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make fix-copies

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This commit is contained in:
Aryan
2025-06-10 23:34:48 +02:00
parent 829545df04
commit 4f8c133580
3 changed files with 103 additions and 28 deletions
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59
src/diffusers/schedulers/scheduling_cosine_dpmsolver_multistep.py
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@@ -87,6 +87,7 @@ class CosineDPMSolverMultistepScheduler(SchedulerMixin, ConfigMixin):
lower_order_final: bool = True,
euler_at_final: bool = False,
final_sigmas_type: Optional[str] = "zero", # "zero", "sigma_min"
use_flow_sigmas: bool = False,
):
if solver_type not in ["midpoint", "heun"]:
if solver_type in ["logrho", "bh1", "bh2"]:
@@ -152,23 +153,19 @@ class CosineDPMSolverMultistepScheduler(SchedulerMixin, ConfigMixin):
if not isinstance(sigma, torch.Tensor):
sigma = torch.tensor([sigma])
return sigma.atan() / math.pi * 2
if self.config.use_flow_sigmas:
c_noise = sigma / (sigma + 1)
else:
c_noise = sigma.atan() / math.pi * 2
return c_noise
# Copied from diffusers.schedulers.scheduling_edm_euler.EDMEulerScheduler.precondition_outputs
def precondition_outputs(self, sample, model_output, sigma):
sigma_data = self.config.sigma_data
c_skip = sigma_data**2 / (sigma**2 + sigma_data**2)
if self.config.prediction_type == "epsilon":
c_out = sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
elif self.config.prediction_type == "v_prediction":
c_out = -sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
if self.config.use_flow_sigmas:
return self._precondition_outputs_flow(sample, model_output, sigma)
else:
raise ValueError(f"Prediction type {self.config.prediction_type} is not supported.")
denoised = c_skip * sample + c_out * model_output
return denoised
return self._precondition_outputs_edm(sample, model_output, sigma)
# Copied from diffusers.schedulers.scheduling_edm_euler.EDMEulerScheduler.scale_model_input
def scale_model_input(self, sample: torch.Tensor, timestep: Union[float, torch.Tensor]) -> torch.Tensor:
@@ -570,8 +567,42 @@ class CosineDPMSolverMultistepScheduler(SchedulerMixin, ConfigMixin):
# Copied from diffusers.schedulers.scheduling_edm_euler.EDMEulerScheduler._get_conditioning_c_in
def _get_conditioning_c_in(self, sigma):
c_in = 1 / ((sigma**2 + self.config.sigma_data**2) ** 0.5)
if self.config.use_flow_sigmas:
t = sigma / (sigma + 1)
c_in = 1.0 - t
else:
c_in = 1 / ((sigma**2 + self.config.sigma_data**2) ** 0.5)
return c_in
# Copied from diffusers.schedulers.scheduling_edm_euler.EDMEulerScheduler._precondition_outputs_flow
def _precondition_outputs_flow(self, sample, model_output, sigma):
t = sigma / (sigma + 1)
c_skip = 1.0 - t
if self.config.prediction_type == "epsilon":
c_out = -t
elif self.config.prediction_type == "v_prediction":
c_out = t
else:
raise ValueError(f"Prediction type {self.config.prediction_type} is not supported.")
denoised = c_skip * sample + c_out * model_output
return denoised
# Copied from diffusers.schedulers.scheduling_edm_euler.EDMEulerScheduler._precondition_outputs_edm
def _precondition_outputs_edm(self, sample, model_output, sigma):
sigma_data = self.config.sigma_data
c_skip = sigma_data**2 / (sigma**2 + sigma_data**2)
if self.config.prediction_type == "epsilon":
c_out = sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
elif self.config.prediction_type == "v_prediction":
c_out = -sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
else:
raise ValueError(f"Prediction type {self.config.prediction_type} is not supported.")
denoised = c_skip * sample + c_out * model_output
return denoised
def __len__(self):
return self.config.num_train_timesteps

57
src/diffusers/schedulers/scheduling_edm_dpmsolver_multistep.py
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@@ -107,6 +107,7 @@ class EDMDPMSolverMultistepScheduler(SchedulerMixin, ConfigMixin):
lower_order_final: bool = True,
euler_at_final: bool = False,
final_sigmas_type: Optional[str] = "zero", # "zero", "sigma_min"
use_flow_sigmas: bool = False,
):
# settings for DPM-Solver
if algorithm_type not in ["dpmsolver++", "sde-dpmsolver++"]:
@@ -185,25 +186,19 @@ class EDMDPMSolverMultistepScheduler(SchedulerMixin, ConfigMixin):
if not isinstance(sigma, torch.Tensor):
sigma = torch.tensor([sigma])
c_noise = 0.25 * torch.log(sigma)
if self.config.use_flow_sigmas:
c_noise = sigma / (sigma + 1)
else:
c_noise = 0.25 * torch.log(sigma)
return c_noise
# Copied from diffusers.schedulers.scheduling_edm_euler.EDMEulerScheduler.precondition_outputs
def precondition_outputs(self, sample, model_output, sigma):
sigma_data = self.config.sigma_data
c_skip = sigma_data**2 / (sigma**2 + sigma_data**2)
if self.config.prediction_type == "epsilon":
c_out = sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
elif self.config.prediction_type == "v_prediction":
c_out = -sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
if self.config.use_flow_sigmas:
return self._precondition_outputs_flow(sample, model_output, sigma)
else:
raise ValueError(f"Prediction type {self.config.prediction_type} is not supported.")
denoised = c_skip * sample + c_out * model_output
return denoised
return self._precondition_outputs_edm(sample, model_output, sigma)
# Copied from diffusers.schedulers.scheduling_edm_euler.EDMEulerScheduler.scale_model_input
def scale_model_input(self, sample: torch.Tensor, timestep: Union[float, torch.Tensor]) -> torch.Tensor:
@@ -705,8 +700,42 @@ class EDMDPMSolverMultistepScheduler(SchedulerMixin, ConfigMixin):
# Copied from diffusers.schedulers.scheduling_edm_euler.EDMEulerScheduler._get_conditioning_c_in
def _get_conditioning_c_in(self, sigma):
c_in = 1 / ((sigma**2 + self.config.sigma_data**2) ** 0.5)
if self.config.use_flow_sigmas:
t = sigma / (sigma + 1)
c_in = 1.0 - t
else:
c_in = 1 / ((sigma**2 + self.config.sigma_data**2) ** 0.5)
return c_in
# Copied from diffusers.schedulers.scheduling_edm_euler.EDMEulerScheduler._precondition_outputs_flow
def _precondition_outputs_flow(self, sample, model_output, sigma):
t = sigma / (sigma + 1)
c_skip = 1.0 - t
if self.config.prediction_type == "epsilon":
c_out = -t
elif self.config.prediction_type == "v_prediction":
c_out = t
else:
raise ValueError(f"Prediction type {self.config.prediction_type} is not supported.")
denoised = c_skip * sample + c_out * model_output
return denoised
# Copied from diffusers.schedulers.scheduling_edm_euler.EDMEulerScheduler._precondition_outputs_edm
def _precondition_outputs_edm(self, sample, model_output, sigma):
sigma_data = self.config.sigma_data
c_skip = sigma_data**2 / (sigma**2 + sigma_data**2)
if self.config.prediction_type == "epsilon":
c_out = sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
elif self.config.prediction_type == "v_prediction":
c_out = -sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
else:
raise ValueError(f"Prediction type {self.config.prediction_type} is not supported.")
denoised = c_skip * sample + c_out * model_output
return denoised
def __len__(self):
return self.config.num_train_timesteps

15
src/diffusers/utils/dummy_torch_and_transformers_objects.py
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@@ -422,6 +422,21 @@ class Cosmos2TextToImagePipeline(metaclass=DummyObject):
requires_backends(cls, ["torch", "transformers"])
class Cosmos2VideoToWorldPipeline(metaclass=DummyObject):
_backends = ["torch", "transformers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch", "transformers"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
class CosmosTextToWorldPipeline(metaclass=DummyObject):
_backends = ["torch", "transformers"]