Update README.md

Dockerfile

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-Dockerfile.bi100

Dockerfile

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+FROM corex:4.3.0
+
+WORKDIR /workspace
+RUN pip install diffusers==0.34.0 sentencepiece transformers==4.55.2
+COPY main.py dataset.json /workspace/

Dockerfile.a100

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-FROM harbor-contest.4pd.io/zhangyiqun/public/pytorch:2.6.0-cuda12.4-cudnn9-devel
-WORKDIR /workspace
-# ENV PT_SDPA_ENABLE_HEAD_DIM_PADDING=1
-RUN pip install diffusers transformers sentencepiece -i https://nexus.4pd.io/repository/pypi-all/simple
-COPY main.py test.sh dataset.json /workspace/

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
-
-WORKDIR /workspace
-ENV PT_SDPA_ENABLE_HEAD_DIM_PADDING=1
-RUN pip install diffusers==0.34.0
-COPY main.py test.sh dataset.json /workspace/

Dockerfile.mrv100

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-FROM git.modelhub.org.cn:980/enginex-iluvatar/mr100_corex:4.3.0
-
-WORKDIR /workspace
-COPY whls-mrv100 /packages
-RUN pip install diffusers==0.34.0 sentencepiece transformers==4.55.2
-# RUN pip install /packages/*.whl
-COPY main.py test.sh dataset.json /workspace/

README.md

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-## Installation
-参考Dockerfile，构建运行镜像
-
 ## Quickstart
 
-### 测试程序
-1. 下载模型：https://modelscope.cn/models/AI-ModelScope/stable-diffusion-v1-5
-
-2. 运行测试程序
-
-修改测试程序`test.py`里面的模型路径，直接执行即可
-
+### 构建镜像
 ```bash
-python3 test.py
+docker build -t diffusers:v0.1 .
 ```
+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
 
-### 批量测试程序
-1. 准备输入数据集`dataset.json`，可以参考示例`dataset.json`
-2. 运行测试程序
+### 模型下载
+模型地址：https://modelscope.cn/models/AI-ModelScope/stable-diffusion-v1-5
+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）
+
+### 测试程序
+1. 准备输入数据集，可以参考示例`dataset.json`
+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。
 ```bash
-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
+./run_in_docker.sh
 ```
 
 ## 测试结果
-|      | A100 平均生成时间（秒） | 天垓100 平均生成时间（秒） |
-|------|-------------------------|----------------------------|
-| 时间 | 1.4289                  | 7.5609                     |
 
+| 模型名称 | A100 平均生成时间（秒） | 智铠100 平均生成时间（秒） | 备注 |
+| ----- | ----- | ----- | ----- |
+| AI-ModelScope/stable-diffusion-3.5-medium | 5.3253 | 56.6237 | |
+| AI-ModelScope/stable-diffusion-v1-5 | 1.3944 | 3.9280 | |
+| AI-ModelScope/stable-diffusion-v1-5 | 3.0271 | 6.9434 | |
+| AI-ModelScope/stable-diffusion-v1-5 | 3.5003 | 6.9644 | |
+| AI-ModelScope/stable-diffusion-v1-5 | 1.5982 | 3.9384 | |
+| Cleaner/lo_dress_gothic_style2_v2 | 3.4047 | 12.7571 | |
+| Cleaner/lo_dress_gothic_style2_v2 | 1.8821 | 2.5121 | |
+| Cleaner/Tyndalleffect_Light | 3.3741 | 12.2595 | |
+| Cleaner/Tyndalleffect_Light | 1.7133 | 2.5138 | |
+| HanYixuan1/Retro_style | 3.6395 | 7.6439 | |
+| HanYixuan1/Retro_style | 1.6764 | 2.4901 | |
+| JeffVan/Oil_Paint_Style_LORA | 3.7807 | 6.9419 | |
+| JeffVan/Oil_Paint_Style_LORA | 1.6673 | 2.7132 | |
+| lljjcc/IndianSarres | 3.8178 | 12.5799 | |
+| lljjcc/IndianSarres | 1.7252 | 2.5209 | |
+| lljjcc/outdoor | 3.7997 | 12.8596 | |
+| lljjcc/outdoor | 1.6684 | 2.5230 | |
+| muse/flux_vae | 1.0812 | 2.5350 | |
+| MusePublic/489_ckpt_FLUX_1/base_model | 1.2286 | 2.5382 | |
+| rewfueranro/cartoon_lora | 2.7893 | 6.9129 | |
+| rewfueranro/cartoon_lora | 1.6880 | 2.6043 | |
+| YorickHe/fairy_lora | 3.6555 | 7.1317 | |
+| YorickHe/fairy_lora | 1.7765 | 2.5139 | |
+| YorickHe/JK_uniform_lora | 2.8396 | 6.8613 | |
+| YorickHe/JK_uniform_lora | 1.7844 | 2.5219 | |
+| YorickHe/outdoor_photo_lora | 3.7066 | 7.0899 | |
+| YorickHe/outdoor_photo_lora | 1.6441 | 2.4851 | |
+| YorickHe/polaroid_lora | 3.7494 | 7.1097 | |
+| YorickHe/polaroid_lora | 1.6272 | 2.5117 | |
+| YorickHe/Winter_hanfu_lora | 2.8050 | 7.1169 | |
+| YorickHe/Winter_hanfu_lora | 1.5962 | 2.4892 | |
+| ZackWang123/filmvelvia_lora | 3.3700 | 6.9867 | |
+| ZackWang123/filmvelvia_lora | 1.8465 | 2.5157 | |
+| zhanghaohit/karlo-v1-alpha | 3.2812 | 18.8275 | |
+| zhanghaohit/karlo-v1-alpha | 3.2040 | 18.8392 | |

run_in_docker.sh

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 #! /usr/bin/env bash
-image=harbor-contest.4pd.io/zhanghao/diffusers:bi100-0.2
-docker run -it -v /root/zhanghao:/workspace -v /mnt:/mnt --device=dev/iluvatar1:/dev/iluvatar0 $image bash
+image=diffusers:v0.1
+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

run_in_docker_a100.sh

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-#! /usr/bin/env bash
-image=harbor-contest.4pd.io/zhanghao/diffusers:a100-0.2
-docker run -it -v /home/zhanghao/workspace:/workspace -v /mnt:/mnt $image bash

run_in_docker_mrv100.sh

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-#! /usr/bin/env bash
-image=harbor-contest.4pd.io/zhanghao/diffusers:mrv100-0.2
-docker run -it -v /root/zhanghao:/workspace -v /mnt:/mnt --device=dev/iluvatar0:/dev/iluvatar0 $image bash

test.py

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-from diffusers import DiffusionPipeline
-import torch
-import time
-
-model_path = "/mnt/contest_ceph/zhanghao/models/stable-diffusion-v1-5"
-# model_path = "/mnt/contest_ceph/zhanghao/models/stable-diffusion-3.5-medium"
-pipeline = DiffusionPipeline.from_pretrained(model_path, torch_dtype=torch.float16)
-pipeline.to("cuda")
-start = time.time()
-image = pipeline("An image of a squirrel in Picasso style").images[0]
-end = time.time()
-print(f"elapsed: {end - start}")
-image.save("squirrel_picasso.png")

test.sh

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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