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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) and self.config.lower_order_final and len(self.timesteps) < 15
)
model_output = self.convert_model_output(model_output, sample=sample)
for i in range(self.config.solver_order - 1):
self.model_outputs[i] = self.model_outputs[i + 1]
self.model_outputs[-1] = model_output | 1,357 | /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_order_update(self.model_outputs, sample=sample)
else:
prev_sample = self.multistep_deis_third_order_update(self.model_outputs, sample=sample)
if self.lower_order_nums < self.config.solver_order:
self.lower_order_nums += 1
# upon completion increase step index by one
self._step_index += 1
if not return_dict:
return (prev_sample,)
return SchedulerOutput(prev_sample=prev_sample) | 1,357 | /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.
Returns:
`torch.Tensor`:
A scaled input sample.
"""
return 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 device and dtype as original_samples
sigmas = self.sigmas.to(device=original_samples.device, dtype=original_samples.dtype)
if original_samples.device.type == "mps" and torch.is_floating_point(timesteps):
# mps does not support float64
schedule_timesteps = self.timesteps.to(original_samples.device, dtype=torch.float32)
timesteps = timesteps.to(original_samples.device, dtype=torch.float32)
else:
schedule_timesteps = self.timesteps.to(original_samples.device)
timesteps = timesteps.to(original_samples.device) | 1,357 | /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 after first denoising step (for inpainting)
step_indices = [self.step_index] * timesteps.shape[0]
else:
# add noise is called before first denoising step to create initial latent(img2img)
step_indices = [self.begin_index] * timesteps.shape[0]
sigma = sigmas[step_indices].flatten()
while len(sigma.shape) < len(original_samples.shape):
sigma = sigma.unsqueeze(-1)
alpha_t, sigma_t = self._sigma_to_alpha_sigma_t(sigma)
noisy_samples = alpha_t * original_samples + sigma_t * noise
return noisy_samples
def __len__(self):
return self.config.num_train_timesteps | 1,357 | /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 next model input in the
denoising loop.
pred_original_sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` for images):
The predicted denoised sample `(x_{0})` based on the model output from the current timestep.
`pred_original_sample` can be used to preview progress or for guidance.
"""
prev_sample: torch.Tensor
pred_original_sample: Optional[torch.Tensor] = None | 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 scheduler has some minor variations in how it calculates the learned range variance and dynamically
re-calculates betas based off the timesteps it is skipping.
The scheduler also uses a slightly different step ratio when computing timesteps to use for inference.
See [`~DDPMScheduler`] for more information on DDPM scheduling | 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`):
option to clip predicted sample between `-clip_sample_range` and `clip_sample_range` for numerical
stability.
clip_sample_range (`float`, default `1.0`):
The range to clip the sample between. See `clip_sample`.
prediction_type (`str`, default `epsilon`, optional):
prediction type of the scheduler function, one of `epsilon` (predicting the noise of the diffusion process)
or `sample` (directly predicting the noisy sample`)
""" | 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",
):
if beta_schedule != "squaredcos_cap_v2":
raise ValueError("UnCLIPScheduler only supports `beta_schedule`: 'squaredcos_cap_v2'")
self.betas = betas_for_alpha_bar(num_train_timesteps)
self.alphas = 1.0 - self.betas
self.alphas_cumprod = torch.cumprod(self.alphas, dim=0)
self.one = torch.tensor(1.0)
# standard deviation of the initial noise distribution
self.init_noise_sigma = 1.0
# setable values
self.num_inference_steps = None
self.timesteps = torch.from_numpy(np.arange(0, num_train_timesteps)[::-1].copy())
self.variance_type = variance_type | 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
timestep (`int`, optional): current timestep
Returns:
`torch.Tensor`: scaled input sample
"""
return sample
def set_timesteps(self, num_inference_steps: int, device: Union[str, torch.device] = None):
"""
Sets the discrete timesteps used for the diffusion chain. Supporting function to be run before inference.
Note that this scheduler uses a slightly different step ratio than the other diffusers schedulers. The
different step ratio is to mimic the original karlo implementation and does not affect the quality or accuracy
of the results. | 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)
timesteps = (np.arange(0, num_inference_steps) * step_ratio).round()[::-1].copy().astype(np.int64)
self.timesteps = torch.from_numpy(timesteps).to(device)
def _get_variance(self, t, prev_timestep=None, predicted_variance=None, variance_type=None):
if prev_timestep is None:
prev_timestep = t - 1
alpha_prod_t = self.alphas_cumprod[t]
alpha_prod_t_prev = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
beta_prod_t = 1 - alpha_prod_t
beta_prod_t_prev = 1 - alpha_prod_t_prev | 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} ~ N(pred_prev_sample, variance) == add variance to pred_sample
variance = beta_prod_t_prev / beta_prod_t * beta
if variance_type is None:
variance_type = self.config.variance_type
# hacks - were probably added for training stability
if variance_type == "fixed_small_log":
variance = torch.log(torch.clamp(variance, min=1e-20))
variance = torch.exp(0.5 * variance)
elif variance_type == "learned_range":
# NOTE difference with DDPM scheduler
min_log = variance.log()
max_log = beta.log() | 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,
return_dict: bool = True,
) -> Union[UnCLIPSchedulerOutput, 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). | 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`, *optional*): The previous timestep to predict the previous sample at.
Used to dynamically compute beta. If not given, `t-1` is used and the pre-computed beta is used.
generator: random number generator.
return_dict (`bool`): option for returning tuple rather than UnCLIPSchedulerOutput class
Returns:
[`~schedulers.scheduling_utils.UnCLIPSchedulerOutput`] or `tuple`:
[`~schedulers.scheduling_utils.UnCLIPSchedulerOutput`] if `return_dict` is True, otherwise a `tuple`. When
returning a tuple, the first element is the sample tensor.
"""
t = timestep | 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:
prev_timestep = t - 1
alpha_prod_t = self.alphas_cumprod[t]
alpha_prod_t_prev = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
beta_prod_t = 1 - alpha_prod_t
beta_prod_t_prev = 1 - alpha_prod_t_prev
if prev_timestep == t - 1:
beta = self.betas[t]
alpha = self.alphas[t]
else:
beta = 1 - alpha_prod_t / alpha_prod_t_prev
alpha = 1 - beta | 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)
elif self.config.prediction_type == "sample":
pred_original_sample = model_output
else:
raise ValueError(
f"prediction_type given as {self.config.prediction_type} must be one of `epsilon` or `sample`"
" for the UnCLIPScheduler."
)
# 3. Clip "predicted x_0"
if self.config.clip_sample:
pred_original_sample = torch.clamp(
pred_original_sample, -self.config.clip_sample_range, self.config.clip_sample_range
) | 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
# 5. Compute predicted previous sample µ_t
# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
pred_prev_sample = pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample
# 6. Add noise
variance = 0
if t > 0:
variance_noise = randn_tensor(
model_output.shape, dtype=model_output.dtype, generator=generator, device=model_output.device
)
variance = self._get_variance(
t,
predicted_variance=predicted_variance,
prev_timestep=prev_timestep,
) | 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 `fixed_small_log` or `learned_range`"
" for the UnCLIPScheduler."
)
variance = variance * variance_noise
pred_prev_sample = pred_prev_sample + variance
if not return_dict:
return (
pred_prev_sample,
pred_original_sample,
)
return UnCLIPSchedulerOutput(prev_sample=pred_prev_sample, pred_original_sample=pred_original_sample) | 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 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) | 1,359 | /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_samples = sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
return noisy_samples | 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 next model input in the
denoising loop.
pred_original_sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` for images):
The predicted denoised sample `(x_{0})` based on the model output from the current timestep.
`pred_original_sample` can be used to preview progress or for guidance.
"""
prev_sample: torch.Tensor
pred_original_sample: torch.Tensor = None | 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(
self,
num_inference_steps: int,
temperature: Union[int, Tuple[int, int], List[int]] = (2, 0),
device: Union[str, torch.device] = None,
):
self.timesteps = torch.arange(num_inference_steps, device=device).flip(0)
if isinstance(temperature, (tuple, list)):
self.temperatures = torch.linspace(temperature[0], temperature[1], num_inference_steps, device=device)
else:
self.temperatures = torch.linspace(temperature, 0.01, num_inference_steps, device=device) | 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 = sample.ndim == 3 and model_output.ndim == 4
if two_dim_input:
batch_size, codebook_size, height, width = model_output.shape
sample = sample.reshape(batch_size, height * width)
model_output = model_output.reshape(batch_size, codebook_size, height * width).permute(0, 2, 1)
unknown_map = sample == self.config.mask_token_id
probs = model_output.softmax(dim=-1) | 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_ = probs_.reshape(-1, probs.size(-1))
pred_original_sample = torch.multinomial(probs_, 1, generator=generator).to(device=device)
pred_original_sample = pred_original_sample[:, 0].view(*probs.shape[:-1])
pred_original_sample = torch.where(unknown_map, pred_original_sample, sample)
if timestep == 0:
prev_sample = pred_original_sample
else:
seq_len = sample.shape[1]
step_idx = (self.timesteps == timestep).nonzero()
ratio = (step_idx + 1) / len(self.timesteps) | 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_ratio = starting_mask_ratio * mask_ratio
mask_len = (seq_len * mask_ratio).floor()
# do not mask more than amount previously masked
mask_len = torch.min(unknown_map.sum(dim=-1, keepdim=True) - 1, mask_len)
# mask at least one
mask_len = torch.max(torch.tensor([1], device=model_output.device), mask_len)
selected_probs = torch.gather(probs, -1, pred_original_sample[:, :, None])[:, :, 0]
# Ignores the tokens given in the input by overwriting their confidence.
selected_probs = torch.where(unknown_map, selected_probs, torch.finfo(selected_probs.dtype).max) | 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(batch_size, height, width)
pred_original_sample = pred_original_sample.reshape(batch_size, height, width)
if not return_dict:
return (prev_sample, pred_original_sample)
return AmusedSchedulerOutput(prev_sample, pred_original_sample)
def add_noise(self, sample, timesteps, generator=None):
step_idx = (self.timesteps == timesteps).nonzero()
ratio = (step_idx + 1) / len(self.timesteps) | 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 = (
torch.rand(
sample.shape, device=generator.device if generator is not None else sample.device, generator=generator
).to(sample.device)
< mask_ratio
)
masked_sample = sample.clone()
masked_sample[mask_indices] = self.config.mask_token_id
return masked_sample | 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 as next model input in the
denoising loop.
"""
prev_sample: torch.Tensor | 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_cumprod = torch.cumprod(alphas, dim=0)
self.sqrt_alphas_cumprod = torch.sqrt(alphas_cumprod)
self.sqrt_one_minus_alphas_cumprod = torch.sqrt(1.0 - alphas_cumprod)
sigmas = torch.sqrt(1.0 / alphas_cumprod - 1)
sqrt_recip_alphas_cumprod = torch.sqrt(1.0 / alphas_cumprod)
self.c_skip = sqrt_recip_alphas_cumprod * sigma_data**2 / (sigmas**2 + sigma_data**2)
self.c_out = sigmas * sigma_data / (sigmas**2 + sigma_data**2) ** 0.5
self.c_in = sqrt_recip_alphas_cumprod / (sigmas**2 + sigma_data**2) ** 0.5 | 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], dtype=torch.long, device=device)
self.sqrt_alphas_cumprod = self.sqrt_alphas_cumprod.to(device)
self.sqrt_one_minus_alphas_cumprod = self.sqrt_one_minus_alphas_cumprod.to(device)
self.c_skip = self.c_skip.to(device)
self.c_out = self.c_out.to(device)
self.c_in = self.c_in.to(device)
@property
def init_noise_sigma(self):
return self.sqrt_one_minus_alphas_cumprod[self.timesteps[0]]
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. | 1,363 | /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]
def step(
self,
model_output: torch.Tensor,
timestep: Union[float, torch.Tensor],
sample: torch.Tensor,
generator: Optional[torch.Generator] = None,
return_dict: bool = True,
) -> Union[ConsistencyDecoderSchedulerOutput, 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). | 1,363 | /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 process.
generator (`torch.Generator`, *optional*):
A random number generator.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a
[`~schedulers.scheduling_consistency_models.ConsistencyDecoderSchedulerOutput`] or `tuple`. | 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 first element is the sample tensor.
"""
x_0 = self.c_out[timestep] * model_output + self.c_skip[timestep] * sample
timestep_idx = torch.where(self.timesteps == timestep)[0]
if timestep_idx == len(self.timesteps) - 1:
prev_sample = x_0
else:
noise = randn_tensor(x_0.shape, generator=generator, dtype=x_0.dtype, device=x_0.device)
prev_sample = (
self.sqrt_alphas_cumprod[self.timesteps[timestep_idx + 1]].to(x_0.dtype) * x_0
+ self.sqrt_one_minus_alphas_cumprod[self.timesteps[timestep_idx + 1]].to(x_0.dtype) * noise
) | 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 the
denoising loop.
"""
prev_sample: torch.LongTensor | 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 (`float`, defaults to 0.99999):
The starting cumulative alpha value.
alpha_cum_end (`float`, defaults to 0.00009):
The ending cumulative alpha value.
gamma_cum_start (`float`, defaults to 0.00009):
The starting cumulative gamma value.
gamma_cum_end (`float`, defaults to 0.99999):
The ending cumulative gamma value.
"""
order = 1 | 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 = num_vec_classes
# By convention, the index for the mask class is the last class index
self.mask_class = self.num_embed - 1
at, att = alpha_schedules(num_train_timesteps, alpha_cum_start=alpha_cum_start, alpha_cum_end=alpha_cum_end)
ct, ctt = gamma_schedules(num_train_timesteps, gamma_cum_start=gamma_cum_start, gamma_cum_end=gamma_cum_end)
num_non_mask_classes = self.num_embed - 1
bt = (1 - at - ct) / num_non_mask_classes
btt = (1 - att - ctt) / num_non_mask_classes | 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("float64"))
ctt = torch.tensor(ctt.astype("float64"))
log_cumprod_at = torch.log(att)
log_cumprod_bt = torch.log(btt)
log_cumprod_ct = torch.log(ctt)
self.log_at = log_at.float()
self.log_bt = log_bt.float()
self.log_ct = log_ct.float()
self.log_cumprod_at = log_cumprod_at.float()
self.log_cumprod_bt = log_cumprod_bt.float()
self.log_cumprod_ct = log_cumprod_ct.float()
# setable values
self.num_inference_steps = None
self.timesteps = torch.from_numpy(np.arange(0, num_train_timesteps)[::-1].copy()) | 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 generating samples with a pre-trained model.
device (`str` or `torch.device`, *optional*):
The device to which the timesteps and diffusion process parameters (alpha, beta, gamma) should be moved
to.
"""
self.num_inference_steps = num_inference_steps
timesteps = np.arange(0, self.num_inference_steps)[::-1].copy()
self.timesteps = torch.from_numpy(timesteps).to(device) | 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 step(
self,
model_output: torch.Tensor,
timestep: torch.long,
sample: torch.LongTensor,
generator: Optional[torch.Generator] = None,
return_dict: bool = True,
) -> Union[VQDiffusionSchedulerOutput, Tuple]:
"""
Predict the sample from the previous timestep by the reverse transition distribution. See
[`~VQDiffusionScheduler.q_posterior`] for more details about how the distribution is computer. | 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.
t (`torch.long`):
The timestep that determines which transition matrices are used.
x_t (`torch.LongTensor` of shape `(batch size, num latent pixels)`):
The classes of each latent pixel at time `t`.
generator (`torch.Generator`, or `None`):
A random number generator for the noise applied to `p(x_{t-1} | x_t)` before it is sampled from.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~schedulers.scheduling_vq_diffusion.VQDiffusionSchedulerOutput`] or
`tuple`. | 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.
"""
if timestep == 0:
log_p_x_t_min_1 = model_output
else:
log_p_x_t_min_1 = self.q_posterior(model_output, sample, timestep)
log_p_x_t_min_1 = gumbel_noised(log_p_x_t_min_1, generator)
x_t_min_1 = log_p_x_t_min_1.argmax(dim=1)
if not return_dict:
return (x_t_min_1,)
return VQDiffusionSchedulerOutput(prev_sample=x_t_min_1)
def q_posterior(self, log_p_x_0, x_t, t):
"""
Calculates the log probabilities for the predicted classes of the image at timestep `t-1`:
```
p(x_{t-1} | x_t) = sum( q(x_t | x_{t-1}) * q(x_{t-1} | x_0) * p(x_0) / q(x_t | x_0) )
``` | 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.
x_t (`torch.LongTensor` of shape `(batch size, num latent pixels)`):
The classes of each latent pixel at time `t`.
t (`torch.Long`):
The timestep that determines which transition matrix is used.
Returns:
`torch.Tensor` of shape `(batch size, num classes, num latent pixels)`:
The log probabilities for the predicted classes of the image at timestep `t-1`.
"""
log_onehot_x_t = index_to_log_onehot(x_t, self.num_embed)
log_q_x_t_given_x_0 = self.log_Q_t_transitioning_to_known_class(
t=t, x_t=x_t, log_onehot_x_t=log_onehot_x_t, cumulative=True
) | 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)
# . . .
# . . .
# . . .
# p_0(x_0=C_{k-1} | x_t) / q(x_t | x_0=C_{k-1}) ... p_n(x_0=C_{k-1} | x_t) / q(x_t | x_0=C_{k-1})
q = log_p_x_0 - log_q_x_t_given_x_0
# sum_0 = p_0(x_0=C_0 | x_t) / q(x_t | x_0=C_0) + ... + p_0(x_0=C_{k-1} | x_t) / q(x_t | x_0=C_{k-1}), ... ,
# sum_n = p_n(x_0=C_0 | x_t) / q(x_t | x_0=C_0) + ... + p_n(x_0=C_{k-1} | x_t) / q(x_t | x_0=C_{k-1})
q_log_sum_exp = torch.logsumexp(q, dim=1, keepdim=True) | 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
# . . .
# . . .
# . . .
# p_0(x_0=C_{k-1} | x_t) / q(x_t | x_0=C_{k-1}) / sum_0 ... p_n(x_0=C_{k-1} | x_t) / q(x_t | x_0=C_{k-1}) / sum_n
q = q - q_log_sum_exp | 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}
# . . .
# . . .
# . . .
# (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} ... (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}
# c_cumulative_{t-1} ... c_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 | x_{t-1}=C_k) * sum_0 ... c_cumulative_{t-1} * q(x_t | x_{t-1}=C_k) * sum_0
log_p_x_t_min_1 = q + log_q_t_given_x_t_min_1 + q_log_sum_exp | 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. x_t is masked i.e. x_t = c_k
#
# Simplifying the expression, the column vector is:
# .
# .
# .
# (c_t / c_cumulative_t) * (a_cumulative_{t-1} * p_n(x_0 = C_i | x_t) + b_cumulative_{t-1} * sum(p_n(x_0)))
# .
# .
# .
# (c_cumulative_{t-1} / c_cumulative_t) * sum(p_n(x_0))
# | 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) + ... + b_cumulative_{k-1} * p(x_0=c_{k-1})]
#
# This verifies the other rows.
#
# 2. x_t is not masked
#
# Simplifying the expression, there are two cases for the rows of the column vector, where C_j = C_i and where C_j != 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}))
# .
# .
# .
# C_j = C_i: (a_t + b_t) * ((b_cumulative_{t-1} / b_cumulative_t) * p_n(x_0 = c_0) + ... + ((a_cumulative_{t-1} + b_cumulative_{t-1}) / (a_cumulative_t + b_cumulative_t)) * p_n(x_0 = C_i = C_j) + ... + (b_cumulative_{t-1} / b_cumulative_t) * p_n(x_0 = c_{k-1}))
# .
# .
# .
# 0
# | 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`.
Args:
t (`torch.Long`):
The timestep that determines which transition matrix is used.
x_t (`torch.LongTensor` of shape `(batch size, num latent pixels)`):
The classes of each latent pixel at time `t`.
log_onehot_x_t (`torch.Tensor` of shape `(batch size, num classes, num latent pixels)`):
The log one-hot vectors of `x_t`.
cumulative (`bool`):
If cumulative is `False`, the single step transition matrix `t-1`->`t` is used. If cumulative is
`True`, the cumulative transition matrix `0`->`t` is used. | 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 because the initial latent pixel cannot be
masked.
Where:
- `q_n` is the probability distribution for the forward process of the `n`th latent pixel.
- C_0 is a class of a latent pixel embedding
- C_k is the class of the masked latent pixel | 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)
. . .
. . .
. . .
q_0(x_t | x_{t-1} = C_k) ... q_n(x_t | x_{t-1} = C_k)
``` | 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)
. . .
. . .
. . .
q_0_cumulative(x_t | x_0 = C_{k-1}) ... q_n_cumulative(x_t | x_0 = C_{k-1})
```
"""
if cumulative:
a = self.log_cumprod_at[t]
b = self.log_cumprod_bt[t]
c = self.log_cumprod_ct[t]
else:
a = self.log_at[t]
b = self.log_bt[t]
c = self.log_ct[t] | 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
# aren't overwritten.
#
# `P(x_t!=mask|x_{t-1=mask}) = 0` and 0 will be the value of the last row of the onehot vector
# if x_t is not masked
#
# `P(x_t=mask|x_{t-1=mask}) = 1` and 1 will be the value of the last row of the onehot vector
# if x_t is masked
log_onehot_x_t_transitioning_from_masked = log_onehot_x_t[:, -1, :].unsqueeze(1) | 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 the transition probabilities using log one-hot vectors.
#
# Don't worry about what values this sets in the columns that mark transitions
# to masked latent pixels. They are overwrote later with the `mask_class_mask`.
#
# Looking at the below logspace formula in non-logspace, each value will evaluate to either
# `1 * a + b = a + b` where `log_Q_t` has the one hot value in the column
# or
# `0 * a + b = b` where `log_Q_t` has the 0 values in the column.
#
# See equation 7 for more details.
log_Q_t = (log_onehot_x_t + a).logaddexp(b) | 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_transitioning_from_masked), dim=1)
return log_Q_t
def apply_cumulative_transitions(self, q, t):
bsz = q.shape[0]
a = self.log_cumprod_at[t]
b = self.log_cumprod_bt[t]
c = self.log_cumprod_ct[t]
num_latent_pixels = q.shape[2]
c = c.expand(bsz, 1, num_latent_pixels)
q = (q + a).logaddexp(b)
q = torch.cat((q, c), dim=1)
return q | 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 model input in the
denoising loop.
pred_original_sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` for images):
The predicted denoised sample (x_{0}) based on the model output from
the current timestep. `pred_original_sample` can be used to preview progress or for guidance.
"""
prev_sample: torch.Tensor
pred_original_sample: torch.Tensor | 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 as loading and saving. | 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_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`, `squaredcos_cap_v2`, or `sigmoid`.
eta (`float`):
The weight of noise for added noise in diffusion step. If its value is between 0.0 and 1.0 it corresponds
to the DDIM scheduler, and if its value is between -0.0 and 1.0 it corresponds to the DDPM scheduler.
trained_betas (`np.ndarray`, *optional*):
Pass an array of betas directly to the constructor to bypass `beta_start` and `beta_end`.
clip_sample (`bool`, defaults to `True`): | 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,
):
if trained_betas is not None:
self.betas = torch.from_numpy(trained_betas)
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
self.betas = betas_for_alpha_bar(num_train_timesteps)
elif beta_schedule == "sigmoid": | 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
self.num_inference_steps = None
self.timesteps = torch.from_numpy(np.arange(0, num_train_timesteps)[::-1].copy())
self.eta = eta
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. | 1,367 | /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,
):
"""
Sets the discrete timesteps used for the diffusion chain (to be run before inference). | 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 backward in time for a single jump (“j” in
RePaint paper). Take a look at Figure 9 and 10 in the paper.
jump_n_sample (`int`, defaults to 10):
The number of times to make a forward time jump for a given chosen time sample. Take a look at Figure 9
and 10 in the paper.
device (`str` or `torch.device`, *optional*):
The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.
"""
num_inference_steps = min(self.config.num_train_timesteps, num_inference_steps)
self.num_inference_steps = num_inference_steps
timesteps = [] | 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
for _ in range(jump_length):
t = t + 1
timesteps.append(t)
timesteps = np.array(timesteps) * (self.config.num_train_timesteps // self.num_inference_steps)
self.timesteps = torch.from_numpy(timesteps).to(device)
def _get_variance(self, t):
prev_timestep = t - self.config.num_train_timesteps // self.num_inference_steps
alpha_prod_t = self.alphas_cumprod[t]
alpha_prod_t_prev = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
beta_prod_t = 1 - alpha_prod_t
beta_prod_t_prev = 1 - alpha_prod_t_prev | 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/2010.02502.pdf
# without eta.
# variance = (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * self.betas[t]
variance = (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)
return variance | 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]:
"""
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). | 1,367 | /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 diffusion process.
original_image (`torch.Tensor`):
The original image to inpaint on.
mask (`torch.Tensor`):
The mask where a value of 0.0 indicates which part of the original image to inpaint.
generator (`torch.Generator`, *optional*):
A random number generator.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~schedulers.scheduling_repaint.RePaintSchedulerOutput`] or `tuple`. | 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 = timestep
prev_timestep = timestep - self.config.num_train_timesteps // self.num_inference_steps
# 1. compute alphas, betas
alpha_prod_t = self.alphas_cumprod[t]
alpha_prod_t_prev = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
beta_prod_t = 1 - alpha_prod_t
# 2. compute predicted original sample from predicted noise also called
# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
pred_original_sample = (sample - beta_prod_t**0.5 * model_output) / alpha_prod_t**0.5 | 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) Algorithm 2 - Sampling) with formula (12) from DDIM paper.
# DDIM schedule gives the same results as DDPM with eta = 1.0
# Noise is being reused in 7. and 8., but no impact on quality has
# been observed.
# 5. Add noise
device = model_output.device
noise = randn_tensor(model_output.shape, generator=generator, device=device, dtype=model_output.dtype)
std_dev_t = self.eta * self._get_variance(timestep) ** 0.5
variance = 0
if t > 0 and self.eta > 0:
variance = std_dev_t * noise | 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 = alpha_prod_t_prev**0.5 * pred_original_sample + pred_sample_direction + variance
# 8. Algorithm 1 Line 5 https://arxiv.org/pdf/2201.09865.pdf
prev_known_part = (alpha_prod_t_prev**0.5) * original_image + (1 - alpha_prod_t_prev) * noise
# 9. Algorithm 1 Line 8 https://arxiv.org/pdf/2201.09865.pdf
pred_prev_sample = mask * prev_known_part + (1.0 - mask) * prev_unknown_part
if not return_dict:
return (
pred_prev_sample,
pred_original_sample,
)
return RePaintSchedulerOutput(prev_sample=pred_prev_sample, pred_original_sample=pred_original_sample) | 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
noise = randn_tensor(sample.shape, dtype=sample.dtype, generator=generator)
noise = noise.to(sample.device)
else:
noise = randn_tensor(sample.shape, generator=generator, device=sample.device, dtype=sample.dtype)
# 10. Algorithm 1 Line 10 https://arxiv.org/pdf/2201.09865.pdf
sample = (1 - beta) ** 0.5 * sample + beta**0.5 * noise
return sample
def add_noise(
self,
original_samples: torch.Tensor,
noise: torch.Tensor,
timesteps: torch.IntTensor,
) -> torch.Tensor:
raise NotImplementedError("Use `DDPMScheduler.add_noise()` to train for sampling with RePaint.") | 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 model input in the
denoising loop.
pred_original_sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` for images):
The predicted denoised sample `(x_{0})` based on the model output from the current timestep.
`pred_original_sample` can be used to preview progress or for guidance.
"""
prev_sample: torch.Tensor
pred_original_sample: Optional[torch.Tensor] = None | 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 generic
methods the library implements for all schedulers such as loading and saving. | 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_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`.
clip_sample (`bool`, defaults to `True`):
Clip the predicted sample for numerical stability.
clip_sample_range (`float`, defaults to 1.0):
The maximum magnitude for sample clipping. Valid only when `clip_sample=True`.
set_alpha_to_one (`bool`, defaults to `True`): | 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):
An offset added to the inference steps, as required by some model families.
prediction_type (`str`, defaults to `epsilon`, *optional*):
Prediction type of the scheduler function; can be `epsilon` (predicts the noise of the diffusion process),
`sample` (directly predicts the noisy sample`) or `v_prediction` (see section 2.4 of [Imagen
Video](https://imagen.research.google/video/paper.pdf) paper).
thresholding (`bool`, defaults to `False`):
Whether to use the "dynamic thresholding" method. This is unsuitable for latent-space diffusion models such
as Stable Diffusion. | 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_spacing (`str`, defaults to `"leading"`):
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.
rescale_betas_zero_snr (`bool`, defaults to `False`):
Whether to rescale the betas to have zero terminal SNR. This enables the model to generate very bright and
dark samples instead of limiting it to samples with medium brightness. Loosely related to | 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_alpha_to_one: bool = True,
steps_offset: int = 0,
prediction_type: str = "epsilon",
thresholding: bool = False,
dynamic_thresholding_ratio: float = 0.995,
clip_sample_range: float = 1.0,
sample_max_value: float = 1.0,
timestep_spacing: str = "leading",
rescale_betas_zero_snr: bool = False,
):
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": | 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_train_timesteps)
else:
raise NotImplementedError(f"{beta_schedule} is not implemented for {self.__class__}") | 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 the final step, there is no previous alphas_cumprod because we are already at 0
# `set_alpha_to_one` decides whether we set this parameter simply to one or
# whether we use the final alpha of the "non-previous" one.
self.final_alpha_cumprod = torch.tensor(1.0) if set_alpha_to_one else self.alphas_cumprod[0]
# standard deviation of the initial noise distribution
self.init_noise_sigma = 1.0
# setable values
self.num_inference_steps = None
self.timesteps = torch.from_numpy(np.arange(0, num_train_timesteps)[::-1].copy().astype(np.int64)) | 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`):
The input sample.
timestep (`int`, *optional*):
The current timestep in the diffusion chain.
Returns:
`torch.Tensor`:
A scaled input sample.
"""
return sample
def _get_variance(self, timestep, prev_timestep):
alpha_prod_t = self.alphas_cumprod[timestep]
alpha_prod_t_prev = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
beta_prod_t = 1 - alpha_prod_t
beta_prod_t_prev = 1 - alpha_prod_t_prev
variance = (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)
return variance | 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 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 | 1,369 | /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))
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 | 1,369 | /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 generating samples with a pre-trained model.
"""
if num_inference_steps > self.config.num_train_timesteps:
raise ValueError(
f"`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:"
f" {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle"
f" maximal {self.config.num_train_timesteps} timesteps."
)
self.num_inference_steps = num_inference_steps | 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]
.copy()
.astype(np.int64)
)
elif self.config.timestep_spacing == "leading":
step_ratio = self.config.num_train_timesteps // self.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(0, num_inference_steps) * step_ratio).round()[::-1].copy().astype(np.int64)
timesteps += self.config.steps_offset
elif self.config.timestep_spacing == "trailing":
step_ratio = self.config.num_train_timesteps / self.num_inference_steps | 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(
f"{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'leading' or 'trailing'."
) | 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] = None,
return_dict: bool = True,
) -> Union[DDIMSchedulerOutput, 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). | 1,369 | /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 diffusion process.
eta (`float`):
The weight of noise for added noise in diffusion step.
use_clipped_model_output (`bool`, defaults to `False`):
If `True`, computes "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` has no effect.
generator (`torch.Generator`, *optional*):
A random number generator. | 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 return a [`~schedulers.scheduling_ddim.DDIMSchedulerOutput`] or `tuple`. | 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_inference_steps is None:
raise ValueError(
"Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"
)
# 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" | 1,369 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_ddim.py |