58 lines
1.8 KiB
Python
58 lines
1.8 KiB
Python
# Copyright 2023 The HuggingFace Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from typing import Optional, Union
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import torch
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from torch import nn
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from ...configuration_utils import ConfigMixin, register_to_config
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from ...models.modeling_utils import ModelMixin
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class StableUnCLIPImageNormalizer(ModelMixin, ConfigMixin):
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"""
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This class is used to hold the mean and standard deviation of the CLIP embedder used in stable unCLIP.
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It is used to normalize the image embeddings before the noise is applied and un-normalize the noised image
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embeddings.
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"""
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@register_to_config
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def __init__(
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self,
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embedding_dim: int = 768,
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):
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super().__init__()
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self.mean = nn.Parameter(torch.zeros(1, embedding_dim))
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self.std = nn.Parameter(torch.ones(1, embedding_dim))
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def to(
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self,
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torch_device: Optional[Union[str, torch.device]] = None,
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torch_dtype: Optional[torch.dtype] = None,
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):
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self.mean = nn.Parameter(self.mean.to(torch_device).to(torch_dtype))
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self.std = nn.Parameter(self.std.to(torch_device).to(torch_dtype))
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return self
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def scale(self, embeds):
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embeds = (embeds - self.mean) * 1.0 / self.std
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return embeds
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def unscale(self, embeds):
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embeds = (embeds * self.std) + self.mean
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return embeds
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