add ldm model

main.py

@@ -4,14 +4,14 @@ import argparse
 import io
 import multiprocessing
 import os
-import time
-from distutils.util import strtobool
 from typing import Union
 
 import cv2
-import numpy as np
 import torch
 
+from lama_cleaner.lama import LaMa
+from lama_cleaner.ldm import LDM
+
 try:
     torch._C._jit_override_can_fuse_on_cpu(False)
     torch._C._jit_override_can_fuse_on_gpu(False)
@@ -24,13 +24,10 @@ from flask import Flask, request, send_file
 from flask_cors import CORS
 
 from lama_cleaner.helper import (
-    download_model,
     load_img,
     norm_img,
     numpy_to_bytes,
-    pad_img_to_modulo,
-    resize_max_size,
-)
+    resize_max_size, )
 
 NUM_THREADS = str(multiprocessing.cpu_count())
 
@@ -55,6 +52,7 @@ device = None
 @app.route("/inpaint", methods=["POST"])
 def process():
     input = request.files
+    # RGB
     image = load_img(input["image"].read())
     original_shape = image.shape
     interpolation = cv2.INTER_CUBIC
@@ -74,14 +72,7 @@ def process():
     mask = resize_max_size(mask, size_limit=size_limit, interpolation=interpolation)
     mask = norm_img(mask)
 
-    res_np_img = run(image, mask)
-
-    # resize to original size
-    # res_np_img = cv2.resize(
-    #     res_np_img,
-    #     dsize=(original_shape[1], original_shape[0]),
-    #     interpolation=interpolation,
-    # )
+    res_np_img = model(image, mask)
 
     return send_file(
         io.BytesIO(numpy_to_bytes(res_np_img)),
@@ -96,35 +87,12 @@ def index():
     return send_file(os.path.join(BUILD_DIR, "index.html"))
 
 
-def run(image, mask):
-    """
-    image: [C, H, W]
-    mask: [1, H, W]
-    return: BGR IMAGE
-    """
-    origin_height, origin_width = image.shape[1:]
-    image = pad_img_to_modulo(image, mod=8)
-    mask = pad_img_to_modulo(mask, mod=8)
-
-    mask = (mask > 0) * 1
-    image = torch.from_numpy(image).unsqueeze(0).to(device)
-    mask = torch.from_numpy(mask).unsqueeze(0).to(device)
-
-    start = time.time()
-    with torch.no_grad():
-        inpainted_image = model(image, mask)
-
-    print(f"process time: {(time.time() - start)*1000}ms")
-    cur_res = inpainted_image[0].permute(1, 2, 0).detach().cpu().numpy()
-    cur_res = cur_res[0:origin_height, 0:origin_width, :]
-    cur_res = np.clip(cur_res * 255, 0, 255).astype("uint8")
-    cur_res = cv2.cvtColor(cur_res, cv2.COLOR_BGR2RGB)
-    return cur_res
-
-
 def get_args_parser():
     parser = argparse.ArgumentParser()
     parser.add_argument("--port", default=8080, type=int)
+    parser.add_argument("--model", default="lama", choices=["lama", "ldm"])
+    parser.add_argument("--ldm-steps", default=50, type=int, help="Steps for DDIM sampling process."
+                                                                  "The larger the value, the better the result, but it will be more time-consuming")
     parser.add_argument("--device", default="cuda", type=str)
     parser.add_argument("--debug", action="store_true")
     return parser.parse_args()
@@ -136,16 +104,13 @@ def main():
     args = get_args_parser()
     device = torch.device(args.device)
 
-    if os.environ.get("LAMA_MODEL"):
-        model_path = os.environ.get("LAMA_MODEL")
-        if not os.path.exists(model_path):
-            raise FileNotFoundError(f"lama torchscript model not found: {model_path}")
+    if args.model == "lama":
+        model = LaMa(device)
+    elif args.model == "ldm":
+        model = LDM(device, steps=args.ldm_steps)
     else:
-        model_path = download_model()
+        raise NotImplementedError(f"Not supported model: {args.model}")
 
-    model = torch.jit.load(model_path, map_location="cpu")
-    model = model.to(device)
-    model.eval()
     app.run(host="0.0.0.0", port=args.port, debug=args.debug)