130 lines
4.8 KiB
Python
130 lines
4.8 KiB
Python
import torch
|
|
import numpy as np
|
|
from comfy.ldm.modules.diffusionmodules.util import make_beta_schedule
|
|
import math
|
|
|
|
class EPS:
|
|
def calculate_input(self, sigma, noise):
|
|
sigma = sigma.view(sigma.shape[:1] + (1,) * (noise.ndim - 1))
|
|
return noise / (sigma ** 2 + self.sigma_data ** 2) ** 0.5
|
|
|
|
def calculate_denoised(self, sigma, model_output, model_input):
|
|
sigma = sigma.view(sigma.shape[:1] + (1,) * (model_output.ndim - 1))
|
|
return model_input - model_output * sigma
|
|
|
|
|
|
class V_PREDICTION(EPS):
|
|
def calculate_denoised(self, sigma, model_output, model_input):
|
|
sigma = sigma.view(sigma.shape[:1] + (1,) * (model_output.ndim - 1))
|
|
return model_input * self.sigma_data ** 2 / (sigma ** 2 + self.sigma_data ** 2) - model_output * sigma * self.sigma_data / (sigma ** 2 + self.sigma_data ** 2) ** 0.5
|
|
|
|
|
|
class ModelSamplingDiscrete(torch.nn.Module):
|
|
def __init__(self, model_config=None):
|
|
super().__init__()
|
|
beta_schedule = "linear"
|
|
if model_config is not None:
|
|
beta_schedule = model_config.sampling_settings.get("beta_schedule", beta_schedule)
|
|
self._register_schedule(given_betas=None, beta_schedule=beta_schedule, timesteps=1000, linear_start=0.00085, linear_end=0.012, cosine_s=8e-3)
|
|
self.sigma_data = 1.0
|
|
|
|
def _register_schedule(self, given_betas=None, beta_schedule="linear", timesteps=1000,
|
|
linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
|
|
if given_betas is not None:
|
|
betas = given_betas
|
|
else:
|
|
betas = make_beta_schedule(beta_schedule, timesteps, linear_start=linear_start, linear_end=linear_end, cosine_s=cosine_s)
|
|
alphas = 1. - betas
|
|
alphas_cumprod = torch.tensor(np.cumprod(alphas, axis=0), dtype=torch.float32)
|
|
# alphas_cumprod_prev = np.append(1., alphas_cumprod[:-1])
|
|
|
|
timesteps, = betas.shape
|
|
self.num_timesteps = int(timesteps)
|
|
self.linear_start = linear_start
|
|
self.linear_end = linear_end
|
|
|
|
# self.register_buffer('betas', torch.tensor(betas, dtype=torch.float32))
|
|
# self.register_buffer('alphas_cumprod', torch.tensor(alphas_cumprod, dtype=torch.float32))
|
|
# self.register_buffer('alphas_cumprod_prev', torch.tensor(alphas_cumprod_prev, dtype=torch.float32))
|
|
|
|
sigmas = ((1 - alphas_cumprod) / alphas_cumprod) ** 0.5
|
|
self.set_sigmas(sigmas)
|
|
|
|
def set_sigmas(self, sigmas):
|
|
self.register_buffer('sigmas', sigmas)
|
|
self.register_buffer('log_sigmas', sigmas.log())
|
|
|
|
@property
|
|
def sigma_min(self):
|
|
return self.sigmas[0]
|
|
|
|
@property
|
|
def sigma_max(self):
|
|
return self.sigmas[-1]
|
|
|
|
def timestep(self, sigma):
|
|
log_sigma = sigma.log()
|
|
dists = log_sigma.to(self.log_sigmas.device) - self.log_sigmas[:, None]
|
|
return dists.abs().argmin(dim=0).view(sigma.shape).to(sigma.device)
|
|
|
|
def sigma(self, timestep):
|
|
t = torch.clamp(timestep.float().to(self.log_sigmas.device), min=0, max=(len(self.sigmas) - 1))
|
|
low_idx = t.floor().long()
|
|
high_idx = t.ceil().long()
|
|
w = t.frac()
|
|
log_sigma = (1 - w) * self.log_sigmas[low_idx] + w * self.log_sigmas[high_idx]
|
|
return log_sigma.exp().to(timestep.device)
|
|
|
|
def percent_to_sigma(self, percent):
|
|
if percent <= 0.0:
|
|
return 999999999.9
|
|
if percent >= 1.0:
|
|
return 0.0
|
|
percent = 1.0 - percent
|
|
return self.sigma(torch.tensor(percent * 999.0)).item()
|
|
|
|
|
|
class ModelSamplingContinuousEDM(torch.nn.Module):
|
|
def __init__(self, model_config=None):
|
|
super().__init__()
|
|
self.sigma_data = 1.0
|
|
|
|
if model_config is not None:
|
|
sampling_settings = model_config.sampling_settings
|
|
else:
|
|
sampling_settings = {}
|
|
|
|
sigma_min = sampling_settings.get("sigma_min", 0.002)
|
|
sigma_max = sampling_settings.get("sigma_max", 120.0)
|
|
self.set_sigma_range(sigma_min, sigma_max)
|
|
|
|
def set_sigma_range(self, sigma_min, sigma_max):
|
|
sigmas = torch.linspace(math.log(sigma_min), math.log(sigma_max), 1000).exp()
|
|
|
|
self.register_buffer('sigmas', sigmas) #for compatibility with some schedulers
|
|
self.register_buffer('log_sigmas', sigmas.log())
|
|
|
|
@property
|
|
def sigma_min(self):
|
|
return self.sigmas[0]
|
|
|
|
@property
|
|
def sigma_max(self):
|
|
return self.sigmas[-1]
|
|
|
|
def timestep(self, sigma):
|
|
return 0.25 * sigma.log()
|
|
|
|
def sigma(self, timestep):
|
|
return (timestep / 0.25).exp()
|
|
|
|
def percent_to_sigma(self, percent):
|
|
if percent <= 0.0:
|
|
return 999999999.9
|
|
if percent >= 1.0:
|
|
return 0.0
|
|
percent = 1.0 - percent
|
|
|
|
log_sigma_min = math.log(self.sigma_min)
|
|
return math.exp((math.log(self.sigma_max) - log_sigma_min) * percent + log_sigma_min)
|