8bit/4bit Linear layer (a.k.a. QLyCORIS) with LoHa/LoKr/OFT/BOFT.
    • This will force module to enable bypass mode.

Fixes, slight changes

  • Refine some details about code quality. Based on the report from GitRoll. (Thx you gitroll!)
  • Remove redundant calculation in BOFT
  • rank_dropout has been removed from OFT/BOFT temporarily untill we ensure how to apply it.
  • Fix bugs in lokr when lokr_w1_a not exist.
  • Fix bugs in conversion scritps.

2024/02/18 update to 2.1.0

New Algo

Improvements

  • Faster, better extract script
  • support kohya-ss/sd-scripts image gen
  • support regex name in kohya-ss/sd-scripts
  • support resume on:
    • full
    • loha
    • oft
    • boft
  • Add logger into LyCORIS

Fixes, slight changes

  • Update HCP convert for the case where only UNet or TE is trained.
  • Change arg names for conversion scripts.
  • Fix wrong TE prefix in merge scripts.
  • Fix warnings and confusing logging.

2023/12/15 quick fixes of 2.0.2

  • Fix bugs in full module.
  • Related: Fix bugs in stable-diffusion-webui/extensions-builtin/Lora

2023/12/14 quick fixes of 2.0.1

  • Support merge sdxl loras which trained on plain diffusers with Kohya's LoRA implementation.
    • Can be found in LECO or other similar projects.
  • Refactor the batch convert scripts for pivotal bundle and hcp.
  • Change the class name lycoris.kohya.LycorisNetwork to lycoris.kohya.LycorisNetworkKohya to avoid confusion.
  • Fix bugs in merge scripts for Norm module and LoKr module.
  • Fix bugs in scaled weight norms of OFT.
  • Fix bugs in extract scripts for SDXL.
  • Fix bugs in full module which consume 2x vram.
  • Fix bugs in create_network_from_weights which caused bugs in "resume" feature for SDXL.

2023/12/02 update to 2.0.0

  • Start supporting HCP-Diffusion (The reason to name this version "2.0.0")
    • Now LyCORIS support LoHa/LoKr/Diag-OFT algorithm in HCP-Diffusion
    • Add Pivotal tuning utilities
    • Add hcp convert utilities
    • Have no plan at this time to support full/lora and train_norms since HCP can do them natively
  • Add Diag-OFT modules
  • Add standalone usage support
    • Can wrap any pytorch module which contains Linear/Conv2d/LayerNorm/GroupNorm modules
    • Will support more module in the future
  • Add SDXL support in Merge script
  • Add SDXL support in Extract-locon
  • More efficient (speed/vram) implementation for full module
  • Better implementation of custom state_dict
  • Fix errors of dropouts
  • Fix errors of apply_max_norms
  • Fix errors of resume

2023/09/27 update to 1.9.0

  • Add norm modules (for training LayerNorm and GroupNorm, which should be good for style)
  • Add full modules (So you can "native finetune" with lycoris now, should be convinient to try different weight)
  • Add preset config system
  • Add custom config system
  • Support resuming from models
  • Merge script support norm and full modules
  • Fix errors with optional requirements
  • Fix errors with not necessary import
  • Fix wrong factorization behaviours

2023/07/27 update to 1.8.2

  • Update utils in kohya-ss/sd-scripts

2023/07/27 update to 1.8.1

  • Add config/preset system
  • Improve the project structure

2023/07/19 update to 1.8.0

  • reimplement weight init method
  • implement HyperDreamBooth into LyCORIS
  • better file structure

2023/06/28 update to 1.7.1

  • rearrange the version format, previous 0.1.7 should be 1.7.0
  • fix the bug in scale weight norm

2023/06/26 Update to 0.1.7

  • Add support for rank_dropout and module_dropout on LoCon/LoHa/LoKr
  • Add support for scale_weight_norms on LoCon/LoHa/LoKr
  • Will support SDXL on 0.1.8 (you can follow the dev branch)

2023/06/04 update to 0.1.6

  • add dylora and IA^3 algorithm

2023/03/29 Update to 0.1.4

  • cp decomposition is default to disable now
  • add 4 more layer to train (conv_in/out, time_embedding)

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)