42 lines
1.5 KiB
Python
42 lines
1.5 KiB
Python
# SPDX-FileCopyrightText: Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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# SPDX-License-Identifier: Apache-2.0
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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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"""The distribution modes to use for continuous image tokenizers."""
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import torch
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class IdentityDistribution(torch.nn.Module):
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def __init__(self):
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super().__init__()
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def forward(self, parameters):
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return parameters, (torch.tensor([0.0]), torch.tensor([0.0]))
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class GaussianDistribution(torch.nn.Module):
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def __init__(self, min_logvar: float = -30.0, max_logvar: float = 20.0):
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super().__init__()
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self.min_logvar = min_logvar
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self.max_logvar = max_logvar
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def sample(self, mean, logvar):
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std = torch.exp(0.5 * logvar)
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return mean + std * torch.randn_like(mean)
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def forward(self, parameters):
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mean, logvar = torch.chunk(parameters, 2, dim=1)
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logvar = torch.clamp(logvar, self.min_logvar, self.max_logvar)
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return self.sample(mean, logvar), (mean, logvar)
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