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sd_schedulers.py

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+import dataclasses
+import torch
+import k_diffusion
+import numpy as np
+from scipy import stats
+from modules import shared
+from modules.sd_simple_kes.simple_kes import simple_kes_scheduler
+from modules.sd_simple_kes_v1.simple_kes_v1 import simple_kes_scheduler_v1 
+from modules.sd_simple_kes_v2.simple_kes_v2 import simple_kes_scheduler_v22
+from modules.sd_simple_kes_v2_old.simple_kes_v2 import simple_kes_scheduler_v2 
+
+def to_d(x, sigma, denoised):
+    """Converts a denoiser output to a Karras ODE derivative."""
+    return (x - denoised) / sigma
+
+
+k_diffusion.sampling.to_d = to_d
+
+
+@dataclasses.dataclass
+class Scheduler:
+    name: str
+    label: str
+    function: any
+
+    default_rho: float = -1
+    need_inner_model: bool = False
+    aliases: list = None
+
+
+def uniform(n, sigma_min, sigma_max, inner_model, device):
+    return inner_model.get_sigmas(n).to(device)
+
+
+def sgm_uniform(n, sigma_min, sigma_max, inner_model, device):
+    start = inner_model.sigma_to_t(torch.tensor(sigma_max))
+    end = inner_model.sigma_to_t(torch.tensor(sigma_min))
+    sigs = [
+        inner_model.t_to_sigma(ts)
+        for ts in torch.linspace(start, end, n + 1)[:-1]
+    ]
+    sigs += [0.0]
+    return torch.FloatTensor(sigs).to(device)
+
+
+def get_align_your_steps_sigmas(n, sigma_min, sigma_max, device):
+    # https://research.nvidia.com/labs/toronto-ai/AlignYourSteps/howto.html
+    def loglinear_interp(t_steps, num_steps):
+        """
+        Performs log-linear interpolation of a given array of decreasing numbers.
+        """
+        xs = np.linspace(0, 1, len(t_steps))
+        ys = np.log(t_steps[::-1])
+
+        new_xs = np.linspace(0, 1, num_steps)
+        new_ys = np.interp(new_xs, xs, ys)
+
+        interped_ys = np.exp(new_ys)[::-1].copy()
+        return interped_ys
+
+    if shared.sd_model.is_sdxl:
+        sigmas = [14.615, 6.315, 3.771, 2.181, 1.342, 0.862, 0.555, 0.380, 0.234, 0.113, 0.029]
+    else:
+        # Default to SD 1.5 sigmas.
+        sigmas = [14.615, 6.475, 3.861, 2.697, 1.886, 1.396, 0.963, 0.652, 0.399, 0.152, 0.029]
+
+    if n != len(sigmas):
+        sigmas = np.append(loglinear_interp(sigmas, n), [0.0])
+    else:
+        sigmas.append(0.0)
+
+    return torch.FloatTensor(sigmas).to(device)
+
+
+def kl_optimal(n, sigma_min, sigma_max, device):
+    alpha_min = torch.arctan(torch.tensor(sigma_min, device=device))
+    alpha_max = torch.arctan(torch.tensor(sigma_max, device=device))
+    step_indices = torch.arange(n + 1, device=device)
+    sigmas = torch.tan(step_indices / n * alpha_min + (1.0 - step_indices / n) * alpha_max)
+    return sigmas
+
+
+def simple_scheduler(n, sigma_min, sigma_max, inner_model, device):
+    sigs = []
+    ss = len(inner_model.sigmas) / n
+    for x in range(n):
+        sigs += [float(inner_model.sigmas[-(1 + int(x * ss))])]
+    sigs += [0.0]
+    return torch.FloatTensor(sigs).to(device)
+
+
+def normal_scheduler(n, sigma_min, sigma_max, inner_model, device, sgm=False, floor=False):
+    start = inner_model.sigma_to_t(torch.tensor(sigma_max))
+    end = inner_model.sigma_to_t(torch.tensor(sigma_min))
+
+    if sgm:
+        timesteps = torch.linspace(start, end, n + 1)[:-1]
+    else:
+        timesteps = torch.linspace(start, end, n)
+
+    sigs = []
+    for x in range(len(timesteps)):
+        ts = timesteps[x]
+        sigs.append(inner_model.t_to_sigma(ts))
+    sigs += [0.0]
+    return torch.FloatTensor(sigs).to(device)
+
+
+def ddim_scheduler(n, sigma_min, sigma_max, inner_model, device):
+    sigs = []
+    ss = max(len(inner_model.sigmas) // n, 1)
+    x = 1
+    while x < len(inner_model.sigmas):
+        sigs += [float(inner_model.sigmas[x])]
+        x += ss
+    sigs = sigs[::-1]
+    sigs += [0.0]
+    return torch.FloatTensor(sigs).to(device)
+
+
+def beta_scheduler(n, sigma_min, sigma_max, inner_model, device):
+    # From "Beta Sampling is All You Need" [arXiv:2407.12173] (Lee et. al, 2024) """
+    alpha = shared.opts.beta_dist_alpha
+    beta = shared.opts.beta_dist_beta
+    timesteps = 1 - np.linspace(0, 1, n)
+    timesteps = [stats.beta.ppf(x, alpha, beta) for x in timesteps]
+    sigmas = [sigma_min + (x * (sigma_max-sigma_min)) for x in timesteps]
+    sigmas += [0.0]
+    return torch.FloatTensor(sigmas).to(device)
+
+def beta_scheduler_old(n, sigma_min, sigma_max, inner_model, device):
+    # From "Beta Sampling is All You Need" [arXiv:2407.12173] (Lee et. al, 2024) """
+    alpha = shared.opts.beta_dist_alpha_old
+    beta = shared.opts.beta_dist_beta_old
+    timesteps = 1 - np.linspace(0, 1, n)
+    timesteps = [stats.beta.ppf(x, alpha, beta) for x in timesteps]
+    sigmas = [sigma_min + (x * (sigma_max-sigma_min)) for x in timesteps]
+
+
+
+    sigmas += [0.0]
+    return torch.FloatTensor(sigmas).to(device)
+
+schedulers = [
+    Scheduler('automatic', 'Automatic', None),
+    Scheduler('uniform', 'Uniform', uniform, need_inner_model=True),
+    Scheduler('karras', 'Karras', k_diffusion.sampling.get_sigmas_karras, default_rho=7.0),
+    Scheduler('exponential', 'Exponential', k_diffusion.sampling.get_sigmas_exponential),
+    Scheduler('polyexponential', 'Polyexponential', k_diffusion.sampling.get_sigmas_polyexponential, default_rho=1.0),
+    Scheduler('sgm_uniform', 'SGM Uniform', sgm_uniform, need_inner_model=True, aliases=["SGMUniform"]),
+    Scheduler('kl_optimal', 'KL Optimal', kl_optimal),
+    Scheduler('align_your_steps', 'Align Your Steps', get_align_your_steps_sigmas),
+    Scheduler('simple', 'Simple', simple_scheduler, need_inner_model=True),
+    Scheduler('normal', 'Normal', normal_scheduler, need_inner_model=True),
+    Scheduler('ddim', 'DDIM', ddim_scheduler, need_inner_model=True),
+    Scheduler('beta', 'Beta', beta_scheduler, need_inner_model=True),
+    Scheduler('beta_old', 'Beta Old', beta_scheduler_old, need_inner_model=True),
+    Scheduler('karras_exponential', 'Karras Exponential', simple_kes_scheduler),
+    Scheduler('karras_exponential_v1', 'Karras Exponential v1', simple_kes_scheduler_v1),
+    Scheduler('karras_exponential_v2_olds', 'Karras Exponential v2 old', simple_kes_scheduler_v2),
+    Scheduler('karras_exponential_v2', 'Karras Exponential v2', simple_kes_scheduler_v22),
+]
+
+schedulers_map = {**{x.name: x for x in schedulers}, **{x.label: x for x in schedulers}}