LCM_Dreamshaper_v7 / lcm_scheduler.py

Duplicate from SimianLuo/LCM_Dreamshaper_v7

b24946b 7 months ago

No virus

22.3 kB

	# Copyright 2023 Stanford University Team and The HuggingFace Team. All rights reserved.
	#
	# Licensed under the Apache License, Version 2.0 (the "License");
	# you may not use this file except in compliance with the License.
	# You may obtain a copy of the License at
	#
	# http://www.apache.org/licenses/LICENSE-2.0
	#
	# Unless required by applicable law or agreed to in writing, software
	# distributed under the License is distributed on an "AS IS" BASIS,
	# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
	# See the License for the specific language governing permissions and
	# limitations under the License.

	# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
	# and https://github.com/hojonathanho/diffusion

	import math
	from dataclasses import dataclass
	from typing import List, Optional, Tuple, Union

	import numpy as np
	import torch

	from diffusers import ConfigMixin, SchedulerMixin
	from diffusers.configuration_utils import register_to_config
	from diffusers.utils import BaseOutput


	@dataclass
	# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->DDIM
	class LCMSchedulerOutput(BaseOutput):
	"""
	Output class for the scheduler's `step` function output.

	Args:
	prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):
	Computed sample `(x_{t-1})` of previous timestep. `prev_sample` should be used as next model input in the
	denoising loop.
	pred_original_sample (`torch.FloatTensor` 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.FloatTensor
	denoised: Optional[torch.FloatTensor] = None


	# Copied from diffusers.schedulers.scheduling_ddpm.betas_for_alpha_bar
	def betas_for_alpha_bar(
	num_diffusion_timesteps,
	max_beta=0.999,
	alpha_transform_type="cosine",
	):
	"""
	Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of
	(1-beta) over time from t = [0,1].

	Contains a function alpha_bar that takes an argument t and transforms it to the cumulative product of (1-beta) up
	to that part of the diffusion process.


	Args:
	num_diffusion_timesteps (`int`): the number of betas to produce.
	max_beta (`float`): the maximum beta to use; use values lower than 1 to
	prevent singularities.
	alpha_transform_type (`str`, optional, default to `cosine`): the type of noise schedule for alpha_bar.
	Choose from `cosine` or `exp`

	Returns:
	betas (`np.ndarray`): the betas used by the scheduler to step the model outputs
	"""
	if alpha_transform_type == "cosine":

	def alpha_bar_fn(t):
	return math.cos((t + 0.008) / 1.008 * math.pi / 2) ** 2

	elif alpha_transform_type == "exp":

	def alpha_bar_fn(t):
	return math.exp(t * -12.0)

	else:
	raise ValueError(f"Unsupported alpha_tranform_type: {alpha_transform_type}")

	betas = []
	for i in range(num_diffusion_timesteps):
	t1 = i / num_diffusion_timesteps
	t2 = (i + 1) / num_diffusion_timesteps
	betas.append(min(1 - alpha_bar_fn(t2) / alpha_bar_fn(t1), max_beta))
	return torch.tensor(betas, dtype=torch.float32)


	def rescale_zero_terminal_snr(betas):
	"""
	Rescales betas to have zero terminal SNR Based on https://arxiv.org/pdf/2305.08891.pdf (Algorithm 1)


	Args:
	betas (`torch.FloatTensor`):
	the betas that the scheduler is being initialized with.

	Returns:
	`torch.FloatTensor`: rescaled betas with zero terminal SNR
	"""
	# Convert betas to alphas_bar_sqrt
	alphas = 1.0 - betas
	alphas_cumprod = torch.cumprod(alphas, dim=0)
	alphas_bar_sqrt = alphas_cumprod.sqrt()

	# Store old values.
	alphas_bar_sqrt_0 = alphas_bar_sqrt[0].clone()
	alphas_bar_sqrt_T = alphas_bar_sqrt[-1].clone()

	# Shift so the last timestep is zero.
	alphas_bar_sqrt -= alphas_bar_sqrt_T

	# Scale so the first timestep is back to the old value.
	alphas_bar_sqrt *= alphas_bar_sqrt_0 / (alphas_bar_sqrt_0 - alphas_bar_sqrt_T)

	# Convert alphas_bar_sqrt to betas
	alphas_bar = alphas_bar_sqrt**2 # Revert sqrt
	alphas = alphas_bar[1:] / alphas_bar[:-1] # Revert cumprod
	alphas = torch.cat([alphas_bar[0:1], alphas])
	betas = 1 - alphas

	return betas


	class LCMScheduler(SchedulerMixin, ConfigMixin):
	"""
	`LCMScheduler` 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.

	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`):
	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. You can use a combination of `offset=1` and
	`set_alpha_to_one=False` to make the last step use step 0 for the previous alpha product like in Stable
	Diffusion.
	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.
	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
	[`--offset_noise`](https://github.com/huggingface/diffusers/blob/74fd735eb073eb1d774b1ab4154a0876eb82f055/examples/dreambooth/train_dreambooth.py#L506).
	"""

	# _compatibles = [e.name for e in KarrasDiffusionSchedulers]
	order = 1

	@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":
	# this schedule is very specific to the latent diffusion model.
	self.betas = (
	torch.linspace(beta_start0.5, beta_end0.5, 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} does is not implemented for {self.__class__}")

	# 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))

	def scale_model_input(self, sample: torch.FloatTensor, timestep: Optional[int] = None) -> torch.FloatTensor:
	"""
	Ensures interchangeability with schedulers that need to scale the denoising model input depending on the
	current timestep.

	Args:
	sample (`torch.FloatTensor`):
	The input sample.
	timestep (`int`, optional):
	The current timestep in the diffusion chain.

	Returns:
	`torch.FloatTensor`:
	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

	# Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler._threshold_sample
	def _threshold_sample(self, sample: torch.FloatTensor) -> torch.FloatTensor:
	"""
	"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, height, width = sample.shape

	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 * height * width)

	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, height, width)
	sample = sample.to(dtype)

	return sample

	def set_timesteps(self, num_inference_steps: int, lcm_origin_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

	# LCM Timesteps Setting: # Linear Spacing
	c = self.config.num_train_timesteps // lcm_origin_steps
	lcm_origin_timesteps = np.asarray(list(range(1, lcm_origin_steps + 1))) * c - 1 # LCM Training Steps Schedule
	skipping_step = len(lcm_origin_timesteps) // num_inference_steps
	timesteps = lcm_origin_timesteps[::-skipping_step][:num_inference_steps] # LCM Inference Steps Schedule

	self.timesteps = torch.from_numpy(timesteps.copy()).to(device)

	def get_scalings_for_boundary_condition_discrete(self, t):
	self.sigma_data = 0.5 # Default: 0.5

	# By dividing 0.1: This is almost a delta function at t=0.
	c_skip = self.sigma_data**2 / (
	(t / 0.1) 2 + self.sigma_data2
	)
	c_out = (( t / 0.1) / ((t / 0.1) 2 + self.sigma_data2) ** 0.5)
	return c_skip, c_out


	def step(
	self,
	model_output: torch.FloatTensor,
	timeindex: int,
	timestep: int,
	sample: torch.FloatTensor,
	eta: float = 0.0,
	use_clipped_model_output: bool = False,
	generator=None,
	variance_noise: Optional[torch.FloatTensor] = None,
	return_dict: bool = True,
	) -> Union[LCMSchedulerOutput, 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).

	Args:
	model_output (`torch.FloatTensor`):
	The direct output from learned diffusion model.
	timestep (`float`):
	The current discrete timestep in the diffusion chain.
	sample (`torch.FloatTensor`):
	A current instance of a sample created by 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.
	variance_noise (`torch.FloatTensor`):
	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_lcm.LCMSchedulerOutput`] or `tuple`.

	Returns:
	[`~schedulers.scheduling_utils.LCMSchedulerOutput`] or `tuple`:
	If return_dict is `True`, [`~schedulers.scheduling_lcm.LCMSchedulerOutput`] is returned, otherwise a
	tuple is returned where the first element is the sample tensor.

	"""
	if self.num_inference_steps is None:
	raise ValueError(
	"Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"
	)

	# 1. get previous step value
	prev_timeindex = timeindex + 1
	if prev_timeindex < len(self.timesteps):
	prev_timestep = self.timesteps[prev_timeindex]
	else:
	prev_timestep = timestep

	# 2. compute alphas, betas
	alpha_prod_t = self.alphas_cumprod[timestep]
	alpha_prod_t_prev = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod

	beta_prod_t = 1 - alpha_prod_t
	beta_prod_t_prev = 1 - alpha_prod_t_prev

	# 3. Get scalings for boundary conditions
	c_skip, c_out = self.get_scalings_for_boundary_condition_discrete(timestep)

	# 4. Different Parameterization:
	parameterization = self.config.prediction_type

	if parameterization == "epsilon": # noise-prediction
	pred_x0 = (sample - beta_prod_t.sqrt() * model_output) / alpha_prod_t.sqrt()

	elif parameterization == "sample": # x-prediction
	pred_x0 = model_output

	elif parameterization == "v_prediction": # v-prediction
	pred_x0 = alpha_prod_t.sqrt() * sample - beta_prod_t.sqrt() * model_output

	# 4. Denoise model output using boundary conditions
	denoised = c_out * pred_x0 + c_skip * sample

	# 5. Sample z ~ N(0, I), For MultiStep Inference
	# Noise is not used for one-step sampling.
	if len(self.timesteps) > 1:
	noise = torch.randn(model_output.shape).to(model_output.device)
	prev_sample = alpha_prod_t_prev.sqrt() * denoised + beta_prod_t_prev.sqrt() * noise
	else:
	prev_sample = denoised

	if not return_dict:
	return (prev_sample, denoised)

	return LCMSchedulerOutput(prev_sample=prev_sample, denoised=denoised)


	# Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler.add_noise
	def add_noise(
	self,
	original_samples: torch.FloatTensor,
	noise: torch.FloatTensor,
	timesteps: torch.IntTensor,
	) -> torch.FloatTensor:
	# Make sure alphas_cumprod and timestep have same device and dtype as original_samples
	alphas_cumprod = self.alphas_cumprod.to(device=original_samples.device, 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)

	sqrt_one_minus_alpha_prod = (1 - alphas_cumprod[timesteps]) ** 0.5
	sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.flatten()
	while len(sqrt_one_minus_alpha_prod.shape) < len(original_samples.shape):
	sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.unsqueeze(-1)

	noisy_samples = sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
	return noisy_samples

	# Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler.get_velocity
	def get_velocity(
	self, sample: torch.FloatTensor, noise: torch.FloatTensor, timesteps: torch.IntTensor
	) -> torch.FloatTensor:
	# Make sure alphas_cumprod and timestep have same device and dtype as sample
	alphas_cumprod = self.alphas_cumprod.to(device=sample.device, dtype=sample.dtype)
	timesteps = timesteps.to(sample.device)

	sqrt_alpha_prod = alphas_cumprod[timesteps] ** 0.5
	sqrt_alpha_prod = sqrt_alpha_prod.flatten()
	while len(sqrt_alpha_prod.shape) < len(sample.shape):
	sqrt_alpha_prod = sqrt_alpha_prod.unsqueeze(-1)

	sqrt_one_minus_alpha_prod = (1 - alphas_cumprod[timesteps]) ** 0.5
	sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.flatten()
	while len(sqrt_one_minus_alpha_prod.shape) < len(sample.shape):
	sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.unsqueeze(-1)

	velocity = sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
	return velocity

	def __len__(self):
	return self.config.num_train_timesteps