[Docs] More docs update (#394)
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William Lin
@@ -11,11 +11,52 @@ FastVideo currently only supports Linux and NVIDIA CUDA GPUs.
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- **CUDA 12.4**
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- **At least 1 NVIDIA GPU**
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## Quick start
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We recommend using an environment manager such as [Conda](#miniconda-recommended)
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## Set up using Python
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### Create a new Python environment
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#### Conda
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You can create a new python environment using [Conda](https://docs.conda.io/projects/conda/en/stable/user-guide/getting-started.html)
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##### 1. Install Miniconda (if not already installed)
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```bash
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wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
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bash Miniconda3-latest-Linux-x86_64.sh
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source ~/.bashrc
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```
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##### 2. Create and activate a Conda environment for FastVideo
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```bash
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# (Recommended) Create a new conda environment.
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conda create -n fastvideo python=3.12 -y
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conda activate fastvideo
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```
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:::{note}
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[PyTorch has deprecated the conda release channel](https://github.com/pytorch/pytorch/issues/138506). If you use `conda`, please only use it to create Python environment rather than installing packages.
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:::
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#### uv
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:::{tip}
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We highly recommend using `uv` to install FastVideo. In our experience, `uv` speeds up installation by at least 3x.
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:::
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Or you can create a new Python environment using [uv](https://docs.astral.sh/uv/), a very fast Python environment manager. Please follow the [documentation](https://docs.astral.sh/uv/#getting-started) to install `uv`. After installing `uv`, you can create a new Python environment using the following command:
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```console
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# (Recommended) Create a new uv environment. Use `--seed` to install `pip` and `setuptools` in the environment.
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uv venv --python 3.12 --seed
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source .venv/bin/activate
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```
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### Installation
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```bash
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pip install fastvideo
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# or if you are using uv
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uv pip install fastvideo
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```
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Also optionally install flash-attn:
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@@ -24,30 +65,9 @@ Also optionally install flash-attn:
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pip install flash-attn==2.7.4.post1 --no-build-isolation
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```
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[Click here to install from source](#installation-from-source)
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### Installation from Source
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## Environment Setup
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We highly recommend using a `conda` or `virtualenv` environment:
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### Miniconda (Recommended)
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#### 1. Install Miniconda (if not already installed)
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```bash
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wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
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bash Miniconda3-latest-Linux-x86_64.sh
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source ~/.bashrc
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```
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#### 2. Create and activate a Conda environment for FastVideo
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```bash
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conda create -n fastvideo python=3.12 -y
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conda activate fastvideo
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```
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## Installation from Source
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### 1. Clone the FastVideo repository
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#### 1. Clone the FastVideo repository
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```bash
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git clone https://github.com/hao-ai-lab/FastVideo.git && cd FastVideo
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@@ -59,23 +79,23 @@ Basic installation:
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```bash
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pip install -e .
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# or if you are using uv
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uv pip install -e .
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```
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## Optional Dependencies
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### Optional Dependencies
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### Flash Attention
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#### Flash Attention
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```bash
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pip install flash-attn==2.7.4.post1 --no-build-isolation
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```
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### Sliding Tile Attention (STA) (Requires CUDA 12.4 and H100)
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To try Sliding Tile Attention (optional), please follow the instructions in [csrc/sliding_tile_attention/README.md](#sta-installation) to install STA.
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## Docker Images
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## Set up using Docker
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We also have prebuilt docker images with FastVideo dependencies pre-installed:
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[Docker Images](#docker)
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## Development Environment Setup
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If you're planning to contribute to FastVideo please see the following page:
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@@ -84,7 +104,7 @@ If you're planning to contribute to FastVideo please see the following page:
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## Hardware Requirements
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### For Basic Inference
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- NVIDIA GPU with CUDA 12.4support
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- NVIDIA GPU with CUDA 12.4 support
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### For Lora Finetuning
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- 40GB GPU memory each for 2 GPUs with lora
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@@ -10,9 +10,74 @@ This class will be the primary Python API for generating videos and images.
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fastvideo.VideoGenerator
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```
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VideoGenerator.from_pretrained() should be the primary way of creating a new video generator.
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`````{py:class} VideoGenerator(fastvideo_args: fastvideo.v1.fastvideo_args.FastVideoArgs, executor_class: type[fastvideo.v1.worker.executor.Executor], log_stats: bool)
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:canonical: fastvideo.v1.entrypoints.video_generator.VideoGenerator
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````{py:method} from_pretrained(model_path: str, device: typing.optional[str] = none, torch_dtype: typing.optional[torch.dtype] = none, pipeline_config: typing.optional[typing.union[str | fastvideo.v1.configs.pipelines.pipelineconfig]] = none, **kwargs) -> v1.entrypoints.video_generator.videogenerator
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:canonical: v1.entrypoints.video_generator.videogenerator.from_pretrained
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```{autodoc2-docstring} fastvideo.v1.entrypoints.video_generator.VideoGenerator
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:parser: docs.source.autodoc2_docstring_parser
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```
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`VideoGenerator.from_pretrained()` should be the primary way of creating a new video generator.
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````{py:method} from_pretrained(model_path: str, device: typing.Optional[str] = None, torch_dtype: typing.Optional[torch.dtype] = None, pipeline_config: typing.Optional[typing.Union[str | fastvideo.v1.configs.pipelines.PipelineConfig]] = None, **kwargs) -> fastvideo.v1.entrypoints.video_generator.VideoGenerator
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:canonical: fastvideo.v1.entrypoints.video_generator.VideoGenerator.from_pretrained
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:classmethod:
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```{autodoc2-docstring} fastvideo.v1.entrypoints.video_generator.VideoGenerator.from_pretrained
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:parser: docs.source.autodoc2_docstring_parser
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```
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## Configuring FastVideo
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The follow two classes `PipelineConfig` and `SamplingParam` are used to configure initialization and sampling parameters, respectively.
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### PipelineConfig
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```{autodoc2-summary}
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fastvideo.PipelineConfig
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```
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`````{py:class} PipelineConfig
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:canonical: fastvideo.v1.configs.pipelines.base.PipelineConfig
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```{autodoc2-docstring} fastvideo.v1.configs.pipelines.base.PipelineConfig
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:parser: docs.source.autodoc2_docstring_parser
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```
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````{py:method} from_pretrained(model_path: str) -> fastvideo.v1.configs.pipelines.base.PipelineConfig
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:canonical: fastvideo.v1.configs.pipelines.base.PipelineConfig.from_pretrained
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:classmethod:
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```{autodoc2-docstring} fastvideo.v1.configs.pipelines.base.PipelineConfig.from_pretrained
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:parser: docs.source.autodoc2_docstring_parser
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```
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````{py:method} dump_to_json(file_path: str)
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:canonical: fastvideo.v1.configs.pipelines.base.PipelineConfig.dump_to_json
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```{autodoc2-docstring} fastvideo.v1.configs.pipelines.base.PipelineConfig.dump_to_json
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:parser: docs.source.autodoc2_docstring_parser
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```
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### SamplingParam
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```{autodoc2-summary}
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fastvideo.SamplingParam
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```
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`````{py:class} SamplingParam
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:canonical: fastvideo.v1.configs.sample.base.SamplingParam
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```{autodoc2-docstring} fastvideo.v1.configs.sample.base.SamplingParam
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:parser: docs.source.autodoc2_docstring_parser
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```
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````{py:method} from_pretrained(model_path: str) -> fastvideo.v1.configs.sample.base.SamplingParam
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:canonical: fastvideo.v1.configs.sample.base.SamplingParam.from_pretrained
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:classmethod:
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```{autodoc2-docstring} fastvideo.v1.configs.sample.base.SamplingParam.from_pretrained
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:parser: docs.source.autodoc2_docstring_parser
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```
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