Apply black

invokeai/backend/util/util.py

@@ -32,9 +32,7 @@ def log_txt_as_img(wh, xc, size=10):
         draw = ImageDraw.Draw(txt)
         font = ImageFont.load_default()
         nc = int(40 * (wh[0] / 256))
-        lines = "\n".join(
-            xc[bi][start : start + nc] for start in range(0, len(xc[bi]), nc)
-        )
+        lines = "\n".join(xc[bi][start : start + nc] for start in range(0, len(xc[bi]), nc))
 
         try:
             draw.text((0, 0), lines, fill="black", font=font)
@@ -81,9 +79,7 @@ def mean_flat(tensor):
 def count_params(model, verbose=False):
     total_params = sum(p.numel() for p in model.parameters())
     if verbose:
-        logger.debug(
-            f"{model.__class__.__name__} has {total_params * 1.e-6:.2f} M params."
-        )
+        logger.debug(f"{model.__class__.__name__} has {total_params * 1.e-6:.2f} M params.")
     return total_params
 
 
@@ -154,21 +150,12 @@ def parallel_data_prefetch(
         proc = Thread
     # spawn processes
     if target_data_type == "ndarray":
-        arguments = [
-            [func, Q, part, i, use_worker_id]
-            for i, part in enumerate(np.array_split(data, n_proc))
-        ]
+        arguments = [[func, Q, part, i, use_worker_id] for i, part in enumerate(np.array_split(data, n_proc))]
     else:
-        step = (
-            int(len(data) / n_proc + 1)
-            if len(data) % n_proc != 0
-            else int(len(data) / n_proc)
-        )
+        step = int(len(data) / n_proc + 1) if len(data) % n_proc != 0 else int(len(data) / n_proc)
         arguments = [
             [func, Q, part, i, use_worker_id]
-            for i, part in enumerate(
-                [data[i : i + step] for i in range(0, len(data), step)]
-            )
+            for i, part in enumerate([data[i : i + step] for i in range(0, len(data), step)])
         ]
     processes = []
     for i in range(n_proc):
@@ -220,9 +207,7 @@ def parallel_data_prefetch(
         return gather_res
 
 
-def rand_perlin_2d(
-    shape, res, device, fade=lambda t: 6 * t**5 - 15 * t**4 + 10 * t**3
-):
+def rand_perlin_2d(shape, res, device, fade=lambda t: 6 * t**5 - 15 * t**4 + 10 * t**3):
     delta = (res[0] / shape[0], res[1] / shape[1])
     d = (shape[0] // res[0], shape[1] // res[1])
 
@@ -265,9 +250,9 @@ def rand_perlin_2d(
     n01 = dot(tile_grads([0, -1], [1, None]), [0, -1]).to(device)
     n11 = dot(tile_grads([1, None], [1, None]), [-1, -1]).to(device)
     t = fade(grid[: shape[0], : shape[1]])
-    noise = math.sqrt(2) * torch.lerp(
-        torch.lerp(n00, n10, t[..., 0]), torch.lerp(n01, n11, t[..., 0]), t[..., 1]
-    ).to(device)
+    noise = math.sqrt(2) * torch.lerp(torch.lerp(n00, n10, t[..., 0]), torch.lerp(n01, n11, t[..., 0]), t[..., 1]).to(
+        device
+    )
     return noise.to(dtype=torch_dtype(device))
 
 
@@ -276,9 +261,7 @@ def ask_user(question: str, answers: list):
 
     user_prompt = f"\n>> {question} {answers}: "
     invalid_answer_msg = "Invalid answer. Please try again."
-    pose_question = chain(
-        [user_prompt], repeat("\n".join([invalid_answer_msg, user_prompt]))
-    )
+    pose_question = chain([user_prompt], repeat("\n".join([invalid_answer_msg, user_prompt])))
     user_answers = map(input, pose_question)
     valid_response = next(filter(answers.__contains__, user_answers))
     return valid_response
@@ -303,9 +286,7 @@ def download_with_resume(url: str, dest: Path, access_token: str = None) -> Path
 
     if dest.is_dir():
         try:
-            file_name = re.search(
-                'filename="(.+)"', resp.headers.get("Content-Disposition")
-            ).group(1)
+            file_name = re.search('filename="(.+)"', resp.headers.get("Content-Disposition")).group(1)
         except:
             file_name = os.path.basename(url)
         dest = dest / file_name
@@ -322,7 +303,7 @@ def download_with_resume(url: str, dest: Path, access_token: str = None) -> Path
         logger.warning("corrupt existing file found. re-downloading")
         os.remove(dest)
         exist_size = 0
-        
+
     if resp.status_code == 416 or (content_length > 0 and exist_size == content_length):
         logger.warning(f"{dest}: complete file found. Skipping.")
         return dest
@@ -377,16 +358,16 @@ def image_to_dataURL(image: Image.Image, image_format: str = "PNG") -> str:
     buffered = io.BytesIO()
     image.save(buffered, format=image_format)
     mime_type = Image.MIME.get(image_format.upper(), "image/" + image_format.lower())
-    image_base64 = f"data:{mime_type};base64," + base64.b64encode(
-        buffered.getvalue()
-    ).decode("UTF-8")
+    image_base64 = f"data:{mime_type};base64," + base64.b64encode(buffered.getvalue()).decode("UTF-8")
     return image_base64
 
+
 class Chdir(object):
-    '''Context manager to chdir to desired directory and change back after context exits:
+    """Context manager to chdir to desired directory and change back after context exits:
     Args:
         path (Path): The path to the cwd
-    '''
+    """
+
     def __init__(self, path: Path):
         self.path = path
         self.original = Path().absolute()
@@ -394,5 +375,5 @@ class Chdir(object):
     def __enter__(self):
         os.chdir(self.path)
 
-    def __exit__(self,*args):
+    def __exit__(self, *args):
         os.chdir(self.original)