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https://github.com/NVIDIA/Model-Optimizer.git
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122 lines
5.0 KiB
Bash
Executable File
122 lines
5.0 KiB
Bash
Executable File
#!/bin/bash
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# SPDX-FileCopyrightText: Copyright (c) 2024 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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set -e
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set -x
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set -o pipefail
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start_time=$(date +%s)
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script_dir="$(dirname "$(readlink -f "$0")")"
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cuda_capability=$(nvidia-smi --query-gpu=compute_cap --format=csv,noheader | head -n 1 | tr -d .)
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# Use the tiny models for faster testing
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# NOTE: For tiny sdxl and sd3, we have to use Float dtype instead of Half from transformers v4.49.0
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SDXL="--model sdxl-1.0 --override-model-path hf-internal-testing/tiny-sdxl-pipe"
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SDXL_DTYPE="Float"
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SD3="--model sd3-medium --override-model-path hf-internal-testing/tiny-sd3-pipe"
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SD3_DTYPE="Float"
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FLUX="--model flux-schnell --override-model-path hf-internal-testing/tiny-flux-pipe"
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FLUX_DTYPE="BFloat16"
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##### Quantization #####
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pushd $script_dir/../../examples/diffusers/quantization
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INT8_ARGS="--format int8 --calib-size 8 --collect-method min-mean --percentile 1.0 --alpha 0.8 --quant-level 3.0 --n-steps 20 --quant-algo smoothquant"
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FP8_ARGS="--format fp8 --calib-size 8 --quant-level 3.0 --n-steps 20 --batch-size 2"
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# SDXL FP8 Level 3 + Restore
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python quantize.py $SDXL --model-dtype $SDXL_DTYPE --trt-high-precision-dtype $SDXL_DTYPE $FP8_ARGS --quantized-torch-ckpt-save-path sdxl.fp8.level3.pt --onnx-dir sdxl.fp8.level3.onnx
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python quantize.py $SDXL --model-dtype $SDXL_DTYPE --trt-high-precision-dtype $SDXL_DTYPE $FP8_ARGS --restore-from sdxl.fp8.level3.pt --onnx-dir sdxl.fp8.level3.restore.onnx
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# SDXL INT8 Level 3
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python quantize.py $SDXL --model-dtype $SDXL_DTYPE --trt-high-precision-dtype $SDXL_DTYPE $INT8_ARGS --batch-size 2 --quantized-torch-ckpt-save-path sdxl.int8.level3.pt --onnx-dir sdxl.int8.level3.onnx
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# SD3 Medium INT8 Level 3 + Restore
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python quantize.py $SD3 --model-dtype $SD3_DTYPE --trt-high-precision-dtype $SD3_DTYPE $INT8_ARGS --batch-size 2 --quantized-torch-ckpt-save-path sd3-medium.int8.level3.pt --onnx-dir sd3-medium.int8.level3.onnx
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python quantize.py $SD3 --model-dtype $SD3_DTYPE --trt-high-precision-dtype $SD3_DTYPE $INT8_ARGS --restore-from sd3-medium.int8.level3.pt --onnx-dir sd3-medium.int8.level3.restore.onnx
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# Flux INT8 level 3
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python quantize.py $FLUX --model-dtype $FLUX_DTYPE --trt-high-precision-dtype $FLUX_DTYPE $INT8_ARGS --batch-size 1 --quantized-torch-ckpt-save-path flux-schnell.int8.level3.pt --onnx-dir flux-schnell.int8.level3.onnx
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##### Inference #####
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# INT8
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python diffusion_trt.py $SDXL --model-dtype $SDXL_DTYPE --restore-from sdxl.int8.level3.pt
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rm -rf build
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# INT8 DQ only models
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python diffusion_trt.py $SDXL --model-dtype $SDXL_DTYPE --onnx-load-path sdxl.int8.level3.onnx/model.onnx --dq-only
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python diffusion_trt.py $SD3 --model-dtype $SD3_DTYPE --onnx-load-path sd3-medium.int8.level3.onnx/model.onnx --dq-only
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python diffusion_trt.py $FLUX --model-dtype $FLUX_DTYPE --onnx-load-path flux-schnell.int8.level3.onnx/model.onnx --dq-only
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# FP8 DQ only models
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if [ $cuda_capability -ge 89 ]; then
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python diffusion_trt.py $SDXL --model-dtype $SDXL_DTYPE --onnx-load-path sdxl.fp8.level3.onnx/model.onnx --dq-only
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fi
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popd
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##### Cache Diffusion #####
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pushd $script_dir/../../examples/diffusers/cache_diffusion
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# SDXL
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python -c "
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import torch
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from cache_diffusion import cachify
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from cache_diffusion.utils import SDXL_DEFAULT_CONFIG
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from diffusers import DiffusionPipeline
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pipe = DiffusionPipeline.from_pretrained(
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'stabilityai/stable-diffusion-xl-base-1.0',
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torch_dtype=torch.float16,
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variant='fp16',
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use_safetensors=True,
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).to('cuda')
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cachify.prepare(pipe, SDXL_DEFAULT_CONFIG)
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prompt = 'A random person with a head that is made of flowers, photo by James C. Leyendecker, \
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Afrofuturism, studio portrait, dynamic pose, national geographic photo, retrofuturism, biomorphicy'
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generator = torch.Generator(device='cuda').manual_seed(2946901)
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pipe(prompt=prompt, num_inference_steps=30, generator=generator).images[0]
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"
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# PIXART
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python -c "
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import torch
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from cache_diffusion import cachify
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from cache_diffusion.utils import PIXART_DEFAULT_CONFIG
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from diffusers import PixArtAlphaPipeline
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pipe = PixArtAlphaPipeline.from_pretrained(
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'PixArt-alpha/PixArt-XL-2-1024-MS', torch_dtype=torch.float16
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).to('cuda')
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cachify.prepare(pipe, PIXART_DEFAULT_CONFIG)
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prompt = 'a small cactus with a happy face in the Sahara desert'
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generator = torch.Generator(device='cuda').manual_seed(2946901)
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pipe(prompt=prompt, generator=generator, num_inference_steps=30).images[0]
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"
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# SDXL
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python benchmarks.py --model-id sdxl
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popd
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echo "Total wall time: $(($(date +%s) - start_time)) seconds"
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