Merge remote-tracking branch 'remotes/bluelovers/patch-3' into dev-local-202406

* remotes/bluelovers/patch-3:
  chore(js): avoid lots of `Wake Lock is not supported.`
  Fix DAT models download (AUTOMATIC1111#16302)

Author: bluelovers

javascript/progressbar.js

@@ -79,11 +79,12 @@ function requestProgress(id_task, progressbarContainer, gallery, atEnd, onProgre
     var wakeLock = null;
 
     var requestWakeLock = async function() {
-        if (!opts.prevent_screen_sleep_during_generation || wakeLock) return;
+        if (!opts.prevent_screen_sleep_during_generation || wakeLock !== null) return;
         try {
             wakeLock = await navigator.wakeLock.request('screen');
         } catch (err) {
             console.error('Wake Lock is not supported.');
+            wakeLock = false;
         }
     };
 

modules/dat_model.py

@@ -49,7 +49,18 @@ def load_model(self, path):
                     scaler.local_data_path = modelloader.load_file_from_url(
                         scaler.data_path,
                         model_dir=self.model_download_path,
+                        hash_prefix=scaler.sha256,
                     )
+
+                    if os.path.getsize(scaler.local_data_path) < 200:
+                        # Re-download if the file is too small, probably an LFS pointer
+                        scaler.local_data_path = modelloader.load_file_from_url(
+                            scaler.data_path,
+                            model_dir=self.model_download_path,
+                            hash_prefix=scaler.sha256,
+                            re_download=True,
+                        )
+
                 if not os.path.exists(scaler.local_data_path):
                     raise FileNotFoundError(f"DAT data missing: {scaler.local_data_path}")
                 return scaler
@@ -60,20 +71,23 @@ def get_dat_models(scaler):
     return [
         UpscalerData(
             name="DAT x2",
-            path="https://github.com/n0kovo/dat_upscaler_models/raw/main/DAT/DAT_x2.pth",
+            path="https://huggingface.co/w-e-w/DAT/resolve/main/experiments/pretrained_models/DAT/DAT_x2.pth",
             scale=2,
             upscaler=scaler,
+            sha256='7760aa96e4ee77e29d4f89c3a4486200042e019461fdb8aa286f49aa00b89b51',
         ),
         UpscalerData(
             name="DAT x3",
-            path="https://github.com/n0kovo/dat_upscaler_models/raw/main/DAT/DAT_x3.pth",
+            path="https://huggingface.co/w-e-w/DAT/resolve/main/experiments/pretrained_models/DAT/DAT_x3.pth",
             scale=3,
             upscaler=scaler,
+            sha256='581973e02c06f90d4eb90acf743ec9604f56f3c2c6f9e1e2c2b38ded1f80d197',
         ),
         UpscalerData(
             name="DAT x4",
-            path="https://github.com/n0kovo/dat_upscaler_models/raw/main/DAT/DAT_x4.pth",
+            path="https://huggingface.co/w-e-w/DAT/resolve/main/experiments/pretrained_models/DAT/DAT_x4.pth",
             scale=4,
             upscaler=scaler,
+            sha256='391a6ce69899dff5ea3214557e9d585608254579217169faf3d4c353caff049e',
         ),
     ]

modules/modelloader.py

@@ -10,37 +10,14 @@
 
 from modules import shared
 from modules.upscaler import Upscaler, UpscalerLanczos, UpscalerNearest, UpscalerNone
+from modules.util import load_file_from_url  # noqa, backwards compatibility
 
 if TYPE_CHECKING:
     import spandrel
 
 logger = logging.getLogger(__name__)
 
 
-def load_file_from_url(
-    url: str,
-    *,
-    model_dir: str,
-    progress: bool = True,
-    file_name: str | None = None,
-    hash_prefix: str | None = None,
-) -> str:
-    """Download a file from `url` into `model_dir`, using the file present if possible.
-
-    Returns the path to the downloaded file.
-    """
-    os.makedirs(model_dir, exist_ok=True)
-    if not file_name:
-        parts = urlparse(url)
-        file_name = os.path.basename(parts.path)
-    cached_file = os.path.abspath(os.path.join(model_dir, file_name))
-    if not os.path.exists(cached_file):
-        print(f'Downloading: "{url}" to {cached_file}\n')
-        from torch.hub import download_url_to_file
-        download_url_to_file(url, cached_file, progress=progress, hash_prefix=hash_prefix)
-    return cached_file
-
-
 def load_models(model_path: str, model_url: str = None, command_path: str = None, ext_filter=None, download_name=None, ext_blacklist=None, hash_prefix=None) -> list:
     """
     A one-and done loader to try finding the desired models in specified directories.

modules/upscaler.py

@@ -93,13 +93,14 @@ class UpscalerData:
     scaler: Upscaler = None
     model: None
 
-    def __init__(self, name: str, path: str, upscaler: Upscaler = None, scale: int = 4, model=None):
+    def __init__(self, name: str, path: str, upscaler: Upscaler = None, scale: int = 4, model=None, sha256: str = None):
         self.name = name
         self.data_path = path
         self.local_data_path = path
         self.scaler = upscaler
         self.scale = scale
         self.model = model
+        self.sha256 = sha256
 
     def __repr__(self):
         return f"<UpscalerData name={self.name} path={self.data_path} scale={self.scale}>"

modules/util.py

@@ -211,3 +211,80 @@ def open_folder(path):
         subprocess.Popen(["explorer.exe", subprocess.check_output(["wslpath", "-w", path])])
     else:
         subprocess.Popen(["xdg-open", path])
+
+
+def load_file_from_url(
+    url: str,
+    *,
+    model_dir: str,
+    progress: bool = True,
+    file_name: str | None = None,
+    hash_prefix: str | None = None,
+    re_download: bool = False,
+) -> str:
+    """Download a file from `url` into `model_dir`, using the file present if possible.
+    Returns the path to the downloaded file.
+
+    file_name: if specified, it will be used as the filename, otherwise the filename will be extracted from the url.
+        file is downloaded to {file_name}.tmp then moved to the final location after download is complete.
+    hash_prefix: sha256 hex string, if provided, the hash of the downloaded file will be checked against this prefix.
+        if the hash does not match, the temporary file is deleted and a ValueError is raised.
+    re_download: forcibly re-download the file even if it already exists.
+    """
+    from urllib.parse import urlparse
+    import requests
+    try:
+        from tqdm import tqdm
+    except ImportError:
+        class tqdm:
+            def __init__(self, *args, **kwargs):
+                pass
+
+            def update(self, n=1, *args, **kwargs):
+                pass
+
+            def __enter__(self):
+                return self
+
+            def __exit__(self, exc_type, exc_val, exc_tb):
+                pass
+
+    if not file_name:
+        parts = urlparse(url)
+        file_name = os.path.basename(parts.path)
+
+    cached_file = os.path.abspath(os.path.join(model_dir, file_name))
+
+    if re_download or not os.path.exists(cached_file):
+        os.makedirs(model_dir, exist_ok=True)
+        temp_file = os.path.join(model_dir, f"{file_name}.tmp")
+        print(f'\nDownloading: "{url}" to {cached_file}')
+        response = requests.get(url, stream=True)
+        response.raise_for_status()
+        total_size = int(response.headers.get('content-length', 0))
+        with tqdm(total=total_size, unit='B', unit_scale=True, desc=file_name, disable=not progress) as progress_bar:
+            with open(temp_file, 'wb') as file:
+                for chunk in response.iter_content(chunk_size=1024):
+                    if chunk:
+                        file.write(chunk)
+                        progress_bar.update(len(chunk))
+
+        if hash_prefix and not compare_sha256(temp_file, hash_prefix):
+            print(f"Hash mismatch for {temp_file}. Deleting the temporary file.")
+            os.remove(temp_file)
+            raise ValueError(f"File hash does not match the expected hash prefix {hash_prefix}!")
+
+        os.rename(temp_file, cached_file)
+    return cached_file
+
+
+def compare_sha256(file_path: str, hash_prefix: str) -> bool:
+    """Check if the SHA256 hash of the file matches the given prefix."""
+    import hashlib
+    hash_sha256 = hashlib.sha256()
+    blksize = 1024 * 1024
+
+    with open(file_path, "rb") as f:
+        for chunk in iter(lambda: f.read(blksize), b""):
+            hash_sha256.update(chunk)
+    return hash_sha256.hexdigest().startswith(hash_prefix.strip().lower())