mirror of
https://github.com/Stability-AI/generative-models.git
synced 2025-12-19 14:24:21 +01:00
236 lines
8.0 KiB
Python
Executable File
236 lines
8.0 KiB
Python
Executable File
import os
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import sys
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from glob import glob
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from typing import List, Optional
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from tqdm import tqdm
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sys.path.append(os.path.realpath(os.path.join(os.path.dirname(__file__), "../../")))
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import numpy as np
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import torch
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from fire import Fire
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from scripts.demo.sv4d_helpers import (
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load_model,
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preprocess_video,
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read_video,
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run_img2vid,
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save_video,
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)
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from sgm.modules.encoders.modules import VideoPredictionEmbedderWithEncoder
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sv4d2_configs = {
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"sv4d2": {
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"T": 12, # number of frames per sample
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"V": 4, # number of views per sample
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"model_config": "scripts/sampling/configs/sv4d2.yaml",
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"version_dict": {
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"T": 12 * 4,
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"options": {
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"discretization": 1,
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"cfg": 2.0,
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"min_cfg": 2.0,
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"num_views": 4,
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"sigma_min": 0.002,
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"sigma_max": 700.0,
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"rho": 7.0,
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"guider": 2,
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"force_uc_zero_embeddings": [
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"cond_frames",
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"cond_frames_without_noise",
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"cond_view",
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"cond_motion",
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],
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"additional_guider_kwargs": {
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"additional_cond_keys": ["cond_view", "cond_motion"]
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},
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},
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},
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},
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"sv4d2_8views": {
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"T": 5, # number of frames per sample
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"V": 8, # number of views per sample
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"model_config": "scripts/sampling/configs/sv4d2_8views.yaml",
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"version_dict": {
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"T": 5 * 8,
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"options": {
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"discretization": 1,
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"cfg": 2.5,
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"min_cfg": 1.5,
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"num_views": 8,
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"sigma_min": 0.002,
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"sigma_max": 700.0,
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"rho": 7.0,
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"guider": 5,
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"force_uc_zero_embeddings": [
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"cond_frames",
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"cond_frames_without_noise",
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"cond_view",
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"cond_motion",
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],
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"additional_guider_kwargs": {
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"additional_cond_keys": ["cond_view", "cond_motion"]
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},
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},
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},
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},
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}
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def sample(
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input_path: str = "assets/sv4d_videos/camel.gif", # Can either be image file or folder with image files
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model_path: Optional[str] = "checkpoints/sv4d2.safetensors",
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output_folder: Optional[str] = "outputs",
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num_steps: Optional[int] = 50,
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img_size: int = 576, # image resolution
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n_frames: int = 21, # number of input and output video frames
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seed: int = 23,
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encoding_t: int = 8, # Number of frames encoded at a time! This eats most VRAM. Reduce if necessary.
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decoding_t: int = 4, # Number of frames decoded at a time! This eats most VRAM. Reduce if necessary.
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device: str = "cuda",
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elevations_deg: Optional[List[float]] = 0.0,
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azimuths_deg: Optional[List[float]] = None,
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image_frame_ratio: Optional[float] = 0.9,
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verbose: Optional[bool] = False,
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remove_bg: bool = False,
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):
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"""
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Simple script to generate multiple novel-view videos conditioned on a video `input_path` or multiple frames, one for each
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image file in folder `input_path`. If you run out of VRAM, try decreasing `decoding_t` and `encoding_t`.
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"""
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# Set model config
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assert os.path.basename(model_path) in [
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"sv4d2.safetensors",
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"sv4d2_8views.safetensors",
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]
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sv4d2_model = os.path.splitext(os.path.basename(model_path))[0]
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config = sv4d2_configs[sv4d2_model]
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print(sv4d2_model, config)
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T = config["T"]
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V = config["V"]
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model_config = config["model_config"]
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version_dict = config["version_dict"]
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F = 8 # vae factor to downsize image->latent
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C = 4
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H, W = img_size, img_size
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n_views = V + 1 # number of output video views (1 input view + 8 novel views)
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subsampled_views = np.arange(n_views)
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version_dict["H"] = H
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version_dict["W"] = W
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version_dict["C"] = C
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version_dict["f"] = F
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version_dict["options"]["num_steps"] = num_steps
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torch.manual_seed(seed)
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output_folder = os.path.join(output_folder, sv4d2_model)
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os.makedirs(output_folder, exist_ok=True)
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# Read input video frames i.e. images at view 0
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print(f"Reading {input_path}")
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base_count = len(glob(os.path.join(output_folder, "*.mp4"))) // n_views
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processed_input_path = preprocess_video(
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input_path,
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remove_bg=remove_bg,
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n_frames=n_frames,
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W=W,
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H=H,
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output_folder=output_folder,
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image_frame_ratio=image_frame_ratio,
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base_count=base_count,
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)
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images_v0 = read_video(processed_input_path, n_frames=n_frames, device=device)
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images_t0 = torch.zeros(n_views, 3, H, W).float().to(device)
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# Get camera viewpoints
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if isinstance(elevations_deg, float) or isinstance(elevations_deg, int):
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elevations_deg = [elevations_deg] * n_views
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assert (
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len(elevations_deg) == n_views
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), f"Please provide 1 value, or a list of {n_views} values for elevations_deg! Given {len(elevations_deg)}"
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if azimuths_deg is None:
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# azimuths_deg = np.linspace(0, 360, n_views + 1)[1:] % 360
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azimuths_deg = (
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np.array([0, 60, 120, 180, 240])
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if sv4d2_model == "sv4d2"
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else np.array([0, 30, 75, 120, 165, 210, 255, 300, 330])
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)
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assert (
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len(azimuths_deg) == n_views
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), f"Please provide a list of {n_views} values for azimuths_deg! Given {len(azimuths_deg)}"
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polars_rad = np.array([np.deg2rad(90 - e) for e in elevations_deg])
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azimuths_rad = np.array(
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[np.deg2rad((a - azimuths_deg[-1]) % 360) for a in azimuths_deg]
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)
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# Initialize image matrix
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img_matrix = [[None] * n_views for _ in range(n_frames)]
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for i, v in enumerate(subsampled_views):
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img_matrix[0][i] = images_t0[v].unsqueeze(0)
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for t in range(n_frames):
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img_matrix[t][0] = images_v0[t]
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# Load SV4D++ model
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model, _ = load_model(
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model_config,
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device,
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version_dict["T"],
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num_steps,
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verbose,
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model_path,
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)
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model.en_and_decode_n_samples_a_time = decoding_t
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for emb in model.conditioner.embedders:
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if isinstance(emb, VideoPredictionEmbedderWithEncoder):
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emb.en_and_decode_n_samples_a_time = encoding_t
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# Sampling novel-view videos
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v0 = 0
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view_indices = np.arange(V) + 1
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t0_list = (
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range(0, n_frames, T-1)
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if sv4d2_model == "sv4d2"
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else range(0, n_frames - T + 1, T - 1)
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)
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for t0 in tqdm(t0_list):
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if t0 + T > n_frames:
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t0 = n_frames - T
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frame_indices = t0 + np.arange(T)
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print(f"Sampling frames {frame_indices}")
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image = img_matrix[t0][v0]
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cond_motion = torch.cat([img_matrix[t][v0] for t in frame_indices], 0)
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cond_view = torch.cat([img_matrix[t0][v] for v in view_indices], 0)
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polars = polars_rad[subsampled_views[1:]][None].repeat(T, 0).flatten()
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azims = azimuths_rad[subsampled_views[1:]][None].repeat(T, 0).flatten()
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polars = (polars - polars_rad[v0] + torch.pi / 2) % (torch.pi * 2)
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azims = (azims - azimuths_rad[v0]) % (torch.pi * 2)
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cond_mv = False if t0 == 0 else True
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samples = run_img2vid(
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version_dict,
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model,
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image,
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seed,
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polars,
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azims,
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cond_motion,
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cond_view,
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decoding_t,
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cond_mv=cond_mv,
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)
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samples = samples.view(T, V, 3, H, W)
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for i, t in enumerate(frame_indices):
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for j, v in enumerate(view_indices):
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img_matrix[t][v] = samples[i, j][None] * 2 - 1
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# Save output videos
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for v in view_indices:
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vid_file = os.path.join(output_folder, f"{base_count:06d}_v{v:03d}.mp4")
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print(f"Saving {vid_file}")
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save_video(
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vid_file,
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[img_matrix[t][v] for t in range(n_frames) if img_matrix[t][v] is not None],
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)
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if __name__ == "__main__":
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Fire(sample)
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