Fix pylint errors

comfy/app/frontend_management.py

@@ -151,7 +151,7 @@ class FrontendManager:
     def get_installed_templates_version(cls) -> str:
         """Get the currently installed workflow templates package version."""
         try:
-            templates_version_str = version("comfyui-workflow-templates")
+            templates_version_str = importlib.metadata.version("comfyui-workflow-templates")
             return templates_version_str
         except Exception:
             return None

comfy/cmd/main.py

@@ -55,7 +55,7 @@ async def _prompt_worker(q: AbstractPromptQueue, server_instance: server_module.
     if args.cache_lru > 0:
         cache_type = execution.CacheType.LRU
     elif args.cache_none:
-        cache_type = execution.CacheType.DEPENDENCY_AWARE
+        cache_type = execution.CacheType.NONE
 
     e = execution.PromptExecutor(server_instance, cache_type=cache_type, cache_size=args.cache_lru)
     last_gc_collect = 0

comfy/ldm/mmaudio/vae/activations.py

@@ -2,7 +2,8 @@
 #   LICENSE is in incl_licenses directory.
 
 import torch
-from torch import nn, sin, pow
+from torch import nn
+from torch import sin, pow  # pylint: disable=no-name-in-module
 from torch.nn import Parameter
 from ....model_management import cast_to
 

comfy/ldm/mmaudio/vae/vae.py

@@ -9,6 +9,7 @@ from .distributions import DiagonalGaussianDistribution
 
 from ....ops import disable_weight_init as ops
 
+from ....model_management import cast_to
 
 DATA_MEAN_80D = [
     -1.6058, -1.3676, -1.2520, -1.2453, -1.2078, -1.2224, -1.2419, -1.2439, -1.2922, -1.2927,
@@ -126,10 +127,10 @@ class VAE(nn.Module):
         return dec
 
     def normalize(self, x: torch.Tensor) -> torch.Tensor:
-        return (x - comfy.model_management.cast_to(self.data_mean, dtype=x.dtype, device=x.device)) / comfy.model_management.cast_to(self.data_std, dtype=x.dtype, device=x.device)
+        return (x - cast_to(self.data_mean, dtype=x.dtype, device=x.device)) / cast_to(self.data_std, dtype=x.dtype, device=x.device)
 
     def unnormalize(self, x: torch.Tensor) -> torch.Tensor:
-        return x * comfy.model_management.cast_to(self.data_std, dtype=x.dtype, device=x.device) + comfy.model_management.cast_to(self.data_mean, dtype=x.dtype, device=x.device)
+        return x * cast_to(self.data_std, dtype=x.dtype, device=x.device) + cast_to(self.data_mean, dtype=x.dtype, device=x.device)
 
     def forward(
             self,
@@ -142,7 +143,9 @@ class VAE(nn.Module):
 
         posterior = self.encode(x, normalize=normalize)
         if sample_posterior:
-            z = posterior.sample(rng)
+            raise RuntimeError("error in implementation, posterior doesn't accept this arg")
+            # todo: fix this in upstream?
+            # z = posterior.sample(rng)
         else:
             z = posterior.mode()
         dec = self.decode(z, unnormalize=unnormalize)

comfy/model_downloader.py

@@ -40,6 +40,10 @@ def get_filename_list(folder_name: str) -> Sequence[str]:
     return get_filename_list_with_downloadable(folder_name)
 
 
+def get_folder_paths(*args, **kwargs):
+    return folder_paths.get_folder_paths(*args, **kwargs)
+
+
 def get_filename_list_with_downloadable(folder_name: str, known_files: Optional[List[Downloadable] | KnownDownloadables] = None) -> DownloadableFileList | list[str]:
     if known_files is None:
         known_files = _get_known_models_for_folder_name(folder_name)

comfy/model_patcher.py

@@ -156,7 +156,7 @@ class LowVramPatch:
             intermediate_dtype = torch.float32
             out = lora.calculate_weight(self.patches[self.key], weight.to(intermediate_dtype), self.key, intermediate_dtype=intermediate_dtype)
             if self.set_func is None:
-                return float.stochastic_rounding(out, weight.dtype, seed=string_to_seed(self.key))
+                return stochastic_rounding(out, weight.dtype, seed=string_to_seed(self.key))
             else:
                 return self.set_func(out, seed=string_to_seed(self.key), return_weight=True)
 

comfy/ops.py

@@ -77,11 +77,11 @@ except Exception as exc_info:
 
 NVIDIA_MEMORY_CONV_BUG_WORKAROUND = False
 try:
-    if comfy.model_management.is_nvidia():
-        if torch.backends.cudnn.version() >= 91002 and comfy.model_management.torch_version_numeric >= (2, 9) and comfy.model_management.torch_version_numeric <= (2, 10):
+    if model_management.is_nvidia():
+        if torch.backends.cudnn.version() >= 91002 and model_management.torch_version_numeric >= (2, 9) and model_management.torch_version_numeric <= (2, 10):
             # TODO: change upper bound version once it's fixed'
             NVIDIA_MEMORY_CONV_BUG_WORKAROUND = True
-            logging.info("working around nvidia conv3d memory bug.")
+            logger.info("working around nvidia conv3d memory bug.")
 except:
     pass
 

comfy/samplers.py

@@ -324,7 +324,7 @@ def _calc_cond_batch(model: BaseModel, conds, x_in: torch.Tensor, timestep: torc
                                                                            copy_dict1=False)
 
             if patches is not None:
-                transformer_options["patches"] = comfy.patcher_extension.merge_nested_dicts(
+                transformer_options["patches"] = patcher_extension.merge_nested_dicts(
                     transformer_options.get("patches", {}),
                     patches
                 )

comfy/text_encoders/lumina2.py

@@ -1,4 +1,4 @@
-from .llama import Gemma2_2B
+from .llama import Gemma2_2B, Gemma3_4B
 from .spiece_tokenizer import SPieceTokenizer
 from .. import sd1_clip
 
@@ -40,7 +40,7 @@ class Gemma2_2BModel(sd1_clip.SDClipModel):
 
 class Gemma3_4BModel(sd1_clip.SDClipModel):
     def __init__(self, device="cpu", layer="hidden", layer_idx=-2, dtype=None, attention_mask=True, model_options={}):
-        super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config={}, dtype=dtype, special_tokens={"start": 2, "pad": 0}, layer_norm_hidden_state=False, model_class=comfy.text_encoders.llama.Gemma3_4B, enable_attention_masks=attention_mask, return_attention_masks=attention_mask, model_options=model_options)
+        super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config={}, dtype=dtype, special_tokens={"start": 2, "pad": 0}, layer_norm_hidden_state=False, model_class=Gemma3_4B, enable_attention_masks=attention_mask, return_attention_masks=attention_mask, model_options=model_options)
 
 class LuminaModel(sd1_clip.SD1ClipModel):
     def __init__(self, device="cpu", dtype=None, model_options=None, name="gemma2_2b", clip_model=Gemma2_2BModel):
@@ -51,6 +51,7 @@ class LuminaModel(sd1_clip.SD1ClipModel):
 
 
 def te(dtype_llama=None, llama_scaled_fp8=None, model_type="gemma2_2b"):
+    model = None
     if model_type == "gemma2_2b":
         model = Gemma2_2BModel
     elif model_type == "gemma3_4b":

comfy_extras/nodes/nodes_clip_sdxl.py

@@ -1,5 +1,5 @@
 from typing_extensions import override
-from comfy.common import MAX_RESOLUTION
+from comfy.nodes.common import MAX_RESOLUTION
 from comfy_api.latest import ComfyExtension, io
 
 

comfy_extras/nodes/nodes_compositing.py

@@ -7,7 +7,7 @@ from skimage import exposure
 import comfy.utils
 from comfy.component_model.tensor_types import RGBImageBatch, ImageBatch, MaskBatch
 from comfy.nodes.package_typing import CustomNode
-
+from comfy_api.latest import io
 
 def resize_mask(mask, shape):
     return torch.nn.functional.interpolate(mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])), size=(shape[0], shape[1]), mode="bilinear").squeeze(1)

comfy_extras/nodes/nodes_flux.py

@@ -29,7 +29,8 @@ class CLIPTextEncodeFlux(io.ComfyNode):
         tokens["t5xxl"] = clip.tokenize(t5xxl)["t5xxl"]
 
         return io.NodeOutput(clip.encode_from_tokens_scheduled(tokens, add_dict={"guidance": guidance}))
-        encode = execute  # TODO: remove
+
+    encode = execute  # TODO: remove
 
 
 class FluxGuidance(io.ComfyNode):
@@ -75,7 +76,8 @@ class FluxDisableGuidance(io.ComfyNode):
     def execute(cls, conditioning) -> io.NodeOutput:
         c = node_helpers.conditioning_set_values(conditioning, {"guidance": None})
         return io.NodeOutput(c)
-        append = execute  # TODO: remove
+
+    append = execute  # TODO: remove
 
 
 class FluxKontextImageScale(io.ComfyNode):

comfy_extras/nodes/nodes_lt.py

@@ -4,6 +4,8 @@ import av
 import numpy as np
 import torch
 
+from io import BytesIO
+
 import comfy.model_management
 import comfy.model_sampling
 import comfy.utils
@@ -11,6 +13,7 @@ from comfy import node_helpers
 from comfy.ldm.lightricks.symmetric_patchifier import SymmetricPatchifier, latent_to_pixel_coords
 from comfy.nodes import base_nodes as nodes
 from comfy_api.latest import ComfyExtension, io
+from typing_extensions import override
 
 class EmptyLTXVLatentVideo(io.ComfyNode):
     @classmethod