wehub-resource-sync
136 lines
4.6 KiB
Python
136 lines
4.6 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("sd")
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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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# Stable Diffusion 3.5 configuration
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parser = argparse.ArgumentParser(
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description="Options for Stable Diffusion 3.5 Txt2Img Demo", conflict_handler="resolve"
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)
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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="3.5-medium",
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choices={"3.5-medium", "3.5-large"},
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help="Version of Stable Diffusion 3.5",
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)
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parser.add_argument("--height", type=int, default=1024, help="Height of image to generate (must be multiple of 8)")
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parser.add_argument("--width", type=int, default=1024, help="Height of image to generate (must be multiple of 8)")
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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(
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"--max-sequence-length",
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type=int,
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default=256,
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help="Maximum sequence length to use with the prompt.",
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)
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parser.add_argument("--denoising-steps", type=int, default=50, help="Number of denoising steps")
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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 prompt
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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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if len(negative_prompt) == 1:
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negative_prompt = negative_prompt * batch_size
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if args.height % 8 != 0 or args.width % 8 != 0:
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raise ValueError(
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f"Image height and width have to be divisible by 8 but specified as: {args.image_height} and {args.width}."
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)
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max_batch_size = 4
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if args.batch_size > max_batch_size:
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raise ValueError(f"Batch size {args.batch_size} is larger than allowed {max_batch_size}.")
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if args.use_cuda_graph and (not args.build_static_batch or args.build_dynamic_shape):
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raise ValueError(
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"Using CUDA graph requires static dimensions. Enable `--build-static-batch` and do not specify `--build-dynamic-shape`"
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)
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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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}
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return kwargs_run_demo
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if __name__ == "__main__":
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print("[I] Initializing Stable Diffusion 3.5 demo using TensorRT")
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args = parseArgs()
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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.StableDiffusion35Pipeline.FromArgs(args, pipeline_type=pipeline_module.PIPELINE_TYPE.TXT2IMG)
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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 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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# Load resources
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demo.load_resources(
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image_height=args.height,
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image_width=args.width,
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batch_size=args.batch_size,
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seed=args.seed,
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)
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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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