Face to Sticker AI: Turn faces into Stickers

Face to Sticker AI uses style transfer to transform any image of a person into a personalized sticker, retaining the unique facial features of the individual. Dive into our blog post to learn how you can create your own custom stickers with just a click!

Face to Sticker AI: Turn faces into Stickers

Face to sticker AI model takes an image of a person and creates a sticker image. This is based on style transfer, where essentially the sticker style is created for an input image of a person. The output is a new image that looks like a sticker but retains the facial features of the person in the input image. This model helps in the creation of personalized stickers from just about any image of a person.

Face-to-Sticker AI 

Key Components of Face to Sticker AI

Under the hood of Face to sticker model is a combination of Instant ID, IP Adapter, ControlNet Depth and Background removal.

  1. Instant ID is responsible for identifying the unique features of the face of the person in the input image.
  2. An image encoder (IP Adapter) helps in transferring the sticker style on to the face image of the person in the input image.
  3. ControlNet Depth estimates the depth of different parts of the face. This helps in creating a 3D representation of the face, which can then be used to apply the sticker style in a way that looks natural and realistic.
  4. Background Removal removes the background, resulting in a clean sticker image.

How to use Face to Sticker AI?

  1. Input image: Choose an image that you want to transform into a sticker. A close-up portrait shot is ideal because it allows the model to clearly identify and process the facial features.
  2. Prompt: Provide a text prompt based on the input image. This could be a simple description of the person in the image, such as “a man” etc. The model uses this prompt to guide the style transfer process.
Face-to-Sticker AI Model Playground

How to Get the Best Results with Face to Sticker AI?

Adjust the below parameters to guide the final image output.

a. Prompt Strength: This parameter is similar to the CGF scale. It determines how closely the image generation follows the text prompt. A higher value will result in an output image that more closely matches the prompt.

b. IP Adapter Noise: This parameter determines the degree of influence of the sticker style. A higher value will result in a more stylized output image

c. IP Adapter Strength: This parameter determines the weight of influence of the sticker style. A higher value will result in a stronger application of the sticker style to the output image.

d. Instant ID strength: This parameter determines how closely the output image resembles the person in the input image. A higher value will result in an output image that more closely resembles the input image.

Face to Sticker AI Examples

Here are few examples of images generated with Face-to-Sticker AI model.

Face to Sticker AI Example 1

Face to Sticker AI Example 2

Face to Sticker AI Example 3