o }oiI!@sdZddlmZddlmZddlZddlZddlm Z ddlm Z ddl m Z ddlmZdd lmZmZmZmZmZmZmZdd lmZdd lmZGd d d eZdS)z/A library for Causal Video Tokenizer inference.)Path)OptionalN) get_token)hf_hub_download) DictConfig)tqdm)get_tokenizer_configload_jit_modelload_pytorch_model numpy2tensorpad_video_batch tensor2numpyunpad_video_batch)PretrainedModelInfo)ModelPTcseZdZdZdeddffdd Ze    d"d d Ze d ej dej fddZ e d ej de ej fddZ e dej dej fddZ d#dejdedejfddZdeefddZdeefddZedeefd d!ZZS)$CausalVideoTokenizerz:Causal Video tokenization with the NVIDIA Cosmos TokenizercfgreturnNcs$t|t|j}t|d|_t|d|_t|d|_tt |j |_ d|_ |j rit|j}|j |d<|jrCt|j|d|j nd|_|jrRt|j|d|j nd|_|jrdt|j|d|j |_dSd|_dS|jrst|j|j nd|_|jrt|j|j nd|_|jrt|j|j nd|_dS) Nautoencoder.jit encoder.jit decoder.jitcudadtypefullencdec)super__init__rcheckpoint_dirstr_full_model_path_enc_model_path_dec_model_pathgetattrtorchr_dtype_device use_pytorchrtokenizer_typeload_full_modelr _full_modelload_enc_model _enc_modelload_dec_model _dec_modelr )selfr checkpointtokenizer_config __class__m/home/ubuntu/.local/lib/python3.10/site-packages/nemo/collections/common/video_tokenizers/cosmos_tokenizer.pyr&s4    zCausalVideoTokenizer.__init__Cosmos-Tokenizer-DV4x8x8TFbfloat16c Cs|d||_t}t|jd|d}t|jd|d} t|jd|d} |r'd}d}tt|j} t| ||||||d} || S)Nznvidia/r)repo_idfilenametokenrrF)rrr+r-r)r(r')_hf_model_name get_hf_tokenrrrparentr) clsr( load_encoder load_decoderr)r'rhf_tokenfull_model_path_ckpt_dirrr4r4r5from_pretrainedHs@  z$CausalVideoTokenizer.from_pretrained input_tensorcCsN|jdur||}t|tr|d}|S|}|S||d}||}|S)zReconstructs a batch of video tensors after embedding into a latent. Args: video: The input video Bx3xTxHxW layout, range [-1..1]. Returns: The reconstructed video, layout Bx3xTxHxW, range [-1..1]. Nr)r* isinstancetupleencodedecode)r/rF output_tensor output_latentr4r4r5 autoencode~s  zCausalVideoTokenizer.autoencodecCs8|jdks Jd||}t|tjr|S|ddS)a Encodes a numpy video into a CausalVideo latent or code. Args: input_tensor: The input tensor Bx3xTxHxW layout, range [-1..1]. Returns: For causal continuous video (CausalCV) tokenizer, the tuple contains: - The latent embedding, Bx16x(t)x(h)x(w), where the compression rate is (T/t x H/h x W/w), and channel dimension of 16. For causal discrete video (CausalDV) tokenizer, the tuple contains: 1) The indices, Bx(t)x(h)x(w), from a codebook of size 64K, which is formed by FSQ levels of (8,8,8,5,5,5). 2) The discrete code, Bx6x(t)x(h)x(w), where the compression rate is again (T/t x H/h x W/w), and channel dimension of 6. input video should be of 5D.N)ndimr,rGr$Tensor)r/rFrLr4r4r5rIs    zCausalVideoTokenizer.encode input_latentcCs|jdks Jd||S)aAEncodes a numpy video into a CausalVideo latent. Args: input_latent: The continuous latent Bx16xtxhxw for CausalCV, or the discrete indices Bxtxhxw for CausalDV. Returns: The reconstructed tensor, layout [B,3,1+(T-1)*8,H*16,W*16] in range [-1..1]. z@input latent should be of 5D for continuous and 4D for discrete.)rQr.)r/rSr4r4r5rJs zCausalVideoTokenizer.decodevideotemporal_windowcCs|jdks Jd|jd}g}ttd|d|dD]:}|||d|}}|dd||df}t|\} } t| |j|jd} || } t | } t | | }| |qt j |ddS) aReconstructs video using a pre-trained CausalTokenizer autoencoder. Given a video of arbitrary length, the forward invokes the CausalVideoTokenizer in a sliding manner with a `temporal_window` size. Args: video: The input video BxTxHxWx3 layout, range [0..255]. temporal_window: The length of the temporal window to process, default=25. Returns: The reconstructed video in range [0..255], layout BxTxHxWx3. rNrOrN.)rdevice)axis)rQshaperranger r r%r&rMr rappendnp concatenate)r/rVrW num_framesoutput_video_listidxstartend input_videopadded_input_video crop_regionrFrKpadded_output_video output_videor4r4r5forwards     zCausalVideoTokenizer.forwardtrain_data_configcCdSNr4)r/rkr4r4r5setup_training_dataz(CausalVideoTokenizer.setup_training_dataval_data_configcCrlrmr4)r/rpr4r4r5setup_validation_dataroz*CausalVideoTokenizer.setup_validation_datacCrlrmr4)r>r4r4r5list_available_modelssz*CausalVideoTokenizer.list_available_models)r6TTFFr7)rU)__name__ __module__ __qualname____doc__rr classmethodrEr$no_gradrRrMrHrIrJr^ndarrayintrjrrnrqrrr __classcell__r4r4r2r5r#s:" 5 !r)rvpathlibrtypingrnumpyr^r$huggingface_hubrr<r omegaconfrr.nemo.collections.common.video_tokenizers.utilsrr r r r r rnemo.core.classes.commonrnemo.core.classes.modelPTrrr4r4r4r5s      $