
    +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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+\RX                  " \-5      r.Sr/S r0    SS\\1   S\\	\2\
Rf                  4      S\\\1      S\\\4      4S jjr5 " S S\\5      r6g)    N)AnyCallableDictListOptionalTupleUnion)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   )CogVideoXPipelineOutputTFa  
    Examples:
        ```python
        >>> import torch
        >>> from diffusers import CogVideoXPipeline
        >>> from diffusers.utils import export_to_video

        >>> # Models: "THUDM/CogVideoX-2b" or "THUDM/CogVideoX-5b"
        >>> pipe = CogVideoXPipeline.from_pretrained("THUDM/CogVideoX-2b", torch_dtype=torch.float16).to("cuda")
        >>> prompt = (
        ...     "A panda, dressed in a small, red jacket and a tiny hat, sits on a wooden stool in a serene bamboo forest. "
        ...     "The panda's fluffy paws strum a miniature acoustic guitar, producing soft, melodic tunes. Nearby, a few other "
        ...     "pandas gather, watching curiously and some clapping in rhythm. Sunlight filters through the tall bamboo, "
        ...     "casting a gentle glow on the scene. The panda's face is expressive, showing concentration and joy as it plays. "
        ...     "The background includes a small, flowing stream and vibrant green foliage, enhancing the peaceful and magical "
        ...     "atmosphere of this unique musical performance."
        ... )
        >>> video = pipe(prompt=prompt, 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$ )Ng       @)intround)src	tgt_width
tgt_heighttwthhwrresize_heightresize_widthcrop_top	crop_lefts               i/home/james-whalen/.local/lib/python3.13/site-packages/diffusers/pipelines/cogvideo/pipeline_cogvideox.pyget_resize_crop_region_for_gridr-   E   s    	B	BDA	AG}5!,-E"&1*-.5",345HE2,345I 8#;Y=U"VVV    num_inference_stepsdevice	timestepssigmasc                    Ub  Ub  [        S5      eUb  S[        [        R                  " U R                  5      R
                  R                  5       5      ;   nU(       d  [        SU R                   S35      eU R                  " S
X2S.UD6  U R                  n[        U5      nX14$ 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 valuesr1   zThe current scheduler class zx's `set_timesteps` does not support custom timestep schedules. Please check whether you are using the correct scheduler.)r1   r0   r2   zv's `set_timesteps` does not support custom sigmas schedules. Please check whether you are using the correct scheduler.)r2   r0   r0    )

ValueErrorsetinspect	signatureset_timesteps
parameterskeys	__class__r1   len)	schedulerr/   r0   r1   r2   kwargsaccepts_timestepsaccept_sigmass           r,   retrieve_timestepsrB   X   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$ee.y/B/B.C D_ `  	GvGG''	!)n )) 	 3MFMfM''	))r.   c            1         ^  \ rS rSrSr/ rSr/ SQrS\S\	S\
S\S	\\\4   4
U 4S
 jjr     S=S\\\\   4   S\S\S\\R*                     S\\R,                     4
S jjr        S>S\\\\   4   S\\\\\   4      S\S\S\\R2                     S\\R2                     S\S\\R*                     S\\R,                     4S jjr S?S jrS\R2                  S\R2                  4S jrS r  S@S jrSAS  jrSAS! jr S"\S#\S$\S\R*                  S\!\R2                  \R2                  4   4
S% jr"\#S& 5       r$\#S' 5       r%\#S( 5       r&\#S) 5       r'\#S* 5       r(\RR                  " 5       \*" \+5      SSSSSS+SS,S-SS.SSSSS/SSSS/S4S\\\\\   4      S\\\\\   4      S"\\   S#\\   S$\\   S0\S1\\\      S2\,S3\S\S4\,S5\\\RZ                  \\RZ                     4      S\\R\                     S\\R\                     S\\R\                     S6\S7\S8\\/\\04      S9\\\1\\\//S4   \2\34      S:\\   S\S\\4\!4   4,S; jj5       5       r5S<r6U =r7$ )BCogVideoXPipeline   a  
Pipeline for text-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)latents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)rI   rJ   rK   rL   r>   rK      r         gffffff?)vae_scale_factor)super__init__register_modulesgetattrr=   rK   configblock_out_channelsvae_scale_factor_spatialtemporal_compression_ratiovae_scale_factor_temporalscaling_factorvae_scaling_factor_imager   video_processor)selfrI   rJ   rK   rL   r>   r<   s         r,   rS   CogVideoXPipeline.__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_lengthr0   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)paddingrf   
truncationadd_special_tokensreturn_tensorslongest)rh   rk   r   zXThe following part of your input was truncated because `max_sequence_length` is set to  z	 tokens: r   )rd   r0   )_execution_devicerJ   rd   
isinstancestrr=   rI   	input_idsshapetorchequalbatch_decodeloggerwarningtorepeatview)r^   ra   rb   rc   r0   rd   
batch_sizetext_inputstext_input_idsuntruncated_idsremoved_textrG   _seq_lens                 r,   _get_t5_prompt_embeds'CogVideoXPipeline._get_t5_prompt_embeds   s    14110**00'44&&[
nn *# % 
 %....SW.Xbb  $(<(<R(@@UcIuIu>>66qJ]`aJadfJfGf7ghLNN'(	,A
 )).*;*;F*CDQG%((u(D &++A%,,Q0EqI%**:+MwXZ[r.   Tnegative_promptdo_classifier_free_guidancerG   rH   c
                 2   U=(       d    U R                   n[        U[        5      (       a  U/OUnUb  [        U5      n
OUR                  S   n
Uc  U R                  UUUUU	S9nU(       a  Uc  U=(       d    Sn[        U[        5      (       a  X/-  OUnUb;  [        U5      [        U5      La$  [        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   )ra   rb   rc   r0   rd    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`.)	rn   ro   rp   r=   rr   r   type	TypeErrorr5   )r^   ra   r   r   rb   rG   rH   rc   r0   rd   r{   s              r,   encode_promptCogVideoXPipeline.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.   c
                 V   [        U[        5      (       a*  [        U5      U:w  a  [        S[        U5       SU S35      eUUS-
  U R                  -  S-   UX@R
                  -  XPR
                  -  4n
U	c  [        XXvS9n	OU	R                  U5      n	XR                  R                  -  n	U	$ )Nz/You have passed a list of generators of length z+, but requested an effective batch size of z@. Make sure the batch size matches the length of the generators.r   )	generatorr0   rd   )
ro   listr=   r5   rZ   rX   r   rx   r>   init_noise_sigma)r^   r{   num_channels_latents
num_framesheightwidthrd   r0   r   rF   rr   s              r,   prepare_latents!CogVideoXPipeline.prepare_latentsE  s     i&&3y>Z+GA#i.AQ R&<'gi  !^ > >>B 333222
 ?"5fZGjj(G NN;;;r.   rF   returnc                     UR                  SSSSS5      nSU R                  -  U-  nU R                  R                  U5      R                  nU$ )Nr   rN   r   r   rP   )permuter\   rK   decodesample)r^   rF   framess      r,   decode_latents CogVideoXPipeline.decode_latents_  sJ    //!Q1a0d333g=)00r.   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etar   )r6   r7   r8   r>   stepr:   r;   )r^   r   r   accepts_etaextra_step_kwargsaccepts_generators         r,   prepare_extra_step_kwargs+CogVideoXPipeline.prepare_extra_step_kwargsg  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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bE  UbA  UR                  UR                  :w  a&  [        SUR                   SUR                   S35      eg g g s  snf )NrO   r   z7`height` and `width` have to be divisible by 8 but are z and r   c              3   @   >#    U  H  oTR                   ;   v   M     g 7fN)_callback_tensor_inputs).0kr^   s     r,   	<genexpr>1CogVideoXPipeline.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` )r5   allr   ro   rp   r   r   rr   )	r^   ra   r   r   r   "callback_on_step_end_tensor_inputsrG   rH   r   s	   `        r,   check_inputsCogVideoXPipeline.check_inputsy  s9    A:?eai1nVW]V^^cdicjjklmm-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  C *L$5 pHs   E&1E&c                 F    SU l         U R                  R                  5         g)zEnables fused QKV projections.TN)fusing_transformerrL   fuse_qkv_projectionsr^   s    r,   r   &CogVideoXPipeline.fuse_qkv_projections  s    "&--/r.   c                     U R                   (       d  [        R                  S5        gU R                  R	                  5         SU l         g)z)Disable QKV projection fusion if enabled.zKThe Transformer was not initially fused for QKV projections. Doing nothing.FN)r   rv   rw   rL   unfuse_qkv_projectionsr   s    r,   r   (CogVideoXPipeline.unfuse_qkv_projections  s2    &&NNhi335&+D#r.   r   r   r   c           
         XR                   U R                  R                  R                  -  -  nX R                   U R                  R                  R                  -  -  nU R                  R                  R                  nU R                  R                  R                  nU R                  R                  R
                  U-  n	U R                  R                  R                  U-  n
Uc>  [        XV4X5      n[        U R                  R                  R                  UXV4UUS9u  pX4$ X8-   S-
  U-  n[        U R                  R                  R                  S XV4USX4US9u  pX4$ )N)	embed_dimcrops_coords	grid_sizetemporal_sizer0   r   slice)r   r   r   r   	grid_typemax_sizer0   )
rX   rL   rV   
patch_sizepatch_size_tsample_widthsample_heightr-   r   attention_head_dim)r^   r   r   r   r0   grid_height
grid_widthpp_tbase_size_widthbase_size_heightgrid_crops_coords	freqs_cos	freqs_sinbase_num_framess                  r,   %_prepare_rotary_positional_embeddings7CogVideoXPipeline._prepare_rotary_positional_embeddings  sd    !>!>AQAQAXAXAcAc!cd<<t?O?O?V?V?a?aab
##..%%22**11>>!C++22@@AE; ?)?! $;**11DD.&3($ I* ##  */!3;O#:**11DD!&3-!*<$ I ##r.   c                     U R                   $ r   )_guidance_scaler   s    r,   guidance_scale CogVideoXPipeline.guidance_scale  s    ###r.   c                     U R                   $ r   )_num_timestepsr   s    r,   num_timestepsCogVideoXPipeline.num_timesteps  s    """r.   c                     U R                   $ r   )_attention_kwargsr   s    r,   attention_kwargs"CogVideoXPipeline.attention_kwargs      %%%r.   c                     U R                   $ r   )_current_timestepr   s    r,   current_timestep"CogVideoXPipeline.current_timestep  r   r.   c                     U R                   $ r   )
_interruptr   s    r,   	interruptCogVideoXPipeline.interrupt  s    r.   2      Fg        pilr/   r1   r   use_dynamic_cfgr   r   output_typereturn_dictr   callback_on_step_endr   c                    [        U[        [        45      (       a  UR                  nU=(       d-    U R                  R
                  R                  U R                  -  nU=(       d-    U R                  R
                  R                  U R                  -  nU=(       d     U R                  R
                  R                  nSn
U R                  UUUUUUU5        Xl        UU l        SU l        SU l        Ub  [        U[        5      (       a  SnO3Ub!  [        U[         5      (       a  [#        U5      nOUR$                  S   nU R&                  nUS:  nU R)                  UUUU
UUUUS9u  pU(       a  [*        R,                  " X/SS9n[/        U R0                  UUU5      u  pv[#        U5      U l        US-
  U R4                  -  S-   nU R                  R
                  R6                  nSnUb#  UU-  S:w  a  UUU-  -
  nUUU R4                  -  -  nU R                  R
                  R8                  nU R;                  UU
-  UUUUUR<                  UUU5	      nU R?                  X5      nU R                  R
                  R@                  (       a"  U RC                  X4URE                  S5      U5      OSn[G        [#        U5      X`R0                  RH                  -  -
  S5      nU RK                  US9 n Sn![M        U5       GH  u  n"n#U RN                  (       a  M  U#U l        U(       a  [*        R,                  " U/S	-  5      OUn$U R0                  RQ                  U$U#5      n$U#RS                  U$R$                  S   5      n%U R                  RU                  S
5         U R	                  U$UU%UUSS9S   n&SSS5        W&RW                  5       n&U	(       aO  SUS[X        RZ                  " [X        R\                  UU#R_                  5       -
  U-  S-  -  5      -
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  :X  d)  U"S-   U:  a0  U"S-   U R0                  RH                  -  S:X  a  U Ro                  5         [p        (       d  GM  [r        Rt                  " 5         GM     SSS5        SU l        US:X  d7  USS2US24   nU Rw                  U5      n,U Rx                  R{                  U,US9n,OUn,U R}                  5         U(       d  U,4$ [        U,S9$ ! , (       d  f       GN= f! , (       d  f       N= f)aC  
Function invoked when calling the pipeline for generation.

Args:
    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_frames (`int`, defaults to `48`):
        Number of frames to generate. Must be divisible by self.vae_scale_factor_temporal. Generated video will
        contain 1 extra frame because CogVideoX is conditioned with (num_seconds * fps + 1) frames where
        num_seconds is 6 and fps is 8. However, since videos can be saved at any fps, the only condition that
        needs to be satisfied is that of divisibility mentioned above.
    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.
    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_cogvideox.CogVideoXPipelineOutput`] or `tuple`:
    [`~pipelines.cogvideo.pipeline_cogvideox.CogVideoXPipelineOutput`] if `return_dict` is True, otherwise a
    `tuple`. When returning a tuple, the first element is a list with the generated images.
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 ! 	."	
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