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Signed-off-by: Balarama Raju Buddharaju <[email protected]>
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1 +1 @@ | ||
| 10.2.0.19 | ||
| 10.3.0.26 |
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,117 @@ | ||
| # | ||
| # SPDX-FileCopyrightText: Copyright (c) 1993-2024 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 | ||
|
|
||
| from PIL import Image | ||
|
|
||
| from stable_video_diffusion_pipeline import StableVideoDiffusionPipeline | ||
| from utilities import ( | ||
| PIPELINE_TYPE, | ||
| add_arguments, | ||
| download_image, | ||
| ) | ||
|
|
||
| def parseArgs(): | ||
| parser = argparse.ArgumentParser(description="Options for Stable Diffusion Img2Vid Demo", conflict_handler='resolve') | ||
| parser = add_arguments(parser) | ||
| parser.add_argument('--version', type=str, default="svd-xt-1.1", choices=["svd-xt-1.1"], help="Version of Stable Video Diffusion") | ||
| parser.add_argument('--input-image', type=str, default="", help="Path to the input image") | ||
| parser.add_argument('--height', type=int, default=576, help="Height of image to generate (must be multiple of 8)") | ||
| parser.add_argument('--width', type=int, default=1024, help="Width of image to generate (must be multiple of 8)") | ||
| parser.add_argument('--min-guidance-scale', type=float, default=1.0, help="The minimum guidance scale. Used for the classifier free guidance with first frame") | ||
| parser.add_argument('--max-guidance-scale', type=float, default=3.0, help="The maximum guidance scale. Used for the classifier free guidance with last frame") | ||
| parser.add_argument('--denoising-steps', type=int, default=25, help="Number of denoising steps") | ||
| parser.add_argument('--num-warmup-runs', type=int, default=1, help="Number of warmup runs before benchmarking performance") | ||
| return parser.parse_args() | ||
|
|
||
| def process_pipeline_args(args): | ||
|
|
||
| if not args.input_image: | ||
| args.input_image = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/svd/rocket.png?download=true" | ||
| if isinstance(args.input_image, str): | ||
| input_image = download_image(args.input_image).resize((args.width, args.height)) | ||
| elif isinstance(args.input_image, Image.Image): | ||
| input_image = Image.open(args.input_image) | ||
| else: | ||
| raise ValueError(f"Input image(s) must be of type `PIL.Image.Image` or `str` (URL) but is {type(args.input_image)}") | ||
|
|
||
| if args.height % 8 != 0 or args.width % 8 != 0: | ||
| raise ValueError(f"Image height and width have to be divisible by 8 but are: {args.image_height} and {args.width}.") | ||
|
|
||
| # TODO enable BS>1 | ||
| max_batch_size = 1 | ||
| args.build_static_batch = True | ||
|
|
||
| if args.batch_size > max_batch_size: | ||
| raise ValueError(f"Batch size {args.batch_size} is larger than allowed {max_batch_size}.") | ||
|
|
||
| if not args.build_static_batch or args.build_dynamic_shape: | ||
| raise ValueError(f"Dynamic shapes not supported. Do not specify `--build-dynamic-shape`") | ||
|
|
||
| kwargs_init_pipeline = { | ||
| 'version': args.version, | ||
| 'max_batch_size': max_batch_size, | ||
| 'denoising_steps': args.denoising_steps, | ||
| 'scheduler': args.scheduler, | ||
| 'min_guidance_scale': args.min_guidance_scale, | ||
| 'max_guidance_scale': args.max_guidance_scale, | ||
| 'output_dir': args.output_dir, | ||
| 'hf_token': args.hf_token, | ||
| 'verbose': args.verbose, | ||
| 'nvtx_profile': args.nvtx_profile, | ||
| 'use_cuda_graph': args.use_cuda_graph, | ||
| 'framework_model_dir': args.framework_model_dir, | ||
| 'torch_inference': args.torch_inference, | ||
| } | ||
|
|
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| kwargs_load_engine = { | ||
| 'onnx_opset': args.onnx_opset, | ||
| 'opt_batch_size': args.batch_size, | ||
| 'opt_image_height': args.height, | ||
| 'opt_image_width': args.width, | ||
| 'static_batch': args.build_static_batch, | ||
| 'static_shape': not args.build_dynamic_shape, | ||
| 'enable_all_tactics': args.build_all_tactics, | ||
| 'enable_refit': args.build_enable_refit, | ||
| 'timing_cache': args.timing_cache, | ||
| } | ||
|
|
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| args_run_demo = (input_image, args.height, args.width, args.batch_size, args.batch_count, args.num_warmup_runs, args.use_cuda_graph) | ||
|
|
||
| return kwargs_init_pipeline, kwargs_load_engine, args_run_demo | ||
|
|
||
| if __name__ == "__main__": | ||
| print("[I] Initializing StableDiffusion img2vid demo using TensorRT") | ||
| args = parseArgs() | ||
| kwargs_init_pipeline, kwargs_load_engine, args_run_demo = process_pipeline_args(args) | ||
|
|
||
| # Initialize demo | ||
| demo = StableVideoDiffusionPipeline( | ||
| pipeline_type=PIPELINE_TYPE.IMG2VID, | ||
| **kwargs_init_pipeline) | ||
| demo.loadEngines( | ||
| args.engine_dir, | ||
| args.framework_model_dir, | ||
| args.onnx_dir, | ||
| **kwargs_load_engine) | ||
| demo.loadResources(args.height, args.width, args.batch_size, args.seed) | ||
|
|
||
| # Run inference | ||
| demo.run(*args_run_demo) | ||
|
|
||
| demo.teardown() |
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