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.
Explore all available models and find the one that suits you best here.
No snippet available for this task.
| 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 |
| Body | | | :--- | :--- | :--- | | image | unknown | The output image returned as raw bytes in the payload. |