Merge pull request modelscope#14 from modelscope/readme_refine

refine readme and optimize requirements.txt
Wangzehong111 · Aug 14, 2023 · 415e14d · 415e14d
2 parents d3f1205 + 1f6e92e
commit 415e14d
Show file tree

Hide file tree

Showing 3 changed files with 47 additions and 30 deletions.
diff --git a/README.md b/README.md
@@ -25,7 +25,7 @@ FaceChain is powered by [ModelScope](https://github.com/modelscope/modelscope).
 The following are the environment dependencies that have been verified:
 - python: py3.8, py3.10
 - pytorch: torch2.0.0, torch2.0.1
-- tensorflow: 2.7.0, tensorflow-cpu
+- tensorflow: 2.8.0, tensorflow-cpu
 - CUDA: 11.7
 - CUDNN: 8+
 - OS: Ubuntu 20.04, CentOS 7.9
@@ -39,22 +39,19 @@ The following are the environment dependencies that have been verified:
 The following installation methods are supported:
 
 
-1. ModelScope notebook【recommended】
+### 1. ModelScope notebook【recommended】
 
    The ModelScope notebook has a free tier that allows you to run the FaceChain application, refer to [ModelScope Notebook](https://modelscope.cn/my/mynotebook/preset)
 
     In addition to ModelScope notebook and ECS, I would suggest that we add that user may also start DSW instance with the option of ModelScope (GPU) image, to create a ready-to-use environment.
 
 ```shell
-# Step1
-我的notebook -> PAI-DSW -> GPU环境
+# Step1: 我的notebook -> PAI-DSW -> GPU环境
 
-# Step2
-Open the Terminal，clone FaceChain from github:
+# Step2: Open the Terminal，clone FaceChain from github:
 GIT_LFS_SKIP_SMUDGE=1 git clone https://github.com/modelscope/facechain.git --depth 1
 
-# Step3
-Entry the Notebook cell:
+# Step3: Entry the Notebook cell:
 import os
 os.chdir('/mnt/workspace/facechain')
 print(os.getcwd())
@@ -63,33 +60,29 @@ print(os.getcwd())
 !python3 app.py
 
 
-# Step4
-click "public URL" or "local URL"
+# Step4: click "public URL" or "local URL", upload your images to 
+# train your own model and then generate your digital twin.
 ```
 
 
-2. Docker
+### 2. Docker
 
 If you are familiar with using docker, we recommend to use this way:
 
 ```shell
-# Step1
-Prepare the environment with GPU on local or cloud, we recommend to use Alibaba Cloud ECS, refer to: https://www.aliyun.com/product/ecs
+# Step1: Prepare the environment with GPU on local or cloud, we recommend to use Alibaba Cloud ECS, refer to: https://www.aliyun.com/product/ecs
 
-# Step2
-Download the docker image (for installing docker engine, refer to https://docs.docker.com/engine/install/）
+# Step2: Download the docker image (for installing docker engine, refer to https://docs.docker.com/engine/install/）
 docker pull registry.cn-hangzhou.aliyuncs.com/modelscope-repo/modelscope:ubuntu20.04-cuda11.7.1-py38-torch2.0.1-tf1.15.5-1.8.0
 
-# Step3
-docker images
-docker run -it --name facechain -p 7860:7860 --gpus all your_xxx_image_id /bin/bash
+# Step3: run the docker container
+docker run -it --name facechain -p 7860:7860 --gpus all registry.cn-hangzhou.aliyuncs.com/modelscope-repo/modelscope:ubuntu20.04-cuda11.7.1-py38-torch2.0.1-tf1.15.5-1.8.0 /bin/bash
 (Note: you may need to install the nvidia-container-runtime, refer to https://github.com/NVIDIA/nvidia-container-runtime)
 
-# Step4
-Install the gradio in the docker container:
+# Step4: Install the gradio in the docker container:
 pip3 install gradio
 
-# Step5
+# Step5 clone facechain from github
 GIT_LFS_SKIP_SMUDGE=1 git clone https://github.com/modelscope/facechain.git --depth 1
 cd facechain
 python3 app.py
@@ -98,6 +91,31 @@ python3 app.py
 Run the app server: click "public URL" --> in the form of: https://xxx.gradio.live
 ```
 
+### 3. conda Virtual Environment
+
+Use the conda virtual environment, and refer to [Anaconda](https://docs.anaconda.com/anaconda/install/) to manage your dependencies. After installation, execute the following commands:
+(Note: mmcv has strict environment requirements and might not be compatible in some cases. It's recommended to use Docker.)
+
+```shell
+conda create -n facechain python=3.8    # Verified environments: 3.8 and 3.10
+conda activate facechain
+
+GIT_LFS_SKIP_SMUDGE=1 git clone https://github.com/modelscope/facechain.git --depth 1
+cd facechain
+
+pip3 install -r requirements.txt
+pip3 install -U openmim 
+mim install mmcv-full==1.7.0
+
+# Navigate to the facechain directory and run:
+python3 app.py
+
+# Finally, click on the URL generated in the log to access the web page.
+```
+
+**Note**: After the app service is successfully launched, go to the URL in the log, enter the "Image Customization" tab, click "Select Image to Upload", and choose at least one image with a face. Then, click "Start Training" to begin model training. After the training is completed, there will be corresponding displays in the log. Afterwards, switch to the "Image Experience" tab and click "Start Inference" to generate your own digital image.
+
+
 
 # Script Execution
 

diff --git a/README_ZH.md b/README_ZH.md
@@ -27,7 +27,7 @@ FaceChain的模型由[ModelScope](https://github.com/modelscope/modelscope)开
 FaceChain是一个组合模型，使用了包括pytorch和tensorflow在内的机器学习框架，以下是已经验证过的主要环境依赖：
 - python环境: py3.8, py3.10
 - pytorch版本: torch2.0.0, torch2.0.1
-- tensorflow版本: 2.7.0, tensorflow-cpu
+- tensorflow版本: 2.8.0, tensorflow-cpu
 - CUDA版本: 11.7
 - CUDNN版本: 8+
 - 操作系统版本: Ubuntu 20.04, CentOS 7.9
@@ -42,7 +42,7 @@ FaceChain是一个组合模型，使用了包括pytorch和tensorflow在内的机
 ## 安装指南
 支持以下几种安装方式，任选其一：
 
-1. 使用ModelScope提供的notebook环境【推荐】
+### 1. 使用ModelScope提供的notebook环境【推荐】
 
     ModelScope(魔搭社区)提供给新用户初始的免费计算资源，参考[ModelScope Notebook](https://modelscope.cn/my/mynotebook/preset)
 
@@ -68,7 +68,7 @@ print(os.getcwd())
 ```
 
 
-2. docker镜像
+### 2. docker镜像
 
 如果您熟悉docker，可以使用我们提供的docker镜像，其包含了模型依赖的所有组件，无需复杂的环境安装：
 ```shell
@@ -79,9 +79,8 @@ print(os.getcwd())
 # Step2: 将镜像下载到本地 （前提是已经安装了docker engine并启动服务，具体可参考： https://docs.docker.com/engine/install/）
 docker pull registry.cn-hangzhou.aliyuncs.com/modelscope-repo/modelscope:ubuntu20.04-cuda11.7.1-py38-torch2.0.1-tf1.15.5-1.8.0
 
-# Step3: 获取image id，并运行
-docker images
-docker run -it --name facechain -p 7860:7860 --gpus all your_xxx_image_id /bin/bash  # 注意 your_xxx_image_id 替换成你的镜像id
+# Step3: 拉起镜像运行
+docker run -it --name facechain -p 7860:7860 --gpus all registry.cn-hangzhou.aliyuncs.com/modelscope-repo/modelscope:ubuntu20.04-cuda11.7.1-py38-torch2.0.1-tf1.15.5-1.8.0 /bin/bash  # 注意 your_xxx_image_id 替换成你的镜像id
 (注意： 如果提示无法使用宿主机GPU的错误，可能需要安装nvidia-container-runtime, 参考：https://github.com/NVIDIA/nvidia-container-runtime)
 
 # Step4: 在容器中安装gradio
@@ -96,7 +95,7 @@ python3 app.py
 ```
 
 
-3. conda虚拟环境
+### 3. conda虚拟环境
 
 使用conda虚拟环境，参考[Anaconda](https://docs.anaconda.com/anaconda/install/)来管理您的依赖，安装完成后，执行如下命令：
 (提示： mmcv对环境要求较高，可能出现不适配的情况，推荐使用docker方式)

diff --git a/requirements.txt b/requirements.txt
@@ -2,13 +2,13 @@ accelerate
 transformers
 diffusers
 onnxruntime
-modelscope[framework]
+modelscope
 Pillow
 opencv-python
 torchvision
 mmdet==2.26.0
 mmengine
-tensorflow==2.7.0
+tensorflow==2.8.0
 #tensorflow-cpu  # slower but no cuda conflicts
 numpy==1.22.0
 protobuf==3.20.1