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from dataclasses import dataclass
import torch
import torch.nn.functional as F
from src.losses.diffusion_loss import DiffusionLoss
from src.diffusion.noise_schedule import NoiseSchedule, create_noise_schedule, extract
from src.diffusion.prediction import (
get_training_target,
model_output_to_x0_and_eps,
)
@dataclass
class DiffusionTrainingOutput:
loss: torch.Tensor
simple_loss: torch.Tensor
model_output: torch.Tensor
target: torch.Tensor
z_t: torch.Tensor
noise: torch.Tensor
timesteps: torch.Tensor
class GaussianDiffusion:
"""
Core latent diffusion utilities.
This handles:
- sampling timesteps
- adding noise q(z_t | z_0)
- creating v-prediction targets
- computing diffusion training loss
- computing DDPM posterior mean/variance for sampling
"""
def __init__(
self,
schedule: NoiseSchedule | None = None,
schedule_type: str = "cosine",
num_timesteps: int = 1000,
prediction_type: str = "v",
loss_type: str = "mse",
beta_start: float = 1e-4,
beta_end: float = 2e-2,
cosine_s: float = 0.008,
max_beta: float = 0.999,
snr_gamma: float | None = None,
snr_weighting: str = "none",
normalize_snr_weights: bool = False,
):
if schedule is None:
schedule = create_noise_schedule(
schedule_type=schedule_type,
num_timesteps=num_timesteps,
beta_start=beta_start,
beta_end=beta_end,
cosine_s=cosine_s,
max_beta=max_beta,
)
self.schedule = schedule
self.prediction_type = prediction_type.lower()
self.loss_type = loss_type.lower()
self.snr_gamma = snr_gamma
self.snr_weighting = snr_weighting.lower()
self.normalize_snr_weights = normalize_snr_weights
if self.prediction_type not in {"v", "v_prediction", "eps", "epsilon", "x0", "sample"}:
raise ValueError(
f"Unknown prediction_type={prediction_type}. "
"Use 'v', 'eps', or 'x0'."
)
if self.loss_type not in {"mse", "l1", "huber"}:
raise ValueError(
f"Unknown loss_type={loss_type}. "
"Use 'mse', 'l1', or 'huber'."
)
self.diffusion_loss = DiffusionLoss(
prediction_type=self.prediction_type,
loss_type=self.loss_type,
snr_gamma=self.snr_gamma,
snr_weighting=self.snr_weighting,
normalize_snr_weights=self.normalize_snr_weights,
)
@property
def num_timesteps(self) -> int:
return self.schedule.num_timesteps
def to(self, device: torch.device | str) -> "GaussianDiffusion":
self.schedule = self.schedule.to(device)
return self
def sample_timesteps(
self,
batch_size: int,
device: torch.device | str,
) -> torch.Tensor:
"""
Sample random diffusion timesteps.
Returns:
t: [B], values in [0, num_timesteps - 1]
"""
return torch.randint(
low=0,
high=self.num_timesteps,
size=(batch_size,),
device=device,
dtype=torch.long,
)
def q_sample(
self,
z_0: torch.Tensor,
t: torch.Tensor,
noise: torch.Tensor | None = None,
) -> tuple[torch.Tensor, torch.Tensor]:
"""
Forward diffusion process:
q(z_t | z_0)
Formula:
z_t = sqrt(alpha_bar_t) * z_0
+ sqrt(1 - alpha_bar_t) * eps
Args:
z_0:
Clean latent [B, C, H, W].
t:
Timesteps [B].
noise:
Optional epsilon noise. If None, sampled from N(0, I).
Returns:
z_t:
Noisy latent.
noise:
The epsilon noise used.
"""
if noise is None:
noise = torch.randn_like(z_0)
sqrt_alpha_bar = extract(
self.schedule.sqrt_alphas_cumprod,
t,
z_0.shape,
)
sqrt_one_minus_alpha_bar = extract(
self.schedule.sqrt_one_minus_alphas_cumprod,
t,
z_0.shape,
)
z_t = sqrt_alpha_bar * z_0 + sqrt_one_minus_alpha_bar * noise
return z_t, noise
def training_target(
self,
z_0: torch.Tensor,
noise: torch.Tensor,
t: torch.Tensor,
) -> torch.Tensor:
"""
Get target for current prediction type
"""
return get_training_target(
z_0=z_0,
eps=noise,
t=t,
schedule=self.schedule,
prediction_type=self.prediction_type,
)
def p_losses(
self,
model,
z_0: torch.Tensor,
context: torch.Tensor | None = None,
t: torch.Tensor | None = None,
noise: torch.Tensor | None = None,
model_kwargs: dict | None = None,
) -> DiffusionTrainingOutput:
"""
Full diffusion training step using loss module.
"""
if model_kwargs is None:
model_kwargs = {}
batch_size = z_0.shape[0]
device = z_0.device
if t is None:
t = self.sample_timesteps(batch_size, device)
z_t, noise = self.q_sample(
z_0=z_0,
t=t,
noise=noise,
)
target = self.training_target(
z_0=z_0,
noise=noise,
t=t,
)
if context is None:
model_output = model(z_t, t, **model_kwargs)
else:
model_output = model(z_t, t, context=context, **model_kwargs)
alpha_t = extract(
self.schedule.sqrt_alphas_cumprod,
t,
z_0.shape,
)
sigma_t = extract(
self.schedule.sqrt_one_minus_alphas_cumprod,
t,
z_0.shape,
)
alpha_bar_t = self.schedule.alphas_cumprod.gather(
0,
t,
)
snr = alpha_bar_t / (1.0 - alpha_bar_t).clamp(min=1e-8)
loss_out = self.diffusion_loss(
model_output=model_output,
x0=z_0,
noise=noise,
alpha_t=alpha_t,
sigma_t=sigma_t,
snr=snr,
return_dict=True,
)
loss = loss_out["loss"]
raw_loss = loss_out["raw_loss"]
return DiffusionTrainingOutput(
loss=loss,
simple_loss=raw_loss.detach(),
model_output=model_output,
target=target,
z_t=z_t,
noise=noise,
timesteps=t,
)
def predict_x0_and_eps(
self,
model_output: torch.Tensor,
z_t: torch.Tensor,
t: torch.Tensor,
) -> tuple[torch.Tensor, torch.Tensor]:
"""
Convert model output to:
z_0 prediction
epsilon prediction
"""
return model_output_to_x0_and_eps(
model_output=model_output,
z_t=z_t,
t=t,
schedule=self.schedule,
prediction_type=self.prediction_type,
)
def q_posterior(
self,
z_0: torch.Tensor,
z_t: torch.Tensor,
t: torch.Tensor,
) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
"""
Compute posterior:
q(z_{t-1} | z_t, z_0)
Returns:
posterior_mean
posterior_variance
posterior_log_variance_clipped
"""
posterior_mean_coef1 = extract(
self.schedule.posterior_mean_coef1,
t,
z_t.shape,
)
posterior_mean_coef2 = extract(
self.schedule.posterior_mean_coef2,
t,
z_t.shape,
)
posterior_mean = (
posterior_mean_coef1 * z_0
+ posterior_mean_coef2 * z_t
)
posterior_variance = extract(
self.schedule.posterior_variance,
t,
z_t.shape,
)
posterior_log_variance_clipped = extract(
self.schedule.posterior_log_variance_clipped,
t,
z_t.shape,
)
return (
posterior_mean,
posterior_variance,
posterior_log_variance_clipped,
)
@torch.no_grad()
def p_mean_variance(
self,
model,
z_t: torch.Tensor,
t: torch.Tensor,
context: torch.Tensor | None = None,
clip_denoised: bool = False,
model_kwargs: dict | None = None,
) -> dict[str, torch.Tensor]:
"""
One reverse-process prediction.
Model predicts v/eps/x0.
We convert to predicted z_0
"""
if model_kwargs is None:
model_kwargs = {}
if context is None:
model_output = model(
z_t,
t,
**model_kwargs,
)
else:
model_output = model(
z_t,
t,
context=context,
**model_kwargs,
)
pred_z0, pred_eps = self.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.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 p_sample(
self,
model,
z_t: torch.Tensor,
t: torch.Tensor,
context: torch.Tensor | None = None,
clip_denoised: bool = False,
model_kwargs: dict | None = None,
) -> torch.Tensor:
"""
This is one reverse step
"""
out = self.p_mean_variance(
model=model,
z_t=z_t,
t=t,
context=context,
clip_denoised=clip_denoised,
model_kwargs=model_kwargs,
)
noise = torch.randn_like(z_t)
# No noise when t == 0.
nonzero_mask = (t != 0).float()
while len(nonzero_mask.shape) < len(z_t.shape):
nonzero_mask = nonzero_mask[..., None]
z_prev = (
out["mean"]
+ nonzero_mask
* torch.exp(0.5 * out["log_variance"])
* noise
)
return z_prev |