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[doc] updated installation doc (#119)

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README.md
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@ -150,7 +150,7 @@ For CUDA 12.1, you can install the dependencies with the following commands. Oth
```bash
# create a virtual env and activate (conda as an example)
conda create -n opensora python=3.10
conda create -n opensora python=3.9
conda activate opensora
# install torch, torchvision and xformers

117
docs/installation.md
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@ -3,27 +3,34 @@
Requirements are listed in `requirements` folder.
Note that besides these packages, some packages needs to be mannually installed, and are detailed in the following sections.
## Different CUDA versions
## Training & Inference
You need to manually install `torch`, `torchvision` and `xformers` for different CUDA versions.
You need to install `opensora` for training and inference. You can follow the steps below for installation. We also provide guideline for different CUDA versions for compatiblity.
For CUDA 12.1,
Please note that the default installation is for training and inference only. Other optional dependencies are detailed in the sections [Data Proessing](#data-processing) and [Evaluation](#evaluation) respectively.
### Step 1: Install PyTorch and xformers
First of all, make sure you have the latest build toolkit for Python.
```bash
pip install -U pip
# update, otherwise may run into weird issues with apex
pip install -U setuptools
pip install -U wheel
# update build libs
pip install -U pip setuptools wheel
```
If you are using **CUDA 12.1**, you can execute the command below to directly install PyTorch, torchvision and xformers.
```bash
# install pytorch, torchvision, and xformers
pip install -r requirements/requirements-cu121.txt
```
For other CUDA versions, you need to manually install the corresponding packages.
If you are using different CUDA versions, you need to manually install `torch`, `torchvision` and `xformers`. You can find the compatible distributions according to the links below.
- PyTorch: choose install commands from [PyTorch installation page](https://pytorch.org/get-started/locally/) based on your own CUDA version.
- xformers: choose install commands from [xformers repo](https://github.com/facebookresearch/xformers?tab=readme-ov-file#installing-xformers) based on your own CUDA version.
### Step 2: Install Open-Sora
Then, you can install the project for training and inference with the following commands:
```bash
@ -35,7 +42,9 @@ cd Open-Sora
pip install -v . # NOTE: for development mode, run `pip install -v -e .`
```
(Optional, recommended for fast speed, especially for training) To enable `layernorm_kernel` and `flash_attn`, you need to install `apex` and `flash-attn` with the following commands.
### Step 3: Install Acceleration Tools (Optional)
This is optional but recommended for faster speed, especially for training. To enable `layernorm_kernel` and `flash_attn`, you need to install `apex` and `flash-attn` with the following commands.
```bash
# install flash attention
@ -43,17 +52,54 @@ pip install -v . # NOTE: for development mode, run `pip install -v -e .`
pip install packaging ninja
pip install flash-attn --no-build-isolation
# install apex, the compilation will take a long time
# set enable_layernorm_kernel=False in config to disable apex
pip install -v --disable-pip-version-check --no-cache-dir --no-build-isolation --config-settings "--build-option=--cpp_ext" --config-settings "--build-option=--cuda_ext" git+https://github.com/NVIDIA/apex.git
```
## Optional Dependencies
The default installation is for inference only. Other optional dependencies are detailed in sections below, namel "Data Dependencies" and "Evaluation Dependencies".
### Data Dependencies
## Evaluation
## Step 1: Install Requirements
To conduct evaluation, run the following command to install requirements:
```bash
pip install -v .[eval]
# For development:`pip install -v -e .[eval]`
```
## Step 2: Install VBench
You need to manually install [VBench](https://github.com/Vchitect/VBench):
```bash
pip install --no-deps vbench==0.1.1
# If the installation shows a warning about the intalled vbench not in PATH, you need to add it by:
export PATH="/path/to/vbench:$PATH"
```
#### Step 3: Install `cupy` for Potential VAE Errors
You need to mannually install [cupy](https://docs.cupy.dev/en/stable/install.html).
- For CUDA v11.2~11.8 (x86_64 / aarch64), `pip install cupy-cuda11x`
- For CUDA v12.x (x86_64 / aarch64), `pip install cupy-cuda12x`
Note that for VAE evaluation, you may run into error with `ModuleNotFoundError: No module named 'torchvision.transforms.functional_tensor'`, in this case, you need to go to the corresponding file (`.../pytorchvideo/transforms/augmentations.py`) reporting this error, then change as following:
```python
# find the original line:
import torchvision.transforms.functional_tensor as F_t
# change to:
import torchvision.transforms._functional_tensor as F_t
```
## Data Processing
### Step 1: Install Requirements
First, run the following command to install requirements:
@ -64,7 +110,7 @@ pip install -v .[data]
Next, you need to manually install the packages listed in the following sections specific to your data processing needs.
#### OpenCV
### Step 2: Install OpenCV
To get image and video information, we use [opencv-python](https://github.com/opencv/opencv-python). You can install it with pip:
@ -79,6 +125,10 @@ pip uninstall opencv-python
conda install -c conda-forge opencv
```
### Step 3: Install Task-specific Dependencies
We have a variety of data processing pipelines, each requires its own dependencies. You can refer to the sections below to install dependencies according to your own needs.
#### LLaVA Captioning
You need to manually install LLaVA with the following command:
@ -125,40 +175,3 @@ and change the assert of `mmcv_version < digit_version(mmcv_maximum_version)` to
If you are unsure of your path to the mmdet init file, simply run our [OCR command](../tools/scoring/README.md), wait for the mmdeet assertion error on mmcv versions.
The error will contain the exact path to the mmdet init file.
### Evaluation Dependencies
First, run the following command to install requirements:
```bash
pip install -v .[eval]
# For development:`pip install -v -e .[eval]`
```
Next, you need to manually install the packages listed in the following sections specific to different evaluation methods.
#### VBench
You need to manually install [VBench](https://github.com/Vchitect/VBench):
```bash
pip install --no-deps vbench==0.1.1
# If the installation shows a warning about the intalled vbench not in PATH, you need to add it by:
export PATH="/path/to/vbench:$PATH"
```
#### VAE
You need to mannually install [cupy](https://docs.cupy.dev/en/stable/install.html).
- For CUDA v11.2~11.8 (x86_64 / aarch64), `pip install cupy-cuda11x`
- For CUDA v12.x (x86_64 / aarch64), `pip install cupy-cuda12x`
Note that for VAE evaluation, you may run into error with `ModuleNotFoundError: No module named 'torchvision.transforms.functional_tensor'`, in this case, you need to go to the corresponding file (`.../pytorchvideo/transforms/augmentations.py`) reporting this error, then change as following:
```python
# find the original line:
import torchvision.transforms.functional_tensor as F_t
# change to:
import torchvision.transforms._functional_tensor as F_t
```