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@ -213,16 +213,13 @@ class Chain(ContextModule):
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return Chain(*self, *other)
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@overload
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def __getitem__(self, key: int) -> Module:
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...
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def __getitem__(self, key: int) -> Module: ...
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@overload
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def __getitem__(self, key: str) -> Module:
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...
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def __getitem__(self, key: str) -> Module: ...
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@overload
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def __getitem__(self, key: slice) -> "Chain":
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...
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def __getitem__(self, key: slice) -> "Chain": ...
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def __getitem__(self, key: int | str | slice) -> Module:
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if isinstance(key, slice):
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@ -270,12 +267,10 @@ class Chain(ContextModule):
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@overload
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def walk(
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self, predicate: Callable[[Module, "Chain"], bool] | None = None, recurse: bool = False
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) -> Iterator[tuple[Module, "Chain"]]:
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...
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) -> Iterator[tuple[Module, "Chain"]]: ...
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@overload
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def walk(self, predicate: type[T], recurse: bool = False) -> Iterator[tuple[T, "Chain"]]:
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...
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def walk(self, predicate: type[T], recurse: bool = False) -> Iterator[tuple[T, "Chain"]]: ...
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def walk(
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self, predicate: type[T] | Callable[[Module, "Chain"], bool] | None = None, recurse: bool = False
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@ -66,8 +66,7 @@ class LatentDiffusionModel(fl.Module, ABC):
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return self.scheduler.steps
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@abstractmethod
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def set_unet_context(self, *, timestep: Tensor, clip_text_embedding: Tensor, **_: Tensor) -> None:
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...
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def set_unet_context(self, *, timestep: Tensor, clip_text_embedding: Tensor, **_: Tensor) -> None: ...
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def forward(
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self, x: Tensor, step: int, *, clip_text_embedding: Tensor, condition_scale: float = 7.5, **kwargs: Tensor
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@ -42,56 +42,39 @@ T = TypeVar("T")
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class Callback(Generic[T]):
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def on_train_begin(self, trainer: T) -> None:
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...
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def on_train_begin(self, trainer: T) -> None: ...
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def on_train_end(self, trainer: T) -> None:
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...
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def on_train_end(self, trainer: T) -> None: ...
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def on_epoch_begin(self, trainer: T) -> None:
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...
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def on_epoch_begin(self, trainer: T) -> None: ...
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def on_epoch_end(self, trainer: T) -> None:
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...
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def on_epoch_end(self, trainer: T) -> None: ...
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def on_batch_begin(self, trainer: T) -> None:
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...
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def on_batch_begin(self, trainer: T) -> None: ...
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def on_batch_end(self, trainer: T) -> None:
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...
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def on_batch_end(self, trainer: T) -> None: ...
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def on_backward_begin(self, trainer: T) -> None:
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...
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def on_backward_begin(self, trainer: T) -> None: ...
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def on_backward_end(self, trainer: T) -> None:
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...
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def on_backward_end(self, trainer: T) -> None: ...
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def on_optimizer_step_begin(self, trainer: T) -> None:
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...
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def on_optimizer_step_begin(self, trainer: T) -> None: ...
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def on_optimizer_step_end(self, trainer: T) -> None:
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...
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def on_optimizer_step_end(self, trainer: T) -> None: ...
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def on_compute_loss_begin(self, trainer: T) -> None:
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...
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def on_compute_loss_begin(self, trainer: T) -> None: ...
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def on_compute_loss_end(self, trainer: T) -> None:
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...
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def on_compute_loss_end(self, trainer: T) -> None: ...
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def on_evaluate_begin(self, trainer: T) -> None:
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...
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def on_evaluate_begin(self, trainer: T) -> None: ...
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def on_evaluate_end(self, trainer: T) -> None:
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...
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def on_evaluate_end(self, trainer: T) -> None: ...
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def on_lr_scheduler_step_begin(self, trainer: T) -> None:
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...
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def on_lr_scheduler_step_begin(self, trainer: T) -> None: ...
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def on_lr_scheduler_step_end(self, trainer: T) -> None:
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...
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def on_lr_scheduler_step_end(self, trainer: T) -> None: ...
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def on_checkpoint_save(self, trainer: T) -> None:
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...
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def on_checkpoint_save(self, trainer: T) -> None: ...
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class ClockCallback(Callback["Trainer[BaseConfig, Any]"]):
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@ -8,11 +8,9 @@ T = TypeVar("T", covariant=True)
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class HuggingfaceDataset(Generic[T], Protocol):
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def __getitem__(self, index: int) -> T:
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...
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def __getitem__(self, index: int) -> T: ...
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def __len__(self) -> int:
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...
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def __len__(self) -> int: ...
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def load_hf_dataset(
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@ -18,8 +18,7 @@ class DiffusersSDXL(Protocol):
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tokenizer_2: fl.Module
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vae: fl.Module
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def __call__(self, prompt: str, *args: Any, **kwargs: Any) -> Any:
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...
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def __call__(self, prompt: str, *args: Any, **kwargs: Any) -> Any: ...
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def encode_prompt(
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self,
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@ -27,8 +26,7 @@ class DiffusersSDXL(Protocol):
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prompt_2: str | None = None,
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negative_prompt: str | None = None,
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negative_prompt_2: str | None = None,
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) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
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...
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) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: ...
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@pytest.fixture(scope="module")
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