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201 changes: 201 additions & 0 deletions LICENSE.md
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74 changes: 46 additions & 28 deletions README.md
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# Kohya Trainer V4 Colab UI - VRAM 12GB
### Best way to train Stable Diffusion model for peeps who didn't have good GPU

Adapted to Google Colab based on [Kohya Guide](https://note.com/kohya_ss/n/nbf7ce8d80f29#c9d7ee61-5779-4436-b4e6-9053741c46bb) <br>
Adapted to Google Colab by [Linaqruf](https://github.com/Linaqruf)<br>
You can find latest notebook update [here](https://github.com/Linaqruf/kohya-trainer/blob/main/kohya-trainer.ipynb)

---
## What is this?
---
### **_Q: So what's differences between `Kohya Trainer` and other Stable Diffusion trainer out there?_**
### A: **Kohya Trainer** have some new features like
1. Using the U-Net learning
2. Automatic captioning/tagging for every image automatically with BLIP/DeepDanbooru
3. Implemented [NovelAI Aspect Ratio Bucketing Tool](https://github.com/NovelAI/novelai-aspect-ratio-bucketing) so you don't need to crop image dataset 512x512 ever again
- Use the output of the second-to-last layer of CLIP (Text Encoder) instead of the last layer.
- Learning at non-square resolutions (Aspect Ratio Bucketing) .
- Extend token length from 75 to 225.
4. By preparing a certain number of images (several hundred or more seems to be desirable), you can make learning even more flexible than with DreamBooth.
5. It also support Hypernetwork learning
6. `NEW!` 23/11 - Implemented Waifu Diffusion 1.4 Tagger for alternative DeepDanbooru to auto-tagging
7.

### **_Q: And what's differences between this notebook and other dreambooth notebook out there?_**
### A: We're adding Quality of Life features such as:
- Install **gallery-dl** to scrap images, so you can get your own dataset fast with google bandwidth
- Huggingface Integration, here you can login to huggingface-hub and upload your trained model/dataset to huggingface
---
# Kohya Trainer V6 - VRAM 12GB
### The Best Way for People Without Good GPUs to Fine-Tune the Stable Diffusion Model

This notebook has been adapted for use in Google Colab based on the [Kohya Guide](https://note.com/kohya_ss/n/nbf7ce8d80f29#c9d7ee61-5779-4436-b4e6-9053741c46bb). </br>
This notebook was adapted by [Linaqruf](https://github.com/Linaqruf)</br>
You can find the latest update to the notebook [here](https://github.com/Linaqruf/kohya-trainer/blob/main/kohya-trainer.ipynb).

## Overview
- Fine tuning of Stable Diffusion's U-Net using Diffusers
- Addressing improvements from the NovelAI article, such as using the output of the penultimate layer of CLIP (Text Encoder) instead of the last layer and learning at non-square resolutions with aspect ratio bucketing.
- Extends token length from 75 to 225 and offers automatic caption and automatic tagging with BLIP, DeepDanbooru, and WD14Tagger
- Supports hypernetwork learning and is compatible with Stable Diffusion v2.0 (base and 768/v)
- By default, does not train Text Encoder for fine tuning of the entire model, but option to train Text Encoder is available.
- Ability to make learning even more flexible than with DreamBooth by preparing a certain number of images (several hundred or more seems to be desirable).

## Change Logs:
##### v6 (6/12):
- Temporary fix for an error when saving in the .safetensors format with some models. If you experienced this error with v5, please try v6.

##### v5 (5/12):
- Added support for the .safetensors format. Install safetensors with `pip install safetensors` and specify the `use_safetensors` option when saving.
- Added the `log_prefix` option.
- The cleaning script can now be used even when one of the captions or tags is missing.

##### v4 (14/12):
- The script name has changed to fine_tune.py.
- Added the option `--train_text_encoder` to train the Text Encoder.
- Added the option `--save_precision` to specify the data format of the saved checkpoint. Can be selected from float, fp16, or bf16.
- Added the option `--save_state` to save the training state, including the optimizer. Can be resumed with the `--resume` option.

##### v3 (29/11):
- Requires Diffusers 0.9.0. To update it, run `pip install -U diffusers[torch]==0.9.0`.
- Supports Stable Diffusion v2.0. Use the `--v2` option when training (and when pre-acquiring latents). If you are using 768-v-ema.ckpt or stable-diffusion-2 instead of stable-diffusion-v2-base, also use the `--v_parameterization` option when training.
- Added options to specify the minimum and maximum resolutions of the bucket when pre-acquiring latents.
- Modified the loss calculation formula.
- Added options for the learning rate scheduler.
- Added support for downloading Diffusers models directly from Hugging Face and for saving during training.
- The cleaning script can now be used even when only one of the captions or tags is missing.
- Added options for the learning rate scheduler.

##### v2 (23/11):
- Implemented Waifu Diffusion 1.4 Tagger for alternative DeepDanbooru for auto-tagging
- Added a tagging script using WD14Tagger.
- Fixed a bug that caused data to be shuffled twice.
- Corrected spelling mistakes in the options for each script.

## Credit
[Kohya](https://twitter.com/kohya_ss) | Just for my part

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