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import numpy as np
from fastapi import FastAPI, Body
from fastapi.exceptions import HTTPException
from PIL import Image
import gradio as gr
from modules.api.models import *
from modules.api import api
from scripts import external_code, global_state
from scripts.processor import preprocessor_filters
from scripts.logging import logger
def encode_to_base64(image):
if type(image) is str:
return image
elif type(image) is Image.Image:
return api.encode_pil_to_base64(image)
elif type(image) is np.ndarray:
return encode_np_to_base64(image)
else:
return ""
def encode_np_to_base64(image):
pil = Image.fromarray(image)
return api.encode_pil_to_base64(pil)
def controlnet_api(_: gr.Blocks, app: FastAPI):
@app.get("/controlnet/version")
async def version():
return {"version": external_code.get_api_version()}
@app.get("/controlnet/model_list")
async def model_list(update: bool = True):
up_to_date_model_list = external_code.get_models(update=update)
logger.debug(up_to_date_model_list)
return {"model_list": up_to_date_model_list}
@app.get("/controlnet/module_list")
async def module_list(alias_names: bool = False):
_module_list = external_code.get_modules(alias_names)
logger.debug(_module_list)
return {
"module_list": _module_list,
"module_detail": external_code.get_modules_detail(alias_names)
}
@app.get("/controlnet/control_types")
async def control_types():
def format_control_type(
filtered_preprocessor_list,
filtered_model_list,
default_option,
default_model,
):
return {
"module_list": filtered_preprocessor_list,
"model_list": filtered_model_list,
"default_option": default_option,
"default_model": default_model,
}
return {
'control_types': {
control_type: format_control_type(*global_state.select_control_type(control_type))
for control_type in preprocessor_filters.keys()
}
}
@app.get("/controlnet/settings")
async def settings():
max_models_num = external_code.get_max_models_num()
return {"control_net_max_models_num":max_models_num}
cached_cn_preprocessors = global_state.cache_preprocessors(global_state.cn_preprocessor_modules)
@app.post("/controlnet/detect")
async def detect(
controlnet_module: str = Body("none", title='Controlnet Module'),
controlnet_input_images: List[str] = Body([], title='Controlnet Input Images'),
controlnet_processor_res: int = Body(512, title='Controlnet Processor Resolution'),
controlnet_threshold_a: float = Body(64, title='Controlnet Threshold a'),
controlnet_threshold_b: float = Body(64, title='Controlnet Threshold b')
):
controlnet_module = global_state.reverse_preprocessor_aliases.get(controlnet_module, controlnet_module)
if controlnet_module not in cached_cn_preprocessors:
raise HTTPException(
status_code=422, detail="Module not available")
if len(controlnet_input_images) == 0:
raise HTTPException(
status_code=422, detail="No image selected")
logger.info(f"Detecting {str(len(controlnet_input_images))} images with the {controlnet_module} module.")
results = []
processor_module = cached_cn_preprocessors[controlnet_module]
for input_image in controlnet_input_images:
img = external_code.to_base64_nparray(input_image)
results.append(processor_module(img, res=controlnet_processor_res, thr_a=controlnet_threshold_a, thr_b=controlnet_threshold_b)[0])
global_state.cn_preprocessor_unloadable.get(controlnet_module, lambda: None)()
results64 = list(map(encode_to_base64, results))
return {"images": results64, "info": "Success"}
try:
import modules.script_callbacks as script_callbacks
script_callbacks.on_app_started(controlnet_api)
except:
pass
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