text stringlengths 1 1.02k | class_index int64 0 1.38k | source stringclasses 431
values |
|---|---|---|
"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`",
)
model_output_list = self.model_outputs
sigma_t, sigma_s0 = (
self.sigmas[self... | 1,278 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_sasolver.py |
gradient_part = torch.zeros_like(sample)
h = lambda_t - lambda_s0
lambda_list = []
for i in range(order):
si = self.step_index - i
alpha_si, sigma_si = self._sigma_to_alpha_sigma_t(self.sigmas[si])
lambda_si = torch.log(alpha_si) - torch.log(sigma_si)
... | 1,278 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_sasolver.py |
if self.predict_x0:
if (
order == 2
): ## if order = 2 we do a modification that does not influence the convergence order similar to unipc. Note: This is used only for few steps sampling.
# The added term is O(h^3). Empirically we find it will slightly improve th... | 1,278 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_sasolver.py |
gradient_coefficients[0] += (
1.0
* torch.exp((1 + tau**2) * lambda_t)
* (h**2 / 2 - (h * (1 + tau**2) - 1 + torch.exp((1 + tau**2) * (-h))) / ((1 + tau**2) ** 2))
/ (lambda_s0 - temp_lambda_s)
)
gradient_coe... | 1,278 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_sasolver.py |
for i in range(order):
if self.predict_x0:
gradient_part += (
(1 + tau**2)
* sigma_t
* torch.exp(-(tau**2) * lambda_t)
* gradient_coefficients[i]
* model_output_list[-(i + 1)]
... | 1,278 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_sasolver.py |
def stochastic_adams_moulton_update(
self,
this_model_output: torch.Tensor,
*args,
last_sample: torch.Tensor,
last_noise: torch.Tensor,
this_sample: torch.Tensor,
order: int,
tau: torch.Tensor,
**kwargs,
) -> torch.Tensor:
"""
O... | 1,278 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_sasolver.py |
this_timestep = args[0] if len(args) > 0 else kwargs.pop("this_timestep", None)
if last_sample is None:
if len(args) > 1:
last_sample = args[1]
else:
raise ValueError(" missing`last_sample` as a required keyward argument")
if last_noise is None:
... | 1,278 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_sasolver.py |
raise ValueError(" missing`tau` as a required keyward argument")
if this_timestep is not None:
deprecate(
"this_timestep",
"1.0.0",
"Passing `this_timestep` is deprecated and has no effect as model output conversion is now handled via an internal count... | 1,278 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_sasolver.py |
model_output_list = self.model_outputs
sigma_t, sigma_s0 = (
self.sigmas[self.step_index],
self.sigmas[self.step_index - 1],
)
alpha_t, sigma_t = self._sigma_to_alpha_sigma_t(sigma_t)
alpha_s0, sigma_s0 = self._sigma_to_alpha_sigma_t(sigma_s0)
lambda_t = ... | 1,278 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_sasolver.py |
if self.predict_x0:
if (
order == 2
): ## if order = 2 we do a modification that does not influence the convergence order similar to UniPC. Note: This is used only for few steps sampling.
# The added term is O(h^3). Empirically we find it will slightly improve th... | 1,278 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_sasolver.py |
* (h / 2 - (h * (1 + tau**2) - 1 + torch.exp((1 + tau**2) * (-h))) / ((1 + tau**2) ** 2 * h))
) | 1,278 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_sasolver.py |
for i in range(order):
if self.predict_x0:
gradient_part += (
(1 + tau**2)
* sigma_t
* torch.exp(-(tau**2) * lambda_t)
* gradient_coefficients[i]
* model_prev_list[-(i + 1)]
)
... | 1,278 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_sasolver.py |
# Copied from diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler.index_for_timestep
def index_for_timestep(self, timestep, schedule_timesteps=None):
if schedule_timesteps is None:
schedule_timesteps = self.timesteps
index_candidates = (schedule_timesteps == ... | 1,278 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_sasolver.py |
# Copied from diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler._init_step_index
def _init_step_index(self, timestep):
"""
Initialize the step_index counter for the scheduler.
"""
if self.begin_index is None:
if isinstance(timestep, torch.Te... | 1,278 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_sasolver.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... | 1,278 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_sasolver.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)
use_corrector = self.... | 1,278 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_sasolver.py |
for i in range(max(self.config.predictor_order, self.config.corrector_order - 1) - 1):
self.model_outputs[i] = self.model_outputs[i + 1]
self.timestep_list[i] = self.timestep_list[i + 1]
self.model_outputs[-1] = model_output_convert
self.timestep_list[-1] = timestep
noi... | 1,278 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_sasolver.py |
self.this_predictor_order = min(this_predictor_order, self.lower_order_nums + 1) # warmup for multistep
self.this_corrector_order = min(this_corrector_order, self.lower_order_nums + 2) # warmup for multistep
assert self.this_predictor_order > 0
assert self.this_corrector_order > 0
sel... | 1,278 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_sasolver.py |
return SchedulerOutput(prev_sample=prev_sample)
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 the
current timestep.
Args:
sampl... | 1,278 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_sasolver.py |
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler.add_noise
def add_noise(
self,
original_samples: torch.Tensor,
noise: torch.Tensor,
timesteps: torch.IntTensor,
) -> torch.Tensor:
# Make sure alphas_cumprod and timestep have same device and dtype as origin... | 1,278 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_sasolver.py |
sqrt_one_minus_alpha_prod = (1 - alphas_cumprod[timesteps]) ** 0.5
sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.flatten()
while len(sqrt_one_minus_alpha_prod.shape) < len(original_samples.shape):
sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.unsqueeze(-1)
noisy_samp... | 1,278 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_sasolver.py |
class KarrasVeSchedulerState:
# setable values
num_inference_steps: Optional[int] = None
timesteps: Optional[jnp.ndarray] = None
schedule: Optional[jnp.ndarray] = None # sigma(t_i)
@classmethod
def create(cls):
return cls() | 1,279 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_karras_ve_flax.py |
class FlaxKarrasVeOutput(BaseOutput):
"""
Output class for the scheduler's step function output.
Args:
prev_sample (`jnp.ndarray` 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 model ... | 1,280 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_karras_ve_flax.py |
class FlaxKarrasVeScheduler(FlaxSchedulerMixin, ConfigMixin):
"""
Stochastic sampling from Karras et al. [1] tailored to the Variance-Expanding (VE) models [2]. Use Algorithm 2 and
the VE column of Table 1 from [1] for reference.
[1] Karras, Tero, et al. "Elucidating the Design Space of Diffusion-Based... | 1,281 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_karras_ve_flax.py |
For more details on the parameters, see the original paper's Appendix E.: "Elucidating the Design Space of
Diffusion-Based Generative Models." https://arxiv.org/abs/2206.00364. The grid search values used to find the
optimal {s_noise, s_churn, s_min, s_max} for a specific model are described in Table 5 of the p... | 1,281 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_karras_ve_flax.py |
@property
def has_state(self):
return True
@register_to_config
def __init__(
self,
sigma_min: float = 0.02,
sigma_max: float = 100,
s_noise: float = 1.007,
s_churn: float = 80,
s_min: float = 0.05,
s_max: float = 50,
):
pass
d... | 1,281 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_karras_ve_flax.py |
"""
timesteps = jnp.arange(0, num_inference_steps)[::-1].copy()
schedule = [
(
self.config.sigma_max**2
* (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
)
for i in timesteps
]
r... | 1,281 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_karras_ve_flax.py |
TODO Args:
"""
if self.config.s_min <= sigma <= self.config.s_max:
gamma = min(self.config.s_churn / state.num_inference_steps, 2**0.5 - 1)
else:
gamma = 0
# sample eps ~ N(0, S_noise^2 * I)
key = random.split(key, num=1)
eps = self.config.s_noise... | 1,281 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_karras_ve_flax.py |
Args:
state (`KarrasVeSchedulerState`): the `FlaxKarrasVeScheduler` state data class.
model_output (`torch.Tensor` or `np.ndarray`): direct output from learned diffusion model.
sigma_hat (`float`): TODO
sigma_prev (`float`): TODO
sample_hat (`torch.Tensor` or ... | 1,281 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_karras_ve_flax.py |
if not return_dict:
return (sample_prev, derivative, state)
return FlaxKarrasVeOutput(prev_sample=sample_prev, derivative=derivative, state=state)
def step_correct(
self,
state: KarrasVeSchedulerState,
model_output: jnp.ndarray,
sigma_hat: float,
sigma_p... | 1,281 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_karras_ve_flax.py |
Args:
state (`KarrasVeSchedulerState`): the `FlaxKarrasVeScheduler` state data class.
model_output (`torch.Tensor` or `np.ndarray`): direct output from learned diffusion model.
sigma_hat (`float`): TODO
sigma_prev (`float`): TODO
sample_hat (`torch.Tensor` or ... | 1,281 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_karras_ve_flax.py |
if not return_dict:
return (sample_prev, derivative, state)
return FlaxKarrasVeOutput(prev_sample=sample_prev, derivative=derivative, state=state)
def add_noise(self, state: KarrasVeSchedulerState, original_samples, noise, timesteps):
raise NotImplementedError() | 1,281 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_karras_ve_flax.py |
class FlowMatchEulerDiscreteSchedulerOutput(BaseOutput):
"""
Output class for the scheduler's `step` function output.
Args:
prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):
Computed sample `(x_{t-1})` of previous timestep. `prev_sample` ... | 1,282 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_flow_match_euler_discrete.py |
class FlowMatchEulerDiscreteScheduler(SchedulerMixin, ConfigMixin):
"""
Euler scheduler.
This model inherits from [`SchedulerMixin`] and [`ConfigMixin`]. Check the superclass documentation for the generic
methods the library implements for all schedulers such as loading and saving. | 1,283 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_flow_match_euler_discrete.py |
Args:
num_train_timesteps (`int`, defaults to 1000):
The number of diffusion steps to train the model.
shift (`float`, defaults to 1.0):
The shift value for the timestep schedule.
use_dynamic_shifting (`bool`, defaults to False):
Whether to apply timestep shif... | 1,283 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_flow_match_euler_discrete.py |
invert_sigmas (`bool`, defaults to False):
Whether to invert the sigmas.
shift_terminal (`float`, defaults to None):
The end value of the shifted timestep schedule.
use_karras_sigmas (`bool`, defaults to False):
Whether to use Karras sigmas for step sizes in the noise... | 1,283 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_flow_match_euler_discrete.py |
_compatibles = []
order = 1 | 1,283 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_flow_match_euler_discrete.py |
@register_to_config
def __init__(
self,
num_train_timesteps: int = 1000,
shift: float = 1.0,
use_dynamic_shifting=False,
base_shift: Optional[float] = 0.5,
max_shift: Optional[float] = 1.15,
base_image_seq_len: Optional[int] = 256,
max_image_seq_len: O... | 1,283 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_flow_match_euler_discrete.py |
"Only one of `config.use_beta_sigmas`, `config.use_exponential_sigmas`, `config.use_karras_sigmas` can be used."
)
timesteps = np.linspace(1, num_train_timesteps, num_train_timesteps, dtype=np.float32)[::-1].copy()
timesteps = torch.from_numpy(timesteps).to(dtype=torch.float32) | 1,283 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_flow_match_euler_discrete.py |
sigmas = timesteps / num_train_timesteps
if not use_dynamic_shifting:
# when use_dynamic_shifting is True, we apply the timestep shifting on the fly based on the image resolution
sigmas = shift * sigmas / (1 + (shift - 1) * sigmas)
self.timesteps = sigmas * num_train_timesteps
... | 1,283 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_flow_match_euler_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... | 1,283 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_flow_match_euler_discrete.py |
Args:
sample (`torch.FloatTensor`):
The input sample.
timestep (`int`, *optional*):
The current timestep in the diffusion chain.
Returns:
`torch.FloatTensor`:
A scaled input sample.
"""
# Make sure sigmas and ti... | 1,283 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_flow_match_euler_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 timestep]
elif self.step_index is not None:
# add_noise is called af... | 1,283 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_flow_match_euler_discrete.py |
def time_shift(self, mu: float, sigma: float, t: torch.Tensor):
return math.exp(mu) / (math.exp(mu) + (1 / t - 1) ** sigma)
def stretch_shift_to_terminal(self, t: torch.Tensor) -> torch.Tensor:
r"""
Stretches and shifts the timestep schedule to ensure it terminates at the configured `shift_... | 1,283 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_flow_match_euler_discrete.py |
def set_timesteps(
self,
num_inference_steps: int = None,
device: Union[str, torch.device] = None,
sigmas: Optional[List[float]] = None,
mu: Optional[float] = None,
):
"""
Sets the discrete timesteps used for the diffusion chain (to be run before inference).
... | 1,283 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_flow_match_euler_discrete.py |
sigmas = timesteps / self.config.num_train_timesteps
else:
sigmas = np.array(sigmas).astype(np.float32)
num_inference_steps = len(sigmas)
self.num_inference_steps = num_inference_steps
if self.config.use_dynamic_shifting:
sigmas = self.time_shift(mu, 1.0, sig... | 1,283 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_flow_match_euler_discrete.py |
sigmas = torch.from_numpy(sigmas).to(dtype=torch.float32, device=device)
timesteps = sigmas * self.config.num_train_timesteps
if self.config.invert_sigmas:
sigmas = 1.0 - sigmas
timesteps = sigmas * self.config.num_train_timesteps
sigmas = torch.cat([sigmas, torch.on... | 1,283 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_flow_match_euler_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... | 1,283 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_flow_match_euler_discrete.py |
def step(
self,
model_output: torch.FloatTensor,
timestep: Union[float, torch.FloatTensor],
sample: torch.FloatTensor,
s_churn: float = 0.0,
s_tmin: float = 0.0,
s_tmax: float = float("inf"),
s_noise: float = 1.0,
generator: Optional[torch.Generato... | 1,283 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_flow_match_euler_discrete.py |
Args:
model_output (`torch.FloatTensor`):
The direct output from learned diffusion model.
timestep (`float`):
The current discrete timestep in the diffusion chain.
sample (`torch.FloatTensor`):
A current instance of a sample created by ... | 1,283 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_flow_match_euler_discrete.py |
Returns:
[`~schedulers.scheduling_euler_discrete.EulerDiscreteSchedulerOutput`] or `tuple`:
If return_dict is `True`, [`~schedulers.scheduling_euler_discrete.EulerDiscreteSchedulerOutput`] is
returned, otherwise a tuple is returned where the first element is the sample tensor... | 1,283 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_flow_match_euler_discrete.py |
# Upcast to avoid precision issues when computing prev_sample
sample = sample.to(torch.float32)
sigma = self.sigmas[self.step_index]
sigma_next = self.sigmas[self.step_index + 1]
prev_sample = sample + (sigma_next - sigma) * model_output
# Cast sample back to model compatible ... | 1,283 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_flow_match_euler_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"):
... | 1,283 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_flow_match_euler_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... | 1,283 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_flow_match_euler_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] ... | 1,283 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_flow_match_euler_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... | 1,283 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_flow_match_euler_discrete.py |
class EulerDiscreteSchedulerOutput(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... | 1,284 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_euler_discrete.py |
class EulerDiscreteScheduler(SchedulerMixin, ConfigMixin):
"""
Euler scheduler.
This model inherits from [`SchedulerMixin`] and [`ConfigMixin`]. Check the superclass documentation for the generic
methods the library implements for all schedulers such as loading and saving. | 1,285 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_euler_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_... | 1,285 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_euler_discrete.py |
Video](https://imagen.research.google/video/paper.pdf) paper).
interpolation_type(`str`, defaults to `"linear"`, *optional*):
The interpolation type to compute intermediate sigmas for the scheduler denoising steps. Should be on of
`"linear"` or `"log_linear"`.
use_karras_sigmas (... | 1,285 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_euler_discrete.py |
Sampling is All You Need](https://huggingface.co/papers/2407.12173) for more information.
timestep_spacing (`str`, defaults to `"linspace"`):
The way the timesteps should be scaled. Refer to Table 2 of the [Common Diffusion Noise Schedules and
Sample Steps are Flawed](https://huggingface... | 1,285 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_euler_discrete.py |
The final `sigma` value for the noise schedule during the sampling process. If `"sigma_min"`, the final
sigma is the same as the last sigma in the training schedule. If `zero`, the final sigma is set to 0.
""" | 1,285 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_euler_discrete.py |
_compatibles = [e.name for e in KarrasDiffusionSchedulers]
order = 1 | 1,285 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_euler_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,
prediction_type: str = "epsilon",
... | 1,285 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_euler_discrete.py |
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.config.use_karras_sigmas]) > 1:
raise ValueError(
"Only one of `config.use_beta_sigmas`, `config.use_exponential_sigmas`, `con... | 1,285 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_euler_discrete.py |
self.betas = betas_for_alpha_bar(num_train_timesteps)
else:
raise NotImplementedError(f"{beta_schedule} is not implemented for {self.__class__}") | 1,285 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_euler_discrete.py |
if rescale_betas_zero_snr:
self.betas = rescale_zero_terminal_snr(self.betas)
self.alphas = 1.0 - self.betas
self.alphas_cumprod = torch.cumprod(self.alphas, dim=0)
if rescale_betas_zero_snr:
# Close to 0 without being 0 so first sigma is not inf
# FP16 smal... | 1,285 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_euler_discrete.py |
# TODO: Support the full EDM scalings for all prediction types and timestep types
if timestep_type == "continuous" and prediction_type == "v_prediction":
self.timesteps = torch.Tensor([0.25 * sigma.log() for sigma in sigmas])
else:
self.timesteps = timesteps
self.sigmas ... | 1,285 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_euler_discrete.py |
@property
def init_noise_sigma(self):
# standard deviation of the initial noise distribution
max_sigma = max(self.sigmas) if isinstance(self.sigmas, list) else self.sigmas.max()
if self.config.timestep_spacing in ["linspace", "trailing"]:
return max_sigma
return (max_sig... | 1,285 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_euler_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_... | 1,285 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_euler_discrete.py |
Returns:
`torch.Tensor`:
A scaled input sample.
"""
if self.step_index is None:
self._init_step_index(timestep)
sigma = self.sigmas[self.step_index]
sample = sample / ((sigma**2 + 1) ** 0.5)
self.is_scale_input_called = True
retur... | 1,285 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_euler_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.
... | 1,285 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_euler_discrete.py |
`num_inference_steps` and `timesteps` must be `None`, and the timesteps will be generated based on the
custom sigmas schedule.
""" | 1,285 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_euler_discrete.py |
if timesteps is not None and sigmas is not None:
raise ValueError("Only one of `timesteps` or `sigmas` should be set.")
if num_inference_steps is None and timesteps is None and sigmas is None:
raise ValueError("Must pass exactly one of `num_inference_steps` or `timesteps` or `sigmas.")
... | 1,285 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_euler_discrete.py |
if (
timesteps is not None
and self.config.timestep_type == "continuous"
and self.config.prediction_type == "v_prediction"
):
raise ValueError(
"Cannot set `timesteps` with `config.timestep_type = 'continuous'` and `config.prediction_type = 'v_pred... | 1,285 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_euler_discrete.py |
if num_inference_steps is None:
num_inference_steps = len(timesteps) if timesteps is not None else len(sigmas) - 1
self.num_inference_steps = num_inference_steps
if sigmas is not None:
log_sigmas = np.log(np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5))
... | 1,285 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_euler_discrete.py |
else:
if timesteps is not None:
timesteps = np.array(timesteps).astype(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":
... | 1,285 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_euler_discrete.py |
timesteps += self.config.steps_offset
elif self.config.timestep_spacing == "trailing":
step_ratio = self.config.num_train_timesteps / self.num_inference_steps
# creates integer timesteps by multiplying by ratio
# casting to int to avoid issues ... | 1,285 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_euler_discrete.py |
sigmas = np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)
log_sigmas = np.log(sigmas)
if self.config.interpolation_type == "linear":
sigmas = np.interp(timesteps, np.arange(0, len(sigmas)), sigmas)
elif self.config.interpolation_type == "log_linear":... | 1,285 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_euler_discrete.py |
elif self.config.use_exponential_sigmas:
sigmas = self._convert_to_exponential(in_sigmas=sigmas, num_inference_steps=num_inference_steps)
timesteps = np.array([self._sigma_to_t(sigma, log_sigmas) for sigma in sigmas])
elif self.config.use_beta_sigmas:
sigmas ... | 1,285 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_euler_discrete.py |
sigmas = np.concatenate([sigmas, [sigma_last]]).astype(np.float32)
sigmas = torch.from_numpy(sigmas).to(dtype=torch.float32, device=device)
# TODO: Support the full EDM scalings for all prediction types and timestep types
if self.config.timestep_type == "continuous" and self.config.prediction_... | 1,285 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_euler_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,... | 1,285 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_euler_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"):
... | 1,285 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_euler_discrete.py |
# Copied from https://github.com/crowsonkb/k-diffusion/blob/686dbad0f39640ea25c8a8c6a6e56bb40eacefa2/k_diffusion/sampling.py#L26
def _convert_to_exponential(self, in_sigmas: torch.Tensor, num_inference_steps: int) -> torch.Tensor:
"""Constructs an exponential noise schedule."""
# Hack to make sure ... | 1,285 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_euler_discrete.py |
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] (Lee et. al, 2024)"""
# Hack to make sure that other schedulers which copy this function don't b... | 1,285 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_euler_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... | 1,285 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_euler_discrete.py |
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_index = self.index_for_timestep(timestep)
else:
self._step_index = self._begin_index
... | 1,285 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_euler_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... | 1,285 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_euler_discrete.py |
Returns:
[`~schedulers.scheduling_euler_discrete.EulerDiscreteSchedulerOutput`] or `tuple`:
If return_dict is `True`, [`~schedulers.scheduling_euler_discrete.EulerDiscreteSchedulerOutput`] is
returned, otherwise a tuple is returned where the first element is the sample tensor... | 1,285 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_euler_discrete.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."
)
if self.step_index is None:
self._init_st... | 1,285 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_euler_discrete.py |
# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
# NOTE: "original_sample" should not be an expected prediction_type but is left in for
# backwards compatibility
if self.config.prediction_type == "original_sample" or self.config.prediction_type == "sample":
... | 1,285 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_euler_discrete.py |
dt = self.sigmas[self.step_index + 1] - sigma_hat
prev_sample = sample + derivative * dt
# Cast sample back to model compatible dtype
prev_sample = prev_sample.to(model_output.dtype)
# upon completion increase step index by one
self._step_index += 1
if not return_dict... | 1,285 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_euler_discrete.py |
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 dtype as original_samples
sigmas = self.sigmas.to(device=original_samples.device, dtype=orig... | 1,285 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_euler_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... | 1,285 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_euler_discrete.py |
def get_velocity(self, sample: torch.Tensor, noise: torch.Tensor, timesteps: torch.Tensor) -> torch.Tensor:
if (
isinstance(timesteps, int)
or isinstance(timesteps, torch.IntTensor)
or isinstance(timesteps, torch.LongTensor)
):
raise ValueError(
... | 1,285 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_euler_discrete.py |
if sample.device.type == "mps" and torch.is_floating_point(timesteps):
# mps does not support float64
schedule_timesteps = self.timesteps.to(sample.device, dtype=torch.float32)
timesteps = timesteps.to(sample.device, dtype=torch.float32)
else:
schedule_timesteps =... | 1,285 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_euler_discrete.py |
sqrt_one_minus_alpha_prod = (1 - alphas_cumprod[step_indices]) ** 0.5
sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.flatten()
while len(sqrt_one_minus_alpha_prod.shape) < len(sample.shape):
sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.unsqueeze(-1)
velocity = sqrt_a... | 1,285 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_euler_discrete.py |
class CosineDPMSolverMultistepScheduler(SchedulerMixin, ConfigMixin):
"""
Implements a variant of `DPMSolverMultistepScheduler` with cosine schedule, proposed by Nichol and Dhariwal (2021).
This scheduler was used in Stable Audio Open [1].
[1] Evans, Parker, et al. "Stable Audio Open" https://arxiv.org... | 1,286 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_cosine_dpmsolver_multistep.py |
Args:
sigma_min (`float`, *optional*, defaults to 0.3):
Minimum noise magnitude in the sigma schedule. This was set to 0.3 in Stable Audio Open [1].
sigma_max (`float`, *optional*, defaults to 500):
Maximum noise magnitude in the sigma schedule. This was set to 500 in Stable Audi... | 1,286 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_cosine_dpmsolver_multistep.py |
solver_order (`int`, defaults to 2):
The DPMSolver order which can be `1` or `2`. It is recommended to use `solver_order=2`.
prediction_type (`str`, defaults to `v_prediction`, *optional*):
Prediction type of the scheduler function; can be `epsilon` (predicts the noise of the diffusion p... | 1,286 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_cosine_dpmsolver_multistep.py |
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