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BentoText2Video

(★ 45)

Text-to-Video generation pipeline using BentoML, VLLM (text), XTTS (audio), and SDXL-Turbo(image)

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bentofile.yaml
female.wav
output.mp4
README.md
requirements.txt
service.py

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Install a package (see `pip install` for more install examples):

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## Text-2-Video Generation: <a target="_blank" href="https://www.canva.com/design/DAF_O6pGDy8/9sxeLm9LmnVvzbPtdgCspg/view?utm_content=DAF_O6pGDy8&utm_campaign=designshare&utm_medium=link&utm_source=editor#14">Slides Here</a>
This repo demonstrates how to build a text-to-video application using  [BentoML](https://github.com/bentoml/BentoML), powered by [XTTS](https://huggingface.co/coqui/XTTS-v2), [VLLM](https://github.com/bentoml/BentoML), [SDXL-TURBO](https://huggingface.co/coqui/XTTS-v2).

<img src="https://github.com/CharlesCreativeContent/myImages/blob/main/images/service.png?raw=true" width="100%" alt="Slideshow Picture"/>

Used BentoML demos, including [BentoVLLM](https://github.com/bentoml/BentoVLLM), [BentoXTTS](https://github.com/bentoml/BentoXTTS), and [BentoSDXLTurbo](https://github.com/bentoml/BentoSDXLTurbo), and used MoviePy to edit them into a video.

## Prerequisites

- Install Python 3.9+ and `pip`. See the [Python downloads page](https://www.python.org/downloads/) to learn more.
- (Recommended) Basic understanding of key concepts in BentoML, such as Services. We recommend you read [Quickstart](https://docs.bentoml.com/en/latest/get-started/quickstart.html) first.
- (Recommended) Create a virtual environment for dependency isolation. See the [Conda documentation](https://conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html) or the [Python documentation](https://docs.python.org/3/library/venv.html) for details.

## Install dependencies

```bash
git clone https://github.com/CharlesCreativeContent/BentoText2Video.git
cd BentoText2Video
pip install -r requirements.txt
```

## Run the BentoML Service

BentoML Service is defined in `service.py`.

Run `bentoml serve` in your project directory to start the Service.

You may also set the environment variable `COQUI_TTS_AGREED=1` to agree to the terms of Coqui TTS.

Lock_packages are currently set to _False_ in the bentofile.yaml, which bypasses local builds.

```python
$ COQUI_TOS_AGREED=1 bentoml serve .

2024-01-18T11:13:54+0800 [INFO] [cli] Starting production HTTP BentoServer from "service:XTTS" listening on http://localhost:3000 (Press CTRL+C to quit)
/workspace/codes/examples/xtts/venv/lib/python3.10/site-packages/TTS/api.py:70: UserWarning: `gpu` will be deprecated. Please use `tts.to(device)` instead.
  warnings.warn("`gpu` will be deprecated. Please use `tts.to(device)` instead.")
 > tts_models/multilingual/multi-dataset/xtts_v2 is already downloaded.
 > Using model: xtts
```

The server is now active at [http://localhost:3000](http://localhost:3000/).

You can interact with it using the Swagger UI, curl, etc.

CURL

```bash
curl -X 'POST' \
  'http://localhost:3000/synthesize' \
  -H 'accept: */*' \
  -H 'Content-Type: application/json' \
  -d '{
  "text": "It took me quite a long time to develop a voice and now that I have it I am not going to be silent.",
  "lang": "en"
}' -o output.mp4
```

## Deploy to production

After the Service is ready, you can deploy the application to BentoCloud for better management and scalability.

A YAML configuration file (`bentofile.yaml`) is used to define the build options and package your application into a Bento. See [Bento build options](https://docs.bentoml.com/en/latest/concepts/bento.html#bento-build-options) to learn more.

Make sure you have [logged in to BentoCloud](https://docs.bentoml.com/en/latest/bentocloud/how-tos/manage-access-token.html), then run the following command in your project directory to deploy the application to BentoCloud.

```bash
bentoml deploy .
```

Once the application is running on BentoCloud, you can access it with the exposed URL.

**Note**: You can also use BentoML to generate a [Docker image](https://docs.bentoml.com/en/latest/guides/containerization.html) for custom deployments.

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