# 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, validate_args from cosmos_predict1.diffusion.inference.world_generation_pipeline import DiffusionText2WorldGenerationPipeline 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="Text to world generation demo script") # Add common arguments add_common_arguments(parser) # Add text2world specific arguments parser.add_argument( "--diffusion_transformer_dir", type=str, default="Cosmos-Predict1-7B-Text2World", help="DiT model weights directory name relative to checkpoint_dir", choices=[ "Cosmos-Predict1-7B-Text2World", "Cosmos-Predict1-14B-Text2World", "Cosmos-Predict1-7B-Text2World_post-trained", "Cosmos-Predict1-7B-Text2World_post-trained-4gpu_80gb", "Cosmos-Predict1-7B-Text2World_post-trained-8gpu_40gb", "Cosmos-Predict1-7B-Text2World_post-trained-4gpu_40gb", "Cosmos-Predict1-7B-Text2World_post-trained-lora", "Cosmos-Predict1-14B-Text2World_post-trained", ], ) parser.add_argument( "--prompt_upsampler_dir", type=str, default="Cosmos-UpsamplePrompt1-12B-Text2World", help="Prompt upsampler weights directory relative to checkpoint_dir", ) parser.add_argument( "--word_limit_to_skip_upsampler", type=int, default=250, help="Skip prompt upsampler for better robustness if the number of words in the prompt is greater than this value", ) return parser.parse_args() def demo(args): """Run text-to-world generation demo. This function handles the main text-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 from input - Generating videos from text prompts - 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, 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 = "text2world" 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 text2world generation model pipeline pipeline = DiffusionText2WorldGenerationPipeline( inference_type=inference_type, checkpoint_dir=args.checkpoint_dir, checkpoint_name=args.diffusion_transformer_dir, prompt_upsampler_dir=args.prompt_upsampler_dir, enable_prompt_upsampler=not args.disable_prompt_upsampler, offload_network=args.offload_diffusion_transformer, offload_tokenizer=args.offload_tokenizer, offload_text_encoder_model=args.offload_text_encoder_model, offload_prompt_upsampler=args.offload_prompt_upsampler, 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, seed=args.seed, ) 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}] os.makedirs(args.video_save_folder, exist_ok=True) for i, input_dict in enumerate(prompts): current_prompt = input_dict.get("prompt", None) if current_prompt is None: log.critical("Prompt is missing, skipping world generation.") continue # Generate video generated_output = pipeline.generate(current_prompt, args.negative_prompt, args.word_limit_to_skip_upsampler) if generated_output is None: log.critical("Guardrail blocked text2world generation.") continue video, prompt = generated_output if args.batch_input_path: video_save_path = os.path.join(args.video_save_folder, f"{i}.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") 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=5, video_save_path=video_save_path, ) # Save prompt to text file alongside video with open(prompt_save_path, "wb") as f: f.write(prompt.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)