
    
3j                    |    S SK Jr  S SKrS SKJr  S SKrSSKJr  SSKJ	r	J
r
Jr  \(       a  SSKJr   " S	 S
\	5      rg)    )annotationsN)TYPE_CHECKING   )register_to_config   )BaseGuidanceGuiderOutputrescale_noise_cfg)
BlockStatec                     ^  \ rS rSrSrSS/r\      S           SU 4S jjj5       rSS jr      SS jr	SSS jjr
\SS	 j5       r\SS
 j5       rSS jrSrU =r$ )ClassifierFreeGuidance   a  
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).
	pred_condpred_uncondc                J   > [         TU ]  XEU5        Xl        X l        X0l        g N)super__init__guidance_scaleguidance_rescaleuse_original_formulation)selfr   r   r   startstopenabled	__class__s          d/home/wildlama/miniconda3/lib/python3.13/site-packages/diffusers/guiders/classifier_free_guidance.pyr   ClassifierFreeGuidance.__init__P   s&     	g., 0(@%    c                    U R                   S:X  a  S/OSS/n/ n[        X R                  5       H(  u  pEU R                  XU5      nUR	                  U5        M*     U$ Nr   r   )num_conditionszip_input_predictions_prepare_batchappend)r   datatuple_indicesdata_batches	tuple_idxinput_prediction
data_batchs          r   prepare_inputs%ClassifierFreeGuidance.prepare_inputs`   sc    #22a7aV+.}>U>U+V'I,,T>NOJ
+ ,W r   c                    U R                   S:X  a  S/OSS/n/ n[        X0R                  5       H(  u  pVU R                  X!XV5      nUR	                  U5        M*     U$ r!   )r"   r#   r$   _prepare_batch_from_block_stater&   )r   r'   input_fieldsr(   r)   r*   r+   r,   s           r   prepare_inputs_from_block_state6ClassifierFreeGuidance.prepare_inputs_from_block_stateh   se      $22a7aV+.}>U>U+V'I==lR[nJ
+ ,W r   c                    S nU R                  5       (       d  UnO*X-
  nU R                  (       a  UOUnX0R                  U-  -   nU R                  S:  a  [	        X1U R                  5      n[        X1US9$ )N        )predr   r   )_is_cfg_enabledr   r   r   r
   r	   )r   r   r   r6   shifts        r   forwardClassifierFreeGuidance.forwardr   sr    ##%%D+E $ = =9;D--55D  3&$Td6K6KLDTTr   c                     U R                   S:H  $ Nr   )_count_prepared)r   s    r   is_conditional%ClassifierFreeGuidance.is_conditional   s    ##q((r   c                >    SnU R                  5       (       a  US-  nU$ r<   )r7   )r   r"   s     r   r"   %ClassifierFreeGuidance.num_conditions   s&    !!aNr   c                   U R                   (       d  gSnU R                  bb  [        U R                  U R                  -  5      n[        U R                  U R                  -  5      nX R
                  s=:*  =(       a    U:  Os  nSnU R                  (       a"  [        R                  " U R                  S5      nO![        R                  " U R                  S5      nU=(       a    U(       + $ )NFTr5         ?)
_enabled_num_inference_stepsint_start_stop_stepr   mathiscloser   )r   is_within_rangeskip_start_stepskip_stop_stepis_closes        r   r7   &ClassifierFreeGuidance._is_cfg_enabled   s    }}$$0!$++0I0I"IJO d.G.G!GHN-LLnLO((||D$7$7=H||D$7$7=H/x</r   )r   r   r   )g      @r5   Fr5   rC   T)r   floatr   rQ   r   boolr   rQ   r   rQ   r   rR   )r'   z,dict[str, tuple[torch.Tensor, torch.Tensor]]returnlist['BlockState'])r'   z'BlockState'r1   z dict[str, str | tuple[str, str]]rS   rT   r   )r   ztorch.Tensorr   ztorch.Tensor | NonerS   r	   )rS   rR   )rS   rF   )__name__
__module____qualname____firstlineno____doc__r$   r   r   r-   r2   r9   propertyr>   r"   r7   __static_attributes____classcell__)r   s   @r   r   r      s    -^ &}5 !$"%).AA  A #'	A
 A A A A 0P	U ) )  0 0r   r   )
__future__r   rJ   typingr   torchconfiguration_utilsr   guider_utilsr   r	   r
   "modular_pipelines.modular_pipeliner   r    r   r   <module>rd      s2    #     4 G G ?~0\ ~0r   