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'''
Thanks SpenserCai for the original version of the roop api script
-----------------------------------
--- ReActor External API v1.0.1 ---
-----------------------------------
'''
import os, glob
from datetime import datetime, date
from fastapi import FastAPI, Body
# from modules.api.models import *
from modules import scripts, shared
from modules.api import api
import gradio as gr
from scripts.reactor_swapper import EnhancementOptions, swap_face
from scripts.reactor_logger import logger
def default_file_path():
time = datetime.now()
today = date.today()
current_date = today.strftime('%Y-%m-%d')
current_time = time.strftime('%H-%M-%S')
output_file = 'output_'+current_date+'_'+current_time+'.png'
return os.path.join(os.path.abspath("outputs/api"), output_file)
def get_face_restorer(name):
for restorer in shared.face_restorers:
if restorer.name() == name:
return restorer
return None
def get_upscaler(name):
for upscaler in shared.sd_upscalers:
if upscaler.name == name:
return upscaler
return None
def get_models():
models_path = os.path.join(scripts.basedir(), "models/insightface/*")
models = glob.glob(models_path)
models = [x for x in models if x.endswith(".onnx") or x.endswith(".pth")]
return models
def get_full_model(model_name):
models = get_models()
for model in models:
model_path = os.path.split(model)
if model_path[1] == model_name:
return model
return None
def reactor_api(_: gr.Blocks, app: FastAPI):
@app.post("/reactor/image")
async def reactor_image(
source_image: str = Body("",title="Source Face Image"),
target_image: str = Body("",title="Target Image"),
source_faces_index: list[int] = Body([0],title="Comma separated face number(s) from swap-source image"),
face_index: list[int] = Body([0],title="Comma separated face number(s) for target image (result)"),
upscaler: str = Body("None",title="Upscaler"),
scale: float = Body(1,title="Scale by"),
upscale_visibility: float = Body(1,title="Upscaler visibility (if scale = 1)"),
face_restorer: str = Body("None",title="Restore Face: 0 - None; 1 - CodeFormer; 2 - GFPGA"),
restorer_visibility: float = Body(1,title="Restore visibility value"),
codeformer_weight: float = Body(0.5,title="CodeFormer Weight"),
restore_first: int = Body(1,title="Restore face -> Then upscale, 1 - True, 0 - False"),
model: str = Body("inswapper_128.onnx",title="Model"),
gender_source: int = Body(0,title="Gender Detection (Source) (0 - No, 1 - Female Only, 2 - Male Only)"),
gender_target: int = Body(0,title="Gender Detection (Target) (0 - No, 1 - Female Only, 2 - Male Only)"),
save_to_file: int = Body(0,title="Save Result to file, 0 - No, 1 - Yes"),
result_file_path: str = Body("",title="(if 'save_to_file = 1') Result file path"),
device: str = Body("CPU",title="CPU or CUDA (if you have it)"),
mask_face: int = Body(0,title="Face Mask Correction, 1 - True, 0 - False"),
select_source: int = Body(0,title="Select Source, 0 - Image, 1 - Face Model, 2 - Source Folder"),
face_model: str = Body("None",title="Filename of the face model (from 'models/reactor/faces'), e.g. elena.safetensors"),
source_folder: str = Body("",title="The path to the folder containing source faces images")
):
s_image = api.decode_base64_to_image(source_image)
t_image = api.decode_base64_to_image(target_image)
sf_index = source_faces_index
f_index = face_index
gender_s = gender_source
gender_t = gender_target
restore_first_bool = True if restore_first == 1 else False
mask_face = True if mask_face == 1 else False
up_options = EnhancementOptions(do_restore_first=restore_first_bool, scale=scale, upscaler=get_upscaler(upscaler), upscale_visibility=upscale_visibility,face_restorer=get_face_restorer(face_restorer),restorer_visibility=restorer_visibility,codeformer_weight=codeformer_weight)
use_model = get_full_model(model)
if use_model is None:
Exception("Model not found")
result = swap_face(s_image, t_image, use_model, sf_index, f_index, up_options, gender_s, gender_t, True, True, device, mask_face, select_source, face_model, source_folder, None)
if save_to_file == 1:
if result_file_path == "":
result_file_path = default_file_path()
try:
result[0].save(result_file_path, format='PNG')
logger.status("Result has been saved to: %s", result_file_path)
except Exception as e:
logger.error("Error while saving result: %s",e)
return {"image": api.encode_pil_to_base64(result[0])}
@app.get("/reactor/models")
async def reactor_models():
model_names = [os.path.split(model)[1] for model in get_models()]
return {"models": model_names}
@app.get("/reactor/upscalers")
async def reactor_upscalers():
names = [upscaler.name for upscaler in shared.sd_upscalers]
return {"upscalers": names}
try:
import modules.script_callbacks as script_callbacks
script_callbacks.on_app_started(reactor_api)
except:
pass
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