text stringlengths 1 1.02k | class_index int64 0 1.38k | source stringclasses 431
values |
|---|---|---|
raise NotImplementedError(f"{algorithm_type} is not implemented for {self.__class__}") | 1,312 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_edm_dpmsolver_multistep.py |
if solver_type not in ["midpoint", "heun"]:
if solver_type in ["logrho", "bh1", "bh2"]:
self.register_to_config(solver_type="midpoint")
else:
raise NotImplementedError(f"{solver_type} is not implemented for {self.__class__}")
if algorithm_type not in ["dp... | 1,312 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_edm_dpmsolver_multistep.py |
# setable values
self.num_inference_steps = None
self.model_outputs = [None] * solver_order
self.lower_order_nums = 0
self._step_index = None
self._begin_index = None
self.sigmas = self.sigmas.to("cpu") # to avoid too much CPU/GPU communication
@property
def ini... | 1,312 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_edm_dpmsolver_multistep.py |
# Copied from diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler.set_begin_index
def set_begin_index(self, begin_index: int = 0):
"""
Sets the begin index for the scheduler. This function should be run from pipeline before the inference.
Args:
begin_... | 1,312 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_edm_dpmsolver_multistep.py |
# 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":
... | 1,312 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_edm_dpmsolver_multistep.py |
# 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:
"""
Ensures interchangeability with schedulers that need to scale the denoising model input depending o... | 1,312 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_edm_dpmsolver_multistep.py |
def set_timesteps(self, num_inference_steps: int = None, device: Union[str, torch.device] = None):
"""
Sets the discrete timesteps used for the diffusion chain (to be run before inference).
Args:
num_inference_steps (`int`):
The number of diffusion steps used when ge... | 1,312 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_edm_dpmsolver_multistep.py |
if self.config.final_sigmas_type == "sigma_min":
sigma_last = self.config.sigma_min
elif self.config.final_sigmas_type == "zero":
sigma_last = 0
else:
raise ValueError(
f"`final_sigmas_type` must be one of 'zero', or 'sigma_min', but got {self.config.f... | 1,312 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_edm_dpmsolver_multistep.py |
# Copied from diffusers.schedulers.scheduling_edm_euler.EDMEulerScheduler._compute_karras_sigmas
def _compute_karras_sigmas(self, ramp, sigma_min=None, sigma_max=None) -> torch.Tensor:
"""Constructs the noise schedule of Karras et al. (2022)."""
sigma_min = sigma_min or self.config.sigma_min
... | 1,312 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_edm_dpmsolver_multistep.py |
https://github.com/crowsonkb/k-diffusion/blob/6ab5146d4a5ef63901326489f31f1d8e7dd36b48/k_diffusion/sampling.py#L26
"""
sigma_min = sigma_min or self.config.sigma_min
sigma_max = sigma_max or self.config.sigma_max
sigmas = torch.linspace(math.log(sigma_min), math.log(sigma_max), len(ramp)... | 1,312 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_edm_dpmsolver_multistep.py |
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler._threshold_sample
def _threshold_sample(self, sample: torch.Tensor) -> torch.Tensor:
"""
"Dynamic thresholding: At each sampling step we set s to a certain percentile absolute pixel value in xt0 (the
prediction of x_0 at timest... | 1,312 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_edm_dpmsolver_multistep.py |
if dtype not in (torch.float32, torch.float64):
sample = sample.float() # upcast for quantile calculation, and clamp not implemented for cpu half
# Flatten sample for doing quantile calculation along each image
sample = sample.reshape(batch_size, channels * np.prod(remaining_dims))
... | 1,312 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_edm_dpmsolver_multistep.py |
# Copied from diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler._sigma_to_t
def _sigma_to_t(self, sigma, log_sigmas):
# get log sigma
log_sigma = np.log(np.maximum(sigma, 1e-10))
# get distribution
dists = log_sigma - log_sigmas[:, np.newaxis]
# get sigm... | 1,312 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_edm_dpmsolver_multistep.py |
def convert_model_output(
self,
model_output: torch.Tensor,
sample: torch.Tensor = None,
) -> torch.Tensor:
"""
Convert the model output to the corresponding type the DPMSolver/DPMSolver++ algorithm needs. DPM-Solver is
designed to discretize an integral of the noise ... | 1,312 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_edm_dpmsolver_multistep.py |
Returns:
`torch.Tensor`:
The converted model output.
"""
sigma = self.sigmas[self.step_index]
x0_pred = self.precondition_outputs(sample, model_output, sigma)
if self.config.thresholding:
x0_pred = self._threshold_sample(x0_pred)
return x... | 1,312 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_edm_dpmsolver_multistep.py |
Returns:
`torch.Tensor`:
The sample tensor at the previous timestep.
"""
sigma_t, sigma_s = self.sigmas[self.step_index + 1], self.sigmas[self.step_index]
alpha_t, sigma_t = self._sigma_to_alpha_sigma_t(sigma_t)
alpha_s, sigma_s = self._sigma_to_alpha_sigma_t(... | 1,312 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_edm_dpmsolver_multistep.py |
def multistep_dpm_solver_second_order_update(
self,
model_output_list: List[torch.Tensor],
sample: torch.Tensor = None,
noise: Optional[torch.Tensor] = None,
) -> torch.Tensor:
"""
One step for the second-order multistep DPMSolver.
Args:
model_out... | 1,312 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_edm_dpmsolver_multistep.py |
alpha_t, sigma_t = self._sigma_to_alpha_sigma_t(sigma_t)
alpha_s0, sigma_s0 = self._sigma_to_alpha_sigma_t(sigma_s0)
alpha_s1, sigma_s1 = self._sigma_to_alpha_sigma_t(sigma_s1)
lambda_t = torch.log(alpha_t) - torch.log(sigma_t)
lambda_s0 = torch.log(alpha_s0) - torch.log(sigma_s0)
... | 1,312 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_edm_dpmsolver_multistep.py |
h, h_0 = lambda_t - lambda_s0, lambda_s0 - lambda_s1
r0 = h_0 / h
D0, D1 = m0, (1.0 / r0) * (m0 - m1)
if self.config.algorithm_type == "dpmsolver++":
# See https://arxiv.org/abs/2211.01095 for detailed derivations
if self.config.solver_type == "midpoint":
... | 1,312 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_edm_dpmsolver_multistep.py |
(sigma_t / sigma_s0 * torch.exp(-h)) * sample
+ (alpha_t * (1 - torch.exp(-2.0 * h))) * D0
+ 0.5 * (alpha_t * (1 - torch.exp(-2.0 * h))) * D1
+ sigma_t * torch.sqrt(1.0 - torch.exp(-2 * h)) * noise
)
elif self.config.solver_type == ... | 1,312 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_edm_dpmsolver_multistep.py |
return x_t
def multistep_dpm_solver_third_order_update(
self,
model_output_list: List[torch.Tensor],
sample: torch.Tensor = None,
) -> torch.Tensor:
"""
One step for the third-order multistep DPMSolver.
Args:
model_output_list (`List[torch.Tensor]`):... | 1,312 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_edm_dpmsolver_multistep.py |
alpha_t, sigma_t = self._sigma_to_alpha_sigma_t(sigma_t)
alpha_s0, sigma_s0 = self._sigma_to_alpha_sigma_t(sigma_s0)
alpha_s1, sigma_s1 = self._sigma_to_alpha_sigma_t(sigma_s1)
alpha_s2, sigma_s2 = self._sigma_to_alpha_sigma_t(sigma_s2)
lambda_t = torch.log(alpha_t) - torch.log(sigma_t)... | 1,312 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_edm_dpmsolver_multistep.py |
h, h_0, h_1 = lambda_t - lambda_s0, lambda_s0 - lambda_s1, lambda_s1 - lambda_s2
r0, r1 = h_0 / h, h_1 / h
D0 = m0
D1_0, D1_1 = (1.0 / r0) * (m0 - m1), (1.0 / r1) * (m1 - m2)
D1 = D1_0 + (r0 / (r0 + r1)) * (D1_0 - D1_1)
D2 = (1.0 / (r0 + r1)) * (D1_0 - D1_1)
if self.confi... | 1,312 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_edm_dpmsolver_multistep.py |
index_candidates = (schedule_timesteps == timestep).nonzero()
if len(index_candidates) == 0:
step_index = len(self.timesteps) - 1
# The sigma index that is taken for the **very** first `step`
# is always the second index (or the last index if there is only 1)
# This way we c... | 1,312 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_edm_dpmsolver_multistep.py |
if self.begin_index is None:
if isinstance(timestep, torch.Tensor):
timestep = timestep.to(self.timesteps.device)
self._step_index = self.index_for_timestep(timestep)
else:
self._step_index = self._begin_index
def step(
self,
model_output:... | 1,312 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_edm_dpmsolver_multistep.py |
Args:
model_output (`torch.Tensor`):
The direct output from learned diffusion model.
timestep (`int`):
The current discrete timestep in the diffusion chain.
sample (`torch.Tensor`):
A current instance of a sample created by the diffusio... | 1,312 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_edm_dpmsolver_multistep.py |
"""
if self.num_inference_steps is None:
raise ValueError(
"Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"
)
if self.step_index is None:
self._init_step_index(timestep)
# Improve numerical s... | 1,312 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_edm_dpmsolver_multistep.py |
model_output = self.convert_model_output(model_output, sample=sample)
for i in range(self.config.solver_order - 1):
self.model_outputs[i] = self.model_outputs[i + 1]
self.model_outputs[-1] = model_output
if self.config.algorithm_type == "sde-dpmsolver++":
noise = randn_t... | 1,312 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_edm_dpmsolver_multistep.py |
if self.config.solver_order == 1 or self.lower_order_nums < 1 or lower_order_final:
prev_sample = self.dpm_solver_first_order_update(model_output, sample=sample, noise=noise)
elif self.config.solver_order == 2 or self.lower_order_nums < 2 or lower_order_second:
prev_sample = self.multist... | 1,312 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_edm_dpmsolver_multistep.py |
# Copied from diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler.add_noise
def add_noise(
self,
original_samples: torch.Tensor,
noise: torch.Tensor,
timesteps: torch.Tensor,
) -> torch.Tensor:
# Make sure sigmas and timesteps have the same device and dt... | 1,312 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_edm_dpmsolver_multistep.py |
# self.begin_index is None when scheduler is used for training, or pipeline does not implement set_begin_index
if self.begin_index is None:
step_indices = [self.index_for_timestep(t, schedule_timesteps) for t in timesteps]
elif self.step_index is not None:
# add_noise is called a... | 1,312 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_edm_dpmsolver_multistep.py |
class LMSDiscreteSchedulerState:
common: CommonSchedulerState
# setable values
init_noise_sigma: jnp.ndarray
timesteps: jnp.ndarray
sigmas: jnp.ndarray
num_inference_steps: Optional[int] = None
# running values
derivatives: Optional[jnp.ndarray] = None
@classmethod
def create(... | 1,313 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_lms_discrete_flax.py |
class FlaxLMSSchedulerOutput(FlaxSchedulerOutput):
state: LMSDiscreteSchedulerState | 1,314 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_lms_discrete_flax.py |
class FlaxLMSDiscreteScheduler(FlaxSchedulerMixin, ConfigMixin):
"""
Linear Multistep Scheduler for discrete beta schedules. Based on the original k-diffusion implementation by
Katherine Crowson:
https://github.com/crowsonkb/k-diffusion/blob/481677d114f6ea445aa009cf5bd7a9cdee909e47/k_diffusion/sampling.... | 1,315 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_lms_discrete_flax.py |
Args:
num_train_timesteps (`int`): number of diffusion steps used to train the model.
beta_start (`float`): the starting `beta` value of inference.
beta_end (`float`): the final `beta` value.
beta_schedule (`str`):
the beta schedule, a mapping from a beta range to a sequence ... | 1,315 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_lms_discrete_flax.py |
the `dtype` used for params and computation.
""" | 1,315 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_lms_discrete_flax.py |
_compatibles = [e.name for e in FlaxKarrasDiffusionSchedulers]
dtype: jnp.dtype
@property
def has_state(self):
return True
@register_to_config
def __init__(
self,
num_train_timesteps: int = 1000,
beta_start: float = 0.0001,
beta_end: float = 0.02,
b... | 1,315 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_lms_discrete_flax.py |
return LMSDiscreteSchedulerState.create(
common=common,
init_noise_sigma=init_noise_sigma,
timesteps=timesteps,
sigmas=sigmas,
)
def scale_model_input(self, state: LMSDiscreteSchedulerState, sample: jnp.ndarray, timestep: int) -> jnp.ndarray:
"""
... | 1,315 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_lms_discrete_flax.py |
sigma = state.sigmas[step_index]
sample = sample / ((sigma**2 + 1) ** 0.5)
return sample
def get_lms_coefficient(self, state: LMSDiscreteSchedulerState, order, t, current_order):
"""
Compute a linear multistep coefficient.
Args:
order (TODO):
t (TODO... | 1,315 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_lms_discrete_flax.py |
def set_timesteps(
self, state: LMSDiscreteSchedulerState, num_inference_steps: int, shape: Tuple = ()
) -> LMSDiscreteSchedulerState:
"""
Sets the timesteps used for the diffusion chain. Supporting function to be run before inference.
Args:
state (`LMSDiscreteSchedulerS... | 1,315 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_lms_discrete_flax.py |
sigmas = ((1 - state.common.alphas_cumprod) / state.common.alphas_cumprod) ** 0.5
sigmas = (1 - frac) * sigmas[low_idx] + frac * sigmas[high_idx]
sigmas = jnp.concatenate([sigmas, jnp.array([0.0], dtype=self.dtype)])
timesteps = timesteps.astype(jnp.int32)
# initial running values
... | 1,315 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_lms_discrete_flax.py |
def step(
self,
state: LMSDiscreteSchedulerState,
model_output: jnp.ndarray,
timestep: int,
sample: jnp.ndarray,
order: int = 4,
return_dict: bool = True,
) -> Union[FlaxLMSSchedulerOutput, Tuple]:
"""
Predict the sample at the previous timeste... | 1,315 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_lms_discrete_flax.py |
Args:
state (`LMSDiscreteSchedulerState`): the `FlaxLMSDiscreteScheduler` state data class instance.
model_output (`jnp.ndarray`): direct output from learned diffusion model.
timestep (`int`): current discrete timestep in the diffusion chain.
sample (`jnp.ndarray`):
... | 1,315 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_lms_discrete_flax.py |
sigma = state.sigmas[timestep]
# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
if self.config.prediction_type == "epsilon":
pred_original_sample = sample - sigma * model_output
elif self.config.prediction_type == "v_prediction":
# * c_out +... | 1,315 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_lms_discrete_flax.py |
# 3. Compute linear multistep coefficients
order = min(timestep + 1, order)
lms_coeffs = [self.get_lms_coefficient(state, order, timestep, curr_order) for curr_order in range(order)]
# 4. Compute previous sample based on the derivatives path
prev_sample = sample + sum(
coeff... | 1,315 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_lms_discrete_flax.py |
def __len__(self):
return self.config.num_train_timesteps | 1,315 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_lms_discrete_flax.py |
class DPMSolverMultistepInverseScheduler(SchedulerMixin, ConfigMixin):
"""
`DPMSolverMultistepInverseScheduler` is the reverse scheduler of [`DPMSolverMultistepScheduler`].
This model inherits from [`SchedulerMixin`] and [`ConfigMixin`]. Check the superclass documentation for the generic
methods the li... | 1,316 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_multistep_inverse.py |
Args:
num_train_timesteps (`int`, defaults to 1000):
The number of diffusion steps to train the model.
beta_start (`float`, defaults to 0.0001):
The starting `beta` value of inference.
beta_end (`float`, defaults to 0.02):
The final `beta` value.
beta_... | 1,316 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_multistep_inverse.py |
Prediction type of the scheduler function; can be `epsilon` (predicts the noise of the diffusion process),
`sample` (directly predicts the noisy sample`) or `v_prediction` (see section 2.4 of [Imagen
Video](https://imagen.research.google/video/paper.pdf) paper).
thresholding (`bool`, def... | 1,316 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_multistep_inverse.py |
Algorithm type for the solver; can be `dpmsolver`, `dpmsolver++`, `sde-dpmsolver` or `sde-dpmsolver++`. The
`dpmsolver` type implements the algorithms in the [DPMSolver](https://huggingface.co/papers/2206.00927)
paper, and the `dpmsolver++` type implements the algorithms in the
[DPMS... | 1,316 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_multistep_inverse.py |
stabilize the sampling of DPMSolver for steps < 15, especially for steps <= 10.
euler_at_final (`bool`, defaults to `False`):
Whether to use Euler's method in the final step. It is a trade-off between numerical stability and detail
richness. This can stabilize the sampling of the SDE var... | 1,316 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_multistep_inverse.py |
Whether to use beta sigmas for step sizes in the noise schedule during the sampling process. Refer to [Beta
Sampling is All You Need](https://huggingface.co/papers/2407.12173) for more information.
lambda_min_clipped (`float`, defaults to `-inf`):
Clipping threshold for the minimum value... | 1,316 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_multistep_inverse.py |
An offset added to the inference steps, as required by some model families.
""" | 1,316 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_multistep_inverse.py |
_compatibles = [e.name for e in KarrasDiffusionSchedulers]
order = 1 | 1,316 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_multistep_inverse.py |
@register_to_config
def __init__(
self,
num_train_timesteps: int = 1000,
beta_start: float = 0.0001,
beta_end: float = 0.02,
beta_schedule: str = "linear",
trained_betas: Optional[Union[np.ndarray, List[float]]] = None,
solver_order: int = 2,
predictio... | 1,316 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_multistep_inverse.py |
timestep_spacing: str = "linspace",
steps_offset: int = 0,
):
if self.config.use_beta_sigmas and not is_scipy_available():
raise ImportError("Make sure to install scipy if you want to use beta sigmas.")
if sum([self.config.use_beta_sigmas, self.config.use_exponential_sigmas, self... | 1,316 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_multistep_inverse.py |
if trained_betas is not None:
self.betas = torch.tensor(trained_betas, dtype=torch.float32)
elif beta_schedule == "linear":
self.betas = torch.linspace(beta_start, beta_end, num_train_timesteps, dtype=torch.float32)
elif beta_schedule == "scaled_linear":
# this schedu... | 1,316 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_multistep_inverse.py |
self.alphas = 1.0 - self.betas
self.alphas_cumprod = torch.cumprod(self.alphas, dim=0)
# Currently we only support VP-type noise schedule
self.alpha_t = torch.sqrt(self.alphas_cumprod)
self.sigma_t = torch.sqrt(1 - self.alphas_cumprod)
self.lambda_t = torch.log(self.alpha_t) - to... | 1,316 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_multistep_inverse.py |
if solver_type not in ["midpoint", "heun"]:
if solver_type in ["logrho", "bh1", "bh2"]:
self.register_to_config(solver_type="midpoint")
else:
raise NotImplementedError(f"{solver_type} is not implemented for {self.__class__}")
# setable values
self... | 1,316 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_multistep_inverse.py |
@property
def step_index(self):
"""
The index counter for current timestep. It will increase 1 after each scheduler step.
"""
return self._step_index
def set_timesteps(self, num_inference_steps: int = None, device: Union[str, torch.device] = None):
"""
Sets the d... | 1,316 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_multistep_inverse.py |
Args:
num_inference_steps (`int`):
The number of diffusion steps used when generating samples with a pre-trained model.
device (`str` or `torch.device`, *optional*):
The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.
... | 1,316 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_multistep_inverse.py |
# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
if self.config.timestep_spacing == "linspace":
timesteps = (
np.linspace(0, self.noisiest_timestep, num_inference_steps + 1).round()[:-1].copy().astype(np.int64)
... | 1,316 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_multistep_inverse.py |
# casting to int to avoid issues when num_inference_step is power of 3
timesteps = np.arange(self.noisiest_timestep + 1, 0, -step_ratio).round()[::-1].copy().astype(np.int64)
timesteps -= 1
else:
raise ValueError(
f"{self.config.timestep_spacing} is not suppor... | 1,316 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_multistep_inverse.py |
sigmas = np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)
log_sigmas = np.log(sigmas) | 1,316 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_multistep_inverse.py |
if self.config.use_karras_sigmas:
sigmas = self._convert_to_karras(in_sigmas=sigmas, num_inference_steps=num_inference_steps)
timesteps = np.array([self._sigma_to_t(sigma, log_sigmas) for sigma in sigmas]).round()
timesteps = timesteps.copy().astype(np.int64)
sigmas = np.... | 1,316 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_multistep_inverse.py |
sigmas = np.concatenate([sigmas, sigmas[-1:]]).astype(np.float32)
elif self.config.use_flow_sigmas:
alphas = np.linspace(1, 1 / self.config.num_train_timesteps, num_inference_steps + 1)
sigmas = 1.0 - alphas
sigmas = np.flip(self.config.flow_shift * sigmas / (1 + (self.config... | 1,316 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_multistep_inverse.py |
self.sigmas = torch.from_numpy(sigmas)
# when num_inference_steps == num_train_timesteps, we can end up with
# duplicates in timesteps.
_, unique_indices = np.unique(timesteps, return_index=True)
timesteps = timesteps[np.sort(unique_indices)]
self.timesteps = torch.from_numpy(t... | 1,316 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_multistep_inverse.py |
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler._threshold_sample
def _threshold_sample(self, sample: torch.Tensor) -> torch.Tensor:
"""
"Dynamic thresholding: At each sampling step we set s to a certain percentile absolute pixel value in xt0 (the
prediction of x_0 at timest... | 1,316 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_multistep_inverse.py |
if dtype not in (torch.float32, torch.float64):
sample = sample.float() # upcast for quantile calculation, and clamp not implemented for cpu half
# Flatten sample for doing quantile calculation along each image
sample = sample.reshape(batch_size, channels * np.prod(remaining_dims))
... | 1,316 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_multistep_inverse.py |
# Copied from diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler._sigma_to_t
def _sigma_to_t(self, sigma, log_sigmas):
# get log sigma
log_sigma = np.log(np.maximum(sigma, 1e-10))
# get distribution
dists = log_sigma - log_sigmas[:, np.newaxis]
# get sigm... | 1,316 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_multistep_inverse.py |
# Copied from diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler._sigma_to_alpha_sigma_t
def _sigma_to_alpha_sigma_t(self, sigma):
if self.config.use_flow_sigmas:
alpha_t = 1 - sigma
sigma_t = sigma
else:
alpha_t = 1 / ((sigma**2 + 1) ... | 1,316 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_multistep_inverse.py |
if hasattr(self.config, "sigma_max"):
sigma_max = self.config.sigma_max
else:
sigma_max = None
sigma_min = sigma_min if sigma_min is not None else in_sigmas[-1].item()
sigma_max = sigma_max if sigma_max is not None else in_sigmas[0].item()
rho = 7.0 # 7.0 is th... | 1,316 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_multistep_inverse.py |
# Hack to make sure that other schedulers which copy this function don't break
# TODO: Add this logic to the other schedulers
if hasattr(self.config, "sigma_min"):
sigma_min = self.config.sigma_min
else:
sigma_min = None
if hasattr(self.config, "sigma_max"):
... | 1,316 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_multistep_inverse.py |
# Copied from diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler._convert_to_beta
def _convert_to_beta(
self, in_sigmas: torch.Tensor, num_inference_steps: int, alpha: float = 0.6, beta: float = 0.6
) -> torch.Tensor:
"""From "Beta Sampling is All You Need" [arXiv:2407.12173] ... | 1,316 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_multistep_inverse.py |
sigmas = np.array(
[
sigma_min + (ppf * (sigma_max - sigma_min))
for ppf in [
scipy.stats.beta.ppf(timestep, alpha, beta)
for timestep in 1 - np.linspace(0, 1, num_inference_steps)
]
]
)
retur... | 1,316 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_multistep_inverse.py |
The algorithm and model type are decoupled. You can use either DPMSolver or DPMSolver++ for both noise
prediction and data prediction models.
</Tip>
Args:
model_output (`torch.Tensor`):
The direct output from the learned diffusion model.
sample (`torch.T... | 1,316 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_multistep_inverse.py |
Returns:
`torch.Tensor`:
The converted model output.
"""
timestep = args[0] if len(args) > 0 else kwargs.pop("timestep", None)
if sample is None:
if len(args) > 1:
sample = args[1]
else:
raise ValueError("missing... | 1,316 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_multistep_inverse.py |
# DPM-Solver++ needs to solve an integral of the data prediction model.
if self.config.algorithm_type in ["dpmsolver++", "sde-dpmsolver++"]:
if self.config.prediction_type == "epsilon":
# DPM-Solver and DPM-Solver++ only need the "mean" output.
if self.config.variance... | 1,316 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_multistep_inverse.py |
elif self.config.prediction_type == "flow_prediction":
sigma_t = self.sigmas[self.step_index]
x0_pred = sample - sigma_t * model_output
else:
raise ValueError(
f"prediction_type given as {self.config.prediction_type} must be one of `epsilon... | 1,316 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_multistep_inverse.py |
if self.config.thresholding:
x0_pred = self._threshold_sample(x0_pred)
return x0_pred | 1,316 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_multistep_inverse.py |
# DPM-Solver needs to solve an integral of the noise prediction model.
elif self.config.algorithm_type in ["dpmsolver", "sde-dpmsolver"]:
if self.config.prediction_type == "epsilon":
# DPM-Solver and DPM-Solver++ only need the "mean" output.
if self.config.variance_ty... | 1,316 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_multistep_inverse.py |
raise ValueError(
f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or"
" `v_prediction` for the DPMSolverMultistepScheduler."
) | 1,316 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_multistep_inverse.py |
if self.config.thresholding:
sigma = self.sigmas[self.step_index]
alpha_t, sigma_t = self._sigma_to_alpha_sigma_t(sigma)
x0_pred = (sample - sigma_t * epsilon) / alpha_t
x0_pred = self._threshold_sample(x0_pred)
epsilon = (sample - alpha_t ... | 1,316 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_multistep_inverse.py |
Args:
model_output (`torch.Tensor`):
The direct output from the learned diffusion model.
sample (`torch.Tensor`):
A current instance of a sample created by the diffusion process.
Returns:
`torch.Tensor`:
The sample tensor at th... | 1,316 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_multistep_inverse.py |
if prev_timestep is not None:
deprecate(
"prev_timestep",
"1.0.0",
"Passing `prev_timestep` is deprecated and has no effect as model output conversion is now handled via an internal counter `self.step_index`",
)
sigma_t, sigma_s = self.sig... | 1,316 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_multistep_inverse.py |
h = lambda_t - lambda_s
if self.config.algorithm_type == "dpmsolver++":
x_t = (sigma_t / sigma_s) * sample - (alpha_t * (torch.exp(-h) - 1.0)) * model_output
elif self.config.algorithm_type == "dpmsolver":
x_t = (alpha_t / alpha_s) * sample - (sigma_t * (torch.exp(h) - 1.0)) * mo... | 1,316 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_multistep_inverse.py |
# Copied from diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler.multistep_dpm_solver_second_order_update
def multistep_dpm_solver_second_order_update(
self,
model_output_list: List[torch.Tensor],
*args,
sample: torch.Tensor = None,
noise: Optiona... | 1,316 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_multistep_inverse.py |
Returns:
`torch.Tensor`:
The sample tensor at the previous timestep.
"""
timestep_list = args[0] if len(args) > 0 else kwargs.pop("timestep_list", None)
prev_timestep = args[1] if len(args) > 1 else kwargs.pop("prev_timestep", None)
if sample is None:
... | 1,316 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_multistep_inverse.py |
if prev_timestep is not None:
deprecate(
"prev_timestep",
"1.0.0",
"Passing `prev_timestep` is deprecated and has no effect as model output conversion is now handled via an internal counter `self.step_index`",
)
sigma_t, sigma_s0, sigma_s1... | 1,316 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_multistep_inverse.py |
h, h_0 = lambda_t - lambda_s0, lambda_s0 - lambda_s1
r0 = h_0 / h
D0, D1 = m0, (1.0 / r0) * (m0 - m1)
if self.config.algorithm_type == "dpmsolver++":
# See https://arxiv.org/abs/2211.01095 for detailed derivations
if self.config.solver_type == "midpoint":
... | 1,316 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_multistep_inverse.py |
(alpha_t / alpha_s0) * sample
- (sigma_t * (torch.exp(h) - 1.0)) * D0
- 0.5 * (sigma_t * (torch.exp(h) - 1.0)) * D1
)
elif self.config.solver_type == "heun":
x_t = (
(alpha_t / alpha_s0) * sample
... | 1,316 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_multistep_inverse.py |
(sigma_t / sigma_s0 * torch.exp(-h)) * sample
+ (alpha_t * (1 - torch.exp(-2.0 * h))) * D0
+ (alpha_t * ((1.0 - torch.exp(-2.0 * h)) / (-2.0 * h) + 1.0)) * D1
+ sigma_t * torch.sqrt(1.0 - torch.exp(-2 * h)) * noise
)
elif self.config.al... | 1,316 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_multistep_inverse.py |
- 2.0 * (sigma_t * ((torch.exp(h) - 1.0) / h - 1.0)) * D1
+ sigma_t * torch.sqrt(torch.exp(2 * h) - 1.0) * noise
)
return x_t | 1,316 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_multistep_inverse.py |
# Copied from diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler.multistep_dpm_solver_third_order_update
def multistep_dpm_solver_third_order_update(
self,
model_output_list: List[torch.Tensor],
*args,
sample: torch.Tensor = None,
noise: Optional[... | 1,316 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_multistep_inverse.py |
timestep_list = args[0] if len(args) > 0 else kwargs.pop("timestep_list", None)
prev_timestep = args[1] if len(args) > 1 else kwargs.pop("prev_timestep", None)
if sample is None:
if len(args) > 2:
sample = args[2]
else:
raise ValueError(" missing`s... | 1,316 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_multistep_inverse.py |
sigma_t, sigma_s0, sigma_s1, sigma_s2 = (
self.sigmas[self.step_index + 1],
self.sigmas[self.step_index],
self.sigmas[self.step_index - 1],
self.sigmas[self.step_index - 2],
)
alpha_t, sigma_t = self._sigma_to_alpha_sigma_t(sigma_t)
alpha_s0, sigm... | 1,316 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_multistep_inverse.py |
h, h_0, h_1 = lambda_t - lambda_s0, lambda_s0 - lambda_s1, lambda_s1 - lambda_s2
r0, r1 = h_0 / h, h_1 / h
D0 = m0
D1_0, D1_1 = (1.0 / r0) * (m0 - m1), (1.0 / r1) * (m1 - m2)
D1 = D1_0 + (r0 / (r0 + r1)) * (D1_0 - D1_1)
D2 = (1.0 / (r0 + r1)) * (D1_0 - D1_1)
if self.confi... | 1,316 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_multistep_inverse.py |
- (sigma_t * ((torch.exp(h) - 1.0) / h - 1.0)) * D1
- (sigma_t * ((torch.exp(h) - 1.0 - h) / h**2 - 0.5)) * D2
)
elif self.config.algorithm_type == "sde-dpmsolver++":
assert noise is not None
x_t = (
(sigma_t / sigma_s0 * torch.exp(-h)) * sampl... | 1,316 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_multistep_inverse.py |
def _init_step_index(self, timestep):
if isinstance(timestep, torch.Tensor):
timestep = timestep.to(self.timesteps.device)
index_candidates = (self.timesteps == timestep).nonzero()
if len(index_candidates) == 0:
step_index = len(self.timesteps) - 1
# The sigma i... | 1,316 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_multistep_inverse.py |
def step(
self,
model_output: torch.Tensor,
timestep: Union[int, torch.Tensor],
sample: torch.Tensor,
generator=None,
variance_noise: Optional[torch.Tensor] = None,
return_dict: bool = True,
) -> Union[SchedulerOutput, Tuple]:
"""
Predict the s... | 1,316 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_multistep_inverse.py |
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