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Our Observations

We have deployed this model using A100 GPU and observed that the model took an average cold start time of 7.02 sec and an average inference time of 34 sec for generating a video of 4 sec with 6fps

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Defining Dependencies

We are using the HuggingFace Diffusers library for the deployment. You can also use this script for deployment provided by Stability AI.

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Constructing the GitHub/GitLab Template

Now quickly construct the GitHub/GitLab template, this process is mandatory and make sure you don’t add any file named model.py

stable-video-diffusion/
├── app.py
├── inferless-runtime-config.yaml
├── inferless.yaml
└── input_schema.py

You can also add other files to this directory.

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Create the class for inference

In the app.py we will define the class and import all the required functions

  1. def initialize: In this function, you will initialize your model and define any variable that you want to use during inference. We are using torch.compile which improves the latency but requires a large GPU(A10/A100). If you are using Nvidia T4 then remove those lines and use model CPU offloading to reduce memory usage.
self.pipe.enable_model_cpu_offload()

class InferlessPythonModel:
    
    def initialize(self):
        self.pipe = StableVideoDiffusionPipeline.from_pretrained("stabilityai/stable-video-diffusion-img2vid-xt", torch_dtype=torch.float16, variant="fp16")
        self.pipe.to("cuda")
        self.pipe.unet = torch.compile(self.pipe.unet, mode="reduce-overhead", fullgraph=True)
        self.pipe.vae = torch.compile(self.pipe.vae, mode="reduce-overhead", fullgraph=True)
        self.seed = 42
        self.randomize_seed = True
        self.motion_bucket_id = 127
        self.fps_id = 6
        self.version = "svd_xt"
        self.cond_aug = 0.02,
        self.decoding_t = 3
        self.device = "cuda"
        self.output_folder = "outputs"
  1. def resize: This is a utility function required during inference, it will download the image and then resize it. You can also add any other functions which are required during inference.
    def resize_image(self,image_url, output_size=(1024, 576)):
        # Calculate aspect ratio
        response = requests.get(image_url)
        image_data = BytesIO(response.content)

        # Open the image using PIL
        image = Image.open(image_data)
        target_aspect = output_size[0] / output_size[1]  # Aspect ratio of the desired size
        image_aspect = image.width / image.height  # Aspect ratio of the original image

        # Resize then crop if the original image is larger
        if image_aspect > target_aspect:
            # Resize the image to match the target height, maintaining the aspect ratio
            new_height = output_size[1]
            new_width = int(new_height * image_aspect)
            resized_image = image.resize((new_width, new_height), Image.LANCZOS)
            # Calculate coordinates for cropping
            left = (new_width - output_size[0]) / 2
            top = 0
            right = (new_width + output_size[0]) / 2
            bottom = output_size[1]
        else:
            # Resize the image to match the target width, maintaining the aspect ratio
            new_width = output_size[0]
            new_height = int(new_width / image_aspect)
            resized_image = image.resize((new_width, new_height), Image.LANCZOS)
            # Calculate coordinates for cropping
            left = 0
            top = (new_height - output_size[1]) / 2
            right = output_size[0]
            bottom = (new_height + output_size[1]) / 2

        # Crop the image
        cropped_image = resized_image.crop((left, top, right, bottom))
        return cropped_image
  1. def infer: This function gets called for every request that you send. Here you can define all the steps that are required for the inference. You can also pass custom values for inference, for example fps_id is fixed in the tutorial, you can pass it through inputs parameter.
    def infer(self,inputs):
        image_url = inputs['image_url']
        max_64_bit_int = 2**63 - 1
        image = self.resize_image(image_url)

        if image.mode == "RGBA":
            image = image.convert("RGB")

        if(self.randomize_seed):
            seed = random.randint(0, max_64_bit_int)
        generator = torch.manual_seed(seed)

        os.makedirs(self.output_folder, exist_ok=True)
        base_count = len(glob(os.path.join(self.output_folder, "*.mp4")))
        video_path = os.path.join(self.output_folder, f"{base_count:06d}.mp4")

        frames = self.pipe(image, decode_chunk_size=self.decoding_t, generator=generator, motion_bucket_id=self.motion_bucket_id, noise_aug_strength=0.1).frames[0]
        export_to_video(frames, video_path, fps=self.fps_id)
        torch.manual_seed(seed)

        #Video convert to base64
        with open(video_path, "rb") as video_file:
            video_binary_data = video_file.read()
            video_bytes_io = BytesIO(video_binary_data)
            base64_encoded_data = base64.b64encode(video_bytes_io.read())
            base64_string = base64_encoded_data.decode("utf-8")

        return {"generated_video": base64_string}
  1. def finalize: This function cleans up all the allocated memory.
    def finalize(self,*args):
        self.pipe = None

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Creating the Custom Runtime

This is a mandatory step where we allow the users to upload their own custom runtime through inferless-runtime-config.yaml.

build:
  cuda_version: "12.1.1"
  system_packages:
    - "libssl-dev"
    - "libx11-6"
    - "libxext6"
    - "libgl1-mesa-glx"
  python_packages:
    - "accelerate==0.25.0"
    - "opencv-python==4.8.1.78"
    - "git+https://github.com/huggingface/diffusers.git@refs/pull/6103/head"
    - "transformers==4.35.2"
    - "requests==2.31.0"

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Method A: Deploying the model on Inferless Platform

Inferless supports multiple ways of importing your model. For this tutorial, we will use GitHub.

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Step 1: Login to the inferless dashboard can click on Import model button

Navigate to your desired workspace in Inferless and Click on Add a custom model button that you see on the top right. An import wizard will open up.

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Step 2: Follow the UI to complete the model Import

  • Select the GitHub/GitLab Integration option to connect your source code repository with the deployment environment.
  • Navigate to the specific GitHub repository that contains your model’s code. Here, you will need to identify and enter the name of the model you wish to import.
  • Choose the appropriate type of machine that suits your model’s requirements. Additionally, specify the minimum and maximum number of replicas to define the scalability range for deploying your model.
  • Optionally, you have the option to enable automatic build and deployment. This feature triggers a new deployment automatically whenever there is a new code push to your repository.
  • If your model requires additional software packages, configure the Custom Runtime settings by including necessary pip or apt packages. Also, set up environment variables such as Inference Timeout, Container Concurrency, and Scale Down Timeout to tailor the runtime environment according to your needs.
  • Wait for the validation process to complete, ensuring that all settings are correct and functional. Once validation is successful, click on the “Import” button to finalize the import of your model.

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Step 3: Wait for the model build to complete usually takes ~5-10 minutes

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Step 4: Use the APIs to call the model

Once the model is in ‘Active’ status you can click on the ‘API’ page to call the model

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Here is the Demo:

Note: This demo GIF shows the deployment of the Stable Video Diffusion model using webhook.

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Method B: Deploying the model on Inferless CLI

Inferless allows you to deploy your model using Inferless-CLI. Follow the steps to deploy using Inferless CLI.

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Clone the repository of the model

Let’s begin by cloning the model repository:

git clone https://github.com/inferless/stable-video-diffusion.git

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Deploy the Model

To deploy the model using Inferless CLI, execute the following command:

inferless deploy --gpu A100 --runtime inferless-runtime-config.yaml

Explanation of the Command:

  • --gpu A100: Specifies the GPU type for deployment. Available options include A10, A100, and T4.
  • --runtime inferless-runtime-config.yaml: Defines the runtime configuration file. If not specified, the default Inferless runtime is used.
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