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if self.step_index is None:
self._init_step_index(timestep)
lower_order_final = (
(self.step_index == len(self.timesteps) - 1) and self.config.lower_order_final and len(self.timesteps) < 15
)
lower_order_second = (
(self.step_index == len(self.timesteps) - 2)... | 1,357 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_deis_multistep.py |
if self.config.solver_order == 1 or self.lower_order_nums < 1 or lower_order_final:
prev_sample = self.deis_first_order_update(model_output, sample=sample)
elif self.config.solver_order == 2 or self.lower_order_nums < 2 or lower_order_second:
prev_sample = self.multistep_deis_second_orde... | 1,357 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_deis_multistep.py |
def scale_model_input(self, sample: torch.Tensor, *args, **kwargs) -> torch.Tensor:
"""
Ensures interchangeability with schedulers that need to scale the denoising model input depending on the
current timestep.
Args:
sample (`torch.Tensor`):
The input sample.... | 1,357 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_deis_multistep.py |
# Copied from diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler.add_noise
def add_noise(
self,
original_samples: torch.Tensor,
noise: torch.Tensor,
timesteps: torch.IntTensor,
) -> torch.Tensor:
# Make sure sigmas and timesteps have the same ... | 1,357 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_deis_multistep.py |
# begin_index is None when the scheduler is used for training or pipeline does not implement set_begin_index
if self.begin_index is None:
step_indices = [self.index_for_timestep(t, schedule_timesteps) for t in timesteps]
elif self.step_index is not None:
# add_noise is called aft... | 1,357 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_deis_multistep.py |
class UnCLIPSchedulerOutput(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 nex... | 1,358 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_unclip.py |
class UnCLIPScheduler(SchedulerMixin, ConfigMixin):
"""
NOTE: do not use this scheduler. The DDPM scheduler has been updated to support the changes made here. This
scheduler will be removed and replaced with DDPM.
This is a modified DDPM Scheduler specifically for the karlo unCLIP model.
This sche... | 1,359 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_unclip.py |
Args:
num_train_timesteps (`int`): number of diffusion steps used to train the model.
variance_type (`str`):
options to clip the variance used when adding noise to the denoised sample. Choose from `fixed_small_log`
or `learned_range`.
clip_sample (`bool`, default `True`):... | 1,359 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_unclip.py |
@register_to_config
def __init__(
self,
num_train_timesteps: int = 1000,
variance_type: str = "fixed_small_log",
clip_sample: bool = True,
clip_sample_range: Optional[float] = 1.0,
prediction_type: str = "epsilon",
beta_schedule: str = "squaredcos_cap_v2",
... | 1,359 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_unclip.py |
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`): input sample
... | 1,359 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_unclip.py |
Args:
num_inference_steps (`int`):
the number of diffusion steps used when generating samples with a pre-trained model.
"""
self.num_inference_steps = num_inference_steps
step_ratio = (self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
times... | 1,359 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_unclip.py |
if prev_timestep == t - 1:
beta = self.betas[t]
else:
beta = 1 - alpha_prod_t / alpha_prod_t_prev
# For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
# and sample from it to get previous sample
# x_{t-1} ... | 1,359 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_unclip.py |
frac = (predicted_variance + 1) / 2
variance = frac * max_log + (1 - frac) * min_log
return variance
def step(
self,
model_output: torch.Tensor,
timestep: int,
sample: torch.Tensor,
prev_timestep: Optional[int] = None,
generator=None,
ret... | 1,359 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_unclip.py |
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.
prev_timestep (`int... | 1,359 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_unclip.py |
if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
model_output, predicted_variance = torch.split(model_output, sample.shape[1], dim=1)
else:
predicted_variance = None
# 1. compute alphas, betas
if prev_timestep is None:
... | 1,359 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_unclip.py |
# 2. compute predicted original sample from predicted noise also called
# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
if self.config.prediction_type == "epsilon":
pred_original_sample = (sample - beta_prod_t ** (0.5) * model_output) / alpha_prod_t ** (0.5)
... | 1,359 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_unclip.py |
# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
pred_original_sample_coeff = (alpha_prod_t_prev ** (0.5) * beta) / beta_prod_t
current_sample_coeff = alpha ** (0.5) * beta_prod_t_prev / beta_prod_t
... | 1,359 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_unclip.py |
if self.variance_type == "fixed_small_log":
variance = variance
elif self.variance_type == "learned_range":
variance = (0.5 * variance).exp()
else:
raise ValueError(
f"variance_type given as {self.variance_type} must be one of `... | 1,359 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_unclip.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... | 1,359 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_unclip.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... | 1,359 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_unclip.py |
class AmusedSchedulerOutput(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 nex... | 1,360 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_amused.py |
class AmusedScheduler(SchedulerMixin, ConfigMixin):
order = 1
temperatures: torch.Tensor
@register_to_config
def __init__(
self,
mask_token_id: int,
masking_schedule: str = "cosine",
):
self.temperatures = None
self.timesteps = None
def set_timesteps(
... | 1,361 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_amused.py |
def step(
self,
model_output: torch.Tensor,
timestep: torch.long,
sample: torch.LongTensor,
starting_mask_ratio: int = 1,
generator: Optional[torch.Generator] = None,
return_dict: bool = True,
) -> Union[AmusedSchedulerOutput, Tuple]:
two_dim_input = s... | 1,361 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_amused.py |
device = probs.device
probs_ = probs.to(generator.device) if generator is not None else probs # handles when generator is on CPU
if probs_.device.type == "cpu" and probs_.dtype != torch.float32:
probs_ = probs_.float() # multinomial is not implemented for cpu half precision
probs_ ... | 1,361 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_amused.py |
if self.config.masking_schedule == "cosine":
mask_ratio = torch.cos(ratio * math.pi / 2)
elif self.config.masking_schedule == "linear":
mask_ratio = 1 - ratio
else:
raise ValueError(f"unknown masking schedule {self.config.masking_schedule}")
... | 1,361 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_amused.py |
masking = mask_by_random_topk(mask_len, selected_probs, self.temperatures[step_idx], generator)
# Masks tokens with lower confidence.
prev_sample = torch.where(masking, self.config.mask_token_id, pred_original_sample)
if two_dim_input:
prev_sample = prev_sample.reshape(batc... | 1,361 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_amused.py |
if self.config.masking_schedule == "cosine":
mask_ratio = torch.cos(ratio * math.pi / 2)
elif self.config.masking_schedule == "linear":
mask_ratio = 1 - ratio
else:
raise ValueError(f"unknown masking schedule {self.config.masking_schedule}")
mask_indices = (
... | 1,361 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_amused.py |
class ConsistencyDecoderSchedulerOutput(BaseOutput):
"""
Output class for the scheduler's `step` function.
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 a... | 1,362 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_consistency_decoder.py |
class ConsistencyDecoderScheduler(SchedulerMixin, ConfigMixin):
order = 1
@register_to_config
def __init__(
self,
num_train_timesteps: int = 1024,
sigma_data: float = 0.5,
):
betas = betas_for_alpha_bar(num_train_timesteps)
alphas = 1.0 - betas
alphas_cu... | 1,363 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_consistency_decoder.py |
def set_timesteps(
self,
num_inference_steps: Optional[int] = None,
device: Union[str, torch.device] = None,
):
if num_inference_steps != 2:
raise ValueError("Currently more than 2 inference steps are not supported.")
self.timesteps = torch.tensor([1008, 512], dt... | 1,363 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_consistency_decoder.py |
Args:
sample (`torch.Tensor`):
The input sample.
timestep (`int`, *optional*):
The current timestep in the diffusion chain.
Returns:
`torch.Tensor`:
A scaled input sample.
"""
return sample * self.c_in[timestep]... | 1,363 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_consistency_decoder.py |
Args:
model_output (`torch.Tensor`):
The direct output from the learned diffusion model.
timestep (`float`):
The current timestep in the diffusion chain.
sample (`torch.Tensor`):
A current instance of a sample created by the diffusion p... | 1,363 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_consistency_decoder.py |
Returns:
[`~schedulers.scheduling_consistency_models.ConsistencyDecoderSchedulerOutput`] or `tuple`:
If return_dict is `True`,
[`~schedulers.scheduling_consistency_models.ConsistencyDecoderSchedulerOutput`] is returned, otherwise
a tuple is returned where the ... | 1,363 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_consistency_decoder.py |
if not return_dict:
return (prev_sample,)
return ConsistencyDecoderSchedulerOutput(prev_sample=prev_sample) | 1,363 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_consistency_decoder.py |
class VQDiffusionSchedulerOutput(BaseOutput):
"""
Output class for the scheduler's step function output.
Args:
prev_sample (`torch.LongTensor` of shape `(batch size, num latent pixels)`):
Computed sample x_{t-1} of previous timestep. `prev_sample` should be used as next model input in t... | 1,364 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_vq_diffusion.py |
class VQDiffusionScheduler(SchedulerMixin, ConfigMixin):
"""
A scheduler for vector quantized diffusion.
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. | 1,365 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_vq_diffusion.py |
Args:
num_vec_classes (`int`):
The number of classes of the vector embeddings of the latent pixels. Includes the class for the masked
latent pixel.
num_train_timesteps (`int`, defaults to 100):
The number of diffusion steps to train the model.
alpha_cum_start ... | 1,365 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_vq_diffusion.py |
@register_to_config
def __init__(
self,
num_vec_classes: int,
num_train_timesteps: int = 100,
alpha_cum_start: float = 0.99999,
alpha_cum_end: float = 0.000009,
gamma_cum_start: float = 0.000009,
gamma_cum_end: float = 0.99999,
):
self.num_embed = ... | 1,365 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_vq_diffusion.py |
at = torch.tensor(at.astype("float64"))
bt = torch.tensor(bt.astype("float64"))
ct = torch.tensor(ct.astype("float64"))
log_at = torch.log(at)
log_bt = torch.log(bt)
log_ct = torch.log(ct)
att = torch.tensor(att.astype("float64"))
btt = torch.tensor(btt.astype("f... | 1,365 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_vq_diffusion.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... | 1,365 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_vq_diffusion.py |
self.log_at = self.log_at.to(device)
self.log_bt = self.log_bt.to(device)
self.log_ct = self.log_ct.to(device)
self.log_cumprod_at = self.log_cumprod_at.to(device)
self.log_cumprod_bt = self.log_cumprod_bt.to(device)
self.log_cumprod_ct = self.log_cumprod_ct.to(device)
def s... | 1,365 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_vq_diffusion.py |
Args:
log_p_x_0: (`torch.Tensor` of shape `(batch size, num classes - 1, num latent pixels)`):
The log probabilities for the predicted classes of the initial latent pixels. Does not include a
prediction for the masked class as the initial unnoised image cannot be masked.
... | 1,365 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_vq_diffusion.py |
Returns:
[`~schedulers.scheduling_vq_diffusion.VQDiffusionSchedulerOutput`] or `tuple`:
If return_dict is `True`, [`~schedulers.scheduling_vq_diffusion.VQDiffusionSchedulerOutput`] is
returned, otherwise a tuple is returned where the first element is the sample tensor.
... | 1,365 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_vq_diffusion.py |
Args:
log_p_x_0 (`torch.Tensor` of shape `(batch size, num classes - 1, num latent pixels)`):
The log probabilities for the predicted classes of the initial latent pixels. Does not include a
prediction for the masked class as the initial unnoised image cannot be masked.
... | 1,365 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_vq_diffusion.py |
log_q_t_given_x_t_min_1 = self.log_Q_t_transitioning_to_known_class(
t=t, x_t=x_t, log_onehot_x_t=log_onehot_x_t, cumulative=False
)
# p_0(x_0=C_0 | x_t) / q(x_t | x_0=C_0) ... p_n(x_0=C_0 | x_t) / q(x_t | x_0=C_0)
# . . ... | 1,365 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_vq_diffusion.py |
# p_0(x_0=C_0 | x_t) / q(x_t | x_0=C_0) / sum_0 ... p_n(x_0=C_0 | x_t) / q(x_t | x_0=C_0) / sum_n
# . . .
# . . .
... | 1,365 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_vq_diffusion.py |
# (p_0(x_0=C_0 | x_t) / q(x_t | x_0=C_0) / sum_0) * a_cumulative_{t-1} + b_cumulative_{t-1} ... (p_n(x_0=C_0 | x_t) / q(x_t | x_0=C_0) / sum_n) * a_cumulative_{t-1} + b_cumulative_{t-1}
# . . ... | 1,365 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_vq_diffusion.py |
q = self.apply_cumulative_transitions(q, t - 1) | 1,365 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_vq_diffusion.py |
# ((p_0(x_0=C_0 | x_t) / q(x_t | x_0=C_0) / sum_0) * a_cumulative_{t-1} + b_cumulative_{t-1}) * q(x_t | x_{t-1}=C_0) * sum_0 ... ((p_n(x_0=C_0 | x_t) / q(x_t | x_0=C_0) / sum_n) * a_cumulative_{t-1} + b_cumulative_{t-1}) * q(x_t | x_{t-1}=C_0) * sum_n
# ... | 1,365 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_vq_diffusion.py |
# ((p_0(x_0=C_{k-1} | x_t) / q(x_t | x_0=C_{k-1}) / sum_0) * a_cumulative_{t-1} + b_cumulative_{t-1}) * q(x_t | x_{t-1}=C_{k-1}) * sum_0 ... ((p_n(x_0=C_{k-1} | x_t) / q(x_t | x_0=C_{k-1}) / sum_n) * a_cumulative_{t-1} + b_cumulative_{t-1}) * q(x_t | x_{t-1}=C_{k-1}) * sum_n
# c_cumulative_{t-1} * q(x_t |... | 1,365 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_vq_diffusion.py |
# For each column, there are two possible cases.
#
# Where:
# - sum(p_n(x_0))) is summing over all classes for x_0
# - C_i is the class transitioning from (not to be confused with c_t and c_cumulative_t being used for gamma's)
# - C_j is the class transitioning to
#
... | 1,365 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_vq_diffusion.py |
# From equation (11) stated in terms of forward probabilities, the last row is trivially verified.
#
# For the other rows, we can state the equation as ...
#
# (c_t / c_cumulative_t) * [b_cumulative_{t-1} * p(x_0=c_0) + ... + (a_cumulative_{t-1} + b_cumulative_{t-1}) * p(x_0=C_i) + ... +... | 1,365 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_vq_diffusion.py |
# C_j != C_i: b_t * ((b_cumulative_{t-1} / b_cumulative_t) * p_n(x_0 = c_0) + ... + ((a_cumulative_{t-1} + b_cumulative_{t-1}) / b_cumulative_t) * p_n(x_0 = C_i) + ... + (b_cumulative_{t-1} / (a_cumulative_t + b_cumulative_t)) * p_n(c_0=C_j) + ... + (b_cumulative_{t-1} / b_cumulative_t) * p_n(x_0 = c_{k-1}))
... | 1,365 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_vq_diffusion.py |
# The last row is trivially verified. The other rows can be verified by directly expanding equation (11) stated in terms of forward probabilities.
return log_p_x_t_min_1 | 1,365 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_vq_diffusion.py |
def log_Q_t_transitioning_to_known_class(
self, *, t: torch.int, x_t: torch.LongTensor, log_onehot_x_t: torch.Tensor, cumulative: bool
):
"""
Calculates the log probabilities of the rows from the (cumulative or non-cumulative) transition matrix for each
latent pixel in `x_t`.
... | 1,365 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_vq_diffusion.py |
Returns:
`torch.Tensor` of shape `(batch size, num classes - 1, num latent pixels)`:
Each _column_ of the returned matrix is a _row_ of log probabilities of the complete probability
transition matrix.
When non cumulative, returns `self.num_classes - 1` rows b... | 1,365 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_vq_diffusion.py |
non-cumulative result (omitting logarithms):
```
q_0(x_t | x_{t-1} = C_0) ... q_n(x_t | x_{t-1} = C_0)
. . .
. . .
. . .
... | 1,365 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_vq_diffusion.py |
cumulative result (omitting logarithms):
```
q_0_cumulative(x_t | x_0 = C_0) ... q_n_cumulative(x_t | x_0 = C_0)
. . .
. . .
. ... | 1,365 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_vq_diffusion.py |
if not cumulative:
# The values in the onehot vector can also be used as the logprobs for transitioning
# from masked latent pixels. If we are not calculating the cumulative transitions,
# we need to save these vectors to be re-appended to the final matrix so the values
#... | 1,365 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_vq_diffusion.py |
# `index_to_log_onehot` will add onehot vectors for masked pixels,
# so the default one hot matrix has one too many rows. See the doc string
# for an explanation of the dimensionality of the returned matrix.
log_onehot_x_t = log_onehot_x_t[:, :-1, :]
# this is a cheeky trick to produce ... | 1,365 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_vq_diffusion.py |
# The whole column of each masked pixel is `c`
mask_class_mask = x_t == self.mask_class
mask_class_mask = mask_class_mask.unsqueeze(1).expand(-1, self.num_embed - 1, -1)
log_Q_t[mask_class_mask] = c
if not cumulative:
log_Q_t = torch.cat((log_Q_t, log_onehot_x_t_transitionin... | 1,365 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_vq_diffusion.py |
class RePaintSchedulerOutput(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 m... | 1,366 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_repaint.py |
class RePaintScheduler(SchedulerMixin, ConfigMixin):
"""
`RePaintScheduler` is a scheduler for DDPM inpainting inside a given mask.
This model inherits from [`SchedulerMixin`] and [`ConfigMixin`]. Check the superclass documentation for the generic
methods the library implements for all schedulers such ... | 1,367 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_repaint.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_... | 1,367 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_repaint.py |
Clip the predicted sample between -1 and 1 for numerical stability. | 1,367 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_repaint.py |
"""
order = 1 | 1,367 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_repaint.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",
eta: float = 0.0,
trained_betas: Optional[np.ndarray] = None,
clip_sample: bool = True,
):
... | 1,367 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_repaint.py |
# GeoDiff sigmoid schedule
betas = torch.linspace(-6, 6, num_train_timesteps)
self.betas = torch.sigmoid(betas) * (beta_end - beta_start) + beta_start
else:
raise NotImplementedError(f"{beta_schedule} is not implemented for {self.__class__}") | 1,367 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_repaint.py |
self.alphas = 1.0 - self.betas
self.alphas_cumprod = torch.cumprod(self.alphas, dim=0)
self.one = torch.tensor(1.0)
self.final_alpha_cumprod = torch.tensor(1.0)
# standard deviation of the initial noise distribution
self.init_noise_sigma = 1.0
# setable values
... | 1,367 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_repaint.py |
Returns:
`torch.Tensor`:
A scaled input sample.
"""
return sample
def set_timesteps(
self,
num_inference_steps: int,
jump_length: int = 10,
jump_n_sample: int = 10,
device: Union[str, torch.device] = None,
):
"""
... | 1,367 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_repaint.py |
Args:
num_inference_steps (`int`):
The number of diffusion steps used when generating samples with a pre-trained model. If used,
`timesteps` must be `None`.
jump_length (`int`, defaults to 10):
The number of steps taken forward in time before going... | 1,367 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_repaint.py |
jumps = {}
for j in range(0, num_inference_steps - jump_length, jump_length):
jumps[j] = jump_n_sample - 1
t = num_inference_steps
while t >= 1:
t = t - 1
timesteps.append(t)
if jumps.get(t, 0) > 0:
jumps[t] = jumps[t] - 1
... | 1,367 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_repaint.py |
# For t > 0, compute predicted variance βt (see formula (6) and (7) from
# https://arxiv.org/pdf/2006.11239.pdf) and sample from it to get
# previous sample x_{t-1} ~ N(pred_prev_sample, variance) == add
# variance to pred_sample
# Is equivalent to formula (16) in https://arxiv.org/pdf/2... | 1,367 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_repaint.py |
def step(
self,
model_output: torch.Tensor,
timestep: int,
sample: torch.Tensor,
original_image: torch.Tensor,
mask: torch.Tensor,
generator: Optional[torch.Generator] = None,
return_dict: bool = True,
) -> Union[RePaintSchedulerOutput, Tuple]:
... | 1,367 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_repaint.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... | 1,367 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_repaint.py |
Returns:
[`~schedulers.scheduling_repaint.RePaintSchedulerOutput`] or `tuple`:
If return_dict is `True`, [`~schedulers.scheduling_repaint.RePaintSchedulerOutput`] is returned,
otherwise a tuple is returned where the first element is the sample tensor.
"""
t =... | 1,367 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_repaint.py |
# 3. Clip "predicted x_0"
if self.config.clip_sample:
pred_original_sample = torch.clamp(pred_original_sample, -1, 1)
# We choose to follow RePaint Algorithm 1 to get x_{t-1}, however we
# substitute formula (7) in the algorithm coming from DDPM paper
# (formula (4) Algorith... | 1,367 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_repaint.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 * model_output
# 7. compute x_{t-1} of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
prev_unknown_part = al... | 1,367 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_repaint.py |
def undo_step(self, sample, timestep, generator=None):
n = self.config.num_train_timesteps // self.num_inference_steps
for i in range(n):
beta = self.betas[timestep + i]
if sample.device.type == "mps":
# randn does not work reproducibly on mps
noi... | 1,367 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_repaint.py |
def __len__(self):
return self.config.num_train_timesteps | 1,367 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_repaint.py |
class DDIMSchedulerOutput(BaseOutput):
"""
Output class for the scheduler's `step` function output.
Args:
prev_sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` for images):
Computed sample `(x_{t-1})` of previous timestep. `prev_sample` should be used as next ... | 1,368 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_ddim.py |
class DDIMScheduler(SchedulerMixin, ConfigMixin):
"""
`DDIMScheduler` extends the denoising procedure introduced in denoising diffusion probabilistic models (DDPMs) with
non-Markovian guidance.
This model inherits from [`SchedulerMixin`] and [`ConfigMixin`]. Check the superclass documentation for the g... | 1,369 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_ddim.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_... | 1,369 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_ddim.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.
steps_offset (`int`, defaults to 0):
... | 1,369 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_ddim.py |
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`.
timestep_sp... | 1,369 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_ddim.py |
[`--offset_noise`](https://github.com/huggingface/diffusers/blob/74fd735eb073eb1d774b1ab4154a0876eb82f055/examples/dreambooth/train_dreambooth.py#L506).
""" | 1,369 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_ddim.py |
_compatibles = [e.name for e in KarrasDiffusionSchedulers]
order = 1 | 1,369 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_ddim.py |
@register_to_config
def __init__(
self,
num_train_timesteps: int = 1000,
beta_start: float = 0.0001,
beta_end: float = 0.02,
beta_schedule: str = "linear",
trained_betas: Optional[Union[np.ndarray, List[float]]] = None,
clip_sample: bool = True,
set_al... | 1,369 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_ddim.py |
# 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
self.betas = betas_for_alpha_bar(num_t... | 1,369 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_ddim.py |
# Rescale for zero SNR
if rescale_betas_zero_snr:
self.betas = rescale_zero_terminal_snr(self.betas)
self.alphas = 1.0 - self.betas
self.alphas_cumprod = torch.cumprod(self.alphas, dim=0)
# At every step in ddim, we are looking into the previous alphas_cumprod
# For... | 1,369 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_ddim.py |
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`):
Th... | 1,369 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_ddim.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... | 1,369 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_ddim.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))
... | 1,369 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_ddim.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... | 1,369 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_ddim.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, self.config.num_train_timesteps - 1, num_inference_steps)
.round()[::-1]
... | 1,369 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_ddim.py |
# creates integer timesteps by multiplying by ratio
# casting to int to avoid issues when num_inference_step is power of 3
timesteps = np.round(np.arange(self.config.num_train_timesteps, 0, -step_ratio)).astype(np.int64)
timesteps -= 1
else:
raise ValueError(
... | 1,369 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_ddim.py |
self.timesteps = torch.from_numpy(timesteps).to(device)
def step(
self,
model_output: torch.Tensor,
timestep: int,
sample: torch.Tensor,
eta: float = 0.0,
use_clipped_model_output: bool = False,
generator=None,
variance_noise: Optional[torch.Tensor] =... | 1,369 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_ddim.py |
Args:
model_output (`torch.Tensor`):
The direct output from learned diffusion model.
timestep (`float`):
The current discrete timestep in the diffusion chain.
sample (`torch.Tensor`):
A current instance of a sample created by the diffus... | 1,369 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_ddim.py |
variance_noise (`torch.Tensor`):
Alternative to generating noise with `generator` by directly providing the noise for the variance
itself. Useful for methods such as [`CycleDiffusion`].
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to re... | 1,369 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_ddim.py |
Returns:
[`~schedulers.scheduling_ddim.DDIMSchedulerOutput`] or `tuple`:
If return_dict is `True`, [`~schedulers.scheduling_ddim.DDIMSchedulerOutput`] is returned, otherwise a
tuple is returned where the first element is the sample tensor.
"""
if self.num_inf... | 1,369 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_ddim.py |
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