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e81cc3b | 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 | import torch
import numpy as np
def expand_t_like_x(t, x_cur):
"""Function to reshape time t to broadcastable dimension of x
Args:
t: [batch_dim,], time vector
x: [batch_dim,...], data point
"""
dims = [1] * (len(x_cur.size()) - 1)
t = t.view(t.size(0), *dims)
return t
def get_score_from_velocity(vt, xt, t, path_type="linear"):
"""Wrapper function: transfrom velocity prediction model to score
Args:
velocity: [batch_dim, ...] shaped tensor; velocity model output
x: [batch_dim, ...] shaped tensor; x_t data point
t: [batch_dim,] time tensor
"""
t = expand_t_like_x(t, xt)
if path_type == "linear":
alpha_t, d_alpha_t = 1 - t, torch.ones_like(xt, device=xt.device) * -1
sigma_t, d_sigma_t = t, torch.ones_like(xt, device=xt.device)
elif path_type == "cosine":
alpha_t = torch.cos(t * np.pi / 2)
sigma_t = torch.sin(t * np.pi / 2)
d_alpha_t = -np.pi / 2 * torch.sin(t * np.pi / 2)
d_sigma_t = np.pi / 2 * torch.cos(t * np.pi / 2)
else:
raise NotImplementedError
mean = xt
reverse_alpha_ratio = alpha_t / d_alpha_t
var = sigma_t**2 - reverse_alpha_ratio * d_sigma_t * sigma_t
score = (reverse_alpha_ratio * vt - mean) / var
return score
def compute_diffusion(t_cur):
return 2 * t_cur
def euler_sampler(
model,
latents,
y,
context,
num_steps=20,
heun=False,
cfg_scale=1.0,
guidance_low=0.0,
guidance_high=1.0,
path_type="linear", # not used, just for compatability
):
# setup conditioning
if cfg_scale > 1.0:
y_null = torch.zeros_like(y).to(y.device)
context_null = torch.zeros_like(context).to(context.device)
_dtype = latents.dtype
t_steps = torch.linspace(1, 0, num_steps+1, dtype=torch.bfloat16)
x_next = latents.to(torch.bfloat16)
device = x_next.device
with torch.no_grad():
for i, (t_cur, t_next) in enumerate(zip(t_steps[:-1], t_steps[1:])):
x_cur = x_next
if cfg_scale > 1.0 and t_cur <= guidance_high and t_cur >= guidance_low:
model_input = torch.cat([x_cur] * 2, dim=0)
y_cur = torch.cat([y, y_null], dim=0)
context_cur = torch.cat([context, context_null], dim=0)
else:
model_input = x_cur
y_cur = y
context_cur = context
do_guidance = (cfg_scale > 1.0 and t_cur <= guidance_high and t_cur >= guidance_low)
kwargs = dict(y=y_cur, context=context_cur, do_guidance=do_guidance)
time_input = torch.ones(model_input.size(0)).to(device=device, dtype=torch.bfloat16) * t_cur
d_cur = model(
model_input.to(dtype=_dtype), time_input.to(dtype=_dtype), **kwargs
)[0]
if cfg_scale > 1. and t_cur <= guidance_high and t_cur >= guidance_low:
d_cur_cond, d_cur_uncond = d_cur.chunk(2)
d_cur = d_cur_uncond + cfg_scale * (d_cur_cond - d_cur_uncond)
x_next = x_cur + (t_next - t_cur) * d_cur
if heun and (i < num_steps - 1):
if cfg_scale > 1.0 and t_cur <= guidance_high and t_cur >= guidance_low:
model_input = torch.cat([x_next] * 2)
y_cur = torch.cat([y, y_null], dim=0)
context_cur = torch.cat([context, context_null], dim=0)
else:
model_input = x_next
y_cur = y
context_cur = context
do_guidance = (cfg_scale > 1.0 and t_cur <= guidance_high and t_cur >= guidance_low)
kwargs = dict(y=y_cur, context=context_cur, do_guidance=do_guidance)
time_input = torch.ones(model_input.size(0)).to(
device=model_input.device, dtype=torch.bfloat16
) * t_next
d_prime = model(
model_input.to(dtype=_dtype), time_input.to(dtype=_dtype), **kwargs
)[0]
if cfg_scale > 1.0 and t_cur <= guidance_high and t_cur >= guidance_low:
d_prime_cond, d_prime_uncond = d_prime.chunk(2)
d_prime = d_prime_uncond + cfg_scale * (d_prime_cond - d_prime_uncond)
x_next = x_cur + (t_next - t_cur) * (0.5 * d_cur + 0.5 * d_prime)
return x_next
def euler_maruyama_sampler(
model,
latents,
y,
context,
num_steps=20,
heun=False, # not used, just for compatability
cfg_scale=1.0,
guidance_low=0.0,
guidance_high=1.0,
path_type="linear",
):
# setup conditioning
if cfg_scale > 1.0:
y_null = torch.zeros_like(y).to(y.device)
context_null = torch.zeros_like(context).to(context.device)
_dtype = latents.dtype
t_steps = torch.linspace(1., 0.04, num_steps, dtype=torch.bfloat16)
t_steps = torch.cat([t_steps, torch.tensor([0.], dtype=torch.bfloat16)])
x_next = latents.to(torch.bfloat16)
device = x_next.device
with torch.no_grad():
for i, (t_cur, t_next) in enumerate(zip(t_steps[:-2], t_steps[1:-1])):
dt = t_next - t_cur
x_cur = x_next
if cfg_scale > 1.0 and t_cur <= guidance_high and t_cur >= guidance_low:
model_input = torch.cat([x_cur] * 2, dim=0)
y_cur = torch.cat([y, y_null], dim=0)
context_cur = torch.cat([context, context_null], dim=0)
else:
model_input = x_cur
y_cur = y
context_cur = context
do_guidance = (cfg_scale > 1.0 and t_cur <= guidance_high and t_cur >= guidance_low)
kwargs = dict(y=y_cur, context=context_cur, do_guidance=do_guidance)
time_input = torch.ones(model_input.size(0)).to(device=device, dtype=torch.bfloat16) * t_cur
diffusion = compute_diffusion(t_cur)
eps_i = torch.randn_like(x_cur).to(device)
deps = eps_i * torch.sqrt(torch.abs(dt))
# compute drift
v_cur = model(
model_input.to(dtype=_dtype), time_input.to(dtype=_dtype), **kwargs
)[0]
s_cur = get_score_from_velocity(v_cur, model_input, time_input, path_type=path_type)
d_cur = v_cur - 0.5 * diffusion * s_cur
if cfg_scale > 1. and t_cur <= guidance_high and t_cur >= guidance_low:
d_cur_cond, d_cur_uncond = d_cur.chunk(2)
d_cur = d_cur_uncond + cfg_scale * (d_cur_cond - d_cur_uncond)
x_next = x_cur + d_cur * dt + torch.sqrt(diffusion) * deps
# last step
t_cur, t_next = t_steps[-2], t_steps[-1]
dt = t_next - t_cur
x_cur = x_next
if cfg_scale > 1.0 and t_cur <= guidance_high and t_cur >= guidance_low:
model_input = torch.cat([x_cur] * 2, dim=0)
y_cur = torch.cat([y, y_null], dim=0)
context_cur = torch.cat([context, context_null], dim=0)
else:
model_input = x_cur
y_cur = y
context_cur = context
do_guidance = (cfg_scale > 1.0 and t_cur <= guidance_high and t_cur >= guidance_low)
kwargs = dict(y=y_cur, context=context_cur, do_guidance=do_guidance)
time_input = torch.ones(model_input.size(0)).to(
device=device, dtype=torch.bfloat16
) * t_cur
# compute drift
v_cur = model(
model_input.to(dtype=_dtype), time_input.to(dtype=_dtype), **kwargs
)[0]
s_cur = get_score_from_velocity(v_cur, model_input, time_input, path_type=path_type)
diffusion = compute_diffusion(t_cur)
d_cur = v_cur - 0.5 * diffusion * s_cur
if cfg_scale > 1. and t_cur <= guidance_high and t_cur >= guidance_low:
d_cur_cond, d_cur_uncond = d_cur.chunk(2)
d_cur = d_cur_uncond + cfg_scale * (d_cur_cond - d_cur_uncond)
mean_x = x_cur + dt * d_cur
return mean_x
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