Text to Image

Generate an image based on a given text prompt.

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

Recommended models

black-forest-labs/FLUX.1-dev: One of the most powerful image generation models that can generate realistic outputs.
Kwai-Kolors/Kolors: Text-to-image model for photorealistic generation.
stabilityai/stable-diffusion-3-medium-diffusers: A powerful text-to-image model.

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

Using the API

Language

Client

Provider

Settings

from huggingface_hub import InferenceClient

client = InferenceClient(
    provider="fal-ai",
    api_key="hf_xxxxxxxxxxxxxxxxxxxxxxxx",
)

# output is a PIL.Image object
image = client.text_to_image(
    "Astronaut riding a horse",
    model="black-forest-labs/FLUX.1-dev",
)

API specification

Request

Payload
inputs*	string	The input text data (sometimes called “prompt”)
parameters	object
guidance_scale	number	A higher guidance scale value encourages the model to generate images closely linked to the text prompt, but values too high may cause saturation and other artifacts.
negative_prompt	string	One prompt to guide what NOT to include in image generation.
num_inference_steps	integer	The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference.
width	integer	The width in pixels of the output image
height	integer	The height in pixels of the output image
scheduler	string	Override the scheduler with a compatible one.
seed	integer	Seed for the random number generator.

Some options can be configured by passing headers to the Inference API. Here are the available headers:

Headers
authorization	string	Authentication header in the form `'Bearer: hf_**'` when `hf_**` is a personal user access token with Inference API permission. You can generate one from your settings page.
x-use-cache	boolean, default to `true`	There is a cache layer on the inference API to speed up requests we have already seen. Most models can use those results as they are deterministic (meaning the outputs will be the same anyway). However, if you use a nondeterministic model, you can set this parameter to prevent the caching mechanism from being used, resulting in a real new query. Read more about caching here.
x-wait-for-model	boolean, default to `false`	If the model is not ready, wait for it instead of receiving 503. It limits the number of requests required to get your inference done. It is advised to only set this flag to true after receiving a 503 error, as it will limit hanging in your application to known places. Read more about model availability here.

For more information about Inference API headers, check out the parameters guide.

Response

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