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src/diffusers/pipelines/wuerstchen/pipeline_wuerstchen_combined.py

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@@ -227,8 +227,9 @@ def __call__(
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return_dict (`bool`, *optional*, defaults to `True`):
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Whether or not to return a [`~pipelines.ImagePipelineOutput`] instead of a plain tuple.
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prior_callback (`Callable`, *optional*):
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A function that will be called every `prior_callback_steps` steps during inference. The function will be
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called with the following arguments: `prior_callback(step: int, timestep: int, latents: torch.FloatTensor)`.
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A function that will be called every `prior_callback_steps` steps during inference. The function will
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be called with the following arguments: `prior_callback(step: int, timestep: int, latents:
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torch.FloatTensor)`.
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prior_callback_steps (`int`, *optional*, defaults to 1):
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The frequency at which the `callback` function will be called. If not specified, the callback will be
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called at every step.

src/diffusers/schedulers/scheduling_utils.py

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@@ -15,7 +15,7 @@
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import os
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from dataclasses import dataclass
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from enum import Enum
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from typing import Any, Dict, Optional, Union
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from typing import Optional, Union
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import torch
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src/diffusers/schedulers/scheduling_utils_flax.py

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@@ -16,7 +16,7 @@
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import os
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from dataclasses import dataclass
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from enum import Enum
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from typing import Any, Dict, Optional, Tuple, Union
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from typing import Optional, Tuple, Union
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import flax
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import jax.numpy as jnp

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