# 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 os import torch from cosmos_predict1.diffusion.inference.inference_utils import ( add_common_arguments, check_input_frames, get_input_sizes, remove_argument, validate_args, ) from cosmos_predict1.diffusion.inference.world_generation_pipeline import ( DiffusionVideo2WorldMultiviewGenerationPipeline, ) from cosmos_predict1.utils import log, misc from cosmos_predict1.utils.io import read_prompts_from_file, save_video torch.enable_grad(False) def parse_arguments() -> argparse.Namespace: parser = argparse.ArgumentParser(description="Video to world generation demo script") # Add common arguments add_common_arguments(parser) remove_argument(parser, "width") remove_argument(parser, "height") remove_argument(parser, "num_video_frames") parser.add_argument("--height", type=int, default=480, help="Height of video to sample") parser.add_argument("--width", type=int, default=848, help="Width of video to sample") parser.add_argument( "--num_video_frames", type=int, default=57, choices=[57], help="Number of video frames to sample, this is per-camera frame number.", ) # Add video2world specific arguments parser.add_argument( "--diffusion_transformer_dir", type=str, default="Cosmos-Predict1-7B-Video2World-Sample-AV-Multiview", help="DiT model weights directory name relative to checkpoint_dir", choices=[ "Cosmos-Predict1-7B-Video2World-Sample-AV-Multiview", ], ) parser.add_argument( "--prompt_left", type=str, default="The video is captured from a camera mounted on a car. The camera is facing to the left. ", help="Text prompt for generating left camera view video", ) parser.add_argument( "--prompt_right", type=str, default="The video is captured from a camera mounted on a car. The camera is facing to the right.", help="Text prompt for generating right camera view video", ) parser.add_argument( "--prompt_back", type=str, default="The video is captured from a camera mounted on a car. The camera is facing backwards.", help="Text prompt for generating rear camera view video", ) parser.add_argument( "--prompt_back_left", type=str, default="The video is captured from a camera mounted on a car. The camera is facing the rear left side.", help="Text prompt for generating left camera view video", ) parser.add_argument( "--prompt_back_right", type=str, default="The video is captured from a camera mounted on a car. The camera is facing the rear right side.", help="Text prompt for generating right camera view video", ) parser.add_argument( "--frame_repeat_negative_condition", type=float, default=10.0, help="frame_repeat number to be used as negative condition", ) parser.add_argument( "--input_image_or_video_path", type=str, help="Input video/image path for generating a single video", ) parser.add_argument( "--num_input_frames", type=int, default=1, help="Number of input frames for video2world prediction", choices=[1, 9], ) return parser.parse_args() def demo(args): """Run multi-view video-to-world generation demo. This function handles the main video-to-world generation pipeline, including: - Setting up the random seed for reproducibility - Initializing the generation pipeline with the provided configuration - Processing single or multiple prompts/images/videos from input - Generating videos from prompts and images/videos - Saving the generated videos and corresponding prompts to disk Args: cfg (argparse.Namespace): Configuration namespace containing: - Model configuration (checkpoint paths, model settings) - Generation parameters (guidance, steps, dimensions) - Input/output settings (prompts/images/videos, save paths) - Performance options (model offloading settings) The function will save: - Generated MP4 video files - Text files containing the processed prompts If guardrails block the generation, a critical log message is displayed and the function continues to the next prompt if available. """ misc.set_random_seed(args.seed) inference_type = "video2world" validate_args(args, inference_type) if args.num_gpus > 1: from megatron.core import parallel_state from cosmos_predict1.utils import distributed distributed.init() parallel_state.initialize_model_parallel(context_parallel_size=args.num_gpus) process_group = parallel_state.get_context_parallel_group() # Initialize video2world generation model pipeline pipeline = DiffusionVideo2WorldMultiviewGenerationPipeline( inference_type=inference_type, checkpoint_dir=args.checkpoint_dir, checkpoint_name=args.diffusion_transformer_dir, offload_network=args.offload_diffusion_transformer, offload_tokenizer=args.offload_tokenizer, offload_text_encoder_model=args.offload_text_encoder_model, offload_guardrail_models=args.offload_guardrail_models, disable_guardrail=args.disable_guardrail, guidance=args.guidance, num_steps=args.num_steps, height=args.height, width=args.width, fps=args.fps, num_video_frames=args.num_video_frames, frame_repeat_negative_condition=args.frame_repeat_negative_condition, seed=args.seed, num_input_frames=args.num_input_frames, ) if args.num_gpus > 1: pipeline.model.net.enable_context_parallel(process_group) # Handle multiple prompts if prompt file is provided if args.batch_input_path: log.info(f"Reading batch inputs from path: {args.batch_input_path}") prompts = read_prompts_from_file(args.batch_input_path) else: # Single prompt case prompts = [ { "prompt": args.prompt, "prompt_left": args.prompt_left, "prompt_right": args.prompt_right, "prompt_back": args.prompt_back, "prompt_back_left": args.prompt_back_left, "prompt_back_right": args.prompt_back_right, "visual_input": args.input_image_or_video_path, } ] os.makedirs(args.video_save_folder, exist_ok=True) for i, input_dict in enumerate(prompts): current_image_or_video_path = input_dict.pop("visual_input", None) if current_image_or_video_path is None: log.critical("Visual input is missing, skipping world generation.") continue current_prompt = input_dict # Check input frames if not check_input_frames(current_image_or_video_path, args.num_input_frames): continue log.warning("Visual input is provided, overriding --height and --width arguments.") args.height, args.width = get_input_sizes(current_image_or_video_path) # Generate video generated_output = pipeline.generate( prompt=current_prompt, image_or_video_path=current_image_or_video_path, ) if generated_output is None: log.critical("Guardrail blocked video2world generation.") continue [video_grid, video], prompt = generated_output if args.batch_input_path: video_save_path = os.path.join(args.video_save_folder, f"{i}.mp4") video_grid_save_path = os.path.join(args.video_save_folder, f"{i}_grid.mp4") prompt_save_path = os.path.join(args.video_save_folder, f"{i}.txt") else: video_save_path = os.path.join(args.video_save_folder, f"{args.video_save_name}.mp4") video_grid_save_path = os.path.join(args.video_save_folder, f"{args.video_save_name}_grid.mp4") prompt_save_path = os.path.join(args.video_save_folder, f"{args.video_save_name}.txt") # Save video save_video( video=video, fps=args.fps, H=args.height, W=args.width, video_save_quality=10, video_save_path=video_save_path, ) save_video( video=video_grid, fps=args.fps, H=args.height * 2, W=args.width * 3, video_save_quality=5, video_save_path=video_grid_save_path, ) # Save prompt to text file alongside video with open(prompt_save_path, "wb") as f: for key, value in prompt.items(): f.write(value.encode("utf-8")) f.write("\n".encode("utf-8")) log.info(f"Saved video to {video_save_path}") log.info(f"Saved prompt to {prompt_save_path}") # clean up properly if args.num_gpus > 1: parallel_state.destroy_model_parallel() import torch.distributed as dist dist.destroy_process_group() if __name__ == "__main__": args = parse_arguments() demo(args)