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import torch
import random
import numpy as np
from tqdm.auto import tqdm
from diffusionsfm.utils.rays import compute_ndc_coordinates
def inference_ddim(
model,
images,
device,
crop_parameters=None,
eta=0,
num_inference_steps=100,
pbar=True,
num_patches_x=16,
num_patches_y=16,
visualize=False,
seed=0,
):
"""
Implements DDIM-style inference.
To get multiple samples, batch the images multiple times.
Args:
model: Ray Diffuser.
images (torch.Tensor): (B, N, C, H, W).
patch_rays_gt (torch.Tensor): If provided, the patch rays which are ground
truth (B, N, P, 6).
eta (float, optional): Stochasticity coefficient. 0 is completely deterministic,
1 is equivalent to DDPM. (Default: 0)
num_inference_steps (int, optional): Number of inference steps. (Default: 100)
pbar (bool, optional): Whether to show progress bar. (Default: True)
"""
timesteps = model.noise_scheduler.compute_inference_timesteps(num_inference_steps)
batch_size = images.shape[0]
num_images = images.shape[1]
if isinstance(eta, list):
eta_0, eta_1 = float(eta[0]), float(eta[1])
else:
eta_0, eta_1 = 0, 0
# Fixing seed
if seed is not None:
torch.manual_seed(seed)
random.seed(seed)
np.random.seed(seed)
with torch.no_grad():
x_tau = torch.randn(
batch_size,
num_images,
model.ray_out if hasattr(model, "ray_out") else model.ray_dim,
num_patches_x,
num_patches_y,
device=device,
)
if visualize:
x_taus = [x_tau]
all_pred = []
noise_samples = []
image_features = model.feature_extractor(images, autoresize=True)
if model.append_ndc:
ndc_coordinates = compute_ndc_coordinates(
crop_parameters=crop_parameters,
no_crop_param_device="cpu",
num_patches_x=model.width,
num_patches_y=model.width,
distortion_coeffs=None,
)[..., :2].to(device)
ndc_coordinates = ndc_coordinates.permute(0, 1, 4, 2, 3)
else:
ndc_coordinates = None
loop = tqdm(range(len(timesteps))) if pbar else range(len(timesteps))
for t in loop:
tau = timesteps[t]
if tau > 0 and eta_1 > 0:
z = torch.randn(
batch_size,
num_images,
model.ray_out if hasattr(model, "ray_out") else model.ray_dim,
num_patches_x,
num_patches_y,
device=device,
)
else:
z = 0
alpha = model.noise_scheduler.alphas_cumprod[tau]
if tau > 0:
tau_prev = timesteps[t + 1]
alpha_prev = model.noise_scheduler.alphas_cumprod[tau_prev]
else:
alpha_prev = torch.tensor(1.0, device=device).float()
sigma_t = (
torch.sqrt((1 - alpha_prev) / (1 - alpha))
* torch.sqrt(1 - alpha / alpha_prev)
)
eps_pred, noise_sample = model(
features=image_features,
rays_noisy=x_tau,
t=int(tau),
ndc_coordinates=ndc_coordinates,
)
if model.use_homogeneous:
p1 = eps_pred[:, :, :4]
p2 = eps_pred[:, :, 4:]
c1 = torch.linalg.norm(p1, dim=2, keepdim=True)
c2 = torch.linalg.norm(p2, dim=2, keepdim=True)
eps_pred[:, :, :4] = p1 / c1
eps_pred[:, :, 4:] = p2 / c2
if visualize:
all_pred.append(eps_pred.clone())
noise_samples.append(noise_sample)
# TODO: Can simplify this a lot
x0_pred = eps_pred.clone()
eps_pred = (x_tau - torch.sqrt(alpha) * eps_pred) / torch.sqrt(
1 - alpha
)
dir_x_tau = torch.sqrt(1 - alpha_prev - eta_0*sigma_t**2) * eps_pred
noise = eta_1 * sigma_t * z
new_x_tau = torch.sqrt(alpha_prev) * x0_pred + dir_x_tau + noise
x_tau = new_x_tau
if visualize:
x_taus.append(x_tau.detach().clone())
if visualize:
return x_tau, x_taus, all_pred, noise_samples
return x_tau
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