feat(mm): download upscaling & lama models as they are requested

invokeai/app/util/download_with_progress.py

@@ -0,0 +1,51 @@
+from pathlib import Path
+from urllib import request
+
+from tqdm import tqdm
+
+from invokeai.backend.util.logging import InvokeAILogger
+
+
+class ProgressBar:
+    """Simple progress bar for urllib.request.urlretrieve using tqdm."""
+
+    def __init__(self, model_name: str = "file"):
+        self.pbar = None
+        self.name = model_name
+
+    def __call__(self, block_num: int, block_size: int, total_size: int):
+        if not self.pbar:
+            self.pbar = tqdm(
+                desc=self.name,
+                initial=0,
+                unit="iB",
+                unit_scale=True,
+                unit_divisor=1000,
+                total=total_size,
+            )
+        self.pbar.update(block_size)
+
+
+def download_with_progress_bar(name: str, url: str, dest_path: Path) -> bool:
+    """Download a file from a URL to a destination path, with a progress bar.
+    If the file already exists, it will not be downloaded again.
+
+    Exceptions are not caught.
+
+    Args:
+        name (str): Name of the file being downloaded.
+        url (str): URL to download the file from.
+        dest_path (Path): Destination path to save the file to.
+
+    Returns:
+        bool: True if the file was downloaded, False if it already existed.
+    """
+    if dest_path.exists():
+        return False  # already downloaded
+
+    InvokeAILogger.get_logger().info(f"Downloading {name}...")
+
+    dest_path.parent.mkdir(parents=True, exist_ok=True)
+    request.urlretrieve(url, dest_path, ProgressBar(name))
+
+    return True