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timestep_list = args[0] if len(args) > 0 else kwargs.pop("timestep_list", None)
prev_timestep = args[1] if len(args) > 1 else kwargs.pop("prev_timestep", None)
if sample is None:
if len(args) > 2:
sample = args[2]
else:
raise ValueError(" missing`sample` as a required keyward argument")
if timestep_list is not None:
deprecate(
"timestep_list",
"1.0.0",
"Passing `timestep_list` is deprecated and has no effect as model output conversion is now handled via an internal counter `self.step_index`",
)
if prev_timestep is not None:
deprecate(
"prev_timestep",
"1.0.0",
"Passing `prev_timestep` is deprecated and has no effect as model output conversion is now handled via an internal counter `self.step_index`",
) | 1,373 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_multistep.py |
sigma_t, sigma_s0, sigma_s1, sigma_s2 = (
self.sigmas[self.step_index + 1],
self.sigmas[self.step_index],
self.sigmas[self.step_index - 1],
self.sigmas[self.step_index - 2],
)
alpha_t, sigma_t = self._sigma_to_alpha_sigma_t(sigma_t)
alpha_s0, sigma_s0 = self._sigma_to_alpha_sigma_t(sigma_s0)
alpha_s1, sigma_s1 = self._sigma_to_alpha_sigma_t(sigma_s1)
alpha_s2, sigma_s2 = self._sigma_to_alpha_sigma_t(sigma_s2)
lambda_t = torch.log(alpha_t) - torch.log(sigma_t)
lambda_s0 = torch.log(alpha_s0) - torch.log(sigma_s0)
lambda_s1 = torch.log(alpha_s1) - torch.log(sigma_s1)
lambda_s2 = torch.log(alpha_s2) - torch.log(sigma_s2)
m0, m1, m2 = model_output_list[-1], model_output_list[-2], model_output_list[-3] | 1,373 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_multistep.py |
h, h_0, h_1 = lambda_t - lambda_s0, lambda_s0 - lambda_s1, lambda_s1 - lambda_s2
r0, r1 = h_0 / h, h_1 / h
D0 = m0
D1_0, D1_1 = (1.0 / r0) * (m0 - m1), (1.0 / r1) * (m1 - m2)
D1 = D1_0 + (r0 / (r0 + r1)) * (D1_0 - D1_1)
D2 = (1.0 / (r0 + r1)) * (D1_0 - D1_1)
if self.config.algorithm_type == "dpmsolver++":
# See https://arxiv.org/abs/2206.00927 for detailed derivations
x_t = (
(sigma_t / sigma_s0) * sample
- (alpha_t * (torch.exp(-h) - 1.0)) * D0
+ (alpha_t * ((torch.exp(-h) - 1.0) / h + 1.0)) * D1
- (alpha_t * ((torch.exp(-h) - 1.0 + h) / h**2 - 0.5)) * D2
)
elif self.config.algorithm_type == "dpmsolver":
# See https://arxiv.org/abs/2206.00927 for detailed derivations
x_t = (
(alpha_t / alpha_s0) * sample
- (sigma_t * (torch.exp(h) - 1.0)) * D0 | 1,373 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_multistep.py |
- (sigma_t * ((torch.exp(h) - 1.0) / h - 1.0)) * D1
- (sigma_t * ((torch.exp(h) - 1.0 - h) / h**2 - 0.5)) * D2
)
elif self.config.algorithm_type == "sde-dpmsolver++":
assert noise is not None
x_t = (
(sigma_t / sigma_s0 * torch.exp(-h)) * sample
+ (alpha_t * (1.0 - torch.exp(-2.0 * h))) * D0
+ (alpha_t * ((1.0 - torch.exp(-2.0 * h)) / (-2.0 * h) + 1.0)) * D1
+ (alpha_t * ((1.0 - torch.exp(-2.0 * h) - 2.0 * h) / (2.0 * h) ** 2 - 0.5)) * D2
+ sigma_t * torch.sqrt(1.0 - torch.exp(-2 * h)) * noise
)
return x_t | 1,373 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_multistep.py |
def index_for_timestep(self, timestep, schedule_timesteps=None):
if schedule_timesteps is None:
schedule_timesteps = self.timesteps
index_candidates = (schedule_timesteps == timestep).nonzero()
if len(index_candidates) == 0:
step_index = len(self.timesteps) - 1
# The sigma index that is taken for the **very** first `step`
# is always the second index (or the last index if there is only 1)
# This way we 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)
elif len(index_candidates) > 1:
step_index = index_candidates[1].item()
else:
step_index = index_candidates[0].item()
return step_index
def _init_step_index(self, timestep):
"""
Initialize the step_index counter for the scheduler.
""" | 1,373 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_multistep.py |
if self.begin_index is None:
if isinstance(timestep, torch.Tensor):
timestep = timestep.to(self.timesteps.device)
self._step_index = self.index_for_timestep(timestep)
else:
self._step_index = self._begin_index
def step(
self,
model_output: torch.Tensor,
timestep: Union[int, torch.Tensor],
sample: torch.Tensor,
generator=None,
variance_noise: Optional[torch.Tensor] = None,
return_dict: bool = True,
) -> Union[SchedulerOutput, Tuple]:
"""
Predict the sample from the previous timestep by reversing the SDE. This function propagates the sample with
the multistep DPMSolver. | 1,373 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_multistep.py |
Args:
model_output (`torch.Tensor`):
The direct output from learned diffusion model.
timestep (`int`):
The current discrete timestep in the diffusion chain.
sample (`torch.Tensor`):
A current instance of a sample created by the diffusion process.
generator (`torch.Generator`, *optional*):
A random number generator.
variance_noise (`torch.Tensor`):
Alternative to generating noise with `generator` by directly providing the noise for the variance
itself. Useful for methods such as [`LEdits++`].
return_dict (`bool`):
Whether or not to return a [`~schedulers.scheduling_utils.SchedulerOutput`] or `tuple`. | 1,373 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_multistep.py |
Returns:
[`~schedulers.scheduling_utils.SchedulerOutput`] or `tuple`:
If return_dict is `True`, [`~schedulers.scheduling_utils.SchedulerOutput`] is returned, otherwise a
tuple is returned where the first element is the sample tensor.
"""
if self.num_inference_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) | 1,373 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_multistep.py |
# Improve numerical stability for small number of steps
lower_order_final = (self.step_index == len(self.timesteps) - 1) and (
self.config.euler_at_final
or (self.config.lower_order_final and len(self.timesteps) < 15)
or self.config.final_sigmas_type == "zero"
)
lower_order_second = (
(self.step_index == len(self.timesteps) - 2) and self.config.lower_order_final and len(self.timesteps) < 15
)
model_output = self.convert_model_output(model_output, sample=sample)
for i in range(self.config.solver_order - 1):
self.model_outputs[i] = self.model_outputs[i + 1]
self.model_outputs[-1] = model_output | 1,373 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_multistep.py |
# Upcast to avoid precision issues when computing prev_sample
sample = sample.to(torch.float32)
if self.config.algorithm_type in ["sde-dpmsolver", "sde-dpmsolver++"] and variance_noise is None:
noise = randn_tensor(
model_output.shape, generator=generator, device=model_output.device, dtype=torch.float32
)
elif self.config.algorithm_type in ["sde-dpmsolver", "sde-dpmsolver++"]:
noise = variance_noise.to(device=model_output.device, dtype=torch.float32)
else:
noise = None | 1,373 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_multistep.py |
if self.config.solver_order == 1 or self.lower_order_nums < 1 or lower_order_final:
prev_sample = self.dpm_solver_first_order_update(model_output, sample=sample, noise=noise)
elif self.config.solver_order == 2 or self.lower_order_nums < 2 or lower_order_second:
prev_sample = self.multistep_dpm_solver_second_order_update(self.model_outputs, sample=sample, noise=noise)
else:
prev_sample = self.multistep_dpm_solver_third_order_update(self.model_outputs, sample=sample, noise=noise)
if self.lower_order_nums < self.config.solver_order:
self.lower_order_nums += 1
# Cast sample back to expected dtype
prev_sample = prev_sample.to(model_output.dtype)
# upon completion increase step index by one
self._step_index += 1
if not return_dict:
return (prev_sample,)
return SchedulerOutput(prev_sample=prev_sample) | 1,373 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_multistep.py |
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:
sample (`torch.Tensor`):
The input sample.
Returns:
`torch.Tensor`:
A scaled input sample.
"""
return sample | 1,373 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_multistep.py |
def add_noise(
self,
original_samples: torch.Tensor,
noise: torch.Tensor,
timesteps: torch.IntTensor,
) -> torch.Tensor:
# Make sure sigmas and timesteps have the same device and dtype as original_samples
sigmas = self.sigmas.to(device=original_samples.device, dtype=original_samples.dtype)
if original_samples.device.type == "mps" and torch.is_floating_point(timesteps):
# mps does not support float64
schedule_timesteps = self.timesteps.to(original_samples.device, dtype=torch.float32)
timesteps = timesteps.to(original_samples.device, dtype=torch.float32)
else:
schedule_timesteps = self.timesteps.to(original_samples.device)
timesteps = timesteps.to(original_samples.device) | 1,373 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_multistep.py |
# begin_index is None when the 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 after first denoising step (for inpainting)
step_indices = [self.step_index] * timesteps.shape[0]
else:
# add noise is called before first denoising step to create initial latent(img2img)
step_indices = [self.begin_index] * timesteps.shape[0]
sigma = sigmas[step_indices].flatten()
while len(sigma.shape) < len(original_samples.shape):
sigma = sigma.unsqueeze(-1)
alpha_t, sigma_t = self._sigma_to_alpha_sigma_t(sigma)
noisy_samples = alpha_t * original_samples + sigma_t * noise
return noisy_samples
def __len__(self):
return self.config.num_train_timesteps | 1,373 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_multistep.py |
class UniPCMultistepScheduler(SchedulerMixin, ConfigMixin):
"""
`UniPCMultistepScheduler` is a training-free framework designed for the fast sampling of diffusion models.
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. | 1,374 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_unipc_multistep.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_schedule (`str`, defaults to `"linear"`):
The beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from
`linear`, `scaled_linear`, or `squaredcos_cap_v2`.
trained_betas (`np.ndarray`, *optional*):
Pass an array of betas directly to the constructor to bypass `beta_start` and `beta_end`.
solver_order (`int`, default `2`):
The UniPC order which can be any positive integer. The effective order of accuracy is `solver_order + 1`
due to the UniC. It is recommended to use `solver_order=2` for guided sampling, and `solver_order=3` for
unconditional sampling. | 1,374 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_unipc_multistep.py |
prediction_type (`str`, defaults to `epsilon`, *optional*):
Prediction type of the scheduler function; can be `epsilon` (predicts the noise of the diffusion process),
`sample` (directly predicts the noisy sample`) or `v_prediction` (see section 2.4 of [Imagen
Video](https://imagen.research.google/video/paper.pdf) paper).
thresholding (`bool`, defaults to `False`):
Whether to use the "dynamic thresholding" method. This is unsuitable for latent-space diffusion models such
as Stable Diffusion.
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` and `predict_x0=True`.
predict_x0 (`bool`, defaults to `True`):
Whether to use the updating algorithm on the predicted x0. | 1,374 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_unipc_multistep.py |
solver_type (`str`, default `bh2`):
Solver type for UniPC. It is recommended to use `bh1` for unconditional sampling when steps < 10, and `bh2`
otherwise.
lower_order_final (`bool`, default `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.
disable_corrector (`list`, default `[]`):
Decides which step to disable the corrector to mitigate the misalignment between `epsilon_theta(x_t, c)`
and `epsilon_theta(x_t^c, c)` which can influence convergence for a large guidance scale. Corrector is
usually disabled during the first few steps.
solver_p (`SchedulerMixin`, default `None`):
Any other scheduler that if specified, the algorithm becomes `solver_p + UniC`.
use_karras_sigmas (`bool`, *optional*, defaults to `False`): | 1,374 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_unipc_multistep.py |
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 levels {σi}.
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 during the sampling process. Refer to [Beta
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.co./papers/2305.08891) for more information.
steps_offset (`int`, defaults to 0): | 1,374 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_unipc_multistep.py |
An offset added to the inference steps, as required by some model families.
final_sigmas_type (`str`, defaults to `"zero"`):
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.
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 samples with medium brightness. Loosely related to
[`--offset_noise`](https://github.com/huggingface/diffusers/blob/74fd735eb073eb1d774b1ab4154a0876eb82f055/examples/dreambooth/train_dreambooth.py#L506).
""" | 1,374 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_unipc_multistep.py |
_compatibles = [e.name for e in KarrasDiffusionSchedulers]
order = 1 | 1,374 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_unipc_multistep.py |
@register_to_config
def __init__(
self,
num_train_timesteps: int = 1000,
beta_start: float = 0.0001,
beta_end: float = 0.02,
beta_schedule: str = "linear",
trained_betas: Optional[Union[np.ndarray, List[float]]] = None,
solver_order: int = 2,
prediction_type: str = "epsilon",
thresholding: bool = False,
dynamic_thresholding_ratio: float = 0.995,
sample_max_value: float = 1.0,
predict_x0: bool = True,
solver_type: str = "bh2",
lower_order_final: bool = True,
disable_corrector: List[int] = [],
solver_p: SchedulerMixin = None,
use_karras_sigmas: Optional[bool] = False,
use_exponential_sigmas: Optional[bool] = False,
use_beta_sigmas: Optional[bool] = False,
use_flow_sigmas: Optional[bool] = False,
flow_shift: Optional[float] = 1.0,
timestep_spacing: str = "linspace",
steps_offset: int = 0, | 1,374 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_unipc_multistep.py |
final_sigmas_type: Optional[str] = "zero", # "zero", "sigma_min"
rescale_betas_zero_snr: bool = False,
):
if self.config.use_beta_sigmas and not is_scipy_available():
raise ImportError("Make sure to install scipy if you want to use beta sigmas.")
if sum([self.config.use_beta_sigmas, self.config.use_exponential_sigmas, self.config.use_karras_sigmas]) > 1:
raise ValueError(
"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(beta_start, beta_end, num_train_timesteps, dtype=torch.float32)
elif beta_schedule == "scaled_linear":
# this schedule is very specific to the latent diffusion model. | 1,374 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_unipc_multistep.py |
self.betas = torch.linspace(beta_start**0.5, beta_end**0.5, num_train_timesteps, dtype=torch.float32) ** 2
elif beta_schedule == "squaredcos_cap_v2":
# Glide cosine schedule
self.betas = betas_for_alpha_bar(num_train_timesteps)
else:
raise NotImplementedError(f"{beta_schedule} is not implemented for {self.__class__}") | 1,374 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_unipc_multistep.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 smallest positive subnormal works well here
self.alphas_cumprod[-1] = 2**-24
# Currently we only support VP-type noise schedule
self.alpha_t = torch.sqrt(self.alphas_cumprod)
self.sigma_t = torch.sqrt(1 - self.alphas_cumprod)
self.lambda_t = torch.log(self.alpha_t) - torch.log(self.sigma_t)
self.sigmas = ((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5
# standard deviation of the initial noise distribution
self.init_noise_sigma = 1.0 | 1,374 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_unipc_multistep.py |
if solver_type not in ["bh1", "bh2"]:
if solver_type in ["midpoint", "heun", "logrho"]:
self.register_to_config(solver_type="bh2")
else:
raise NotImplementedError(f"{solver_type} is not implemented for {self.__class__}")
self.predict_x0 = predict_x0
# setable values
self.num_inference_steps = None
timesteps = np.linspace(0, num_train_timesteps - 1, num_train_timesteps, dtype=np.float32)[::-1].copy()
self.timesteps = torch.from_numpy(timesteps)
self.model_outputs = [None] * solver_order
self.timestep_list = [None] * solver_order
self.lower_order_nums = 0
self.disable_corrector = disable_corrector
self.solver_p = solver_p
self.last_sample = None
self._step_index = None
self._begin_index = None
self.sigmas = self.sigmas.to("cpu") # to avoid too much CPU/GPU communication | 1,374 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_unipc_multistep.py |
@property
def step_index(self):
"""
The index counter for current timestep. It will increase 1 after each scheduler step.
"""
return self._step_index
@property
def begin_index(self):
"""
The index for the first timestep. It should be set from pipeline with `set_begin_index` method.
"""
return self._begin_index
# 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_index (`int`):
The begin index for the scheduler.
"""
self._begin_index = begin_index | 1,374 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_unipc_multistep.py |
def set_timesteps(self, num_inference_steps: int, device: Union[str, torch.device] = None):
"""
Sets the discrete timesteps used for the diffusion chain (to be run before inference). | 1,374 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_unipc_multistep.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.
"""
# "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 + 1)
.round()[::-1][:-1]
.copy()
.astype(np.int64)
)
elif self.config.timestep_spacing == "leading":
step_ratio = self.config.num_train_timesteps // (num_inference_steps + 1)
# creates integer timesteps by multiplying by ratio | 1,374 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_unipc_multistep.py |
# casting to int to avoid issues when num_inference_step is power of 3
timesteps = (np.arange(0, num_inference_steps + 1) * step_ratio).round()[::-1][:-1].copy().astype(np.int64)
timesteps += self.config.steps_offset
elif self.config.timestep_spacing == "trailing":
step_ratio = self.config.num_train_timesteps / num_inference_steps
# creates integer timesteps by multiplying by ratio
# casting to int to avoid issues when num_inference_step is power of 3
timesteps = np.arange(self.config.num_train_timesteps, 0, -step_ratio).round().copy().astype(np.int64)
timesteps -= 1
else:
raise ValueError(
f"{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."
) | 1,374 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_unipc_multistep.py |
sigmas = np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)
if self.config.use_karras_sigmas:
log_sigmas = np.log(sigmas)
sigmas = np.flip(sigmas).copy()
sigmas = self._convert_to_karras(in_sigmas=sigmas, num_inference_steps=num_inference_steps)
timesteps = np.array([self._sigma_to_t(sigma, log_sigmas) for sigma in sigmas]).round()
if self.config.final_sigmas_type == "sigma_min":
sigma_last = sigmas[-1]
elif self.config.final_sigmas_type == "zero":
sigma_last = 0
else:
raise ValueError(
f"`final_sigmas_type` must be one of 'zero', or 'sigma_min', but got {self.config.final_sigmas_type}"
)
sigmas = np.concatenate([sigmas, [sigma_last]]).astype(np.float32)
elif self.config.use_exponential_sigmas:
log_sigmas = np.log(sigmas)
sigmas = np.flip(sigmas).copy() | 1,374 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_unipc_multistep.py |
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])
if self.config.final_sigmas_type == "sigma_min":
sigma_last = sigmas[-1]
elif self.config.final_sigmas_type == "zero":
sigma_last = 0
else:
raise ValueError(
f"`final_sigmas_type` must be one of 'zero', or 'sigma_min', but got {self.config.final_sigmas_type}"
)
sigmas = np.concatenate([sigmas, [sigma_last]]).astype(np.float32)
elif self.config.use_beta_sigmas:
log_sigmas = np.log(sigmas)
sigmas = np.flip(sigmas).copy()
sigmas = self._convert_to_beta(in_sigmas=sigmas, num_inference_steps=num_inference_steps)
timesteps = np.array([self._sigma_to_t(sigma, log_sigmas) for sigma in sigmas]) | 1,374 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_unipc_multistep.py |
if self.config.final_sigmas_type == "sigma_min":
sigma_last = sigmas[-1]
elif self.config.final_sigmas_type == "zero":
sigma_last = 0
else:
raise ValueError(
f"`final_sigmas_type` must be one of 'zero', or 'sigma_min', but got {self.config.final_sigmas_type}"
)
sigmas = np.concatenate([sigmas, [sigma_last]]).astype(np.float32)
elif self.config.use_flow_sigmas:
alphas = np.linspace(1, 1 / self.config.num_train_timesteps, num_inference_steps + 1)
sigmas = 1.0 - alphas
sigmas = np.flip(self.config.flow_shift * sigmas / (1 + (self.config.flow_shift - 1) * sigmas))[:-1].copy()
timesteps = (sigmas * self.config.num_train_timesteps).copy()
if self.config.final_sigmas_type == "sigma_min":
sigma_last = sigmas[-1]
elif self.config.final_sigmas_type == "zero":
sigma_last = 0 | 1,374 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_unipc_multistep.py |
else:
raise ValueError(
f"`final_sigmas_type` must be one of 'zero', or 'sigma_min', but got {self.config.final_sigmas_type}"
)
sigmas = np.concatenate([sigmas, [sigma_last]]).astype(np.float32)
else:
sigmas = np.interp(timesteps, np.arange(0, len(sigmas)), sigmas)
if self.config.final_sigmas_type == "sigma_min":
sigma_last = ((1 - self.alphas_cumprod[0]) / self.alphas_cumprod[0]) ** 0.5
elif self.config.final_sigmas_type == "zero":
sigma_last = 0
else:
raise ValueError(
f"`final_sigmas_type` must be one of 'zero', or 'sigma_min', but got {self.config.final_sigmas_type}"
)
sigmas = np.concatenate([sigmas, [sigma_last]]).astype(np.float32) | 1,374 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_unipc_multistep.py |
self.sigmas = torch.from_numpy(sigmas)
self.timesteps = torch.from_numpy(timesteps).to(device=device, dtype=torch.int64)
self.num_inference_steps = len(timesteps)
self.model_outputs = [
None,
] * self.config.solver_order
self.lower_order_nums = 0
self.last_sample = None
if self.solver_p:
self.solver_p.set_timesteps(self.num_inference_steps, device=device)
# add an index counter for schedulers that allow duplicated timesteps
self._step_index = None
self._begin_index = None
self.sigmas = self.sigmas.to("cpu") # to avoid too much CPU/GPU communication | 1,374 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_unipc_multistep.py |
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler._threshold_sample
def _threshold_sample(self, sample: torch.Tensor) -> torch.Tensor:
"""
"Dynamic thresholding: At each sampling step we set s to a certain percentile absolute pixel value in xt0 (the
prediction of x_0 at timestep t), and if s > 1, then we threshold xt0 to the range [-s, s] and then divide by
s. Dynamic thresholding pushes saturated pixels (those near -1 and 1) inwards, thereby actively preventing
pixels from saturation at each step. We find that dynamic thresholding results in significantly better
photorealism as well as better image-text alignment, especially when using very large guidance weights."
https://arxiv.org/abs/2205.11487
"""
dtype = sample.dtype
batch_size, channels, *remaining_dims = sample.shape | 1,374 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_unipc_multistep.py |
if dtype not in (torch.float32, torch.float64):
sample = sample.float() # upcast for quantile calculation, and clamp not implemented for cpu half
# Flatten sample for doing quantile calculation along each image
sample = sample.reshape(batch_size, channels * np.prod(remaining_dims))
abs_sample = sample.abs() # "a certain percentile absolute pixel value"
s = torch.quantile(abs_sample, self.config.dynamic_thresholding_ratio, dim=1)
s = torch.clamp(
s, min=1, max=self.config.sample_max_value
) # When clamped to min=1, equivalent to standard clipping to [-1, 1]
s = s.unsqueeze(1) # (batch_size, 1) because clamp will broadcast along dim=0
sample = torch.clamp(sample, -s, s) / s # "we threshold xt0 to the range [-s, s] and then divide by s"
sample = sample.reshape(batch_size, channels, *remaining_dims)
sample = sample.to(dtype)
return sample | 1,374 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_unipc_multistep.py |
# Copied from diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler._sigma_to_t
def _sigma_to_t(self, sigma, log_sigmas):
# get log sigma
log_sigma = np.log(np.maximum(sigma, 1e-10))
# get distribution
dists = log_sigma - log_sigmas[:, np.newaxis]
# get 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)
# transform interpolation to time range
t = (1 - w) * low_idx + w * high_idx
t = t.reshape(sigma.shape)
return t | 1,374 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_unipc_multistep.py |
# Copied from diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler._sigma_to_alpha_sigma_t
def _sigma_to_alpha_sigma_t(self, sigma):
if self.config.use_flow_sigmas:
alpha_t = 1 - sigma
sigma_t = sigma
else:
alpha_t = 1 / ((sigma**2 + 1) ** 0.5)
sigma_t = sigma * alpha_t
return alpha_t, sigma_t
# Copied from diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler._convert_to_karras
def _convert_to_karras(self, in_sigmas: torch.Tensor, num_inference_steps) -> torch.Tensor:
"""Constructs the noise schedule of Karras et al. (2022)."""
# 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 | 1,374 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_unipc_multistep.py |
if hasattr(self.config, "sigma_max"):
sigma_max = self.config.sigma_max
else:
sigma_max = None
sigma_min = sigma_min if sigma_min is not None else in_sigmas[-1].item()
sigma_max = sigma_max if sigma_max is not None else in_sigmas[0].item()
rho = 7.0 # 7.0 is the value used in the paper
ramp = np.linspace(0, 1, num_inference_steps)
min_inv_rho = sigma_min ** (1 / rho)
max_inv_rho = sigma_max ** (1 / rho)
sigmas = (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
return sigmas
# 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.""" | 1,374 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_unipc_multistep.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"):
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.linspace(math.log(sigma_max), math.log(sigma_min), num_inference_steps))
return sigmas | 1,374 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_unipc_multistep.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] (Lee et. al, 2024)"""
# 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"):
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() | 1,374 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_unipc_multistep.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)
]
]
)
return sigmas
def convert_model_output(
self,
model_output: torch.Tensor,
*args,
sample: torch.Tensor = None,
**kwargs,
) -> torch.Tensor:
r"""
Convert the model output to the corresponding type the UniPC algorithm needs.
Args:
model_output (`torch.Tensor`):
The direct output from the 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 diffusion process. | 1,374 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_unipc_multistep.py |
Returns:
`torch.Tensor`:
The converted model output.
"""
timestep = args[0] if len(args) > 0 else kwargs.pop("timestep", None)
if sample is None:
if len(args) > 1:
sample = args[1]
else:
raise ValueError("missing `sample` as a required keyward argument")
if timestep is not None:
deprecate(
"timesteps",
"1.0.0",
"Passing `timesteps` is deprecated and has no effect as model output conversion is now handled via an internal counter `self.step_index`",
)
sigma = self.sigmas[self.step_index]
alpha_t, sigma_t = self._sigma_to_alpha_sigma_t(sigma) | 1,374 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_unipc_multistep.py |
if self.predict_x0:
if self.config.prediction_type == "epsilon":
x0_pred = (sample - sigma_t * model_output) / alpha_t
elif self.config.prediction_type == "sample":
x0_pred = model_output
elif self.config.prediction_type == "v_prediction":
x0_pred = alpha_t * sample - sigma_t * model_output
elif self.config.prediction_type == "flow_prediction":
sigma_t = self.sigmas[self.step_index]
x0_pred = sample - sigma_t * model_output
else:
raise ValueError(
f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, "
"`v_prediction`, or `flow_prediction` for the UniPCMultistepScheduler."
)
if self.config.thresholding:
x0_pred = self._threshold_sample(x0_pred) | 1,374 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_unipc_multistep.py |
return x0_pred
else:
if self.config.prediction_type == "epsilon":
return model_output
elif self.config.prediction_type == "sample":
epsilon = (sample - alpha_t * model_output) / sigma_t
return epsilon
elif self.config.prediction_type == "v_prediction":
epsilon = alpha_t * model_output + sigma_t * sample
return epsilon
else:
raise ValueError(
f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or"
" `v_prediction` for the UniPCMultistepScheduler."
) | 1,374 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_unipc_multistep.py |
def multistep_uni_p_bh_update(
self,
model_output: torch.Tensor,
*args,
sample: torch.Tensor = None,
order: int = None,
**kwargs,
) -> torch.Tensor:
"""
One step for the UniP (B(h) version). Alternatively, `self.solver_p` is used if is specified.
Args:
model_output (`torch.Tensor`):
The direct output from the learned diffusion model at the current timestep.
prev_timestep (`int`):
The previous discrete timestep in the diffusion chain.
sample (`torch.Tensor`):
A current instance of a sample created by the diffusion process.
order (`int`):
The order of UniP at this timestep (corresponds to the *p* in UniPC-p). | 1,374 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_unipc_multistep.py |
Returns:
`torch.Tensor`:
The sample tensor at the previous timestep.
"""
prev_timestep = args[0] if len(args) > 0 else kwargs.pop("prev_timestep", None)
if sample is None:
if len(args) > 1:
sample = args[1]
else:
raise ValueError(" missing `sample` as a required keyward argument")
if order is None:
if len(args) > 2:
order = args[2]
else:
raise ValueError(" missing `order` as a required keyward argument")
if prev_timestep is not None:
deprecate(
"prev_timestep",
"1.0.0",
"Passing `prev_timestep` is deprecated and has no effect as model output conversion is now handled via an internal counter `self.step_index`",
)
model_output_list = self.model_outputs
s0 = self.timestep_list[-1]
m0 = model_output_list[-1]
x = sample | 1,374 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_unipc_multistep.py |
if self.solver_p:
x_t = self.solver_p.step(model_output, s0, x).prev_sample
return x_t
sigma_t, sigma_s0 = self.sigmas[self.step_index + 1], self.sigmas[self.step_index]
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 = torch.log(alpha_t) - torch.log(sigma_t)
lambda_s0 = torch.log(alpha_s0) - torch.log(sigma_s0)
h = lambda_t - lambda_s0
device = sample.device
rks = []
D1s = []
for i in range(1, order):
si = self.step_index - i
mi = model_output_list[-(i + 1)]
alpha_si, sigma_si = self._sigma_to_alpha_sigma_t(self.sigmas[si])
lambda_si = torch.log(alpha_si) - torch.log(sigma_si)
rk = (lambda_si - lambda_s0) / h
rks.append(rk)
D1s.append((mi - m0) / rk)
rks.append(1.0)
rks = torch.tensor(rks, device=device) | 1,374 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_unipc_multistep.py |
R = []
b = []
hh = -h if self.predict_x0 else h
h_phi_1 = torch.expm1(hh) # h\phi_1(h) = e^h - 1
h_phi_k = h_phi_1 / hh - 1
factorial_i = 1
if self.config.solver_type == "bh1":
B_h = hh
elif self.config.solver_type == "bh2":
B_h = torch.expm1(hh)
else:
raise NotImplementedError()
for i in range(1, order + 1):
R.append(torch.pow(rks, i - 1))
b.append(h_phi_k * factorial_i / B_h)
factorial_i *= i + 1
h_phi_k = h_phi_k / hh - 1 / factorial_i
R = torch.stack(R)
b = torch.tensor(b, device=device) | 1,374 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_unipc_multistep.py |
if len(D1s) > 0:
D1s = torch.stack(D1s, dim=1) # (B, K)
# for order 2, we use a simplified version
if order == 2:
rhos_p = torch.tensor([0.5], dtype=x.dtype, device=device)
else:
rhos_p = torch.linalg.solve(R[:-1, :-1], b[:-1]).to(device).to(x.dtype)
else:
D1s = None
if self.predict_x0:
x_t_ = sigma_t / sigma_s0 * x - alpha_t * h_phi_1 * m0
if D1s is not None:
pred_res = torch.einsum("k,bkc...->bc...", rhos_p, D1s)
else:
pred_res = 0
x_t = x_t_ - alpha_t * B_h * pred_res
else:
x_t_ = alpha_t / alpha_s0 * x - sigma_t * h_phi_1 * m0
if D1s is not None:
pred_res = torch.einsum("k,bkc...->bc...", rhos_p, D1s)
else:
pred_res = 0
x_t = x_t_ - sigma_t * B_h * pred_res
x_t = x_t.to(x.dtype)
return x_t | 1,374 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_unipc_multistep.py |
def multistep_uni_c_bh_update(
self,
this_model_output: torch.Tensor,
*args,
last_sample: torch.Tensor = None,
this_sample: torch.Tensor = None,
order: int = None,
**kwargs,
) -> torch.Tensor:
"""
One step for the UniC (B(h) version).
Args:
this_model_output (`torch.Tensor`):
The model outputs at `x_t`.
this_timestep (`int`):
The current timestep `t`.
last_sample (`torch.Tensor`):
The generated sample before the last predictor `x_{t-1}`.
this_sample (`torch.Tensor`):
The generated sample after the last predictor `x_{t}`.
order (`int`):
The `p` of UniC-p at this step. The effective order of accuracy should be `order + 1`. | 1,374 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_unipc_multistep.py |
Returns:
`torch.Tensor`:
The corrected sample tensor at the current timestep.
"""
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 this_sample is None:
if len(args) > 2:
this_sample = args[2]
else:
raise ValueError(" missing`this_sample` as a required keyward argument")
if order is None:
if len(args) > 3:
order = args[3]
else:
raise ValueError(" missing`order` as a required keyward argument")
if this_timestep is not None:
deprecate(
"this_timestep",
"1.0.0", | 1,374 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_unipc_multistep.py |
"Passing `this_timestep` is deprecated and has no effect as model output conversion is now handled via an internal counter `self.step_index`",
) | 1,374 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_unipc_multistep.py |
model_output_list = self.model_outputs
m0 = model_output_list[-1]
x = last_sample
x_t = this_sample
model_t = this_model_output
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 = torch.log(alpha_t) - torch.log(sigma_t)
lambda_s0 = torch.log(alpha_s0) - torch.log(sigma_s0)
h = lambda_t - lambda_s0
device = this_sample.device
rks = []
D1s = []
for i in range(1, order):
si = self.step_index - (i + 1)
mi = model_output_list[-(i + 1)]
alpha_si, sigma_si = self._sigma_to_alpha_sigma_t(self.sigmas[si])
lambda_si = torch.log(alpha_si) - torch.log(sigma_si)
rk = (lambda_si - lambda_s0) / h
rks.append(rk)
D1s.append((mi - m0) / rk) | 1,374 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_unipc_multistep.py |
rks.append(1.0)
rks = torch.tensor(rks, device=device)
R = []
b = []
hh = -h if self.predict_x0 else h
h_phi_1 = torch.expm1(hh) # h\phi_1(h) = e^h - 1
h_phi_k = h_phi_1 / hh - 1
factorial_i = 1
if self.config.solver_type == "bh1":
B_h = hh
elif self.config.solver_type == "bh2":
B_h = torch.expm1(hh)
else:
raise NotImplementedError()
for i in range(1, order + 1):
R.append(torch.pow(rks, i - 1))
b.append(h_phi_k * factorial_i / B_h)
factorial_i *= i + 1
h_phi_k = h_phi_k / hh - 1 / factorial_i
R = torch.stack(R)
b = torch.tensor(b, device=device)
if len(D1s) > 0:
D1s = torch.stack(D1s, dim=1)
else:
D1s = None | 1,374 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_unipc_multistep.py |
# for order 1, we use a simplified version
if order == 1:
rhos_c = torch.tensor([0.5], dtype=x.dtype, device=device)
else:
rhos_c = torch.linalg.solve(R, b).to(device).to(x.dtype)
if self.predict_x0:
x_t_ = sigma_t / sigma_s0 * x - alpha_t * h_phi_1 * m0
if D1s is not None:
corr_res = torch.einsum("k,bkc...->bc...", rhos_c[:-1], D1s)
else:
corr_res = 0
D1_t = model_t - m0
x_t = x_t_ - alpha_t * B_h * (corr_res + rhos_c[-1] * D1_t)
else:
x_t_ = alpha_t / alpha_s0 * x - sigma_t * h_phi_1 * m0
if D1s is not None:
corr_res = torch.einsum("k,bkc...->bc...", rhos_c[:-1], D1s)
else:
corr_res = 0
D1_t = model_t - m0
x_t = x_t_ - sigma_t * B_h * (corr_res + rhos_c[-1] * D1_t)
x_t = x_t.to(x.dtype)
return x_t | 1,374 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_unipc_multistep.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 == timestep).nonzero()
if len(index_candidates) == 0:
step_index = len(self.timesteps) - 1
# The sigma index that is taken for the **very** first `step`
# is always the second index (or the last index if there is only 1)
# This way we 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)
elif len(index_candidates) > 1:
step_index = index_candidates[1].item()
else:
step_index = index_candidates[0].item()
return step_index | 1,374 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_unipc_multistep.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.Tensor):
timestep = timestep.to(self.timesteps.device)
self._step_index = self.index_for_timestep(timestep)
else:
self._step_index = self._begin_index
def step(
self,
model_output: torch.Tensor,
timestep: Union[int, torch.Tensor],
sample: torch.Tensor,
return_dict: bool = True,
) -> Union[SchedulerOutput, Tuple]:
"""
Predict the sample from the previous timestep by reversing the SDE. This function propagates the sample with
the multistep UniPC. | 1,374 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_unipc_multistep.py |
Args:
model_output (`torch.Tensor`):
The direct output from learned diffusion model.
timestep (`int`):
The current discrete timestep in the diffusion chain.
sample (`torch.Tensor`):
A current instance of a sample created by the diffusion process.
return_dict (`bool`):
Whether or not to return a [`~schedulers.scheduling_utils.SchedulerOutput`] or `tuple`.
Returns:
[`~schedulers.scheduling_utils.SchedulerOutput`] or `tuple`:
If return_dict is `True`, [`~schedulers.scheduling_utils.SchedulerOutput`] is returned, otherwise a
tuple is returned where the first element is the sample tensor.
"""
if self.num_inference_steps is None:
raise ValueError(
"Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"
) | 1,374 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_unipc_multistep.py |
if self.step_index is None:
self._init_step_index(timestep)
use_corrector = (
self.step_index > 0 and self.step_index - 1 not in self.disable_corrector and self.last_sample is not None
)
model_output_convert = self.convert_model_output(model_output, sample=sample)
if use_corrector:
sample = self.multistep_uni_c_bh_update(
this_model_output=model_output_convert,
last_sample=self.last_sample,
this_sample=sample,
order=self.this_order,
)
for i in range(self.config.solver_order - 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 | 1,374 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_unipc_multistep.py |
if self.config.lower_order_final:
this_order = min(self.config.solver_order, len(self.timesteps) - self.step_index)
else:
this_order = self.config.solver_order
self.this_order = min(this_order, self.lower_order_nums + 1) # warmup for multistep
assert self.this_order > 0
self.last_sample = sample
prev_sample = self.multistep_uni_p_bh_update(
model_output=model_output, # pass the original non-converted model output, in case solver-p is used
sample=sample,
order=self.this_order,
)
if self.lower_order_nums < self.config.solver_order:
self.lower_order_nums += 1
# upon completion increase step index by one
self._step_index += 1
if not return_dict:
return (prev_sample,)
return SchedulerOutput(prev_sample=prev_sample) | 1,374 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_unipc_multistep.py |
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:
sample (`torch.Tensor`):
The input sample.
Returns:
`torch.Tensor`:
A scaled input sample.
"""
return sample | 1,374 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_unipc_multistep.py |
# Copied from diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler.add_noise
def add_noise(
self,
original_samples: torch.Tensor,
noise: torch.Tensor,
timesteps: torch.IntTensor,
) -> torch.Tensor:
# Make sure sigmas and timesteps have the same device and dtype as original_samples
sigmas = self.sigmas.to(device=original_samples.device, dtype=original_samples.dtype)
if original_samples.device.type == "mps" and torch.is_floating_point(timesteps):
# mps does not support float64
schedule_timesteps = self.timesteps.to(original_samples.device, dtype=torch.float32)
timesteps = timesteps.to(original_samples.device, dtype=torch.float32)
else:
schedule_timesteps = self.timesteps.to(original_samples.device)
timesteps = timesteps.to(original_samples.device) | 1,374 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_unipc_multistep.py |
# begin_index is None when the 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 after first denoising step (for inpainting)
step_indices = [self.step_index] * timesteps.shape[0]
else:
# add noise is called before first denoising step to create initial latent(img2img)
step_indices = [self.begin_index] * timesteps.shape[0]
sigma = sigmas[step_indices].flatten()
while len(sigma.shape) < len(original_samples.shape):
sigma = sigma.unsqueeze(-1)
alpha_t, sigma_t = self._sigma_to_alpha_sigma_t(sigma)
noisy_samples = alpha_t * original_samples + sigma_t * noise
return noisy_samples
def __len__(self):
return self.config.num_train_timesteps | 1,374 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_unipc_multistep.py |
class ScoreSdeVpScheduler(SchedulerMixin, ConfigMixin):
"""
`ScoreSdeVpScheduler` is a variance preserving stochastic differential equation (SDE) 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.
Args:
num_train_timesteps (`int`, defaults to 2000):
The number of diffusion steps to train the model.
beta_min (`int`, defaults to 0.1):
beta_max (`int`, defaults to 20):
sampling_eps (`int`, defaults to 1e-3):
The end value of sampling where timesteps decrease progressively from 1 to epsilon.
"""
order = 1
@register_to_config
def __init__(self, num_train_timesteps=2000, beta_min=0.1, beta_max=20, sampling_eps=1e-3):
self.sigmas = None
self.discrete_sigmas = None
self.timesteps = None | 1,375 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/deprecated/scheduling_sde_vp.py |
def set_timesteps(self, num_inference_steps, device: Union[str, torch.device] = None):
"""
Sets the continuous timesteps used for the diffusion chain (to be run before inference).
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.
"""
self.timesteps = torch.linspace(1, self.config.sampling_eps, num_inference_steps, device=device)
def step_pred(self, score, x, t, generator=None):
"""
Predict the sample from the previous timestep by reversing the SDE. This function propagates the diffusion
process from the learned model outputs (most often the predicted noise). | 1,375 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/deprecated/scheduling_sde_vp.py |
Args:
score ():
x ():
t ():
generator (`torch.Generator`, *optional*):
A random number generator.
"""
if self.timesteps is None:
raise ValueError(
"`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"
)
# TODO(Patrick) better comments + non-PyTorch
# postprocess model score
log_mean_coeff = -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
std = torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff))
std = std.flatten()
while len(std.shape) < len(score.shape):
std = std.unsqueeze(-1)
score = -score / std
# compute
dt = -1.0 / len(self.timesteps) | 1,375 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/deprecated/scheduling_sde_vp.py |
beta_t = self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
beta_t = beta_t.flatten()
while len(beta_t.shape) < len(x.shape):
beta_t = beta_t.unsqueeze(-1)
drift = -0.5 * beta_t * x
diffusion = torch.sqrt(beta_t)
drift = drift - diffusion**2 * score
x_mean = x + drift * dt
# add noise
noise = randn_tensor(x.shape, layout=x.layout, generator=generator, device=x.device, dtype=x.dtype)
x = x_mean + diffusion * math.sqrt(-dt) * noise
return x, x_mean
def __len__(self):
return self.config.num_train_timesteps | 1,375 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/deprecated/scheduling_sde_vp.py |
class KarrasVeOutput(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 model input in the
denoising loop.
derivative (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` for images):
Derivative of predicted original image sample (x_0).
pred_original_sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` for images):
The predicted denoised sample (x_{0}) based on the model output from the current timestep.
`pred_original_sample` can be used to preview progress or for guidance.
"""
prev_sample: torch.Tensor
derivative: torch.Tensor
pred_original_sample: Optional[torch.Tensor] = None | 1,376 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/deprecated/scheduling_karras_ve.py |
class KarrasVeScheduler(SchedulerMixin, ConfigMixin):
"""
A stochastic scheduler tailored to variance-expanding models.
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.
<Tip>
For more details on the parameters, see [Appendix E](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 paper.
</Tip> | 1,377 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/deprecated/scheduling_karras_ve.py |
Args:
sigma_min (`float`, defaults to 0.02):
The minimum noise magnitude.
sigma_max (`float`, defaults to 100):
The maximum noise magnitude.
s_noise (`float`, defaults to 1.007):
The amount of additional noise to counteract loss of detail during sampling. A reasonable range is [1.000,
1.011].
s_churn (`float`, defaults to 80):
The parameter controlling the overall amount of stochasticity. A reasonable range is [0, 100].
s_min (`float`, defaults to 0.05):
The start value of the sigma range to add noise (enable stochasticity). A reasonable range is [0, 10].
s_max (`float`, defaults to 50):
The end value of the sigma range to add noise. A reasonable range is [0.2, 80].
"""
order = 2 | 1,377 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/deprecated/scheduling_karras_ve.py |
@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,
):
# standard deviation of the initial noise distribution
self.init_noise_sigma = sigma_max
# setable values
self.num_inference_steps: int = None
self.timesteps: np.IntTensor = None
self.schedule: torch.Tensor = None # sigma(t_i)
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
current timestep.
Args:
sample (`torch.Tensor`):
The input sample.
timestep (`int`, *optional*):
The current timestep in the diffusion chain. | 1,377 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/deprecated/scheduling_karras_ve.py |
Returns:
`torch.Tensor`:
A scaled input sample.
"""
return sample
def set_timesteps(self, num_inference_steps: int, device: Union[str, torch.device] = None):
"""
Sets the discrete timesteps used for the diffusion chain (to be run before inference). | 1,377 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/deprecated/scheduling_karras_ve.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.
"""
self.num_inference_steps = num_inference_steps
timesteps = np.arange(0, self.num_inference_steps)[::-1].copy()
self.timesteps = torch.from_numpy(timesteps).to(device)
schedule = [
(
self.config.sigma_max**2
* (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
)
for i in self.timesteps
]
self.schedule = torch.tensor(schedule, dtype=torch.float32, device=device) | 1,377 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/deprecated/scheduling_karras_ve.py |
def add_noise_to_input(
self, sample: torch.Tensor, sigma: float, generator: Optional[torch.Generator] = None
) -> Tuple[torch.Tensor, float]:
"""
Explicit Langevin-like "churn" step of adding noise to the sample according to a `gamma_i ≥ 0` to reach a
higher noise level `sigma_hat = sigma_i + gamma_i*sigma_i`.
Args:
sample (`torch.Tensor`):
The input sample.
sigma (`float`):
generator (`torch.Generator`, *optional*):
A random number generator.
"""
if self.config.s_min <= sigma <= self.config.s_max:
gamma = min(self.config.s_churn / self.num_inference_steps, 2**0.5 - 1)
else:
gamma = 0
# sample eps ~ N(0, S_noise^2 * I)
eps = self.config.s_noise * randn_tensor(sample.shape, generator=generator).to(sample.device)
sigma_hat = sigma + gamma * sigma
sample_hat = sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps) | 1,377 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/deprecated/scheduling_karras_ve.py |
return sample_hat, sigma_hat
def step(
self,
model_output: torch.Tensor,
sigma_hat: float,
sigma_prev: float,
sample_hat: torch.Tensor,
return_dict: bool = True,
) -> Union[KarrasVeOutput, Tuple]:
"""
Predict the sample from the previous timestep by reversing the SDE. This function propagates the diffusion
process from the learned model outputs (most often the predicted noise).
Args:
model_output (`torch.Tensor`):
The direct output from learned diffusion model.
sigma_hat (`float`):
sigma_prev (`float`):
sample_hat (`torch.Tensor`):
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~schedulers.scheduling_karras_ve.KarrasVESchedulerOutput`] or `tuple`. | 1,377 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/deprecated/scheduling_karras_ve.py |
Returns:
[`~schedulers.scheduling_karras_ve.KarrasVESchedulerOutput`] or `tuple`:
If return_dict is `True`, [`~schedulers.scheduling_karras_ve.KarrasVESchedulerOutput`] is returned,
otherwise a tuple is returned where the first element is the sample tensor.
"""
pred_original_sample = sample_hat + sigma_hat * model_output
derivative = (sample_hat - pred_original_sample) / sigma_hat
sample_prev = sample_hat + (sigma_prev - sigma_hat) * derivative
if not return_dict:
return (sample_prev, derivative)
return KarrasVeOutput(
prev_sample=sample_prev, derivative=derivative, pred_original_sample=pred_original_sample
) | 1,377 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/deprecated/scheduling_karras_ve.py |
def step_correct(
self,
model_output: torch.Tensor,
sigma_hat: float,
sigma_prev: float,
sample_hat: torch.Tensor,
sample_prev: torch.Tensor,
derivative: torch.Tensor,
return_dict: bool = True,
) -> Union[KarrasVeOutput, Tuple]:
"""
Corrects the predicted sample based on the `model_output` of the network.
Args:
model_output (`torch.Tensor`):
The direct output from learned diffusion model.
sigma_hat (`float`): TODO
sigma_prev (`float`): TODO
sample_hat (`torch.Tensor`): TODO
sample_prev (`torch.Tensor`): TODO
derivative (`torch.Tensor`): TODO
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~schedulers.scheduling_ddpm.DDPMSchedulerOutput`] or `tuple`.
Returns:
prev_sample (TODO): updated sample in the diffusion chain. derivative (TODO): TODO | 1,377 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/deprecated/scheduling_karras_ve.py |
"""
pred_original_sample = sample_prev + sigma_prev * model_output
derivative_corr = (sample_prev - pred_original_sample) / sigma_prev
sample_prev = sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)
if not return_dict:
return (sample_prev, derivative)
return KarrasVeOutput(
prev_sample=sample_prev, derivative=derivative, pred_original_sample=pred_original_sample
)
def add_noise(self, original_samples, noise, timesteps):
raise NotImplementedError() | 1,377 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/deprecated/scheduling_karras_ve.py |