wehub-resource-sync
129 lines
4.9 KiB
Python
129 lines
4.9 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 demo_diffusion import dd_argparse
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from demo_diffusion import pipeline as pipeline_module
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def parseArgs():
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parser = argparse.ArgumentParser(description="Options for Wan 2.2 Txt2Vid Demo", conflict_handler='resolve')
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parser = dd_argparse.add_arguments(parser)
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parser.add_argument('--version', type=str, default="wan2.2-t2v-a14b", help="Version of Wan")
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parser.add_argument('--guidance-scale', type=float, default=4.0, help="Guidance scale for high-noise stage (Wan default: 4.0)")
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parser.add_argument('--guidance-scale-2', type=float, default=3.0, help="Guidance scale for low-noise stage (Wan default: 3.0)")
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parser.add_argument('--boundary-ratio', type=float, default=0.875, help="Boundary ratio for two-stage denoising (default: 0.875)")
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parser.add_argument('--denoising-steps', type=int, default=40, help="Number of denoising steps (Wan default: 40)")
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parser.add_argument('--num-warmup-runs', type=int, default=1, help="Number of warmup runs before benchmarking")
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parser.add_argument(
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'--negative-prompt',
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nargs='*',
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default= (
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"vivid colors, overexposed, static, blurry details, subtitles, style, "
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"work of art, painting, picture, still, overall grayish, worst quality, "
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"low quality, JPEG artifacts, ugly, deformed, extra fingers, poorly drawn hands, "
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"poorly drawn face, deformed, disfigured, deformed limbs, fused fingers, "
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"static image, cluttered background, three legs, many people in the background, "
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"walking backwards"
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),
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help="Negative prompt (Wan team default, English translation)"
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)
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parser.add_argument(
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"--onnx-opset",
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type=int,
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default=23,
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choices=range(7, 24),
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help="Select ONNX opset version to target for exported models",
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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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args.height = 720
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args.width = 1280
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args.num_frames = 81
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args.max_sequence_length = 512
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# require static batch = 1
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if args.batch_size > 1:
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raise ValueError(f"Batch size {args.batch_size} is larger than allowed (max=1 for Wan).")
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args.batch_size = 1
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args.build_static_batch = True
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args.build_dynamic_shape = False
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print(f"[I] Building Wan 2.2 T2V with fixed resolution: {args.height}×{args.width}, {args.num_frames} frames")
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negative_prompt = args.negative_prompt
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if isinstance(negative_prompt, list):
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negative_prompt = ' '.join(negative_prompt) if negative_prompt else ""
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kwargs_run_demo = {
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'prompt': args.prompt,
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'height': args.height,
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'width': args.width,
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'num_frames': args.num_frames,
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'batch_size': args.batch_size,
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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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'negative_prompt': negative_prompt,
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'num_inference_steps': args.denoising_steps,
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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 Wan 2.2 txt2vid demo using TensorRT")
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args = parseArgs()
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kwargs_run_demo = process_demo_args(args)
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_, kwargs_load_engine, _ = dd_argparse.process_pipeline_args(args)
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# Initialize demo
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demo = pipeline_module.WanPipeline.FromArgs(args, pipeline_type=pipeline_module.PIPELINE_TYPE.TXT2VID)
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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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# In low-vram mode we allocate the required device memory individually before each model is run
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if args.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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# Load resources
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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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demo.run(**kwargs_run_demo)
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demo.teardown()
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