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fix(types): resolve pre-existing basedpyright errors in py/ and standalone.py

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Fix ~950 basedpyright errors across the backend:
- Convert ineffective # type: ignore comments to # pyright: ignore[rule]
- Add missing generic type arguments (Dict[str, Any], list[Any], ...)
- Annotate dynamic dict literals and runtime-initialized attributes
- Widen CivitAI provider tuple signatures in recipe parsers
- Remove dead LoraRoutes handlers calling nonexistent LoraService methods
- Suppress unavoidable ServiceRegistry import cycles (basedpyright counts
  function-local imports as cycle edges)
This commit is contained in:
Will Miao
2026-08-08 20:12:52 +08:00
parent 6fcdeb799d
commit 8e724538bd
103 changed files with 1184 additions and 1015 deletions
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12
py/nodes/checkpoint_loader.py
View File

@@ -1,7 +1,7 @@
import logging
from typing import List, Tuple
import comfy.sd # type: ignore
import folder_paths # type: ignore
from typing import Any, List, Tuple
import comfy.sd # pyright: ignore[reportMissingImports]
import folder_paths # pyright: ignore[reportMissingImports]
from ..utils.utils import get_checkpoint_info_absolute, _format_model_name_for_comfyui
logger = logging.getLogger(__name__)
@@ -18,9 +18,9 @@ class CheckpointLoaderLM:
CATEGORY = "Lora Manager/loaders"
@classmethod
def INPUT_TYPES(s):
def INPUT_TYPES(cls):
# Get list of checkpoint names from scanner (includes extra folder paths)
checkpoint_names = s._get_checkpoint_names()
checkpoint_names = cls._get_checkpoint_names()
return {
"required": {
"ckpt_name": (
@@ -89,7 +89,7 @@ class CheckpointLoaderLM:
logger.error(f"Error getting checkpoint names: {e}")
return []
def load_checkpoint(self, ckpt_name: str) -> Tuple:
def load_checkpoint(self, ckpt_name: str) -> Tuple[Any, Any, Any]:
"""Load a checkpoint by name, supporting extra folder paths
Args:

4
py/nodes/create_hook_lora.py
View File

@@ -57,8 +57,8 @@ class CreateHookLoraLM:
del text # used by the frontend widget only
# Lazy imports: comfy is not available in CI/test environment at module level
import comfy.hooks # type: ignore # noqa: C0415
import comfy.utils # type: ignore # noqa: C0415
import comfy.hooks # pyright: ignore[reportMissingImports] # noqa: C0415
import comfy.utils # pyright: ignore[reportMissingImports] # noqa: C0415
prev_hooks: comfy.hooks.HookGroup | None = kwargs.get("prev_hooks")

4
py/nodes/lora_loader.py
View File

@@ -1,8 +1,8 @@
import importlib
import logging
import comfy.sd # type: ignore
import comfy.utils # type: ignore
import comfy.sd # pyright: ignore[reportMissingImports]
import comfy.utils # pyright: ignore[reportMissingImports]
from ..utils.utils import get_lora_info_absolute
from .utils import (

2
py/nodes/lora_stack_combiner.py
View File

@@ -73,7 +73,7 @@ class LoraStackCombinerLM:
stack = inspect.stack()
if len(stack) > 2 and stack[2].function == "get_input_info":
optional_inputs = _LoraStackOptionalInputs(optional_inputs) # type: ignore[assignment]
optional_inputs = _LoraStackOptionalInputs(optional_inputs) # pyright: ignore[reportAssignmentType]
return {
"required": {},

25
py/nodes/nunchaku_qwen.py
View File

@@ -15,15 +15,15 @@ import os
import re
from collections import defaultdict
from pathlib import Path
from typing import Dict, List, Optional, Tuple, Union
from typing import Any, Dict, List, Optional, Tuple, Union, cast
import comfy.utils # type: ignore
import folder_paths # type: ignore
import comfy.utils # pyright: ignore[reportMissingImports]
import folder_paths # pyright: ignore[reportMissingImports]
import torch
import torch.nn as nn
from safetensors import safe_open
from nunchaku.lora.flux.nunchaku_converter import (
from nunchaku.lora.flux.nunchaku_converter import ( # pyright: ignore[reportMissingTypeStubs]
pack_lowrank_weight,
unpack_lowrank_weight,
)
@@ -87,10 +87,6 @@ def _rename_layer_underscore_layer_name(old_name: str) -> str:
return new_name
def _is_indexable_module(module):
return isinstance(module, (nn.ModuleList, nn.Sequential, list, tuple))
def _get_module_by_name(model: nn.Module, name: str) -> Optional[nn.Module]:
if not name:
return model
@@ -100,7 +96,7 @@ def _get_module_by_name(model: nn.Module, name: str) -> Optional[nn.Module]:
continue
if hasattr(module, part):
module = getattr(module, part)
elif part.isdigit() and _is_indexable_module(module):
elif part.isdigit() and isinstance(module, (nn.ModuleList, nn.Sequential, list, tuple)):
try:
module = module[int(part)]
except (IndexError, TypeError):
@@ -267,7 +263,9 @@ def _handle_proj_out_split(lora_dict: Dict[str, Dict[str, torch.Tensor]], base_k
return result, consumed
def _apply_lora_to_module(module: nn.Module, a_tensor: torch.Tensor, b_tensor: torch.Tensor, module_name: str, model: nn.Module) -> None:
def _apply_lora_to_module(module: Any, a_tensor: torch.Tensor, b_tensor: torch.Tensor, module_name: str, model: Any) -> None:
# These modules are dynamic torch containers; monkey-patched attributes
# below are set at runtime, so the module/model types are deliberately Any.
if not hasattr(module, "in_features") or not hasattr(module, "out_features"):
raise ValueError(f"{module_name}: unsupported module without in/out features")
if a_tensor.shape[1] != module.in_features or b_tensor.shape[0] != module.out_features:
@@ -336,7 +334,7 @@ def _apply_lora_to_module(module: nn.Module, a_tensor: torch.Tensor, b_tensor: t
raise ValueError(f"{module_name}: unsupported module type {type(module)}")
def reset_lora_v2(model: nn.Module) -> None:
def reset_lora_v2(model: Any) -> None:
slots = getattr(model, "_lora_slots", None)
if not slots:
return
@@ -344,6 +342,7 @@ def reset_lora_v2(model: nn.Module) -> None:
module = _get_module_by_name(model, name)
if module is None:
continue
module = cast(Any, module)
module_type = info.get("type", "nunchaku")
if module_type == "nunchaku":
base_rank = info["base_rank"]
@@ -371,7 +370,7 @@ def reset_lora_v2(model: nn.Module) -> None:
def compose_loras_v2(model: nn.Module, lora_configs: List[Tuple[Union[str, Path, Dict[str, torch.Tensor]], float]], apply_awq_mod: bool = True) -> bool:
del apply_awq_mod # retained for interface compatibility
reset_lora_v2(model)
aggregated_weights: Dict[str, List[Dict[str, object]]] = defaultdict(list)
aggregated_weights: Dict[str, List[Dict[str, Any]]] = defaultdict(list)
saw_supported_format = False
unresolved_targets = 0
@@ -471,7 +470,7 @@ def compose_loras_v2(model: nn.Module, lora_configs: List[Tuple[Union[str, Path,
class ComfyQwenImageWrapperLM(nn.Module):
def __init__(self, model: nn.Module, config=None, apply_awq_mod: bool = True):
super().__init__()
self.model = model
self.model: Any = model
self.config = {} if config is None else config
self.dtype = next(model.parameters()).dtype
self.loras: List[Tuple[Union[str, Path, Dict[str, torch.Tensor]], float]] = []

4
py/nodes/prompt.py
View File

@@ -67,7 +67,7 @@ class PromptLM:
stack = inspect.stack()
if len(stack) > 2 and stack[2].function == "get_input_info":
optional_inputs = _PromptOptionalInputs(optional_inputs) # type: ignore[assignment]
optional_inputs = _PromptOptionalInputs(optional_inputs) # pyright: ignore[reportAssignmentType]
return {
"required": {
@@ -126,7 +126,7 @@ class PromptLM:
else:
prompt = expanded_text
from nodes import CLIPTextEncode # type: ignore
from nodes import CLIPTextEncode # pyright: ignore[reportMissingImports, reportAttributeAccessIssue]
conditioning = CLIPTextEncode().encode(clip, prompt)[0]
return (conditioning, prompt)

14
py/nodes/save_image.py
View File

@@ -5,7 +5,7 @@ import time
import uuid
from typing import Any, Dict, Optional
import numpy as np
import folder_paths # type: ignore
import folder_paths # pyright: ignore[reportMissingImports]
from ..services.service_registry import ServiceRegistry
from ..metadata_collector.metadata_processor import MetadataProcessor
from ..metadata_collector import get_metadata
@@ -13,7 +13,7 @@ from ..utils.constants import CARD_PREVIEW_WIDTH
from ..utils.exif_utils import ExifUtils
from ..utils.utils import calculate_recipe_fingerprint, sanitize_folder_name
from PIL import Image, PngImagePlugin
import piexif
import piexif # pyright: ignore[reportMissingTypeStubs]
import logging
# Civitai-compatible sampler name mapping: ComfyUI internal → A1111 display name
@@ -355,7 +355,7 @@ class SaveImageLM:
type_lower = model_type.lower() if model_type else "other"
return f"urn:air:{slug}:{type_lower}:civitai:{model_id}@{version_id}"
def format_metadata(self, metadata_dict: dict, add_loras_to_prompt: bool = False) -> str:
def format_metadata(self, metadata_dict: dict[str, Any], add_loras_to_prompt: bool = False) -> str:
"""Format metadata as A1111-compatible parameters string with Hashes JSON and Civitai resources."""
if not metadata_dict: return ""
@@ -396,7 +396,7 @@ class SaveImageLM:
ckpt_display_name = os.path.splitext(os.path.basename(checkpoint))[0]
# Resolve LoRA hash and Civitai data from local cache
loras_data: list[dict] = []
loras_data: list[dict[str, Any]] = []
for lora_name, strength in lora_entries:
lora_hash, lora_civitai, lora_base_model = self._resolve_model_cache_entry(
"lora_scanner", lora_name
@@ -418,9 +418,9 @@ class SaveImageLM:
hashes[f"LORA:{lora['name']}"] = lora["hash"][:10].upper()
# Build Civitai resources JSON array
civitai_resources: list[dict] = []
civitai_resources: list[dict[str, Any]] = []
if ckpt_civitai.get("id", 0) > 0:
ckpt_resource: dict = {}
ckpt_resource: dict[str, Any] = {}
ckpt_type = (ckpt_civitai.get("model") or {}).get("type", "Checkpoint")
model_id = ckpt_civitai.get("modelId", 0)
version_id = ckpt_civitai.get("id", 0)
@@ -439,7 +439,7 @@ class SaveImageLM:
lora_civitai = lora["civitai"]
if not lora_civitai or lora_civitai.get("id", 0) <= 0:
continue
lora_resource: dict = {"weight": lora["strength"]}
lora_resource: dict[str, Any] = {"weight": lora["strength"]}
lora_type = (lora_civitai.get("model") or {}).get("type", "LORA")
model_id = lora_civitai.get("modelId", 0)
version_id = lora_civitai.get("id", 0)

12
py/nodes/unet_loader.py
View File

@@ -1,7 +1,7 @@
import logging
import os
from typing import List, Tuple
import comfy.sd # type: ignore
from typing import Any, List, Tuple
import comfy.sd # pyright: ignore[reportMissingImports]
from ..utils.utils import get_checkpoint_info_absolute, _format_model_name_for_comfyui
logger = logging.getLogger(__name__)
@@ -34,9 +34,9 @@ class UNETLoaderLM:
CATEGORY = "Lora Manager/loaders"
@classmethod
def INPUT_TYPES(s):
def INPUT_TYPES(cls):
# Get list of unet names from scanner (includes extra folder paths)
unet_names = s._get_unet_names()
unet_names = cls._get_unet_names()
return {
"required": {
"unet_name": (
@@ -105,7 +105,7 @@ class UNETLoaderLM:
logger.error(f"Error getting unet names: {e}")
return []
def load_unet(self, unet_name: str, weight_dtype: str) -> Tuple:
def load_unet(self, unet_name: str, weight_dtype: str) -> Tuple[Any, ...]:
"""Load a diffusion model by name, supporting extra folder paths
Args:
@@ -148,7 +148,7 @@ class UNETLoaderLM:
def _load_gguf_unet(
self, unet_path: str, unet_name: str, weight_dtype: str
) -> Tuple:
) -> Tuple[Any, ...]:
"""Load a GGUF format diffusion model
Args:

10
py/nodes/utils.py
View File

@@ -1,3 +1,6 @@
from typing import Any
class AnyType(str):
"""A special class that is always equal in not equal comparisons. Credit to pythongosssss"""
@@ -6,7 +9,7 @@ class AnyType(str):
# Credit to Regis Gaughan, III (rgthree)
class FlexibleOptionalInputType(dict):
class FlexibleOptionalInputType(dict[str, Any]):
"""A special class to make flexible nodes that pass data to our python handlers.
Enables both flexible/dynamic input types (like for Any Switch) or a dynamic number of inputs
@@ -23,6 +26,7 @@ class FlexibleOptionalInputType(dict):
"""
def __init__(self, type):
super().__init__()
self.type = type
def __getitem__(self, key):
@@ -40,7 +44,7 @@ import re
import logging
import copy
import sys
import folder_paths # type: ignore
import folder_paths # pyright: ignore[reportMissingImports]
logger = logging.getLogger(__name__)
@@ -70,7 +74,7 @@ def extract_lora_name(lora_path):
return apply_lora_syntax_format(name_no_ext)
def parse_lora_syntax(text: str) -> list[dict]:
def parse_lora_syntax(text: str) -> list[dict[str, Any]]:
"""Parse <lora:name:strength> syntax from text input into a list of dicts.
Each entry contains: name, model_strength, clip_strength.