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# 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, remove_argument, validate_args
from cosmos_predict1.diffusion.inference.world_generation_pipeline import DiffusionText2WorldMultiviewGenerationPipeline
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)
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 text2world specific arguments
parser.add_argument(
"--diffusion_transformer_dir",
type=str,
default="Cosmos-Predict1-7B-Text2World-Sample-AV-Multiview",
help="DiT model weights directory name relative to checkpoint_dir",
choices=[
"Cosmos-Predict1-7B-Text2World-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",
)
return parser.parse_args()
def demo(args):
"""Run multi-view 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 = DiffusionText2WorldMultiviewGenerationPipeline(
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,
)
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,
}
]
os.makedirs(args.video_save_folder, exist_ok=True)
for i, current_prompt in enumerate(prompts):
# Generate video
generated_output = pipeline.generate(current_prompt)
if generated_output is None:
log.critical("Guardrail blocked text2world 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)
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