(tiddlZddlmZddlmZddlmZeje Z ddejde dededed ed ed ejfd ZGd dej"ZGddej"Zy)N)logging timesteps embedding_dim freq_shift min_timescale max_timescaleflip_sin_to_cosscalereturnc|jdk(sJd|dzdk(s Jd|dt|dz}tj||z ||z z }|t j t j |tj| zz} t j|dt j| dz} || z} |r@t jt j| t j| gd} n?t jt j| t j| gd} t j| t j|d|g} | S) aReturns the positional encoding (same as Tensor2Tensor). Args: timesteps (`jnp.ndarray` of shape `(N,)`): A 1-D array of N indices, one per batch element. These may be fractional. embedding_dim (`int`): The number of output channels. freq_shift (`float`, *optional*, defaults to `1`): Shift applied to the frequency scaling of the embeddings. min_timescale (`float`, *optional*, defaults to `1`): The smallest time unit used in the sinusoidal calculation (should probably be 0.0). max_timescale (`float`, *optional*, defaults to `1.0e4`): The largest time unit used in the sinusoidal calculation. flip_sin_to_cos (`bool`, *optional*, defaults to `False`): Whether to flip the order of sinusoidal components to cosine first. scale (`float`, *optional*, defaults to `1.0`): A scaling factor applied to the positional embeddings. Returns: a Tensor of timing signals [N, num_channels] zTimesteps should be a 1d-arrayrrzEmbedding dimension z should be even)dtype)axis)ndimfloatmathlogjnpexparangefloat32 expand_dims concatenatecossinreshapeshape) rrrrr r r num_timescaleslog_timescale_incrementinv_timescalesemb scaled_timesignals o/Volumes/fast/ai/experiments/MLX_z-image/.venv/lib/python3.12/site-packages/diffusers/models/embeddings_flax.pyget_sinusoidal_embeddingsr&s=< >>Q @ @@  1  !X%9-#XX !=A-.N"hh}}'DEZdIde"SWWSZZckk-Z^u]u-u%vvN //)Q '#//.!*L LC#+K#''+"6 8L!MTUV#''+"6 8L!MTUV [[#))I"6q"9=!I JF MceZdZUdZej ddZeed<e jZ e jed<e jdZy)FlaxTimestepEmbeddinga= Time step Embedding Module. Learns embeddings for input time steps. Args: time_embed_dim (`int`, *optional*, defaults to `32`): Time step embedding dimension. dtype (`jnp.dtype`, *optional*, defaults to `jnp.float32`): The data type for the embedding parameters. Flax classes are deprecated and will be removed in Diffusers v1.0.0. We recommend migrating to PyTorch classes or pinning your version of Diffusers. time_embed_dimrctj|j|jd|}tj|}tj|j|jd|}|S)Nlinear_1)rnamelinear_2)nnDenser,rsilu)selftembs r%__call__zFlaxTimestepEmbedding.__call__\s\Orxx++4::JOPTUwwt}Orxx++4::JOPTU r'N)__name__ __module__ __qualname____doc__loggerwarningr,int__annotations__rrrr1compactr6r'r%r)r)IsO NN W NC{{E399"ZZr'r)ceZdZUdZdZeed<dZeed<dZ e ed<e jde jd Zy ) FlaxTimestepsa Wrapper Module for sinusoidal Time step Embeddings as described in https://huggingface.co/papers/2006.11239 Args: dim (`int`, *optional*, defaults to `32`): Time step embedding dimension. flip_sin_to_cos (`bool`, *optional*, defaults to `False`): Whether to flip the sinusoidal function from sine to cosine. freq_shift (`float`, *optional*, defaults to `1`): Frequency shift applied to the sinusoidal embeddings. r+dimFr rrr*c\t||j|j|jS)N)rr r)r&rCr r)r4rs r%r6zFlaxTimesteps.__call__zs)( TXXt?S?S`d`o`o  r'N)r7r8r9r:rCr=r>r boolrrr;r<r1r?r6r@r'r%rBrBdsP CM!OT!J NN W ZZ  r'rB)rrg@Fg?)r flax.linenlinenr1 jax.numpynumpyrutilsr get_loggerr7r;ndarrayr=rrEr&Moduler)rBr@r'r%rNs    H %  !-{{--- -  -  - - [[-`BII6 BII r'