Spaces:
Configuration error
Configuration error
File size: 9,119 Bytes
87e21d1 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 |
# Copyright 2024 NVIDIA CORPORATION & AFFILIATES
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
# SPDX-License-Identifier: Apache-2.0
# Modified from OpenAI's diffusion repos
# GLIDE: https://github.com/openai/glide-text2im/blob/main/glide_text2im/gaussian_diffusion.py
# ADM: https://github.com/openai/guided-diffusion/blob/main/guided_diffusion
# IDDPM: https://github.com/openai/improved-diffusion/blob/main/improved_diffusion/gaussian_diffusion.py
import random
import numpy as np
from tqdm import tqdm
from diffusion.model.utils import *
# ----------------------------------------------------------------------------
# Proposed EDM sampler (Algorithm 2).
def edm_sampler(
net,
latents,
class_labels=None,
cfg_scale=None,
randn_like=torch.randn_like,
num_steps=18,
sigma_min=0.002,
sigma_max=80,
rho=7,
S_churn=0,
S_min=0,
S_max=float("inf"),
S_noise=1,
**kwargs
):
# Adjust noise levels based on what's supported by the network.
sigma_min = max(sigma_min, net.sigma_min)
sigma_max = min(sigma_max, net.sigma_max)
# Time step discretization.
step_indices = torch.arange(num_steps, dtype=torch.float64, device=latents.device)
t_steps = (
sigma_max ** (1 / rho) + step_indices / (num_steps - 1) * (sigma_min ** (1 / rho) - sigma_max ** (1 / rho))
) ** rho
t_steps = torch.cat([net.round_sigma(t_steps), torch.zeros_like(t_steps[:1])]) # t_N = 0
# Main sampling loop.
x_next = latents.to(torch.float64) * t_steps[0]
for i, (t_cur, t_next) in tqdm(list(enumerate(zip(t_steps[:-1], t_steps[1:])))): # 0, ..., N-1
x_cur = x_next
# Increase noise temporarily.
gamma = min(S_churn / num_steps, np.sqrt(2) - 1) if S_min <= t_cur <= S_max else 0
t_hat = net.round_sigma(t_cur + gamma * t_cur)
x_hat = x_cur + (t_hat**2 - t_cur**2).sqrt() * S_noise * randn_like(x_cur)
# Euler step.
denoised = net(x_hat.float(), t_hat, class_labels, cfg_scale, **kwargs)["x"].to(torch.float64)
d_cur = (x_hat - denoised) / t_hat
x_next = x_hat + (t_next - t_hat) * d_cur
# Apply 2nd order correction.
if i < num_steps - 1:
denoised = net(x_next.float(), t_next, class_labels, cfg_scale, **kwargs)["x"].to(torch.float64)
d_prime = (x_next - denoised) / t_next
x_next = x_hat + (t_next - t_hat) * (0.5 * d_cur + 0.5 * d_prime)
return x_next
# ----------------------------------------------------------------------------
# Generalized ablation sampler, representing the superset of all sampling
# methods discussed in the paper.
def ablation_sampler(
net,
latents,
class_labels=None,
cfg_scale=None,
feat=None,
randn_like=torch.randn_like,
num_steps=18,
sigma_min=None,
sigma_max=None,
rho=7,
solver="heun",
discretization="edm",
schedule="linear",
scaling="none",
epsilon_s=1e-3,
C_1=0.001,
C_2=0.008,
M=1000,
alpha=1,
S_churn=0,
S_min=0,
S_max=float("inf"),
S_noise=1,
):
assert solver in ["euler", "heun"]
assert discretization in ["vp", "ve", "iddpm", "edm"]
assert schedule in ["vp", "ve", "linear"]
assert scaling in ["vp", "none"]
# Helper functions for VP & VE noise level schedules.
vp_sigma = lambda beta_d, beta_min: lambda t: (np.e ** (0.5 * beta_d * (t**2) + beta_min * t) - 1) ** 0.5
vp_sigma_deriv = lambda beta_d, beta_min: lambda t: 0.5 * (beta_min + beta_d * t) * (sigma(t) + 1 / sigma(t))
vp_sigma_inv = (
lambda beta_d, beta_min: lambda sigma: ((beta_min**2 + 2 * beta_d * (sigma**2 + 1).log()).sqrt() - beta_min)
/ beta_d
)
ve_sigma = lambda t: t.sqrt()
ve_sigma_deriv = lambda t: 0.5 / t.sqrt()
ve_sigma_inv = lambda sigma: sigma**2
# Select default noise level range based on the specified time step discretization.
if sigma_min is None:
vp_def = vp_sigma(beta_d=19.1, beta_min=0.1)(t=epsilon_s)
sigma_min = {"vp": vp_def, "ve": 0.02, "iddpm": 0.002, "edm": 0.002}[discretization]
if sigma_max is None:
vp_def = vp_sigma(beta_d=19.1, beta_min=0.1)(t=1)
sigma_max = {"vp": vp_def, "ve": 100, "iddpm": 81, "edm": 80}[discretization]
# Adjust noise levels based on what's supported by the network.
sigma_min = max(sigma_min, net.sigma_min)
sigma_max = min(sigma_max, net.sigma_max)
# Compute corresponding betas for VP.
vp_beta_d = 2 * (np.log(sigma_min**2 + 1) / epsilon_s - np.log(sigma_max**2 + 1)) / (epsilon_s - 1)
vp_beta_min = np.log(sigma_max**2 + 1) - 0.5 * vp_beta_d
# Define time steps in terms of noise level.
step_indices = torch.arange(num_steps, dtype=torch.float64, device=latents.device)
if discretization == "vp":
orig_t_steps = 1 + step_indices / (num_steps - 1) * (epsilon_s - 1)
sigma_steps = vp_sigma(vp_beta_d, vp_beta_min)(orig_t_steps)
elif discretization == "ve":
orig_t_steps = (sigma_max**2) * ((sigma_min**2 / sigma_max**2) ** (step_indices / (num_steps - 1)))
sigma_steps = ve_sigma(orig_t_steps)
elif discretization == "iddpm":
u = torch.zeros(M + 1, dtype=torch.float64, device=latents.device)
alpha_bar = lambda j: (0.5 * np.pi * j / M / (C_2 + 1)).sin() ** 2
for j in torch.arange(M, 0, -1, device=latents.device): # M, ..., 1
u[j - 1] = ((u[j] ** 2 + 1) / (alpha_bar(j - 1) / alpha_bar(j)).clip(min=C_1) - 1).sqrt()
u_filtered = u[torch.logical_and(u >= sigma_min, u <= sigma_max)]
sigma_steps = u_filtered[((len(u_filtered) - 1) / (num_steps - 1) * step_indices).round().to(torch.int64)]
else:
assert discretization == "edm"
sigma_steps = (
sigma_max ** (1 / rho) + step_indices / (num_steps - 1) * (sigma_min ** (1 / rho) - sigma_max ** (1 / rho))
) ** rho
# Define noise level schedule.
if schedule == "vp":
sigma = vp_sigma(vp_beta_d, vp_beta_min)
sigma_deriv = vp_sigma_deriv(vp_beta_d, vp_beta_min)
sigma_inv = vp_sigma_inv(vp_beta_d, vp_beta_min)
elif schedule == "ve":
sigma = ve_sigma
sigma_deriv = ve_sigma_deriv
sigma_inv = ve_sigma_inv
else:
assert schedule == "linear"
sigma = lambda t: t
sigma_deriv = lambda t: 1
sigma_inv = lambda sigma: sigma
# Define scaling schedule.
if scaling == "vp":
s = lambda t: 1 / (1 + sigma(t) ** 2).sqrt()
s_deriv = lambda t: -sigma(t) * sigma_deriv(t) * (s(t) ** 3)
else:
assert scaling == "none"
s = lambda t: 1
s_deriv = lambda t: 0
# Compute final time steps based on the corresponding noise levels.
t_steps = sigma_inv(net.round_sigma(sigma_steps))
t_steps = torch.cat([t_steps, torch.zeros_like(t_steps[:1])]) # t_N = 0
# Main sampling loop.
t_next = t_steps[0]
x_next = latents.to(torch.float64) * (sigma(t_next) * s(t_next))
for i, (t_cur, t_next) in enumerate(zip(t_steps[:-1], t_steps[1:])): # 0, ..., N-1
x_cur = x_next
# Increase noise temporarily.
gamma = min(S_churn / num_steps, np.sqrt(2) - 1) if S_min <= sigma(t_cur) <= S_max else 0
t_hat = sigma_inv(net.round_sigma(sigma(t_cur) + gamma * sigma(t_cur)))
x_hat = s(t_hat) / s(t_cur) * x_cur + (sigma(t_hat) ** 2 - sigma(t_cur) ** 2).clip(min=0).sqrt() * s(
t_hat
) * S_noise * randn_like(x_cur)
# Euler step.
h = t_next - t_hat
denoised = net(x_hat.float() / s(t_hat), sigma(t_hat), class_labels, cfg_scale, feat=feat)["x"].to(
torch.float64
)
d_cur = (sigma_deriv(t_hat) / sigma(t_hat) + s_deriv(t_hat) / s(t_hat)) * x_hat - sigma_deriv(t_hat) * s(
t_hat
) / sigma(t_hat) * denoised
x_prime = x_hat + alpha * h * d_cur
t_prime = t_hat + alpha * h
# Apply 2nd order correction.
if solver == "euler" or i == num_steps - 1:
x_next = x_hat + h * d_cur
else:
assert solver == "heun"
denoised = net(x_prime.float() / s(t_prime), sigma(t_prime), class_labels, cfg_scale, feat=feat)["x"].to(
torch.float64
)
d_prime = (sigma_deriv(t_prime) / sigma(t_prime) + s_deriv(t_prime) / s(t_prime)) * x_prime - sigma_deriv(
t_prime
) * s(t_prime) / sigma(t_prime) * denoised
x_next = x_hat + h * ((1 - 1 / (2 * alpha)) * d_cur + 1 / (2 * alpha) * d_prime)
return x_next
|