diff options
Diffstat (limited to 'training')
| -rw-r--r-- | training/functional.py | 12 |
1 files changed, 2 insertions, 10 deletions
diff --git a/training/functional.py b/training/functional.py index 36269f0..1c38635 100644 --- a/training/functional.py +++ b/training/functional.py | |||
| @@ -253,7 +253,6 @@ def loss_step( | |||
| 253 | text_encoder: CLIPTextModel, | 253 | text_encoder: CLIPTextModel, |
| 254 | with_prior_preservation: bool, | 254 | with_prior_preservation: bool, |
| 255 | prior_loss_weight: float, | 255 | prior_loss_weight: float, |
| 256 | noise_offset: float, | ||
| 257 | seed: int, | 256 | seed: int, |
| 258 | step: int, | 257 | step: int, |
| 259 | batch: dict[str, Any], | 258 | batch: dict[str, Any], |
| @@ -268,17 +267,12 @@ def loss_step( | |||
| 268 | generator = torch.Generator(device=latents.device).manual_seed(seed + step) if eval else None | 267 | generator = torch.Generator(device=latents.device).manual_seed(seed + step) if eval else None |
| 269 | 268 | ||
| 270 | # Sample noise that we'll add to the latents | 269 | # Sample noise that we'll add to the latents |
| 271 | offsets = noise_offset * torch.randn( | 270 | noise = torch.randn( |
| 272 | latents.shape[0], 1, 1, 1, | 271 | latents.shape, |
| 273 | dtype=latents.dtype, | 272 | dtype=latents.dtype, |
| 274 | layout=latents.layout, | 273 | layout=latents.layout, |
| 275 | device=latents.device, | 274 | device=latents.device, |
| 276 | generator=generator | 275 | generator=generator |
| 277 | ).expand(latents.shape) | ||
| 278 | noise = torch.normal( | ||
| 279 | mean=offsets, | ||
| 280 | std=1, | ||
| 281 | generator=generator, | ||
| 282 | ) | 276 | ) |
| 283 | 277 | ||
| 284 | # Sample a random timestep for each image | 278 | # Sample a random timestep for each image |
| @@ -565,7 +559,6 @@ def train( | |||
| 565 | global_step_offset: int = 0, | 559 | global_step_offset: int = 0, |
| 566 | with_prior_preservation: bool = False, | 560 | with_prior_preservation: bool = False, |
| 567 | prior_loss_weight: float = 1.0, | 561 | prior_loss_weight: float = 1.0, |
| 568 | noise_offset: float = 0.2, | ||
| 569 | **kwargs, | 562 | **kwargs, |
| 570 | ): | 563 | ): |
| 571 | text_encoder, unet, optimizer, train_dataloader, val_dataloader, lr_scheduler, extra = strategy.prepare( | 564 | text_encoder, unet, optimizer, train_dataloader, val_dataloader, lr_scheduler, extra = strategy.prepare( |
| @@ -600,7 +593,6 @@ def train( | |||
| 600 | text_encoder, | 593 | text_encoder, |
| 601 | with_prior_preservation, | 594 | with_prior_preservation, |
| 602 | prior_loss_weight, | 595 | prior_loss_weight, |
| 603 | noise_offset, | ||
| 604 | seed, | 596 | seed, |
| 605 | ) | 597 | ) |
| 606 | 598 | ||
