
    h                        S r SSKrSSKJrJr  SSKrSSKrSSKJ	r	  SSK
Jr  SSKJr  SSKJrJrJrJrJr  SSKJrJrJr  \R.                  R1                  S	S
5        \ " S S5      5       r " S S\R4                  5      r " S S\R                  R4                  5      rS r/ SQrS rS r \!S:X  a  \" \\45      r"\"RG                  5       u  r$r%SSSSS.\%l&        \" \$RN                  \$RP                  \$RR                  S9r*\" \%\" \$RV                  \$RX                  5      \\*\ S9r-\-R]                  5         \-R_                  \%R`                  5        \%Rb                  (       a  \-Rc                  \$Rd                  S9  ggg)a[  
python examples/scripts/ddpo.py     --num_epochs 200     --train_gradient_accumulation_steps 1     --sample_num_steps 50     --sample_batch_size 6     --train_batch_size 3     --sample_num_batches_per_epoch 4     --per_prompt_stat_tracking True     --per_prompt_stat_tracking_buffer_size 32     --tracker_project_name "stable_diffusion_training"
    N)	dataclassfield)hf_hub_download)EntryNotFoundError)nn)	CLIPModelCLIPProcessorHfArgumentParseris_torch_npu_availableis_torch_xpu_available)
DDPOConfigDDPOTrainer"DefaultDDPOStableDiffusionPipelineTRACKIO_SPACE_IDztrl-trackioc                       \ rS rSr% Sr\" SSS0S9r\\S'   \" SSS	0S9r	\\S
'   \" SSS0S9r
\\S'   \" SSS0S9r\\S'   \" SSS0S9r\\S'   \" SSS0S9r\\S'   Srg)ScriptArguments7   a8  
Arguments for the script.

Args:
    pretrained_model (`str`, *optional*, defaults to `"runwayml/stable-diffusion-v1-5"`):
        Pretrained model to use.
    pretrained_revision (`str`, *optional*, defaults to `"main"`):
        Pretrained model revision to use.
    hf_hub_model_id (`str`, *optional*, defaults to `"ddpo-finetuned-stable-diffusion"`):
        HuggingFace repo to save model weights to.
    hf_hub_aesthetic_model_id (`str`, *optional*, defaults to `"trl-lib/ddpo-aesthetic-predictor"`):
        Hugging Face model ID for aesthetic scorer model weights.
    hf_hub_aesthetic_model_filename (`str`, *optional*, defaults to `"aesthetic-model.pth"`):
        Hugging Face model filename for aesthetic scorer model weights.
    use_lora (`bool`, *optional*, defaults to `True`):
        Whether to use LoRA.
zrunwayml/stable-diffusion-v1-5helpzPretrained model to use.)defaultmetadatapretrained_modelmainz!Pretrained model revision to use.pretrained_revisionzddpo-finetuned-stable-diffusionz*HuggingFace repo to save model weights to.hf_hub_model_idz trl-lib/ddpo-aesthetic-predictorz9Hugging Face model ID for aesthetic scorer model weights.hf_hub_aesthetic_model_idzaesthetic-model.pthz?Hugging Face model filename for aesthetic scorer model weights.hf_hub_aesthetic_model_filenameTzWhether to use LoRA.use_lora N)__name__
__module____qualname____firstlineno____doc__r   r   str__annotations__r   r   r   r   r   bool__static_attributes__r       O/home/james-whalen/.local/lib/python3.13/site-packages/examples/scripts/ddpo.pyr   r   7   s    $ "0FD^;_c   %VvGj>kll 1VEq<rOS  &+2UV&s  ,1%[\,#S  46;Q2RSHdSr(   r   c                   V   ^  \ rS rSrU 4S jr\R                  " 5       S 5       rSrU =r	$ )MLP]   c                   > [         TU ]  5         [        R                  " [        R                  " SS5      [        R
                  " S5      [        R                  " SS5      [        R
                  " S5      [        R                  " SS5      [        R
                  " S5      [        R                  " SS5      [        R                  " SS5      5      U l        g )	Ni   i   g?   @   g?      )super__init__r   
SequentialLinearDropoutlayers)self	__class__s    r)   r3   MLP.__init__^   s    mmIIc4 JJsOIIdC JJsOIIc2JJsOIIb"IIb!	
r(   c                 $    U R                  U5      $ Nr7   )r8   embeds     r)   forwardMLP.forwardk   s    {{5!!r(   r=   )
r   r    r!   r"   r3   torchno_gradr?   r'   __classcell__r9   s   @r)   r+   r+   ]   s"    
 ]]_" "r(   r+   c                   Z   ^  \ rS rSrSrU 4S jr\R                  " 5       S 5       rSr	U =r
$ )AestheticScorerp   z
This model attempts to predict the aesthetic score of an image. The aesthetic score
is a numerical approximation of how much a specific image is liked by humans on average.
This is from https://github.com/christophschuhmann/improved-aesthetic-predictor
c                  > [         TU ]  5         [        R                  " S5      U l        [
        R                  " S5      U l        [        5       U l         [        X#5      n[        R                  " U[        R                   " S5      SS9nU R                  R#                  U5        Xl        U R'                  5         g ! [         a"    [        R                  R                  X#5      n Nf = f)Nzopenai/clip-vit-large-patch14cpuT)map_locationweights_only)r2   r3   r   from_pretrainedclipr	   	processorr+   mlpr   r   ospathjoinrA   loaddeviceload_state_dictdtypeeval)r8   rV   model_idmodel_filenamecached_path
state_dictr9   s         r)   r3   AestheticScorer.__init__w   s    --.MN	&667VW5	A)(CK ZZ%,,u:M\`a
  ,
		 " 	A'',,x@K	As   B= =)C)(C)c                    [        U R                  5       5      R                  nU R                  USS9nUR	                  5        VVs0 s H/  u  pEXER                  U R                  5      R                  U5      _M1     nnnU R                  R                  " S0 UD6nU[        R                  R                  USSS9-  nU R                  U5      R                  S5      $ s  snnf )Npt)imagesreturn_tensorsT)dimkeepdimr1   r   )next
parametersrT   rN   itemstorV   rM   get_image_featuresrA   linalgvector_normrO   squeeze)r8   r_   rT   inputskvr>   s          r)   __call__AestheticScorer.__call__   s    doo'(//vdC=C\\^L^TQ!TT$**%((00^L		,,6v600B0MMxx&&q))	 Ms   6C)rM   rV   rO   rN   )r   r    r!   r"   r#   r3   rA   rB   ro   r'   rC   rD   s   @r)   rF   rF   p   s'     ]]_* *r(   rF   c                    ^ [        U U[        R                  S9m[        5       (       a  TR	                  5       mO0[        5       (       a  TR                  5       mOTR                  5       mU4S jnU$ )N)rX   rY   rV   c                    > U S-  R                  5       R                  SS5      R                  [        R                  5      n T" U 5      nU0 4$ )N   r   )roundclamprg   rA   uint8)r_   promptsr   scoresscorers       r)   _fnaesthetic_scorer.<locals>._fn   sD    3,%%'--a588Erzr(   )rF   rA   float32r   npur   xpucuda)hub_model_idrY   rz   ry   s      @r)   aesthetic_scorerr      s[    %mmF
 		!	!
 Jr(   )catdoghorsemonkeyrabbitzebraspiderbirdsheepdeercowgoatlionfrogchickenduckgoosebeepigturkeyflyllamacamelbatgorillahedgehogkangarooc                  L    [         R                  R                  [        5      0 4$ r<   )nprandomchoiceanimalsr   r(   r)   	prompt_fnr      s    99G$b((r(   c                     0 nU S   u  pEpgn[        U5       HC  u  pXX   n
Xx   R                  5       nU	R                  S5      R                  5       X:S SUS 3'   ME     UR	                  UUS9  g )Nra   r   z.25z | z.2f)step)	enumerateitem	unsqueezefloat
log_images)
image_dataglobal_stepaccelerate_loggerresultr_   rw   _rewardsiimagepromptrewards               r)   image_outputs_loggerr      s     F%/^"FQf%"161C1I1I1KS-. &
    ! r(   __main__z./logsT   z./save)logging_dirautomatic_checkpoint_namingtotal_limitproject_dir)pretrained_model_revisionr   )image_samples_hook)dataset_name)3r#   rP   dataclassesr   r   numpyr   rA   huggingface_hubr   huggingface_hub.utilsr   r   transformersr   r	   r
   r   r   trlr   r   r   environ
setdefaultr   Moduler+   rF   r   r   r   r   r   parserparse_args_into_dataclassesscript_argstraining_argsproject_kwargsr   r   r   pipeliner   r   trainertrain
save_model
output_dirpush_to_hubr   r   r(   r)   <module>r      s~  2 
 (   + 4  s s K K 

  (- 8 "T "T "TJ"")) "&*ehhoo *@,>)" z
;<F!'!C!C!EK'+	$M  2$$"-"A"A%%H >>@k@kl/G MMO }//0  )A)AB !9 r(   