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# 1. get previous step value (=t-1)
prev_timestep = timestep - self.config.num_train_timesteps // self.num_inference_steps
# 2. compute alphas, betas
alpha_prod_t = self.alphas_cumprod[timestep]
alpha_prod_t_prev = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final... | 1,299 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpm_cogvideox.py |
# 3. compute predicted original sample from predicted noise also called
# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
# To make style tests pass, commented out `pred_epsilon` as it is an unused variable
if self.config.prediction_type == "epsilon":
pred_o... | 1,299 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpm_cogvideox.py |
f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or"
" `v_prediction`"
) | 1,299 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpm_cogvideox.py |
h, r, lamb, lamb_next = self.get_variables(alpha_prod_t, alpha_prod_t_prev, alpha_prod_t_back)
mult = list(self.get_mult(h, r, alpha_prod_t, alpha_prod_t_prev, alpha_prod_t_back))
mult_noise = (1 - alpha_prod_t_prev) ** 0.5 * (1 - (-2 * h).exp()) ** 0.5
noise = randn_tensor(sample.shape, genera... | 1,299 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpm_cogvideox.py |
prev_sample = x_advanced
if not return_dict:
return (prev_sample, pred_original_sample)
return DDIMSchedulerOutput(prev_sample=prev_sample, pred_original_sample=pred_original_sample)
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler.add_noise
def add_noise(
... | 1,299 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpm_cogvideox.py |
sqrt_alpha_prod = alphas_cumprod[timesteps] ** 0.5
sqrt_alpha_prod = sqrt_alpha_prod.flatten()
while len(sqrt_alpha_prod.shape) < len(original_samples.shape):
sqrt_alpha_prod = sqrt_alpha_prod.unsqueeze(-1)
sqrt_one_minus_alpha_prod = (1 - alphas_cumprod[timesteps]) ** 0.5
s... | 1,299 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpm_cogvideox.py |
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler.get_velocity
def get_velocity(self, sample: torch.Tensor, noise: torch.Tensor, timesteps: torch.IntTensor) -> torch.Tensor:
# Make sure alphas_cumprod and timestep have same device and dtype as sample
self.alphas_cumprod = self.alphas_... | 1,299 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpm_cogvideox.py |
velocity = sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
return velocity
def __len__(self):
return self.config.num_train_timesteps | 1,299 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpm_cogvideox.py |
class DDPMSchedulerState:
common: CommonSchedulerState
# setable values
init_noise_sigma: jnp.ndarray
timesteps: jnp.ndarray
num_inference_steps: Optional[int] = None
@classmethod
def create(cls, common: CommonSchedulerState, init_noise_sigma: jnp.ndarray, timesteps: jnp.ndarray):
... | 1,300 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_ddpm_flax.py |
class FlaxDDPMSchedulerOutput(FlaxSchedulerOutput):
state: DDPMSchedulerState | 1,301 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_ddpm_flax.py |
class FlaxDDPMScheduler(FlaxSchedulerMixin, ConfigMixin):
"""
Denoising diffusion probabilistic models (DDPMs) explores the connections between denoising score matching and
Langevin dynamics sampling.
[`~ConfigMixin`] takes care of storing all config attributes that are passed in the scheduler's `__ini... | 1,302 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_ddpm_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 ... | 1,302 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_ddpm_flax.py |
indicates whether the model predicts the noise (epsilon), or the samples. One of `epsilon`, `sample`.
`v-prediction` is not supported for this scheduler.
dtype (`jnp.dtype`, *optional*, defaults to `jnp.float32`):
the `dtype` used for params and computation.
""" | 1,302 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_ddpm_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... | 1,302 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_ddpm_flax.py |
return DDPMSchedulerState.create(
common=common,
init_noise_sigma=init_noise_sigma,
timesteps=timesteps,
)
def scale_model_input(
self, state: DDPMSchedulerState, sample: jnp.ndarray, timestep: Optional[int] = None
) -> jnp.ndarray:
"""
Args:
... | 1,302 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_ddpm_flax.py |
Args:
state (`DDIMSchedulerState`):
the `FlaxDDPMScheduler` state data class instance.
num_inference_steps (`int`):
the number of diffusion steps used when generating samples with a pre-trained model.
"""
step_ratio = self.config.num_train_timeste... | 1,302 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_ddpm_flax.py |
# For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
# and sample from it to get previous sample
# x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
variance = (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.comm... | 1,302 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_ddpm_flax.py |
# hacks - were probably added for training stability
if variance_type == "fixed_small":
variance = jnp.clip(variance, a_min=1e-20)
# for rl-diffuser https://arxiv.org/abs/2205.09991
elif variance_type == "fixed_small_log":
variance = jnp.log(jnp.clip(variance, a_min=1e-20... | 1,302 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_ddpm_flax.py |
def step(
self,
state: DDPMSchedulerState,
model_output: jnp.ndarray,
timestep: int,
sample: jnp.ndarray,
key: Optional[jax.Array] = None,
return_dict: bool = True,
) -> Union[FlaxDDPMSchedulerOutput, Tuple]:
"""
Predict the sample at the previ... | 1,302 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_ddpm_flax.py |
Returns:
[`FlaxDDPMSchedulerOutput`] or `tuple`: [`FlaxDDPMSchedulerOutput`] if `return_dict` is True, otherwise a
`tuple`. When returning a tuple, the first element is the sample tensor.
"""
t = timestep
if key is None:
key = jax.random.key(0)
if (... | 1,302 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_ddpm_flax.py |
# 2. compute predicted original sample from predicted noise also called
# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
if self.config.prediction_type == "epsilon":
pred_original_sample = (sample - beta_prod_t ** (0.5) * model_output) / alpha_prod_t ** (0.5)
... | 1,302 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_ddpm_flax.py |
# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
pred_original_sample_coeff = (alpha_prod_t_prev ** (0.5) * state.common.betas[t]) / beta_prod_t
current_sample_coeff = state.common.alphas[t] ** (0.5) * beta_... | 1,302 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_ddpm_flax.py |
pred_prev_sample = pred_prev_sample + variance
if not return_dict:
return (pred_prev_sample, state)
return FlaxDDPMSchedulerOutput(prev_sample=pred_prev_sample, state=state)
def add_noise(
self,
state: DDPMSchedulerState,
original_samples: jnp.ndarray,
... | 1,302 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_ddpm_flax.py |
class DPMSolverSDESchedulerOutput(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 ... | 1,303 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_sde.py |
class BatchedBrownianTree:
"""A wrapper around torchsde.BrownianTree that enables batches of entropy."""
def __init__(self, x, t0, t1, seed=None, **kwargs):
t0, t1, self.sign = self.sort(t0, t1)
w0 = kwargs.get("w0", torch.zeros_like(x))
if seed is None:
seed = torch.randint... | 1,304 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_sde.py |
def __call__(self, t0, t1):
t0, t1, sign = self.sort(t0, t1)
w = torch.stack([tree(t0, t1) for tree in self.trees]) * (self.sign * sign)
return w if self.batched else w[0] | 1,304 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_sde.py |
class BrownianTreeNoiseSampler:
"""A noise sampler backed by a torchsde.BrownianTree.
Args:
x (Tensor): The tensor whose shape, device and dtype to use to generate
random samples.
sigma_min (float): The low end of the valid interval.
sigma_max (float): The high end of the va... | 1,305 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_sde.py |
def __call__(self, sigma, sigma_next):
t0, t1 = self.transform(torch.as_tensor(sigma)), self.transform(torch.as_tensor(sigma_next))
return self.tree(t0, t1) / (t1 - t0).abs().sqrt() | 1,305 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_sde.py |
class DPMSolverSDEScheduler(SchedulerMixin, ConfigMixin):
"""
DPMSolverSDEScheduler implements the stochastic sampler from the [Elucidating the Design Space of Diffusion-Based
Generative Models](https://huggingface.co/papers/2206.00364) paper.
This model inherits from [`SchedulerMixin`] and [`ConfigMix... | 1,306 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_sde.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... | 1,306 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_sde.py |
Video](https://imagen.research.google/video/paper.pdf) paper).
use_karras_sigmas (`bool`, *optional*, defaults to `False`):
Whether to use Karras sigmas for step sizes in the noise schedule during the sampling process. If `True`,
the sigmas are determined according to a sequence of noise... | 1,306 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_sde.py |
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.co/papers/2305.08891) for more information.
steps_offset (`int`, defaults to 0):
... | 1,306 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_sde.py |
_compatibles = [e.name for e in KarrasDiffusionSchedulers]
order = 2 | 1,306 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_sde.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,
prediction_type: ... | 1,306 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_sde.py |
"Only one of `config.use_beta_sigmas`, `config.use_exponential_sigmas`, `config.use_karras_sigmas` can be used."
)
if trained_betas is not None:
self.betas = torch.tensor(trained_betas, dtype=torch.float32)
elif beta_schedule == "linear":
self.betas = torch.linspace(b... | 1,306 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_sde.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.use_karras_sigmas = use_karras_sigmas
self.noise_sampler = None
self.noise_sampler_seed = nois... | 1,306 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_sde.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... | 1,306 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_sde.py |
@property
def init_noise_sigma(self):
# standard deviation of the initial noise distribution
if self.config.timestep_spacing in ["linspace", "trailing"]:
return self.sigmas.max()
return (self.sigmas.max() ** 2 + 1) ** 0.5
@property
def step_index(self):
"""
... | 1,306 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_sde.py |
Args:
begin_index (`int`):
The begin index for the scheduler.
"""
self._begin_index = begin_index
def scale_model_input(
self,
sample: torch.Tensor,
timestep: Union[float, torch.Tensor],
) -> torch.Tensor:
"""
Ensures interchan... | 1,306 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_sde.py |
def set_timesteps(
self,
num_inference_steps: int,
device: Union[str, torch.device] = None,
num_train_timesteps: Optional[int] = None,
):
"""
Sets the discrete timesteps used for the diffusion chain (to be run before inference).
Args:
num_inferenc... | 1,306 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_sde.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=float)[::-1].copy()
elif self.config.timestep_spacing ==... | 1,306 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_sde.py |
timesteps = (np.arange(num_train_timesteps, 0, -step_ratio)).round().copy().astype(float)
timesteps -= 1
else:
raise ValueError(
f"{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."
) | 1,306 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_sde.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... | 1,306 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_sde.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)
second_order_timesteps = torch.from_numpy(second_... | 1,306 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_sde.py |
def _second_order_timesteps(self, sigmas, log_sigmas):
def sigma_fn(_t):
return np.exp(-_t)
def t_fn(_sigma):
return -np.log(_sigma)
midpoint_ratio = 0.5
t = t_fn(sigmas)
delta_time = np.diff(t)
t_proposed = t[:-1] + delta_time * midpoint_ratio
... | 1,306 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_sde.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... | 1,306 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_sde.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... | 1,306 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_sde.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] ... | 1,306 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_sde.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... | 1,306 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_sde.py |
Args:
model_output (`torch.Tensor` or `np.ndarray`):
The direct output from learned diffusion model.
timestep (`float` or `torch.Tensor`):
The current discrete timestep in the diffusion chain.
sample (`torch.Tensor` or `np.ndarray`):
A ... | 1,306 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_sde.py |
Returns:
[`~schedulers.scheduling_dpmsolver_sde.DPMSolverSDESchedulerOutput`] or `tuple`:
If return_dict is `True`, [`~schedulers.scheduling_dpmsolver_sde.DPMSolverSDESchedulerOutput`] is
returned, otherwise a tuple is returned where the first element is the sample tensor.
... | 1,306 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_sde.py |
if self.state_in_first_order:
sigma = self.sigmas[self.step_index]
sigma_next = self.sigmas[self.step_index + 1]
else:
# 2nd order
sigma = self.sigmas[self.step_index - 1]
sigma_next = self.sigmas[self.step_index]
# Set the midpoint and step s... | 1,306 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_sde.py |
# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
if self.config.prediction_type == "epsilon":
sigma_input = sigma if self.state_in_first_order else sigma_fn(t_proposed)
pred_original_sample = sample - sigma_input * model_output
elif self.config.predi... | 1,306 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_sde.py |
if sigma_next == 0:
derivative = (sample - pred_original_sample) / sigma
dt = sigma_next - sigma
prev_sample = sample + derivative * dt
else:
if self.state_in_first_order:
t_next = t_proposed
else:
sample = self.sample
... | 1,306 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_sde.py |
if self.state_in_first_order:
# store for 2nd order step
self.sample = sample
self.mid_point_sigma = sigma_fn(t_next)
else:
# free for "first order mode"
self.sample = None
self.mid_point_sigma = None
# ... | 1,306 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_sde.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... | 1,306 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_sde.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... | 1,306 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_sde.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 ... | 1,307 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_ddim_inverse.py |
class DDIMInverseScheduler(SchedulerMixin, ConfigMixin):
"""
`DDIMInverseScheduler` is the reverse scheduler of [`DDIMScheduler`].
This model inherits from [`SchedulerMixin`] and [`ConfigMixin`]. Check the superclass documentation for the generic
methods the library implements for all schedulers such a... | 1,308 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_ddim_inverse.py |
Args:
num_train_timesteps (`int`, defaults to 1000):
The number of diffusion steps to train the model.
beta_start (`float`, defaults to 0.0001):
The starting `beta` value of inference.
beta_end (`float`, defaults to 0.02):
The final `beta` value.
beta_... | 1,308 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_ddim_inverse.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 0, otherwise
it uses the alpha value at step `num_train_timesteps - 1`.
steps_offset (`... | 1,308 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_ddim_inverse.py |
Sample Steps are Flawed](https://huggingface.co/papers/2305.08891) for more information.
rescale_betas_zero_snr (`bool`, defaults to `False`):
Whether to rescale the betas to have zero terminal SNR. This enables the model to generate very bright and
dark samples instead of limiting it to... | 1,308 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_ddim_inverse.py |
order = 1
ignore_for_config = ["kwargs"]
_deprecated_kwargs = ["set_alpha_to_zero"] | 1,308 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_ddim_inverse.py |
@register_to_config
def __init__(
self,
num_train_timesteps: int = 1000,
beta_start: float = 0.0001,
beta_end: float = 0.02,
beta_schedule: str = "linear",
trained_betas: Optional[Union[np.ndarray, List[float]]] = None,
clip_sample: bool = True,
set_al... | 1,308 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_ddim_inverse.py |
self.betas = torch.tensor(trained_betas, dtype=torch.float32)
elif beta_schedule == "linear":
self.betas = torch.linspace(beta_start, beta_end, num_train_timesteps, dtype=torch.float32)
elif beta_schedule == "scaled_linear":
# this schedule is very specific to the latent diffusio... | 1,308 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_ddim_inverse.py |
# Rescale for zero SNR
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)
# At every step in inverted ddim, we are looking into the next alphas_cumprod
... | 1,308 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_ddim_inverse.py |
# Copied from diffusers.schedulers.scheduling_ddim.DDIMScheduler.scale_model_input
def scale_model_input(self, sample: torch.Tensor, timestep: Optional[int] = None) -> torch.Tensor:
"""
Ensures interchangeability with schedulers that need to scale the denoising model input depending on the
c... | 1,308 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_ddim_inverse.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"... | 1,308 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_ddim_inverse.py |
# "leading" and "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
if self.config.timestep_spacing == "leading":
step_ratio = self.config.num_train_timesteps // self.num_inference_steps
# creates integer timesteps by multiplying by ratio
# ca... | 1,308 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_ddim_inverse.py |
raise ValueError(
f"{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'leading' or 'trailing'."
) | 1,308 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_ddim_inverse.py |
self.timesteps = torch.from_numpy(timesteps).to(device)
def step(
self,
model_output: torch.Tensor,
timestep: int,
sample: torch.Tensor,
return_dict: bool = True,
) -> Union[DDIMSchedulerOutput, Tuple]:
"""
Predict the sample from the previous timestep by... | 1,308 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_ddim_inverse.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... | 1,308 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_ddim_inverse.py |
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 return a [`~schedulers.scheduling_ddim_inverse.DDIM... | 1,308 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_ddim_inverse.py |
Returns:
[`~schedulers.scheduling_ddim_inverse.DDIMInverseSchedulerOutput`] or `tuple`:
If return_dict is `True`, [`~schedulers.scheduling_ddim_inverse.DDIMInverseSchedulerOutput`] is
returned, otherwise a tuple is returned where the first element is the sample tensor.
... | 1,308 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_ddim_inverse.py |
# 3. compute predicted original sample from predicted noise also called
# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
if self.config.prediction_type == "epsilon":
pred_original_sample = (sample - beta_prod_t ** (0.5) * model_output) / alpha_prod_t ** (0.5)
... | 1,308 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_ddim_inverse.py |
) | 1,308 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_ddim_inverse.py |
# 4. Clip or threshold "predicted x_0"
if self.config.clip_sample:
pred_original_sample = pred_original_sample.clamp(
-self.config.clip_sample_range, self.config.clip_sample_range
)
# 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/p... | 1,308 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_ddim_inverse.py |
class ScoreSdeVeSchedulerState:
# setable values
timesteps: Optional[jnp.ndarray] = None
discrete_sigmas: Optional[jnp.ndarray] = None
sigmas: Optional[jnp.ndarray] = None
@classmethod
def create(cls):
return cls() | 1,309 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_sde_ve_flax.py |
class FlaxSdeVeOutput(FlaxSchedulerOutput):
"""
Output class for the ScoreSdeVeScheduler's step function output.
Args:
state (`ScoreSdeVeSchedulerState`):
prev_sample (`jnp.ndarray` of shape `(batch_size, num_channels, height, width)` for images):
Computed sample (x_{t-1}) of pr... | 1,310 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_sde_ve_flax.py |
class FlaxScoreSdeVeScheduler(FlaxSchedulerMixin, ConfigMixin):
"""
The variance exploding stochastic differential equation (SDE) scheduler.
For more information, see the original paper: https://arxiv.org/abs/2011.13456
[`~ConfigMixin`] takes care of storing all config attributes that are passed in th... | 1,311 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_sde_ve_flax.py |
Args:
num_train_timesteps (`int`): number of diffusion steps used to train the model.
snr (`float`):
coefficient weighting the step from the model_output sample (from the network) to the random noise.
sigma_min (`float`):
initial noise scale for sigma sequence in samp... | 1,311 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_sde_ve_flax.py |
@register_to_config
def __init__(
self,
num_train_timesteps: int = 2000,
snr: float = 0.15,
sigma_min: float = 0.01,
sigma_max: float = 1348.0,
sampling_eps: float = 1e-5,
correct_steps: int = 1,
):
pass
def create_state(self):
state =... | 1,311 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_sde_ve_flax.py |
Args:
state (`ScoreSdeVeSchedulerState`): the `FlaxScoreSdeVeScheduler` state data class instance.
num_inference_steps (`int`):
the number of diffusion steps used when generating samples with a pre-trained model.
sampling_eps (`float`, optional):
final... | 1,311 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_sde_ve_flax.py |
The sigmas control the weight of the `drift` and `diffusion` components of sample update. | 1,311 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_sde_ve_flax.py |
Args:
state (`ScoreSdeVeSchedulerState`): the `FlaxScoreSdeVeScheduler` state data class instance.
num_inference_steps (`int`):
the number of diffusion steps used when generating samples with a pre-trained model.
sigma_min (`float`, optional):
initial ... | 1,311 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_sde_ve_flax.py |
state = self.set_timesteps(state, num_inference_steps, sampling_eps) | 1,311 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_sde_ve_flax.py |
discrete_sigmas = jnp.exp(jnp.linspace(jnp.log(sigma_min), jnp.log(sigma_max), num_inference_steps))
sigmas = jnp.array([sigma_min * (sigma_max / sigma_min) ** t for t in state.timesteps])
return state.replace(discrete_sigmas=discrete_sigmas, sigmas=sigmas)
def get_adjacent_sigma(self, state, time... | 1,311 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_sde_ve_flax.py |
Args:
state (`ScoreSdeVeSchedulerState`): the `FlaxScoreSdeVeScheduler` state data class instance.
model_output (`jnp.ndarray`): direct output from learned diffusion model.
timestep (`int`): current discrete timestep in the diffusion chain.
sample (`jnp.ndarray`):
... | 1,311 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_sde_ve_flax.py |
timestep = timestep * jnp.ones(
sample.shape[0],
)
timesteps = (timestep * (len(state.timesteps) - 1)).long()
sigma = state.discrete_sigmas[timesteps]
adjacent_sigma = self.get_adjacent_sigma(state, timesteps, timestep)
drift = jnp.zeros_like(sample)
diffusio... | 1,311 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_sde_ve_flax.py |
# equation 6: sample noise for the diffusion term of
key = random.split(key, num=1)
noise = random.normal(key=key, shape=sample.shape)
prev_sample_mean = sample - drift # subtract because `dt` is a small negative timestep
# TODO is the variable diffusion the correct scaling term for th... | 1,311 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_sde_ve_flax.py |
def step_correct(
self,
state: ScoreSdeVeSchedulerState,
model_output: jnp.ndarray,
sample: jnp.ndarray,
key: jax.Array,
return_dict: bool = True,
) -> Union[FlaxSdeVeOutput, Tuple]:
"""
Correct the predicted sample based on the output model_output of ... | 1,311 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_sde_ve_flax.py |
Returns:
[`FlaxSdeVeOutput`] or `tuple`: [`FlaxSdeVeOutput`] if `return_dict` is True, otherwise a `tuple`. When
returning a tuple, the first element is the sample tensor.
"""
if state.timesteps is None:
raise ValueError(
"`state.timesteps` is not set... | 1,311 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_sde_ve_flax.py |
# compute corrected sample: model_output term and noise term
step_size = step_size.flatten()
step_size = broadcast_to_shape_from_left(step_size, sample.shape)
prev_sample_mean = sample + step_size * model_output
prev_sample = prev_sample_mean + ((step_size * 2) ** 0.5) * noise
i... | 1,311 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_sde_ve_flax.py |
class EDMDPMSolverMultistepScheduler(SchedulerMixin, ConfigMixin):
"""
Implements DPMSolverMultistepScheduler in EDM formulation as presented in Karras et al. 2022 [1].
`EDMDPMSolverMultistepScheduler` is a fast dedicated high-order solver for diffusion ODEs.
[1] Karras, Tero, et al. "Elucidating the D... | 1,312 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_edm_dpmsolver_multistep.py |
Args:
sigma_min (`float`, *optional*, defaults to 0.002):
Minimum noise magnitude in the sigma schedule. This was set to 0.002 in the EDM paper [1]; a reasonable
range is [0, 10].
sigma_max (`float`, *optional*, defaults to 80.0):
Maximum noise magnitude in the sigma ... | 1,312 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_edm_dpmsolver_multistep.py |
The number of diffusion steps to train the model.
solver_order (`int`, defaults to 2):
The DPMSolver order which can be `1` or `2` or `3`. It is recommended to use `solver_order=2` for guided
sampling, and `solver_order=3` for unconditional sampling.
prediction_type (`str`, defau... | 1,312 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_edm_dpmsolver_multistep.py |
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` and
`algorithm_type="dpmsolver++"`.
algorithm_type (`str`, defaults t... | 1,312 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_edm_dpmsolver_multistep.py |
lower_order_final (`bool`, defaults to `True`):
Whether to use lower-order solvers in the final steps. Only valid for < 15 inference steps. This can
stabilize the sampling of DPMSolver for steps < 15, especially for steps <= 10.
euler_at_final (`bool`, defaults to `False`):
W... | 1,312 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_edm_dpmsolver_multistep.py |
_compatibles = []
order = 1 | 1,312 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_edm_dpmsolver_multistep.py |
@register_to_config
def __init__(
self,
sigma_min: float = 0.002,
sigma_max: float = 80.0,
sigma_data: float = 0.5,
sigma_schedule: str = "karras",
num_train_timesteps: int = 1000,
prediction_type: str = "epsilon",
rho: float = 7.0,
solver_orde... | 1,312 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_edm_dpmsolver_multistep.py |
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