fal_api.py

import base64
from fal_client import FalClient
from src.utils import numpy_to_base64,resize_image
import json 
# Initialize FalClient
fal_client = FalClient()
styles_dic= json.dump(open("src/styles.json"))

def fal_api(prompt,ar,nis,seed,style,model="fal-ai/flux-general"):
  inputs={
      "prompt":prompt,

      "num_inference_steps":nis,

  }
  if ar is not None:
    inputs["image_size"]=ar

  if seed is not None:
    inputs["seed"]=seed
  if style is not None:
    style_type=styles_dic[style][1]
    if style_type=="model":
      model=styles_dic[style][0]
    if style_type=="lora":
      #inputs["LoraWeight"]=styles_dic[style][0]
      inputs["loras"]= {  "path": styles_dic[style][0]},
      if len(styles_dic[style])==3: #Add in prompt triggers
         inputs["prompt"]+=styles_dic[style][2]

    if style_type=="prompt":
      inputs["prompt"]+=styles_dic[style][0]

  handler = fal_client.submit(
        model,
        arguments=inputs
    )
  return handler.get()["images"][0]["url"]


def fal_ipadapter_api(input_image,ip_image,seg_prompt):
  print(input_image,ip_image,seg_prompt)

  handler = fal_client.submit(
        "comfy/JarvisSan22/cloth_ipadapter",
        arguments={
            "loadimage_1":numpy_to_base64(input_image),
             "loadimage_2":numpy_to_base64(ip_image),
            "groundingdinosamsegment (segment anything)_prompt":seg_prompt
        },
    )
  print(handler)
  result= handler.get()
  #image_urls=[]
  print(result["outputs"])

  #print(result)
  return result["outputs"]["20"]["images"][0]["url"]