text stringlengths 1 1.02k | class_index int64 0 1.38k | source stringclasses 431
values |
|---|---|---|
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`s... | 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, sigm... | 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.confi... | 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)) * sampl... | 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
... | 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:... | 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 diffusio... | 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... | 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"
)
... | 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... | 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.multist... | 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.... | 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=o... | 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 aft... | 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 libr... | 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_... | 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.re... | 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 in... | 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 s... | 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 schedul... | 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,
predictio... | 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_sig... | 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_... | 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 smal... | 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
... | 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 `s... | 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.
... | 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... | 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)
ti... | 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.f... | 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 {... | 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.ar... | 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_s... | 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 timest... | 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))
... | 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 sigm... | 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) ... | 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 th... | 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"):
... | 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] ... | 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)
]
]
)
retur... | 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... | 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":
... | 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... | 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.
... | 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:
... | 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_si... | 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... | 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).... | 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).
... | 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]
el... | 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)
... | 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 = h... | 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
... | 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 == ... | 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.Te... | 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 diffusio... | 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)
... | 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... | 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.... | 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 ... | 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 aft... | 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... | 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 s... | 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' aft... | 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 * s... | 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 inp... | 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 a... | 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 re... | 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.ini... | 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.
... | 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 =... | 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 ... | 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.
"""
... | 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]:
"""
Cor... | 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, de... | 1,377 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/deprecated/scheduling_karras_ve.py |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.