Exclude downloader modules via .comfyignore with conditional startup registration

.comfyignore

@@ -1 +1,4 @@
 tests/
+nodes/downloader_nodes.py
+nodes/downloader_utils.py
+

__init__.py

@@ -49,10 +49,18 @@ from .nodes.lora_resize import (
     LoRAResizeFrobenius, LoRAResizeCumulative,
     LoRAMergeToModel
 )
-from .nodes.downloader_nodes import (
-    CheckpointInfoMetaDownloader, DiffusionModelInfoMetaDownloader, LoRAInfoMetaDownloader, EmbeddingInfoMetaDownloader,
-    VAEInfoMetaDownloader, ControlNetInfoMetaDownloader, ManualPathInfoMetaDownloader
-)
+try:
+    from .nodes.downloader_nodes import (
+        CheckpointInfoMetaDownloader, DiffusionModelInfoMetaDownloader, LoRAInfoMetaDownloader, EmbeddingInfoMetaDownloader,
+        VAEInfoMetaDownloader, ControlNetInfoMetaDownloader, ManualPathInfoMetaDownloader
+    )
+    DOWNLOADER_NODES: list[type[io.ComfyNode]] = [
+        CheckpointInfoMetaDownloader, DiffusionModelInfoMetaDownloader, LoRAInfoMetaDownloader, EmbeddingInfoMetaDownloader,
+        VAEInfoMetaDownloader, ControlNetInfoMetaDownloader, ManualPathInfoMetaDownloader,
+    ]
+except ImportError:
+    DOWNLOADER_NODES = []
+
 from .nodes.model_info_nodes import (
     CheckpointInfoLoader, LoRAInfoLoader, EmbeddingInfoLoader,
     VAEInfoLoader, ControlNetInfoLoader, DiffusionModelInfoLoader
@@ -125,9 +133,8 @@ class ModelUtilsExtension(ComfyExtension):
             DiffusionModelDtypeConversion,
             MiniMaxH3FoldAdaLN,
             MiniMaxH3UnfoldAdaLN,
-            # Downloaders
-            CheckpointInfoMetaDownloader, DiffusionModelInfoMetaDownloader, LoRAInfoMetaDownloader, EmbeddingInfoMetaDownloader,
-            VAEInfoMetaDownloader, ControlNetInfoMetaDownloader, ManualPathInfoMetaDownloader,
+            # Downloaders (available in manual git/source installations)
+            *DOWNLOADER_NODES,
             # Info Loaders
             CheckpointInfoLoader, LoRAInfoLoader, EmbeddingInfoLoader,
             VAEInfoLoader, ControlNetInfoLoader, DiffusionModelInfoLoader,

nodes/downloader_utils.py

@@ -32,11 +32,15 @@ except ImportError:
     HAS_AV = False
     print("Warning: av (PyAV) not found. MP4 metadata extraction fallback disabled.")
 
-# Add the reference module to the Python path
 # Assuming downloader_utils.py is inside nodes/, the root is one level up
 root_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
-sys.path.append(os.path.join(root_dir, 'unifiedefficientloader-reference'))
 from unifiedefficientloader import UnifiedSafetensorsLoader
+from .model_info_nodes import (
+    get_potential_preview_files,
+    load_image_tensor,
+    get_model_workflows,
+    get_model_metadata_file,
+)
 
 # --- Database Path Logic ---
 try:
@@ -381,14 +385,6 @@ class Downloader:
         os.rename(temp_path, target_path)
         return True
 
-def get_potential_preview_files(base_path):
-    exts = ["png", "jpg", "jpeg", "webp", "mp4"]
-    files = []
-    for ext in exts:
-        files.append(f"{base_path}.preview.{ext}")
-        files.append(f"{base_path}.{ext}")
-    return files
-
 def preview_exists(base_path):
     previews = get_potential_preview_files(base_path)
     for p in previews:
@@ -569,59 +565,6 @@ def process_file(filepath, cache, api, nsfw_threshold, max_examples):
                         for k in stats: stats[k] += res.get(k, 0)
     return stats
 
-import numpy as np
-from PIL import Image, ImageOps
-import torch
-
-def load_image_tensor(path):
-    if not os.path.exists(path):
-        return None
-    try:
-        img = Image.open(path)
-        img = ImageOps.exif_transpose(img)
-        image = img.convert("RGB")
-        image = np.array(image).astype(np.float32) / 255.0
-        image = torch.from_numpy(image)[None,]
-        return image
-    except Exception as e:
-        print(f"Error loading image {path}: {e}")
-        return None
-
-def get_model_workflows(base_path):
-    workflows = []
-    # Main workflow
-    main_json = f"{base_path}.json"
-    if os.path.exists(main_json):
-        workflows.append(main_json)
-
-    # Example workflows
-    # They are named like base_path.example.N.json
-    # or base_path.preview.json (if extracted from preview)
-    preview_json = f"{base_path}.preview.json"
-    if os.path.exists(preview_json) and preview_json not in workflows:
-        workflows.append(preview_json)
-
-    example_jsons = glob.glob(f"{base_path}.example.*.json")
-    # Sort them by index
-    example_jsons.sort()
-    for ej in example_jsons:
-        if ej not in workflows:
-            workflows.append(ej)
-
-    return workflows
-
-def get_model_metadata_file(base_path):
-    # Prefer civitai.info if available
-    civitai_info = f"{base_path}.civitai.info"
-    if os.path.exists(civitai_info):
-        return civitai_info
-
-    # Fallback to metadata.json (safetensors metadata)
-    metadata_json = f"{base_path}.metadata.json"
-    if os.path.exists(metadata_json):
-        return metadata_json
-
-    return None
 
 def scan_and_process(scan_dirs, recursive=True, db_path=None, nsfw_level="All", max_examples=0, api_key=None, threads=4):
     """

nodes/model_info_nodes.py

@@ -1,7 +1,64 @@
+import glob
 import os
+import numpy as np
+from PIL import Image, ImageOps
+import torch
 import folder_paths
 from comfy_api.latest import io
-from .downloader_utils import get_potential_preview_files, load_image_tensor, get_model_workflows, get_model_metadata_file
+
+
+def get_potential_preview_files(base_path: str) -> list[str]:
+    exts = ["png", "jpg", "jpeg", "webp", "mp4"]
+    files = []
+    for ext in exts:
+        files.append(f"{base_path}.preview.{ext}")
+        files.append(f"{base_path}.{ext}")
+    return files
+
+
+def load_image_tensor(path: str):
+    if not os.path.exists(path):
+        return None
+    try:
+        img = Image.open(path)
+        img = ImageOps.exif_transpose(img)
+        image = img.convert("RGB")
+        image_np = np.array(image).astype(np.float32) / 255.0
+        return torch.from_numpy(image_np)[None,]
+    except Exception as e:
+        print(f"Error loading image {path}: {e}")
+        return None
+
+
+def get_model_workflows(base_path: str) -> list[str]:
+    workflows = []
+    main_json = f"{base_path}.json"
+    if os.path.exists(main_json):
+        workflows.append(main_json)
+
+    preview_json = f"{base_path}.preview.json"
+    if os.path.exists(preview_json) and preview_json not in workflows:
+        workflows.append(preview_json)
+
+    example_jsons = glob.glob(f"{base_path}.example.*.json")
+    example_jsons.sort()
+    for ej in example_jsons:
+        if ej not in workflows:
+            workflows.append(ej)
+
+    return workflows
+
+
+def get_model_metadata_file(base_path: str) -> str | None:
+    civitai_info = f"{base_path}.civitai.info"
+    if os.path.exists(civitai_info):
+        return civitai_info
+
+    metadata_json = f"{base_path}.metadata.json"
+    if os.path.exists(metadata_json):
+        return metadata_json
+
+    return None
 
 class BaseModelInfoLoader(io.ComfyNode):
     CATEGORY = "ModelUtils/Info"

tests/test_conditional_registration.py

@@ -0,0 +1,49 @@
+import asyncio
+import importlib.util
+import sys
+from pathlib import Path
+from comfy_api.latest import io
+
+REPO_ROOT = Path(__file__).resolve().parents[1]
+
+
+def _load_extension_module():
+    package_name = "ComfyUI_ModelUtils_init_test"
+    if package_name not in sys.modules:
+        spec = importlib.util.spec_from_file_location(
+            package_name,
+            REPO_ROOT / "__init__.py",
+            submodule_search_locations=[str(REPO_ROOT)],
+        )
+        module = importlib.util.module_from_spec(spec)
+        sys.modules[package_name] = module
+        spec.loader.exec_module(module)
+    return sys.modules[package_name]
+
+
+def test_startup_node_registration_when_downloaders_absent(monkeypatch):
+    ext_module = _load_extension_module()
+
+    ext = ext_module.ModelUtilsExtension()
+    nodes_with_downloader = asyncio.run(ext.get_node_list())
+    assert len(nodes_with_downloader) > 0
+
+    # Simulate registry install by setting DOWNLOADER_NODES to empty
+    monkeypatch.setattr(ext_module, "DOWNLOADER_NODES", [])
+    nodes_registry = asyncio.run(ext.get_node_list())
+    assert len(nodes_registry) < len(nodes_with_downloader)
+    assert all(issubclass(n, io.ComfyNode) for n in nodes_registry)
+
+
+def test_model_info_nodes_importable_standalone():
+    spec = importlib.util.spec_from_file_location(
+        "modelutils_model_info_nodes_test",
+        REPO_ROOT / "nodes" / "model_info_nodes.py",
+    )
+    module = importlib.util.module_from_spec(spec)
+    spec.loader.exec_module(module)
+
+    assert hasattr(module, "get_potential_preview_files")
+    assert hasattr(module, "load_image_tensor")
+    assert hasattr(module, "get_model_workflows")
+    assert hasattr(module, "get_model_metadata_file")

tests/test_groups.toml

@@ -1,6 +1,7 @@
 [groups.repository_contract]
 paths = ["__init__.py", "nodes/*.py"]
 python_tests = [
+  "tests/test_conditional_registration.py",
   "tests/test_schema_tooltips.py",
   "tests/test_uel_repository_contract.py",
 ]