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FYI: This had covered #1409 already. Meanwhile this gives a lot more optimizers to explore other than parser.add_argument(
"--optimizer_type",
type=str,
default="",
help="Optimizer to use / オプティマイザの種類: AdamW (default), AdamW8bit, PagedAdamW, PagedAdamW8bit, PagedAdamW32bit, "
"Lion8bit, PagedLion8bit, Lion, SGDNesterov, SGDNesterov8bit, "
"DAdaptation(DAdaptAdamPreprint), DAdaptAdaGrad, DAdaptAdam, DAdaptAdan, DAdaptAdanIP, DAdaptLion, DAdaptSGD, "
"AdaFactor. "
"Also, you can use any optimizer by specifying the full path to the class, like 'bitsandbytes.optim.AdEMAMix8bit' or 'bitsandbytes.optim.PagedAdEMAMix8bit'.",
) |
Recommend adding missed prodigy-plus-schedule-free and updating prodigy to the latest version. |
Thank you for this PR! The latest update is nice, because I think specifying a specific version is preferable. I will merge this today. |
# for T5XXL tokenizer (SD3/FLUX) | ||
sentencepiece==0.2.0 | ||
# for kohya_ss library | ||
pytorch-optimizer |
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Sorry, I didn't notice, but is the latest version of pytorch-optimizer okay?
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okay
But this is a rapidly updating library.🤣
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Hi! I stumbled upon this PR by chance. Thanks for opening the PR and introducing pytorch-optimizer to this repo, @gesen2egee @kohya-ss.
IMO, it might be great to fix the version to the latest release (3.5.0
) for reproducibility, consistency, and to prevent unexpected issues or bugs.
I usually update the PATCH version (z
in the x.y.z scheme) every couple of weeks to a month and update the minor version (y
) every couple of months. Maybe, we could update the minor (or patch) version here when needed.
For your reference, In PATCH updates usually include bug fixes or adding a new optimizer, and I update the minor version when there are enough new features and each new feature looks okay!
feel free to leave a comment if you need anything :)
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Thank you for your comment! I will update to specify 3.5.0 after merging.
We will make appropriate changes as new versions are released!
https://github.com/kozistr/pytorch_optimizer
A very practical optimizer library that includes many newer optimizer (or learning rate scheduling) options.
For example, to use CAME:
To use Lion with cautious mode:
You can check pytorch_optimizer to see what other options are available.
The author kozistr is very active. If you have an optimizer you’d like to try,
you can provide a paper link or code and open a issue—he implements things quickly!