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minor update to ema docs

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Victor Hall 2023-09-18 15:07:39 -04:00
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doc/ADVANCED_TWEAKING.md
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@ -230,7 +230,7 @@ In this mode, the EMA model will be saved alongside the regular checkpoint from
For more information, consult the [research paper](https://arxiv.org/abs/2101.08482) or continue reading the tuning notes below.
**Parameters:**
- `--ema_decay_rate`: Determines the EMA decay rate. It defines how much the EMA model is updated from training at each update. Values should be close to 1 but not exceed it. Activating this parameter triggers the EMA decay feature.
- `--ema_strength_target`: Set the EMA decay target value within the (0,1) range. The `ema_decay_rate` is computed based on the relation: decay_rate to the power of (total_steps/decay_interval) equals decay_target. Enabling this parameter will override `ema_decay_rate` and will enable EMA feature.
- `--ema_strength_target`: Set the EMA strength target value within the (0,1) range. The `ema_decay_rate` is computed based on the relation: decay_rate to the power of (total_steps/decay_interval) equals decay_target. Enabling this parameter will override `ema_decay_rate` and will enable EMA feature. See [ema_strength_target](#ema_strength_target) for more information.
- `--ema_update_interval`: Set the interval in steps between EMA updates. The update occurs at each optimizer step. If you use grad_accum, actual update interval will be multipled by your grad_accum value.
- `--ema_device`: Choose between `cpu` and `cuda` for EMA. Opting for 'cpu' takes around 4 seconds per update and uses approximately 3.2GB RAM, while 'cuda' is much faster but requires a similar amount of VRAM.
- `--ema_sample_raw_training`: Activate to display samples from the trained model, mirroring conventional training. They will not be presented by default with EMA decay enabled.