Text-to-Video Inference with Flexai

This experiment demonstrates how to deploy and use a text-to-video model (Wan2.2-TI2V-5B) using Flexai's inference serving capabilities.

VIEW CODE ON GITHUB

About Wan2.2

Wan2.2 is developed by Alibaba. It represents a major upgrade to visual generative models, now open-sourced with more powerful capabilities, better performance, and superior visual quality making it an excellent choice for text-to-video applications.

Prerequisites

Before starting, make sure you have:

• A Flexai account with access to the platform
• A Hugging Face token with access to the Wan-AI/Wan2.2-TI2V-5B-Diffusers model
• The flexai CLI installed and configured

Setup FlexAI Secret for Hugging Face Token

First, create a FlexAI secret that contains your Hugging Face token to access the inference model:

# Enter your HF token value when prompted
flexai secret create MY_HF_TOKEN

Note: Make sure your Hugging Face token has access to the Wan-AI/Wan2.2-TI2V-5B-Diffusers model. You may need to accept the model's license terms on Hugging Face first.

Start the FlexAI Inference Endpoint

Start the FlexAI endpoint for the Wan2.2-TI2V-5B model:

INFERENCE_NAME=wan-text-to-video
flexai inference serve $INFERENCE_NAME --runtime flexserve --hf-token-secret MY_HF_TOKEN -- --task text-to-video --model Wan-AI/Wan2.2-TI2V-5B-Diffusers --quantization-config bitsandbytes_4bit

This command will:

• Create an inference endpoint named wan-text-to-video
• Use the flexserve runtime optimized for text-to-video tasks
• Load the Wan2.2-TI2V-5B model from Hugging Face
• Configure it for text-to-video generation with 4-bit quantization for memory efficiency

Get Endpoint Information

Once the endpoint is deployed, you'll see the API key displayed in the output. Store it in an environment variable:

export INFERENCE_API_KEY=<API_KEY_FROM_ENDPOINT_CREATION_OUTPUT>

Then retrieve the endpoint URL:

export INFERENCE_URL=$(flexai inference inspect $INFERENCE_NAME -j | jq .config.endpointUrl -r)

You'll notice these export lines use the jq tool to extract values from the JSON output of the inspect command.

If you don't have it already, you can get jq from its official website: https://jqlang.org/

Generate Videos

Now you can generate videos by making HTTP POST requests to your endpoint. Here's an example that generates a high-quality video of anthropomorphic cats boxing:

curl -X POST \
  -H "Authorization: Bearer $INFERENCE_API_KEY" \
  -H 'Content-Type: application/json' \
  -d '{
    "inputs": "Two anthropomorphic cats in comfy boxing gear and bright gloves fight intensely on a spotlighted stage.",
    "parameters": {
      "num_frames": 81,
      "height": 704,
      "width": 1280,
      "num_inference_steps": 20,
      "guidance_scale": 4.0,
      "guidance_scale_2": 3.0
    }
  }' \
  -o boxing_cats.mp4 \
  "$INFERENCE_URL/v1/videos/generations"

This will save the generated video as boxing_cats.mp4 in your current directory.

Parameters Explanation

The API accepts the following parameters:

• inputs: The text prompt describing the video you want to generate
• num_frames: Number of frames in the output video (affects duration)
• height: Output video height in pixels
• width: Output video width in pixels
• num_inference_steps: Number of denoising steps (higher = better quality but slower)
• guidance_scale and guidance_scale_2: As you increase the value, the model tries harder to match your prompt.