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This commit is contained in:
YiYi Xu
2025-04-29 18:43:46 -10:00
committed by GitHub
parent c1084b8cb8
commit 9bfddfe65d
2 changed files with 0 additions and 47 deletions
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8
src/diffusers/schedulers/scheduling_ddim.py
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@@ -349,8 +349,6 @@ class DDIMScheduler(SchedulerMixin, ConfigMixin):
generator=None,
variance_noise: Optional[torch.Tensor] = None,
return_dict: bool = True,
_model_output_uncond: Optional[torch.Tensor] = None,
_use_cfgpp: bool = True,
) -> Union[DDIMSchedulerOutput, Tuple]:
"""
Predict the sample from the previous timestep by reversing the SDE. This function propagates the diffusion
@@ -388,11 +386,6 @@ class DDIMScheduler(SchedulerMixin, ConfigMixin):
raise ValueError(
"Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"
)
if _use_cfgpp and self.config.prediction_type != "epsilon":
raise ValueError(
f"CFG++ is only supported for prediction type `epsilon`, but got {self.config.prediction_type}."
)
# See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
# Ideally, read DDIM paper in-detail understanding
@@ -418,7 +411,6 @@ class DDIMScheduler(SchedulerMixin, ConfigMixin):
# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
if self.config.prediction_type == "epsilon":
pred_original_sample = (sample - beta_prod_t ** (0.5) * model_output) / alpha_prod_t ** (0.5)
pred_epsilon = model_output if not _use_cfgpp else _model_output_uncond
elif self.config.prediction_type == "sample":
pred_original_sample = model_output
pred_epsilon = (sample - alpha_prod_t ** (0.5) * pred_original_sample) / beta_prod_t ** (0.5)

39
src/diffusers/schedulers/scheduling_euler_discrete.py
View File

@@ -584,8 +584,6 @@ class EulerDiscreteScheduler(SchedulerMixin, ConfigMixin):
s_noise: float = 1.0,
generator: Optional[torch.Generator] = None,
return_dict: bool = True,
_model_output_uncond: Optional[torch.Tensor] = None,
_use_cfgpp: bool = False,
) -> Union[EulerDiscreteSchedulerOutput, Tuple]:
"""
Predict the sample from the previous timestep by reversing the SDE. This function propagates the diffusion
@@ -629,11 +627,6 @@ class EulerDiscreteScheduler(SchedulerMixin, ConfigMixin):
"The `scale_model_input` function should be called before `step` to ensure correct denoising. "
"See `StableDiffusionPipeline` for a usage example."
)
if _use_cfgpp and self.config.prediction_type != "epsilon":
raise ValueError(
f"CFG++ is only supported for prediction type `epsilon`, but got {self.config.prediction_type}."
)
if self.step_index is None:
self._init_step_index(timestep)
@@ -675,38 +668,6 @@ class EulerDiscreteScheduler(SchedulerMixin, ConfigMixin):
dt = self.sigmas[self.step_index + 1] - sigma_hat
prev_sample = sample + derivative * dt
if _use_cfgpp:
prev_sample = prev_sample + (_model_output_uncond - model_output) * self.sigmas[self.step_index + 1]
# denoised = sample - model_output * sigmas[i]
# d = (sample - denoised) / sigmas[i]
# new_sample = denoised + d * sigmas[i + 1]
# new_sample = denoised + (sample - denoised) * sigmas[i + 1] / sigmas[i]
# new_sample = sample - model_output * sigmas[i] + model_output * sigmas[i + 1]
# new_sample = sample + model_output * (sigmas[i + 1] - sigmas[i])
# new_sample = sample - model_output * sigmas[i] + model_output * sigmas[i + 1] --- (1)
# CFG++ =====
# denoised = sample - model_output * sigmas[i]
# uncond_denoised = sample - model_output_uncond * sigmas[i]
# d = (sample - uncond_denoised) / sigmas[i]
# new_sample = denoised + d * sigmas[i + 1]
# new_sample = denoised + (sample - uncond_denoised) * sigmas[i + 1] / sigmas[i]
# new_sample = sample - model_output * sigmas[i] + model_output_uncond * sigmas[i + 1] --- (2)
# To go from (1) to (2):
# new_sample_2 = new_sample_1 - model_output * sigmas[i + 1] + model_output_uncond * sigmas[i + 1]
# new_sample_2 = new_sample_1 + (model_output_uncond - model_output) * sigmas[i + 1]
# new_sample_2 = new_sample_1 + diff * sigmas[i + 1]
# diff = model_output_uncond - model_output
# diff = model_output_uncond - (model_output_uncond + g * (model_output_cond - model_output_uncond))
# diff = model_output_uncond - (g * model_output_cond + (1 - g) * model_output_uncond)
# diff = model_output_uncond - g * model_output_cond + (g - 1) * model_output_uncond
# diff = g * (model_output_uncond - model_output_cond)
# Cast sample back to model compatible dtype
prev_sample = prev_sample.to(model_output.dtype)