File size: 10,060 Bytes
01fdb75 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 | # Sampling for Medical Image Generation with Mask Conditioning
# Based on sampling.py with mask support
import torch
import torch.nn as nn
from typing import Callable
import logging
from src.diffusion.base.guidance import *
from src.diffusion.base.scheduling import BaseScheduler
from src.diffusion.base.sampling import BaseSampler
logger = logging.getLogger(__name__)
def shift_respace_fn(t, shift=3.0):
return t / (t + (1 - t) * shift)
def ode_step_fn(x, v, dt, s, w):
return x + v * dt
class EulerSamplerMedical(BaseSampler):
"""
Euler sampler for mask-conditional medical image generation.
"""
def __init__(
self,
w_scheduler: BaseScheduler = None,
timeshift: float = 1.0,
guidance_interval_min: float = 0.0,
guidance_interval_max: float = 1.0,
step_fn: Callable = ode_step_fn,
last_step: float = None,
last_step_fn: Callable = ode_step_fn,
t_eps: float = 0.05,
*args,
**kwargs
):
super().__init__(*args, **kwargs)
self.step_fn = step_fn
self.last_step = last_step
self.last_step_fn = last_step_fn
self.w_scheduler = w_scheduler
self.timeshift = timeshift
self.guidance_interval_min = guidance_interval_min
self.guidance_interval_max = guidance_interval_max
self.t_eps = t_eps
if self.last_step is None or self.num_steps == 1:
self.last_step = 1.0 / self.num_steps
timesteps = torch.linspace(0.0, 1 - self.last_step, self.num_steps)
timesteps = torch.cat([timesteps, torch.tensor([1.0])], dim=0)
self.timesteps = shift_respace_fn(timesteps, self.timeshift)
assert self.last_step > 0.0
assert self.scheduler is not None
def _impl_sampling(self, net, noise, condition, uncondition, mask=None):
"""
Sampling with mask conditioning.
Args:
net: Denoiser network
noise: Initial noise [N, 3, H, W]
condition: Mask embedding [N, hidden_size]
uncondition: Null embedding [N, hidden_size]
mask: Optional mask tensor [N, C, H, W] for direct conditioning
Returns:
x_trajs: List of intermediate samples
v_trajs: List of velocity predictions
"""
batch_size = noise.shape[0]
steps = self.timesteps.to(noise.device, noise.dtype)
# CFG: concatenate uncondition and condition
# Note: For mask conditioning, we handle this differently
x = noise
x_trajs = [noise]
v_trajs = []
for i, (t_cur, t_next) in enumerate(zip(steps[:-1], steps[1:])):
dt = t_next - t_cur
t_cur_batch = t_cur.repeat(batch_size)
sigma = self.scheduler.sigma(t_cur_batch)
dalpha_over_alpha = self.scheduler.dalpha_over_alpha(t_cur_batch)
dsigma_mul_sigma = self.scheduler.dsigma_mul_sigma(t_cur_batch)
if self.w_scheduler:
w = self.w_scheduler.w(t_cur_batch)
else:
w = 0.0
# CFG forward pass
cfg_x = torch.cat([x, x], dim=0)
cfg_t = t_cur_batch.repeat(2)
# For mask conditioning, we need to handle y (class label)
# and pass mask separately to the model
y_uncond = torch.zeros(batch_size, dtype=torch.long, device=noise.device)
y_cond = torch.zeros(batch_size, dtype=torch.long, device=noise.device)
cfg_y = torch.cat([y_uncond, y_cond], dim=0)
if mask is not None:
cfg_mask = torch.cat([
torch.zeros_like(mask), # Unconditional: zero mask
mask # Conditional: actual mask
], dim=0)
out = net(cfg_x, cfg_t, cfg_y, mask=cfg_mask)
else:
out = net(cfg_x, cfg_t, cfg_y, mask=None)
# Convert x-prediction to velocity
out = (out - cfg_x) / (1.0 - cfg_t.view(-1, 1, 1, 1)).clamp_min(self.t_eps)
# Apply CFG
if t_cur > self.guidance_interval_min and t_cur <= self.guidance_interval_max:
out = self.guidance_fn(out, self.guidance)
else:
out = self.guidance_fn(out, 1.0)
v = out
s = ((1 / dalpha_over_alpha) * v - x) / (sigma ** 2 - (1 / dalpha_over_alpha) * dsigma_mul_sigma)
if i < self.num_steps - 1:
x = self.step_fn(x, v, dt, s=s, w=w)
else:
x = self.last_step_fn(x, v, dt, s=s, w=w)
x_trajs.append(x)
v_trajs.append(v)
v_trajs.append(torch.zeros_like(x))
return x_trajs, v_trajs
class HeunSamplerMedical(BaseSampler):
"""
Heun sampler for mask-conditional medical image generation.
Second-order ODE solver for better quality.
"""
def __init__(
self,
scheduler: BaseScheduler = None,
w_scheduler: BaseScheduler = None,
exact_heun: bool = True,
guidance_interval_min: float = 0.0,
guidance_interval_max: float = 1.0,
timeshift: float = 1.0,
step_fn: Callable = ode_step_fn,
last_step: float = None,
last_step_fn: Callable = ode_step_fn,
t_eps: float = 0.05,
*args,
**kwargs
):
super().__init__(*args, **kwargs)
self.scheduler = scheduler
self.exact_heun = exact_heun
self.step_fn = step_fn
self.last_step = last_step
self.last_step_fn = last_step_fn
self.w_scheduler = w_scheduler
self.timeshift = timeshift
self.guidance_interval_min = guidance_interval_min
self.guidance_interval_max = guidance_interval_max
self.t_eps = t_eps
if self.last_step is None or self.num_steps == 1:
self.last_step = 1.0 / self.num_steps
timesteps = torch.linspace(0.0, 1 - self.last_step, self.num_steps)
timesteps = torch.cat([timesteps, torch.tensor([1.0])], dim=0)
self.timesteps = shift_respace_fn(timesteps, self.timeshift)
assert self.last_step > 0.0
assert self.scheduler is not None
def _forward_with_cfg(self, net, x, t, mask=None):
"""Forward pass with CFG for mask conditioning."""
batch_size = x.shape[0] // 2
y = torch.zeros(x.shape[0], dtype=torch.long, device=x.device)
if mask is not None:
out = net(x, t, y, mask=mask)
else:
out = net(x, t, y, mask=None)
return out
def _impl_sampling(self, net, noise, condition, uncondition, mask=None):
"""
Heun sampling with mask conditioning.
"""
batch_size = noise.shape[0]
steps = self.timesteps.to(noise.device)
x = noise
v_hat, s_hat = 0.0, 0.0
x_trajs = [noise]
v_trajs = []
for i, (t_cur, t_next) in enumerate(zip(steps[:-1], steps[1:])):
dt = t_next - t_cur
t_cur_batch = t_cur.repeat(batch_size)
sigma = self.scheduler.sigma(t_cur_batch)
alpha_over_dalpha = 1 / self.scheduler.dalpha_over_alpha(t_cur_batch)
dsigma_mul_sigma = self.scheduler.dsigma_mul_sigma(t_cur_batch)
t_hat = t_next.repeat(batch_size)
sigma_hat = self.scheduler.sigma(t_hat)
alpha_over_dalpha_hat = 1 / self.scheduler.dalpha_over_alpha(t_hat)
dsigma_mul_sigma_hat = self.scheduler.dsigma_mul_sigma(t_hat)
if self.w_scheduler:
w = self.w_scheduler.w(t_cur_batch)
else:
w = 0.0
if i == 0 or self.exact_heun:
# First evaluation
cfg_x = torch.cat([x, x], dim=0)
cfg_t = t_cur_batch.repeat(2)
if mask is not None:
cfg_mask = torch.cat([torch.zeros_like(mask), mask], dim=0)
else:
cfg_mask = None
out = self._forward_with_cfg(net, cfg_x, cfg_t, cfg_mask)
out = (out - cfg_x) / (1.0 - cfg_t.view(-1, 1, 1, 1)).clamp_min(self.t_eps)
if t_cur > self.guidance_interval_min and t_cur <= self.guidance_interval_max:
out = self.guidance_fn(out, self.guidance)
else:
out = self.guidance_fn(out, 1.0)
v = out
s = (alpha_over_dalpha * v - x) / (sigma ** 2 - alpha_over_dalpha * dsigma_mul_sigma)
else:
v = v_hat
s = s_hat
x_hat = self.step_fn(x, v, dt, s=s, w=w)
# Heun correction
if i < self.num_steps - 1:
cfg_x_hat = torch.cat([x_hat, x_hat], dim=0)
cfg_t_hat = t_hat.repeat(2)
if mask is not None:
cfg_mask = torch.cat([torch.zeros_like(mask), mask], dim=0)
else:
cfg_mask = None
out = self._forward_with_cfg(net, cfg_x_hat, cfg_t_hat, cfg_mask)
out = (out - cfg_x_hat) / (1.0 - cfg_t_hat.view(-1, 1, 1, 1)).clamp_min(self.t_eps)
if t_cur > self.guidance_interval_min and t_cur <= self.guidance_interval_max:
out = self.guidance_fn(out, self.guidance)
else:
out = self.guidance_fn(out, 1.0)
v_hat = out
s_hat = (alpha_over_dalpha_hat * v_hat - x_hat) / (sigma_hat ** 2 - alpha_over_dalpha_hat * dsigma_mul_sigma_hat)
v = (v + v_hat) / 2
s = (s + s_hat) / 2
x = self.step_fn(x, v, dt, s=s, w=w)
else:
x = self.last_step_fn(x, v, dt, s=s, w=w)
x_trajs.append(x)
v_trajs.append(v)
v_trajs.append(torch.zeros_like(x))
return x_trajs, v_trajs
|