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| # MIT License | |
| # Copyright (c) Microsoft | |
| # Permission is hereby granted, free of charge, to any person obtaining a copy | |
| # of this software and associated documentation files (the "Software"), to deal | |
| # in the Software without restriction, including without limitation the rights | |
| # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell | |
| # copies of the Software, and to permit persons to whom the Software is | |
| # furnished to do so, subject to the following conditions: | |
| # The above copyright notice and this permission notice shall be included in all | |
| # copies or substantial portions of the Software. | |
| # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR | |
| # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, | |
| # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE | |
| # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER | |
| # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, | |
| # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE | |
| # SOFTWARE. | |
| # Copyright (c) [2025] [Microsoft] | |
| # Copyright (c) [2025] [Chongjie Ye] | |
| # SPDX-License-Identifier: MIT | |
| # This file has been modified by Chongjie Ye on 2025/04/10 | |
| # Original file was released under MIT, with the full license text # available at https://github.com/atong01/conditional-flow-matching/blob/1.0.7/LICENSE. | |
| # This modified file is released under the same license. | |
| from typing import * | |
| import torch | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| from ...modules import sparse as sp | |
| from .base import SparseTransformerBase | |
| class SLatEncoder(SparseTransformerBase): | |
| def __init__( | |
| self, | |
| resolution: int, | |
| in_channels: int, | |
| model_channels: int, | |
| latent_channels: int, | |
| num_blocks: int, | |
| num_heads: Optional[int] = None, | |
| num_head_channels: Optional[int] = 64, | |
| mlp_ratio: float = 4, | |
| attn_mode: Literal["full", "shift_window", "shift_sequence", "shift_order", "swin"] = "swin", | |
| window_size: int = 8, | |
| pe_mode: Literal["ape", "rope"] = "ape", | |
| use_fp16: bool = False, | |
| use_checkpoint: bool = False, | |
| qk_rms_norm: bool = False, | |
| ): | |
| super().__init__( | |
| in_channels=in_channels, | |
| model_channels=model_channels, | |
| num_blocks=num_blocks, | |
| num_heads=num_heads, | |
| num_head_channels=num_head_channels, | |
| mlp_ratio=mlp_ratio, | |
| attn_mode=attn_mode, | |
| window_size=window_size, | |
| pe_mode=pe_mode, | |
| use_fp16=use_fp16, | |
| use_checkpoint=use_checkpoint, | |
| qk_rms_norm=qk_rms_norm, | |
| ) | |
| self.resolution = resolution | |
| self.out_layer = sp.SparseLinear(model_channels, 2 * latent_channels) | |
| self.initialize_weights() | |
| if use_fp16: | |
| self.convert_to_fp16() | |
| def initialize_weights(self) -> None: | |
| super().initialize_weights() | |
| # Zero-out output layers: | |
| nn.init.constant_(self.out_layer.weight, 0) | |
| nn.init.constant_(self.out_layer.bias, 0) | |
| def forward(self, x: sp.SparseTensor, sample_posterior=True, return_raw=False): | |
| h = super().forward(x) | |
| h = h.type(x.dtype) | |
| h = h.replace(F.layer_norm(h.feats, h.feats.shape[-1:])) | |
| h = self.out_layer(h) | |
| # Sample from the posterior distribution | |
| mean, logvar = h.feats.chunk(2, dim=-1) | |
| if sample_posterior: | |
| std = torch.exp(0.5 * logvar) | |
| z = mean + std * torch.randn_like(std) | |
| else: | |
| z = mean | |
| z = h.replace(z) | |
| if return_raw: | |
| return z, mean, logvar | |
| else: | |
| return z | |