| from typing import Tuple | |
| from transformers import PretrainedConfig | |
| class VQGANConfig(PretrainedConfig): | |
| def __init__( | |
| self, | |
| ch: int = 128, | |
| out_ch: int = 3, | |
| in_channels: int = 3, | |
| num_res_blocks: int = 2, | |
| resolution: int = 256, | |
| z_channels: int = 256, | |
| ch_mult: Tuple = (1, 1, 2, 2, 4), | |
| attn_resolutions: int = (16,), | |
| n_embed: int = 1024, | |
| embed_dim: int = 256, | |
| dropout: float = 0.0, | |
| double_z: bool = False, | |
| resamp_with_conv: bool = True, | |
| give_pre_end: bool = False, | |
| **kwargs, | |
| ): | |
| super().__init__(**kwargs) | |
| self.ch = ch | |
| self.out_ch = out_ch | |
| self.in_channels = in_channels | |
| self.num_res_blocks = num_res_blocks | |
| self.resolution = resolution | |
| self.z_channels = z_channels | |
| self.ch_mult = list(ch_mult) | |
| self.attn_resolutions = list(attn_resolutions) | |
| self.n_embed = n_embed | |
| self.embed_dim = embed_dim | |
| self.dropout = dropout | |
| self.double_z = double_z | |
| self.resamp_with_conv = resamp_with_conv | |
| self.give_pre_end = give_pre_end | |
| self.num_resolutions = len(ch_mult) |