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# 1. get previous step value (=t-1)
prev_timestep = timestep - self.config.num_train_timesteps // self.num_inference_steps
# 2. compute alphas, betas
alpha_prod_t = self.alphas_cumprod[timestep]
alpha_prod_t_prev = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final... | 1,369 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_ddim.py |
# 3. compute predicted original sample from predicted noise also called
# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
if self.config.prediction_type == "epsilon":
pred_original_sample = (sample - beta_prod_t ** (0.5) * model_output) / alpha_prod_t ** (0.5)
... | 1,369 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_ddim.py |
) | 1,369 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_ddim.py |
# 4. Clip or threshold "predicted x_0"
if self.config.thresholding:
pred_original_sample = self._threshold_sample(pred_original_sample)
elif self.config.clip_sample:
pred_original_sample = pred_original_sample.clamp(
-self.config.clip_sample_range, self.config.cli... | 1,369 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_ddim.py |
# 6. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
pred_sample_direction = (1 - alpha_prod_t_prev - std_dev_t**2) ** (0.5) * pred_epsilon
# 7. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
prev_sample... | 1,369 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_ddim.py |
prev_sample = prev_sample + variance
if not return_dict:
return (
prev_sample,
pred_original_sample,
)
return DDIMSchedulerOutput(prev_sample=prev_sample, pred_original_sample=pred_original_sample)
# Copied from diffusers.schedulers.scheduli... | 1,369 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_ddim.py |
sqrt_alpha_prod = alphas_cumprod[timesteps] ** 0.5
sqrt_alpha_prod = sqrt_alpha_prod.flatten()
while len(sqrt_alpha_prod.shape) < len(original_samples.shape):
sqrt_alpha_prod = sqrt_alpha_prod.unsqueeze(-1)
sqrt_one_minus_alpha_prod = (1 - alphas_cumprod[timesteps]) ** 0.5
s... | 1,369 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_ddim.py |
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler.get_velocity
def get_velocity(self, sample: torch.Tensor, noise: torch.Tensor, timesteps: torch.IntTensor) -> torch.Tensor:
# Make sure alphas_cumprod and timestep have same device and dtype as sample
self.alphas_cumprod = self.alphas_... | 1,369 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_ddim.py |
velocity = sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
return velocity
def __len__(self):
return self.config.num_train_timesteps | 1,369 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_ddim.py |
class FlowMatchHeunDiscreteSchedulerOutput(BaseOutput):
"""
Output class for the scheduler's `step` function output.
Args:
prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):
Computed sample `(x_{t-1})` of previous timestep. `prev_sample` s... | 1,370 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_flow_match_heun_discrete.py |
class FlowMatchHeunDiscreteScheduler(SchedulerMixin, ConfigMixin):
"""
Heun 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... | 1,371 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_flow_match_heun_discrete.py |
@register_to_config
def __init__(
self,
num_train_timesteps: int = 1000,
shift: float = 1.0,
):
timesteps = np.linspace(1, num_train_timesteps, num_train_timesteps, dtype=np.float32)[::-1].copy()
timesteps = torch.from_numpy(timesteps).to(dtype=torch.float32)
sig... | 1,371 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_flow_match_heun_discrete.py |
@property
def begin_index(self):
"""
The index for the first timestep. It should be set from pipeline with `set_begin_index` method.
"""
return self._begin_index
# Copied from diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler.set_begin_index
def... | 1,371 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_flow_match_heun_discrete.py |
Args:
sample (`torch.FloatTensor`):
The input sample.
timestep (`int`, *optional*):
The current timestep in the diffusion chain.
Returns:
`torch.FloatTensor`:
A scaled input sample.
"""
if self.step_index is Non... | 1,371 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_flow_match_heun_discrete.py |
Args:
num_inference_steps (`int`):
The number of diffusion steps used when generating samples with a pre-trained model.
device (`str` or `torch.device`, *optional*):
The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.
... | 1,371 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_flow_match_heun_discrete.py |
sigmas = torch.cat([sigmas, torch.zeros(1, device=sigmas.device)])
self.sigmas = torch.cat([sigmas[:1], sigmas[1:-1].repeat_interleave(2), sigmas[-1:]])
# empty dt and derivative
self.prev_derivative = None
self.dt = None
self._step_index = None
self._begin_index = None... | 1,371 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_flow_match_heun_discrete.py |
def _init_step_index(self, timestep):
if self.begin_index is None:
if isinstance(timestep, torch.Tensor):
timestep = timestep.to(self.timesteps.device)
self._step_index = self.index_for_timestep(timestep)
else:
self._step_index = self._begin_index
... | 1,371 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_flow_match_heun_discrete.py |
def step(
self,
model_output: torch.FloatTensor,
timestep: Union[float, torch.FloatTensor],
sample: torch.FloatTensor,
s_churn: float = 0.0,
s_tmin: float = 0.0,
s_tmax: float = float("inf"),
s_noise: float = 1.0,
generator: Optional[torch.Generato... | 1,371 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_flow_match_heun_discrete.py |
Args:
model_output (`torch.FloatTensor`):
The direct output from learned diffusion model.
timestep (`float`):
The current discrete timestep in the diffusion chain.
sample (`torch.FloatTensor`):
A current instance of a sample created by ... | 1,371 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_flow_match_heun_discrete.py |
Returns:
[`~schedulers.scheduling_Heun_discrete.HeunDiscreteSchedulerOutput`] or `tuple`:
If return_dict is `True`, [`~schedulers.scheduling_Heun_discrete.HeunDiscreteSchedulerOutput`] is
returned, otherwise a tuple is returned where the first element is the sample tensor.
... | 1,371 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_flow_match_heun_discrete.py |
# Upcast to avoid precision issues when computing prev_sample
sample = sample.to(torch.float32)
if self.state_in_first_order:
sigma = self.sigmas[self.step_index]
sigma_next = self.sigmas[self.step_index + 1]
else:
# 2nd order / Heun's method
sigm... | 1,371 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_flow_match_heun_discrete.py |
if self.state_in_first_order:
# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
denoised = sample - model_output * sigma
# 2. convert to an ODE derivative for 1st order
derivative = (sample - denoised) / sigma_hat
# 3. Delta timest... | 1,371 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_flow_match_heun_discrete.py |
# free dt and derivative
# Note, this puts the scheduler in "first order mode"
self.prev_derivative = None
self.dt = None
self.sample = None
prev_sample = sample + derivative * dt
# Cast sample back to model compatible dtype
prev_sample = prev_sam... | 1,371 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_flow_match_heun_discrete.py |
class PNDMScheduler(SchedulerMixin, ConfigMixin):
"""
`PNDMScheduler` uses pseudo numerical methods for diffusion models such as the Runge-Kutta and linear multi-step
method.
This model inherits from [`SchedulerMixin`] and [`ConfigMixin`]. Check the superclass documentation for the generic
methods ... | 1,372 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_pndm.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_... | 1,372 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_pndm.py |
Each diffusion step uses the alphas product value at that step and at the previous one. For the final step
there is no previous alpha. When this option is `True` the previous alpha product is fixed to `1`,
otherwise it uses the alpha value at step 0.
prediction_type (`str`, defaults to `... | 1,372 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_pndm.py |
_compatibles = [e.name for e in KarrasDiffusionSchedulers]
order = 1 | 1,372 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_pndm.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,
skip_prk_steps: bool = False,
se... | 1,372 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_pndm.py |
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__}") | 1,372 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_pndm.py |
self.alphas = 1.0 - self.betas
self.alphas_cumprod = torch.cumprod(self.alphas, dim=0)
self.final_alpha_cumprod = torch.tensor(1.0) if set_alpha_to_one else self.alphas_cumprod[0]
# standard deviation of the initial noise distribution
self.init_noise_sigma = 1.0
# For now we o... | 1,372 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_pndm.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).
Args:
num_inference_steps (`int`):
The number of diffusion steps used when generatin... | 1,372 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_pndm.py |
self.num_inference_steps = num_inference_steps
# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
if self.config.timestep_spacing == "linspace":
self._timesteps = (
np.linspace(0, self.config.num_train_timesteps - 1, ... | 1,372 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_pndm.py |
# creates integer timesteps by multiplying by ratio
# casting to int to avoid issues when num_inference_step is power of 3
self._timesteps = np.round(np.arange(self.config.num_train_timesteps, 0, -step_ratio))[::-1].astype(
np.int64
)
self._timesteps -= 1
... | 1,372 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_pndm.py |
if self.config.skip_prk_steps:
# for some models like stable diffusion the prk steps can/should be skipped to
# produce better results. When using PNDM with `self.config.skip_prk_steps` the implementation
# is based on crowsonkb's PLMS sampler implementation: https://github.com/CompV... | 1,372 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_pndm.py |
].copy() # we copy to avoid having negative strides which are not supported by torch.from_numpy | 1,372 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_pndm.py |
timesteps = np.concatenate([self.prk_timesteps, self.plms_timesteps]).astype(np.int64)
self.timesteps = torch.from_numpy(timesteps).to(device)
self.ets = []
self.counter = 0
self.cur_model_output = 0
def step(
self,
model_output: torch.Tensor,
timestep: int,... | 1,372 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_pndm.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... | 1,372 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_pndm.py |
"""
if self.counter < len(self.prk_timesteps) and not self.config.skip_prk_steps:
return self.step_prk(model_output=model_output, timestep=timestep, sample=sample, return_dict=return_dict)
else:
return self.step_plms(model_output=model_output, timestep=timestep, sample=sample, re... | 1,372 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_pndm.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... | 1,372 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_pndm.py |
diff_to_prev = 0 if self.counter % 2 else self.config.num_train_timesteps // self.num_inference_steps // 2
prev_timestep = timestep - diff_to_prev
timestep = self.prk_timesteps[self.counter // 4 * 4]
if self.counter % 4 == 0:
self.cur_model_output += 1 / 6 * model_output
... | 1,372 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_pndm.py |
if not return_dict:
return (prev_sample,)
return SchedulerOutput(prev_sample=prev_sample)
def step_plms(
self,
model_output: torch.Tensor,
timestep: int,
sample: torch.Tensor,
return_dict: bool = True,
) -> Union[SchedulerOutput, Tuple]:
"""
... | 1,372 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_pndm.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... | 1,372 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_pndm.py |
if not self.config.skip_prk_steps and len(self.ets) < 3:
raise ValueError(
f"{self.__class__} can only be run AFTER scheduler has been run "
"in 'prk' mode for at least 12 iterations "
"See: https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipe... | 1,372 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_pndm.py |
if len(self.ets) == 1 and self.counter == 0:
model_output = model_output
self.cur_sample = sample
elif len(self.ets) == 1 and self.counter == 1:
model_output = (model_output + self.ets[-1]) / 2
sample = self.cur_sample
self.cur_sample = None
el... | 1,372 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_pndm.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.... | 1,372 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_pndm.py |
# Notation (<variable name> -> <name in paper>
# alpha_prod_t -> α_t
# alpha_prod_t_prev -> α_(t−δ)
# beta_prod_t -> (1 - α_t)
# beta_prod_t_prev -> (1 - α_(t−δ))
# sample -> x_t
# model_output -> e_θ(x_t, t)
# prev_sample -> x_(t−δ)
alpha_prod_t = self.al... | 1,372 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_pndm.py |
# corresponds to (α_(t−δ) - α_t) divided by
# denominator of x_t in formula (9) and plus 1
# Note: (α_(t−δ) - α_t) / (sqrt(α_t) * (sqrt(α_(t−δ)) + sqr(α_t))) =
# sqrt(α_(t−δ)) / sqrt(α_t))
sample_coeff = (alpha_prod_t_prev / alpha_prod_t) ** (0.5)
# corresponds to denominator of... | 1,372 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_pndm.py |
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler.add_noise
def add_noise(
self,
original_samples: torch.Tensor,
noise: torch.Tensor,
timesteps: torch.IntTensor,
) -> torch.Tensor:
# Make sure alphas_cumprod and timestep have same device and dtype as origin... | 1,372 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_pndm.py |
sqrt_one_minus_alpha_prod = (1 - alphas_cumprod[timesteps]) ** 0.5
sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.flatten()
while len(sqrt_one_minus_alpha_prod.shape) < len(original_samples.shape):
sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.unsqueeze(-1)
noisy_samp... | 1,372 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_pndm.py |
class DPMSolverMultistepScheduler(SchedulerMixin, ConfigMixin):
"""
`DPMSolverMultistepScheduler` is a fast dedicated high-order solver for diffusion ODEs.
This model inherits from [`SchedulerMixin`] and [`ConfigMixin`]. Check the superclass documentation for the generic
methods the library implements ... | 1,373 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_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_... | 1,373 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_multistep.py |
Prediction type of the scheduler function; can be `epsilon` (predicts the noise of the diffusion process),
`sample` (directly predicts the noisy sample), `v_prediction` (see section 2.4 of [Imagen
Video](https://imagen.research.google/video/paper.pdf) paper), or `flow_prediction`.
thresh... | 1,373 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_multistep.py |
Algorithm type for the solver; can be `dpmsolver`, `dpmsolver++`, `sde-dpmsolver` or `sde-dpmsolver++`. The
`dpmsolver` type implements the algorithms in the [DPMSolver](https://huggingface.co/papers/2206.00927)
paper, and the `dpmsolver++` type implements the algorithms in the
[DPMS... | 1,373 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_multistep.py |
stabilize the sampling of DPMSolver for steps < 15, especially for steps <= 10.
euler_at_final (`bool`, defaults to `False`):
Whether to use Euler's method in the final step. It is a trade-off between numerical stability and detail
richness. This can stabilize the sampling of the SDE var... | 1,373 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_multistep.py |
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.
use_lu_lambdas (`bool`, *optional*, defaults to `False`):
Whether to use the uniform-logSN... | 1,373 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_multistep.py |
sigma is the same as the last sigma in the training schedule. If `zero`, the final sigma is set to 0.
lambda_min_clipped (`float`, defaults to `-inf`):
Clipping threshold for the minimum value of `lambda(t)` for numerical stability. This is critical for the
cosine (`squaredcos_cap_v2`) n... | 1,373 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_multistep.py |
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... | 1,373 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_multistep.py |
_compatibles = [e.name for e in KarrasDiffusionSchedulers]
order = 1 | 1,373 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_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... | 1,373 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_multistep.py |
lambda_min_clipped: float = -float("inf"),
variance_type: Optional[str] = None,
timestep_spacing: str = "linspace",
steps_offset: int = 0,
rescale_betas_zero_snr: bool = False,
):
if self.config.use_beta_sigmas and not is_scipy_available():
raise ImportError("Make... | 1,373 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_multistep.py |
if trained_betas is not None:
self.betas = torch.tensor(trained_betas, dtype=torch.float32)
elif beta_schedule == "linear":
self.betas = torch.linspace(beta_start, beta_end, num_train_timesteps, dtype=torch.float32)
elif beta_schedule == "scaled_linear":
# this schedu... | 1,373 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_multistep.py |
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)
... | 1,373 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_multistep.py |
if solver_type not in ["midpoint", "heun"]:
if solver_type in ["logrho", "bh1", "bh2"]:
self.register_to_config(solver_type="midpoint")
else:
raise NotImplementedError(f"{solver_type} is not implemented for {self.__class__}")
if algorithm_type not in ["dp... | 1,373 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_multistep.py |
# 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.lower_order_nums = 0
self.... | 1,373 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_multistep.py |
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_in... | 1,373 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_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.
... | 1,373 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_multistep.py |
raise ValueError("Can only pass one of `num_inference_steps` or `custom_timesteps`.")
if timesteps is not None and self.config.use_karras_sigmas:
raise ValueError("Cannot use `timesteps` with `config.use_karras_sigmas = True`")
if timesteps is not None and self.config.use_lu_lambdas:
... | 1,373 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_multistep.py |
if timesteps is not None:
timesteps = np.array(timesteps).astype(np.int64)
else:
# Clipping the minimum of all lambda(t) for numerical stability.
# This is critical for cosine (squaredcos_cap_v2) noise schedule.
clipped_idx = torch.searchsorted(torch.flip(self.lam... | 1,373 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_multistep.py |
# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
if self.config.timestep_spacing == "linspace":
timesteps = (
np.linspace(0, last_timestep - 1, num_inference_steps + 1)
.round()[::-1][:-1]
... | 1,373 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_multistep.py |
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(last_timestep, 0, -step_ratio).round().copy().astype(np.int6... | 1,373 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_multistep.py |
sigmas = np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)
log_sigmas = np.log(sigmas) | 1,373 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_multistep.py |
if self.config.use_karras_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()
elif self.config.use_lu_lambda... | 1,373 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_multistep.py |
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])
elif self.config.use_flow_sigmas:
alphas = np.linspace(1, 1 / self.config.num_train_timesteps, num_inference_steps + 1... | 1,373 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_multistep.py |
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... | 1,373 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_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... | 1,373 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_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))
... | 1,373 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_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... | 1,373 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_multistep.py |
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.sch... | 1,373 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_multistep.py |
sigma_min = sigma_min if sigma_min is not None else in_sigmas[-1].item()
sigma_max = sigma_max if sigma_max is not None else in_sigmas[0].item()
rho = 7.0 # 7.0 is the value used in the paper
ramp = np.linspace(0, 1, num_inference_steps)
min_inv_rho = sigma_min ** (1 / rho)
max... | 1,373 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_multistep.py |
# Copied from diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler._convert_to_exponential
def _convert_to_exponential(self, in_sigmas: torch.Tensor, num_inference_steps: int) -> torch.Tensor:
"""Constructs an exponential noise schedule."""
# Hack to make sure that other schedulers... | 1,373 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_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] ... | 1,373 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_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... | 1,373 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_multistep.py |
Args:
model_output (`torch.Tensor`):
The direct output from the learned diffusion model.
sample (`torch.Tensor`):
A current instance of a sample created by the diffusion process.
Returns:
`torch.Tensor`:
The converted model out... | 1,373 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_multistep.py |
# DPM-Solver++ needs to solve an integral of the data prediction model.
if self.config.algorithm_type in ["dpmsolver++", "sde-dpmsolver++"]:
if self.config.prediction_type == "epsilon":
# DPM-Solver and DPM-Solver++ only need the "mean" output.
if self.config.variance... | 1,373 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_multistep.py |
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... | 1,373 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_multistep.py |
if self.config.thresholding:
x0_pred = self._threshold_sample(x0_pred)
return x0_pred | 1,373 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_multistep.py |
# DPM-Solver needs to solve an integral of the noise prediction model.
elif self.config.algorithm_type in ["dpmsolver", "sde-dpmsolver"]:
if self.config.prediction_type == "epsilon":
# DPM-Solver and DPM-Solver++ only need the "mean" output.
if self.config.variance_ty... | 1,373 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_multistep.py |
raise ValueError(
f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or"
" `v_prediction` for the DPMSolverMultistepScheduler."
) | 1,373 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_multistep.py |
if self.config.thresholding:
sigma = self.sigmas[self.step_index]
alpha_t, sigma_t = self._sigma_to_alpha_sigma_t(sigma)
x0_pred = (sample - sigma_t * epsilon) / alpha_t
x0_pred = self._threshold_sample(x0_pred)
epsilon = (sample - alpha_t ... | 1,373 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_multistep.py |
Returns:
`torch.Tensor`:
The sample tensor at the previous timestep.
"""
timestep = args[0] if len(args) > 0 else kwargs.pop("timestep", None)
prev_timestep = args[1] if len(args) > 1 else kwargs.pop("prev_timestep", None)
if sample is None:
if len... | 1,373 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_multistep.py |
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`",
)
sigma_t, sigma_s = self.sig... | 1,373 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_multistep.py |
h = lambda_t - lambda_s
if self.config.algorithm_type == "dpmsolver++":
x_t = (sigma_t / sigma_s) * sample - (alpha_t * (torch.exp(-h) - 1.0)) * model_output
elif self.config.algorithm_type == "dpmsolver":
x_t = (alpha_t / alpha_s) * sample - (sigma_t * (torch.exp(h) - 1.0)) * mo... | 1,373 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_multistep.py |
def multistep_dpm_solver_second_order_update(
self,
model_output_list: List[torch.Tensor],
*args,
sample: torch.Tensor = None,
noise: Optional[torch.Tensor] = None,
**kwargs,
) -> torch.Tensor:
"""
One step for the second-order multistep DPMSolver.
... | 1,373 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_multistep.py |
Returns:
`torch.Tensor`:
The sample tensor at the previous timestep.
"""
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:
... | 1,373 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_multistep.py |
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`",
)
sigma_t, sigma_s0, sigma_s1... | 1,373 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_multistep.py |
h, h_0 = lambda_t - lambda_s0, lambda_s0 - lambda_s1
r0 = h_0 / h
D0, D1 = m0, (1.0 / r0) * (m0 - m1)
if self.config.algorithm_type == "dpmsolver++":
# See https://arxiv.org/abs/2211.01095 for detailed derivations
if self.config.solver_type == "midpoint":
... | 1,373 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_multistep.py |
(alpha_t / alpha_s0) * sample
- (sigma_t * (torch.exp(h) - 1.0)) * D0
- 0.5 * (sigma_t * (torch.exp(h) - 1.0)) * D1
)
elif self.config.solver_type == "heun":
x_t = (
(alpha_t / alpha_s0) * sample
... | 1,373 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_multistep.py |
(sigma_t / sigma_s0 * torch.exp(-h)) * sample
+ (alpha_t * (1 - torch.exp(-2.0 * h))) * D0
+ (alpha_t * ((1.0 - torch.exp(-2.0 * h)) / (-2.0 * h) + 1.0)) * D1
+ sigma_t * torch.sqrt(1.0 - torch.exp(-2 * h)) * noise
)
elif self.config.al... | 1,373 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_multistep.py |
- 2.0 * (sigma_t * ((torch.exp(h) - 1.0) / h - 1.0)) * D1
+ sigma_t * torch.sqrt(torch.exp(2 * h) - 1.0) * noise
)
return x_t | 1,373 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_multistep.py |
def multistep_dpm_solver_third_order_update(
self,
model_output_list: List[torch.Tensor],
*args,
sample: torch.Tensor = None,
noise: Optional[torch.Tensor] = None,
**kwargs,
) -> torch.Tensor:
"""
One step for the third-order multistep DPMSolver.
... | 1,373 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpmsolver_multistep.py |
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