text stringlengths 1 1.02k | class_index int64 0 1.38k | source stringclasses 431
values |
|---|---|---|
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.use_karras_sigmas = use_karras_sigmas
self._step_index = None
self._begin_index = None
... | 1,292 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_heun_discrete.py |
# 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 denoising schedule (e.g. for image-to-image)
pos = 1 if le... | 1,292 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_heun_discrete.py |
@property
def begin_index(self):
"""
The index for the first timestep. It should be set from pipeline with `set_begin_index` method.
"""
return self._begin_index
# Copied from diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler.set_begin_index
def... | 1,292 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_heun_discrete.py |
Args:
sample (`torch.Tensor`):
The input sample.
timestep (`int`, *optional*):
The current timestep in the diffusion chain.
Returns:
`torch.Tensor`:
A scaled input sample.
"""
if self.step_index is None:
... | 1,292 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_heun_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.
... | 1,292 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_heun_discrete.py |
raise ValueError("Must pass exactly one of `num_inference_steps` or `custom_timesteps`.")
if num_inference_steps is not None and timesteps is not None:
raise ValueError("Can only pass one of `num_inference_steps` or `custom_timesteps`.")
if timesteps is not None and self.config.use_karras_si... | 1,292 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_heun_discrete.py |
num_inference_steps = num_inference_steps or len(timesteps)
self.num_inference_steps = num_inference_steps
num_train_timesteps = num_train_timesteps or self.config.num_train_timesteps | 1,292 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_heun_discrete.py |
if timesteps is not None:
timesteps = np.array(timesteps, dtype=np.float32)
else:
# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
if self.config.timestep_spacing == "linspace":
timesteps = np.linspa... | 1,292 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_heun_discrete.py |
# creates integer timesteps by multiplying by ratio
# casting to int to avoid issues when num_inference_step is power of 3
timesteps = (np.arange(num_train_timesteps, 0, -step_ratio)).round().copy().astype(np.float32)
timesteps -= 1
else:
raise... | 1,292 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_heun_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=self.num_... | 1,292 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_heun_discrete.py |
sigmas = np.concatenate([sigmas, [0.0]]).astype(np.float32)
sigmas = torch.from_numpy(sigmas).to(device=device)
self.sigmas = torch.cat([sigmas[:1], sigmas[1:-1].repeat_interleave(2), sigmas[-1:]])
timesteps = torch.from_numpy(timesteps)
timesteps = torch.cat([timesteps[:1], timesteps[1... | 1,292 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_heun_discrete.py |
# get sigmas range
low_idx = np.cumsum((dists >= 0), axis=0).argmax(axis=0).clip(max=log_sigmas.shape[0] - 2)
high_idx = low_idx + 1
low = log_sigmas[low_idx]
high = log_sigmas[high_idx]
# interpolate sigmas
w = (low - log_sigma) / (low - high)
w = np.clip(w, 0,... | 1,292 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_heun_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"):
... | 1,292 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_heun_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... | 1,292 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_heun_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] ... | 1,292 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_heun_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... | 1,292 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_heun_discrete.py |
def step(
self,
model_output: Union[torch.Tensor, np.ndarray],
timestep: Union[float, torch.Tensor],
sample: Union[torch.Tensor, np.ndarray],
return_dict: bool = True,
) -> Union[HeunDiscreteSchedulerOutput, Tuple]:
"""
Predict the sample from the previous tim... | 1,292 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_heun_discrete.py |
Returns:
[`~schedulers.scheduling_heun_discrete.HeunDiscreteSchedulerOutput`] or `tuple`:
If return_dict is `True`, [`~schedulers.scheduling_heun_discrete.HeunDiscreteSchedulerOutput`] is
returned, otherwise a tuple is returned where the first element is the sample tensor.
... | 1,292 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_heun_discrete.py |
# currently only gamma=0 is supported. This usually works best anyways.
# We can support gamma in the future but then need to scale the timestep before
# passing it to the model which requires a change in API
gamma = 0
sigma_hat = sigma * (gamma + 1) # Note: sigma_hat == sigma for now | 1,292 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_heun_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_next
pred_original_sample = sample - sigma_input * model_output
elif self.config.prediction_... | 1,292 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_heun_discrete.py |
if self.config.clip_sample:
pred_original_sample = pred_original_sample.clamp(
-self.config.clip_sample_range, self.config.clip_sample_range
)
if self.state_in_first_order:
# 2. Convert to an ODE derivative for 1st order
derivative = (sample - pre... | 1,292 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_heun_discrete.py |
# free dt and derivative
# Note, this puts the scheduler in "first order mode"
self.prev_derivative = None
self.dt = None
self.sample = None
prev_sample = sample + derivative * dt
# upon completion increase step index by one
self._step_index += 1... | 1,292 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_heun_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... | 1,292 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_heun_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... | 1,292 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_heun_discrete.py |
class KDPM2DiscreteSchedulerOutput(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... | 1,293 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_k_dpm_2_discrete.py |
class KDPM2DiscreteScheduler(SchedulerMixin, ConfigMixin):
"""
KDPM2DiscreteScheduler 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 inherits from [`SchedulerMixin`] a... | 1,294 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_k_dpm_2_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... | 1,294 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_k_dpm_2_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... | 1,294 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_k_dpm_2_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.
""" | 1,294 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_k_dpm_2_discrete.py |
_compatibles = [e.name for e in KarrasDiffusionSchedulers]
order = 2 | 1,294 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_k_dpm_2_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... | 1,294 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_k_dpm_2_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... | 1,294 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_k_dpm_2_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 t... | 1,294 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_k_dpm_2_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_... | 1,294 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_k_dpm_2_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... | 1,294 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_k_dpm_2_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.
... | 1,294 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_k_dpm_2_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... | 1,294 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_k_dpm_2_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'."
) | 1,294 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_k_dpm_2_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_infer... | 1,294 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_k_dpm_2_discrete.py |
self.log_sigmas = torch.from_numpy(log_sigmas).to(device=device)
sigmas = np.concatenate([sigmas, [0.0]]).astype(np.float32)
sigmas = torch.from_numpy(sigmas).to(device=device)
# interpolate sigmas
sigmas_interpol = sigmas.log().lerp(sigmas.roll(1).log(), 0.5).exp()
self.sigmas... | 1,294 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_k_dpm_2_discrete.py |
# interpolate timesteps
sigmas_interpol = sigmas_interpol.cpu()
log_sigmas = self.log_sigmas.cpu()
timesteps_interpol = np.array(
[self._sigma_to_t(sigma_interpol, log_sigmas) for sigma_interpol in sigmas_interpol]
)
timesteps_interpol = torch.from_numpy(timesteps_int... | 1,294 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_k_dpm_2_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()... | 1,294 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_k_dpm_2_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... | 1,294 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_k_dpm_2_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... | 1,294 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_k_dpm_2_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... | 1,294 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_k_dpm_2_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... | 1,294 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_k_dpm_2_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"):
... | 1,294 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_k_dpm_2_discrete.py |
def step(
self,
model_output: Union[torch.Tensor, np.ndarray],
timestep: Union[float, torch.Tensor],
sample: Union[torch.Tensor, np.ndarray],
return_dict: bool = True,
) -> Union[KDPM2DiscreteSchedulerOutput, Tuple]:
"""
Predict the sample from the previous ti... | 1,294 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_k_dpm_2_discrete.py |
Returns:
[`~schedulers.scheduling_k_dpm_2_discrete.KDPM2DiscreteSchedulerOutput`] or `tuple`:
If return_dict is `True`, [`~schedulers.scheduling_k_dpm_2_discrete.KDPM2DiscreteSchedulerOutput`] is
returned, otherwise a tuple is returned where the first element is the sample te... | 1,294 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_k_dpm_2_discrete.py |
# currently only gamma=0 is supported. This usually works best anyways.
# We can support gamma in the future but then need to scale the timestep before
# passing it to the model which requires a change in API
gamma = 0
sigma_hat = sigma * (gamma + 1) # Note: sigma_hat == sigma for now | 1,294 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_k_dpm_2_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... | 1,294 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_k_dpm_2_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
else... | 1,294 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_k_dpm_2_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... | 1,294 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_k_dpm_2_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... | 1,294 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_k_dpm_2_discrete.py |
class PNDMSchedulerState:
common: CommonSchedulerState
final_alpha_cumprod: jnp.ndarray
# setable values
init_noise_sigma: jnp.ndarray
timesteps: jnp.ndarray
num_inference_steps: Optional[int] = None
prk_timesteps: Optional[jnp.ndarray] = None
plms_timesteps: Optional[jnp.ndarray] = Non... | 1,295 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_pndm_flax.py |
class FlaxPNDMSchedulerOutput(FlaxSchedulerOutput):
state: PNDMSchedulerState | 1,296 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_pndm_flax.py |
class FlaxPNDMScheduler(FlaxSchedulerMixin, ConfigMixin):
"""
Pseudo numerical methods for diffusion models (PNDM) proposes using more advanced ODE integration techniques,
namely Runge-Kutta method and a linear multi-step method.
[`~ConfigMixin`] takes care of storing all config attributes that are pas... | 1,297 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_pndm_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 ... | 1,297 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_pndm_flax.py |
step there is no previous alpha. When this option is `True` the previous alpha product is fixed to `1`,
otherwise it uses the value of alpha at step 0.
steps_offset (`int`, default `0`):
An offset added to the inference steps, as required by some model families.
prediction_type (... | 1,297 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_pndm_flax.py |
_compatibles = [e.name for e in FlaxKarrasDiffusionSchedulers]
dtype: jnp.dtype
pndm_order: int
@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: flo... | 1,297 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_pndm_flax.py |
def create_state(self, common: Optional[CommonSchedulerState] = None) -> PNDMSchedulerState:
if common is None:
common = CommonSchedulerState.create(self)
# At every step in ddim, we are looking into the previous alphas_cumprod
# For the final step, there is no previous alphas_cumpr... | 1,297 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_pndm_flax.py |
return PNDMSchedulerState.create(
common=common,
final_alpha_cumprod=final_alpha_cumprod,
init_noise_sigma=init_noise_sigma,
timesteps=timesteps,
)
def set_timesteps(self, state: PNDMSchedulerState, num_inference_steps: int, shape: Tuple) -> PNDMSchedulerStat... | 1,297 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_pndm_flax.py |
step_ratio = self.config.num_train_timesteps // num_inference_steps
# creates integer timesteps by multiplying by ratio
# rounding to avoid issues when num_inference_step is power of 3
_timesteps = (jnp.arange(0, num_inference_steps) * step_ratio).round() + self.config.steps_offset
if s... | 1,297 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_pndm_flax.py |
else:
prk_timesteps = _timesteps[-self.pndm_order :].repeat(2) + jnp.tile(
jnp.array([0, self.config.num_train_timesteps // num_inference_steps // 2], dtype=jnp.int32),
self.pndm_order,
)
prk_timesteps = (prk_timesteps[:-1].repeat(2)[1:-1])[::-1]
... | 1,297 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_pndm_flax.py |
return state.replace(
timesteps=timesteps,
num_inference_steps=num_inference_steps,
prk_timesteps=prk_timesteps,
plms_timesteps=plms_timesteps,
cur_model_output=cur_model_output,
counter=counter,
cur_sample=cur_sample,
ets=e... | 1,297 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_pndm_flax.py |
def step(
self,
state: PNDMSchedulerState,
model_output: jnp.ndarray,
timestep: int,
sample: jnp.ndarray,
return_dict: bool = True,
) -> Union[FlaxPNDMSchedulerOutput, Tuple]:
"""
Predict the sample at the previous timestep by reversing the SDE. Core f... | 1,297 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_pndm_flax.py |
Args:
state (`PNDMSchedulerState`): the `FlaxPNDMScheduler` 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`):
cur... | 1,297 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_pndm_flax.py |
if self.config.skip_prk_steps:
prev_sample, state = self.step_plms(state, model_output, timestep, sample)
else:
prk_prev_sample, prk_state = self.step_prk(state, model_output, timestep, sample)
plms_prev_sample, plms_state = self.step_plms(state, model_output, timestep, sampl... | 1,297 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_pndm_flax.py |
return FlaxPNDMSchedulerOutput(prev_sample=prev_sample, state=state)
def step_prk(
self,
state: PNDMSchedulerState,
model_output: jnp.ndarray,
timestep: int,
sample: jnp.ndarray,
) -> Union[FlaxPNDMSchedulerOutput, Tuple]:
"""
Step function propagating th... | 1,297 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_pndm_flax.py |
Returns:
[`FlaxPNDMSchedulerOutput`] or `tuple`: [`FlaxPNDMSchedulerOutput`] if `return_dict` is True, otherwise a
`tuple`. When returning a tuple, the first element is the sample tensor.
"""
if state.num_inference_steps is None:
raise ValueError(
"N... | 1,297 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_pndm_flax.py |
state = state.replace(
cur_model_output=jax.lax.select_n(
state.counter % 4,
state.cur_model_output + 1 / 6 * model_output, # remainder 0
state.cur_model_output + 1 / 3 * model_output, # remainder 1
state.cur_model_output + 1 / 3 * model_outp... | 1,297 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_pndm_flax.py |
cur_sample = state.cur_sample
prev_sample = self._get_prev_sample(state, cur_sample, timestep, prev_timestep, model_output)
state = state.replace(counter=state.counter + 1)
return (prev_sample, state)
def step_plms(
self,
state: PNDMSchedulerState,
model_output: jnp... | 1,297 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_pndm_flax.py |
Args:
state (`PNDMSchedulerState`): the `FlaxPNDMScheduler` 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`):
cur... | 1,297 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_pndm_flax.py |
# NOTE: There is no way to check in the jitted runtime if the prk mode was ran before
prev_timestep = timestep - self.config.num_train_timesteps // state.num_inference_steps
prev_timestep = jnp.where(prev_timestep > 0, prev_timestep, 0)
# Reference:
# if state.counter != 1:
# ... | 1,297 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_pndm_flax.py |
# Reference:
# if len(state.ets) == 1 and state.counter == 0:
# model_output = model_output
# state.cur_sample = sample
# elif len(state.ets) == 1 and state.counter == 1:
# model_output = (model_output + state.ets[-1]) / 2
# sample = state.cur_sample
... | 1,297 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_pndm_flax.py |
state = state.replace(
ets=jax.lax.select(
state.counter != 1,
state.ets.at[0:3].set(state.ets[1:4]).at[3].set(model_output), # counter != 1
state.ets, # counter 1
),
cur_sample=jax.lax.select(
state.counter != 1,
... | 1,297 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_pndm_flax.py |
sample = state.cur_sample
model_output = state.cur_model_output
prev_sample = self._get_prev_sample(state, sample, timestep, prev_timestep, model_output)
state = state.replace(counter=state.counter + 1)
return (prev_sample, state)
def _get_prev_sample(self, state: PNDMSchedulerStat... | 1,297 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_pndm_flax.py |
# Notation (<variable name> -> <name in paper>
# alpha_prod_t -> α_t
# alpha_prod_t_prev -> α_(t−δ)
# beta_prod_t -> (1 - α_t)
# beta_prod_t_prev -> (1 - α_(t−δ))
# sample -> x_t
# model_output -> e_θ(x_t, t)
# prev_sample -> x_(t−δ)
alpha_prod_t = state.c... | 1,297 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_pndm_flax.py |
# corresponds to (α_(t−δ) - α_t) divided by
# denominator of x_t in formula (9) and plus 1
# Note: (α_(t−δ) - α_t) / (sqrt(α_t) * (sqrt(α_(t−δ)) + sqr(α_t))) =
# sqrt(α_(t−δ)) / sqrt(α_t))
sample_coeff = (alpha_prod_t_prev / alpha_prod_t) ** (0.5)
# corresponds to denominator of... | 1,297 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_pndm_flax.py |
def __len__(self):
return self.config.num_train_timesteps | 1,297 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_pndm_flax.py |
class DDIMSchedulerOutput(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 as next ... | 1,298 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpm_cogvideox.py |
class CogVideoXDPMScheduler(SchedulerMixin, ConfigMixin):
"""
`DDIMScheduler` extends the denoising procedure introduced in denoising diffusion probabilistic models (DDPMs) with
non-Markovian guidance.
This model inherits from [`SchedulerMixin`] and [`ConfigMixin`]. Check the superclass documentation f... | 1,299 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpm_cogvideox.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_... | 1,299 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpm_cogvideox.py |
Each diffusion step uses the alphas product value at that step and at the previous one. For the final step
there is no previous alpha. When this option is `True` the previous alpha product is fixed to `1`,
otherwise it uses the alpha value at step 0.
steps_offset (`int`, defaults to 0):
... | 1,299 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpm_cogvideox.py |
dynamic_thresholding_ratio (`float`, defaults to 0.995):
The ratio for the dynamic thresholding method. Valid only when `thresholding=True`.
sample_max_value (`float`, defaults to 1.0):
The threshold value for dynamic thresholding. Valid only when `thresholding=True`.
timestep_sp... | 1,299 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpm_cogvideox.py |
[`--offset_noise`](https://github.com/huggingface/diffusers/blob/74fd735eb073eb1d774b1ab4154a0876eb82f055/examples/dreambooth/train_dreambooth.py#L506).
""" | 1,299 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpm_cogvideox.py |
_compatibles = [e.name for e in KarrasDiffusionSchedulers]
order = 1 | 1,299 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpm_cogvideox.py |
@register_to_config
def __init__(
self,
num_train_timesteps: int = 1000,
beta_start: float = 0.00085,
beta_end: float = 0.0120,
beta_schedule: str = "scaled_linear",
trained_betas: Optional[Union[np.ndarray, List[float]]] = None,
clip_sample: bool = True,
... | 1,299 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpm_cogvideox.py |
# 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.float64) ** 2
elif beta_schedule == "squaredcos_cap_v2":
# Glide cosine schedule
self.betas = betas_for_alpha_bar(num_t... | 1,299 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpm_cogvideox.py |
self.alphas = 1.0 - self.betas
self.alphas_cumprod = torch.cumprod(self.alphas, dim=0)
# Modify: SNR shift following SD3
self.alphas_cumprod = self.alphas_cumprod / (snr_shift_scale + (1 - snr_shift_scale) * self.alphas_cumprod)
# Rescale for zero SNR
if rescale_betas_zero_snr:... | 1,299 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpm_cogvideox.py |
# setable values
self.num_inference_steps = None
self.timesteps = torch.from_numpy(np.arange(0, num_train_timesteps)[::-1].copy().astype(np.int64))
def _get_variance(self, timestep, prev_timestep):
alpha_prod_t = self.alphas_cumprod[timestep]
alpha_prod_t_prev = self.alphas_cumprod[... | 1,299 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpm_cogvideox.py |
Args:
sample (`torch.Tensor`):
The input sample.
timestep (`int`, *optional*):
The current timestep in the diffusion chain.
Returns:
`torch.Tensor`:
A scaled input sample.
"""
return sample
def set_timestep... | 1,299 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpm_cogvideox.py |
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_timesteps`:"
f" {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle"... | 1,299 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpm_cogvideox.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)
.round()[::-1]
... | 1,299 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpm_cogvideox.py |
# creates integer timesteps by multiplying by ratio
# casting to int to avoid issues when num_inference_step is power of 3
timesteps = np.round(np.arange(self.config.num_train_timesteps, 0, -step_ratio)).astype(np.int64)
timesteps -= 1
else:
raise ValueError(
... | 1,299 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpm_cogvideox.py |
self.timesteps = torch.from_numpy(timesteps).to(device)
def get_variables(self, alpha_prod_t, alpha_prod_t_prev, alpha_prod_t_back=None):
lamb = ((alpha_prod_t / (1 - alpha_prod_t)) ** 0.5).log()
lamb_next = ((alpha_prod_t_prev / (1 - alpha_prod_t_prev)) ** 0.5).log()
h = lamb_next - lamb
... | 1,299 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpm_cogvideox.py |
if alpha_prod_t_back is not None:
mult3 = 1 + 1 / (2 * r)
mult4 = 1 / (2 * r)
return mult1, mult2, mult3, mult4
else:
return mult1, mult2
def step(
self,
model_output: torch.Tensor,
old_pred_original_sample: torch.Tensor,
times... | 1,299 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpm_cogvideox.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... | 1,299 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpm_cogvideox.py |
variance_noise (`torch.Tensor`):
Alternative to generating noise with `generator` by directly providing the noise for the variance
itself. Useful for methods such as [`CycleDiffusion`].
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to re... | 1,299 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpm_cogvideox.py |
Returns:
[`~schedulers.scheduling_ddim.DDIMSchedulerOutput`] or `tuple`:
If return_dict is `True`, [`~schedulers.scheduling_ddim.DDIMSchedulerOutput`] is returned, otherwise a
tuple is returned where the first element is the sample tensor.
"""
if self.num_inf... | 1,299 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpm_cogvideox.py |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.