| from transformers import PretrainedConfig | |
| class UpscalerConfig(PretrainedConfig): | |
| model_type = "upscaler" | |
| def __init__( | |
| self, | |
| scale: int = 2, | |
| in_channels: int = 3, | |
| width: int = 32, | |
| num_blocks: int = 3, | |
| feat1: int = 64, | |
| feat2: int = 32, | |
| use_refine: bool = False, | |
| **kwargs, | |
| ): | |
| super().__init__(**kwargs) | |
| self.scale = int(scale) | |
| self.in_channels = int(in_channels) | |
| self.width = int(width) | |
| self.num_blocks = int(num_blocks) | |
| self.feat1 = int(feat1) | |
| self.feat2 = int(feat2) | |
| self.use_refine = bool(use_refine) |