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from dataclasses import dataclass
import torch
from tqdm import tqdm
from src.diffusion.gaussian_diffusion import GaussianDiffusion
@dataclass
class DDIMSamplerOutput:
latents: torch.Tensor
trajectory: list[torch.Tensor] | None = None
class DDIMSampler:
"""
DDIM sampler.
eta controls stochasticity:
eta = 0.0 -> deterministic DDIM
eta > 0.0 -> more stochastic
"""
def __init__(
self,
diffusion: GaussianDiffusion,
):
self.diffusion = diffusion
def make_timesteps(
self,
num_steps: int,
device: torch.device | str,
) -> torch.Tensor:
"""
Select evenly spaced timesteps from the original diffusion schedule.
Example:
original T = 1000
num_steps = 50
returns 50 timesteps descending from high noise to low noise.
"""
if num_steps > self.diffusion.num_timesteps:
raise ValueError(
f"num_steps={num_steps} cannot be larger than "
f"num_timesteps={self.diffusion.num_timesteps}"
)
timesteps = torch.linspace(
0,
self.diffusion.num_timesteps - 1,
steps=num_steps,
device=device,
).long()
timesteps = torch.flip(timesteps, dims=[0])
return timesteps
@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 v/eps/x0 with optional classifier-free guidance.
"""
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,
)
cond_output = model(
z_t,
t,
context=context,
attention_mask=attention_mask,
)
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 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,
num_steps: int = 50,
eta: float = 0.0,
clip_denoised: bool = False,
return_trajectory: bool = False,
progress: bool = True,
) -> DDIMSamplerOutput:
"""
DDIM sampling.
Returns:
clean latent estimate z_0 at the final step.
"""
device = torch.device(device)
model.eval()
z_t = torch.randn(
shape,
device=device,
)
trajectory = [] if return_trajectory else None
ddim_timesteps = self.make_timesteps(
num_steps=num_steps,
device=device,
)
if progress:
iterator = tqdm(
range(len(ddim_timesteps)),
desc=f"DDIM sampling ({num_steps} steps)",
)
else:
iterator = range(len(ddim_timesteps))
for i in iterator:
step = ddim_timesteps[i]
t = torch.full(
(shape[0],),
int(step.item()),
device=device,
dtype=torch.long,
)
if i == len(ddim_timesteps) - 1:
prev_step = torch.tensor(
-1,
device=device,
dtype=torch.long,
)
else:
prev_step = ddim_timesteps[i + 1]
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)
alpha_t = self.diffusion.schedule.alphas_cumprod[t]
alpha_t = alpha_t.view(shape[0], 1, 1, 1)
if prev_step.item() < 0:
alpha_prev = torch.ones_like(alpha_t)
else:
alpha_prev = self.diffusion.schedule.alphas_cumprod[
torch.full(
(shape[0],),
int(prev_step.item()),
device=device,
dtype=torch.long,
)
]
alpha_prev = alpha_prev.view(shape[0], 1, 1, 1)
sigma_t = eta * torch.sqrt(
(1.0 - alpha_prev)
/ (1.0 - alpha_t)
* (1.0 - alpha_t / alpha_prev)
)
# Direction pointing to z_t.
dir_xt = torch.sqrt(
torch.clamp(
1.0 - alpha_prev - sigma_t ** 2,
min=0.0,
)
) * pred_eps
noise = sigma_t * torch.randn_like(z_t)
z_t = torch.sqrt(alpha_prev) * pred_z0 + dir_xt + noise
if return_trajectory:
trajectory.append(z_t.detach().cpu())
return DDIMSamplerOutput(
latents=z_t,
trajectory=trajectory,
) |