add ldm model

lama_cleaner/lama/__init__.py

@@ -0,0 +1,56 @@
+import os
+import time
+
+import cv2
+import torch
+import numpy as np
+
+from lama_cleaner.helper import pad_img_to_modulo, download_model
+
+LAMA_MODEL_URL = os.environ.get(
+    "LAMA_MODEL_URL",
+    "https://github.com/Sanster/models/releases/download/add_big_lama/big-lama.pt",
+)
+
+
+class LaMa:
+    def __init__(self, device):
+        self.device = 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}")
+        else:
+            model_path = download_model(LAMA_MODEL_URL)
+
+        model = torch.jit.load(model_path, map_location="cpu")
+        model = model.to(device)
+        model.eval()
+        self.model = model
+
+    @torch.no_grad()
+    def __call__(self, image, mask):
+        """
+        image: [C, H, W] RGB
+        mask: [1, H, W]
+        return: BGR IMAGE
+        """
+        device = self.device
+        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()
+        inpainted_image = self.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