ar_utils_inference.py

# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse
import json
import math
import os
from pathlib import Path
from typing import List

import numpy as np
import torch
import torchvision
from PIL import Image

from .ar_config_inference_inference_config import SamplingConfig
from .log import log

_IMAGE_EXTENSIONS = [".png", ".jpg", ".jpeg", "webp"]
_VIDEO_EXTENSIONS = [".mp4"]
_SUPPORTED_CONTEXT_LEN = [1, 9]  # Input frames
NUM_TOTAL_FRAMES = 33


def add_common_arguments(parser):
    """Add common command line arguments.

    Args:
        parser (ArgumentParser): Argument parser to add arguments to
    """
    parser.add_argument(
        "--checkpoint_dir", type=str, default="checkpoints", help="Base directory containing model checkpoints"
    )
    parser.add_argument(
        "--video_save_name",
        type=str,
        default="output",
        help="Output filename for generating a single video",
    )
    parser.add_argument("--video_save_folder", type=str, default="outputs/", help="Output folder for saving videos")
    parser.add_argument(
        "--input_image_or_video_path",
        type=str,
        help="Input path for input image or video",
    )
    parser.add_argument(
        "--batch_input_path",
        type=str,
        help="Input folder containing all input images or videos",
    )
    parser.add_argument(
        "--num_input_frames",
        type=int,
        default=9,
        help="Number of input frames for world generation",
        choices=_SUPPORTED_CONTEXT_LEN,
    )
    parser.add_argument("--temperature", type=float, default=1.0, help="Temperature for sampling")
    parser.add_argument("--top_p", type=float, default=0.8, help="Top-p value for sampling")
    parser.add_argument("--seed", type=int, default=0, help="Random seed")
    parser.add_argument("--disable_diffusion_decoder", action="store_true", help="Disable diffusion decoder")
    parser.add_argument(
        "--offload_guardrail_models",
        action="store_true",
        help="Offload guardrail models after inference",
    )
    parser.add_argument(
        "--offload_diffusion_decoder",
        action="store_true",
        help="Offload diffusion decoder after inference",
    )
    parser.add_argument(
        "--offload_ar_model",
        action="store_true",
        help="Offload AR model after inference",
    )
    parser.add_argument(
        "--offload_tokenizer",
        action="store_true",
        help="Offload discrete tokenizer model after inference",
    )


def validate_args(args: argparse.Namespace, inference_type: str):
    """Validate command line arguments for base and video2world generation."""
    assert inference_type in [
        "base",
        "video2world",
    ], "Invalid inference_type, must be 'base' or 'video2world'"
    if args.input_type in ["image", "text_and_image"] and args.num_input_frames != 1:
        args.num_input_frames = 1
        log.info(f"Set num_input_frames to 1 for {args.input_type} input")

    if args.num_input_frames == 1:
        if "4B" in args.ar_model_dir:
            log.warning(
                "The failure rate for 4B model with image input is ~15%. 12B / 13B model have a smaller failure rate. Please be cautious and refer to README.md for more details."
            )
        elif "5B" in args.ar_model_dir:
            log.warning(
                "The failure rate for 5B model with image input is ~7%. 12B / 13B model have a smaller failure rate. Please be cautious and refer to README.md for more details."
            )

    # Validate prompt/image/video args for single or batch generation
    assert (
        args.input_image_or_video_path or args.batch_input_path
    ), "--input_image_or_video_path or --batch_input_path must be provided."
    if inference_type == "video2world" and (not args.batch_input_path):
        assert args.prompt, "--prompt is required for single video generation."
    args.data_resolution = [640, 1024]

    # Validate number of GPUs
    num_gpus = int(os.getenv("WORLD_SIZE", 1))
    assert num_gpus <= 1, "We support only single GPU inference for now"

    # Create output folder
    Path(args.video_save_folder).mkdir(parents=True, exist_ok=True)

    sampling_config = SamplingConfig(
        echo=True,
        temperature=args.temperature,
        top_p=args.top_p,
        compile_sampling=True,
    )
    return sampling_config


def resize_input(video: torch.Tensor, resolution: list[int]):
    r"""
    Function to perform aspect ratio preserving resizing and center cropping.
    This is needed to make the video into target resolution.
    Args:
        video (torch.Tensor): Input video tensor
        resolution (list[int]): Data resolution
    Returns:
        Cropped video
    """

    orig_h, orig_w = video.shape[2], video.shape[3]
    target_h, target_w = resolution

    scaling_ratio = max((target_w / orig_w), (target_h / orig_h))
    resizing_shape = (int(math.ceil(scaling_ratio * orig_h)), int(math.ceil(scaling_ratio * orig_w)))
    video_resized = torchvision.transforms.functional.resize(video, resizing_shape)
    video_cropped = torchvision.transforms.functional.center_crop(video_resized, resolution)
    return video_cropped


def load_image_from_list(flist, data_resolution: List[int]) -> dict:
    """
    Function to load images from a list of image paths.
    Args:
        flist (List[str]): List of image paths
        data_resolution (List[int]): Data resolution
    Returns:
        Dict containing input images
    """
    all_videos = dict()
    for img_path in flist:
        ext = os.path.splitext(img_path)[1]
        if ext in _IMAGE_EXTENSIONS:
            # Read the image
            img = Image.open(img_path)

            # Convert to tensor
            img = torchvision.transforms.functional.to_tensor(img)
            static_vid = img.unsqueeze(0).repeat(NUM_TOTAL_FRAMES, 1, 1, 1)
            static_vid = static_vid * 2 - 1

            log.debug(
                f"Resizing input image of shape ({static_vid.shape[2]}, {static_vid.shape[3]}) -> ({data_resolution[0]}, {data_resolution[1]})"
            )
            static_vid = resize_input(static_vid, data_resolution)
            fname = os.path.basename(img_path)
            all_videos[fname] = static_vid.transpose(0, 1).unsqueeze(0)

    return all_videos


def read_input_images(batch_input_path: str, data_resolution: List[int]) -> dict:
    """
    Function to read input images from a JSONL file.

    Args:
        batch_input_path (str): Path to JSONL file containing visual input paths
        data_resolution (list[int]): Data resolution

    Returns:
        Dict containing input images
    """
    # Read visual inputs from JSONL
    flist = []
    with open(batch_input_path, "r") as f:
        for line in f:
            data = json.loads(line.strip())
            flist.append(data["visual_input"])

    return load_image_from_list(flist, data_resolution=data_resolution)


def read_input_image(input_path: str, data_resolution: List[int]) -> dict:
    """
    Function to read input image.
    Args:
        input_path (str): Path to input image
        data_resolution (List[int]): Data resolution
    Returns:
        Dict containing input image
    """
    flist = [input_path]
    return load_image_from_list(flist, data_resolution=data_resolution)


def read_input_videos(batch_input_path: str, data_resolution: List[int], num_input_frames: int) -> dict:
    r"""
    Function to read input videos.
    Args:
        batch_input_path (str): Path to JSONL file containing visual input paths
        data_resolution (list[int]): Data resolution
    Returns:
        Dict containing input videos
    """
    # Read visual inputs from JSONL
    flist = []
    with open(batch_input_path, "r") as f:
        for line in f:
            data = json.loads(line.strip())
            flist.append(data["visual_input"])
    return load_videos_from_list(flist, data_resolution=data_resolution, num_input_frames=num_input_frames)


def read_input_video(input_path: str, data_resolution: List[int], num_input_frames: int) -> dict:
    """
    Function to read input video.
    Args:
        input_path (str): Path to input video
        data_resolution (List[int]): Data resolution
        num_input_frames (int): Number of frames in context
    Returns:
        Dict containing input video
    """
    flist = [input_path]
    return load_videos_from_list(flist, data_resolution=data_resolution, num_input_frames=num_input_frames)


def load_videos_from_list(flist: List[str], data_resolution: List[int], num_input_frames: int) -> dict:
    """
    Function to load videos from a list of video paths.
    Args:
        flist (List[str]): List of video paths
        data_resolution (List[int]): Data resolution
        num_input_frames (int): Number of frames in context
    Returns:
        Dict containing input videos
    """
    all_videos = dict()

    for video_path in flist:
        ext = os.path.splitext(video_path)[-1]
        if ext in _VIDEO_EXTENSIONS:
            video, _, _ = torchvision.io.read_video(video_path, pts_unit="sec")
            video = video.float() / 255.0
            video = video * 2 - 1

            # Resize the videos to the required dimension
            nframes_in_video = video.shape[0]
            if nframes_in_video < num_input_frames:
                fname = os.path.basename(video_path)
                log.warning(
                    f"Video {fname} has {nframes_in_video} frames, less than the requried {num_input_frames} frames. Skipping."
                )
                continue

            video = video[-num_input_frames:, :, :, :]

            # Pad the video to NUM_TOTAL_FRAMES (because the tokenizer expects inputs of NUM_TOTAL_FRAMES)
            video = torch.cat(
                (video, video[-1, :, :, :].unsqueeze(0).repeat(NUM_TOTAL_FRAMES - num_input_frames, 1, 1, 1)),
                dim=0,
            )

            video = video.permute(0, 3, 1, 2)

            log.debug(
                f"Resizing input video of shape ({video.shape[2]}, {video.shape[3]}) -> ({data_resolution[0]}, {data_resolution[1]})"
            )
            video = resize_input(video, data_resolution)

            fname = os.path.basename(video_path)
            all_videos[fname] = video.transpose(0, 1).unsqueeze(0)

    return all_videos


def load_vision_input(
    input_type: str,
    batch_input_path: str,
    input_image_or_video_path: str,
    data_resolution: List[int],
    num_input_frames: int,
):
    """
    Function to load vision input.
    Note: We pad the frames of the input image/video to NUM_TOTAL_FRAMES here, and feed the padded video tensors to the video tokenizer to obtain tokens. The tokens will be truncated based on num_input_frames when feeding to the autoregressive model.
    Args:
        input_type (str): Type of input
        batch_input_path (str): Folder containing input images or videos
        input_image_or_video_path (str): Path to input image or video
        data_resolution (List[int]): Data resolution
        num_input_frames (int): Number of frames in context
    Returns:
        Dict containing input videos
    """
    if batch_input_path:
        log.info(f"Reading batch inputs from path: {batch_input_path}")
        if input_type == "image" or input_type == "text_and_image":
            input_videos = read_input_images(batch_input_path, data_resolution=data_resolution)
        elif input_type == "video" or input_type == "text_and_video":
            input_videos = read_input_videos(
                batch_input_path,
                data_resolution=data_resolution,
                num_input_frames=num_input_frames,
            )
        else:
            raise ValueError(f"Invalid input type {input_type}")
    else:
        if input_type == "image" or input_type == "text_and_image":
            input_videos = read_input_image(input_image_or_video_path, data_resolution=data_resolution)
        elif input_type == "video" or input_type == "text_and_video":
            input_videos = read_input_video(
                input_image_or_video_path,
                data_resolution=data_resolution,
                num_input_frames=num_input_frames,
            )
        else:
            raise ValueError(f"Invalid input type {input_type}")
    return input_videos


def prepare_video_batch_for_saving(video_batch: List[torch.Tensor]) -> List[np.ndarray]:
    """
    Function to convert output tensors to numpy format for saving.
    Args:
        video_batch (List[torch.Tensor]): List of output tensors
    Returns:
        List of numpy arrays
    """
    return [(video * 255).to(torch.uint8).permute(1, 2, 3, 0).cpu().numpy() for video in video_batch]