
    +h                        S SK r S SKrS SKJrJrJrJrJrJrJ	r	  S SK
r
S SKJr  S SKJrJr  SSKJrJr  SSKJr  SSKJrJr  SS	KJr  SS
KJr  SSKJrJr  SSKJ r J!r!J"r"  SSK#J$r$  SSK%J&r&  SSK'J(r(  \ " 5       (       a  S SK)J*s  J+r,  Sr-OSr-\!R\                  " \/5      r0Sr1S r2    S S\\3   S\\	\4\
Rj                  4      S\\\3      S\\\6      4S jjr7 S!S\
Rp                  S\\
Rr                     S\44S jjr: " S S\\5      r;g)"    N)AnyCallableDictListOptionalTupleUnion)Image)T5EncoderModelT5Tokenizer   )MultiPipelineCallbacksPipelineCallback)CogVideoXLoraLoaderMixin)AutoencoderKLCogVideoXCogVideoXTransformer3DModel)get_3d_rotary_pos_embed)DiffusionPipeline)CogVideoXDDIMSchedulerCogVideoXDPMScheduler)is_torch_xla_availableloggingreplace_example_docstring)randn_tensor)VideoProcessor   )CogVideoXPipelineOutputTFaX  
    Examples:
        ```python
        >>> import torch
        >>> from diffusers import CogVideoXDPMScheduler, CogVideoXVideoToVideoPipeline
        >>> from diffusers.utils import export_to_video, load_video

        >>> # Models: "THUDM/CogVideoX-2b" or "THUDM/CogVideoX-5b"
        >>> pipe = CogVideoXVideoToVideoPipeline.from_pretrained("THUDM/CogVideoX-5b", torch_dtype=torch.bfloat16)
        >>> pipe.to("cuda")
        >>> pipe.scheduler = CogVideoXDPMScheduler.from_config(pipe.scheduler.config)

        >>> input_video = load_video(
        ...     "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/hiker.mp4"
        ... )
        >>> prompt = (
        ...     "An astronaut stands triumphantly at the peak of a towering mountain. Panorama of rugged peaks and "
        ...     "valleys. Very futuristic vibe and animated aesthetic. Highlights of purple and golden colors in "
        ...     "the scene. The sky is looks like an animated/cartoonish dream of galaxies, nebulae, stars, planets, "
        ...     "moons, but the remainder of the scene is mostly realistic."
        ... )

        >>> video = pipe(
        ...     video=input_video, prompt=prompt, strength=0.8, guidance_scale=6, num_inference_steps=50
        ... ).frames[0]
        >>> export_to_video(video, "output.mp4", fps=8)
        ```
c                    UnUnU u  pVXV-  nXtU-  :  a  Un[        [        XE-  U-  5      5      n	OUn	[        [        X6-  U-  5      5      n[        [        XH-
  S-  5      5      n
[        [        X9-
  S-  5      5      nX4X-   X-   44$ )Ng       @)intround)src	tgt_width
tgt_heighttwthhwrresize_heightresize_widthcrop_top	crop_lefts               u/home/james-whalen/.local/lib/python3.13/site-packages/diffusers/pipelines/cogvideo/pipeline_cogvideox_video2video.pyget_resize_crop_region_for_gridr.   M   s    	B	BDA	AG}5!,-E"&1*-.5",345HE2,345I 8#;Y=U"VVV    num_inference_stepsdevice	timestepssigmasc                    Ub  Ub  [        S5      eUb  S[        [        R                  " U R                  5      R
                  R                  5       5      ;   nU(       d  [        SU R                   S35      eU R                  " S
X2S.UD6  U R                  n[        U5      nX14$ Ub  S[        [        R                  " U R                  5      R
                  R                  5       5      ;   nU(       d  [        SU R                   S35      eU R                  " S
XBS.UD6  U R                  n[        U5      nX14$ U R                  " U4S	U0UD6  U R                  nX14$ )a  
Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles
custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`.

Args:
    scheduler (`SchedulerMixin`):
        The scheduler to get timesteps from.
    num_inference_steps (`int`):
        The number of diffusion steps used when generating samples with a pre-trained model. If used, `timesteps`
        must be `None`.
    device (`str` or `torch.device`, *optional*):
        The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.
    timesteps (`List[int]`, *optional*):
        Custom timesteps used to override the timestep spacing strategy of the scheduler. If `timesteps` is passed,
        `num_inference_steps` and `sigmas` must be `None`.
    sigmas (`List[float]`, *optional*):
        Custom sigmas used to override the timestep spacing strategy of the scheduler. If `sigmas` is passed,
        `num_inference_steps` and `timesteps` must be `None`.

Returns:
    `Tuple[torch.Tensor, int]`: A tuple where the first element is the timestep schedule from the scheduler and the
    second element is the number of inference steps.
zYOnly one of `timesteps` or `sigmas` can be passed. Please choose one to set custom valuesr2   zThe current scheduler class zx's `set_timesteps` does not support custom timestep schedules. Please check whether you are using the correct scheduler.)r2   r1   r3   zv's `set_timesteps` does not support custom sigmas schedules. Please check whether you are using the correct scheduler.)r3   r1   r1    )

ValueErrorsetinspect	signatureset_timesteps
parameterskeys	__class__r2   len)	schedulerr0   r1   r2   r3   kwargsaccepts_timestepsaccept_sigmass           r-   retrieve_timestepsrC   `   s}   > !3tuu'3w/@/@AXAX/Y/d/d/i/i/k+ll .y/B/B.C Da b  	M)MfM''	!)n )) 
	 C(9(9):Q:Q(R(](](b(b(d$ee.y/B/B.C D_ `  	GvGG''	!)n )) 	 3MFMfM''	))r/   encoder_output	generatorsample_modec                    [        U S5      (       a!  US:X  a  U R                  R                  U5      $ [        U S5      (       a   US:X  a  U R                  R                  5       $ [        U S5      (       a  U R                  $ [        S5      e)Nlatent_distsampleargmaxlatentsz3Could not access latents of provided encoder_output)hasattrrH   rI   moderK   AttributeError)rD   rE   rF   s      r-   retrieve_latentsrO      s}     ~}--+2I))00;;		/	/K84K))..00		+	+%%%RSSr/   c            3         ^  \ rS rSrSr/ rSr/ SQrS\S\	S\
S\S	\\\4   4
U 4S
 jjr     SDS\\\\   4   S\S\S\\R*                     S\\R,                     4
S jjr        SES\\\\   4   S\\\\\   4      S\S\S\\R2                     S\\R2                     S\S\\R*                     S\\R,                     4S jjr          SFS\\R2                     S\S\S\S\S\\R,                     S\\R*                     S\\R6                     S \\R2                     S!\\R2                     4S" jjrS \R2                  S#\R2                  4S$ jrS% rS& r    SGS' jr SHS( jr!SHS) jr"S\S\S*\S\R*                  S#\#\R2                  \R2                  4   4
S+ jr$\%S, 5       r&\%S- 5       r'\%S. 5       r(\%S/ 5       r)\%S0 5       r*\RV                  " 5       \," \-5      SSSSSS1SS2S3S4SS5SSSSS6SSSS /S4S\\.R\                     S\\\\\   4      S\\\\\   4      S\\   S\\   S7\S8\\\      S9\/S:\/S;\S\S<\/S\\\R6                  \\R6                     4      S \\R`                     S\\R`                     S\\R`                     S=\S>\S?\\1\\24      S@\\\3\\\1/S4   \4\54      SA\\   S\S#\\6\#4   4.SB jj5       5       r7SCr8U =r9$ )ICogVideoXVideoToVideoPipeline   a  
Pipeline for video-to-video generation using CogVideoX.

This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)

Args:
    vae ([`AutoencoderKL`]):
        Variational Auto-Encoder (VAE) Model to encode and decode videos to and from latent representations.
    text_encoder ([`T5EncoderModel`]):
        Frozen text-encoder. CogVideoX uses
        [T5](https://huggingface.co/docs/transformers/model_doc/t5#transformers.T5EncoderModel); specifically the
        [t5-v1_1-xxl](https://huggingface.co/PixArt-alpha/PixArt-alpha/tree/main/t5-v1_1-xxl) variant.
    tokenizer (`T5Tokenizer`):
        Tokenizer of class
        [T5Tokenizer](https://huggingface.co/docs/transformers/model_doc/t5#transformers.T5Tokenizer).
    transformer ([`CogVideoXTransformer3DModel`]):
        A text conditioned `CogVideoXTransformer3DModel` to denoise the encoded video latents.
    scheduler ([`SchedulerMixin`]):
        A scheduler to be used in combination with `transformer` to denoise the encoded video latents.
ztext_encoder->transformer->vae)rK   prompt_embedsnegative_prompt_embeds	tokenizertext_encodervaetransformerr?   c                   > [         TU ]  5         U R                  XX4US9  [        U SS 5      (       a/  S[	        U R
                  R                  R                  5      S-
  -  OSU l        [        U SS 5      (       a   U R
                  R                  R                  OSU l
        [        U SS 5      (       a   U R
                  R                  R                  OSU l        [        U R                  S9U l        g )	N)rU   rV   rW   rX   r?   rW      r         gffffff?)vae_scale_factor)super__init__register_modulesgetattrr>   rW   configblock_out_channelsvae_scale_factor_spatialtemporal_compression_ratiovae_scale_factor_temporalscaling_factorvae_scaling_factor_imager   video_processor)selfrU   rV   rW   rX   r?   r=   s         r-   r_   &CogVideoXVideoToVideoPipeline.__init__   s     	hq 	 	

 CJ$PUW[B\B\A#dhhoo889A=>bc 	% ;B$t:T:TDHHOO66Z[ 	& KRRVX]_cJdJd(F(Fjm%-t?\?\]r/   Nr      promptnum_videos_per_promptmax_sequence_lengthr1   dtypec           	         U=(       d    U R                   nU=(       d    U R                  R                  n[        U[        5      (       a  U/OUn[        U5      nU R                  USUSSSS9nUR                  nU R                  USSS9R                  n	U	R                  S   UR                  S   :  a]  [        R                  " X5      (       dB  U R                  R                  U	S S 2US-
  S24   5      n
[        R                  S	U S
U
 35        U R                  UR                  U5      5      S   nUR                  XTS9nUR                  u  pnUR                  SUS5      nUR!                  Xb-  US5      nU$ )N
max_lengthTpt)paddingrr   
truncationadd_special_tokensreturn_tensorslongest)rt   rw   r   zXThe following part of your input was truncated because `max_sequence_length` is set to  z	 tokens: r   )rp   r1   )_execution_devicerV   rp   
isinstancestrr>   rU   	input_idsshapetorchequalbatch_decodeloggerwarningtorepeatview)rj   rm   rn   ro   r1   rp   
batch_sizetext_inputstext_input_idsuntruncated_idsremoved_textrS   _seq_lens                 r-   _get_t5_prompt_embeds3CogVideoXVideoToVideoPipeline._get_t5_prompt_embeds   s    14110**00'44&&[
nn *# % 
 %....SW.Xbb  $(<(<R(@@UcIuIu>>66qJ]`aJadfJfGf7ghLNN'(	,A
 )).*;*;F*CDQG%((u(D &++A%,,Q0EqI%**:+MwXZ[r/   Tnegative_promptdo_classifier_free_guidancerS   rT   c
                 2   U=(       d    U R                   n[        U[        5      (       a  U/OUnUb  [        U5      n
OUR                  S   n
Uc  U R                  UUUUU	S9nU(       a  Uc  U=(       d    Sn[        U[        5      (       a  X/-  OUnUb;  [        U5      [        U5      La$  [        S[        U5       S[        U5       S35      eU
[        U5      :w  a!  [        SU S[        U5       S	U SU
 S
3	5      eU R                  UUUUU	S9nXV4$ )ab  
Encodes the prompt into text encoder hidden states.

Args:
    prompt (`str` or `List[str]`, *optional*):
        prompt to be encoded
    negative_prompt (`str` or `List[str]`, *optional*):
        The prompt or prompts not to guide the image generation. If not defined, one has to pass
        `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
        less than `1`).
    do_classifier_free_guidance (`bool`, *optional*, defaults to `True`):
        Whether to use classifier free guidance or not.
    num_videos_per_prompt (`int`, *optional*, defaults to 1):
        Number of videos that should be generated per prompt. torch device to place the resulting embeddings on
    prompt_embeds (`torch.Tensor`, *optional*):
        Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
        provided, text embeddings will be generated from `prompt` input argument.
    negative_prompt_embeds (`torch.Tensor`, *optional*):
        Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
        weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
        argument.
    device: (`torch.device`, *optional*):
        torch device
    dtype: (`torch.dtype`, *optional*):
        torch dtype
r   )rm   rn   ro   r1   rp    z?`negative_prompt` should be the same type to `prompt`, but got z != .z`negative_prompt`: z has batch size z, but `prompt`: zT. Please make sure that passed `negative_prompt` matches the batch size of `prompt`.)	rz   r{   r|   r>   r~   r   type	TypeErrorr6   )rj   rm   r   r   rn   rS   rT   ro   r1   rp   r   s              r-   encode_prompt+CogVideoXVideoToVideoPipeline.encode_prompt  sk   L 1411'44&&VJ&,,Q/J  66&;$7 7 M '+A+I-3O@J?\_@`@`j+<<fuO!d6l$:O&OUVZ[jVkUl mV~Q(  s?33 )/)::J3K_J` ax/
| <33  &*%?%?&&;$7 &@ &" 44r/   videor   num_channels_latentsheightwidthrE   rK   timestepc           
         [        U[        5      (       a*  [        U5      U:w  a  [        S[        U5       SU S35      eU	c$  UR	                  S5      S-
  U R
                  -  S-   OU	R	                  S5      nUUUX@R                  -  XPR                  -  4nU	Gc  [        U[        5      (       aR  [        U5       Vs/ s H;  n[        U R                  R                  X   R                  S5      5      X   5      PM=     nnODU Vs/ s H7  n[        U R                  R                  UR                  S5      5      U5      PM9     nn[        R                  " USS9R                  U5      R                  SSSSS	5      nU R                   U-  n[#        XXvS
9nU R$                  R'                  UUU
5      n	OU	R                  U5      n	XR$                  R(                  -  n	U	$ s  snf s  snf )Nz/You have passed a list of generators of length z+, but requested an effective batch size of z@. Make sure the batch size matches the length of the generators.rZ   r   r   dimr   r\   )rE   r1   rp   )r{   listr>   r6   sizerf   rd   rangerO   rW   encode	unsqueezer   catr   permuterh   r   r?   	add_noiseinit_noise_sigma)rj   r   r   r   r   r   rp   r1   rE   rK   r   
num_framesr~   iinit_latentsvidnoises                    r-   prepare_latents-CogVideoXVideoToVideoPipeline.prepare_latents^  s    i&&3y>Z+GA#i.AQ R&<'gi 
 SZRaejjma'D,J,JJQNgngsgstugv
  333222
 ?)T**dijtdu du_`$TXX__UX5G5G5J%KY\Zdu    kppjocf 0qAQ1RT] ^jop 99\q9<<UCKKAqRSUVXYZL88<GL FXEnn..|UHMGjj(G NN;;;!   qs   =AG>G$returnc                     UR                  SSSSS5      nSU R                  -  U-  nU R                  R                  U5      R                  nU$ )Nr   rZ   r   r   r\   )r   rh   rW   decoderI   )rj   rK   framess      r-   decode_latents,CogVideoXVideoToVideoPipeline.decode_latents  sJ    //!Q1a0d333g=)00r/   c                     [        [        X-  5      U5      n[        X-
  S5      nX&U R                  R                  -  S  nX!U-
  4$ )Nr   )minr   maxr?   order)rj   r0   r2   strengthr1   init_timestept_starts          r-   get_timesteps+CogVideoXVideoToVideoPipeline.get_timesteps  sP    C 3 >?ATU)91=(<(<<>?	777r/   c                 n   S[        [        R                  " U R                  R                  5      R
                  R                  5       5      ;   n0 nU(       a  X$S'   S[        [        R                  " U R                  R                  5      R
                  R                  5       5      ;   nU(       a  XS'   U$ )NetarE   )r7   r8   r9   r?   stepr;   r<   )rj   rE   r   accepts_etaextra_step_kwargsaccepts_generators         r-   prepare_extra_step_kwargs7CogVideoXVideoToVideoPipeline.prepare_extra_step_kwargs  s     s7#4#4T^^5H5H#I#T#T#Y#Y#[\\'*e$ (3w/@/@ATAT/U/`/`/e/e/g+hh-6k*  r/   c           
      *  ^  US-  S:w  d	  US-  S:w  a  [        SU SU S35      eUS:  d  US:  a  [        SU 35      eUbW  [        U 4S jU 5       5      (       d=  [        S	T R                   S
U Vs/ s H  oT R                  ;  d  M  UPM     sn 35      eUb  U	b  [        SU SU	 S35      eUc  U	c  [        S5      eUbA  [        U[        5      (       d,  [        U[
        5      (       d  [        S[        U5       35      eUb  U
b  [        SU SU
 S35      eUb  U
b  [        SU SU
 S35      eU	bC  U
b@  U	R                  U
R                  :w  a&  [        SU	R                   SU
R                   S35      eUb  Ub  [        S5      eg g s  snf )Nr[   r   z7`height` and `width` have to be divisible by 8 but are z and r   r   z2The value of strength should in [0.0, 1.0] but is c              3   @   >#    U  H  oTR                   ;   v   M     g 7fN)_callback_tensor_inputs).0krj   s     r-   	<genexpr>=CogVideoXVideoToVideoPipeline.check_inputs.<locals>.<genexpr>  s      F
7Y!---7Ys   z2`callback_on_step_end_tensor_inputs` has to be in z, but found zCannot forward both `prompt`: z and `prompt_embeds`: z2. Please make sure to only forward one of the two.zeProvide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined.z2`prompt` has to be of type `str` or `list` but is z and `negative_prompt_embeds`: z'Cannot forward both `negative_prompt`: zu`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but got: `prompt_embeds` z != `negative_prompt_embeds` z3Only one of `video` or `latents` should be provided)r6   allr   r{   r|   r   r   r~   )rj   rm   r   r   r   r   "callback_on_step_end_tensor_inputsr   rK   rS   rT   r   s   `           r-   check_inputs*CogVideoXVideoToVideoPipeline.check_inputs  sn    A:?eai1nVW]V^^cdicjjklmma<8a<QRZQ[\]]-9# F
7YF
 C
 C
 DTEaEaDbbn  |^  pH  |^vw  ko  kG  kG  bGpq  |^  pH  oI  J  -";08N}o ^0 0  ^ 5w  FC)@)@TZ\`IaIaQRVW]R^Q_`aa"8"D0 9*++]_ 
 &+A+M9/9J K*++]_ 
 $)?)K""&<&B&BB --:-@-@,A B.445Q8  !4RSS "5E pHs   4FFc                 F    SU l         U R                  R                  5         g)zEnables fused QKV projections.TN)fusing_transformerrX   fuse_qkv_projectionsrj   s    r-   r   2CogVideoXVideoToVideoPipeline.fuse_qkv_projections  s    "&--/r/   c                     U R                   (       d  [        R                  S5        gU R                  R	                  5         SU l         g)z)Disable QKV projection fusion if enabled.zKThe Transformer was not initially fused for QKV projections. Doing nothing.FN)r   r   r   rX   unfuse_qkv_projectionsr   s    r-   r   4CogVideoXVideoToVideoPipeline.unfuse_qkv_projections  s2    &&NNhi335&+D#r/   r   c           
         XR                   U R                  R                  R                  -  -  nX R                   U R                  R                  R                  -  -  nU R                  R                  R                  nU R                  R                  R                  nU R                  R                  R
                  U-  n	U R                  R                  R                  U-  n
Uc>  [        XV4X5      n[        U R                  R                  R                  UXV4UUS9u  pX4$ X8-   S-
  U-  n[        U R                  R                  R                  S XV4USX4US9u  pX4$ )N)	embed_dimcrops_coords	grid_sizetemporal_sizer1   r   slice)r   r   r   r   	grid_typemax_sizer1   )
rd   rX   rb   
patch_sizepatch_size_tsample_widthsample_heightr.   r   attention_head_dim)rj   r   r   r   r1   grid_height
grid_widthpp_tbase_size_widthbase_size_heightgrid_crops_coords	freqs_cos	freqs_sinbase_num_framess                  r-   %_prepare_rotary_positional_embeddingsCCogVideoXVideoToVideoPipeline._prepare_rotary_positional_embeddings  sd    !>!>AQAQAXAXAcAc!cd<<t?O?O?V?V?a?aab
##..%%22**11>>!C++22@@AE; ?)?! $;**11DD.&3($ I* ##  */!3;O#:**11DD!&3-!*<$ I ##r/   c                     U R                   $ r   )_guidance_scaler   s    r-   guidance_scale,CogVideoXVideoToVideoPipeline.guidance_scale+  s    ###r/   c                     U R                   $ r   )_num_timestepsr   s    r-   num_timesteps+CogVideoXVideoToVideoPipeline.num_timesteps/  s    """r/   c                     U R                   $ r   )_attention_kwargsr   s    r-   attention_kwargs.CogVideoXVideoToVideoPipeline.attention_kwargs3      %%%r/   c                     U R                   $ r   )_current_timestepr   s    r-   current_timestep.CogVideoXVideoToVideoPipeline.current_timestep7  r  r/   c                     U R                   $ r   )
_interruptr   s    r-   	interrupt'CogVideoXVideoToVideoPipeline.interrupt;  s    r/   2   g?   Fg        pilr0   r2   r   r   use_dynamic_cfgr   output_typereturn_dictr   callback_on_step_endr   c                    [        U[        [        45      (       a  UR                  nU=(       d-    U R                  R
                  R                  U R                  -  nU=(       d-    U R                  R
                  R                  U R                  -  nUc  [        U5      OUR                  S5      nSnU R                  UUUUUUUUUUS9
  Xl        UU l        SU l        SU l        Ub  [        U[         5      (       a  SnO3Ub!  [        U["        5      (       a  [        U5      nOUR$                  S   nU R&                  nU	S:  nU R)                  UUUUUUUUS9u  nnU(       a  [*        R,                  " UU/SS9n[/        U R0                  UUU5      u  pvU R3                  XgUU5      u  pvUSS R5                  UU-  5      n[        U5      U l        US-
  U R8                  -  S-   nU R                  R
                  R:                  nUb  UU-  S:w  a  [=        S	U< S
U< S35      eUc4  U R>                  RA                  XUS9nURC                  UURD                  S9nU R                  R
                  RF                  nU RI                  UUU-  UUUURD                  UUUU5
      nU RK                  X5      nU R                  R
                  RL                  (       a"  U RO                  XEUR                  S5      U5      OSn [Q        [        U5      X`R0                  RR                  -  -
  S5      n!U RU                  US9 n"Sn#[W        U5       GH  u  n$n%U RX                  (       a  M  U%U l        U(       a  [*        R,                  " U/S-  5      OUn&U R0                  R[                  U&U%5      n&U%R]                  U&R$                  S   5      n'U R                  R_                  S5         U R	                  U&UU'U USS9S   n(SSS5        W(Ra                  5       n(U
(       aO  SU	S[b        Rd                  " [b        Rf                  UU%Ri                  5       -
  U-  S-  -  5      -
  S-  -  -   U l        U(       a)  U(Rk                  S5      u  n)n*U)U Rl                  U*U)-
  -  -   n([        U R0                  [n        5      (       d'  U R0                  Rp                  " U(U%U40 UDSS0D6S   nO6U R0                  Rp                  " U(U#U%U$S:  a  UU$S-
     OSU40 UDSS0D6u  nn#URC                  URD                  5      nUb\  0 n+U H  n,[s        5       U,   U+U,'   M     U" U U$U%U+5      n-U-Ru                  SU5      nU-Ru                  SU5      nU-Ru                  SU5      nU$[        U5      S-
  :X  d)  U$S-   U!:  a0  U$S-   U R0                  RR                  -  S:X  a  U"Rw                  5         [x        (       d  GM  [z        R|                  " 5         GM     SSS5        SU l        US:X  d,  U R                  U5      nU R>                  R                  UUS9nOUnU R                  5         U(       d  U4$ [        US9$ ! , (       d  f       GNt= f! , (       d  f       N~= f)a  
Function invoked when calling the pipeline for generation.

Args:
    video (`List[PIL.Image.Image]`):
        The input video to condition the generation on. Must be a list of images/frames of the video.
    prompt (`str` or `List[str]`, *optional*):
        The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
        instead.
    negative_prompt (`str` or `List[str]`, *optional*):
        The prompt or prompts not to guide the image generation. If not defined, one has to pass
        `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
        less than `1`).
    height (`int`, *optional*, defaults to self.transformer.config.sample_height * self.vae_scale_factor_spatial):
        The height in pixels of the generated image. This is set to 480 by default for the best results.
    width (`int`, *optional*, defaults to self.transformer.config.sample_height * self.vae_scale_factor_spatial):
        The width in pixels of the generated image. This is set to 720 by default for the best results.
    num_inference_steps (`int`, *optional*, defaults to 50):
        The number of denoising steps. More denoising steps usually lead to a higher quality image at the
        expense of slower inference.
    timesteps (`List[int]`, *optional*):
        Custom timesteps to use for the denoising process with schedulers which support a `timesteps` argument
        in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is
        passed will be used. Must be in descending order.
    strength (`float`, *optional*, defaults to 0.8):
        Higher strength leads to more differences between original video and generated video.
    guidance_scale (`float`, *optional*, defaults to 7.0):
        Guidance scale as defined in [Classifier-Free Diffusion
        Guidance](https://huggingface.co/papers/2207.12598). `guidance_scale` is defined as `w` of equation 2.
        of [Imagen Paper](https://huggingface.co/papers/2205.11487). Guidance scale is enabled by setting
        `guidance_scale > 1`. Higher guidance scale encourages to generate images that are closely linked to
        the text `prompt`, usually at the expense of lower image quality.
    num_videos_per_prompt (`int`, *optional*, defaults to 1):
        The number of videos to generate per prompt.
    generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
        One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
        to make generation deterministic.
    latents (`torch.FloatTensor`, *optional*):
        Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
        generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
        tensor will ge generated by sampling using the supplied random `generator`.
    prompt_embeds (`torch.FloatTensor`, *optional*):
        Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
        provided, text embeddings will be generated from `prompt` input argument.
    negative_prompt_embeds (`torch.FloatTensor`, *optional*):
        Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
        weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
        argument.
    output_type (`str`, *optional*, defaults to `"pil"`):
        The output format of the generate image. Choose between
        [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
    return_dict (`bool`, *optional*, defaults to `True`):
        Whether or not to return a [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] instead
        of a plain tuple.
    attention_kwargs (`dict`, *optional*):
        A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
        `self.processor` in
        [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
    callback_on_step_end (`Callable`, *optional*):
        A function that calls at the end of each denoising steps during the inference. The function is called
        with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int,
        callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by
        `callback_on_step_end_tensor_inputs`.
    callback_on_step_end_tensor_inputs (`List`, *optional*):
        The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list
        will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the
        `._callback_tensor_inputs` attribute of your pipeline class.
    max_sequence_length (`int`, defaults to `226`):
        Maximum sequence length in encoded prompt. Must be consistent with
        `self.transformer.config.max_text_seq_length` otherwise may lead to poor results.

Examples:

Returns:
    [`~pipelines.cogvideo.pipeline_output.CogVideoXPipelineOutput`] or `tuple`:
    [`~pipelines.cogvideo.pipeline_output.CogVideoXPipelineOutput`] if `return_dict` is True, otherwise a
    `tuple`. When returning a tuple, the first element is a list with the generated images.
Nr   )
rm   r   r   r   r   r   r   rK   rS   rT   Fr   g      ?)rn   rS   rT   ro   r1   r   z?The number of latent frames must be divisible by `patch_size_t=z-` but the given video contains latent_frames=z, which is not divisible.)r   r   )r1   rp   )totalrZ   cond_uncond)hidden_statesencoder_hidden_statesr   image_rotary_embr   r  g      @r  rK   rS   rT   latent)r   r  )r   )Cr{   r   r   tensor_inputsrX   rb   r   rd   r   r>   r   r   r   r   r  r  r|   r   r~   rz   r   r   r   rC   r?   r   r   r   rf   r   r6   ri   preprocess_videor   rp   in_channelsr   r    use_rotary_positional_embeddingsr   r   r   progress_bar	enumerater  scale_model_inputexpandcache_contextfloatmathcospiitemchunkr   r   r   localspopupdateXLA_AVAILABLExm	mark_stepr   postprocess_videomaybe_free_model_hooksr   ).rj   r   rm   r   r   r   r0   r2   r   r   r  rn   r   rE   rK   rS   rT   r  r  r   r  r   ro   r   r   r1   r   latent_timesteplatent_framesr   latent_channelsr   r  num_warmup_stepsr  old_pred_original_sampler   tlatent_model_inputr   
noise_prednoise_pred_uncondnoise_pred_textcallback_kwargsr   callback_outputss.                                                 r-   __call__&CogVideoXVideoToVideoPipeline.__call__?  s   X *-=?U,VWW1E1S1S.`4++22@@4C`C``]))00==@]@]]#*?SZQ
 ! 	+/Q'#9 	 	
  .!1!% *VS"9"9JJvt$<$<VJ&,,Q/J''
 '5s&:# 150B0B'"7'#9 3 1C 	1
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 ?((99%V[9\EHHF-2E2EHFE**11==&&..
 !::9J
 &&GG 66vgllSToW]^ 	 s9~0CnnFZFZ0ZZ\]^%89\'+$!),1>>)*&A\UYYy1}%=bi"%)^^%E%EFXZ[%\" 88$6$<$<Q$?@ %%33MB!%!1!1&8.;!))9)9$) "2 " "J C (--/
 #+,~TXXdgg2E2PTg1glo0o&opptuu0 ,D( /9C9I9I!9L6%!2T5H5HO^oLo5p!pJ "$..2GHH"nn11*aqL]qkpqrstG8<8K8K"0,-E	!a%(t9 ,9 %*95G5 "**]%8%89 (3&(O?-3Xa[* @';D!Q'X$.229gFG$4$8$8-$XM-=-A-ABZ\r-s*I**A9I/IqSTuX\XfXfXlXlNlpqNq '') =LLN} - :F "&h&''0E((::T_:`EE 	##%8O&e44E CB :9s,   >B'Y<%Y*=G(Y<*Y<*
Y94Y<<
Z
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r   r  r   r  r   r   rd   rf   rh   ri   )Nr   rl   NN)NTr   NNrl   NN)
Nr      <   Z   NNNNNNNNN)r   N):__name__
__module____qualname____firstlineno____doc___optional_componentsmodel_cpu_offload_seqr   r   r   r   r   r	   r   r   r_   r|   r   r   r   r   r1   rp   r   boolTensorr   	Generatorr   r   r   r   r   r   r   r   r   propertyr   r   r   r  r  no_gradr   EXAMPLE_DOC_STRINGr
   r!  FloatTensorr   r   r   r   r   r   r;  __static_attributes____classcell__)r=   s   @r-   rQ   rQ      sn   , <^^ %^ $	^
 1^ /1FFG^6 )-%&#&)-'+(c49n%(  #( !	(
 &( $(\ <@,0%&049=#&)-'+O5c49n%O5 "%T#Y"78O5 &*	O5
  #O5  -O5 !) 6O5 !O5 &O5 $O5f )-$&'+)-/3*.+//%/ / "	/
 / / $/ &/ EOO,/ %,,'/ 5<<(/dell u|| 8!2 #:Tz0,*$*$ *$ 	*$
 *$ 
u||U\\)	**$X $ $ # # & & & &   ]]_12 $(26;? $##%)- ! %%&MQ/359>B  59 9B#&3c5EKK c5 sDI~./c5 "%T#Y"78	c5
 c5 }c5 !c5 DI&c5 c5 c5 c5  #c5 c5 E%//43H"HIJc5 %++,c5    1 12!c5" !)):): ;#c5$ %c5& 'c5( #4S>2)c5* '(Cd+T124DF\\]
+c50 -1I1c52 !3c54 
&-	.5c5 3 c5r/   rQ   r@  )NrI   )<r8   r"  typingr   r   r   r   r   r   r	   r   PILr
   transformersr   r   	callbacksr   r   loadersr   modelsr   r   models.embeddingsr   pipelines.pipeline_utilsr   
schedulersr   r   utilsr   r   r   utils.torch_utilsr   ri   r   pipeline_outputr   torch_xla.core.xla_modelcore	xla_modelr+  r*  
get_loggerrA  r   rM  r.   r   r|   r1   r!  rC   rI  rJ  rO   rQ   r5   r/   r-   <module>ra     s#      D D D   4 A / I 8 9 G O O - - 4 ))MM			H	% >W* *.15%)$(8*!#8* U3,-.8* S	"	8*
 T%[!8*z ck
TLL
T-5eoo-F
T\_
T{
5$57O {
5r/   