公告

本地搭建stable-diffusion diffuser docker CUDA10.2 RTX2060 [Updated: 2023-12-13]

系統: Ubuntu 18.04
顯示卡: RTX2060 Super (8G內存)

1.0 CUDA版本

1.1 CUDA Toolkit and Corresponding Driver Versions
https://docs.nvidia.com/cuda/cuda-toolkit-release-notes/index.html#id4

尋找合版本的CUDA。

1.2 CUDA Toolkit 10.2 Download

https://developer.nvidia.com/cuda-10.2-download-archive?target_os=Linux&target_arch=x86_64&target_distro=Ubuntu&target_version=1804&target_type=deblocal

選擇系統，根據Installation Instructions執行。推薦使用.deb本地安裝，更穩定。

1.2 確定安裝是否成功

nvidia-smi

　　成功安裝，會出現完整資料。

2.0 安裝CUDNN加速

2.1 註冊nvidia開發者帳號

https://developer.nvidia.com/cudnn

點進去後，提示註冊nvidia開發者帳號。

2.2 Download NVIDIA cuDNN

https://developer.nvidia.com/rdp/cudnn-archive

選擇 cuDNN v8.6.0 for CUDA 10.2，.deb安裝。

2.3 Install cuDNN

https://docs.nvidia.com/deeplearning/cudnn/install-guide/index.html#installlinux-deb

本地執行安裝。

2.4 確定安裝是否成功

https://docs.nvidia.com/deeplearning/cudnn/install-guide/index.html#verify

如安裝cuDNN成功，會出現 Test passed! 信息。

3.0 安裝libnvidia-container

3.1 why libnvidia-container?

https://github.com/NVIDIA/nvidia-docker

如果要用docker container來執行CUDA，就需要libnvidia-container。

3.2 Install libnvidia-container

https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html#setting-up-nvidia-container-toolkit

distribution=$(. /etc/os-release;echo $ID$VERSION_ID) \
      && curl -fsSL https://nvidia.github.io/libnvidia-container/gpgkey | sudo gpg --dearmor -o /usr/share/keyrings/nvidia-container-toolkit-keyring.gpg \
      && curl -s -L https://nvidia.github.io/libnvidia-container/$distribution/libnvidia-container.list | \
            sed 's#deb https://#deb [signed-by=/usr/share/keyrings/nvidia-container-toolkit-keyring.gpg] https://#g' | \
            sudo tee /etc/apt/sources.list.d/nvidia-container-toolkit.list

　　然後用docker測試是否安裝成功。

sudo apt-get update

sudo apt-get install -y nvidia-container-toolkit

　　安裝nvidia-container-toolkit。

3.2 確定安裝是否成功

https://hub.docker.com/r/nvidia/cuda/tags?page=1&name=10.2-cudnn8-runtime-ubuntu18.04

sudo docker run --rm --gpus all nvidia/cuda:10.2-cudnn8-runtime-ubuntu18.04 nvidia-smi

　　pull了docker image後，在container里執行nvidia-smi。

安裝成功畫面。Docker container能驅動CUDA10.2。

4.0 Install diffusers

https://github.com/huggingface/diffusers

diffusers是SD模型的pipeline庫，SD官方比較推薦用這個。

4.1 Edit dockerfile

現在流行不寫顯式聲明，反而是之後執行setup.py安裝庫。那麼就跟隨官方的操作。

~/diffusers/docker/diffusers-pytorch-cuda/Dockerfile

FROM nvidia/cuda:10.2-cudnn8-runtime-ubuntu18.04
LABEL maintainer="Hugging Face"
LABEL repository="diffusers"

ENV DEBIAN_FRONTEND=noninteractive

RUN apt update && \
    apt install -y bash \
                   build-essential \
                   git \
                   git-lfs \
                   curl \
                   ca-certificates \
                   libsndfile1-dev \
                   python3.8 \
                   python3-pip \
                   python3.8-venv \
                   python3-venv && \
    rm -rf /var/lib/apt/lists
    
# link libcudart.so.10.2 to libcudart.so.10.1
RUN ln -s /usr/local/cuda-10.2/targets/x86_64-linux/lib/libcudart.so.10.2 /usr/lib/x86_64-linux-gnu/libcudart.so.10.1

# link python3 to python3.8
RUN rm /usr/bin/python3 && \
    ln -s /usr/bin/python3.8 /usr/bin/python3

# make sure to use venv
RUN python3 -m venv /opt/venv
ENV PATH="/opt/venv/bin:$PATH"

# pre-install the heavy dependencies (these can later be overridden by the deps from setup.py)
RUN python3 -m pip install --no-cache-dir --upgrade pip && \
    python3 -m pip install --no-cache-dir \
        torch==1.10.1+cu102 \
        torchvision==0.11.2+cu102 \
        torchaudio==0.10.1 \
        -f https://download.pytorch.org/whl/cu102/torch_stable.html && \
    python3 -m pip install --no-cache-dir \
        accelerate \
        datasets \
        hf-doc-builder \
        huggingface-hub \
        diffusers \
        librosa \
        modelcards \
        numpy \
        scipy \
        tensorflow-gpu==2.3.0 \
        tensorboard \
        transformers \
        jupyterlab \
        flask

CMD ["/bin/bash"]

　　降級可使用CUDA10.2。

1. 更改nvidia/cuda:10.2-cudnn8-runtime-ubuntu18.04
2. 再安裝python3-venv，否則venv時報錯。
3. link libcudart.so.10.2 to libcudart.so.10.1。這個是CUDA官方的Bug。
4. link python3 to python3.8。這個是diffusers官方的Bug。
5. 另外安裝常用庫: diffusers, tensorflow-gpu, jupyterlab, flask。

cd ~/PycharmProjects/diffusers/app/outputs
sudo docker build -t diffusers/cuda/v3:10.2-cudnn8-runtime-ubuntu18.04 .

　　Build docker image。

4.2 Run diffusers

https://huggingface.co/runwayml/stable-diffusion-v1-5

from diffusers import StableDiffusionPipeline
import torch

model_id = "runwayml/stable-diffusion-v1-5"
pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
pipe = pipe.to("cuda")

prompt = "a photo of an astronaut riding a horse on mars"
image = pipe(prompt).images[0]  
    
image.save("astronaut_rides_horse.png")

　　Demo Code。

需要注意:

size_base = 64
os.environ["PYTORCH_CUDA_ALLOC_CONF"] = f'max_split_size_mb:{size_base}'
img_width, img_height = size_base*11, size_base*11

　　size必需是*8的數字。由於模型容易內存不足，最好設置size大小。

sudo docker run --name diffusers.002 --rm --gpus all -t -v ~/PycharmProjects/diffusers/app:/app diffusers/cuda/v3:10.2-cudnn8-runtime-ubuntu18.04 python3 /app/main.py >> ~/PycharmProjects/diffusers/log/diffusers_001_`date +\%Y\%m\%d_\%H\%M\%S`.log 2>&1

　　執行docker container。

Project Code: https://github.com/kenny-chen/ai.diffusers