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a04730e | 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 | from __future__ import annotations
from dataclasses import dataclass
import torch
from tqdm import tqdm
from src.diffusion.gaussian_diffusion import GaussianDiffusion
@dataclass
class DDPMSamplerOutput:
latents: torch.Tensor
trajectory: list[torch.Tensor] | None = None
class DDPMSampler:
"""
DDPM sampler.
This sampler uses the learned reverse process:
z_T ~ N(0, I)
z_T -> z_{T-1} -> ... -> z_0
Supports classifier-free guidance if both conditional and unconditional
context are provided.
"""
def __init__(
self,
diffusion: GaussianDiffusion,
):
self.diffusion = diffusion
@torch.no_grad()
def predict_model_output(
self,
model,
z_t: torch.Tensor,
t: torch.Tensor,
context: torch.Tensor | None = None,
attention_mask: torch.Tensor | None = None,
uncond_context: torch.Tensor | None = None,
uncond_attention_mask: torch.Tensor | None = None,
guidance_scale: float = 1.0,
) -> torch.Tensor:
"""
Predict model output with optional classifier-free guidance.
If guidance_scale == 1 or uncond_context is None:
normal conditional prediction.
If guidance_scale > 1:
output = uncond + scale * (cond - uncond)
"""
if uncond_context is None or guidance_scale == 1.0:
if context is None:
return model(
z_t,
t,
)
return model(
z_t,
t,
context=context,
attention_mask=attention_mask,
)
# Conditional prediction
cond_output = model(
z_t,
t,
context=context,
attention_mask=attention_mask,
)
# Unconditional prediction
uncond_output = model(
z_t,
t,
context=uncond_context,
attention_mask=uncond_attention_mask,
)
return uncond_output + guidance_scale * (cond_output - uncond_output)
@torch.no_grad()
def p_mean_variance_with_cfg(
self,
model,
z_t: torch.Tensor,
t: torch.Tensor,
context: torch.Tensor | None = None,
attention_mask: torch.Tensor | None = None,
uncond_context: torch.Tensor | None = None,
uncond_attention_mask: torch.Tensor | None = None,
guidance_scale: float = 1.0,
clip_denoised: bool = False,
) -> dict[str, torch.Tensor]:
"""
Same as GaussianDiffusion.p_mean_variance, but supports CFG.
"""
model_output = self.predict_model_output(
model=model,
z_t=z_t,
t=t,
context=context,
attention_mask=attention_mask,
uncond_context=uncond_context,
uncond_attention_mask=uncond_attention_mask,
guidance_scale=guidance_scale,
)
pred_z0, pred_eps = self.diffusion.predict_x0_and_eps(
model_output=model_output,
z_t=z_t,
t=t,
)
if clip_denoised:
pred_z0 = pred_z0.clamp(-1.0, 1.0)
(
posterior_mean,
posterior_variance,
posterior_log_variance,
) = self.diffusion.q_posterior(
z_0=pred_z0,
z_t=z_t,
t=t,
)
return {
"mean": posterior_mean,
"variance": posterior_variance,
"log_variance": posterior_log_variance,
"pred_z0": pred_z0,
"pred_eps": pred_eps,
"model_output": model_output,
}
@torch.no_grad()
def sample(
self,
model,
shape: tuple[int, int, int, int],
device: torch.device | str,
context: torch.Tensor | None = None,
attention_mask: torch.Tensor | None = None,
uncond_context: torch.Tensor | None = None,
uncond_attention_mask: torch.Tensor | None = None,
guidance_scale: float = 1.0,
clip_denoised: bool = False,
return_trajectory: bool = False,
progress: bool = True,
) -> DDPMSamplerOutput:
"""
Generate clean latents from pure noise.
Args:
shape:
Usually [B, 8, 32, 32] for your model.
context:
Conditional CLIP text context.
uncond_context:
Empty-prompt CLIP context for CFG.
guidance_scale:
CFG scale. Common values: 3.0 to 7.5.
"""
device = torch.device(device)
model.eval()
z_t = torch.randn(
shape,
device=device,
)
trajectory = [] if return_trajectory else None
timesteps = reversed(range(self.diffusion.num_timesteps))
if progress:
timesteps = tqdm(
timesteps,
total=self.diffusion.num_timesteps,
desc="DDPM sampling",
)
for step in timesteps:
t = torch.full(
(shape[0],),
step,
device=device,
dtype=torch.long,
)
out = self.p_mean_variance_with_cfg(
model=model,
z_t=z_t,
t=t,
context=context,
attention_mask=attention_mask,
uncond_context=uncond_context,
uncond_attention_mask=uncond_attention_mask,
guidance_scale=guidance_scale,
clip_denoised=clip_denoised,
)
noise = torch.randn_like(z_t)
nonzero_mask = (t != 0).float()
while len(nonzero_mask.shape) < len(z_t.shape):
nonzero_mask = nonzero_mask[..., None]
z_t = (
out["mean"]
+ nonzero_mask
* torch.exp(0.5 * out["log_variance"])
* noise
)
if return_trajectory:
trajectory.append(z_t.detach().cpu())
return DDPMSamplerOutput(
latents=z_t,
trajectory=trajectory,
) |