init
This commit is contained in:
kijai
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from .scheduling_cosine_ddpm import DDPMCosineScheduler
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from .scheduling_flow_matching import PyramidFlowMatchEulerDiscreteScheduler
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import math
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from dataclasses import dataclass
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from typing import List, Optional, Tuple, Union
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import torch
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from diffusers.configuration_utils import ConfigMixin, register_to_config
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from diffusers.utils import BaseOutput
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from diffusers.utils.torch_utils import randn_tensor
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from diffusers.schedulers.scheduling_utils import SchedulerMixin
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@dataclass
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class DDPMSchedulerOutput(BaseOutput):
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"""
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Output class for the scheduler's step function output.
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Args:
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prev_sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` for images):
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Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the
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denoising loop.
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"""
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prev_sample: torch.Tensor
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class DDPMCosineScheduler(SchedulerMixin, ConfigMixin):
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@register_to_config
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def __init__(
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self,
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scaler: float = 1.0,
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s: float = 0.008,
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):
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self.scaler = scaler
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self.s = torch.tensor([s])
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self._init_alpha_cumprod = torch.cos(self.s / (1 + self.s) * torch.pi * 0.5) ** 2
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# standard deviation of the initial noise distribution
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self.init_noise_sigma = 1.0
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def _alpha_cumprod(self, t, device):
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if self.scaler > 1:
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t = 1 - (1 - t) ** self.scaler
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elif self.scaler < 1:
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t = t**self.scaler
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alpha_cumprod = torch.cos(
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(t + self.s.to(device)) / (1 + self.s.to(device)) * torch.pi * 0.5
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) ** 2 / self._init_alpha_cumprod.to(device)
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return alpha_cumprod.clamp(0.0001, 0.9999)
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def scale_model_input(self, sample: torch.Tensor, timestep: Optional[int] = None) -> torch.Tensor:
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"""
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Ensures interchangeability with schedulers that need to scale the denoising model input depending on the
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current timestep.
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Args:
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sample (`torch.Tensor`): input sample
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timestep (`int`, optional): current timestep
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Returns:
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`torch.Tensor`: scaled input sample
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"""
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return sample
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def set_timesteps(
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self,
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num_inference_steps: int = None,
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timesteps: Optional[List[int]] = None,
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device: Union[str, torch.device] = None,
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):
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"""
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Sets the discrete timesteps used for the diffusion chain. Supporting function to be run before inference.
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Args:
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num_inference_steps (`Dict[float, int]`):
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the number of diffusion steps used when generating samples with a pre-trained model. If passed, then
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`timesteps` must be `None`.
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device (`str` or `torch.device`, optional):
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the device to which the timesteps are moved to. {2 / 3: 20, 0.0: 10}
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"""
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if timesteps is None:
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timesteps = torch.linspace(1.0, 0.0, num_inference_steps + 1, device=device)
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if not isinstance(timesteps, torch.Tensor):
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timesteps = torch.Tensor(timesteps).to(device)
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self.timesteps = timesteps
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def step(
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self,
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model_output: torch.Tensor,
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timestep: int,
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sample: torch.Tensor,
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generator=None,
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return_dict: bool = True,
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) -> Union[DDPMSchedulerOutput, Tuple]:
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dtype = model_output.dtype
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device = model_output.device
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t = timestep
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prev_t = self.previous_timestep(t)
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alpha_cumprod = self._alpha_cumprod(t, device).view(t.size(0), *[1 for _ in sample.shape[1:]])
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alpha_cumprod_prev = self._alpha_cumprod(prev_t, device).view(prev_t.size(0), *[1 for _ in sample.shape[1:]])
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alpha = alpha_cumprod / alpha_cumprod_prev
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mu = (1.0 / alpha).sqrt() * (sample - (1 - alpha) * model_output / (1 - alpha_cumprod).sqrt())
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std_noise = randn_tensor(mu.shape, generator=generator, device=model_output.device, dtype=model_output.dtype)
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std = ((1 - alpha) * (1.0 - alpha_cumprod_prev) / (1.0 - alpha_cumprod)).sqrt() * std_noise
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pred = mu + std * (prev_t != 0).float().view(prev_t.size(0), *[1 for _ in sample.shape[1:]])
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if not return_dict:
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return (pred.to(dtype),)
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return DDPMSchedulerOutput(prev_sample=pred.to(dtype))
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def add_noise(
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self,
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original_samples: torch.Tensor,
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noise: torch.Tensor,
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timesteps: torch.Tensor,
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) -> torch.Tensor:
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device = original_samples.device
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dtype = original_samples.dtype
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alpha_cumprod = self._alpha_cumprod(timesteps, device=device).view(
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timesteps.size(0), *[1 for _ in original_samples.shape[1:]]
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)
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noisy_samples = alpha_cumprod.sqrt() * original_samples + (1 - alpha_cumprod).sqrt() * noise
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return noisy_samples.to(dtype=dtype)
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def __len__(self):
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return self.config.num_train_timesteps
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def previous_timestep(self, timestep):
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index = (self.timesteps - timestep[0]).abs().argmin().item()
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prev_t = self.timesteps[index + 1][None].expand(timestep.shape[0])
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return prev_t
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from dataclasses import dataclass
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from typing import Optional, Tuple, Union, List
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import math
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import numpy as np
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import torch
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from diffusers.configuration_utils import ConfigMixin, register_to_config
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from diffusers.utils import BaseOutput, logging
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from diffusers.utils.torch_utils import randn_tensor
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from diffusers.schedulers.scheduling_utils import SchedulerMixin
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#from IPython import embed
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@dataclass
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class FlowMatchEulerDiscreteSchedulerOutput(BaseOutput):
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"""
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Output class for the scheduler's `step` function output.
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Args:
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prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):
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Computed sample `(x_{t-1})` of previous timestep. `prev_sample` should be used as next model input in the
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denoising loop.
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"""
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prev_sample: torch.FloatTensor
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class PyramidFlowMatchEulerDiscreteScheduler(SchedulerMixin, ConfigMixin):
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"""
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Euler scheduler.
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This model inherits from [`SchedulerMixin`] and [`ConfigMixin`]. Check the superclass documentation for the generic
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methods the library implements for all schedulers such as loading and saving.
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Args:
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num_train_timesteps (`int`, defaults to 1000):
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The number of diffusion steps to train the model.
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timestep_spacing (`str`, defaults to `"linspace"`):
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The way the timesteps should be scaled. Refer to Table 2 of the [Common Diffusion Noise Schedules and
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Sample Steps are Flawed](https://huggingface.co/papers/2305.08891) for more information.
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shift (`float`, defaults to 1.0):
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The shift value for the timestep schedule.
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"""
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_compatibles = []
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order = 1
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@register_to_config
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def __init__(
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self,
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num_train_timesteps: int = 1000,
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shift: float = 1.0, # Following Stable diffusion 3,
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stages: int = 3,
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stage_range: List = [0, 1/3, 2/3, 1],
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gamma: float = 1/3,
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):
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self.timestep_ratios = {} # The timestep ratio for each stage
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self.timesteps_per_stage = {} # The detailed timesteps per stage
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self.sigmas_per_stage = {}
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self.start_sigmas = {}
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self.end_sigmas = {}
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self.ori_start_sigmas = {}
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# self.init_sigmas()
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self.init_sigmas_for_each_stage()
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self.sigma_min = self.sigmas[-1].item()
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self.sigma_max = self.sigmas[0].item()
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self.gamma = gamma
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def init_sigmas(self):
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"""
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initialize the global timesteps and sigmas
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"""
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num_train_timesteps = self.config.num_train_timesteps
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shift = self.config.shift
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timesteps = np.linspace(1, num_train_timesteps, num_train_timesteps, dtype=np.float32)[::-1].copy()
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timesteps = torch.from_numpy(timesteps).to(dtype=torch.float32)
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sigmas = timesteps / num_train_timesteps
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sigmas = shift * sigmas / (1 + (shift - 1) * sigmas)
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self.timesteps = sigmas * num_train_timesteps
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self._step_index = None
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self._begin_index = None
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self.sigmas = sigmas.to("cpu") # to avoid too much CPU/GPU communication
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def init_sigmas_for_each_stage(self):
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"""
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Init the timesteps for each stage
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"""
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self.init_sigmas()
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stage_distance = []
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stages = self.config.stages
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training_steps = self.config.num_train_timesteps
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stage_range = self.config.stage_range
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# Init the start and end point of each stage
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for i_s in range(stages):
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# To decide the start and ends point
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start_indice = int(stage_range[i_s] * training_steps)
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start_indice = max(start_indice, 0)
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end_indice = int(stage_range[i_s+1] * training_steps)
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end_indice = min(end_indice, training_steps)
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start_sigma = self.sigmas[start_indice].item()
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end_sigma = self.sigmas[end_indice].item() if end_indice < training_steps else 0.0
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self.ori_start_sigmas[i_s] = start_sigma
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if i_s != 0:
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ori_sigma = 1 - start_sigma
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gamma = self.config.gamma
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corrected_sigma = (1 / (math.sqrt(1 + (1 / gamma)) * (1 - ori_sigma) + ori_sigma)) * ori_sigma
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# corrected_sigma = 1 / (2 - ori_sigma) * ori_sigma
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start_sigma = 1 - corrected_sigma
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stage_distance.append(start_sigma - end_sigma)
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self.start_sigmas[i_s] = start_sigma
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self.end_sigmas[i_s] = end_sigma
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# Determine the ratio of each stage according to flow length
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tot_distance = sum(stage_distance)
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for i_s in range(stages):
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if i_s == 0:
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start_ratio = 0.0
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else:
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start_ratio = sum(stage_distance[:i_s]) / tot_distance
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if i_s == stages - 1:
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end_ratio = 1.0
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else:
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end_ratio = sum(stage_distance[:i_s+1]) / tot_distance
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self.timestep_ratios[i_s] = (start_ratio, end_ratio)
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# Determine the timesteps and sigmas for each stage
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for i_s in range(stages):
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timestep_ratio = self.timestep_ratios[i_s]
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timestep_max = self.timesteps[int(timestep_ratio[0] * training_steps)]
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timestep_min = self.timesteps[min(int(timestep_ratio[1] * training_steps), training_steps - 1)]
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timesteps = np.linspace(
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timestep_max, timestep_min, training_steps + 1,
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)
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self.timesteps_per_stage[i_s] = torch.from_numpy(timesteps[:-1])
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stage_sigmas = np.linspace(
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1, 0, training_steps + 1,
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)
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self.sigmas_per_stage[i_s] = torch.from_numpy(stage_sigmas[:-1])
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@property
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def step_index(self):
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"""
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The index counter for current timestep. It will increase 1 after each scheduler step.
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"""
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return self._step_index
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@property
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def begin_index(self):
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"""
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The index for the first timestep. It should be set from pipeline with `set_begin_index` method.
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"""
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return self._begin_index
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# Copied from diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler.set_begin_index
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def set_begin_index(self, begin_index: int = 0):
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"""
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Sets the begin index for the scheduler. This function should be run from pipeline before the inference.
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Args:
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begin_index (`int`):
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The begin index for the scheduler.
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"""
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self._begin_index = begin_index
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def _sigma_to_t(self, sigma):
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return sigma * self.config.num_train_timesteps
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def set_timesteps(self, num_inference_steps: int, stage_index: int, device: Union[str, torch.device] = None):
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"""
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Setting the timesteps and sigmas for each stage
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"""
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self.num_inference_steps = num_inference_steps
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training_steps = self.config.num_train_timesteps
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self.init_sigmas()
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stage_timesteps = self.timesteps_per_stage[stage_index]
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timestep_max = stage_timesteps[0].item()
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timestep_min = stage_timesteps[-1].item()
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timesteps = np.linspace(
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timestep_max, timestep_min, num_inference_steps,
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)
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self.timesteps = torch.from_numpy(timesteps).to(device=device)
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stage_sigmas = self.sigmas_per_stage[stage_index]
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sigma_max = stage_sigmas[0].item()
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sigma_min = stage_sigmas[-1].item()
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ratios = np.linspace(
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sigma_max, sigma_min, num_inference_steps
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)
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sigmas = torch.from_numpy(ratios).to(device=device)
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self.sigmas = torch.cat([sigmas, torch.zeros(1, device=sigmas.device)])
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self._step_index = None
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def index_for_timestep(self, timestep, schedule_timesteps=None):
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if schedule_timesteps is None:
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schedule_timesteps = self.timesteps
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indices = (schedule_timesteps == timestep).nonzero()
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# The sigma index that is taken for the **very** first `step`
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# is always the second index (or the last index if there is only 1)
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# This way we can ensure we don't accidentally skip a sigma in
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# case we start in the middle of the denoising schedule (e.g. for image-to-image)
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pos = 1 if len(indices) > 1 else 0
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return indices[pos].item()
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def _init_step_index(self, timestep):
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if self.begin_index is None:
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if isinstance(timestep, torch.Tensor):
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timestep = timestep.to(self.timesteps.device)
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self._step_index = self.index_for_timestep(timestep)
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else:
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self._step_index = self._begin_index
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def step(
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self,
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model_output: torch.FloatTensor,
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timestep: Union[float, torch.FloatTensor],
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sample: torch.FloatTensor,
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generator: Optional[torch.Generator] = None,
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return_dict: bool = True,
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) -> Union[FlowMatchEulerDiscreteSchedulerOutput, Tuple]:
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"""
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Predict the sample from the previous timestep by reversing the SDE. This function propagates the diffusion
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process from the learned model outputs (most often the predicted noise).
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Args:
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model_output (`torch.FloatTensor`):
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The direct output from learned diffusion model.
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timestep (`float`):
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The current discrete timestep in the diffusion chain.
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sample (`torch.FloatTensor`):
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A current instance of a sample created by the diffusion process.
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generator (`torch.Generator`, *optional*):
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A random number generator.
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return_dict (`bool`):
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Whether or not to return a [`~schedulers.scheduling_euler_discrete.EulerDiscreteSchedulerOutput`] or
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tuple.
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Returns:
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[`~schedulers.scheduling_euler_discrete.EulerDiscreteSchedulerOutput`] or `tuple`:
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If return_dict is `True`, [`~schedulers.scheduling_euler_discrete.EulerDiscreteSchedulerOutput`] is
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returned, otherwise a tuple is returned where the first element is the sample tensor.
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"""
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if (
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isinstance(timestep, int)
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or isinstance(timestep, torch.IntTensor)
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or isinstance(timestep, torch.LongTensor)
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):
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raise ValueError(
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(
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"Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to"
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" `EulerDiscreteScheduler.step()` is not supported. Make sure to pass"
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" one of the `scheduler.timesteps` as a timestep."
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),
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)
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if self.step_index is None:
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self._step_index = 0
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# Upcast to avoid precision issues when computing prev_sample
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sample = sample.to(torch.float32)
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sigma = self.sigmas[self.step_index]
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sigma_next = self.sigmas[self.step_index + 1]
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prev_sample = sample + (sigma_next - sigma) * model_output
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# Cast sample back to model compatible dtype
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prev_sample = prev_sample.to(model_output.dtype)
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# upon completion increase step index by one
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self._step_index += 1
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if not return_dict:
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return (prev_sample,)
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return FlowMatchEulerDiscreteSchedulerOutput(prev_sample=prev_sample)
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def __len__(self):
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return self.config.num_train_timesteps
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