mirror of https://github.com/vladmandic/automatic
342 lines
14 KiB
Python
342 lines
14 KiB
Python
from typing import ClassVar
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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 KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput
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from .phi_functions import Phi
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class RESUnifiedScheduler(SchedulerMixin, ConfigMixin):
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"""
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RESUnifiedScheduler (Exponential Integrator) ported from RES4LYF.
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Supports RES 2M, 3M, 2S, 3S, 5S, 6S
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Supports DEIS 1S, 2M, 3M
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"""
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_compatibles: ClassVar[list[str]] = [e.name for e in KarrasDiffusionSchedulers]
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order: ClassVar[int] = 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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prediction_type: str = "epsilon",
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rk_type: str = "res_2m",
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use_analytic_solution: bool = True,
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rescale_betas_zero_snr: bool = False,
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timestep_spacing: str = "linspace",
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steps_offset: int = 0,
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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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shift: float = 1.0,
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use_dynamic_shifting: bool = False,
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base_shift: float = 0.5,
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max_shift: float = 1.15,
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base_image_seq_len: int = 256,
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max_image_seq_len: int = 4096,
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):
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from .scheduler_utils import betas_for_alpha_bar, rescale_zero_terminal_snr
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if 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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elif beta_schedule == "squaredcos_cap_v2":
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self.betas = betas_for_alpha_bar(num_train_timesteps)
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else:
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raise NotImplementedError(f"{beta_schedule} is not implemented")
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if rescale_betas_zero_snr:
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self.betas = rescale_zero_terminal_snr(self.betas)
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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.sigmas = torch.Tensor([])
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self.timesteps = torch.Tensor([])
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self.model_outputs = []
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self.x0_outputs = []
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self.prev_sigmas = []
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self._step_index = None
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self._begin_index = None
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self.init_noise_sigma = 1.0
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def set_sigmas(self, sigmas: torch.Tensor):
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self.sigmas = sigmas
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self._step_index = None
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@property
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def step_index(self) -> int | None:
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return self._step_index
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@property
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def begin_index(self) -> int | None:
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return self._begin_index
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def set_begin_index(self, begin_index: int = 0) -> None:
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self._begin_index = begin_index
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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 set_timesteps(self, num_inference_steps: int, device: str | torch.device = None, mu: float | None = None, dtype: torch.dtype = torch.float32):
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from .scheduler_utils import (
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apply_shift,
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get_dynamic_shift,
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get_sigmas_beta,
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get_sigmas_exponential,
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get_sigmas_karras,
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)
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self.num_inference_steps = num_inference_steps
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timestep_spacing = getattr(self.config, "timestep_spacing", "linspace")
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steps_offset = getattr(self.config, "steps_offset", 0)
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if 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 timestep_spacing == "leading":
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step_ratio = self.config.num_train_timesteps // self.num_inference_steps
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timesteps = (np.arange(0, num_inference_steps) * step_ratio).round()[::-1].copy()
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timesteps += steps_offset
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elif timestep_spacing == "trailing":
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step_ratio = self.config.num_train_timesteps / self.num_inference_steps
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timesteps = (np.arange(self.config.num_train_timesteps, 0, -step_ratio)).round().copy()
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timesteps -= 1
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else:
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raise ValueError(f"timestep_spacing {timestep_spacing} is not supported.")
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# Derived sigma range from alphas_cumprod
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base_sigmas = np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)
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sigmas = base_sigmas[::-1].copy() # Ensure high to low
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if getattr(self.config, "use_karras_sigmas", False):
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sigmas = get_sigmas_karras(num_inference_steps, sigmas[-1], sigmas[0], device=device, dtype=dtype).cpu().numpy()
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elif getattr(self.config, "use_exponential_sigmas", False):
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sigmas = get_sigmas_exponential(num_inference_steps, sigmas[-1], sigmas[0], device=device, dtype=dtype).cpu().numpy()
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elif getattr(self.config, "use_beta_sigmas", False):
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sigmas = get_sigmas_beta(num_inference_steps, sigmas[-1], sigmas[0], device=device, dtype=dtype).cpu().numpy()
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elif getattr(self.config, "use_flow_sigmas", False):
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sigmas = np.linspace(1.0, 1 / 1000, num_inference_steps)
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else:
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if self.config.use_flow_sigmas:
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sigmas = np.linspace(1.0, 1 / 1000, num_inference_steps)
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else:
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# Re-sample the base sigmas at the requested steps
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idx = np.linspace(0, len(base_sigmas) - 1, num_inference_steps)
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sigmas = np.interp(idx, np.arange(len(base_sigmas)), base_sigmas)[::-1].copy()
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shift = getattr(self.config, "shift", 1.0)
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use_dynamic_shifting = getattr(self.config, "use_dynamic_shifting", False)
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if shift != 1.0 or use_dynamic_shifting:
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if use_dynamic_shifting and mu is not None:
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shift = get_dynamic_shift(
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mu,
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getattr(self.config, "base_shift", 0.5),
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getattr(self.config, "max_shift", 1.5),
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getattr(self.config, "base_image_seq_len", 256),
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getattr(self.config, "max_image_seq_len", 4096),
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)
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sigmas = apply_shift(torch.from_numpy(sigmas), shift).numpy()
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if getattr(self.config, "use_flow_sigmas", False):
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timesteps = sigmas * self.config.num_train_timesteps
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self.sigmas = torch.from_numpy(np.concatenate([sigmas, [0.0]])).to(device=device, dtype=dtype)
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self.timesteps = torch.from_numpy(timesteps).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._step_index = None
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self._begin_index = None
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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 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 _get_coefficients(self, sigma, sigma_next):
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h = -torch.log(sigma_next / sigma) if sigma > 0 else torch.zeros_like(sigma)
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phi = Phi(h, [], getattr(self.config, "use_analytic_solution", True))
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phi_1 = phi(1)
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phi_2 = phi(2)
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# phi_2 = phi(2) # Moved inside conditional blocks as needed
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history_len = len(self.x0_outputs)
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# Stability: Force Order 1 for final few steps to prevent degradation at low noise levels
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if self.num_inference_steps is not None and self._step_index >= self.num_inference_steps - 3:
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return [phi_1], h
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if self.config.rk_type in ["res_2m", "deis_2m"] and history_len >= 2:
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h_prev = -torch.log(self.prev_sigmas[-1] / (self.prev_sigmas[-2] + 1e-9))
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r = h_prev / (h + 1e-9)
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h_prev = -torch.log(self.prev_sigmas[-1] / (self.prev_sigmas[-2] + 1e-9))
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r = h_prev / (h + 1e-9)
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# Hard Restart: if step sizes vary too wildly, fallback to order 1
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if r < 0.5 or r > 2.0:
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return [phi_1], h
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phi_2 = phi(2)
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# Correct Adams-Bashforth-like coefficients for Exponential Integrators
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b2 = -phi_2 / (r + 1e-9)
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b1 = phi_1 - b2
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return [b1, b2], h
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elif self.config.rk_type in ["res_3m", "deis_3m"] and history_len >= 3:
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h_prev1 = -torch.log(self.prev_sigmas[-1] / (self.prev_sigmas[-2] + 1e-9))
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h_prev2 = -torch.log(self.prev_sigmas[-1] / (self.prev_sigmas[-3] + 1e-9))
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r1 = h_prev1 / (h + 1e-9)
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r2 = h_prev2 / (h + 1e-9)
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h_prev1 = -torch.log(self.prev_sigmas[-1] / (self.prev_sigmas[-2] + 1e-9))
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h_prev2 = -torch.log(self.prev_sigmas[-1] / (self.prev_sigmas[-3] + 1e-9))
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r1 = h_prev1 / (h + 1e-9)
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r2 = h_prev2 / (h + 1e-9)
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# Hard Restart check
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if r1 < 0.5 or r1 > 2.0 or r2 < 0.5 or r2 > 2.0:
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return [phi_1], h
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phi_2 = phi(2)
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phi_3 = phi(3)
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# Generalized AB3 for Exponential Integrators (Varying steps)
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denom = r2 - r1 + 1e-9
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b3 = (phi_3 + r1 * phi_2) / (r2 * denom)
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b2 = -(phi_3 + r2 * phi_2) / (r1 * denom)
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b1 = phi_1 - (b2 + b3)
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return [b1, b2, b3], h
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return [phi_1], h
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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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sigma = self.sigmas[self._step_index]
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sigma_next = self.sigmas[self._step_index + 1]
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h = -torch.log(sigma_next / sigma) if sigma > 0 and sigma_next > 0 else torch.zeros_like(sigma)
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# RECONSTRUCT X0 (Matching PEC pattern)
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if self.config.prediction_type == "epsilon":
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x0 = sample - sigma * model_output
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elif self.config.prediction_type == "sample":
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x0 = model_output
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elif self.config.prediction_type == "v_prediction":
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alpha_t = 1.0 / (sigma**2 + 1) ** 0.5
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sigma_t = sigma * alpha_t
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x0 = alpha_t * sample - sigma_t * model_output
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elif self.config.prediction_type == "flow_prediction":
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x0 = sample - sigma * model_output
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else:
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x0 = model_output
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self.x0_outputs.append(x0)
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self.model_outputs.append(model_output) # Added for AB support
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self.prev_sigmas.append(sigma)
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if len(self.x0_outputs) > 3:
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self.x0_outputs.pop(0)
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self.model_outputs.pop(0)
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self.prev_sigmas.pop(0)
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if self.config.prediction_type == "flow_prediction":
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# Variable Step Adams-Bashforth for Flow Matching
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dt = sigma_next - sigma
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v_n = model_output
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curr_order = min(len(self.prev_sigmas), 3) # Max order 3 here
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if curr_order == 1:
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x_next = sample + dt * v_n
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elif curr_order == 2:
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sigma_prev = self.prev_sigmas[-2]
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dt_prev = sigma - sigma_prev
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r = dt / dt_prev if abs(dt_prev) > 1e-8 else 0.0
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if dt_prev == 0 or r < -0.9 or r > 2.0:
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x_next = sample + dt * v_n
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else:
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c0 = 1 + 0.5 * r
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c1 = -0.5 * r
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x_next = sample + dt * (c0 * v_n + c1 * self.model_outputs[-2])
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else:
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# AB2 fallback for robustness
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sigma_prev = self.prev_sigmas[-2]
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dt_prev = sigma - sigma_prev
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r = dt / dt_prev if abs(dt_prev) > 1e-8 else 0.0
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c0 = 1 + 0.5 * r
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c1 = -0.5 * r
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x_next = sample + dt * (c0 * v_n + c1 * self.model_outputs[-2])
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self._step_index += 1
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if not return_dict:
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return (x_next,)
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return SchedulerOutput(prev_sample=x_next)
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# GET COEFFICIENTS
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b, h_val = self._get_coefficients(sigma, sigma_next)
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if len(b) == 1:
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res = b[0] * x0
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elif len(b) == 2:
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res = b[0] * self.x0_outputs[-1] + b[1] * self.x0_outputs[-2]
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elif len(b) == 3:
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res = b[0] * self.x0_outputs[-1] + b[1] * self.x0_outputs[-2] + b[2] * self.x0_outputs[-3]
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else:
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res = b[0] * x0
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# UPDATE
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if sigma_next == 0:
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x_next = x0
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else:
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# Propagate in x-space (unnormalized)
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x_next = torch.exp(-h) * sample + h * res
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self._step_index += 1
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if not return_dict:
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return (x_next,)
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return SchedulerOutput(prev_sample=x_next)
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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 __len__(self):
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return self.config.num_train_timesteps
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