Ë
    Õ†iÕ  ã                   óz   — d dl Z d dlmZmZmZmZmZmZ d dlZddl	m
Z
 ddlmZmZmZ erddlmZ  G d„ d	e«      Zy)
é    N)ÚTYPE_CHECKINGÚDictÚListÚOptionalÚTupleÚUnioné   )Úregister_to_configé   )ÚBaseGuidanceÚGuiderOutputÚrescale_noise_cfg)Ú
BlockStatec                   ób  ‡ — e Zd ZdZddgZe	 	 	 	 	 	 ddededededed	efˆ fd
„«       Zde	e
eej                  ej                  f   f   ded   fd„Zddde	e
ee
ee
e
f   f   f   ded   fd„Zddej                  deej                     defd„Zedefd„«       Zedefd„«       Zdefd„Zˆ xZS )ÚClassifierFreeGuidanceaC	  
    Implements Classifier-Free Guidance (CFG) for diffusion models.

    Reference: https://huggingface.co/papers/2207.12598

    CFG improves generation quality and prompt adherence by jointly training models on both conditional and
    unconditional data, then combining predictions during inference. This allows trading off between quality (high
    guidance) and diversity (low guidance).

    **Two CFG Formulations:**

    1. **Original formulation** (from paper):
       ```
       x_pred = x_cond + guidance_scale * (x_cond - x_uncond)
       ```
       Moves conditional predictions further from unconditional ones.

    2. **Diffusers-native formulation** (default, from Imagen paper):
       ```
       x_pred = x_uncond + guidance_scale * (x_cond - x_uncond)
       ```
       Moves unconditional predictions toward conditional ones, effectively suppressing negative features (e.g., "bad
       quality", "watermarks"). Equivalent in theory but more intuitive.

    Use `use_original_formulation=True` to switch to the original formulation.

    Args:
        guidance_scale (`float`, defaults to `7.5`):
            CFG scale applied by this guider during post-processing. Higher values = stronger prompt conditioning but
            may reduce quality. Typical range: 1.0-20.0.
        guidance_rescale (`float`, defaults to `0.0`):
            Rescaling factor to prevent overexposure from high guidance scales. Based on [Common Diffusion Noise
            Schedules and Sample Steps are Flawed](https://huggingface.co/papers/2305.08891). Range: 0.0 (no rescaling)
            to 1.0 (full rescaling).
        use_original_formulation (`bool`, defaults to `False`):
            If `True`, uses the original CFG formulation from the paper. If `False` (default), uses the
            diffusers-native formulation from the Imagen paper.
        start (`float`, defaults to `0.0`):
            Fraction of denoising steps (0.0-1.0) after which CFG starts. Use > 0.0 to disable CFG in early denoising
            steps.
        stop (`float`, defaults to `1.0`):
            Fraction of denoising steps (0.0-1.0) after which CFG stops. Use < 1.0 to disable CFG in late denoising
            steps.
        enabled (`bool`, defaults to `True`):
            Whether CFG is enabled. Set to `False` to disable CFG entirely (uses only conditional predictions).
    Ú	pred_condÚpred_uncondÚguidance_scaleÚguidance_rescaleÚuse_original_formulationÚstartÚstopÚenabledc                 óR   •— t         ‰|   |||«       || _        || _        || _        y ©N)ÚsuperÚ__init__r   r   r   )Úselfr   r   r   r   r   r   Ú	__class__s          €út/home/obispo/Crisostomo_bridge/mision_env/lib/python3.12/site-packages/diffusers/guiders/classifier_free_guidance.pyr   zClassifierFreeGuidance.__init__N   s.   ø€ ô 	‰Ñ˜  gÔ.à,ˆÔØ 0ˆÔØ(@ˆÕ%ó    ÚdataÚreturnr   c                 óº   — | j                   dk(  rdgnddg}g }t        || j                  «      D ])  \  }}| j                  |||«      }|j	                  |«       Œ+ |S ©Nr   r   )Únum_conditionsÚzipÚ_input_predictionsÚ_prepare_batchÚappend)r   r"   Útuple_indicesÚdata_batchesÚ	tuple_idxÚinput_predictionÚ
data_batchs          r    Úprepare_inputsz%ClassifierFreeGuidance.prepare_inputs^   sn   € Ø#×2Ñ2°aÒ7˜™¸aÀ¸VˆØˆÜ+.¨}¸d×>UÑ>UÓ+Vò 	,Ñ'ˆIÐ'Ø×,Ñ,¨T°9Ð>NÓOˆJØ×Ñ 
Õ+ð	,ð Ðr!   Úinput_fieldsc                 ó¼   — | j                   dk(  rdgnddg}g }t        || j                  «      D ]*  \  }}| j                  ||||«      }|j	                  |«       Œ, |S r%   )r&   r'   r(   Ú_prepare_batch_from_block_stater*   )r   r"   r1   r+   r,   r-   r.   r/   s           r    Úprepare_inputs_from_block_statez6ClassifierFreeGuidance.prepare_inputs_from_block_statef   ss   € ð  $×2Ñ2°aÒ7˜™¸aÀ¸VˆØˆÜ+.¨}¸d×>UÑ>UÓ+Vò 	,Ñ'ˆIÐ'Ø×=Ñ=¸lÈDÐR[Ð]mÓnˆJØ×Ñ 
Õ+ð	,ð Ðr!   c                 óâ   — d }| j                  «       s|}n'||z
  }| j                  r|n|}|| j                  |z  z   }| j                  dkD  rt	        ||| j                  «      }t        |||¬«      S )Nç        )Úpredr   r   )Ú_is_cfg_enabledr   r   r   r   r   )r   r   r   r7   Úshifts        r    ÚforwardzClassifierFreeGuidance.forwardp   sw   € Øˆà×#Ñ#Ô%Ø‰Dà Ñ+ˆEØ $× =Ò =‘9À;ˆDØ˜$×-Ñ-°Ñ5Ñ5ˆDà× Ñ  3Ò&Ü$ T¨9°d×6KÑ6KÓLˆDä °ÈÔTÐTr!   c                 ó    — | j                   dk(  S ©Nr   )Ú_count_prepared)r   s    r    Úis_conditionalz%ClassifierFreeGuidance.is_conditional   s   € à×#Ñ# qÑ(Ð(r!   c                 ó4   — d}| j                  «       r|dz  }|S r<   )r8   )r   r&   s     r    r&   z%ClassifierFreeGuidance.num_conditionsƒ   s#   € àˆØ×ÑÔ!Ø˜aÑˆNØÐr!   c                 ó   — | j                   syd}| j                  ^t        | j                  | j                  z  «      }t        | j                  | j                  z  «      }|| j
                  cxk  xr |k  nc }d}| j                  r!t        j                  | j                  d«      }n t        j                  | j                  d«      }|xr | S )NFTr6   ç      ð?)
Ú_enabledÚ_num_inference_stepsÚintÚ_startÚ_stopÚ_stepr   ÚmathÚiscloser   )r   Úis_within_rangeÚskip_start_stepÚskip_stop_stepÚis_closes        r    r8   z&ClassifierFreeGuidance._is_cfg_enabledŠ   s©   € Ø}Š}ØàˆØ×$Ñ$Ð0Ü! $§+¡+°×0IÑ0IÑ"IÓJˆOÜ  §¡¨d×.GÑ.GÑ!GÓHˆNØ-°·±ÖL¸nÔLˆOàˆØ×(Ò(Ü—|‘| D×$7Ñ$7¸Ó=‰Hä—|‘| D×$7Ñ$7¸Ó=ˆHàÒ/ x <Ð/r!   )g      @r6   Fr6   rA   Tr   )Ú__name__Ú
__module__Ú__qualname__Ú__doc__r(   r
   ÚfloatÚboolr   r   Ústrr   ÚtorchÚTensorr   r0   r   r4   r   r   r:   Úpropertyr>   rD   r&   r8   Ú__classcell__)r   s   @r    r   r      sa  ø„ ñ-ð^ & }Ð5Ðàð !$Ø"%Ø).ØØØñAàðAð  ðAð #'ð	Að
 ðAð ðAð ôAó ðAð 4¨¨U°5·<±<ÀÇÁÐ3MÑ-NÐ(NÑ#Oð ÐTXÐYeÑTfó ðØ ðØ04°S¸%ÀÀUÈ3ÐPSÈ8Á_Ð@TÑ:UÐ5UÑ0Vðà	ˆlÑ	óñU §¡ð U¸HÀUÇ\Á\Ñ<Rð UÐ^jó Uð ð) ò )ó ð)ð ð ò ó ðð0 ÷ 0r!   r   )rH   Útypingr   r   r   r   r   r   rU   Úconfiguration_utilsr
   Úguider_utilsr   r   r   Ú"modular_pipelines.modular_pipeliner   r   © r!   r    ú<module>r^      s2   ðó ß D× Dã å 4ß GÑ Gñ Ý?ô~0˜\õ ~0r!   