Deploy Model Endpoint for Stable Video Diffusion xt

Stable Video Diffusion (SVD) is a powerful image-to-video generation model that can generate 2-4 second high resolution (576x1024) videos conditioned on an input image. In this tutorial, we will create a model endpoint against Stability AI’s Stable Video Diffusion xt model.

``` .. raw:: html

<div style=”display: flex; align-items: flex-start; padding: 20px;”>
<div style=”text-align: center; margin-right: 30px; margin-top: 27px;”>

<img src=”../../_static/tir_inference/image_to_video_model/car.png” alt=”Image” style=”width: 340px; height: 170px; object-fit: cover;”> <div>Input Image</div>

</div> <div style=”text-align: center; margin-top:50px”>

<video width=”320px” height=”240px” controls style=”object-fit: cover;”>

<source src=”../../_static/tir_inference/image_to_video_model/video_output.mp4” type=”video/mp4”> Your browser does not support the video tag.

</video> <div>Output Video</div>

</div>

</div>

```

The tutorial will mainly focus on the following:

For the scope of this tutorial, we will use pre-built container (Stable Video Diffusion xt) for the model endpoint but you may choose to create your own custom container by following this tutorial .

In most cases, the pre-built container would work for your use case. The advantage is - you won’t have to worry about building an API handler. API handler will be automatically created for you.

So let’s get started!

A guide on Model Endpoint creation & Image generation

Step 1: Create a Model Endpoint for Stable Video Diffusion xt on TIR

  • Go to TIR AI Platform

  • Choose a project

  • Go to Model Endpoints section

  • Click on Create Endpoint button on the top-right corner

  • Choose Stable Video Diffusion xt model card in the Choose Framework section

  • Pick any suitable GPU plan of your choice. You can proceed with the default values for replicas, disk-size & endpoint details.

  • Add your environment variables, if any. Else, proceed further

  • Model Details: For now, we will skip the model details and continue with the default model weights.

    If you wish to load your custom model weights (fine-tuned or not), select the appropriate model. (See Creating Model endpoint with custom model weights section below)

  • Complete the endpoint creation

Step 2: Generate your API TOKEN

The model endpoint API requires a valid auth token which you’ll need to perform further steps. So, let’s generate one.

  • Go to API Tokens section under the project.

  • Create a new API Token by clicking on the Create Token button on the top right corner. You can also use an existing token, if already created.

  • Once created, you’ll be able to see the list of API Tokens containing the API Key and Auth Token. You will need this Auth Token in the next step.

    ../../_images/AuthToken.png

Step 3: Generate Videos using a Prompt Image

The final step is to send API requests to the created model endpoint & generate video using image prompt. We will use TIR Notebook to do the same.

  • Once your model endpoint is Ready, visit the Sample API Request section of that model endpoint and copy the Python cod

  • Launch a TIR Notebook with PyTorch or any appropriate Image with any basic machine plan. Once it is in Running state, launch it, and start a new notebook untitled.ipynb in the jupyter labs

  • Paste the Sample API Request code (for Python) in the notebook cell. Below is the sample code.

    import requests
import json
import base64

# mandatory fields
auth_token = <your-auth-token>
input_image_path = 'car.png'    # local path for providing input image
video_output_path = 'video_output.avi'    # local path for storing video output
video_fps = 10  # fps of the output video

url = "https://jupyterlabs.e2enetworks.net/project/p-681/endpoint/is-2242/v1/models/stable-video-diffusion-img2vid-xt:predict"
#Read image file and encode it to a base64 string
with open(input_image_path, 'rb') as image_file:
    image_base64 = base64.b64encode(image_file.read()).decode('utf-8')

payload = json.dumps({
    "fps": video_fps,
    "image":image_base64
})
headers = {
'Content-Type': 'application/json',
'Authorization': f'Bearer {auth_token}'
}
response = requests.request("POST", url, headers=headers, data=payload)   # response contains video in base64

video_bytes = base64.b64decode(response.json().get('predictions'))    # saving the video in output video path
with open(video_output_path, "wb") as video_file:
        ideo_file.write(video_bytes)

  • Copy the Auth Token generated in Step-2 & use it in place of $AUTH_TOKEN in the Sample API Request

  • Also mention appropriate input_image_path, video_output_path, video_fps in the above python script.

    Note

    Video output file should of .avi extenstion. As of now we are supporting only .avi files as output.

  • Execute the code & send request

  • You can view your video which has been downloaded in the path mentioned.

That’s it! Your Stable Video Diffusion Xt model endpoint is up & ready for inference.

You can also try providing different prompts & see the generated images. Besides prompt, the model also supports various other parameters for video generation. Simply add new key and the values in the the payload of the above code. See the Supported parameters for image generation section below.

Creating Model endpoint with custom model weights

To create Inference against Stable Video Diffusion xt model with custom model weights, we will:

Step 1.1: Define a model in TIR Dashboard

Before we proceed with downloading or fine-tuning (optional) the model weights, let us first define a model in TIR dashboard.

  • Go to TIR AI Platform

  • Choose a project

  • Go to Model section

  • Click on Create Model

  • Enter a model name of your choosing (e.g. stable-video-diffusion)

  • Select Model Type as Custom

  • Click on CREATE

  • You will now see details of EOS (E2E Object Storage) bucket created for this model.

  • EOS Provides a S3 compatible API to upload or download content. We will be using MinIO CLI in this tutorial.

  • Copy the Setup Host command from Setup Minio CLI tab to a notepad or leave it in the clipboard. We will soon use it to setup MinIO CLI

Note

In case you forget to copy the setup host command for MinIO CLI, don’t worry. You can always go back to model details and get it again.

Step 1.2: Start a new Notebook

To work with the model weights, we will need to first download them to a local machine or a notebook instance.

  • In TIR Dashboard, Go to Notebooks

  • Launch a new Notebook with Diffusers Image and a hardware plan (e.g. A10080). We recommand a GPU plan if you plan to test or fine-tune the model.

  • Click on the Notebook name or Launch Notebook option to start jupyter labs environment

  • In the jupyter labs, Click New Launcher and Select Terminal

  • Now, paste and run the command for setting up MinIO CLI Host from Step 1

  • If the command works, you will have mc cli ready for uploading our model

Step 1.3: Download the Stable-Video-Diffusion-Xt (by Stability AI) model from notebook

Now, our EOS bucket will store the model weights. Let us download the weights from Hugging face.

  • Start a new notebook untitled.ipynb in jupyter labs

  • Run the below commands. The model will be downloaded by huggingface sdk in the $HOME/.cache folder

    import torch

from diffusers import StableVideoDiffusionPipeline
from diffusers.utils import load_image, export_to_video

pipe = StableVideoDiffusionPipeline.from_pretrained(
    "stabilityai/stable-video-diffusion-img2vid-xt", torch_dtype=torch.float16, variant="fp16"
)
pipe.enable_model_cpu_offload()

# Load the conditioning image
image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/svd/rocket.png")
image = image.resize((1024, 576))

generator = torch.manual_seed(42)
frames = pipe(image, decode_chunk_size=8, generator=generator).frames[0]

export_to_video(frames, "generated.mp4", fps=7)

    Note

    If you face any issues running above code in the notebook cell, you may be missing required libraries. This may happen if you did not launch the notebook with Diffusers image. In such situation, you can install the required libraries below:

    pip install diffusers transformers accelerate

Step 2: Upload the model to Model Bucket (EOS)

Now that the model works as expected, you can fine-tune it with your own data or choose to serve the model as-is. This tutorial assumes you are uploading the model as-is to create inference endpoint. In case you fine-tune the model, you can follow similar steps to upload the model to EOS bucket.

# go to the directory that has the huggingface model code.
cd $HOME/.cache/huggingface/hub/models--stabilityai--stable-video-diffusion-img2vid-xt/snapshots

# push the contents of the folder to EOS bucket.
# Go to TIR Dashboard >> Models >> Select your model >> Copy the cp command from **Setup MinIO CLI** tab.

# The copy command would look like this:
# mc cp -r <MODEL_NAME> stable-video-diffusion/stable-video-diffusion-854588

# here we replace <MODEL_NAME> with '*' to upload all contents of snapshots folder

mc cp -r * stable-video-diffusion/stable-video-diffusion-854588

Note

The model directory name may be a little different (we assume it is models–stabilityai–stable-video-diffusion-img2vid-xt). In case, this command does not work, list the directories in the below path to identify the model directory

$HOME/.cache/huggingface/hub

Step 3: Create an endpoint for our model

With model weights uploaded to TIR Model’s EOS Bucket, what remains is to just launch the endpoint and serve API requests.

Head back to the section on A guide on Model Endpoint creation & Image generation above and follow the steps to create the endpoint for your model.

While creating the endpoint, make sure you select the appropriate model in the model details sub-section, i.e., the EOS bucket containing your model weights. If your model is not in the root directory of the bucket, make sure to specify the path where the model is saved in the bucket.

Follow the steps below to find the Model path in the bucket:

  • Go to MyAccount Object Storage

  • Find your Model bucket (in this case: stable-video-diffusion-854588) & click on its Objects tab

  • If the model_index.json file is present in the list of objects, then your model is present in the root directory & you need not give any Model Path

  • Otherwise, navigate to the folder, and find the model_index.json file, copy its path and paste the same in the Model Path field

    ../../_images/ModelPath.png

  • You can click on Validate button to validate the existance of the model at the given path

    ../../_images/ModelDetails.png

Supported parameters for image generation

Below is a brief description about the supported parameters. we pass these parameters in payload dictionary as shown in the above script

Required Parameters

  • image (base64) - an image in base64 formats

  • fps (int) - fps of the video to be generated

the model supports some other optional parameters that you can pass in request payload to generate video.

Advanced Parameters

  • vae (AutoencoderKLTemporalDecoder) — Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.

  • image_encoder (CLIPVisionModelWithProjection) — Frozen CLIP image-encoder (laion/CLIP-ViT-H-14-laion2B-s32B-b79K).

  • unet (UNetSpatioTemporalConditionModel) — A UNetSpatioTemporalConditionModel to denoise the encoded image latents.

  • scheduler (EulerDiscreteScheduler) — A scheduler to be used in combination with unet to denoise the encoded image latents.

  • feature_extractor (CLIPImageProcessor) — A CLIPImageProcessor to extract features from generated images.