VictorMylle/Kohya-ss-sd-scripts
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Kohya S
2024-07-11 08:01:23 +09:00
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@@ -4,7 +4,9 @@ This repository contains training, generation and utility scripts for Stable Dif
SD3 training is done with `sd3_train.py`. SD3 training is done with `sd3_train.py`.
__Jun 29, 2024__: Fixed mixed precision training with fp16 is not working. Fixed the model is in bf16 dtype even without `--full_bf16` option (this could worsen the training result). __Jul 11, 2024__: Fixed to work t5xxl with `fp16`. If you change the dtype to `fp16` for t5xxl, please remove existing latents cache files (`*_sd3.npz`). The shift in `sd3_minimum_inference.py` is fixed to 3.0. Thanks to araleza!
Jun 29, 2024: Fixed mixed precision training with fp16 is not working. Fixed the model is in bf16 dtype even without `--full_bf16` option (this could worsen the training result).
`fp16` and `bf16` are available for mixed precision training. We are not sure which is better. `fp16` and `bf16` are available for mixed precision training. We are not sure which is better.
@@ -12,7 +14,7 @@ __Jun 29, 2024__: Fixed mixed precision training with fp16 is not working. Fixed
`clip_l`, `clip_g` and `t5xxl` can be specified if the checkpoint does not include them. `clip_l`, `clip_g` and `t5xxl` can be specified if the checkpoint does not include them.
t5xxl doesn't seem to work with `fp16`, so 1) use`bf16` for mixed precision, or 2) use `bf16` or `float32` for `t5xxl_dtype`. ~~t5xxl doesn't seem to work with `fp16`, so 1) use`bf16` for mixed precision, or 2) use `bf16` or `float32` for `t5xxl_dtype`. ~~ t5xxl works with `fp16` now.
There are `t5xxl_device` and `t5xxl_dtype` options for `t5xxl` device and dtype. There are `t5xxl_device` and `t5xxl_dtype` options for `t5xxl` device and dtype.