HuggingFace just announced that they now support third party inference providers: fal, Replicate, Sambanova, Together AI both directly through the HuggingFace Hub and their SDKs.
Being a Pro user of HuggingFace, I get $2 of credits to use each month. So I played around with the Flux model:
from huggingface_hub import InferenceClient
client = InferenceClient(provider="fal-ai", token="hf_***") # Enable serverless inference when creating the token
image = client.text_to_image("Close-up of a cheetah's face, direct frontal view. Sharp focus on eye and skin texture and color. Natural lighting to capture authentic eye shine and depth.", model="black-forest-labs/FLUX.1-schnell")
image.save("cheetah.png")The output looks like this:
This is fantastic! I can now run quick experiments with many different models on the huggingface hub. There is a text box on the HuggingFace Hub where I can enter text and start generating images (or text for a LLM) easily:
I can even get the code to run this via their Python SDK:
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"
)The only challenge I found so far is that fal-ai for example does support the Flux 1.1 pro through their API, but since that model is not available on the HuggingFace Hub, I can’t use it. I get a ValueError: Model fal-ai/flux-pro/v1 is not supported with FalAI for task text-to-image. error when I try to use it.