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.
Explore all available models and find the one that suits you best here.
Language
Client
Provider
import os
from huggingface_hub import InferenceClient
client = InferenceClient(
provider="fal-ai",
api_key=os.environ["HF_TOKEN"],
)
# output is a PIL.Image object
image = client.text_to_image(
"Astronaut riding a horse",
model="black-forest-labs/FLUX.1-dev",
)| 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 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. |
| Body | ||
|---|---|---|
| image | unknown | The generated image returned as raw bytes in the payload. |