Passing arguments and leveraging Pydantic

fal Applications and FAST API are fully compatible with Pydantic. Any features of Pydantic used in fal endpoint arguments will also work.

Pydantic features can be used for data validation in your endpoint. In the example below, you can set some of the parameters as optional, set default values, and apply other types of validation such as constraints and types.

import fal
from pydantic import BaseModel
from fal.toolkit import Image

class ImageModelInput(BaseModel):
    seed: int | None = Field(
        default=None,
        description="""
            The same seed and the same prompt given to the same version of Stable Diffusion
            will output the same image every time.
        """,
        examples=[176400],
    )
    num_inference_steps: int = Field(
        default=25,
        description="""
            Increasing the amount of steps tell the model that it should take more steps
            to generate your final result which can increase the amount of detail in your image.
        """,
        gt=0,
        le=100,
    )

class MyApp(fal.App(keep_alive=300)):
    machine_type = "GPU-A100"
    requirements = [
        "diffusers==0.28.0",
        "torch==2.3.0",
        "accelerate",
        "transformers",
    ]

    def setup(self):
        import torch
        from diffusers import StableDiffusionXLPipeline, DPMSolverSinglestepScheduler

        self.pipe = StableDiffusionXLPipeline.from_pretrained(
            "sd-community/sdxl-flash",
            torch_dtype=torch.float16,
        ).to("cuda")
        self.pipe.scheduler = DPMSolverSinglestepScheduler.from_config(
            self.pipe.scheduler.config,
            timestep_spacing="trailing",
        )

    @fal.endpoint("/")
    def generate_image(self, request: ImageModelInput) -> Image:
        result = self.pipe(request.prompt, num_inference_steps=7, guidance_scale=3)
        image = Image.from_pil(result.images[0])
        return image