LyCORIS
Lora beYond Conventional methods, Other Rank adaptation Implementations for Stable diffusion. (by KohakuBlueleaf)
sd-scripts
By kohya-ss
LyCORIS | sd-scripts | |
---|---|---|
13 | 64 | |
1,983 | 4,222 | |
- | - | |
9.6 | 9.7 | |
8 days ago | 6 days ago | |
Python | Python | |
Apache License 2.0 | Apache License 2.0 |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
LyCORIS
Posts with mentions or reviews of LyCORIS.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-06-28.
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LoRA (LyCORIS) iA3 is amazing (info in 1st comment)
Lycoris is another implementation of LoRA done by KohakuBlueleaf: https://github.com/KohakuBlueleaf/LyCORIS
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Training LORAs locally guide in text form?
Most guides focus on LoRa training as that has been around for longer. But I think LoHa can give better results. But the training is about half as fas it/s and it requires different training settings.
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Guide to DreamBooth / LORA / LyCORIS
I've read in some tutorials that it is best that the value should be 64 or below, also here they suggest to not go over 64 ( https://github.com/KohakuBlueleaf/LyCORIS )
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LyCORIS doesn't work with inpainting models
Does anyone know how to make LyCORIS models (https://github.com/KohakuBlueleaf/LyCORIS) work with inpainting models?
- wtf is a lycoris?
- I wonder what to do with this?
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I'm the creator of LoRA. How can I make it better?
I think it was linked already but this is also relevant for LoRa: https://github.com/KohakuBlueleaf/LyCORIS Nice work!
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LoRA: Low-Rank Adaptation of Large Language Models
There are some WIP evolutions of SD Lora in the works, like locon and lycoris.
https://github.com/KohakuBlueleaf/LyCORIS
- What the hell is a Locon/Loha model?
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SD fine-tuning methods compared: a benchmark
You might want to expand LoRA to include LoCon and LoHa, (and also add a column for VRAM requirements) (Think of it as a more complete LoRA that works for the kernels in the convolutional units rather than just the weights for the feed-forward network), support is still quite limited, but it's starting to pick up steam https://github.com/KohakuBlueleaf/LyCORIS
sd-scripts
Posts with mentions or reviews of sd-scripts.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-11-16.
- Everything you know about loss is a lie
- Evidence that LoRA extraction in Kohya is broken?
- Stable Diffusion XL (SDXL) DreamBooth training with EMA (Exponential Moving Average) on the way
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Installing kohya_ss GUI on AWS
This repository mostly provides a Windows-focused Gradio GUI for Kohya's Stable Diffusion trainers... but support for Linux OS is also provided through community contributions.
- Question on SD Finetuning
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Trying to put up a simple dreambooth for sdxl, but an errors pops up
Leaving this here because i'm very tired, so this is the file of the ipynb that uses the sdxl_train.py from the https://github.com/kohya-ss/sd-scripts/tree/sdxl repo, if anybody find out why when getting to the training i get this very empty error : " [00:09:11] WARNING The following values were not passed to "
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Finally SDXL coming to the Automatic1111 Web UI
You can try and test training LoRAs now https://github.com/kohya-ss/sd-scripts/tree/sdxl
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Help with LORA Training - Kohya_ss Regularization
This might help.
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need a lora traning guide for linux
Kohya_ss sd-scripts Seems to be the standard for lora training. The linked page has an English translation, but doesn't really have system specific tips. Someone else has a popular gui for it, but it's designed with windows in mind. There's another, simpler gui, but its still in development and the dev doesn't do any testing on Linux. With any of these, I run into dependency conflicts like crazy.
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SDXL 0.9 is wild but trying to imagine where we go from here is breaking my brain.
"Direct training" is already feasible with masking in kohya-ss: https://github.com/kohya-ss/sd-scripts/pull/589
What are some alternatives?
When comparing LyCORIS and sd-scripts you can also consider the following projects:
lora - Using Low-rank adaptation to quickly fine-tune diffusion models.
kohya_ss
LoRA - Code for loralib, an implementation of "LoRA: Low-Rank Adaptation of Large Language Models"
sd_dreambooth_extension
ComfyUI - The most powerful and modular stable diffusion GUI, api and backend with a graph/nodes interface.
sd-webui-additional-networks
bitsandbytes-rocm
StableTuner - Finetuning SD in style.
kohya-trainer - Adapted from https://note.com/kohya_ss/n/nbf7ce8d80f29 for easier cloning