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feat(agent): 优化Docker全流程，新增Docker启动脚本.
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‎Dockerfile‎

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# Stage 1: Base environment build
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FROM ubuntu:22.04 as builder
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# Copyright 2025 IDDM Authors
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# Disable interactive prompts and configure time zone
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ENV DEBIAN_FRONTEND=noninteractive \
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TZ=Etc/UTC
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# Install apt-get dependencies
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RUN apt-get update && \
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apt-get install -y --no-install-recommends ca-certificates wget git openssh-client && \
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rm -rf /var/lib/apt/lists/*
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# Install Miniconda
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ARG MINICONDA_VERSION=py310_25.1.1-2
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RUN wget --quiet https://repo.anaconda.com/miniconda/Miniconda3-${MINICONDA_VERSION}-Linux-x86_64.sh -O ~/miniconda.sh && \
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/bin/bash ~/miniconda.sh -b -p /opt/conda && \
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rm ~/miniconda.sh
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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# Set conda environment
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ENV PATH=/opt/conda/bin:$PATH
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# http://www.apache.org/licenses/LICENSE-2.0
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# Stage 2: App deployment
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FROM ubuntu:22.04
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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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# Copy necessary content from the stage 1 (builder)
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COPY --from=builder /opt/conda /opt/conda
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# Use NVIDIA CUDA base image for GPU support
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FROM nvidia/cuda:12.8.0-cudnn-runtime-ubuntu22.04
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# For China region
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# FROM swr.cn-north-4.myhuaweicloud.com/ddn-k8s/docker.io/nvidia/cuda:12.8.0-cudnn-runtime-ubuntu22.04
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# Set environment variables
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ENV PATH=/opt/conda/bin:$PATH \
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# Disable interactive prompts and configure time zone
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ENV DEBIAN_FRONTEND=noninteractive \
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TZ=Etc/UTC \
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LANG=C.UTF-8 \
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PYTHONUNBUFFERED=1
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# Git clone the repository
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# Install system dependencies
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RUN apt-get update && \
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apt-get install -y --no-install-recommends python3 python3-pip git && \
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rm -rf /var/lib/apt/lists/* && \
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ln -sf /usr/bin/python3 /usr/bin/python
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# Clone the repository
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RUN git clone --depth 1 https://github.com/chairc/Integrated-Design-Diffusion-Model.git /app
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# For China region
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# RUN git clone --depth 1 https://gitee.com/chairc/Integrated-Design-Diffusion-Model.git /app
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# Set working directory
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WORKDIR /app
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# Install (conda + pip)
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# If pip is not available, use conda
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RUN conda install -y pytorch=1.13.0 torchvision=0.14.0 torchaudio=0.13.0 pytorch-cuda=11.8 -c pytorch -c nvidia && \
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conda clean -ya
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# If conda is not available, use pip
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# RUN pip install torch==1.13.0+cu116 torchvision==0.14.0+cu116 -f https://download.pytorch.org/whl/torch_stable.html
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# Install PyTorch (compatible with CUDA 12.8)
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RUN pip install torch==2.7.0 torchvision==0.22.0 torchaudio==2.7.0 --index-url https://download.pytorch.org/whl/cu128
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# For China region
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# RUN pip install torch==2.7.0 torchvision==0.22.0 torchaudio==2.7.0 --index-url https://mirrors.tuna.tsinghua.edu.cn/pypi/web/simple
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RUN cd /app/Integrated-Design-Diffusion-Model && \
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pip install -r requirements.txt && \
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pip cache purge
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# Install project dependencies
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RUN pip install --no-cache-dir --trusted-host pypi.org --trusted-host files.pythonhosted.org --no-deps -r requirements.txt
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# For China region
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# RUN pip install --no-cache-dir -r requirements.txt --index-url https://mirrors.tuna.tsinghua.edu.cn/pypi/web/simple
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# Verify installation
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RUN python -c "import torch; print(f'PyTorch version: {torch.__version__}')" && \

‎README.md‎

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@@ -82,6 +82,22 @@ python train.py --xxx xxx # Replace --xxx with your training parameters
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python generate.py --xxx xxx # Replace --xxx with your generation parameters
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```
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#### Docker Running
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Modification contents, please refer to the `docker_run.sh` script.
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```bash
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# Mount and build the entire project
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bash docker_run.sh -a /path/to/project
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# Mount and build specified paths
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bash docker_run.sh \
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-d /path/to/datasets \
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-r /path/to/results \
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-w /path/to/weights
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```
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`/path/to/project`, `/path/to/datasets`, `/path/to/results` and `/path/to/weights` are local paths, used to mount the full project, dataset, result and weight directories respectively.
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#### Installation
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In addition to running locally, there are also the following two approachs for installing this code.
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```yaml
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coloredlogs==15.0.1
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gradio>=6.0.0
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gradio==6.0.0
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matplotlib==3.7.1
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numpy>=1.25.0
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Pillow>=12.2.0
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Requests>=2.32.4
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numpy==1.25.0
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Pillow==12.2.0
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Requests==2.32.4
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scikit-image==0.22.0
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torch_summary==1.4.5
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tqdm==4.66.3
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pytorch_fid==0.3.0
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fastapi==0.115.6
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tensorboard==2.19.0
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tensorboardX==2.6.1
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transformers>=5.0.0
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transformers==5.0.0
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# If you want to use flash attention, please install flash-attn.
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# Compile your own environment: pip install flash-attn --no-build-isolation

‎README_zh.md‎

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python generate.py --xxx xxx # 用个人参数替换-xxx
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```
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#### Docker运行
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运行`构建脚本+运行容器`bash脚本，详细修改内容请参考`docker_run.sh`脚本，如遇下载问题请修改`Dockerfile`的中国区域下载地址
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```bash
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# 构建项目挂载
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bash docker_run.sh -a /path/to/project
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# 构建指定路径挂载
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bash docker_run.sh \
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-d /path/to/datasets \
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-r /path/to/results \
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-w /path/to/weights
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```
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其中，`/path/to/project`、`/path/to/datasets`、`/path/to/results`和`/path/to/weights`为本地的路径，用于挂载全项目、数据集、结果和权重目录
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#### 安装
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除了本地运行外，也可采用下列两种方式安装本代码
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```yaml
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coloredlogs==15.0.1
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gradio>=6.0.0
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gradio==6.0.0
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matplotlib==3.7.1
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numpy>=1.25.0
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Pillow>=12.2.0
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Requests>=2.32.4
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numpy==1.25.0
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Pillow==12.2.0
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Requests==2.32.4
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scikit-image==0.22.0
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torch_summary==1.4.5
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tqdm==4.66.3
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pytorch_fid==0.3.0
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fastapi==0.115.6
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tensorboard==2.19.0
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tensorboardX==2.6.1
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transformers>=5.0.0
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transformers==5.0.0
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# 如果您想使用 flash attention，请安装 flash-attn
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# 编译自己的环境：pip install flash-attn --no-build-isolation

‎docker_run.sh‎

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#!/bin/bash
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# Copyright 2025 IDDM Authors
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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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# http://www.apache.org/licenses/LICENSE-2.0
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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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IMAGE_NAME="iddm-image"
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CONTAINER_NAME="iddm-container"
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HOST_DATASET_DIR=""
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HOST_OUTPUT_DIR=""
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HOST_WEIGHT_DIR=""
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HOST_PROJECT_DIR=""
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show_help() {
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echo "Usage: bash docker_run.sh [options]"
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echo ""
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echo "Options:"
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echo " -a, --all Host project root directory (mapped to /app)"
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echo " -d, --dataset Host dataset directory (mapped to /app/datasets)"
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echo " -r, --result Host result directory (mapped to /app/results)"
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echo " -w, --weight Host weight directory (mapped to /app/weights)"
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echo " -b, --build Only build image, do not run container"
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echo " -h, --help Show this help message"
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echo ""
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echo "Example:"
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echo " bash docker_run.sh -a /home/user/project"
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echo " bash docker_run.sh -d /home/user/datasets -r /home/user/results -w /home/user/weights"
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}
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while [[ $# -gt 0 ]]; do
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case "$1" in
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-a|--all)
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HOST_PROJECT_DIR="$2"; shift 2 ;;
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-d|--dataset)
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HOST_DATASET_DIR="$2"; shift 2 ;;
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-r|--result)
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HOST_OUTPUT_DIR="$2"; shift 2 ;;
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-w|--weight)
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HOST_WEIGHT_DIR="$2"; shift 2 ;;
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-b|--build)
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BUILD_ONLY=true; shift ;;
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-h|--help)
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show_help; exit 0 ;;
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*)
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echo "Unknown option: $1"; show_help; exit 1 ;;
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esac
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done
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echo "=============================="
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echo " Step 1: Building Docker image"
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echo "=============================="
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sudo docker build -t "${IMAGE_NAME}" .
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echo "Image '${IMAGE_NAME}' built successfully."
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echo ""
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if [ "${BUILD_ONLY}" = true ]; then
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echo "Build-only mode. Exiting."
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exit 0
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fi
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MOUNT_ARGS=""
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if [ -n "${HOST_PROJECT_DIR}" ]; then
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if [ -n "${HOST_DATASET_DIR}" ] || [ -n "${HOST_OUTPUT_DIR}" ] || [ -n "${HOST_WEIGHT_DIR}" ]; then
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echo "Warning: -a/--all is specified, individual mounts (-d/-r/-w) will be ignored."
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fi
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mkdir -p "${HOST_PROJECT_DIR}"
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MOUNT_ARGS="${MOUNT_ARGS} -v ${HOST_PROJECT_DIR}:/app"
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echo "Mount project: ${HOST_PROJECT_DIR} -> /app"
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else
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if [ -n "${HOST_DATASET_DIR}" ]; then
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mkdir -p "${HOST_DATASET_DIR}"
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MOUNT_ARGS="${MOUNT_ARGS} -v ${HOST_DATASET_DIR}:/app/datasets"
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echo "Mount dataset: ${HOST_DATASET_DIR} -> /app/datasets"
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fi
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if [ -n "${HOST_OUTPUT_DIR}" ]; then
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mkdir -p "${HOST_OUTPUT_DIR}"
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MOUNT_ARGS="${MOUNT_ARGS} -v ${HOST_OUTPUT_DIR}:/app/results"
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echo "Mount results: ${HOST_OUTPUT_DIR} -> /app/results"
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fi
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if [ -n "${HOST_WEIGHT_DIR}" ]; then
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mkdir -p "${HOST_WEIGHT_DIR}"
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MOUNT_ARGS="${MOUNT_ARGS} -v ${HOST_WEIGHT_DIR}:/app/weights"
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echo "Mount weights: ${HOST_WEIGHT_DIR} -> /app/weights"
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fi
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fi
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echo ""
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echo "=============================="
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echo " Step 2: Running container"
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echo "=============================="
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sudo docker run --gpus all -it --rm --name "${CONTAINER_NAME}" \
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${MOUNT_ARGS} \
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"${IMAGE_NAME}"

‎docs/en-US/01_get_started.md‎

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```bash
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git clone https://github.com/chairc/Integrated-Design-Diffusion-Model.git
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cd Integrated-Design-Diffusion-Model
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# Run the project in a virtual environment (recommended)
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conda create -n iddm python=3.10
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pip install -r requirements.txt
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# Train the model
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cd iddm/tools
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python train.py --xxx xxx # Replace --xxx with your training parameters
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# Generate images
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python generate.py --xxx xxx # Replace --xxx with your generation parameters
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```
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#### Docker Running
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Modification contents, please refer to the `docker_run.sh` script.
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```bash
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# Mount and build the entire project
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bash docker_run.sh -a /path/to/project
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# Mount and build specified paths
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bash docker_run.sh \
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-d /path/to/datasets \
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-r /path/to/results \
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-w /path/to/weights
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```
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`/path/to/project`, `/path/to/datasets`, `/path/to/results` and `/path/to/weights` are local paths, used to mount the full project, dataset, result and weight directories respectively.
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#### Installation
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> [!NOTE]
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```yaml
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coloredlogs==15.0.1
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gradio>=6.0.0
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gradio==6.0.0
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matplotlib==3.7.1
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numpy>=1.25.0
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Pillow>=12.2.0
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Requests>=2.32.4
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numpy==1.25.0
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Pillow==12.2.0
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Requests==2.32.4
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scikit-image==0.22.0
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torch_summary==1.4.5
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tqdm==4.66.3
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pytorch_fid==0.3.0
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fastapi==0.115.6
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tensorboard==2.19.0
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tensorboardX==2.6.1
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transformers>=5.0.0
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transformers==5.0.0
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# If you want to use flash attention, please install flash-attn.
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# Compile your own environment: pip install flash-attn --no-build-isolation

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