o vij@s0ddlZddlZddlmZGdddeZdS)N)EDMSDEcsfeZdZdZ       dd ed ed ed ed ededeffdd Zdedej ffdd Z Z S) HybridEDMSDEag Extended EDMSDE for Cosmos Policy that supports hybrid sigma distribution. This class adds a hybrid sampling strategy that combines: - 70% samples from the original log-normal distribution - 30% samples from a uniform distribution over higher sigma values This approach helps put more weight on higher sigma values during training. 333333333333?T@Mb`?F?@U@p_meanp_std sigma_max sigma_minhybrid_sigma_distribution uniform_lower uniform_uppercs*tj||||d||_||_||_dS)N)r r r r )super__init__rrr)selfr r r r rrr __class__J/data/cameron/vidgen/cosmos-policy/cosmos_policy/modules/hybrid_edm_sde.pyr!s  zHybridEDMSDE.__init__ batch_sizereturncsjrStj|dd}tj|dddk}|}||}|dkr=tjj|d}tj fdd|Ddd}t |||<|dkrQtj |dd j j||<|St|S)a Sample timesteps (sigma values) for training. When hybrid_sigma_distribution is enabled, uses a mixture of: - Log-normal distribution (70% of samples) - Uniform distribution between uniform_lower and uniform_upper (30% of samples) Otherwise, uses the base class implementation (pure log-normal). Args: batch_size: Number of samples to generate Returns: Tensor of sigma values with shape (batch_size,) cuda)devicegffffff?r)sizecsg|]}j|qSr) gaussian_distinv_cdf).0cdf_valrrr Osz)HybridEDMSDE.sample_t..)rtorchzerosrandsumitemnprandomuniformtensorexpemptyuniform_rrrsample_t)rrsamplesZdistribution_choiceZ num_lognormalZ num_uniformcdf_valsZ log_sigmasrr!rr/0s   zHybridEDMSDE.sample_t)rrrrFrr ) __name__ __module__ __qualname____doc__floatboolrintr#Tensorr/ __classcell__rrrrrs2  r)numpyr(r#-cosmos_policy._src.imaginaire.modules.edm_sderrrrrrs