Spaces:
Runtime error
Runtime error
File size: 4,665 Bytes
7cc4b41 |
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 |
# Copyright (c) 2024 Bytedance Ltd. and/or its 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.
import math
from typing import List
import torch
def get_betas(name: str, num_steps: int = 1000, shift_snr: float = 1, terminal_pure_noise: bool = False):
# Get betas
max_beta = 1 if terminal_pure_noise else 0.999
if name == "squared_linear":
betas = torch.linspace(0.00085**0.5, 0.012**0.5, num_steps) ** 2
elif name == "cosine":
betas = get_cosine_betas(num_steps, max_beta=max_beta)
elif name == "alphas_cumprod_linear":
betas = get_alphas_cumprod_linear_betas(num_steps, max_beta=max_beta)
elif name == "sigmoid":
betas = get_sigmoid_betas(num_steps, max_beta=max_beta, square=True, slop=0.7)
else:
raise NotImplementedError
# Shift snr
betas = shift_betas_by_snr_factor(betas, shift_snr)
# Ensure terminal pure noise
# Only non-cosine schedule uses rescale, cosine schedule can directly set max_beta=1 to ensure temrinal pure noise.
if name == "squared_linear" and terminal_pure_noise:
betas = rescale_betas_to_ensure_terminal_pure_noise(betas)
return betas
def validate_betas(betas: List[float]) -> bool:
"""
Validate betas is monotonic and within 0 to 1 range, i.e. 0 < beta_{t-1} < beta_{t} <= 1
Args:
betas (List[float]): betas
Returns:
bool: True if betas is correct
"""
return all(b1 < b2 for b1, b2 in zip(betas, betas[1:])) and betas[0] > 0 and betas[-1] <= 1
def betas_for_alpha_bar(num_diffusion_timesteps, alpha_bar_fn, max_beta=0.999):
betas = []
for i in range(num_diffusion_timesteps):
t1 = i / num_diffusion_timesteps
t2 = (i + 1) / num_diffusion_timesteps
betas.append(min(1 - alpha_bar_fn(t2) / alpha_bar_fn(t1), max_beta))
if not validate_betas(betas):
import logging
logging.warning("No feasible betas for given alpha bar")
return torch.tensor(betas, dtype=torch.float32)
def get_cosine_betas(num_diffusion_timesteps, max_beta=0.999) -> torch.Tensor:
def alpha_bar_fn(time_step):
return math.cos((time_step + 0.008) / 1.008 * math.pi / 2) ** 2
return betas_for_alpha_bar(num_diffusion_timesteps, alpha_bar_fn, max_beta)
def get_sigmoid_betas(num_diffusion_timesteps, max_beta, square=False, slop=1):
def alpha_bar_fn(t):
def sigmoid(x):
return 1 / (1 + math.exp(-x * slop))
s = 6 # (-6, 6) from geodiff
vb = sigmoid(-s)
ve = sigmoid(s)
return ((sigmoid(s - t * 2 * s) - vb) / (ve - vb))**(1 if not square else 2)
return betas_for_alpha_bar(num_diffusion_timesteps, alpha_bar_fn, max_beta)
def get_alphas_cumprod_linear_betas(num_diffusion_timesteps, max_beta):
def alpha_bar_fn(t):
return 1 - t
return betas_for_alpha_bar(num_diffusion_timesteps, alpha_bar_fn, max_beta=max_beta)
def shift_betas_by_snr_factor(betas: torch.Tensor, factor: float) -> torch.Tensor:
if factor == 1.0:
return betas
# Convert betas to snr
alphas = 1 - betas
alphas_cumprod = alphas.cumprod(dim=0)
snr = alphas_cumprod / (1 - alphas_cumprod)
# Shift snr
snr *= factor
# Convert snr to betas
alphas_cumprod = snr / (1 + snr)
alphas = torch.cat(
[alphas_cumprod[0:1], alphas_cumprod[1:] / alphas_cumprod[:-1]])
betas = 1 - alphas
return betas
def rescale_betas_to_ensure_terminal_pure_noise(betas: torch.Tensor) -> torch.Tensor:
# Convert betas to alphas_cumprod_sqrt
alphas = 1 - betas
alphas_cumprod = alphas.cumprod(0)
alphas_cumprod_sqrt = alphas_cumprod.sqrt()
# Rescale alphas_cumprod_sqrt such that alphas_cumprod_sqrt[0] remains unchanged but alphas_cumprod_sqrt[-1] = 0
alphas_cumprod_sqrt = (alphas_cumprod_sqrt - alphas_cumprod_sqrt[-1]) / (
alphas_cumprod_sqrt[0] - alphas_cumprod_sqrt[-1]) * alphas_cumprod_sqrt[0]
# Convert alphas_cumprod_sqrt to betas
alphas_cumprod = alphas_cumprod_sqrt ** 2
alphas = torch.cat(
[alphas_cumprod[0:1], alphas_cumprod[1:] / alphas_cumprod[:-1]])
betas = 1 - alphas
return betas
|