mirror of https://github.com/vladmandic/automatic
251 lines
10 KiB
Python
251 lines
10 KiB
Python
|
|
import numpy as np
|
|
import torch
|
|
from diffusers.configuration_utils import ConfigMixin, register_to_config
|
|
from diffusers.schedulers.scheduling_utils import SchedulerMixin, SchedulerOutput
|
|
|
|
|
|
class RungeKutta44Scheduler(SchedulerMixin, ConfigMixin):
|
|
"""
|
|
RK4: Classical 4th-order Runge-Kutta scheduler.
|
|
Adapted from the RES4LYF repository.
|
|
|
|
This scheduler uses 4 stages per step.
|
|
"""
|
|
|
|
order = 1
|
|
|
|
@register_to_config
|
|
def __init__(
|
|
self,
|
|
num_train_timesteps: int = 1000,
|
|
beta_start: float = 0.00085,
|
|
beta_end: float = 0.012,
|
|
beta_schedule: str = "linear",
|
|
trained_betas: np.ndarray | list[float] | None = None,
|
|
prediction_type: str = "epsilon",
|
|
use_karras_sigmas: bool = False,
|
|
use_exponential_sigmas: bool = False,
|
|
use_beta_sigmas: bool = False,
|
|
use_flow_sigmas: bool = False,
|
|
sigma_min: float | None = None,
|
|
sigma_max: float | None = None,
|
|
rho: float = 7.0,
|
|
shift: float | None = None,
|
|
base_shift: float = 0.5,
|
|
max_shift: float = 1.15,
|
|
use_dynamic_shifting: bool = False,
|
|
timestep_spacing: str = "linspace",
|
|
clip_sample: bool = False,
|
|
sample_max_value: float = 1.0,
|
|
set_alpha_to_one: bool = False,
|
|
skip_prk_steps: bool = False,
|
|
interpolation_type: str = "linear",
|
|
steps_offset: int = 0,
|
|
timestep_type: str = "discrete",
|
|
rescale_betas_zero_snr: bool = False,
|
|
final_sigmas_type: str = "zero",
|
|
):
|
|
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":
|
|
self.betas = torch.linspace(beta_start**0.5, beta_end**0.5, num_train_timesteps, dtype=torch.float32) ** 2
|
|
else:
|
|
raise NotImplementedError(f"{beta_schedule} is not implemented for RungeKutta44Scheduler")
|
|
|
|
self.alphas = 1.0 - self.betas
|
|
self.alphas_cumprod = torch.cumprod(self.alphas, dim=0)
|
|
|
|
self.sigmas = None
|
|
self.timesteps = torch.from_numpy(np.arange(0, num_train_timesteps)[::-1].copy())
|
|
self.init_noise_sigma = 1.0
|
|
|
|
# Internal state for multi-stage
|
|
self.model_outputs = []
|
|
self.sample_at_start_of_step = None
|
|
self._sigmas_cpu = None
|
|
self._step_index = None
|
|
|
|
def set_timesteps(self, num_inference_steps: int, device: str | torch.device = None, mu: float | None = None, dtype: torch.dtype = torch.float32):
|
|
self.num_inference_steps = num_inference_steps
|
|
|
|
# 1. Base sigmas
|
|
timesteps = np.linspace(0, self.config.num_train_timesteps - 1, num_inference_steps, dtype=float)[::-1].copy()
|
|
sigmas = np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)
|
|
if self.config.interpolation_type == "linear":
|
|
sigmas = np.interp(timesteps, np.arange(len(sigmas)), sigmas)
|
|
elif self.config.interpolation_type == "log_linear":
|
|
sigmas = np.exp(np.interp(timesteps, np.arange(len(sigmas)), np.log(sigmas)))
|
|
else:
|
|
raise ValueError(f"interpolation_type must be one of 'linear' or 'log_linear', got {self.config.interpolation_type}")
|
|
|
|
if self.config.use_karras_sigmas:
|
|
sigma_min = self.config.sigma_min if self.config.sigma_min is not None else sigmas[-1]
|
|
sigma_max = self.config.sigma_max if self.config.sigma_max is not None else sigmas[0]
|
|
rho = self.config.rho
|
|
ramp = np.linspace(0, 1, num_inference_steps)
|
|
min_inv_rho = sigma_min ** (1 / rho)
|
|
max_inv_rho = sigma_max ** (1 / rho)
|
|
sigmas = (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
|
|
|
|
# 2. Add sub-step sigmas for multi-stage RK
|
|
# RK4 has c = [0, 1/2, 1/2, 1]
|
|
c_values = [0.0, 0.5, 0.5, 1.0]
|
|
|
|
sigmas_expanded = []
|
|
for i in range(len(sigmas) - 1):
|
|
s_curr = sigmas[i]
|
|
s_next = sigmas[i + 1]
|
|
# Intermediate sigmas: s_curr + c * (s_next - s_curr)
|
|
for c in c_values:
|
|
# Add a tiny epsilon to duplicate sigmas to allow distinct indexing if needed,
|
|
# but better to rely on internal counter.
|
|
sigmas_expanded.append(s_curr + c * (s_next - s_curr))
|
|
sigmas_expanded.append(0.0) # terminal sigma
|
|
|
|
# 3. Map back to timesteps
|
|
sigmas_interpolated = np.array(sigmas_expanded)
|
|
# Linear remapping for Flow Matching
|
|
timesteps_expanded = sigmas_interpolated * self.config.num_train_timesteps
|
|
|
|
self.sigmas = torch.from_numpy(sigmas_interpolated).to(device=device, dtype=dtype)
|
|
self.timesteps = torch.from_numpy(timesteps_expanded + self.config.steps_offset).to(device=device, dtype=dtype)
|
|
self.init_noise_sigma = self.sigmas.max().item() if self.sigmas.numel() > 0 else 1.0
|
|
|
|
self._sigmas_cpu = self.sigmas.detach().cpu().numpy()
|
|
|
|
self.model_outputs = []
|
|
self.sample_at_start_of_step = None
|
|
self._step_index = None
|
|
|
|
@property
|
|
def step_index(self):
|
|
"""
|
|
The index counter for the current timestep. It will increase 1 after each scheduler step.
|
|
"""
|
|
return self._step_index
|
|
|
|
def index_for_timestep(self, timestep, schedule_timesteps=None):
|
|
if schedule_timesteps is None:
|
|
schedule_timesteps = self.timesteps
|
|
|
|
# Use argmin for robust float matching
|
|
index = torch.abs(schedule_timesteps - timestep).argmin().item()
|
|
return index
|
|
|
|
def _init_step_index(self, timestep):
|
|
if isinstance(timestep, torch.Tensor):
|
|
timestep = timestep.to(self.timesteps.device)
|
|
self._step_index = self.index_for_timestep(timestep)
|
|
|
|
def scale_model_input(self, sample: torch.Tensor, timestep: float | torch.Tensor) -> torch.Tensor:
|
|
if self._step_index is None:
|
|
self._init_step_index(timestep)
|
|
if self.config.prediction_type == "flow_prediction":
|
|
return sample
|
|
sigma = self._sigmas_cpu[self._step_index]
|
|
return sample / ((sigma**2 + 1) ** 0.5)
|
|
|
|
def step(
|
|
self,
|
|
model_output: torch.Tensor,
|
|
timestep: float | torch.Tensor,
|
|
sample: torch.Tensor,
|
|
return_dict: bool = True,
|
|
) -> SchedulerOutput | tuple:
|
|
if self._step_index is None:
|
|
self._init_step_index(timestep)
|
|
|
|
step_index = self._step_index
|
|
stage_index = step_index % 4
|
|
|
|
# Current and next step interval sigmas
|
|
base_step_index = (step_index // 4) * 4
|
|
sigma_curr = self._sigmas_cpu[base_step_index]
|
|
sigma_next_idx = min(base_step_index + 4, len(self._sigmas_cpu) - 1)
|
|
sigma_next = self._sigmas_cpu[sigma_next_idx] # The sigma at the end of this 4-stage step
|
|
|
|
h = sigma_next - sigma_curr
|
|
|
|
sigma_t = self._sigmas_cpu[step_index]
|
|
alpha_t = 1 / (sigma_t**2 + 1) ** 0.5
|
|
sigma_actual = sigma_t * alpha_t
|
|
|
|
prediction_type = getattr(self.config, "prediction_type", "epsilon")
|
|
if prediction_type == "epsilon":
|
|
denoised = sample - sigma_t * model_output
|
|
elif prediction_type == "v_prediction":
|
|
alpha_t = 1 / (sigma_t**2 + 1) ** 0.5
|
|
sigma_actual = sigma_t * alpha_t
|
|
denoised = alpha_t * sample - sigma_actual * model_output
|
|
elif prediction_type == "flow_prediction":
|
|
denoised = sample - sigma_t * model_output
|
|
elif prediction_type == "sample":
|
|
denoised = model_output
|
|
else:
|
|
raise ValueError(f"prediction_type error: {prediction_type}")
|
|
|
|
if self.config.clip_sample:
|
|
denoised = denoised.clamp(-self.config.sample_max_value, self.config.sample_max_value)
|
|
|
|
# derivative = (x - x0) / sigma
|
|
derivative = (sample - denoised) / sigma_t if sigma_t > 1e-6 else torch.zeros_like(sample)
|
|
|
|
if self.sample_at_start_of_step is None:
|
|
if stage_index > 0:
|
|
# Mid-step fallback for Img2Img/Inpainting
|
|
sigma_next_t = self._sigmas_cpu[self._step_index + 1]
|
|
dt = sigma_next_t - sigma_t
|
|
prev_sample = sample + dt * derivative
|
|
self._step_index += 1
|
|
if not return_dict:
|
|
return (prev_sample,)
|
|
return SchedulerOutput(prev_sample=prev_sample)
|
|
|
|
self.sample_at_start_of_step = sample
|
|
self.model_outputs = [derivative] * stage_index
|
|
|
|
if stage_index == 0:
|
|
self.model_outputs = [derivative]
|
|
self.sample_at_start_of_step = sample
|
|
# Stage 2 input: y + 0.5 * h * k1
|
|
prev_sample = self.sample_at_start_of_step + 0.5 * h * derivative
|
|
elif stage_index == 1:
|
|
self.model_outputs.append(derivative)
|
|
# Stage 3 input: y + 0.5 * h * k2
|
|
prev_sample = self.sample_at_start_of_step + 0.5 * h * derivative
|
|
elif stage_index == 2:
|
|
self.model_outputs.append(derivative)
|
|
# Stage 4 input: y + h * k3
|
|
prev_sample = self.sample_at_start_of_step + h * derivative
|
|
elif stage_index == 3:
|
|
self.model_outputs.append(derivative)
|
|
# Final result: y + (h/6) * (k1 + 2*k2 + 2*k3 + k4)
|
|
k1, k2, k3, k4 = self.model_outputs
|
|
prev_sample = self.sample_at_start_of_step + (h / 6.0) * (k1 + 2 * k2 + 2 * k3 + k4)
|
|
# Clear state
|
|
self.model_outputs = []
|
|
self.sample_at_start_of_step = None
|
|
|
|
# Increment step index
|
|
self._step_index += 1
|
|
|
|
if not return_dict:
|
|
return (prev_sample,)
|
|
|
|
return SchedulerOutput(prev_sample=prev_sample)
|
|
|
|
def add_noise(
|
|
self,
|
|
original_samples: torch.Tensor,
|
|
noise: torch.Tensor,
|
|
timesteps: torch.Tensor,
|
|
) -> torch.Tensor:
|
|
from .scheduler_utils import add_noise_to_sample
|
|
return add_noise_to_sample(original_samples, noise, self.sigmas, timesteps, self.timesteps)
|
|
|
|
def __len__(self):
|
|
return self.config.num_train_timesteps
|