fix(docker): use hf_hub_download for HuggingFace models to fix CDN 504s

urlretrieve against the HuggingFace CDN was consistently returning HTTP
504 in GitHub Actions runners for LaMa, NAFNet, and the OpenCV caffemodel.
The huggingface_hub library has built-in retry logic, resumable downloads,
and better CDN routing than bare urlretrieve.

- download_lama_model: urlretrieve → hf_hub_download (Carve/LaMa-ONNX)
- download_nafnet_model: urlretrieve → hf_hub_download (mikestealth/nafnet-models)
- download_opencv_colorize_models: caffemodel → hf_hub_download (space repo_type)
- _urlretrieve: retry count 3→5, flat 10s delay → exponential backoff (10/20/40/80s)
- Also reverts the SKIP_MODEL_DOWNLOADS=true from CI workflow (wrong approach)

docker/download_models.py

@@ -15,8 +15,8 @@ _opener.addheaders = [("User-Agent", "ashim/1.0")]
 urllib.request.install_opener(_opener)
 
 
-def _urlretrieve(url: str, path: str, max_retries: int = 3) -> None:
-    """Download url to path, retrying on transient network errors (5xx, timeout)."""
+def _urlretrieve(url: str, path: str, max_retries: int = 5) -> None:
+    """Download url to path with exponential backoff on transient errors (5xx, timeout)."""
     for attempt in range(1, max_retries + 1):
         try:
             urllib.request.urlretrieve(url, path)
@@ -24,13 +24,45 @@ def _urlretrieve(url: str, path: str, max_retries: int = 3) -> None:
         except urllib.error.HTTPError as e:
             if attempt == max_retries or e.code < 500:
                 raise
-            print(f"  HTTP {e.code} on attempt {attempt}/{max_retries}, retrying in 10s...")
-            time.sleep(10)
+            delay = 10 * (2 ** (attempt - 1))  # 10s, 20s, 40s, 80s
+            print(f"  HTTP {e.code} on attempt {attempt}/{max_retries}, retrying in {delay}s...")
+            time.sleep(delay)
         except (urllib.error.URLError, OSError) as e:
             if attempt == max_retries:
                 raise
-            print(f"  Network error on attempt {attempt}/{max_retries}: {e}, retrying in 10s...")
-            time.sleep(10)
+            delay = 10 * (2 ** (attempt - 1))
+            print(f"  Network error on attempt {attempt}/{max_retries}: {e}, retrying in {delay}s...")
+            time.sleep(delay)
+
+
+def _hf_download(
+    repo_id: str,
+    filename: str,
+    local_dir: str,
+    min_size: int,
+    label: str,
+    repo_type: str = "model",
+) -> str:
+    """Download a file from HuggingFace Hub with built-in retry and CDN routing.
+
+    Uses hf_hub_download which handles retries, resumable downloads, and respects
+    the HF_ENDPOINT env var for mirror support. repo_type can be "model" or "space".
+    """
+    from huggingface_hub import hf_hub_download
+
+    print(f"  Downloading {label} from HuggingFace ({repo_id}/{filename})...")
+    path = hf_hub_download(
+        repo_id=repo_id, filename=filename, local_dir=local_dir, repo_type=repo_type
+    )
+    # hf_hub_download may place the file in a cache subdir; normalise to local_dir/filename
+    dest = os.path.join(local_dir, filename)
+    if path != dest and os.path.exists(path):
+        os.makedirs(os.path.dirname(dest), exist_ok=True)
+        os.rename(path, dest)
+    size = os.path.getsize(dest)
+    assert size > min_size, f"{label} too small: {size} bytes (expected > {min_size})"
+    print(f"  {label} ready ({size / 1_000_000:.1f} MB)")
+    return dest
 
 # Force CPU mode during build - no GPU driver available at build time.
 # Must be set before any ML library import.
@@ -187,17 +219,11 @@ def download_rembg_models():
 
 
 def download_lama_model():
-    """Download LaMa ONNX inpainting model from HuggingFace."""
+    """Download LaMa ONNX inpainting model from HuggingFace Hub."""
     print("=== Downloading LaMa ONNX model ===")
     os.makedirs(LAMA_MODEL_DIR, exist_ok=True)
-    print(f"  Downloading from {LAMA_MODEL_URL}...")
-    _urlretrieve(LAMA_MODEL_URL, LAMA_MODEL_PATH)
-
-    size = os.path.getsize(LAMA_MODEL_PATH)
-    assert size > LAMA_MIN_SIZE, (
-        f"LaMa model too small: {size} bytes (expected > {LAMA_MIN_SIZE})"
-    )
-    print(f"  lama_fp32.onnx downloaded ({size / 1_000_000:.1f} MB)\n")
+    _hf_download("Carve/LaMa-ONNX", "lama_fp32.onnx", LAMA_MODEL_DIR, LAMA_MIN_SIZE, "LaMa ONNX")
+    print()
 
 
 def download_realesrgan_model():
@@ -349,15 +375,16 @@ def download_scunet_model():
 
 
 def download_nafnet_model():
-    """Download NAFNet SIDD width-64 denoising model."""
-    print(f"Downloading NAFNet model to {NAFNET_MODEL_PATH}...")
+    """Download NAFNet SIDD width-64 denoising model from HuggingFace Hub."""
+    print("=== Downloading NAFNet model ===")
     os.makedirs(NAFNET_MODEL_DIR, exist_ok=True)
-    _urlretrieve(NAFNET_MODEL_URL, NAFNET_MODEL_PATH)
-    size = os.path.getsize(NAFNET_MODEL_PATH)
-    assert size > NAFNET_MIN_SIZE, (
-        f"NAFNet model too small: {size} bytes (expected >{NAFNET_MIN_SIZE})"
+    _hf_download(
+        "mikestealth/nafnet-models",
+        "NAFNet-SIDD-width64.pth",
+        NAFNET_MODEL_DIR,
+        NAFNET_MIN_SIZE,
+        "NAFNet",
     )
-    print(f"  NAFNet model downloaded: {size:,} bytes")
 
 
 def download_facexlib_models():
@@ -394,9 +421,9 @@ def download_opencv_colorize_models():
     print("=== Downloading OpenCV colorization models ===")
     os.makedirs(OPENCV_COLORIZE_DIR, exist_ok=True)
 
+    # Prototxt and cluster-centres file are from GitHub raw content
     for url, path, name in [
         (OPENCV_PROTO_URL, OPENCV_PROTO_PATH, "colorization_deploy_v2.prototxt"),
-        (OPENCV_CAFFE_URL, OPENCV_CAFFE_PATH, "colorization_release_v2.caffemodel"),
         (OPENCV_POINTS_URL, OPENCV_POINTS_PATH, "pts_in_hull.npy"),
     ]:
         print(f"  Downloading {name}...")
@@ -404,10 +431,14 @@ def download_opencv_colorize_models():
         size = os.path.getsize(path)
         print(f"  {name} downloaded ({size / 1_000_000:.1f} MB)")
 
-    # Verify the caffemodel (the big one)
-    size = os.path.getsize(OPENCV_CAFFE_PATH)
-    assert size > OPENCV_CAFFE_MIN_SIZE, (
-        f"Caffemodel too small: {size} bytes (expected > {OPENCV_CAFFE_MIN_SIZE})"
+    # Caffemodel lives in a HuggingFace space — use hf_hub_download for reliability
+    _hf_download(
+        "BilalSardar/Black-N-White-To-Color",
+        "colorization_release_v2.caffemodel",
+        OPENCV_COLORIZE_DIR,
+        OPENCV_CAFFE_MIN_SIZE,
+        "OpenCV caffemodel",
+        repo_type="space",
     )
     print("OpenCV colorization models downloaded.\n")