text stringlengths 1 1.02k | class_index int64 0 1.38k | source stringclasses 431
values |
|---|---|---|
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... | 1,316 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_multistep_inverse.py |
Returns:
[`~schedulers.scheduling_utils.SchedulerOutput`] or `tuple`:
If return_dict is `True`, [`~schedulers.scheduling_utils.SchedulerOutput`] is returned, otherwise a
tuple is returned where the first element is the sample tensor.
"""
if self.num_inference... | 1,316 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_multistep_inverse.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 in ["sde-dpmsolver", "sde-dpmsolver++"] and varia... | 1,316 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_multistep_inverse.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... | 1,316 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_multistep_inverse.py |
# Copied from diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler.scale_model_input
def scale_model_input(self, sample: torch.Tensor, *args, **kwargs) -> torch.Tensor:
"""
Ensures interchangeability with schedulers that need to scale the denoising model input depending on... | 1,316 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_multistep_inverse.py |
def add_noise(
self,
original_samples: torch.Tensor,
noise: torch.Tensor,
timesteps: torch.IntTensor,
) -> torch.Tensor:
# Make sure sigmas and timesteps have the same device and dtype as original_samples
sigmas = self.sigmas.to(device=original_samples.device, dtype=o... | 1,316 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_multistep_inverse.py |
step_indices = []
for timestep in timesteps:
index_candidates = (schedule_timesteps == timestep).nonzero()
if len(index_candidates) == 0:
step_index = len(schedule_timesteps) - 1
elif len(index_candidates) > 1:
step_index = index_candidates[1].... | 1,316 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_multistep_inverse.py |
class KDPM2AncestralDiscreteSchedulerOutput(BaseOutput):
"""
Output class for the scheduler's `step` function output.
Args:
prev_sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` for images):
Computed sample `(x_{t-1})` of previous timestep. `prev_sample` shoul... | 1,317 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_k_dpm_2_ancestral_discrete.py |
class KDPM2AncestralDiscreteScheduler(SchedulerMixin, ConfigMixin):
"""
KDPM2DiscreteScheduler with ancestral sampling is inspired by the DPMSolver2 and Algorithm 2 from the [Elucidating
the Design Space of Diffusion-Based Generative Models](https://huggingface.co/papers/2206.00364) paper.
This model i... | 1,318 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_k_dpm_2_ancestral_discrete.py |
Args:
num_train_timesteps (`int`, defaults to 1000):
The number of diffusion steps to train the model.
beta_start (`float`, defaults to 0.00085):
The starting `beta` value of inference.
beta_end (`float`, defaults to 0.012):
The final `beta` value.
bet... | 1,318 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_k_dpm_2_ancestral_discrete.py |
use_exponential_sigmas (`bool`, *optional*, defaults to `False`):
Whether to use exponential sigmas for step sizes in the noise schedule during the sampling process.
use_beta_sigmas (`bool`, *optional*, defaults to `False`):
Whether to use beta sigmas for step sizes in the noise schedule... | 1,318 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_k_dpm_2_ancestral_discrete.py |
Sample Steps are Flawed](https://huggingface.co/papers/2305.08891) for more information.
steps_offset (`int`, defaults to 0):
An offset added to the inference steps, as required by some model families.
""" | 1,318 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_k_dpm_2_ancestral_discrete.py |
_compatibles = [e.name for e in KarrasDiffusionSchedulers]
order = 2 | 1,318 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_k_dpm_2_ancestral_discrete.py |
@register_to_config
def __init__(
self,
num_train_timesteps: int = 1000,
beta_start: float = 0.00085, # sensible defaults
beta_end: float = 0.012,
beta_schedule: str = "linear",
trained_betas: Optional[Union[np.ndarray, List[float]]] = None,
use_karras_sigmas... | 1,318 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_k_dpm_2_ancestral_discrete.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":
# t... | 1,318 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_k_dpm_2_ancestral_discrete.py |
self.alphas = 1.0 - self.betas
self.alphas_cumprod = torch.cumprod(self.alphas, dim=0)
# set all values
self.set_timesteps(num_train_timesteps, None, num_train_timesteps)
self._step_index = None
self._begin_index = None
self.sigmas = self.sigmas.to("cpu") # to avoid to... | 1,318 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_k_dpm_2_ancestral_discrete.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_... | 1,318 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_k_dpm_2_ancestral_discrete.py |
Returns:
`torch.Tensor`:
A scaled input sample.
"""
if self.step_index is None:
self._init_step_index(timestep)
if self.state_in_first_order:
sigma = self.sigmas[self.step_index]
else:
sigma = self.sigmas_interpol[self.step... | 1,318 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_k_dpm_2_ancestral_discrete.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.
... | 1,318 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_k_dpm_2_ancestral_discrete.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, num_train_timesteps - 1, num_inference_steps, dtype=np.float32)[::-1].copy()
elif self.config.timestep_spaci... | 1,318 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_k_dpm_2_ancestral_discrete.py |
timesteps = (np.arange(num_train_timesteps, 0, -step_ratio)).round().copy().astype(np.float32)
timesteps -= 1
else:
raise ValueError(
f"{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."
) | 1,318 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_k_dpm_2_ancestral_discrete.py |
sigmas = np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)
log_sigmas = np.log(sigmas)
sigmas = np.interp(timesteps, np.arange(0, len(sigmas)), sigmas)
if self.config.use_karras_sigmas:
sigmas = self._convert_to_karras(in_sigmas=sigmas, num_inference_steps=num_infe... | 1,318 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_k_dpm_2_ancestral_discrete.py |
self.log_sigmas = torch.from_numpy(log_sigmas).to(device)
sigmas = np.concatenate([sigmas, [0.0]]).astype(np.float32)
sigmas = torch.from_numpy(sigmas).to(device=device)
# compute up and down sigmas
sigmas_next = sigmas.roll(-1)
sigmas_next[-1] = 0.0
sigmas_up = (sigmas_... | 1,318 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_k_dpm_2_ancestral_discrete.py |
# set sigmas
self.sigmas = torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2), sigmas[-1:]])
self.sigmas_interpol = torch.cat(
[sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2), sigmas_interpol[-1:]]
)
self.sigmas_up = torch.cat([sigmas_up[:1], sigmas_up[1:].... | 1,318 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_k_dpm_2_ancestral_discrete.py |
timesteps_interpol = torch.from_numpy(timesteps_interpol).to(device, dtype=timesteps.dtype)
interleaved_timesteps = torch.stack((timesteps_interpol[:-2, None], timesteps[1:, None]), dim=-1).flatten()
self.timesteps = torch.cat([timesteps[:1], interleaved_timesteps])
self.sample = None
... | 1,318 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_k_dpm_2_ancestral_discrete.py |
# interpolate sigmas
w = (low - log_sigma) / (low - high)
w = np.clip(w, 0, 1)
# transform interpolation to time range
t = (1 - w) * low_idx + w * high_idx
t = t.reshape(sigma.shape)
return t
# Copied from diffusers.schedulers.scheduling_euler_discrete.EulerDiscrete... | 1,318 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_k_dpm_2_ancestral_discrete.py |
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 the value used in the paper
ramp = np.linspace(0, 1, num_inference_steps)
min_inv_rho = sigma_min ** (1 / rho)
max... | 1,318 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_k_dpm_2_ancestral_discrete.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()
sigmas = np.exp(np.lin... | 1,318 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_k_dpm_2_ancestral_discrete.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"):
... | 1,318 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_k_dpm_2_ancestral_discrete.py |
# Copied from diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler.index_for_timestep
def index_for_timestep(self, timestep, schedule_timesteps=None):
if schedule_timesteps is None:
schedule_timesteps = self.timesteps
indices = (schedule_timesteps == timestep).nonzero()... | 1,318 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_k_dpm_2_ancestral_discrete.py |
# Copied from diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler._init_step_index
def _init_step_index(self, timestep):
if self.begin_index is None:
if isinstance(timestep, torch.Tensor):
timestep = timestep.to(self.timesteps.device)
self._step_inde... | 1,318 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_k_dpm_2_ancestral_discrete.py |
Args:
model_output (`torch.Tensor`):
The direct output from learned diffusion model.
timestep (`float`):
The current discrete timestep in the diffusion chain.
sample (`torch.Tensor`):
A current instance of a sample created by the diffus... | 1,318 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_k_dpm_2_ancestral_discrete.py |
Returns:
[`~schedulers.scheduling_k_dpm_2_ancestral_discrete.KDPM2AncestralDiscreteSchedulerOutput`] or `tuple`:
If return_dict is `True`,
[`~schedulers.scheduling_k_dpm_2_ancestral_discrete.KDPM2AncestralDiscreteSchedulerOutput`] is
returned, otherwise a tupl... | 1,318 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_k_dpm_2_ancestral_discrete.py |
if self.state_in_first_order:
sigma = self.sigmas[self.step_index]
sigma_interpol = self.sigmas_interpol[self.step_index]
sigma_up = self.sigmas_up[self.step_index]
sigma_down = self.sigmas_down[self.step_index - 1]
else:
# 2nd order / KPDM2's method
... | 1,318 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_k_dpm_2_ancestral_discrete.py |
# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
if self.config.prediction_type == "epsilon":
sigma_input = sigma_hat if self.state_in_first_order else sigma_interpol
pred_original_sample = sample - sigma_input * model_output
elif self.config.predict... | 1,318 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_k_dpm_2_ancestral_discrete.py |
if self.state_in_first_order:
# 2. Convert to an ODE derivative for 1st order
derivative = (sample - pred_original_sample) / sigma_hat
# 3. delta timestep
dt = sigma_interpol - sigma_hat
# store for 2nd order step
self.sample = sample
... | 1,318 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_k_dpm_2_ancestral_discrete.py |
# upon completion increase step index by one
self._step_index += 1
if not return_dict:
return (
prev_sample,
pred_original_sample,
)
return KDPM2AncestralDiscreteSchedulerOutput(
prev_sample=prev_sample, pred_original_sample=p... | 1,318 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_k_dpm_2_ancestral_discrete.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... | 1,318 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_k_dpm_2_ancestral_discrete.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... | 1,318 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_k_dpm_2_ancestral_discrete.py |
class LMSDiscreteSchedulerOutput(BaseOutput):
"""
Output class for the scheduler's `step` function output.
Args:
prev_sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` for images):
Computed sample `(x_{t-1})` of previous timestep. `prev_sample` should be used a... | 1,319 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_lms_discrete.py |
class LMSDiscreteScheduler(SchedulerMixin, ConfigMixin):
"""
A linear multistep scheduler for discrete beta schedules.
This model inherits from [`SchedulerMixin`] and [`ConfigMixin`]. Check the superclass documentation for the generic
methods the library implements for all schedulers such as loading an... | 1,320 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_lms_discrete.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_... | 1,320 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_lms_discrete.py |
Whether to use exponential sigmas for step sizes in the noise schedule during the sampling process.
use_beta_sigmas (`bool`, *optional*, defaults to `False`):
Whether to use beta sigmas for step sizes in the noise schedule during the sampling process. Refer to [Beta
Sampling is All You N... | 1,320 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_lms_discrete.py |
Sample Steps are Flawed](https://huggingface.co/papers/2305.08891) for more information.
steps_offset (`int`, defaults to 0):
An offset added to the inference steps, as required by some model families.
""" | 1,320 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_lms_discrete.py |
_compatibles = [e.name for e in KarrasDiffusionSchedulers]
order = 1 | 1,320 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_lms_discrete.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,
use_karras_sigmas: Optional[bool] = Fals... | 1,320 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_lms_discrete.py |
self.betas = torch.linspace(beta_start, beta_end, num_train_timesteps, dtype=torch.float32)
elif beta_schedule == "scaled_linear":
# this schedule is very specific to the latent diffusion model.
self.betas = torch.linspace(beta_start**0.5, beta_end**0.5, num_train_timesteps, dtype=torch.... | 1,320 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_lms_discrete.py |
self.alphas = 1.0 - self.betas
self.alphas_cumprod = torch.cumprod(self.alphas, dim=0)
sigmas = np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)
sigmas = np.concatenate([sigmas[::-1], [0.0]]).astype(np.float32)
self.sigmas = torch.from_numpy(sigmas)
# setable ... | 1,320 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_lms_discrete.py |
@property
def step_index(self):
"""
The index counter for current timestep. It will increase 1 after each scheduler step.
"""
return self._step_index
@property
def begin_index(self):
"""
The index for the first timestep. It should be set from pipeline with `s... | 1,320 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_lms_discrete.py |
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 on the
current timestep.
Args:
sample (`torch.Tensor`):
... | 1,320 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_lms_discrete.py |
def lms_derivative(tau):
prod = 1.0
for k in range(order):
if current_order == k:
continue
prod *= (tau - self.sigmas[t - k]) / (self.sigmas[t - current_order] - self.sigmas[t - k])
return prod
integrated_coeff = integrate.... | 1,320 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_lms_discrete.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.
... | 1,320 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_lms_discrete.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.config.num_train_timesteps - 1, num_inference_steps, dtype=np.float32)[
::-1
].copy... | 1,320 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_lms_discrete.py |
# casting to int to avoid issues when num_inference_step is power of 3
timesteps = (np.arange(self.config.num_train_timesteps, 0, -step_ratio)).round().copy().astype(np.float32)
timesteps -= 1
else:
raise ValueError(
f"{self.config.timestep_spacing} is not sup... | 1,320 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_lms_discrete.py |
sigmas = np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)
log_sigmas = np.log(sigmas)
sigmas = np.interp(timesteps, np.arange(0, len(sigmas)), sigmas)
if self.config.use_karras_sigmas:
sigmas = self._convert_to_karras(in_sigmas=sigmas)
timesteps = np.ar... | 1,320 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_lms_discrete.py |
self.sigmas = torch.from_numpy(sigmas).to(device=device)
self.timesteps = torch.from_numpy(timesteps).to(device=device, dtype=torch.float32)
self._step_index = None
self._begin_index = None
self.sigmas = self.sigmas.to("cpu") # to avoid too much CPU/GPU communication
self.deriv... | 1,320 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_lms_discrete.py |
return indices[pos].item()
# Copied from diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler._init_step_index
def _init_step_index(self, timestep):
if self.begin_index is None:
if isinstance(timestep, torch.Tensor):
timestep = timestep.to(self.timesteps.dev... | 1,320 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_lms_discrete.py |
# interpolate sigmas
w = (low - log_sigma) / (low - high)
w = np.clip(w, 0, 1)
# transform interpolation to time range
t = (1 - w) * low_idx + w * high_idx
t = t.reshape(sigma.shape)
return t
# copied from diffusers.schedulers.scheduling_euler_discrete.EulerDiscrete... | 1,320 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_lms_discrete.py |
# Copied from diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler._convert_to_exponential
def _convert_to_exponential(self, in_sigmas: torch.Tensor, num_inference_steps: int) -> torch.Tensor:
"""Constructs an exponential noise schedule."""
# Hack to make sure that other schedulers... | 1,320 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_lms_discrete.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] ... | 1,320 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_lms_discrete.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... | 1,320 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_lms_discrete.py |
Args:
model_output (`torch.Tensor`):
The direct output from learned diffusion model.
timestep (`float` or `torch.Tensor`):
The current discrete timestep in the diffusion chain.
sample (`torch.Tensor`):
A current instance of a sample cre... | 1,320 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_lms_discrete.py |
"""
if not self.is_scale_input_called:
warnings.warn(
"The `scale_model_input` function should be called before `step` to ensure correct denoising. "
"See `StableDiffusionPipeline` for a usage example."
)
if self.step_index is None:
se... | 1,320 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_lms_discrete.py |
# 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 + input * c_skip
pred_origina... | 1,320 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_lms_discrete.py |
# 3. Compute linear multistep coefficients
order = min(self.step_index + 1, order)
lms_coeffs = [self.get_lms_coefficient(order, self.step_index, curr_order) for curr_order in range(order)]
# 4. Compute previous sample based on the derivatives path
prev_sample = sample + sum(
... | 1,320 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_lms_discrete.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... | 1,320 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_lms_discrete.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... | 1,320 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_lms_discrete.py |
class EulerDiscreteSchedulerState:
common: CommonSchedulerState
# setable values
init_noise_sigma: jnp.ndarray
timesteps: jnp.ndarray
sigmas: jnp.ndarray
num_inference_steps: Optional[int] = None
@classmethod
def create(
cls, common: CommonSchedulerState, init_noise_sigma: jnp.... | 1,321 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_euler_discrete_flax.py |
class FlaxEulerDiscreteSchedulerOutput(FlaxSchedulerOutput):
state: EulerDiscreteSchedulerState | 1,322 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_euler_discrete_flax.py |
class FlaxEulerDiscreteScheduler(FlaxSchedulerMixin, ConfigMixin):
"""
Euler scheduler (Algorithm 2) from Karras et al. (2022) https://arxiv.org/abs/2206.00364. . Based on the original
k-diffusion implementation by Katherine Crowson:
https://github.com/crowsonkb/k-diffusion/blob/481677d114f6ea445aa009cf... | 1,323 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_euler_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 ... | 1,323 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_euler_discrete_flax.py |
the `dtype` used for params and computation.
""" | 1,323 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_euler_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... | 1,323 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_euler_discrete_flax.py |
timesteps = jnp.arange(0, self.config.num_train_timesteps).round()[::-1]
sigmas = ((1 - common.alphas_cumprod) / common.alphas_cumprod) ** 0.5
sigmas = jnp.interp(timesteps, jnp.arange(0, len(sigmas)), sigmas)
sigmas = jnp.concatenate([sigmas, jnp.array([0.0], dtype=self.dtype)])
# stan... | 1,323 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_euler_discrete_flax.py |
Args:
state (`EulerDiscreteSchedulerState`):
the `FlaxEulerDiscreteScheduler` state data class instance.
sample (`jnp.ndarray`):
current instance of sample being created by diffusion process.
timestep (`int`):
current discrete timestep ... | 1,323 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_euler_discrete_flax.py |
Args:
state (`EulerDiscreteSchedulerState`):
the `FlaxEulerDiscreteScheduler` state data class instance.
num_inference_steps (`int`):
the number of diffusion steps used when generating samples with a pre-trained model.
"""
if self.config.timestep_... | 1,323 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_euler_discrete_flax.py |
sigmas = ((1 - state.common.alphas_cumprod) / state.common.alphas_cumprod) ** 0.5
sigmas = jnp.interp(timesteps, jnp.arange(0, len(sigmas)), sigmas)
sigmas = jnp.concatenate([sigmas, jnp.array([0.0], dtype=self.dtype)])
# standard deviation of the initial noise distribution
if self.conf... | 1,323 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_euler_discrete_flax.py |
def step(
self,
state: EulerDiscreteSchedulerState,
model_output: jnp.ndarray,
timestep: int,
sample: jnp.ndarray,
return_dict: bool = True,
) -> Union[FlaxEulerDiscreteSchedulerOutput, Tuple]:
"""
Predict the sample at the previous timestep by reversi... | 1,323 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_euler_discrete_flax.py |
Args:
state (`EulerDiscreteSchedulerState`):
the `FlaxEulerDiscreteScheduler` state data class instance.
model_output (`jnp.ndarray`): direct output from learned diffusion model.
timestep (`int`): current discrete timestep in the diffusion chain.
sample (`... | 1,323 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_euler_discrete_flax.py |
"""
if state.num_inference_steps is None:
raise ValueError(
"Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"
)
(step_index,) = jnp.where(state.timesteps == timestep, size=1)
step_index = step_index[0]
... | 1,323 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_euler_discrete_flax.py |
# 2. Convert to an ODE derivative
derivative = (sample - pred_original_sample) / sigma
# dt = sigma_down - sigma
dt = state.sigmas[step_index + 1] - sigma
prev_sample = sample + derivative * dt
if not return_dict:
return (prev_sample, state)
return FlaxEul... | 1,323 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_euler_discrete_flax.py |
class CMStochasticIterativeSchedulerOutput(BaseOutput):
"""
Output class for the scheduler's `step` function.
Args:
prev_sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` for images):
Computed sample `(x_{t-1})` of previous timestep. `prev_sample` should be use... | 1,324 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_consistency_models.py |
class CMStochasticIterativeScheduler(SchedulerMixin, ConfigMixin):
"""
Multistep and onestep sampling for consistency models.
This model inherits from [`SchedulerMixin`] and [`ConfigMixin`]. Check the superclass documentation for the generic
methods the library implements for all schedulers such as loa... | 1,325 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_consistency_models.py |
Args:
num_train_timesteps (`int`, defaults to 40):
The number of diffusion steps to train the model.
sigma_min (`float`, defaults to 0.002):
Minimum noise magnitude in the sigma schedule. Defaults to 0.002 from the original implementation.
sigma_max (`float`, defaults to ... | 1,325 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_consistency_models.py |
The parameter for calculating the Karras sigma schedule from the EDM
[paper](https://huggingface.co/papers/2206.00364). Defaults to 7.0 from the original implementation.
clip_denoised (`bool`, defaults to `True`):
Whether to clip the denoised outputs to `(-1, 1)`.
timesteps (`Lis... | 1,325 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_consistency_models.py |
order = 1
@register_to_config
def __init__(
self,
num_train_timesteps: int = 40,
sigma_min: float = 0.002,
sigma_max: float = 80.0,
sigma_data: float = 0.5,
s_noise: float = 1.0,
rho: float = 7.0,
clip_denoised: bool = True,
):
# stand... | 1,325 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_consistency_models.py |
@property
def step_index(self):
"""
The index counter for current timestep. It will increase 1 after each scheduler step.
"""
return self._step_index
@property
def begin_index(self):
"""
The index for the first timestep. It should be set from pipeline with `s... | 1,325 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_consistency_models.py |
def scale_model_input(self, sample: torch.Tensor, timestep: Union[float, torch.Tensor]) -> torch.Tensor:
"""
Scales the consistency model input by `(sigma**2 + sigma_data**2) ** 0.5`.
Args:
sample (`torch.Tensor`):
The input sample.
timestep (`float` or `... | 1,325 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_consistency_models.py |
Args:
sigmas (`float` or `np.ndarray`):
A single Karras sigma or an array of Karras sigmas.
Returns:
`float` or `np.ndarray`:
A scaled input timestep or scaled input timestep array.
"""
if not isinstance(sigmas, np.ndarray):
si... | 1,325 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_consistency_models.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.
... | 1,325 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_consistency_models.py |
# Follow DDPMScheduler custom timesteps logic
if timesteps is not None:
for i in range(1, len(timesteps)):
if timesteps[i] >= timesteps[i - 1]:
raise ValueError("`timesteps` must be in descending order.")
if timesteps[0] >= self.config.num_train_times... | 1,325 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_consistency_models.py |
timesteps = np.array(timesteps, dtype=np.int64)
self.custom_timesteps = True
else:
if num_inference_steps > self.config.num_train_timesteps:
raise ValueError(
f"`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_times... | 1,325 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_consistency_models.py |
# Map timesteps to Karras sigmas directly for multistep sampling
# See https://github.com/openai/consistency_models/blob/main/cm/karras_diffusion.py#L675
num_train_timesteps = self.config.num_train_timesteps
ramp = timesteps[::-1].copy()
ramp = ramp / (num_train_timesteps - 1)
si... | 1,325 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_consistency_models.py |
# Modified _convert_to_karras implementation that takes in ramp as argument
def _convert_to_karras(self, ramp):
"""Constructs the noise schedule of Karras et al. (2022)."""
sigma_min: float = self.config.sigma_min
sigma_max: float = self.config.sigma_max
rho = self.config.rho
... | 1,325 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_consistency_models.py |
`epsilon` in the equations for `c_skip` and `c_out` is set to `sigma_min`.
</Tip>
Args:
sigma (`torch.Tensor`):
The current sigma in the Karras sigma schedule.
Returns:
`tuple`:
A two-element tuple where `c_skip` (which weights the curre... | 1,325 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_consistency_models.py |
indices = (schedule_timesteps == timestep).nonzero()
# 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 can ensure we don't accidentally skip a sigma in
# case we start in the middle of the deno... | 1,325 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_consistency_models.py |
def step(
self,
model_output: torch.Tensor,
timestep: Union[float, torch.Tensor],
sample: torch.Tensor,
generator: Optional[torch.Generator] = None,
return_dict: bool = True,
) -> Union[CMStochasticIterativeSchedulerOutput, Tuple]:
"""
Predict the samp... | 1,325 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_consistency_models.py |
Args:
model_output (`torch.Tensor`):
The direct output from the learned diffusion model.
timestep (`float`):
The current timestep in the diffusion chain.
sample (`torch.Tensor`):
A current instance of a sample created by the diffusion p... | 1,325 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_consistency_models.py |
Returns:
[`~schedulers.scheduling_consistency_models.CMStochasticIterativeSchedulerOutput`] or `tuple`:
If return_dict is `True`,
[`~schedulers.scheduling_consistency_models.CMStochasticIterativeSchedulerOutput`] is returned,
otherwise a tuple is returned wher... | 1,325 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_consistency_models.py |
if not self.is_scale_input_called:
logger.warning(
"The `scale_model_input` function should be called before `step` to ensure correct denoising. "
"See `StableDiffusionPipeline` for a usage example."
)
sigma_min = self.config.sigma_min
sigma_max =... | 1,325 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_consistency_models.py |
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