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feat/mr100
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1bd42f0dd6
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FROM corex:4.3.0
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WORKDIR /workspace
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RUN pip install diffusers==0.34.0 sentencepiece transformers==4.55.2
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COPY main.py dataset.json /workspace/
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Dockerfile
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Dockerfile
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Dockerfile.mrv100
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Dockerfile.a100
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Dockerfile.a100
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FROM harbor-contest.4pd.io/zhangyiqun/public/pytorch:2.6.0-cuda12.4-cudnn9-devel
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WORKDIR /workspace
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# ENV PT_SDPA_ENABLE_HEAD_DIM_PADDING=1
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RUN pip install diffusers transformers sentencepiece -i https://nexus.4pd.io/repository/pypi-all/simple
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COPY main.py test.sh dataset.json /workspace/
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Dockerfile.bi100
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Dockerfile.bi100
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FROM git.modelhub.org.cn:980/enginex-iluvatar/bi100-3.2.1-x86-ubuntu20.04-py3.10-poc-llm-infer:v1.2.2
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WORKDIR /workspace
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ENV PT_SDPA_ENABLE_HEAD_DIM_PADDING=1
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RUN pip install diffusers==0.34.0
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COPY main.py test.sh dataset.json /workspace/
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Dockerfile.mrv100
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Dockerfile.mrv100
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FROM git.modelhub.org.cn:980/enginex-iluvatar/mr100_corex:4.3.0
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WORKDIR /workspace
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COPY whls-mrv100 /packages
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RUN pip install diffusers==0.34.0 sentencepiece transformers==4.55.2
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# RUN pip install /packages/*.whl
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COPY main.py test.sh dataset.json /workspace/
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README.md
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README.md
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## Installation
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参考Dockerfile,构建运行镜像
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## Quickstart
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## Quickstart
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### 构建镜像
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```bash
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docker build -t diffusers:v0.1 .
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```
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其中,基础镜像 corex:4.3.0 通过联系天数智芯智铠100厂商技术支持可获取
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### 模型下载
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模型地址:https://modelscope.cn/models/AI-ModelScope/stable-diffusion-v1-5
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并放到目录:`/mnt/contest_ceph/zhanghao/models/stable-diffusion-v1-5`(如更改目录,请修改后面的执行脚本中的模型路径)
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### 测试程序
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### 测试程序
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1. 准备输入数据集,可以参考示例`dataset.json`
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1. 下载模型:https://modelscope.cn/models/AI-ModelScope/stable-diffusion-v1-5
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2. 在docker镜像里运行测试程序,会根据`dataset.json`内容,在`output`目录下生成图片文件。
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2. 运行测试程序
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修改测试程序`test.py`里面的模型路径,直接执行即可
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```bash
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```bash
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./run_in_docker.sh
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python3 test.py
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```
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### 批量测试程序
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1. 准备输入数据集`dataset.json`,可以参考示例`dataset.json`
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2. 运行测试程序
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```bash
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python3 main.py --model "/mnt/contest_ceph/zhanghao/models/stable-diffusion-v1-5" --json "dataset.json" --results "results.json" --outdir "output" --device cuda --dtype fp16
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```
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```
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## 测试结果
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## 测试结果
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| | A100 平均生成时间(秒) | 智铠100 平均生成时间(秒) |
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|------|-------------------------|----------------------------|
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| 时间 | 1.5 | 4.1 |
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| 模型名称 | A100 平均生成时间(秒) | 智铠100 平均生成时间(秒) | 备注 |
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| ----- | ----- | ----- | ----- |
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| AI-ModelScope/stable-diffusion-3.5-medium | 5.3253 | 56.6237 | |
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| AI-ModelScope/stable-diffusion-v1-5 | 1.3944 | 3.9280 | |
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| Cleaner/lo_dress_gothic_style2_v2 | 1.8821 | 2.5121 | |
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| Cleaner/Tyndalleffect_Light | 1.7133 | 2.5138 | |
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| HanYixuan1/Retro_style | 1.6764 | 2.4901 | |
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| JeffVan/Oil_Paint_Style_LORA | 1.6673 | 2.7132 | |
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| lljjcc/IndianSarres | 1.7252 | 2.5209 | |
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| lljjcc/outdoor | 1.6684 | 2.5230 | |
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| muse/flux_vae | 1.0812 | 2.5350 | |
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| MusePublic/489_ckpt_FLUX_1/base_model | 1.2286 | 2.5382 | |
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| rewfueranro/cartoon_lora | 1.6880 | 2.6043 | |
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| YorickHe/fairy_lora | 1.7765 | 2.5139 | |
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| YorickHe/JK_uniform_lora | 1.7844 | 2.5219 | |
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| YorickHe/outdoor_photo_lora | 1.6441 | 2.4851 | |
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| YorickHe/polaroid_lora | 1.6272 | 2.5117 | |
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| YorickHe/Winter_hanfu_lora | 1.5962 | 2.4892 | |
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| ZackWang123/filmvelvia_lora | 1.8465 | 2.5157 | |
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#! /usr/bin/env bash
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#! /usr/bin/env bash
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image=diffusers:v0.1
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image=harbor-contest.4pd.io/zhanghao/diffusers:bi100-0.2
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docker run -v `pwd`:/workspace -v /mnt/contest_ceph/zhanghao/models/stable-diffusion-v1-5:/workspace/stable-diffusion-v1-5 --device=dev/iluvatar1:/dev/iluvatar0 $image python3 main.py --model "./stable-diffusion-v1-5" --json "dataset.json" --results "results.json" --outdir "output" --device cuda --dtype fp16
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docker run -it -v /root/zhanghao:/workspace -v /mnt:/mnt --device=dev/iluvatar1:/dev/iluvatar0 $image bash
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run_in_docker_a100.sh
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run_in_docker_a100.sh
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#! /usr/bin/env bash
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image=harbor-contest.4pd.io/zhanghao/diffusers:a100-0.2
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docker run -it -v /home/zhanghao/workspace:/workspace -v /mnt:/mnt $image bash
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run_in_docker_mrv100.sh
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run_in_docker_mrv100.sh
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#! /usr/bin/env bash
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image=harbor-contest.4pd.io/zhanghao/diffusers:mrv100-0.2
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docker run -it -v /root/zhanghao:/workspace -v /mnt:/mnt --device=dev/iluvatar0:/dev/iluvatar0 $image bash
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test.py
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test.py
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from diffusers import DiffusionPipeline
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import torch
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import time
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model_path = "/mnt/contest_ceph/zhanghao/models/stable-diffusion-v1-5"
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# model_path = "/mnt/contest_ceph/zhanghao/models/stable-diffusion-3.5-medium"
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pipeline = DiffusionPipeline.from_pretrained(model_path, torch_dtype=torch.float16)
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pipeline.to("cuda")
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start = time.time()
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image = pipeline("An image of a squirrel in Picasso style").images[0]
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end = time.time()
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print(f"elapsed: {end - start}")
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image.save("squirrel_picasso.png")
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