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from typing import List
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_unit_count": 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 = []
poses = []
processor_module = cached_cn_preprocessors[controlnet_module]
for input_image in controlnet_input_images:
img = external_code.to_base64_nparray(input_image)
class JsonAcceptor:
def __init__(self) -> None:
self.value = None
def accept(self, json_dict: dict) -> None:
self.value = json_dict
json_acceptor = JsonAcceptor()
results.append(
processor_module(
img,
res=controlnet_processor_res,
thr_a=controlnet_threshold_a,
thr_b=controlnet_threshold_b,
json_pose_callback=json_acceptor.accept,
)[0]
)
if "openpose" in controlnet_module:
assert json_acceptor.value is not None
poses.append(json_acceptor.value)
global_state.cn_preprocessor_unloadable.get(controlnet_module, lambda: None)()
results64 = list(map(encode_to_base64, results))
res = {"images": results64, "info": "Success"}
if poses:
res["poses"] = poses
return res
try:
import modules.script_callbacks as script_callbacks
script_callbacks.on_app_started(controlnet_api)
except:
pass
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