File size: 5,116 Bytes
5353f6a |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 |
import torch
import numpy as np
from ldm_patched.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__()
if model_config is not None:
sampling_settings = model_config.sampling_settings
else:
sampling_settings = {}
beta_schedule = sampling_settings.get("beta_schedule", "linear")
linear_start = sampling_settings.get("linear_start", 0.00085)
linear_end = sampling_settings.get("linear_end", 0.012)
self._register_schedule(given_betas=None, beta_schedule=beta_schedule, timesteps=1000, linear_start=linear_start, linear_end=linear_end, 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)
|