EveryDream2trainer/doc/OPTIMIZER.md

Advanced optimizer tweaking

You can set advanced optimizer settings using this arg:

--optimizer_config optimizer.json

or in train.json

"optimizer_config": "optimizer.json"

A default optimizer.json is supplied which you can modify.

This extra json file allows expanded tweaking.

If you do not set optimizer_config at all or set it to null in train.json, the defaults are adamw8bit with standard betas of (0.9,0.999), weight decay 0.01, and epsilon 1e-8.

Optimizers

In optimizer.json the you can set independent optimizer settings for both the text encoder and unet. If you want shared settings, just fill out the base section and leave text_encoder_overrides properties null an they will be copied from the base section.

If you set the text_encder_lr_scale property, the text encoder will be trained with a multiple of the Unet learning rate if it the LR is being copied. If you explicitly set the text encoder LR, the text_encder_lr_scale is ignored. text_encder_lr_scale is likely to be deprecated in the future, but now is left for backwards compatibility.

For each of the unet and text_encoder sections, you can set the following properties:

optimizer value is the type of optimizer to use. Below are the supported optimizers.

• adamw

Standard full precision AdamW optimizer exposed by PyTorch. Not recommended. Slower and uses more memory than adamw8bit. Widely documented on the web.

• adamw8bit
• lion8bit

Tim Dettmers / bitsandbytes AdamW and Lion 8bit optimizer. adamw8bit is the default and recommended setting as it is well understood, and lion8bit is very vram efficient. Widely documented on the web.

• lion

Lucidrains' implementation of the lion optimizer. Click links to read more. Epsilon is not used by lion. You should prefer lion8bit over this optimizer as it is more memory efficient.

Recommended settings for lion based on the paper are as follows:

"optimizer": "lion",
    "lr": 1e-7,
    "lr_scheduler": "constant",
    "betas": [0.9, 0.999],
    "epsilon": 1e-8,
    "weight_decay": 0.10

The recommendations are based on "1/10th LR" but "10x the weight decay" compared to AdamW when training diffusion models. Lion converges quickly, so take care with the learning rate, and even lower learning rates may be effective.

There are no known recommendations for the CLIP text encoder. Using an even larger weight decay, increased epsilon, or even lower LR may be effective for the text encoder. Further investigation on betas for text encoder is needed as well.

D-Adaption optimizers

Dadaptation version of various optimizers.

These require drastically different hyperparameters. Early indications seem to point to LR of 0.1 to 1.0 and weight decay of 0.8 may work well. There is a decouple parameter that appears to need to be set to true for dadaptation to work and is defaulted. Another d0 parameter is defaulted to 1e-6 as suggested and, according to the paper authors, does not need to be tuned, but is optional. See optimizer_dadapt.json for an example of a fully configured dadapt_adam training.

These are not memory efficient. You should use gradient checkpointing even with 24GB GPU.

Available optimizer values for Dadaptation are:

• dadapt_lion, dadapt_adam, dadapt_sgd

These are fairly experimental but tested as working. Gradient checkpointing may be required even on 24GB GPUs. Performance is slower than the compiled and optimized AdamW8bit optimizer unless you increae gradient accumulation as it seems the accumulation steps process slowly with the current implementation of D-Adaption.

Prodigy

Another adaptive optimizer. It is not very VRAM efficient. Github, Paper

• prodigy

Optimizer parameters

LR can be set in optimizer.json and excluded from the main CLI arg or train.json but if you use the main CLI arg or set it in the main train.json it will override the setting. This was done to make sure existing behavior will not break. To set LR in the optimizer.json make sure to delete "lr": 1.3e-6 in your main train.json and exclude the CLI arg.

The text encoder LR can run at a different value to the Unet LR. This may help prevent over-fitting, especially if you're training from SD2 checkpoints.

Text Encoder freezing

If you're training SD2.1 you will likely experience great benefit from partially freezing the text encoder. You can control text encoder freezing using the text_encoder_freezing block in your optimizer.json:

    "text_encoder_freezing": {
        "unfreeze_final_n_layers": 2,
    }

This will freeze the text encoder up to the last 2 layers, leaving the earlier layers and the embeddings intact.

Recommended settings for SD2.1 are provided in optimizerSD21.json. Unfreezing more layers will speed up training at the expense of text encoder stability. You can also try unfreezing the embeddings as well, by setting "freeze_embeddings": false. This may improve training, but it also seems to lead to quicker frying.

General Beta, weight decay, epsilon, etc tuning

Betas, weight decay, and epsilon are documented in the AdamW paper and there is a wealth of information on the web, but consider those experimental to tweak.