wehub-resource-sync
175 lines
6.5 KiB
Python
175 lines
6.5 KiB
Python
#
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# SPDX-FileCopyrightText: Copyright (c) 1993-2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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# SPDX-License-Identifier: Apache-2.0
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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# Configure dependencies before any external imports
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from demo_diffusion import deps
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deps.configure("cosmos")
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import argparse
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from cuda.bindings import runtime as cudart
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from diffusers.utils import load_image, load_video
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from demo_diffusion import dd_argparse
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from demo_diffusion import pipeline as pipeline_module
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def parse_args():
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parser = argparse.ArgumentParser(description="Options for Cosmos video2world Demo", conflict_handler="resolve")
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parser = dd_argparse.add_arguments(parser)
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parser.add_argument(
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"--version",
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type=str,
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default="cosmos-predict2-2b-video2world",
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choices=("cosmos-predict2-2b-video2world", "cosmos-predict2-14b-video2world"),
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help="Version of Cosmos",
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)
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parser.add_argument('--input-image', type=str, default=None, help="Path to the input image")
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parser.add_argument('--input-video', type=str, default=None, help="Path to the input video")
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parser.add_argument(
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"--height",
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type=int,
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default=704,
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help="Height of image to generate (must be multiple of 8)",
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)
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parser.add_argument(
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"--width",
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type=int,
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default=1280,
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help="Width of image to generate (must be multiple of 8)",
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)
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parser.add_argument("--denoising-steps", type=int, default=35, help="Number of denoising steps")
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parser.add_argument(
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"--guidance-scale",
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type=float,
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default=7.0,
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help="Value of classifier-free guidance scale (must be greater than 1)",
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)
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parser.add_argument("--num-frames", type=int, default=93, help="The number of frames in the generated video.")
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parser.add_argument("--fps", type=int, default=16, help="The frames per second of the generated video.")
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parser.add_argument("--num-videos-per-prompt", type=int, default=1, help="The number of videos to generate per prompt.")
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parser.add_argument(
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"--max_sequence_length",
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type=int,
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default=512,
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help="Maximum sequence length to use with the prompt.",
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)
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parser.add_argument(
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"--t5-ws-percentage",
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type=int,
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default=None,
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help="Set runtime weight streaming budget as the percentage of the size of streamable weights for the T5 model. This argument only takes effect when --ws is set. 0 streams the most weights and 100 or None streams no weights. ",
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)
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parser.add_argument(
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"--transformer-ws-percentage",
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type=int,
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default=None,
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help="Set runtime weight streaming budget as the percentage of the size of streamable weights for the transformer model. This argument only takes effect when --ws is set. 0 streams the most weights and 100 or None streams no weights.",
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)
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parser.add_argument(
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"--bf16",
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action="store_true",
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default=True,
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help="Use bfloat16 precision by default.",
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)
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return parser.parse_args()
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def process_demo_args(args):
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batch_size = args.batch_size
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prompt = args.prompt
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negative_prompt = args.negative_prompt
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# Process input args
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if not isinstance(prompt, list):
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raise ValueError(f"`prompt` must be of type `str` list, but is {type(prompt)}")
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prompt = prompt * batch_size
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if not isinstance(negative_prompt, list):
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raise ValueError(f"`negative_prompt` must be of type `str` list, but is {type(negative_prompt)}")
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negative_prompt = negative_prompt * batch_size
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# process input image and input video
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if args.input_image and args.input_video:
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raise ValueError("Only one of --input-image or --input-video can be provided")
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if args.input_image:
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args.input_image = load_image(args.input_image)
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elif args.input_video:
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args.input_video = load_video(args.input_video)
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else:
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# load default image
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args.input_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/yellow-scrubber.png")
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kwargs_run_demo = {
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"prompt": prompt,
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"negative_prompt": negative_prompt,
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"height": args.height,
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"width": args.width,
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"batch_count": args.batch_count,
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"num_warmup_runs": args.num_warmup_runs,
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"use_cuda_graph": args.use_cuda_graph,
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"num_frames": args.num_frames,
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"fps": args.fps,
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"input_image": args.input_image,
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"input_video": args.input_video,
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"num_videos_per_prompt": args.num_videos_per_prompt,
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}
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return kwargs_run_demo
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if __name__ == "__main__":
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print("[I] Initializing Cosmos video2world demo using TensorRT")
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args = parse_args()
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# Enforce torch-inference is enabled
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if not args.torch_inference:
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print("[W] The video2world demo only supports the PyTorch backend. Enabling torch-inference with 'eager' mode.")
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args.torch_inference = "eager"
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_, kwargs_load_engine, _ = dd_argparse.process_pipeline_args(args)
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kwargs_run_demo = process_demo_args(args)
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# Initialize demo
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demo = pipeline_module.CosmosPipeline.FromArgs(args, pipeline_type=pipeline_module.PIPELINE_TYPE.VIDEO2WORLD)
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# Load TensorRT engines and pytorch modules
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demo.load_engines(
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framework_model_dir=args.framework_model_dir,
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**kwargs_load_engine,
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)
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if args.onnx_export_only:
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print("[I] ONNX export completed. Exiting...")
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demo.teardown()
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exit(0)
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# In low-vram mode we allocate the required device memory individually before each model is run.
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if demo.low_vram:
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demo.device_memory_sizes = demo.get_device_memory_sizes()
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else:
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_, shared_device_memory = cudart.cudaMalloc(demo.calculate_max_device_memory())
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demo.activate_engines(shared_device_memory)
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demo.load_resources(args.height, args.width, args.batch_size, args.seed)
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# Run inference
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videos = demo.run(**kwargs_run_demo)
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demo.teardown()
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# save video
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demo.save_video(kwargs_run_demo["prompt"], videos, check_integrity=True)
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