mirror of https://github.com/vladmandic/automatic
321 lines
13 KiB
Python
321 lines
13 KiB
Python
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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.schedulers.scheduling_utils import SchedulerMixin, SchedulerOutput
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class LinearRKScheduler(SchedulerMixin, ConfigMixin):
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"""
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LinearRKScheduler: Standard explicit Runge-Kutta integrators.
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Supports Ralston, Midpoint, Heun, Kutta, and standard RK4.
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Adapted from the RES4LYF repository.
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"""
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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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beta_start: float = 0.00085,
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beta_end: float = 0.012,
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beta_schedule: str = "linear",
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trained_betas: np.ndarray | list[float] | None = None,
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prediction_type: str = "epsilon",
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variant: str = "rk4", # euler, heun, rk2, rk3, rk4, ralston, midpoint
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use_karras_sigmas: bool = False,
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use_exponential_sigmas: bool = False,
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use_beta_sigmas: bool = False,
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use_flow_sigmas: bool = False,
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sigma_min: float | None = None,
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sigma_max: float | None = None,
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rho: float = 7.0,
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shift: float | None = None,
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base_shift: float = 0.5,
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max_shift: float = 1.15,
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use_dynamic_shifting: bool = False,
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timestep_spacing: str = "linspace",
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clip_sample: bool = False,
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sample_max_value: float = 1.0,
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set_alpha_to_one: bool = False,
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skip_prk_steps: bool = False,
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interpolation_type: str = "linear",
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steps_offset: int = 0,
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timestep_type: str = "discrete",
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rescale_betas_zero_snr: bool = False,
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final_sigmas_type: str = "zero",
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):
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if trained_betas is not None:
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self.betas = torch.tensor(trained_betas, dtype=torch.float32)
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elif beta_schedule == "linear":
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self.betas = torch.linspace(beta_start, beta_end, num_train_timesteps, dtype=torch.float32)
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elif beta_schedule == "scaled_linear":
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self.betas = torch.linspace(beta_start**0.5, beta_end**0.5, num_train_timesteps, dtype=torch.float32) ** 2
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else:
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raise NotImplementedError(f"{beta_schedule} is not implemented")
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self.alphas = 1.0 - self.betas
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self.alphas_cumprod = torch.cumprod(self.alphas, dim=0)
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self.num_inference_steps = None
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self.timesteps = torch.from_numpy(np.arange(0, num_train_timesteps)[::-1].copy())
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self.sigmas = None
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self.init_noise_sigma = 1.0
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# Internal state
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self.model_outputs = []
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self.sample_at_start_of_step = None
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self._step_index = None
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def _get_tableau(self):
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v = str(self.config.variant).lower().strip()
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if v in ["ralston", "ralston_2s"]:
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a, b, c = [[2 / 3]], [1 / 4, 3 / 4], [0, 2 / 3]
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elif v in ["midpoint", "midpoint_2s"]:
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a, b, c = [[1 / 2]], [0, 1], [0, 1 / 2]
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elif v in ["heun", "heun_2s"]:
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a, b, c = [[1]], [1 / 2, 1 / 2], [0, 1]
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elif v == "heun_3s":
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a, b, c = [[1 / 3], [0, 2 / 3]], [1 / 4, 0, 3 / 4], [0, 1 / 3, 2 / 3]
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elif v in ["kutta", "kutta_3s"]:
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a, b, c = [[1 / 2], [-1, 2]], [1 / 6, 2 / 3, 1 / 6], [0, 1 / 2, 1]
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elif v in ["rk4", "rk4_4s"]:
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a, b, c = [[1 / 2], [0, 1 / 2], [0, 0, 1]], [1 / 6, 1 / 3, 1 / 3, 1 / 6], [0, 1 / 2, 1 / 2, 1]
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elif v in ["rk2", "heun"]:
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a, b, c = [[1]], [1 / 2, 1 / 2], [0, 1]
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elif v == "rk3":
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a, b, c = [[1 / 2], [-1, 2]], [1 / 6, 2 / 3, 1 / 6], [0, 1 / 2, 1]
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elif v == "euler":
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a, b, c = [], [1], [0]
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else:
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raise ValueError(f"Unknown variant: {v}")
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# Expand 'a' to full matrix
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stages = len(c)
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full_a = np.zeros((stages, stages))
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for i, row in enumerate(a):
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full_a[i + 1, : len(row)] = row
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return full_a, np.array(b), np.array(c)
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def set_timesteps(
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self,
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num_inference_steps: int,
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device: str | torch.device = None,
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mu: float | None = None, dtype: torch.dtype = torch.float32):
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self.num_inference_steps = num_inference_steps
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# 1. Spacing
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if self.config.timestep_spacing == "linspace":
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timesteps = np.linspace(0, self.config.num_train_timesteps - 1, num_inference_steps, dtype=float)[::-1].copy()
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elif self.config.timestep_spacing == "leading":
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step_ratio = self.config.num_train_timesteps // num_inference_steps
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timesteps = (np.arange(0, num_inference_steps) * step_ratio).round()[::-1].copy().astype(float)
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elif self.config.timestep_spacing == "trailing":
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step_ratio = self.config.num_train_timesteps / num_inference_steps
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timesteps = (np.arange(self.config.num_train_timesteps, 0, -step_ratio)).round().copy().astype(float)
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timesteps -= 1
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else:
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raise ValueError(f"timestep_spacing must be one of 'linspace', 'leading', or 'trailing', got {self.config.timestep_spacing}")
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sigmas = np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)
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if self.config.interpolation_type == "linear":
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sigmas = np.interp(timesteps, np.arange(len(sigmas)), sigmas)
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elif self.config.interpolation_type == "log_linear":
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sigmas = np.exp(np.interp(timesteps, np.arange(len(sigmas)), np.log(sigmas)))
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else:
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raise ValueError(f"interpolation_type must be one of 'linear' or 'log_linear', got {self.config.interpolation_type}")
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# 2. Sigma Schedule
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if self.config.use_karras_sigmas:
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sigma_min = self.config.sigma_min if self.config.sigma_min is not None else sigmas[-1]
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sigma_max = self.config.sigma_max if self.config.sigma_max is not None else sigmas[0]
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rho = self.config.rho
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ramp = np.linspace(0, 1, num_inference_steps)
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sigmas = (sigma_max ** (1 / rho) + ramp * (sigma_min ** (1 / rho) - sigma_max ** (1 / rho))) ** rho
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elif self.config.use_exponential_sigmas:
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sigma_min = self.config.sigma_min if self.config.sigma_min is not None else sigmas[-1]
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sigma_max = self.config.sigma_max if self.config.sigma_max is not None else sigmas[0]
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sigmas = np.exp(np.linspace(np.log(sigma_max), np.log(sigma_min), num_inference_steps))
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elif self.config.use_beta_sigmas:
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sigma_min = self.config.sigma_min if self.config.sigma_min is not None else sigmas[-1]
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sigma_max = self.config.sigma_max if self.config.sigma_max is not None else sigmas[0]
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alpha, beta = 0.6, 0.6
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ramp = np.linspace(0, 1, num_inference_steps)
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try:
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import torch.distributions as dist
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b = dist.Beta(alpha, beta)
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ramp = b.sample((num_inference_steps,)).sort().values.numpy()
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except Exception:
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pass
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sigmas = sigma_max * (1 - ramp) + sigma_min * ramp
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elif self.config.use_flow_sigmas:
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sigmas = np.linspace(1.0, 1 / 1000, num_inference_steps)
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# 3. Shifting
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if self.config.use_dynamic_shifting and mu is not None:
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sigmas = mu * sigmas / (1 + (mu - 1) * sigmas)
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elif self.config.shift is not None:
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sigmas = self.config.shift * sigmas / (1 + (self.config.shift - 1) * sigmas)
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# We handle multi-history expansion
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_a_mat, _b_vec, c_vec = self._get_tableau()
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len(c_vec)
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sigmas_expanded = []
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for i in range(len(sigmas) - 1):
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s_curr = sigmas[i]
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s_next = sigmas[i + 1]
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for c_val in c_vec:
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sigmas_expanded.append(s_curr + c_val * (s_next - s_curr))
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sigmas_expanded.append(0.0)
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sigmas_interpolated = np.array(sigmas_expanded)
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# Linear remapping for Flow Matching
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timesteps_expanded = sigmas_interpolated * self.config.num_train_timesteps
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self.sigmas = torch.from_numpy(sigmas_interpolated).to(device=device, dtype=dtype)
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self.timesteps = torch.from_numpy(timesteps_expanded + self.config.steps_offset).to(device=device, dtype=dtype)
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self.init_noise_sigma = self.sigmas.max().item() if self.sigmas.numel() > 0 else 1.0
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self.model_outputs = []
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self.sample_at_start_of_step = None
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self._step_index = None
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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 the 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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def index_for_timestep(self, timestep, schedule_timesteps=None):
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from .scheduler_utils import index_for_timestep
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if schedule_timesteps is None:
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schedule_timesteps = self.timesteps
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return index_for_timestep(timestep, schedule_timesteps)
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def _init_step_index(self, timestep):
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if self._step_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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def scale_model_input(self, sample: torch.Tensor, timestep: float | torch.Tensor) -> torch.Tensor:
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if self._step_index is None:
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self._init_step_index(timestep)
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if self.config.prediction_type == "flow_prediction":
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return sample
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sigma = self.sigmas[self._step_index]
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return sample / ((sigma**2 + 1) ** 0.5)
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def step(
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self,
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model_output: torch.Tensor,
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timestep: float | torch.Tensor,
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sample: torch.Tensor,
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return_dict: bool = True,
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) -> SchedulerOutput | tuple:
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if self._step_index is None:
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self._init_step_index(timestep)
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a_mat, b_vec, c_vec = self._get_tableau()
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num_stages = len(c_vec)
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stage_index = self._step_index % num_stages
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base_step_index = (self._step_index // num_stages) * num_stages
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sigma_curr = self.sigmas[base_step_index]
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sigma_next_idx = min(base_step_index + num_stages, len(self.sigmas) - 1)
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sigma_next = self.sigmas[sigma_next_idx]
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if sigma_next <= 0:
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sigma_t = self.sigmas[self._step_index]
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denoised = sample - sigma_t * model_output if self.config.prediction_type == "epsilon" else model_output
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prev_sample = denoised
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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 SchedulerOutput(prev_sample=prev_sample)
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h = sigma_next - sigma_curr
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sigma_t = self.sigmas[self._step_index]
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if self.config.prediction_type == "epsilon":
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denoised = sample - sigma_t * model_output
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elif self.config.prediction_type == "v_prediction":
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alpha_t = 1 / (sigma_t**2 + 1) ** 0.5
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sigma_actual = sigma_t * alpha_t
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denoised = alpha_t * sample - sigma_actual * model_output
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elif self.config.prediction_type == "flow_prediction":
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denoised = sample - sigma_t * model_output
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elif self.config.prediction_type == "sample":
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denoised = model_output
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else:
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raise ValueError(f"prediction_type {self.config.prediction_type} is not supported.")
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if self.config.clip_sample:
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denoised = denoised.clamp(-self.config.sample_max_value, self.config.sample_max_value)
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# derivative = (x - x0) / sigma
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derivative = (sample - denoised) / sigma_t if sigma_t > 1e-6 else torch.zeros_like(sample)
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if self.sample_at_start_of_step is None:
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if stage_index > 0:
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# Mid-step fallback for Img2Img/Inpainting
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sigma_next_t = self.sigmas[self._step_index + 1]
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dt = sigma_next_t - sigma_t
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prev_sample = sample + dt * derivative
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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 SchedulerOutput(prev_sample=prev_sample)
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self.sample_at_start_of_step = sample
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self.model_outputs = [derivative] * stage_index
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if stage_index == 0:
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self.model_outputs = [derivative]
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self.sample_at_start_of_step = sample
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else:
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self.model_outputs.append(derivative)
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next_stage_idx = stage_index + 1
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if next_stage_idx < num_stages:
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sum_ak = 0
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for j in range(len(self.model_outputs)):
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sum_ak = sum_ak + a_mat[next_stage_idx][j] * self.model_outputs[j]
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sigma_next_stage = self.sigmas[self._step_index + 1]
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# Update x (unnormalized sample)
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prev_sample = self.sample_at_start_of_step + (sigma_next_stage - sigma_curr) * sum_ak
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else:
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sum_bk = 0
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for j in range(len(self.model_outputs)):
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sum_bk = sum_bk + b_vec[j] * self.model_outputs[j]
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prev_sample = self.sample_at_start_of_step + h * sum_bk
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self.model_outputs = []
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self.sample_at_start_of_step = None
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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 SchedulerOutput(prev_sample=prev_sample)
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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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from .scheduler_utils import add_noise_to_sample
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return add_noise_to_sample(original_samples, noise, self.sigmas, timesteps, self.timesteps)
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def __len__(self):
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return self.config.num_train_timesteps
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