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edbe9c6 | 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 | import torch
from torch import nn
import torch.nn.functional as F
class Solver:
def __init__(self, func, y0, sigma=0.25, temperature=1.5) -> None:
self.func = func
self.y0 = y0
self.sigma = sigma
self.temperature = temperature
def integrate(self, t):
solution = torch.empty(len(t), *self.y0.shape, dtype=self.y0.dtype, device=self.y0.device)
solution[0] = self.y0
j = 1
y0 = self.y0
for t0, t1 in zip(t[:-1], t[1:]):
dt = t1 - t0
f0 = self.func(t0, y0)
dy = dt * f0
y1 = y0 + dy
while j < len(t) and t1 >= t[j]:
solution[j] = self._linear_interp(t0, t1, y0, y1, t[j])
j += 1
noise = torch.randn_like(y0)
shift = self.sigma * (self.temperature ** 0.5) * (abs(dt) ** 0.5) * noise
y0 = y1 + shift
return solution
def _linear_interp(self, t0, t1, y0, y1, t):
if t == t0:
return y0
if t == t1:
return y1
slope = (t - t0) / (t1 - t0)
return y0 + slope * (y1 - y0)
def get_epss_timesteps(n, device, dtype):
dt = 1 / 32
predefined_timesteps = {
5: [0, 2, 4, 8, 16, 32],
6: [0, 2, 4, 6, 8, 16, 32],
7: [0, 2, 4, 6, 8, 16, 24, 32],
10: [0, 2, 4, 6, 8, 12, 16, 20, 24, 28, 32],
12: [0, 2, 4, 6, 8, 10, 12, 14, 16, 20, 24, 28, 32],
16: [0, 1, 2, 3, 4, 5, 6, 7, 8, 10, 12, 14, 16, 20, 24, 28, 32],
}
t = predefined_timesteps.get(n, [])
if not t:
return torch.linspace(0, 1, n + 1, device=device, dtype=dtype)
return dt * torch.tensor(t, device=device, dtype=dtype)
class CFM(nn.Module):
def __init__(
self,
model: nn.Module,
):
super().__init__()
self.model = model
@property
def device(self):
return next(self.parameters()).device
def forward(
self,
cond,
target,
latent_history,
mask,
patch_size,
):
x1 = target
batch, dtype = x1.shape[0], x1.dtype
x0 = torch.randn_like(x1)
time = torch.rand((batch,), dtype=dtype, device=self.device)
# sample xt (φ_t(x) in the paper)
t = time.unsqueeze(-1).unsqueeze(-1)
x = (1 - t) * x0 + t * x1 # Corresponds to Equation (22) in the paper
flow = x1 - x0 # Take the derivative of x
pred = self.model(x=x, t=time, c=cond, latent_history=latent_history, mask=mask.to(torch.bool))
pred = pred[:, -patch_size:, :]
loss = F.mse_loss(pred, flow, reduction="none")
mask = (mask == 1)
loss = loss[mask]
return loss.mean()
@torch.no_grad()
def sample(
self,
noise,
c,
latent_history,
steps=10,
cfg_scale=1.0,
sway_sampling_coef=-1.0,
seed: int | None = None,
use_epss=True,
patch_size=1,
):
def fn(t, x):
if cfg_scale < 1e-5:
pred = self.model(
x=x,
time=t,
latent_history=latent_history
)
return pred
# predict flow (cond and uncond), for classifier-free guidance
pred_cfg = self.model.forward_with_cfg(
x=x,
t=t,
c=c,
latent_history=latent_history,
cfg_scale=cfg_scale,
patch_size=patch_size,
)
if not cfg_scale == 1:
pred, null_pred = torch.chunk(pred_cfg, 2, dim=0)
return pred + (pred - null_pred) * cfg_scale
else:
return pred_cfg
y0 = noise.transpose(1, 2)
t_start = 0
if t_start == 0 and use_epss: # use Empirically Pruned Step Sampling for low NFE
t = get_epss_timesteps(steps, device=self.device, dtype=noise.dtype)
else:
t = torch.linspace(t_start, 1, steps + 1, device=self.device, dtype=noise.dtype)
if sway_sampling_coef is not None:
t = t + sway_sampling_coef * (torch.cos(torch.pi / 2 * t) - 1 + t)
solver = Solver(fn, y0)
trajectory = solver.integrate(t)
sampled = trajectory[-1]
out = sampled
return out, trajectory
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