# Copyright 2025 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import math from typing import TYPE_CHECKING, Dict, List, Optional, Tuple, Union import torch from ..configuration_utils import register_to_config from .guider_utils import BaseGuidance, GuiderOutput, rescale_noise_cfg if TYPE_CHECKING: from ..modular_pipelines.modular_pipeline import BlockState class ClassifierFreeZeroStarGuidance(BaseGuidance): """ Classifier-free Zero* (CFG-Zero*): https://huggingface.co/papers/2503.18886 This is an implementation of the Classifier-Free Zero* guidance technique, which is a variant of classifier-free guidance. It proposes zero initialization of the noise predictions for the first few steps of the diffusion process, and also introduces an optimal rescaling factor for the noise predictions, which can help in improving the quality of generated images. The authors of the paper suggest setting zero initialization in the first 4% of the inference steps. Args: guidance_scale (`float`, defaults to `7.5`): The scale parameter for classifier-free guidance. Higher values result in stronger conditioning on the text prompt, while lower values allow for more freedom in generation. Higher values may lead to saturation and deterioration of image quality. zero_init_steps (`int`, defaults to `1`): The number of inference steps for which the noise predictions are zeroed out (see Section 4.2). guidance_rescale (`float`, defaults to `0.0`): The rescale factor applied to the noise predictions. This is used to improve image quality and fix overexposure. Based on Section 3.4 from [Common Diffusion Noise Schedules and Sample Steps are Flawed](https://huggingface.co/papers/2305.08891). use_original_formulation (`bool`, defaults to `False`): Whether to use the original formulation of classifier-free guidance as proposed in the paper. By default, we use the diffusers-native implementation that has been in the codebase for a long time. See [~guiders.classifier_free_guidance.ClassifierFreeGuidance] for more details. start (`float`, defaults to `0.01`): The fraction of the total number of denoising steps after which guidance starts. stop (`float`, defaults to `0.2`): The fraction of the total number of denoising steps after which guidance stops. """ _input_predictions = ["pred_cond", "pred_uncond"] @register_to_config def __init__( self, guidance_scale: float = 7.5, zero_init_steps: int = 1, guidance_rescale: float = 0.0, use_original_formulation: bool = False, start: float = 0.0, stop: float = 1.0, ): super().__init__(start, stop) self.guidance_scale = guidance_scale self.zero_init_steps = zero_init_steps self.guidance_rescale = guidance_rescale self.use_original_formulation = use_original_formulation def prepare_inputs( self, data: "BlockState", input_fields: Optional[Dict[str, Union[str, Tuple[str, str]]]] = None ) -> List["BlockState"]: if input_fields is None: input_fields = self._input_fields tuple_indices = [0] if self.num_conditions == 1 else [0, 1] data_batches = [] for i in range(self.num_conditions): data_batch = self._prepare_batch(input_fields, data, tuple_indices[i], self._input_predictions[i]) data_batches.append(data_batch) return data_batches def forward(self, pred_cond: torch.Tensor, pred_uncond: Optional[torch.Tensor] = None) -> GuiderOutput: pred = None if self._step < self.zero_init_steps: pred = torch.zeros_like(pred_cond) elif not self._is_cfg_enabled(): pred = pred_cond else: pred_cond_flat = pred_cond.flatten(1) pred_uncond_flat = pred_uncond.flatten(1) alpha = cfg_zero_star_scale(pred_cond_flat, pred_uncond_flat) alpha = alpha.view(-1, *(1,) * (len(pred_cond.shape) - 1)) pred_uncond = pred_uncond * alpha shift = pred_cond - pred_uncond pred = pred_cond if self.use_original_formulation else pred_uncond pred = pred + self.guidance_scale * shift if self.guidance_rescale > 0.0: pred = rescale_noise_cfg(pred, pred_cond, self.guidance_rescale) return GuiderOutput(pred=pred, pred_cond=pred_cond, pred_uncond=pred_uncond) @property def is_conditional(self) -> bool: return self._count_prepared == 1 @property def num_conditions(self) -> int: num_conditions = 1 if self._is_cfg_enabled(): num_conditions += 1 return num_conditions def _is_cfg_enabled(self) -> bool: if not self._enabled: return False is_within_range = True if self._num_inference_steps is not None: skip_start_step = int(self._start * self._num_inference_steps) skip_stop_step = int(self._stop * self._num_inference_steps) is_within_range = skip_start_step <= self._step < skip_stop_step is_close = False if self.use_original_formulation: is_close = math.isclose(self.guidance_scale, 0.0) else: is_close = math.isclose(self.guidance_scale, 1.0) return is_within_range and not is_close def cfg_zero_star_scale(cond: torch.Tensor, uncond: torch.Tensor, eps: float = 1e-8) -> torch.Tensor: cond_dtype = cond.dtype cond = cond.float() uncond = uncond.float() dot_product = torch.sum(cond * uncond, dim=1, keepdim=True) squared_norm = torch.sum(uncond**2, dim=1, keepdim=True) + eps # st_star = v_cond^T * v_uncond / ||v_uncond||^2 scale = dot_product / squared_norm return scale.to(dtype=cond_dtype)