VictorMylle/Kohya-ss-sd-scripts
1
0
Fork 1
mirror of https://github.com/kohya-ss/sd-scripts.git synced 2026-04-09 06:45:09 +00:00
Code Issues Packages Projects Releases Wiki Activity

feat: Support multi-resolution training with caching latents to disk

Browse Source
This commit is contained in:
Kohya S
2024-08-20 21:39:43 +09:00
parent 388b3b4b74
commit 6ab48b09d8
4 changed files with 93 additions and 43 deletions
Download Patch File Download Diff File Expand all files Collapse all files

11
README.md
View File

@@ -9,13 +9,20 @@ __Please update PyTorch to 2.4.0. We have tested with `torch==2.4.0` and `torchv
The command to install PyTorch is as follows:
`pip3 install torch==2.4.0 torchvision==0.19.0 --index-url https://download.pytorch.org/whl/cu124`
Aug 20, 2024 (update 3):
__Experimental__ The multi-resolution training is now supported with caching latents to disk.
The cache files now hold latents for multiple resolutions. Since the latents are appended to the current cache file, it is recommended to delete the cache file in advance (if not, the old latents is kept in .npz file).
See [FLUX.1 Multi-resolution training](#flux1-multi-resolution-training) for details.
Aug 20, 2024 (update 2):
`flux_merge_lora.py` now supports LoRA from AI-toolkit (Diffusers based keys). Specify `--diffusers` option to merge LoRA with Diffusers based keys. Thanks to exveria1015!
Aug 20, 2024:
FLUX.1 supports multi-resolution inference, so training at multiple resolutions may be possible and the results may be improved (like 1024x1024, 768x768 and 512x512 ... you can use any resolution).
The script seems to support multi-resolution even in the current version, __if `--cache_latents_to_disk` is not specified__. Please try if you are interested. See [FLUX.1 Multi-resolution training](#flux1-multi-resolution-training) for details.
The script seems to support multi-resolution even in the current version, ~~if `--cache_latents_to_disk` is not specified~~ -> `--cache_latents_to_disk` is now supported for multi-resolution training. Please try if you are interested. See [FLUX.1 Multi-resolution training](#flux1-multi-resolution-training) for details.
We will support multi-resolution caching to disk in the near future.
@@ -171,7 +178,7 @@ The script can merge multiple LoRA models. If you want to merge multiple LoRA mo
### FLUX.1 Multi-resolution training
You can define multiple resolutions in the dataset configuration file. __Caching latents to disk is not supported yet.__
You can define multiple resolutions in the dataset configuration file.
The dataset configuration file is like below. You can define multiple resolutions with different batch sizes. The resolutions are defined in the `[[datasets]]` section. The `[[datasets.subsets]]` section is for the dataset directory. Please specify the same directory for each resolution.