240 lines
6.7 KiB
Python
240 lines
6.7 KiB
Python
#!/usr/bin/env python3
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import io
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import logging
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import multiprocessing
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import os
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import random
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import time
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import imghdr
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from pathlib import Path
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from typing import Union
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import cv2
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import torch
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import numpy as np
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from loguru import logger
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from lama_cleaner.model_manager import ModelManager
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from lama_cleaner.schema import Config
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try:
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torch._C._jit_override_can_fuse_on_cpu(False)
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torch._C._jit_override_can_fuse_on_gpu(False)
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torch._C._jit_set_texpr_fuser_enabled(False)
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torch._C._jit_set_nvfuser_enabled(False)
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except:
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pass
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from flask import Flask, request, send_file, cli, make_response
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# Disable ability for Flask to display warning about using a development server in a production environment.
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# https://gist.github.com/jerblack/735b9953ba1ab6234abb43174210d356
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cli.show_server_banner = lambda *_: None
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from flask_cors import CORS
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from lama_cleaner.helper import (
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load_img,
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numpy_to_bytes,
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resize_max_size,
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)
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NUM_THREADS = str(multiprocessing.cpu_count())
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# fix libomp problem on windows https://github.com/Sanster/lama-cleaner/issues/56
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os.environ["KMP_DUPLICATE_LIB_OK"] = "True"
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os.environ["OMP_NUM_THREADS"] = NUM_THREADS
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os.environ["OPENBLAS_NUM_THREADS"] = NUM_THREADS
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os.environ["MKL_NUM_THREADS"] = NUM_THREADS
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os.environ["VECLIB_MAXIMUM_THREADS"] = NUM_THREADS
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os.environ["NUMEXPR_NUM_THREADS"] = NUM_THREADS
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if os.environ.get("CACHE_DIR"):
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os.environ["TORCH_HOME"] = os.environ["CACHE_DIR"]
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BUILD_DIR = os.environ.get("LAMA_CLEANER_BUILD_DIR", "app/build")
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class NoFlaskwebgui(logging.Filter):
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def filter(self, record):
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return "GET //flaskwebgui-keep-server-alive" not in record.getMessage()
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logging.getLogger("werkzeug").addFilter(NoFlaskwebgui())
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app = Flask(__name__, static_folder=os.path.join(BUILD_DIR, "static"))
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app.config["JSON_AS_ASCII"] = False
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CORS(app, expose_headers=["Content-Disposition"])
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# MAX_BUFFER_SIZE = 50 * 1000 * 1000 # 50 MB
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# async_mode 优先级: eventlet/gevent_uwsgi/gevent/threading
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# only threading works on macOS
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# socketio = SocketIO(app, max_http_buffer_size=MAX_BUFFER_SIZE, async_mode='threading')
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model: ModelManager = None
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device = None
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input_image_path: str = None
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def get_image_ext(img_bytes):
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w = imghdr.what("", img_bytes)
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if w is None:
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w = "jpeg"
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return w
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def diffuser_callback(step: int):
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pass
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# socketio.emit('diffusion_step', {'diffusion_step': step})
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@app.route("/inpaint", methods=["POST"])
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def process():
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input = request.files
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# RGB
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origin_image_bytes = input["image"].read()
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image, alpha_channel = load_img(origin_image_bytes)
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original_shape = image.shape
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interpolation = cv2.INTER_CUBIC
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form = request.form
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size_limit: Union[int, str] = form.get("sizeLimit", "1080")
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if size_limit == "Original":
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size_limit = max(image.shape)
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else:
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size_limit = int(size_limit)
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config = Config(
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ldm_steps=form["ldmSteps"],
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ldm_sampler=form["ldmSampler"],
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hd_strategy=form["hdStrategy"],
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zits_wireframe=form["zitsWireframe"],
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hd_strategy_crop_margin=form["hdStrategyCropMargin"],
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hd_strategy_crop_trigger_size=form["hdStrategyCropTrigerSize"],
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hd_strategy_resize_limit=form["hdStrategyResizeLimit"],
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prompt=form["prompt"],
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use_croper=form["useCroper"],
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croper_x=form["croperX"],
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croper_y=form["croperY"],
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croper_height=form["croperHeight"],
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croper_width=form["croperWidth"],
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sd_mask_blur=form["sdMaskBlur"],
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sd_strength=form["sdStrength"],
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sd_steps=form["sdSteps"],
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sd_guidance_scale=form["sdGuidanceScale"],
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sd_sampler=form["sdSampler"],
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sd_seed=form["sdSeed"],
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cv2_flag=form["cv2Flag"],
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cv2_radius=form['cv2Radius']
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)
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if config.sd_seed == -1:
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config.sd_seed = random.randint(1, 9999999)
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logger.info(f"Origin image shape: {original_shape}")
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image = resize_max_size(image, size_limit=size_limit, interpolation=interpolation)
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logger.info(f"Resized image shape: {image.shape}")
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mask, _ = load_img(input["mask"].read(), gray=True)
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mask = resize_max_size(mask, size_limit=size_limit, interpolation=interpolation)
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start = time.time()
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res_np_img = model(image, mask, config)
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logger.info(f"process time: {(time.time() - start) * 1000}ms")
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torch.cuda.empty_cache()
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if alpha_channel is not None:
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if alpha_channel.shape[:2] != res_np_img.shape[:2]:
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alpha_channel = cv2.resize(
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alpha_channel, dsize=(res_np_img.shape[1], res_np_img.shape[0])
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)
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res_np_img = np.concatenate(
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(res_np_img, alpha_channel[:, :, np.newaxis]), axis=-1
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)
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ext = get_image_ext(origin_image_bytes)
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response = make_response(
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send_file(
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io.BytesIO(numpy_to_bytes(res_np_img, ext)),
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mimetype=f"image/{ext}",
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)
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)
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response.headers["X-Seed"] = str(config.sd_seed)
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return response
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@app.route("/model")
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def current_model():
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return model.name, 200
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@app.route("/model_downloaded/<name>")
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def model_downloaded(name):
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return str(model.is_downloaded(name)), 200
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@app.route("/model", methods=["POST"])
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def switch_model():
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new_name = request.form.get("name")
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if new_name == model.name:
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return "Same model", 200
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try:
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model.switch(new_name)
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except NotImplementedError:
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return f"{new_name} not implemented", 403
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return f"ok, switch to {new_name}", 200
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@app.route("/")
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def index():
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return send_file(os.path.join(BUILD_DIR, "index.html"))
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@app.route("/inputimage")
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def set_input_photo():
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if input_image_path:
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with open(input_image_path, "rb") as f:
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image_in_bytes = f.read()
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return send_file(
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input_image_path,
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as_attachment=True,
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attachment_filename=Path(input_image_path).name,
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mimetype=f"image/{get_image_ext(image_in_bytes)}",
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)
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else:
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return "No Input Image"
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def main(args):
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global model
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global device
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global input_image_path
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device = torch.device(args.device)
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input_image_path = args.input
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model = ModelManager(
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name=args.model,
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device=device,
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hf_access_token=args.hf_access_token,
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sd_disable_nsfw=args.sd_disable_nsfw,
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sd_cpu_textencoder=args.sd_cpu_textencoder,
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sd_run_local=args.sd_run_local,
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callbacks=[diffuser_callback],
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)
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if args.gui:
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app_width, app_height = args.gui_size
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from flaskwebgui import FlaskUI
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ui = FlaskUI(
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app, width=app_width, height=app_height, host=args.host, port=args.port
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)
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ui.run()
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else:
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# TODO: socketio
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app.run(host=args.host, port=args.port, debug=args.debug)
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