Image to Image

Image-to-image is the task of transforming a source image to match the characteristics of a target image or a target image domain.

Example applications:

Transferring the style of an image to another image
Colorizing a black and white image
Increasing the resolution of an image

For more details about the image-to-image task, check out its dedicated page! You will find examples and related materials.

Recommended models

Explore all available models and find the one that suits you best here.

Using the API

Client

Provider

Settings

from huggingface_hub import InferenceClient

client = InferenceClient(
    provider="hf-inference",
    api_key="hf_xxxxxxxxxxxxxxxxxxxxxxxx",
)

# output is a PIL.Image object
image = client.image_to_image(
    "cat.png",
    prompt="Turn the cat into a tiger.",
    model="enhanceaiteam/Flux-Uncensored-V2",
)

API specification

Request

Headers
authorization	string	Authentication header in the form `'Bearer: hf_**'` when `hf_**` is a personal user access token with “Inference Providers” permission. You can generate one from your settings page.

Payload
inputs*	string	The input image data as a base64-encoded string. If no `parameters` are provided, you can also provide the image data as a raw bytes payload.
parameters	object
prompt	string	The text prompt to guide the image generation.
guidance_scale	number	For diffusion models. A higher guidance scale value encourages the model to generate images closely linked to the text prompt at the expense of lower image quality.
negative_prompt	string	One prompt to guide what NOT to include in image generation.
num_inference_steps	integer	For diffusion models. The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference.
target_size	object	The size in pixel of the output image.
width*	integer
height*	integer

Response

Body
image	unknown	The output image returned as raw bytes in the payload.