Merge c2bf857966 into 2ff3104f70

comfy/hooks.py

@@ -510,7 +510,7 @@ def get_sorted_list_via_attr(objects: list, attr: str) -> list:
     unique_attrs = {}
     for o in objects:
         val_attr = getattr(o, attr)
-        attr_list: list = unique_attrs.get(val_attr, list())
+        attr_list: list = unique_attrs.get(val_attr, [])
         attr_list.append(o)
         if val_attr not in unique_attrs:
             unique_attrs[val_attr] = attr_list

comfy/ldm/models/autoencoder.py

@@ -19,7 +19,7 @@ class DiagonalGaussianRegularizer(torch.nn.Module):
         yield from ()
 
     def forward(self, z: torch.Tensor) -> Tuple[torch.Tensor, dict]:
-        log = dict()
+        log = {}
         posterior = DiagonalGaussianDistribution(z)
         if self.sample:
             z = posterior.sample()
@@ -88,7 +88,7 @@ class AbstractAutoencoder(torch.nn.Module):
     def instantiate_optimizer_from_config(self, params, lr, cfg):
         logging.info(f"loading >>> {cfg['target']} <<< optimizer from config")
         return get_obj_from_str(cfg["target"])(
-            params, lr=lr, **cfg.get("params", dict())
+            params, lr=lr, **cfg.get("params", {})
         )
 
     def configure_optimizers(self) -> Any:
@@ -129,7 +129,7 @@ class AutoencodingEngine(AbstractAutoencoder):
     ) -> Union[torch.Tensor, Tuple[torch.Tensor, dict]]:
         z = self.encoder(x)
         if unregularized:
-            return z, dict()
+            return z, {}
         z, reg_log = self.regularization(z)
         if return_reg_log:
             return z, reg_log
@@ -191,7 +191,7 @@ class AutoencodingEngineLegacy(AutoencodingEngine):
             N = x.shape[0]
             bs = self.max_batch_size
             n_batches = int(math.ceil(N / bs))
-            z = list()
+            z = []
             for i_batch in range(n_batches):
                 z_batch = self.encoder(x[i_batch * bs : (i_batch + 1) * bs])
                 z_batch = self.quant_conv(z_batch)
@@ -211,7 +211,7 @@ class AutoencodingEngineLegacy(AutoencodingEngine):
             N = z.shape[0]
             bs = self.max_batch_size
             n_batches = int(math.ceil(N / bs))
-            dec = list()
+            dec = []
             for i_batch in range(n_batches):
                 dec_batch = self.post_quant_conv(z[i_batch * bs : (i_batch + 1) * bs])
                 dec_batch = self.decoder(dec_batch, **decoder_kwargs)

comfy/ldm/util.py

@@ -13,7 +13,7 @@ def log_txt_as_img(wh, xc, size=10):
     # wh a tuple of (width, height)
     # xc a list of captions to plot
     b = len(xc)
-    txts = list()
+    txts = []
     for bi in range(b):
         txt = Image.new("RGB", wh, color="white")
         draw = ImageDraw.Draw(txt)
@@ -77,7 +77,7 @@ def instantiate_from_config(config):
         elif config == "__is_unconditional__":
             return None
         raise KeyError("Expected key `target` to instantiate.")
-    return get_obj_from_str(config["target"])(**config.get("params", dict()))
+    return get_obj_from_str(config["target"])(**config.get("params", {}))
 
 
 def get_obj_from_str(string, reload=False):
@@ -106,9 +106,9 @@ class AdamWwithEMAandWings(optim.Optimizer):
             raise ValueError("Invalid weight_decay value: {}".format(weight_decay))
         if not 0.0 <= ema_decay <= 1.0:
             raise ValueError("Invalid ema_decay value: {}".format(ema_decay))
-        defaults = dict(lr=lr, betas=betas, eps=eps,
+        defaults = {"lr": lr, "betas": betas, "eps": eps,
-                        weight_decay=weight_decay, amsgrad=amsgrad, ema_decay=ema_decay,
+                        "weight_decay": weight_decay, "amsgrad": amsgrad, "ema_decay": ema_decay,
-                        ema_power=ema_power, param_names=param_names)
+                        "ema_power": ema_power, "param_names": param_names}
         super().__init__(params, defaults)
 
     def __setstate__(self, state):

comfy/samplers.py

@@ -185,7 +185,7 @@ def finalize_default_conds(model: 'BaseModel', hooked_to_run: dict[comfy.hooks.H
             p = p._replace(mult=mult)
             if p.hooks is not None:
                 model.current_patcher.prepare_hook_patches_current_keyframe(timestep, p.hooks, model_options)
-            hooked_to_run.setdefault(p.hooks, list())
+            hooked_to_run.setdefault(p.hooks, [])
             hooked_to_run[p.hooks] += [(p, i)]
 
 def calc_cond_batch(model: 'BaseModel', conds: list[list[dict]], x_in: torch.Tensor, timestep, model_options):
@@ -220,7 +220,7 @@ def _calc_cond_batch(model: 'BaseModel', conds: list[list[dict]], x_in: torch.Te
                     continue
                 if p.hooks is not None:
                     model.current_patcher.prepare_hook_patches_current_keyframe(timestep, p.hooks, model_options)
-                hooked_to_run.setdefault(p.hooks, list())
+                hooked_to_run.setdefault(p.hooks, [])
                 hooked_to_run[p.hooks] += [(p, i)]
         default_conds.append(default_c)
 

comfy/sd1_clip.py

@@ -26,7 +26,7 @@ def gen_empty_tokens(special_tokens, length):
 
 class ClipTokenWeightEncoder:
     def encode_token_weights(self, token_weight_pairs):
-        to_encode = list()
+        to_encode = []
         max_token_len = 0
         has_weights = False
         for x in token_weight_pairs:

comfy_extras/nodes_audio.py

@@ -164,7 +164,7 @@ class SaveAudio:
     def save_audio(self, audio, filename_prefix="ComfyUI", prompt=None, extra_pnginfo=None):
         filename_prefix += self.prefix_append
         full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir)
-        results = list()
+        results = []
 
         metadata = {}
         if not args.disable_metadata:

comfy_extras/nodes_images.py

@@ -99,7 +99,7 @@ class SaveAnimatedWEBP:
         method = self.methods.get(method)
         filename_prefix += self.prefix_append
         full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir, images[0].shape[1], images[0].shape[0])
-        results = list()
+        results = []
         pil_images = []
         for image in images:
             i = 255. * image.cpu().numpy()
@@ -160,7 +160,7 @@ class SaveAnimatedPNG:
     def save_images(self, images, fps, compress_level, filename_prefix="ComfyUI", prompt=None, extra_pnginfo=None):
         filename_prefix += self.prefix_append
         full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir, images[0].shape[1], images[0].shape[0])
-        results = list()
+        results = []
         pil_images = []
         for image in images:
             i = 255. * image.cpu().numpy()

execution.py

@@ -232,7 +232,7 @@ def get_output_data(obj, input_data_all, execution_block_cb=None, pre_execute_cb
         output = merge_result_data(results, obj)
     else:
         output = []
-    ui = dict()
+    ui = {}
     if len(uis) > 0:
         ui = {k: [y for x in uis for y in x[k]] for k in uis[0].keys()}
     return output, ui, has_subgraph

nodes.py

@@ -477,7 +477,7 @@ class SaveLatent:
 
         file = f"{filename}_{counter:05}_.latent"
 
-        results = list()
+        results = []
         results.append({
             "filename": file,
             "subfolder": subfolder,
@@ -1596,7 +1596,7 @@ class SaveImage:
     def save_images(self, images, filename_prefix="ComfyUI", prompt=None, extra_pnginfo=None):
         filename_prefix += self.prefix_append
         full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir, images[0].shape[1], images[0].shape[0])
-        results = list()
+        results = []
         for (batch_number, image) in enumerate(images):
             i = 255. * image.cpu().numpy()
             img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8))

ruff.toml

@@ -1,15 +1,17 @@
+target-version = "py39"
+
 # Disable all rules by default
 lint.ignore = ["ALL"]
 
-# Enable specific rules
+# Enable specific rules, see all rules here: https://docs.astral.sh/ruff/rules/
 lint.select = [
     "S307",  # suspicious-eval-usage
     "S102", # exec
     "T",  # print-usage
     "W",
     # The "F" series in Ruff stands for "Pyflakes" rules, which catch various Python syntax errors and undefined names.
-    # See all rules here: https://docs.astral.sh/ruff/rules/#pyflakes-f
     "F",
+    "C408",  # unnecessary dict(), list() or tuple() calls that can be rewritten as empty literals.
 ]
 
 exclude = ["*.ipynb"]

server.py

@@ -171,7 +171,7 @@ class PromptServer():
 
         max_upload_size = round(args.max_upload_size * 1024 * 1024)
         self.app = web.Application(client_max_size=max_upload_size, middlewares=middlewares)
-        self.sockets = dict()
+        self.sockets = {}
         self.web_root = (
             FrontendManager.init_frontend(args.front_end_version)
             if args.front_end_root is None