| from transformers import PretrainedConfig | |
| class in2INConfig(PretrainedConfig): | |
| def __init__(self, | |
| num_layers=8, | |
| num_heads=8, | |
| dropout=0.1, | |
| input_dim=262, | |
| latent_dim=1024, | |
| ff_size=2048, | |
| activation="gelu", | |
| diffusion_steps=1000, | |
| beta_scheduler="cosine", | |
| sampler="uniform", | |
| motion_rep="global", | |
| finetune=False, | |
| text_encoder="clip", | |
| t_bar=700, | |
| control="text", | |
| strategy="ddim50", | |
| cfg_weight=3, | |
| cfg_weight_interaction=3, | |
| cfg_weight_individual=1, | |
| mode="interaction", | |
| **kwargs): | |
| self.NUM_LAYERS = num_layers | |
| self.NUM_HEADS = num_heads | |
| self.DROPOUT = dropout | |
| self.INPUT_DIM = input_dim | |
| self.LATENT_DIM = latent_dim | |
| self.FF_SIZE = ff_size | |
| self.ACTIVATION = activation | |
| self.DIFFUSION_STEPS = diffusion_steps | |
| self.BETA_SCHEDULER = beta_scheduler | |
| self.SAMPLER = sampler | |
| self.MOTION_REP = motion_rep | |
| self.FINETUNE = finetune | |
| self.TEXT_ENCODER = text_encoder | |
| self.T_BAR = t_bar | |
| self.CONTROL = control | |
| self.STRATEGY = strategy | |
| self.CFG_WEIGHT = cfg_weight | |
| self.CFG_WEIGHT_INTERACTION = cfg_weight_interaction | |
| self.CFG_WEIGHT_INDIVIDUAL = cfg_weight_individual | |
| self.MODE = mode | |
| super().__init__(**kwargs) | |