Merge branch 'main' into feat/ip-adapter

invokeai/backend/image_util/cv2_inpaint.py

@@ -0,0 +1,20 @@
+import cv2
+import numpy as np
+from PIL import Image
+
+
+def cv2_inpaint(image: Image.Image) -> Image.Image:
+    # Prepare Image
+    image_array = np.array(image.convert("RGB"))
+    image_cv = cv2.cvtColor(image_array, cv2.COLOR_RGB2BGR)
+
+    # Prepare Mask From Alpha Channel
+    mask = image.split()[3].convert("RGB")
+    mask_array = np.array(mask)
+    mask_cv = cv2.cvtColor(mask_array, cv2.COLOR_BGR2GRAY)
+    mask_inv = cv2.bitwise_not(mask_cv)
+
+    # Inpaint Image
+    inpainted_result = cv2.inpaint(image_cv, mask_inv, 3, cv2.INPAINT_TELEA)
+    inpainted_image = Image.fromarray(cv2.cvtColor(inpainted_result, cv2.COLOR_BGR2RGB))
+    return inpainted_image

invokeai/backend/image_util/lama.py

@@ -5,6 +5,7 @@ import numpy as np
 import torch
 from PIL import Image
 
+import invokeai.backend.util.logging as logger
 from invokeai.app.services.config import get_invokeai_config
 from invokeai.backend.util.devices import choose_torch_device
 
@@ -19,7 +20,7 @@ def norm_img(np_img):
 
 def load_jit_model(url_or_path, device):
     model_path = url_or_path
-    print(f"Loading model from: {model_path}")
+    logger.info(f"Loading model from: {model_path}")
     model = torch.jit.load(model_path, map_location="cpu").to(device)
     model.eval()
     return model
@@ -52,5 +53,6 @@ class LaMA:
 
         del model
         gc.collect()
+        torch.cuda.empty_cache()
 
         return infilled_image

invokeai/backend/install/invokeai_configure.py

@@ -290,9 +290,20 @@ def download_realesrgan():
         download_with_progress_bar(model["url"], config.models_path / model["dest"], model["description"])
 
 
+# ---------------------------------------------
+def download_lama():
+    logger.info("Installing lama infill model")
+    download_with_progress_bar(
+        "https://github.com/Sanster/models/releases/download/add_big_lama/big-lama.pt",
+        config.models_path / "core/misc/lama/lama.pt",
+        "lama infill model",
+    )
+
+
 # ---------------------------------------------
 def download_support_models():
     download_realesrgan()
+    download_lama()
     download_conversion_models()