_ / lama_cleaner /server.py
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#!/usr/bin/env python3
import os
import hashlib
os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1"
import imghdr
import io
import logging
import multiprocessing
import random
import time
from pathlib import Path
import cv2
import numpy as np
import torch
from PIL import Image
from loguru import logger
from lama_cleaner.const import SD15_MODELS
from lama_cleaner.file_manager import FileManager
from lama_cleaner.model.utils import torch_gc
from lama_cleaner.model_manager import ModelManager
from lama_cleaner.plugins import (
InteractiveSeg,
RemoveBG,
RealESRGANUpscaler,
MakeGIF,
GFPGANPlugin,
RestoreFormerPlugin,
AnimeSeg,
)
from lama_cleaner.schema import Config
try:
torch._C._jit_override_can_fuse_on_cpu(False)
torch._C._jit_override_can_fuse_on_gpu(False)
torch._C._jit_set_texpr_fuser_enabled(False)
torch._C._jit_set_nvfuser_enabled(False)
except:
pass
from flask import (
Flask,
request,
send_file,
cli,
make_response,
send_from_directory,
jsonify,
)
from flask_socketio import SocketIO
# Disable ability for Flask to display warning about using a development server in a production environment.
# https://gist.github.com/jerblack/735b9953ba1ab6234abb43174210d356
cli.show_server_banner = lambda *_: None
from flask_cors import CORS
from lama_cleaner.helper import (
load_img,
numpy_to_bytes,
resize_max_size,
pil_to_bytes,
)
NUM_THREADS = str(multiprocessing.cpu_count())
# fix libomp problem on windows https://github.com/Sanster/lama-cleaner/issues/56
os.environ["KMP_DUPLICATE_LIB_OK"] = "True"
os.environ["OMP_NUM_THREADS"] = NUM_THREADS
os.environ["OPENBLAS_NUM_THREADS"] = NUM_THREADS
os.environ["MKL_NUM_THREADS"] = NUM_THREADS
os.environ["VECLIB_MAXIMUM_THREADS"] = NUM_THREADS
os.environ["NUMEXPR_NUM_THREADS"] = NUM_THREADS
if os.environ.get("CACHE_DIR"):
os.environ["TORCH_HOME"] = os.environ["CACHE_DIR"]
BUILD_DIR = os.environ.get("LAMA_CLEANER_BUILD_DIR", "app/build")
class NoFlaskwebgui(logging.Filter):
def filter(self, record):
msg = record.getMessage()
if "Running on http:" in msg:
print(msg[msg.index("Running on http:") :])
return (
"flaskwebgui-keep-server-alive" not in msg
and "socket.io" not in msg
and "This is a development server." not in msg
)
logging.getLogger("werkzeug").addFilter(NoFlaskwebgui())
app = Flask(__name__, static_folder=os.path.join(BUILD_DIR, "static"))
app.config["JSON_AS_ASCII"] = False
CORS(app, expose_headers=["Content-Disposition"])
sio_logger = logging.getLogger("sio-logger")
sio_logger.setLevel(logging.ERROR)
socketio = SocketIO(app, cors_allowed_origins="*", async_mode="threading")
model: ModelManager = None
thumb: FileManager = None
output_dir: str = None
device = None
input_image_path: str = None
is_disable_model_switch: bool = False
is_controlnet: bool = False
controlnet_method: str = "control_v11p_sd15_canny"
is_enable_file_manager: bool = False
is_enable_auto_saving: bool = False
is_desktop: bool = False
image_quality: int = 95
plugins = {}
def get_image_ext(img_bytes):
w = imghdr.what("", img_bytes)
if w is None:
w = "jpeg"
return w
def diffuser_callback(i, t, latents):
socketio.emit("diffusion_progress", {"step": i})
@app.route("/save_image", methods=["POST"])
def save_image():
if output_dir is None:
return "--output-dir is None", 500
input = request.files
filename = request.form["filename"]
origin_image_bytes = input["image"].read() # RGB
ext = get_image_ext(origin_image_bytes)
image, alpha_channel, exif_infos = load_img(origin_image_bytes, return_exif=True)
save_path = os.path.join(output_dir, filename)
if alpha_channel is not None:
if alpha_channel.shape[:2] != image.shape[:2]:
alpha_channel = cv2.resize(
alpha_channel, dsize=(image.shape[1], image.shape[0])
)
image = np.concatenate((image, alpha_channel[:, :, np.newaxis]), axis=-1)
pil_image = Image.fromarray(image)
img_bytes = pil_to_bytes(
pil_image,
ext,
quality=image_quality,
exif_infos=exif_infos,
)
with open(save_path, "wb") as fw:
fw.write(img_bytes)
return "ok", 200
@app.route("/medias/<tab>")
def medias(tab):
if tab == "image":
response = make_response(jsonify(thumb.media_names), 200)
else:
response = make_response(jsonify(thumb.output_media_names), 200)
# response.last_modified = thumb.modified_time[tab]
# response.cache_control.no_cache = True
# response.cache_control.max_age = 0
# response.make_conditional(request)
return response
@app.route("/media/<tab>/<filename>")
def media_file(tab, filename):
if tab == "image":
return send_from_directory(thumb.root_directory, filename)
return send_from_directory(thumb.output_dir, filename)
@app.route("/media_thumbnail/<tab>/<filename>")
def media_thumbnail_file(tab, filename):
args = request.args
width = args.get("width")
height = args.get("height")
if width is None and height is None:
width = 256
if width:
width = int(float(width))
if height:
height = int(float(height))
directory = thumb.root_directory
if tab == "output":
directory = thumb.output_dir
thumb_filename, (width, height) = thumb.get_thumbnail(
directory, filename, width, height
)
thumb_filepath = f"{app.config['THUMBNAIL_MEDIA_THUMBNAIL_ROOT']}{thumb_filename}"
response = make_response(send_file(thumb_filepath))
response.headers["X-Width"] = str(width)
response.headers["X-Height"] = str(height)
return response
@app.route("/inpaint", methods=["POST"])
def process():
input = request.files
# RGB
origin_image_bytes = input["image"].read()
image, alpha_channel, exif_infos = load_img(origin_image_bytes, return_exif=True)
mask, _ = load_img(input["mask"].read(), gray=True)
mask = cv2.threshold(mask, 127, 255, cv2.THRESH_BINARY)[1]
if image.shape[:2] != mask.shape[:2]:
return (
f"Mask shape{mask.shape[:2]} not queal to Image shape{image.shape[:2]}",
400,
)
original_shape = image.shape
interpolation = cv2.INTER_CUBIC
form = request.form
size_limit = max(image.shape)
if "paintByExampleImage" in input:
paint_by_example_example_image, _ = load_img(
input["paintByExampleImage"].read()
)
paint_by_example_example_image = Image.fromarray(paint_by_example_example_image)
else:
paint_by_example_example_image = None
config = Config(
ldm_steps=form["ldmSteps"],
ldm_sampler=form["ldmSampler"],
hd_strategy=form["hdStrategy"],
zits_wireframe=form["zitsWireframe"],
hd_strategy_crop_margin=form["hdStrategyCropMargin"],
hd_strategy_crop_trigger_size=form["hdStrategyCropTrigerSize"],
hd_strategy_resize_limit=form["hdStrategyResizeLimit"],
prompt=form["prompt"],
negative_prompt=form["negativePrompt"],
use_croper=form["useCroper"],
croper_x=form["croperX"],
croper_y=form["croperY"],
croper_height=form["croperHeight"],
croper_width=form["croperWidth"],
sd_scale=form["sdScale"],
sd_mask_blur=form["sdMaskBlur"],
sd_strength=form["sdStrength"],
sd_steps=form["sdSteps"],
sd_guidance_scale=form["sdGuidanceScale"],
sd_sampler=form["sdSampler"],
sd_seed=form["sdSeed"],
sd_match_histograms=form["sdMatchHistograms"],
cv2_flag=form["cv2Flag"],
cv2_radius=form["cv2Radius"],
paint_by_example_steps=form["paintByExampleSteps"],
paint_by_example_guidance_scale=form["paintByExampleGuidanceScale"],
paint_by_example_mask_blur=form["paintByExampleMaskBlur"],
paint_by_example_seed=form["paintByExampleSeed"],
paint_by_example_match_histograms=form["paintByExampleMatchHistograms"],
paint_by_example_example_image=paint_by_example_example_image,
p2p_steps=form["p2pSteps"],
p2p_image_guidance_scale=form["p2pImageGuidanceScale"],
p2p_guidance_scale=form["p2pGuidanceScale"],
controlnet_conditioning_scale=form["controlnet_conditioning_scale"],
controlnet_method=form["controlnet_method"],
)
if config.sd_seed == -1:
config.sd_seed = random.randint(1, 999999999)
if config.paint_by_example_seed == -1:
config.paint_by_example_seed = random.randint(1, 999999999)
logger.info(f"Origin image shape: {original_shape}")
image = resize_max_size(image, size_limit=size_limit, interpolation=interpolation)
mask = resize_max_size(mask, size_limit=size_limit, interpolation=interpolation)
start = time.time()
try:
res_np_img = model(image, mask, config)
except RuntimeError as e:
if "CUDA out of memory. " in str(e):
# NOTE: the string may change?
return "CUDA out of memory", 500
else:
logger.exception(e)
return f"{str(e)}", 500
finally:
logger.info(f"process time: {(time.time() - start) * 1000}ms")
torch_gc()
res_np_img = cv2.cvtColor(res_np_img.astype(np.uint8), cv2.COLOR_BGR2RGB)
if alpha_channel is not None:
if alpha_channel.shape[:2] != res_np_img.shape[:2]:
alpha_channel = cv2.resize(
alpha_channel, dsize=(res_np_img.shape[1], res_np_img.shape[0])
)
res_np_img = np.concatenate(
(res_np_img, alpha_channel[:, :, np.newaxis]), axis=-1
)
ext = get_image_ext(origin_image_bytes)
bytes_io = io.BytesIO(
pil_to_bytes(
Image.fromarray(res_np_img),
ext,
quality=image_quality,
exif_infos=exif_infos,
)
)
response = make_response(
send_file(
# io.BytesIO(numpy_to_bytes(res_np_img, ext)),
bytes_io,
mimetype=f"image/{ext}",
)
)
response.headers["X-Seed"] = str(config.sd_seed)
socketio.emit("diffusion_finish")
return response
@app.route("/run_plugin", methods=["POST"])
def run_plugin():
form = request.form
files = request.files
name = form["name"]
if name not in plugins:
return "Plugin not found", 500
origin_image_bytes = files["image"].read() # RGB
rgb_np_img, alpha_channel, exif_infos = load_img(
origin_image_bytes, return_exif=True
)
start = time.time()
try:
form = dict(form)
if name == InteractiveSeg.name:
img_md5 = hashlib.md5(origin_image_bytes).hexdigest()
form["img_md5"] = img_md5
bgr_res = plugins[name](rgb_np_img, files, form)
except RuntimeError as e:
torch.cuda.empty_cache()
if "CUDA out of memory. " in str(e):
# NOTE: the string may change?
return "CUDA out of memory", 500
else:
logger.exception(e)
return "Internal Server Error", 500
logger.info(f"{name} process time: {(time.time() - start) * 1000}ms")
torch_gc()
if name == MakeGIF.name:
return send_file(
io.BytesIO(bgr_res),
mimetype="image/gif",
as_attachment=True,
download_name=form["filename"],
)
if name == InteractiveSeg.name:
return make_response(
send_file(
io.BytesIO(numpy_to_bytes(bgr_res, "png")),
mimetype="image/png",
)
)
if name in [RemoveBG.name, AnimeSeg.name]:
rgb_res = bgr_res
ext = "png"
else:
rgb_res = cv2.cvtColor(bgr_res, cv2.COLOR_BGR2RGB)
ext = get_image_ext(origin_image_bytes)
if alpha_channel is not None:
if alpha_channel.shape[:2] != rgb_res.shape[:2]:
alpha_channel = cv2.resize(
alpha_channel, dsize=(rgb_res.shape[1], rgb_res.shape[0])
)
rgb_res = np.concatenate(
(rgb_res, alpha_channel[:, :, np.newaxis]), axis=-1
)
response = make_response(
send_file(
io.BytesIO(
pil_to_bytes(
Image.fromarray(rgb_res),
ext,
quality=image_quality,
exif_infos=exif_infos,
)
),
mimetype=f"image/{ext}",
)
)
return response
@app.route("/server_config", methods=["GET"])
def get_server_config():
return {
"isControlNet": is_controlnet,
"controlNetMethod": controlnet_method,
"isDisableModelSwitchState": is_disable_model_switch,
"isEnableAutoSaving": is_enable_auto_saving,
"enableFileManager": is_enable_file_manager,
"plugins": list(plugins.keys()),
}, 200
@app.route("/model")
def current_model():
return model.name, 200
@app.route("/model_downloaded/<name>")
def model_downloaded(name):
return str(model.is_downloaded(name)), 200
@app.route("/is_desktop")
def get_is_desktop():
return str(is_desktop), 200
@app.route("/model", methods=["POST"])
def switch_model():
if is_disable_model_switch:
return "Switch model is disabled", 400
new_name = request.form.get("name")
if new_name == model.name:
return "Same model", 200
try:
model.switch(new_name)
except NotImplementedError:
return f"{new_name} not implemented", 403
return f"ok, switch to {new_name}", 200
@app.route("/")
def index():
return send_file(os.path.join(BUILD_DIR, "index.html"))
@app.route("/inputimage")
def set_input_photo():
if input_image_path:
with open(input_image_path, "rb") as f:
image_in_bytes = f.read()
return send_file(
input_image_path,
as_attachment=True,
download_name=Path(input_image_path).name,
mimetype=f"image/{get_image_ext(image_in_bytes)}",
)
else:
return "No Input Image"
def build_plugins(args):
global plugins
if args.enable_interactive_seg:
logger.info(f"Initialize {InteractiveSeg.name} plugin")
plugins[InteractiveSeg.name] = InteractiveSeg(
args.interactive_seg_model, args.interactive_seg_device
)
if args.enable_remove_bg:
logger.info(f"Initialize {RemoveBG.name} plugin")
plugins[RemoveBG.name] = RemoveBG()
if args.enable_anime_seg:
logger.info(f"Initialize {AnimeSeg.name} plugin")
plugins[AnimeSeg.name] = AnimeSeg()
if args.enable_realesrgan:
logger.info(
f"Initialize {RealESRGANUpscaler.name} plugin: {args.realesrgan_model}, {args.realesrgan_device}"
)
plugins[RealESRGANUpscaler.name] = RealESRGANUpscaler(
args.realesrgan_model,
args.realesrgan_device,
no_half=args.realesrgan_no_half,
)
if args.enable_gfpgan:
logger.info(f"Initialize {GFPGANPlugin.name} plugin")
if args.enable_realesrgan:
logger.info("Use realesrgan as GFPGAN background upscaler")
else:
logger.info(
f"GFPGAN no background upscaler, use --enable-realesrgan to enable it"
)
plugins[GFPGANPlugin.name] = GFPGANPlugin(
args.gfpgan_device, upscaler=plugins.get(RealESRGANUpscaler.name, None)
)
if args.enable_restoreformer:
logger.info(f"Initialize {RestoreFormerPlugin.name} plugin")
plugins[RestoreFormerPlugin.name] = RestoreFormerPlugin(
args.restoreformer_device,
upscaler=plugins.get(RealESRGANUpscaler.name, None),
)
if args.enable_gif:
logger.info(f"Initialize GIF plugin")
plugins[MakeGIF.name] = MakeGIF()
def main(args):
global model
global device
global input_image_path
global is_disable_model_switch
global is_enable_file_manager
global is_desktop
global thumb
global output_dir
global is_enable_auto_saving
global is_controlnet
global controlnet_method
global image_quality
build_plugins(args)
image_quality = args.quality
if args.sd_controlnet and args.model in SD15_MODELS:
is_controlnet = True
controlnet_method = args.sd_controlnet_method
output_dir = args.output_dir
if output_dir:
is_enable_auto_saving = True
device = torch.device(args.device)
is_disable_model_switch = args.disable_model_switch
is_desktop = args.gui
if is_disable_model_switch:
logger.info(
f"Start with --disable-model-switch, model switch on frontend is disable"
)
if args.input and os.path.isdir(args.input):
logger.info(f"Initialize file manager")
thumb = FileManager(app)
is_enable_file_manager = True
app.config["THUMBNAIL_MEDIA_ROOT"] = args.input
app.config["THUMBNAIL_MEDIA_THUMBNAIL_ROOT"] = os.path.join(
args.output_dir, "lama_cleaner_thumbnails"
)
thumb.output_dir = Path(args.output_dir)
# thumb.start()
# try:
# while True:
# time.sleep(1)
# finally:
# thumb.image_dir_observer.stop()
# thumb.image_dir_observer.join()
# thumb.output_dir_observer.stop()
# thumb.output_dir_observer.join()
else:
input_image_path = args.input
model = ModelManager(
name=args.model,
sd_controlnet=args.sd_controlnet,
sd_controlnet_method=args.sd_controlnet_method,
device=device,
no_half=args.no_half,
hf_access_token=args.hf_access_token,
disable_nsfw=args.sd_disable_nsfw or args.disable_nsfw,
sd_cpu_textencoder=args.sd_cpu_textencoder,
sd_run_local=args.sd_run_local,
sd_local_model_path=args.sd_local_model_path,
local_files_only=args.local_files_only,
cpu_offload=args.cpu_offload,
enable_xformers=args.sd_enable_xformers or args.enable_xformers,
callback=diffuser_callback,
)
if args.gui:
app_width, app_height = args.gui_size
from flaskwebgui import FlaskUI
ui = FlaskUI(
app,
socketio=socketio,
width=app_width,
height=app_height,
host=args.host,
port=args.port,
close_server_on_exit=not args.no_gui_auto_close,
)
ui.run()
else:
socketio.run(
app,
host=args.host,
port=args.port,
debug=args.debug,
allow_unsafe_werkzeug=True,
)