mount current dir to /workspace

Dockerfile

@@ -1 +0,0 @@
-Dockerfile.bi100

Dockerfile

@@ -0,0 +1,6 @@
+FROM git.modelhub.org.cn:9443/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 dataset.json /workspace/

Dockerfile.a100

@@ -1,5 +0,0 @@
-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

@@ -1,6 +0,0 @@
-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

@@ -1,7 +0,0 @@
-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

@@ -1,24 +1,19 @@
-## Installation
-参考Dockerfile，构建运行镜像
-
 ## Quickstart
 
-### 测试程序
+### 构建镜像
-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
-
 ```bash
-python3 test.py
+docker build -t diffusers:v0.1 .
 ```
 
-### 批量测试程序
+### 模型下载
-1. 准备输入数据集`dataset.json`，可以参考示例`dataset.json`
+模型地址：https://modelscope.cn/models/AI-ModelScope/stable-diffusion-v1-5
-2. 运行测试程序
+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

run_in_docker.sh

@@ -1,3 +1,3 @@
 #! /usr/bin/env bash
-image=harbor-contest.4pd.io/zhanghao/diffusers:bi100-0.2
+image=diffusers:v0.1
-docker run -it -v /root/zhanghao:/workspace -v /mnt:/mnt --device=dev/iluvatar1:/dev/iluvatar0 $image bash
+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

@@ -1,3 +0,0 @@
-#! /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

@@ -1,3 +0,0 @@
-#! /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

@@ -1,13 +0,0 @@
-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

@@ -1 +0,0 @@
-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