Kohya Trainer V10 - 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.
This notebook was adapted by Linaqruf
You can find the latest update to the notebook here.

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).

Kohya S. Official Github Account

kohya_ss

Run locally

Please refer to bmaltais's repo if you want to run it locally on your terminal

bmaltais's kohya_ss (dreambooth)
bmaltais's kohya_diffusers_fine_tuning
bmaltais's kohya_diffusion (gen_img_diffusers)

Original post for each dedicated script:

Change Logs:

2023

v10 (02/01) separate release

Added a function to automatically download the BLIP weight in make_caption.py
Added functions for LoRA training and generation
Fixed issue where text encoder training was not stopped
Fixed conversion error for v1 Diffusers->ckpt in convert_diffusers20_original_sd.py
Fixed npz file name for images with dots in prepare_buckets_latents.py

Colab UI changes:

Integrated the repository's format with kohya-ss/sd-script to facilitate merging
You can no longer choose older script versions in the clone cell because the new format does not support it
The requirements for both blip and wd tagger have been merged into one requirements.txt file
The blip cell has been simplified because make_caption.py will now automatically download the BLIP weight, as will the wd tagger
A list of sdv2 models has been added to the "download pretrained model" cell
The "v2" option has been added to the bucketing and training cells
An image generation cell using gen_img_diffusers.py has been added below the training cell

2022

v9 (17/12):

Added the save_model_as option to fine_tune.py, which allows you to save the model in any format.
Added the keep_tokens option to fine_tune.py, which allows you to fix the first n tokens of the caption and not shuffle them.
Added support for left-right flipping augmentation in prepare_buckets_latents.py and fine_tune.py with the flip_aug option.

v8 (13/12):

Added support for training with fp16 gradients (experimental feature). This allows training with 8GB VRAM on SD1.x. See "Training with fp16 gradients (experimental feature)" for details.
Updated WD14Tagger script to automatically download weights.

v7 (7/12):

Requires Diffusers 0.10.2 (0.10.0 or later will work, but there are reported issues with 0.10.0 so we recommend using 0.10.2). To update, run pip install -U diffusers[torch]==0.10.2 in your virtual environment.
Added support for Diffusers 0.10 (uses code in Diffusers for v-parameterization training and also supports safetensors).
Added support for accelerate 0.15.0.
Added support for multiple teacher data folders. For caption and tag preprocessing, use the --full_path option. The arguments for the cleaning script have also changed, see "Caption and Tag Preprocessing" for details.

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.

Conclusion

While Stable Diffusion fine tuning is typically based on CompVis, using Diffusers as a base allows for efficient and fast fine tuning with less memory usage. We have also added support for the features proposed by Novel AI, so we hope this article will be useful for those who want to fine tune their models.

— kohya_ss

Credit

Kohya | Lopho for prune script | Just for my part

Fannovel16 / kohya-trainer