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import sys
from glob import glob
from typing import List, Optional, Union
from tqdm import tqdm
sys.path.append(os.path.realpath(os.path.join(os.path.dirname(__file__), "../../")))
import numpy as np
import torch
from fire import Fire
from sgm.modules.encoders.modules import VideoPredictionEmbedderWithEncoder
from scripts.demo.sv4d_helpers import (
decode_latents,
load_model,
initial_model_load,
read_video,
run_img2vid,
prepare_sampling,
prepare_inputs,
do_sample_per_step,
sample_sv3d,
save_video,
preprocess_video,
)
def sample(
input_path: str = "assets/sv4d_videos/test_video1.mp4", # Can either be image file or folder with image files
output_folder: Optional[str] = "outputs/sv4d",
num_steps: Optional[int] = 4,
sv3d_version: str = "sv3d_u", # sv3d_u or sv3d_p
img_size: int = 576, # image resolution
fps_id: int = 6,
motion_bucket_id: int = 127,
cond_aug: float = 1e-5,
seed: int = 23,
encoding_t: int = 8, # Number of frames encoded at a time! This eats most VRAM. Reduce if necessary.
decoding_t: int = 4, # Number of frames decoded at a time! This eats most VRAM. Reduce if necessary.
device: str = "cuda",
elevations_deg: Optional[Union[float, List[float]]] = 10.0,
azimuths_deg: Optional[List[float]] = None,
image_frame_ratio: Optional[float] = 0.917,
verbose: Optional[bool] = False,
remove_bg: bool = False,
):
"""
Simple script to generate multiple novel-view videos conditioned on a video `input_path` or multiple frames, one for each
image file in folder `input_path`. If you run out of VRAM, try decreasing `decoding_t` and `encoding_t`.
"""
# Set model config
T = 5 # number of frames per sample
V = 8 # number of views per sample
F = 8 # vae factor to downsize image->latent
C = 4
H, W = img_size, img_size
n_frames = 21 # number of input and output video frames
n_views = V + 1 # number of output video views (1 input view + 8 novel views)
n_views_sv3d = 21
subsampled_views = np.array(
[0, 2, 5, 7, 9, 12, 14, 16, 19]
) # subsample (V+1=)9 (uniform) views from 21 SV3D views
model_config = "scripts/sampling/configs/sv4d.yaml"
version_dict = {
"T": T * V,
"H": H,
"W": W,
"C": C,
"f": F,
"options": {
"discretization": 1,
"cfg": 6.5,
"num_views": V,
"sigma_min": 0.002,
"sigma_max": 700.0,
"rho": 7.0,
"guider": 5,
"num_steps": num_steps,
"force_uc_zero_embeddings": [
"cond_frames",
"cond_frames_without_noise",
# "cond_view",
"cond_motion",
],
"additional_guider_kwargs": {
"additional_cond_keys": ["cond_view", "cond_motion"]
},
},
}
torch.manual_seed(seed)
os.makedirs(output_folder, exist_ok=True)
# Read input video frames i.e. images at view 0
print(f"Reading {input_path}")
base_count = len(glob(os.path.join(output_folder, "*.mp4"))) // 11
processed_input_path = preprocess_video(
input_path,
remove_bg=remove_bg,
n_frames=n_frames,
W=W,
H=H,
output_folder=output_folder,
image_frame_ratio=image_frame_ratio,
base_count=base_count,
)
images_v0 = read_video(processed_input_path, n_frames=n_frames, device=device)
# Get camera viewpoints
if isinstance(elevations_deg, float) or isinstance(elevations_deg, int):
elevations_deg = [elevations_deg] * n_views_sv3d
assert (
len(elevations_deg) == n_views_sv3d
), f"Please provide 1 value, or a list of {n_views_sv3d} values for elevations_deg! Given {len(elevations_deg)}"
if azimuths_deg is None:
azimuths_deg = np.linspace(0, 360, n_views_sv3d + 1)[1:] % 360
assert (
len(azimuths_deg) == n_views_sv3d
), f"Please provide a list of {n_views_sv3d} values for azimuths_deg! Given {len(azimuths_deg)}"
polars_rad = np.array([np.deg2rad(90 - e) for e in elevations_deg])
azimuths_rad = np.array(
[np.deg2rad((a - azimuths_deg[-1]) % 360) for a in azimuths_deg]
)
# Sample multi-view images of the first frame using SV3D i.e. images at time 0
images_t0 = sample_sv3d(
images_v0[0],
n_views_sv3d,
num_steps,
sv3d_version,
fps_id,
motion_bucket_id,
cond_aug,
decoding_t,
device,
polars_rad,
azimuths_rad,
verbose,
)
images_t0 = torch.roll(images_t0, 1, 0) # move conditioning image to first frame
# Initialize image matrix
img_matrix = [[None] * n_views for _ in range(n_frames)]
for i, v in enumerate(subsampled_views):
img_matrix[0][i] = images_t0[v].unsqueeze(0)
for t in range(n_frames):
img_matrix[t][0] = images_v0[t]
save_video(
os.path.join(output_folder, f"{base_count:06d}_t000.mp4"),
img_matrix[0],
)
# save_video(
# os.path.join(output_folder, f"{base_count:06d}_v000.mp4"),
# [img_matrix[t][0] for t in range(n_frames)],
# )
# Load SV4D model
model, filter = load_model(
model_config,
device,
version_dict["T"],
num_steps,
verbose,
)
model = initial_model_load(model)
for emb in model.conditioner.embedders:
if isinstance(emb, VideoPredictionEmbedderWithEncoder):
emb.en_and_decode_n_samples_a_time = encoding_t
model.en_and_decode_n_samples_a_time = decoding_t
# Interleaved sampling for anchor frames
t0, v0 = 0, 0
frame_indices = np.arange(T - 1, n_frames, T - 1) # [4, 8, 12, 16, 20]
view_indices = np.arange(V) + 1
print(f"Sampling anchor frames {frame_indices}")
image = img_matrix[t0][v0]
cond_motion = torch.cat([img_matrix[t][v0] for t in frame_indices], 0)
cond_view = torch.cat([img_matrix[t0][v] for v in view_indices], 0)
polars = polars_rad[subsampled_views[1:]][None].repeat(T, 0).flatten()
azims = azimuths_rad[subsampled_views[1:]][None].repeat(T, 0).flatten()
azims = (azims - azimuths_rad[v0]) % (torch.pi * 2)
samples = run_img2vid(
version_dict, model, image, seed, polars, azims, cond_motion, cond_view, decoding_t
)
samples = samples.view(T, V, 3, H, W)
for i, t in enumerate(frame_indices):
for j, v in enumerate(view_indices):
if img_matrix[t][v] is None:
img_matrix[t][v] = samples[i, j][None] * 2 - 1
# Dense sampling for the rest
print(f"Sampling dense frames:")
for t0 in tqdm(np.arange(0, n_frames - 1, T - 1)): # [0, 4, 8, 12, 16]
frame_indices = t0 + np.arange(T)
print(f"Sampling dense frames {frame_indices}")
latent_matrix = torch.randn(n_frames, n_views, C, H // F, W // F).to("cuda")
polars = polars_rad[subsampled_views[1:]][None].repeat(T, 0).flatten()
azims = azimuths_rad[subsampled_views[1:]][None].repeat(T, 0).flatten()
azims = (azims - azimuths_rad[v0]) % (torch.pi * 2)
# alternate between forward and backward conditioning
forward_inputs, forward_frame_indices, backward_inputs, backward_frame_indices = prepare_inputs(
frame_indices,
img_matrix,
v0,
view_indices,
model,
version_dict,
seed,
polars,
azims
)
for step in tqdm(range(num_steps)):
if step % 2 == 1:
c, uc, additional_model_inputs, sampler = forward_inputs
frame_indices = forward_frame_indices
else:
c, uc, additional_model_inputs, sampler = backward_inputs
frame_indices = backward_frame_indices
noisy_latents = latent_matrix[frame_indices][:, view_indices].flatten(0, 1)
samples = do_sample_per_step(
model,
sampler,
noisy_latents,
c,
uc,
step,
additional_model_inputs,
)
samples = samples.view(T, V, C, H // F, W // F)
for i, t in enumerate(frame_indices):
for j, v in enumerate(view_indices):
latent_matrix[t, v] = samples[i, j]
img_matrix = decode_latents(model, latent_matrix, img_matrix, frame_indices, view_indices, T)
# Save output videos
for v in view_indices:
vid_file = os.path.join(output_folder, f"{base_count:06d}_v{v:03d}.mp4")
print(f"Saving {vid_file}")
save_video(vid_file, [img_matrix[t][v] for t in range(n_frames)])
# Save diagonal video
diag_frames = [
img_matrix[t][(t // (n_frames // n_views)) % n_views] for t in range(n_frames)
]
vid_file = os.path.join(output_folder, f"{base_count:06d}_diag.mp4")
print(f"Saving {vid_file}")
save_video(vid_file, diag_frames)
if __name__ == "__main__":
Fire(sample)
|