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prev_sample = prev_sample + variance
if not return_dict:
return (
prev_sample,
pred_original_sample,
)
return DDIMParallelSchedulerOutput(prev_sample=prev_sample, pred_original_sample=pred_original_sample)
def batch_step_no_noise(
self,
model_output: torch.Tensor,
timesteps: List[int],
sample: torch.Tensor,
eta: float = 0.0,
use_clipped_model_output: bool = False,
) -> torch.Tensor:
"""
Batched version of the `step` function, to be able to reverse the SDE for multiple samples/timesteps at once.
Also, does not add any noise to the predicted sample, which is necessary for parallel sampling where the noise
is pre-sampled by the pipeline.
Predict the sample at the previous timestep by reversing the SDE. Core function to propagate the diffusion
process from the learned model outputs (most often the predicted noise). | 1,347 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_ddim_parallel.py |
Args:
model_output (`torch.Tensor`): direct output from learned diffusion model.
timesteps (`List[int]`):
current discrete timesteps in the diffusion chain. This is now a list of integers.
sample (`torch.Tensor`):
current instance of sample being created by diffusion process.
eta (`float`): weight of noise for added noise in diffusion step.
use_clipped_model_output (`bool`): if `True`, compute "corrected" `model_output` from the clipped
predicted original sample. Necessary because predicted original sample is clipped to [-1, 1] when
`self.config.clip_sample` is `True`. If no clipping has happened, "corrected" `model_output` would
coincide with the one provided as input and `use_clipped_model_output` will have not effect.
Returns:
`torch.Tensor`: sample tensor at previous timestep. | 1,347 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_ddim_parallel.py |
"""
if self.num_inference_steps is None:
raise ValueError(
"Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"
)
assert eta == 0.0
# See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
# Ideally, read DDIM paper in-detail understanding
# Notation (<variable name> -> <name in paper>
# - pred_noise_t -> e_theta(x_t, t)
# - pred_original_sample -> f_theta(x_t, t) or x_0
# - std_dev_t -> sigma_t
# - eta -> η
# - pred_sample_direction -> "direction pointing to x_t"
# - pred_prev_sample -> "x_t-1"
# 1. get previous step value (=t-1)
t = timesteps
prev_t = t - self.config.num_train_timesteps // self.num_inference_steps
t = t.view(-1, *([1] * (model_output.ndim - 1)))
prev_t = prev_t.view(-1, *([1] * (model_output.ndim - 1))) | 1,347 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_ddim_parallel.py |
# 1. compute alphas, betas
self.alphas_cumprod = self.alphas_cumprod.to(model_output.device)
self.final_alpha_cumprod = self.final_alpha_cumprod.to(model_output.device)
alpha_prod_t = self.alphas_cumprod[t]
alpha_prod_t_prev = self.alphas_cumprod[torch.clip(prev_t, min=0)]
alpha_prod_t_prev[prev_t < 0] = torch.tensor(1.0)
beta_prod_t = 1 - alpha_prod_t | 1,347 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_ddim_parallel.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)
pred_epsilon = model_output
elif self.config.prediction_type == "sample":
pred_original_sample = model_output
pred_epsilon = (sample - alpha_prod_t ** (0.5) * pred_original_sample) / beta_prod_t ** (0.5)
elif self.config.prediction_type == "v_prediction":
pred_original_sample = (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
pred_epsilon = (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
else:
raise ValueError(
f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or"
" `v_prediction`" | 1,347 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_ddim_parallel.py |
) | 1,347 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_ddim_parallel.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.clip_sample_range
)
# 5. compute variance: "sigma_t(η)" -> see formula (16)
# σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
variance = self._batch_get_variance(t, prev_t).to(model_output.device).view(*alpha_prod_t_prev.shape)
std_dev_t = eta * variance ** (0.5)
if use_clipped_model_output:
# the pred_epsilon is always re-derived from the clipped x_0 in Glide
pred_epsilon = (sample - alpha_prod_t ** (0.5) * pred_original_sample) / beta_prod_t ** (0.5) | 1,347 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_ddim_parallel.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 = alpha_prod_t_prev ** (0.5) * pred_original_sample + pred_sample_direction
return prev_sample | 1,347 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_ddim_parallel.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 original_samples
# Move the self.alphas_cumprod to device to avoid redundant CPU to GPU data movement
# for the subsequent add_noise calls
self.alphas_cumprod = self.alphas_cumprod.to(device=original_samples.device)
alphas_cumprod = self.alphas_cumprod.to(dtype=original_samples.dtype)
timesteps = timesteps.to(original_samples.device)
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) | 1,347 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_ddim_parallel.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_samples = sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
return noisy_samples
# 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_cumprod.to(device=sample.device)
alphas_cumprod = self.alphas_cumprod.to(dtype=sample.dtype)
timesteps = timesteps.to(sample.device) | 1,347 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_ddim_parallel.py |
sqrt_alpha_prod = alphas_cumprod[timesteps] ** 0.5
sqrt_alpha_prod = sqrt_alpha_prod.flatten()
while len(sqrt_alpha_prod.shape) < len(sample.shape):
sqrt_alpha_prod = sqrt_alpha_prod.unsqueeze(-1)
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(sample.shape):
sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.unsqueeze(-1)
velocity = sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
return velocity
def __len__(self):
return self.config.num_train_timesteps | 1,347 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_ddim_parallel.py |
class DDPMWuerstchenSchedulerOutput(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.
"""
prev_sample: torch.Tensor | 1,348 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_ddpm_wuerstchen.py |
class DDPMWuerstchenScheduler(SchedulerMixin, ConfigMixin):
"""
Denoising diffusion probabilistic models (DDPMs) explores the connections between denoising score matching and
Langevin dynamics sampling.
[`~ConfigMixin`] takes care of storing all config attributes that are passed in the scheduler's `__init__`
function, such as `num_train_timesteps`. They can be accessed via `scheduler.config.num_train_timesteps`.
[`SchedulerMixin`] provides general loading and saving functionality via the [`SchedulerMixin.save_pretrained`] and
[`~SchedulerMixin.from_pretrained`] functions.
For more details, see the original paper: https://arxiv.org/abs/2006.11239
Args:
scaler (`float`): ....
s (`float`): ....
""" | 1,349 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_ddpm_wuerstchen.py |
@register_to_config
def __init__(
self,
scaler: float = 1.0,
s: float = 0.008,
):
self.scaler = scaler
self.s = torch.tensor([s])
self._init_alpha_cumprod = torch.cos(self.s / (1 + self.s) * torch.pi * 0.5) ** 2
# standard deviation of the initial noise distribution
self.init_noise_sigma = 1.0
def _alpha_cumprod(self, t, device):
if self.scaler > 1:
t = 1 - (1 - t) ** self.scaler
elif self.scaler < 1:
t = t**self.scaler
alpha_cumprod = torch.cos(
(t + self.s.to(device)) / (1 + self.s.to(device)) * torch.pi * 0.5
) ** 2 / self._init_alpha_cumprod.to(device)
return alpha_cumprod.clamp(0.0001, 0.9999)
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. | 1,349 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_ddpm_wuerstchen.py |
Args:
sample (`torch.Tensor`): input sample
timestep (`int`, optional): current timestep
Returns:
`torch.Tensor`: scaled input sample
"""
return sample
def set_timesteps(
self,
num_inference_steps: int = None,
timesteps: Optional[List[int]] = None,
device: Union[str, torch.device] = None,
):
"""
Sets the discrete timesteps used for the diffusion chain. Supporting function to be run before inference. | 1,349 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_ddpm_wuerstchen.py |
Args:
num_inference_steps (`Dict[float, int]`):
the number of diffusion steps used when generating samples with a pre-trained model. If passed, then
`timesteps` must be `None`.
device (`str` or `torch.device`, optional):
the device to which the timesteps are moved to. {2 / 3: 20, 0.0: 10}
"""
if timesteps is None:
timesteps = torch.linspace(1.0, 0.0, num_inference_steps + 1, device=device)
if not isinstance(timesteps, torch.Tensor):
timesteps = torch.Tensor(timesteps).to(device)
self.timesteps = timesteps | 1,349 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_ddpm_wuerstchen.py |
def step(
self,
model_output: torch.Tensor,
timestep: int,
sample: torch.Tensor,
generator=None,
return_dict: bool = True,
) -> Union[DDPMWuerstchenSchedulerOutput, Tuple]:
"""
Predict the sample at the previous timestep by reversing the SDE. Core function to propagate the diffusion
process from the learned model outputs (most often the predicted noise).
Args:
model_output (`torch.Tensor`): direct output from learned diffusion model.
timestep (`int`): current discrete timestep in the diffusion chain.
sample (`torch.Tensor`):
current instance of sample being created by diffusion process.
generator: random number generator.
return_dict (`bool`): option for returning tuple rather than DDPMWuerstchenSchedulerOutput class | 1,349 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_ddpm_wuerstchen.py |
Returns:
[`DDPMWuerstchenSchedulerOutput`] or `tuple`: [`DDPMWuerstchenSchedulerOutput`] if `return_dict` is True,
otherwise a `tuple`. When returning a tuple, the first element is the sample tensor.
"""
dtype = model_output.dtype
device = model_output.device
t = timestep
prev_t = self.previous_timestep(t)
alpha_cumprod = self._alpha_cumprod(t, device).view(t.size(0), *[1 for _ in sample.shape[1:]])
alpha_cumprod_prev = self._alpha_cumprod(prev_t, device).view(prev_t.size(0), *[1 for _ in sample.shape[1:]])
alpha = alpha_cumprod / alpha_cumprod_prev
mu = (1.0 / alpha).sqrt() * (sample - (1 - alpha) * model_output / (1 - alpha_cumprod).sqrt()) | 1,349 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_ddpm_wuerstchen.py |
std_noise = randn_tensor(mu.shape, generator=generator, device=model_output.device, dtype=model_output.dtype)
std = ((1 - alpha) * (1.0 - alpha_cumprod_prev) / (1.0 - alpha_cumprod)).sqrt() * std_noise
pred = mu + std * (prev_t != 0).float().view(prev_t.size(0), *[1 for _ in sample.shape[1:]])
if not return_dict:
return (pred.to(dtype),)
return DDPMWuerstchenSchedulerOutput(prev_sample=pred.to(dtype))
def add_noise(
self,
original_samples: torch.Tensor,
noise: torch.Tensor,
timesteps: torch.Tensor,
) -> torch.Tensor:
device = original_samples.device
dtype = original_samples.dtype
alpha_cumprod = self._alpha_cumprod(timesteps, device=device).view(
timesteps.size(0), *[1 for _ in original_samples.shape[1:]]
)
noisy_samples = alpha_cumprod.sqrt() * original_samples + (1 - alpha_cumprod).sqrt() * noise
return noisy_samples.to(dtype=dtype) | 1,349 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_ddpm_wuerstchen.py |
def __len__(self):
return self.config.num_train_timesteps
def previous_timestep(self, timestep):
index = (self.timesteps - timestep[0]).abs().argmin().item()
prev_t = self.timesteps[index + 1][None].expand(timestep.shape[0])
return prev_t | 1,349 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_ddpm_wuerstchen.py |
class FlaxKarrasDiffusionSchedulers(Enum):
FlaxDDIMScheduler = 1
FlaxDDPMScheduler = 2
FlaxPNDMScheduler = 3
FlaxLMSDiscreteScheduler = 4
FlaxDPMSolverMultistepScheduler = 5
FlaxEulerDiscreteScheduler = 6 | 1,350 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_utils_flax.py |
class FlaxSchedulerOutput(BaseOutput):
"""
Base class for the scheduler's step function output.
Args:
prev_sample (`jnp.ndarray` of shape `(batch_size, num_channels, height, width)` for images):
Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the
denoising loop.
"""
prev_sample: jnp.ndarray | 1,351 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_utils_flax.py |
class FlaxSchedulerMixin(PushToHubMixin):
"""
Mixin containing common functions for the schedulers.
Class attributes:
- **_compatibles** (`List[str]`) -- A list of classes that are compatible with the parent class, so that
`from_config` can be used from a class different than the one used to save the config (should be overridden
by parent class).
"""
config_name = SCHEDULER_CONFIG_NAME
ignore_for_config = ["dtype"]
_compatibles = []
has_compatibles = True
@classmethod
@validate_hf_hub_args
def from_pretrained(
cls,
pretrained_model_name_or_path: Optional[Union[str, os.PathLike]] = None,
subfolder: Optional[str] = None,
return_unused_kwargs=False,
**kwargs,
):
r"""
Instantiate a Scheduler class from a pre-defined JSON-file.
Parameters:
pretrained_model_name_or_path (`str` or `os.PathLike`, *optional*):
Can be either: | 1,352 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_utils_flax.py |
- A string, the *model id* of a model repo on huggingface.co. Valid model ids should have an
organization name, like `google/ddpm-celebahq-256`.
- A path to a *directory* containing model weights saved using [`~SchedulerMixin.save_pretrained`],
e.g., `./my_model_directory/`.
subfolder (`str`, *optional*):
In case the relevant files are located inside a subfolder of the model repo (either remote in
huggingface.co or downloaded locally), you can specify the folder name here.
return_unused_kwargs (`bool`, *optional*, defaults to `False`):
Whether kwargs that are not consumed by the Python class should be returned or not. | 1,352 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_utils_flax.py |
cache_dir (`Union[str, os.PathLike]`, *optional*):
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.
force_download (`bool`, *optional*, defaults to `False`):
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist. | 1,352 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_utils_flax.py |
proxies (`Dict[str, str]`, *optional*):
A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128',
'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
output_loading_info(`bool`, *optional*, defaults to `False`):
Whether or not to also return a dictionary containing missing keys, unexpected keys and error messages.
local_files_only(`bool`, *optional*, defaults to `False`):
Whether or not to only look at local files (i.e., do not try to download the model).
token (`str` or *bool*, *optional*):
The token to use as HTTP bearer authorization for remote files. If `True`, will use the token generated
when running `transformers-cli login` (stored in `~/.huggingface`).
revision (`str`, *optional*, defaults to `"main"`): | 1,352 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_utils_flax.py |
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any
identifier allowed by git. | 1,352 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_utils_flax.py |
<Tip>
It is required to be logged in (`huggingface-cli login`) when you want to use private or [gated
models](https://huggingface.co./docs/hub/models-gated#gated-models).
</Tip>
<Tip>
Activate the special ["offline-mode"](https://huggingface.co./transformers/installation.html#offline-mode) to
use this method in a firewalled environment.
</Tip>
"""
config, kwargs = cls.load_config(
pretrained_model_name_or_path=pretrained_model_name_or_path,
subfolder=subfolder,
return_unused_kwargs=True,
**kwargs,
)
scheduler, unused_kwargs = cls.from_config(config, return_unused_kwargs=True, **kwargs)
if hasattr(scheduler, "create_state") and getattr(scheduler, "has_state", False):
state = scheduler.create_state()
if return_unused_kwargs:
return scheduler, state, unused_kwargs
return scheduler, state | 1,352 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_utils_flax.py |
def save_pretrained(self, save_directory: Union[str, os.PathLike], push_to_hub: bool = False, **kwargs):
"""
Save a scheduler configuration object to the directory `save_directory`, so that it can be re-loaded using the
[`~FlaxSchedulerMixin.from_pretrained`] class method. | 1,352 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_utils_flax.py |
Args:
save_directory (`str` or `os.PathLike`):
Directory where the configuration JSON file will be saved (will be created if it does not exist).
push_to_hub (`bool`, *optional*, defaults to `False`):
Whether or not to push your model to the Hugging Face Hub after saving it. You can specify the
repository you want to push to with `repo_id` (will default to the name of `save_directory` in your
namespace).
kwargs (`Dict[str, Any]`, *optional*):
Additional keyword arguments passed along to the [`~utils.PushToHubMixin.push_to_hub`] method.
"""
self.save_config(save_directory=save_directory, push_to_hub=push_to_hub, **kwargs)
@property
def compatibles(self):
"""
Returns all schedulers that are compatible with this scheduler | 1,352 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_utils_flax.py |
Returns:
`List[SchedulerMixin]`: List of compatible schedulers
"""
return self._get_compatibles()
@classmethod
def _get_compatibles(cls):
compatible_classes_str = list(set([cls.__name__] + cls._compatibles))
diffusers_library = importlib.import_module(__name__.split(".")[0])
compatible_classes = [
getattr(diffusers_library, c) for c in compatible_classes_str if hasattr(diffusers_library, c)
]
return compatible_classes | 1,352 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_utils_flax.py |
class CommonSchedulerState:
alphas: jnp.ndarray
betas: jnp.ndarray
alphas_cumprod: jnp.ndarray
@classmethod
def create(cls, scheduler):
config = scheduler.config | 1,353 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_utils_flax.py |
if config.trained_betas is not None:
betas = jnp.asarray(config.trained_betas, dtype=scheduler.dtype)
elif config.beta_schedule == "linear":
betas = jnp.linspace(config.beta_start, config.beta_end, config.num_train_timesteps, dtype=scheduler.dtype)
elif config.beta_schedule == "scaled_linear":
# this schedule is very specific to the latent diffusion model.
betas = (
jnp.linspace(
config.beta_start**0.5, config.beta_end**0.5, config.num_train_timesteps, dtype=scheduler.dtype
)
** 2
)
elif config.beta_schedule == "squaredcos_cap_v2":
# Glide cosine schedule
betas = betas_for_alpha_bar(config.num_train_timesteps, dtype=scheduler.dtype)
else:
raise NotImplementedError(
f"beta_schedule {config.beta_schedule} is not implemented for scheduler {scheduler.__class__.__name__}"
) | 1,353 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_utils_flax.py |
alphas = 1.0 - betas
alphas_cumprod = jnp.cumprod(alphas, axis=0)
return cls(
alphas=alphas,
betas=betas,
alphas_cumprod=alphas_cumprod,
) | 1,353 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_utils_flax.py |
class SdeVeOutput(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.
prev_sample_mean (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` for images):
Mean averaged `prev_sample` over previous timesteps.
"""
prev_sample: torch.Tensor
prev_sample_mean: torch.Tensor | 1,354 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_sde_ve.py |
class ScoreSdeVeScheduler(SchedulerMixin, ConfigMixin):
"""
`ScoreSdeVeScheduler` is a variance exploding 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. | 1,355 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_sde_ve.py |
Args:
num_train_timesteps (`int`, defaults to 1000):
The number of diffusion steps to train the model.
snr (`float`, defaults to 0.15):
A coefficient weighting the step from the `model_output` sample (from the network) to the random noise.
sigma_min (`float`, defaults to 0.01):
The initial noise scale for the sigma sequence in the sampling procedure. The minimum sigma should mirror
the distribution of the data.
sigma_max (`float`, defaults to 1348.0):
The maximum value used for the range of continuous timesteps passed into the model.
sampling_eps (`float`, defaults to 1e-5):
The end value of sampling where timesteps decrease progressively from 1 to epsilon.
correct_steps (`int`, defaults to 1):
The number of correction steps performed on a produced sample.
"""
order = 1 | 1,355 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_sde_ve.py |
@register_to_config
def __init__(
self,
num_train_timesteps: int = 2000,
snr: float = 0.15,
sigma_min: float = 0.01,
sigma_max: float = 1348.0,
sampling_eps: float = 1e-5,
correct_steps: int = 1,
):
# standard deviation of the initial noise distribution
self.init_noise_sigma = sigma_max
# setable values
self.timesteps = None
self.set_sigmas(num_train_timesteps, sigma_min, sigma_max, sampling_eps)
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. | 1,355 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_sde_ve.py |
Returns:
`torch.Tensor`:
A scaled input sample.
"""
return sample
def set_timesteps(
self, num_inference_steps: int, sampling_eps: float = None, 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.
sampling_eps (`float`, *optional*):
The final timestep value (overrides value given during scheduler instantiation).
device (`str` or `torch.device`, *optional*):
The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.
"""
sampling_eps = sampling_eps if sampling_eps is not None else self.config.sampling_eps | 1,355 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_sde_ve.py |
self.timesteps = torch.linspace(1, sampling_eps, num_inference_steps, device=device)
def set_sigmas(
self, num_inference_steps: int, sigma_min: float = None, sigma_max: float = None, sampling_eps: float = None
):
"""
Sets the noise scales used for the diffusion chain (to be run before inference). The sigmas control the weight
of the `drift` and `diffusion` components of the sample update. | 1,355 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_sde_ve.py |
Args:
num_inference_steps (`int`):
The number of diffusion steps used when generating samples with a pre-trained model.
sigma_min (`float`, optional):
The initial noise scale value (overrides value given during scheduler instantiation).
sigma_max (`float`, optional):
The final noise scale value (overrides value given during scheduler instantiation).
sampling_eps (`float`, optional):
The final timestep value (overrides value given during scheduler instantiation).
"""
sigma_min = sigma_min if sigma_min is not None else self.config.sigma_min
sigma_max = sigma_max if sigma_max is not None else self.config.sigma_max
sampling_eps = sampling_eps if sampling_eps is not None else self.config.sampling_eps
if self.timesteps is None:
self.set_timesteps(num_inference_steps, sampling_eps) | 1,355 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_sde_ve.py |
self.sigmas = sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
self.discrete_sigmas = torch.exp(torch.linspace(math.log(sigma_min), math.log(sigma_max), num_inference_steps))
self.sigmas = torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps])
def get_adjacent_sigma(self, timesteps, t):
return torch.where(
timesteps == 0,
torch.zeros_like(t.to(timesteps.device)),
self.discrete_sigmas[timesteps - 1].to(timesteps.device),
)
def step_pred(
self,
model_output: torch.Tensor,
timestep: int,
sample: torch.Tensor,
generator: Optional[torch.Generator] = None,
return_dict: bool = True,
) -> Union[SdeVeOutput, 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). | 1,355 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_sde_ve.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.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~schedulers.scheduling_sde_ve.SdeVeOutput`] or `tuple`.
Returns:
[`~schedulers.scheduling_sde_ve.SdeVeOutput`] or `tuple`:
If return_dict is `True`, [`~schedulers.scheduling_sde_ve.SdeVeOutput`] is returned, otherwise a tuple
is returned where the first element is the sample tensor. | 1,355 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_sde_ve.py |
"""
if self.timesteps is None:
raise ValueError(
"`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"
)
timestep = timestep * torch.ones(
sample.shape[0], device=sample.device
) # torch.repeat_interleave(timestep, sample.shape[0])
timesteps = (timestep * (len(self.timesteps) - 1)).long()
# mps requires indices to be in the same device, so we use cpu as is the default with cuda
timesteps = timesteps.to(self.discrete_sigmas.device)
sigma = self.discrete_sigmas[timesteps].to(sample.device)
adjacent_sigma = self.get_adjacent_sigma(timesteps, timestep).to(sample.device)
drift = torch.zeros_like(sample)
diffusion = (sigma**2 - adjacent_sigma**2) ** 0.5 | 1,355 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_sde_ve.py |
# equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
# also equation 47 shows the analog from SDE models to ancestral sampling methods
diffusion = diffusion.flatten()
while len(diffusion.shape) < len(sample.shape):
diffusion = diffusion.unsqueeze(-1)
drift = drift - diffusion**2 * model_output
# equation 6: sample noise for the diffusion term of
noise = randn_tensor(
sample.shape, layout=sample.layout, generator=generator, device=sample.device, dtype=sample.dtype
)
prev_sample_mean = sample - drift # subtract because `dt` is a small negative timestep
# TODO is the variable diffusion the correct scaling term for the noise?
prev_sample = prev_sample_mean + diffusion * noise # add impact of diffusion field g
if not return_dict:
return (prev_sample, prev_sample_mean) | 1,355 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_sde_ve.py |
return SdeVeOutput(prev_sample=prev_sample, prev_sample_mean=prev_sample_mean)
def step_correct(
self,
model_output: torch.Tensor,
sample: torch.Tensor,
generator: Optional[torch.Generator] = None,
return_dict: bool = True,
) -> Union[SchedulerOutput, Tuple]:
"""
Correct the predicted sample based on the `model_output` of the network. This is often run repeatedly after
making the prediction for the previous timestep.
Args:
model_output (`torch.Tensor`):
The direct output from learned diffusion model.
sample (`torch.Tensor`):
A current instance of a sample created by the diffusion process.
generator (`torch.Generator`, *optional*):
A random number generator.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~schedulers.scheduling_sde_ve.SdeVeOutput`] or `tuple`. | 1,355 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_sde_ve.py |
Returns:
[`~schedulers.scheduling_sde_ve.SdeVeOutput`] or `tuple`:
If return_dict is `True`, [`~schedulers.scheduling_sde_ve.SdeVeOutput`] is returned, otherwise a tuple
is returned where the first element is the sample tensor.
"""
if self.timesteps is None:
raise ValueError(
"`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"
)
# For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
# sample noise for correction
noise = randn_tensor(sample.shape, layout=sample.layout, generator=generator).to(sample.device) | 1,355 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_sde_ve.py |
# compute step size from the model_output, the noise, and the snr
grad_norm = torch.norm(model_output.reshape(model_output.shape[0], -1), dim=-1).mean()
noise_norm = torch.norm(noise.reshape(noise.shape[0], -1), dim=-1).mean()
step_size = (self.config.snr * noise_norm / grad_norm) ** 2 * 2
step_size = step_size * torch.ones(sample.shape[0]).to(sample.device)
# self.repeat_scalar(step_size, sample.shape[0])
# compute corrected sample: model_output term and noise term
step_size = step_size.flatten()
while len(step_size.shape) < len(sample.shape):
step_size = step_size.unsqueeze(-1)
prev_sample_mean = sample + step_size * model_output
prev_sample = prev_sample_mean + ((step_size * 2) ** 0.5) * noise
if not return_dict:
return (prev_sample,)
return SchedulerOutput(prev_sample=prev_sample) | 1,355 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_sde_ve.py |
def add_noise(
self,
original_samples: torch.Tensor,
noise: torch.Tensor,
timesteps: torch.Tensor,
) -> torch.Tensor:
# Make sure sigmas and timesteps have the same device and dtype as original_samples
timesteps = timesteps.to(original_samples.device)
sigmas = self.discrete_sigmas.to(original_samples.device)[timesteps]
noise = (
noise * sigmas[:, None, None, None]
if noise is not None
else torch.randn_like(original_samples) * sigmas[:, None, None, None]
)
noisy_samples = noise + original_samples
return noisy_samples
def __len__(self):
return self.config.num_train_timesteps | 1,355 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_sde_ve.py |
class IPNDMScheduler(SchedulerMixin, ConfigMixin):
"""
A fourth-order Improved Pseudo Linear Multistep 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 1000):
The number of diffusion steps to train the model.
trained_betas (`np.ndarray`, *optional*):
Pass an array of betas directly to the constructor to bypass `beta_start` and `beta_end`.
"""
order = 1
@register_to_config
def __init__(
self, num_train_timesteps: int = 1000, trained_betas: Optional[Union[np.ndarray, List[float]]] = None
):
# set `betas`, `alphas`, `timesteps`
self.set_timesteps(num_train_timesteps)
# standard deviation of the initial noise distribution
self.init_noise_sigma = 1.0 | 1,356 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_ipndm.py |
# For now we only support F-PNDM, i.e. the runge-kutta method
# For more information on the algorithm please take a look at the paper: https://arxiv.org/pdf/2202.09778.pdf
# mainly at formula (9), (12), (13) and the Algorithm 2.
self.pndm_order = 4
# running values
self.ets = []
self._step_index = None
self._begin_index = None
@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 | 1,356 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_ipndm.py |
# Copied from diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler.set_begin_index
def set_begin_index(self, begin_index: int = 0):
"""
Sets the begin index for the scheduler. This function should be run from pipeline before the inference.
Args:
begin_index (`int`):
The begin index for the scheduler.
"""
self._begin_index = begin_index
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). | 1,356 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_ipndm.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
steps = torch.linspace(1, 0, num_inference_steps + 1)[:-1]
steps = torch.cat([steps, torch.tensor([0.0])])
if self.config.trained_betas is not None:
self.betas = torch.tensor(self.config.trained_betas, dtype=torch.float32)
else:
self.betas = torch.sin(steps * math.pi / 2) ** 2
self.alphas = (1.0 - self.betas**2) ** 0.5
timesteps = (torch.atan2(self.betas, self.alphas) / math.pi * 2)[:-1]
self.timesteps = timesteps.to(device)
self.ets = []
self._step_index = None
self._begin_index = None | 1,356 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_ipndm.py |
# Copied from diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler.index_for_timestep
def index_for_timestep(self, timestep, schedule_timesteps=None):
if schedule_timesteps is None:
schedule_timesteps = self.timesteps
indices = (schedule_timesteps == timestep).nonzero()
# The sigma index that is taken for the **very** first `step`
# is always the second index (or the last index if there is only 1)
# This way we can ensure we don't accidentally skip a sigma in
# case we start in the middle of the denoising schedule (e.g. for image-to-image)
pos = 1 if len(indices) > 1 else 0
return indices[pos].item() | 1,356 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_ipndm.py |
# Copied from diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler._init_step_index
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
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 linear multistep method. It performs one forward pass multiple times to approximate the solution. | 1,356 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_ipndm.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. | 1,356 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_ipndm.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)
timestep_index = self.step_index
prev_timestep_index = self.step_index + 1
ets = sample * self.betas[timestep_index] + model_output * self.alphas[timestep_index]
self.ets.append(ets) | 1,356 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_ipndm.py |
if len(self.ets) == 1:
ets = self.ets[-1]
elif len(self.ets) == 2:
ets = (3 * self.ets[-1] - self.ets[-2]) / 2
elif len(self.ets) == 3:
ets = (23 * self.ets[-1] - 16 * self.ets[-2] + 5 * self.ets[-3]) / 12
else:
ets = (1 / 24) * (55 * self.ets[-1] - 59 * self.ets[-2] + 37 * self.ets[-3] - 9 * self.ets[-4])
prev_sample = self._get_prev_sample(sample, timestep_index, prev_timestep_index, ets)
# upon completion increase step index by one
self._step_index += 1
if not return_dict:
return (prev_sample,)
return SchedulerOutput(prev_sample=prev_sample)
def scale_model_input(self, sample: torch.Tensor, *args, **kwargs) -> torch.Tensor:
"""
Ensures interchangeability with schedulers that need to scale the denoising model input depending on the
current timestep.
Args:
sample (`torch.Tensor`):
The input sample. | 1,356 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_ipndm.py |
Returns:
`torch.Tensor`:
A scaled input sample.
"""
return sample
def _get_prev_sample(self, sample, timestep_index, prev_timestep_index, ets):
alpha = self.alphas[timestep_index]
sigma = self.betas[timestep_index]
next_alpha = self.alphas[prev_timestep_index]
next_sigma = self.betas[prev_timestep_index]
pred = (sample - sigma * ets) / max(alpha, 1e-8)
prev_sample = next_alpha * pred + ets * next_sigma
return prev_sample
def __len__(self):
return self.config.num_train_timesteps | 1,356 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_ipndm.py |
class DEISMultistepScheduler(SchedulerMixin, ConfigMixin):
"""
`DEISMultistepScheduler` is a fast high order solver for diffusion ordinary differential equations (ODEs).
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. | 1,357 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_deis_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`, defaults to 2):
The DEIS order which can be `1` or `2` or `3`. It is recommended to use `solver_order=2` for guided
sampling, and `solver_order=3` for unconditional sampling.
prediction_type (`str`, defaults to `epsilon`): | 1,357 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_deis_multistep.py |
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`.
algorithm_type (`str`, defaults to `deis`):
The algorithm type for the solver.
lower_order_final (`bool`, defaults to `True`): | 1,357 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_deis_multistep.py |
Whether to use lower-order solvers in the final steps. Only valid for < 15 inference steps.
use_karras_sigmas (`bool`, *optional*, defaults to `False`):
Whether to use Karras sigmas for step sizes in the noise schedule during the sampling process. If `True`,
the sigmas are determined according to a sequence of noise 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"`): | 1,357 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_deis_multistep.py |
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):
An offset added to the inference steps, as required by some model families.
""" | 1,357 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_deis_multistep.py |
_compatibles = [e.name for e in KarrasDiffusionSchedulers]
order = 1 | 1,357 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_deis_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[np.ndarray] = None,
solver_order: int = 2,
prediction_type: str = "epsilon",
thresholding: bool = False,
dynamic_thresholding_ratio: float = 0.995,
sample_max_value: float = 1.0,
algorithm_type: str = "deis",
solver_type: str = "logrho",
lower_order_final: bool = True,
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,
):
if self.config.use_beta_sigmas and not is_scipy_available(): | 1,357 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_deis_multistep.py |
raise ImportError("Make sure to install scipy if you want to use beta sigmas.")
if sum([self.config.use_beta_sigmas, self.config.use_exponential_sigmas, self.config.use_karras_sigmas]) > 1:
raise ValueError(
"Only one of `config.use_beta_sigmas`, `config.use_exponential_sigmas`, `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.
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 | 1,357 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_deis_multistep.py |
self.betas = betas_for_alpha_bar(num_train_timesteps)
else:
raise NotImplementedError(f"{beta_schedule} is not implemented for {self.__class__}") | 1,357 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_deis_multistep.py |
self.alphas = 1.0 - self.betas
self.alphas_cumprod = torch.cumprod(self.alphas, dim=0)
# 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
# settings for DEIS
if algorithm_type not in ["deis"]:
if algorithm_type in ["dpmsolver", "dpmsolver++"]:
self.register_to_config(algorithm_type="deis")
else:
raise NotImplementedError(f"{algorithm_type} is not implemented for {self.__class__}") | 1,357 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_deis_multistep.py |
if solver_type not in ["logrho"]:
if solver_type in ["midpoint", "heun", "bh1", "bh2"]:
self.register_to_config(solver_type="logrho")
else:
raise NotImplementedError(f"solver type {solver_type} is not implemented for {self.__class__}")
# 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._step_index = None
self._begin_index = None
self.sigmas = self.sigmas.to("cpu") # to avoid too much CPU/GPU communication
@property
def step_index(self):
"""
The index counter for current timestep. It will increase 1 after each scheduler step.
"""
return self._step_index | 1,357 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_deis_multistep.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 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
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). | 1,357 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_deis_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 | 1,357 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_deis_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'."
) | 1,357 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_deis_multistep.py |
sigmas = np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)
log_sigmas = np.log(sigmas)
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()
sigmas = np.concatenate([sigmas, sigmas[-1:]]).astype(np.float32)
elif self.config.use_exponential_sigmas:
sigmas = np.flip(sigmas).copy()
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])
sigmas = np.concatenate([sigmas, sigmas[-1:]]).astype(np.float32)
elif self.config.use_beta_sigmas:
sigmas = np.flip(sigmas).copy() | 1,357 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_deis_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])
sigmas = np.concatenate([sigmas, sigmas[-1:]]).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()
sigmas = np.concatenate([sigmas, sigmas[-1:]]).astype(np.float32)
else:
sigmas = np.interp(timesteps, np.arange(0, len(sigmas)), sigmas)
sigma_last = ((1 - self.alphas_cumprod[0]) / self.alphas_cumprod[0]) ** 0.5
sigmas = np.concatenate([sigmas, [sigma_last]]).astype(np.float32) | 1,357 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_deis_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
# 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 | 1,357 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_deis_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 | 1,357 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_deis_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 | 1,357 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_deis_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 | 1,357 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_deis_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 | 1,357 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_deis_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.""" | 1,357 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_deis_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 | 1,357 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_deis_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() | 1,357 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_deis_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:
"""
Convert the model output to the corresponding type the DEIS 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. | 1,357 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_deis_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`",
) | 1,357 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_deis_multistep.py |
sigma = self.sigmas[self.step_index]
alpha_t, sigma_t = self._sigma_to_alpha_sigma_t(sigma)
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 DEISMultistepScheduler."
)
if self.config.thresholding:
x0_pred = self._threshold_sample(x0_pred) | 1,357 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_deis_multistep.py |
if self.config.algorithm_type == "deis":
return (sample - alpha_t * x0_pred) / sigma_t
else:
raise NotImplementedError("only support log-rho multistep deis now")
def deis_first_order_update(
self,
model_output: torch.Tensor,
*args,
sample: torch.Tensor = None,
**kwargs,
) -> torch.Tensor:
"""
One step for the first-order DEIS (equivalent to DDIM).
Args:
model_output (`torch.Tensor`):
The direct output from the learned diffusion model.
timestep (`int`):
The current discrete timestep in the diffusion chain.
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. | 1,357 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_deis_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(args) > 2:
sample = args[2]
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`",
) | 1,357 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_deis_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.sigmas[self.step_index + 1], self.sigmas[self.step_index]
alpha_t, sigma_t = self._sigma_to_alpha_sigma_t(sigma_t)
alpha_s, sigma_s = self._sigma_to_alpha_sigma_t(sigma_s)
lambda_t = torch.log(alpha_t) - torch.log(sigma_t)
lambda_s = torch.log(alpha_s) - torch.log(sigma_s)
h = lambda_t - lambda_s
if self.config.algorithm_type == "deis":
x_t = (alpha_t / alpha_s) * sample - (sigma_t * (torch.exp(h) - 1.0)) * model_output
else:
raise NotImplementedError("only support log-rho multistep deis now")
return x_t | 1,357 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_deis_multistep.py |
def multistep_deis_second_order_update(
self,
model_output_list: List[torch.Tensor],
*args,
sample: torch.Tensor = None,
**kwargs,
) -> torch.Tensor:
"""
One step for the second-order multistep DEIS.
Args:
model_output_list (`List[torch.Tensor]`):
The direct outputs from learned diffusion model at current and latter timesteps.
sample (`torch.Tensor`):
A current instance of a sample created by the diffusion process. | 1,357 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_deis_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:
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`",
) | 1,357 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_deis_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 = (
self.sigmas[self.step_index + 1],
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)
alpha_s1, sigma_s1 = self._sigma_to_alpha_sigma_t(sigma_s1)
m0, m1 = model_output_list[-1], model_output_list[-2]
rho_t, rho_s0, rho_s1 = sigma_t / alpha_t, sigma_s0 / alpha_s0, sigma_s1 / alpha_s1
if self.config.algorithm_type == "deis": | 1,357 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_deis_multistep.py |
def ind_fn(t, b, c):
# Integrate[(log(t) - log(c)) / (log(b) - log(c)), {t}]
return t * (-np.log(c) + np.log(t) - 1) / (np.log(b) - np.log(c))
coef1 = ind_fn(rho_t, rho_s0, rho_s1) - ind_fn(rho_s0, rho_s0, rho_s1)
coef2 = ind_fn(rho_t, rho_s1, rho_s0) - ind_fn(rho_s0, rho_s1, rho_s0)
x_t = alpha_t * (sample / alpha_s0 + coef1 * m0 + coef2 * m1)
return x_t
else:
raise NotImplementedError("only support log-rho multistep deis now")
def multistep_deis_third_order_update(
self,
model_output_list: List[torch.Tensor],
*args,
sample: torch.Tensor = None,
**kwargs,
) -> torch.Tensor:
"""
One step for the third-order multistep DEIS. | 1,357 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_deis_multistep.py |
Args:
model_output_list (`List[torch.Tensor]`):
The direct outputs from learned diffusion model at current and latter timesteps.
sample (`torch.Tensor`):
A current instance of a sample created by diffusion process.
Returns:
`torch.Tensor`:
The sample tensor at the previous timestep.
""" | 1,357 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_deis_multistep.py |
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`",
) | 1,357 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_deis_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)
m0, m1, m2 = model_output_list[-1], model_output_list[-2], model_output_list[-3]
rho_t, rho_s0, rho_s1, rho_s2 = (
sigma_t / alpha_t,
sigma_s0 / alpha_s0,
sigma_s1 / alpha_s1,
sigma_s2 / alpha_s2,
)
if self.config.algorithm_type == "deis": | 1,357 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_deis_multistep.py |
def ind_fn(t, b, c, d):
# Integrate[(log(t) - log(c))(log(t) - log(d)) / (log(b) - log(c))(log(b) - log(d)), {t}]
numerator = t * (
np.log(c) * (np.log(d) - np.log(t) + 1)
- np.log(d) * np.log(t)
+ np.log(d)
+ np.log(t) ** 2
- 2 * np.log(t)
+ 2
)
denominator = (np.log(b) - np.log(c)) * (np.log(b) - np.log(d))
return numerator / denominator
coef1 = ind_fn(rho_t, rho_s0, rho_s1, rho_s2) - ind_fn(rho_s0, rho_s0, rho_s1, rho_s2)
coef2 = ind_fn(rho_t, rho_s1, rho_s2, rho_s0) - ind_fn(rho_s0, rho_s1, rho_s2, rho_s0)
coef3 = ind_fn(rho_t, rho_s2, rho_s0, rho_s1) - ind_fn(rho_s0, rho_s2, rho_s0, rho_s1)
x_t = alpha_t * (sample / alpha_s0 + coef1 * m0 + coef2 * m1 + coef3 * m2) | 1,357 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_deis_multistep.py |
return x_t
else:
raise NotImplementedError("only support log-rho multistep deis now")
# 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() | 1,357 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_deis_multistep.py |
return step_index
# 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 DEIS. | 1,357 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_deis_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"
) | 1,357 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_deis_multistep.py |