bi`ddlZddlmZmZmZddlZddlmZddlm Z m Z ddl m Z ddl mZmZddlmZmZdd lmZdd lmZdd lmZdd lmZdd lmZGddej:ZGddej:ZGddej:Z Gddej:Z!Gddej:Z"Gddej:Z#Gddee Z$dZ%y)N)OptionalTupleUnion) ConfigMixinregister_to_config)apply_forward_hook) Attention SpatialNorm) DecoderOutputDiagonalGaussianDistribution) Downsample2D)AutoencoderKLOutput) ModelMixin) ResnetBlock2D) Upsample2DceZdZdZ ddedeededededed ed dffd Ze d e jd e jfd Z d e jded e jfdZ xZS)AllegroTemporalConvLayera  Temporal convolutional layer that can be used for video (sequence of images) input. 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O :::vN O66t~~vNF#.. , #F+ ,^^F+F1aA.66q!<##F+v&!!!j"%56>>q!Q1M##F+("1aA.66q!<v&!!!j"%56>>q!Q1M r5 rKrLrMr$rrrPr#r:rRrHrSrTs@r4rrs- /C7Q !!Q,Q,Q, S/ Q,"#s(OQ,%*$)$4Q,Q,Q,Q,Q,f(ell(u||(r5rceZdZdddgdddddd f d ed ed eed fdeed fdeed fdedededeffd ZdejdejfdZ xZ S)AllegroDecoder3Dr?rrrrrFTTFrr rJrgrouprWrXup_block_types.temporal_upsample_blocksrrrr norm_typec t|tj||dddd|_tj |d|ddd|_d|_tjg|_ | dk(r|nd} t|dd |d| d k(rd n| |d|| |_tt|} | d } t|D]g\} }| }| | } | t|dz k(}|dk(rt|dz|| | || d ||| |  }n t!d|jj#|| }i| dk(rt%|d | |_n tj(|d |d |_tj*|_tj |d |d dd|_tj|d |dd|_d|_y)Nr9rrrrrr!rrrrrrr) rYrWrXrrrZr\r]rer[z?Invalid `UP_block_type` encountered. Must be `AllegroUpBlock3D`rF)r"r#r%rrr)rrrr up_blocksrlistreversedrrrrrqr rr'r(rrrr)r/rWrXrrrrrrrrereversed_block_out_channelsrrx up_block_typeprev_output_channelrup_blockr3s r4r#zAllegroDecoder3D.__init__s " yy  r "   II&8&<>PQS>TV_irsr*'0I'= 4 /*2. !1:g1EI91"5)'  '+84F+G&H#4Q7 ). 9 1 A}"0 8;N#&8"9A"==N 22+/!3 3!/)7%7&>q&A#"("1"/,5 !!bcc NN ! !( +"0 / 14  !!,-?-BM!RD !#;Ma;P]lrv!wD   YY'9!'<>PQR>SU^hqr "4Q"7qRST &+#r5rrcv|jd}|jdddddjdd}|j|}|j d|dfjddddd}|}|j |}||z}t t|jjj}tjrL|jr@|j|j|}|jD]}|j||}n;|j|}|j!|}|jD] }||} |jdddddjdd}|j#|}|j%|}|j d|dfjddddd}|}|j'|}||z}|jdddddjdd}|j)|}|j d|dfjddddd}|Sr)rErCrFrrBrnextiterr parametersdtyper:rrrrtorrrr)r/rr=r upscale_dtypers r4rHzAllegroDecoder3D.forwardus'\\!_ 1aA.66q!<f%!!!j"%56>>q!Q1M""6*("T$..";";"=>?EE  "t'B'B66t~~vNF!NN M::8VL M ^^F+FYY}-F!NN *!&) *1aA.66q!<##F+v&!!!j"%56>>q!Q1M##F+("1aA.66q!<v&!!!j"%56>>q!Q1M r5rrTs@r4rrs+ 6P.B !! U,U,U,c3h U,#(c "2U,"#s(OU,U,U,U,U,n,ell,u||,r5rc"eZdZdZdZe d'dededeedfdeedfdeedfd ee dfd ee dfd ed ed edede dede de ddf fd Z d(dZ d(dZ d(dZd(dZdej"dej"fdZe d)dej"de deeeeffdZdej"dej"fdZed)dej"de deeej"ffd Zdej"dej"fd!Zdej"dej"fd"Z d*d#ej"d$e de d%eej<deeej"ff d&ZxZ S)+AutoencoderKLAllegroa! A VAE model with KL loss for encoding videos into latents and decoding latent representations into videos. Used in [Allegro](https://github.com/rhymes-ai/Allegro). This model inherits from [`ModelMixin`]. Check the superclass documentation for it's generic methods implemented for all models (such as downloading or saving). Parameters: in_channels (int, defaults to `3`): Number of channels in the input image. out_channels (int, defaults to `3`): Number of channels in the output. down_block_types (`Tuple[str, ...]`, defaults to `("AllegroDownBlock3D", "AllegroDownBlock3D", "AllegroDownBlock3D", "AllegroDownBlock3D")`): Tuple of strings denoting which types of down blocks to use. up_block_types (`Tuple[str, ...]`, defaults to `("AllegroUpBlock3D", "AllegroUpBlock3D", "AllegroUpBlock3D", "AllegroUpBlock3D")`): Tuple of strings denoting which types of up blocks to use. block_out_channels (`Tuple[int, ...]`, defaults to `(128, 256, 512, 512)`): Tuple of integers denoting number of output channels in each block. temporal_downsample_blocks (`Tuple[bool, ...]`, defaults to `(True, True, False, False)`): Tuple of booleans denoting which blocks to enable temporal downsampling in. latent_channels (`int`, defaults to `4`): Number of channels in latents. layers_per_block (`int`, defaults to `2`): Number of resnet or attention or temporal convolution layers per down/up block. act_fn (`str`, defaults to `"silu"`): The activation function to use. norm_num_groups (`int`, defaults to `32`): Number of groups to use in normalization layers. temporal_compression_ratio (`int`, defaults to `4`): Ratio by which temporal dimension of samples are compressed. sample_size (`int`, defaults to `320`): Default latent size. scaling_factor (`float`, defaults to `0.13235`): The component-wise standard deviation of the trained latent space computed using the first batch of the training set. This is used to scale the latent space to have unit variance when training the diffusion model. The latents are scaled with the formula `z = z * scaling_factor` before being passed to the diffusion model. When decoding, the latents are scaled back to the original scale with the formula: `z = 1 / scaling_factor * z`. For more details, refer to sections 4.3.2 and D.1 of the [High-Resolution Image Synthesis with Latent Diffusion Models](https://huggingface.co/papers/2112.10752) paper. force_upcast (`bool`, default to `True`): If enabled it will force the VAE to run in float32 for high image resolution pipelines, such as SD-XL. VAE can be fine-tuned / trained to a lower range without losing too much precision in which case `force_upcast` can be set to `False` - see: https://huggingface.co/madebyollin/sdxl-vae-fp16-fix TrWrXr.rrrrlatent_channelsrrrtemporal_compression_ratio sample_sizescaling_factor force_upcastrNc t|t|||||| | | d |_t |||||| | | |_t jd|zd|zd|_t j||d|_ d|_ d|_ dt|dz z|_ d|_d|_d |_d }|| | f|_||jz | |jz | |j z f|_y) NT) rWrXrrrrrrr)rWrXrrrrrrr rFxP)r"r#rencoderrdecoderr%r quant_convpost_quant_conv use_slicing use_tilingrspatial_compression_ratiotile_overlap_ttile_overlap_htile_overlap_wkernelr)r/rWrXrrrrrrrrrrrrr sample_framesr3s r4r#zAutoencoderKLAllegro.__init__s: '#(-'A1-+   ('%)%=1-+   ))A$7_9LaP!yy/1M !)*s3E/F/J)K&!  $k;? D// / $-- - $-- -  r5cd|_y)a Enable tiled VAE decoding. When this option is enabled, the VAE will split the input tensor into tiles to compute decoding and encoding in several steps. This is useful for saving a large amount of memory and to allow processing larger images. TNrr/s r4 enable_tilingz"AutoencoderKLAllegro.enable_tilings r5cd|_y)z Disable tiled VAE decoding. If `enable_tiling` was previously enabled, this method will go back to computing decoding in one step. FNrrs r4disable_tilingz#AutoencoderKLAllegro.disable_tiling&s  r5cd|_y)z Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to compute decoding in several steps. This is useful to save some memory and allow larger batch sizes. TNrrs r4enable_slicingz#AutoencoderKLAllegro.enable_slicing-s  r5cd|_y)z Disable sliced VAE decoding. If `enable_slicing` was previously enabled, this method will go back to computing decoding in one step. FNr rs r4disable_slicingz$AutoencoderKLAllegro.disable_slicing4s !r5xcR|jr|j|Std)Nz5Encoding without tiling has not been implemented yet.)r tiled_encodeNotImplementedError)r/rs r4_encodezAutoencoderKLAllegro._encode;( ??$$Q' '!"YZZr5 return_dictc(|jrU|jddkDrC|jdDcgc]}|j|}}t j |}n|j|}t |}|s|fSt|Scc}w)a Encode a batch of videos into latents. Args: x (`torch.Tensor`): Input batch of videos. return_dict (`bool`, defaults to `True`): Whether to return a [`~models.autoencoder_kl.AutoencoderKLOutput`] instead of a plain tuple. Returns: The latent representations of the encoded videos. If `return_dict` is True, a [`~models.autoencoder_kl.AutoencoderKLOutput`] is returned, otherwise a plain `tuple` is returned. rr) latent_dist)rrEsplitrr:r;rr)r/rrx_sliceencoded_slicesh posteriors r4encodezAutoencoderKLAllegro.encodeCs"    QCD771:Ndll73NNN .)A QA03 < "y99OsBzcR|jr|j|Std)Nz5Decoding without tiling has not been implemented yet.)r tiled_decoder)r/rs r4_decodezAutoencoderKLAllegro._decode`rr5c|jrU|jddkDrC|jdDcgc]}|j|}}t j |}n|j|}|s|fSt |Scc}w)a Decode a batch of videos. Args: z (`torch.Tensor`): Input batch of latent vectors. return_dict (`bool`, defaults to `True`): Whether to return a [`~models.vae.DecoderOutput`] instead of a plain tuple. Returns: [`~models.vae.DecoderOutput`] or `tuple`: If return_dict is True, a [`~models.vae.DecoderOutput`] is returned, otherwise a plain `tuple` is returned. rrr)rrErr!r:r;r )r/rrz_slicedecoded_slicesdecodeds r4decodezAutoencoderKLAllegro.decodehsv    QCD771:Ndll73NNNii/Gll1oG: G,,OsBc  d}|j}|jj}|j\}}}}} t j ||j dz |jdz dz} t j ||j dz |jdz dz} t j | |j dz |jdz dz} d} |j| | z| zd|jjz|j d|z|j d|z|j d|zf}|j|||j d|j d|j df}t| D]}t| D]u}t| D]c}||jdz||jdz|j dz}}||jdz||jdz|j dz}}||jdz||jdz|j dz}}|dddd||||||f}||| |z<| |z|dz k(s| | | z| zdz k(r|j|}| | | z| zdz k(r | |z|dz k7r|d| |zdz|| | |zz dn||| |z dz| dz|j|||j d|j d|j df}| dz } fx|j|d|jjz||z||z| |zf}|j d|z|j d|z|j d|zf}|jd|z|jd|z|jd|zf}|d|dz |d|dz |d|dz f}t| D]}||dz||dz|dz}}t| D]}||dz||dz|dz}}t| D]u}||dz||dz|dz}}t|| |df|| |df|| |df||| z| z|| zz|zjd}|dddd||||||fxx|z cc<w|jdddddjdd}|j!|}|j#d|dfjddddd}|SNrrr rr?r9)rconfigrrEmathfloorrr new_zerosrrpr_prepare_for_blend unsqueezerCrFrrB)r/rlocal_batch_sizersrtr=r num_framesheightwidthoutput_num_frames output_height output_widthcount output_latentvae_batch_inputrxjkn_startn_endh_starth_endw_startw_end video_cubelatent output_kernel output_strideoutput_overlap latent_means r4rz!AutoencoderKLAllegro.tiled_encodes  + + [[ 3 3>?gg; L*fe JJ T[[^(Ct{{ST~'UVYZZ FT[[^$;t{{1~#MNQRR zz54;;q>#9T[[^"KLqP  !M1L@DKK/// A"$ A"$ A"$   ++'7t{{ST~W[WbWbcdWegkgrgrstgu&vw() A=) |,A%&Q%7T[[^9KdkkZ[n9\UG%&Q%7T[[^9KdkkZ[n9\UG%&Q%7T[[^9KdkkZ[n9\UG!"1a wu}#T!UJ@JOE,<$<= 004Dq4HH $5 $E $TWX$XX!%o!>"%6%F%UXY%YY %(8 8!+<=L.*;<%a-&7,&F\IY&Y\]&]^hhijk #K 1a wu}LMQ\\M] ] ]1aA.66q!<(!!!j"%56>>q!Q1M r5c  d}|j}|jj}|jd|z|jd|z|jd|zf}|jd|z|jd|z|jd|zf}|j \}}} } } |j dddddjdd}|j|}|jd|dfj ddddd}tj| |dz |dz dz} tj| |dz |dz dz} tj| |dz |dz dz}d}|j| | z|z|jj|jd|jd|jdf}|j|||d|d|df}t| D] }t| D]}t|D]}||dz||dz|dz}}||dz||dz|dz}}||dz||dz|dz}}|dddd||||||f}||||z<||z|dz k(s|| | z|zdz k(rl|j|}|| | z|zdz k(r ||z|dz k7r|d||zdz||||zz dn||||z dz|dz|j|||d|d|df}|dz }|j||jj| |z| |z| |zf}|jd|jdz |jd|jdz |jd|jdz f}t| D]}||jdz||jdz|jdz}}t| D]}||jdz||jdz|jdz}}t|D]}||jdz||jdz|jdz}}t!|| |df|| |df|||df||| z|z||zz|zj#d}|dddd||||||fxx|z cc<|j dddddj%}|Sr))rr*rrrrErCrFrrBr+r,r-rXrprr.r/ contiguous) r/rr0r1r2 latent_kernel latent_strider=rr3r4r5r6r7r8r9decoded_videosr;rxr<r=r>r?r@rArBrCcurrent_latent current_videovideo video_overlapout_video_blends r4r z!AutoencoderKLAllegro.tiled_decodes  + + [[ 3 3 A",dkk!n.BDKKPQNVXDXX  A",dkk!n.BDKKPQNVXDXX >?gg; L*fe IIaAq! $ , ,Q 2   # KKJ+ , 4 4Q1a C JJ ]15E(EWXIY'YZ]^^ F]1-=$=qAQ#QRUVV zz5=+;#;}Q?O"OPSTT !M1L@ (( A A A   ++ |]1-=}Q?OQ^_`Qa b () A=) |,A%&q)9%91}Q?O;OR_`aRb;bUG%&q)9%91}Q?O;OR_`aRb;bUG%&q)9%91}Q?O;OR_`aRb;bUG%&q!WU]GEM7SX='X%YN@NOE,<$<= 004Dq4HH $5 $E $TWX$XX(, _(E "%6%F%UXY%YY %(8 8%*:":Q">RN55;K3K+K+MNXeN53C+Ca+G%RS)T*+++-|]1=M}]^O_anopaqr+QJE9  @ Z)A)A:PR?TZ]_T_afikaklm KKNT[[^ + KKNT[[^ + KKNT[[^ + () `AQ/T[[^1CdkkRSn1TUG=) `!"T[[^!3QQ5G$++VW.5X|,`A%&Q%7T[[^9KdkkZ[n9\UG&8-}Q/?@M=+;<L-*:;&q='8<'G!lJZ'Z]^'^_iijkl 'O !Q wu}gemKLP__L` ` ` aAq!,779 r5rsample_posterior generatorc|}|j|j}|r|j|}n|j}|j |j}|s|fSt |S)a Args: sample (`torch.Tensor`): Input sample. sample_posterior (`bool`, *optional*, defaults to `False`): Whether to sample from the posterior. return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`DecoderOutput`] instead of a plain tuple. generator (`torch.Generator`, *optional*): PyTorch random number generator. )rUr#)rrrmoder'r ) r/rrTrrUrrrdecs r4rHzAutoencoderKLAllegro.forward.sf" KKN..   9 5A Akk!n##6MC((r5)rrrrrrrr?r rrJr?i@gp= ף?T)rN)T)FTN)!rKrLrMrN _supports_gradient_checkpointingrr$rrrPrOr#rr r rr:rRrr rrrrr!r r'rr r GeneratorrHrSrTs@r4rrs+Z(,$- + /C7Q5O !!,- $!5G G G  S/ G c3hG "#s(O!G "%*$)$4#G $#(c "2%G &'G ()G *+G ,-G .%*/G 01G 23G 45G 6 7G G R  ![[%,,[37::,0: "E*F$GG H::8[[%,,[- -4-5X]XdXdIdCe--2QellQu||QfWellWu||Wx"' /3 ) )) ) EOO, ) }ell* + )r5rc|\}}}|\}}} |\} } } |dkDr|dkDrx|ddddd|ddddftjd|jj|j|z j |ddz|ddddd|ddddf<||dz kr}|dddd| dddddfdtjd|jj|j|z z j |ddz|dddd| dddddf<|dkDrw|ddddddd| ddftjd| jj|j| z j | dz|ddddddd| ddf<||dz kr||dddddd| dddfdtjd| jj|j| z z j | dz|dddddd| dddf<| dkDrg|ddddddddd| ftjd| jj|j| z z|ddddddddd| f<| | dz krl|dddddddd| dfdtjd| jj|j| z z z|dddddddd| df<|S)Nrr)r:arangerOrdevicereshape) n_paramh_paramw_paramrnn_max overlap_nrh_max overlap_hww_max overlap_ws r4r.r.Ms!Aui!Aui!Aui1} q5)*1a9a+B)C Q *00255ahh?)KgiA&*'AaAiKA% & uqy=)*1a)a+B)CELLI.44699!((CiOOgiA&*'AaYJKA% & 1u%&q!Q) Q'>%? 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