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| author | Volpeon <git@volpeon.ink> | 2023-05-16 16:48:51 +0200 |
|---|---|---|
| committer | Volpeon <git@volpeon.ink> | 2023-05-16 16:48:51 +0200 |
| commit | 55a12f2c683b2ecfa4fc8b4015462ad2798abda5 (patch) | |
| tree | feeb3f9a041466e773bb5921cbf0adb208d60a49 /training | |
| parent | Avoid model recompilation due to varying prompt lengths (diff) | |
| download | textual-inversion-diff-55a12f2c683b2ecfa4fc8b4015462ad2798abda5.tar.gz textual-inversion-diff-55a12f2c683b2ecfa4fc8b4015462ad2798abda5.tar.bz2 textual-inversion-diff-55a12f2c683b2ecfa4fc8b4015462ad2798abda5.zip | |
Fix LoRA training with DAdan
Diffstat (limited to 'training')
| -rw-r--r-- | training/functional.py | 4 | ||||
| -rw-r--r-- | training/sampler.py | 2 |
2 files changed, 3 insertions, 3 deletions
diff --git a/training/functional.py b/training/functional.py index 10560e5..fd3f9f4 100644 --- a/training/functional.py +++ b/training/functional.py | |||
| @@ -710,8 +710,8 @@ def train( | |||
| 710 | vae = torch.compile(vae, backend='hidet') | 710 | vae = torch.compile(vae, backend='hidet') |
| 711 | 711 | ||
| 712 | if compile_unet: | 712 | if compile_unet: |
| 713 | # unet = torch.compile(unet, backend='hidet') | 713 | unet = torch.compile(unet, backend='hidet') |
| 714 | unet = torch.compile(unet, mode="reduce-overhead") | 714 | # unet = torch.compile(unet, mode="reduce-overhead") |
| 715 | 715 | ||
| 716 | callbacks = strategy.callbacks( | 716 | callbacks = strategy.callbacks( |
| 717 | accelerator=accelerator, | 717 | accelerator=accelerator, |
diff --git a/training/sampler.py b/training/sampler.py index 8afe255..bdb3e90 100644 --- a/training/sampler.py +++ b/training/sampler.py | |||
| @@ -129,7 +129,7 @@ class LossSecondMomentResampler(LossAwareSampler): | |||
| 129 | self._loss_history = np.zeros( | 129 | self._loss_history = np.zeros( |
| 130 | [self.num_timesteps, history_per_term], dtype=np.float64 | 130 | [self.num_timesteps, history_per_term], dtype=np.float64 |
| 131 | ) | 131 | ) |
| 132 | self._loss_counts = np.zeros([self.num_timesteps], dtype=np.int) | 132 | self._loss_counts = np.zeros([self.num_timesteps], dtype=int) |
| 133 | 133 | ||
| 134 | def weights(self): | 134 | def weights(self): |
| 135 | if not self._warmed_up(): | 135 | if not self._warmed_up(): |