admin/ComfyUI-OpenClaw
1
0
Fork 0
mirror of https://github.com/rookiestar28/ComfyUI-OpenClaw.git synced 2026-08-14 00:48:07 +00:00
Code Issues Packages Projects Releases Wiki Activity

feat(models): align managed folder type parity

Browse Source
This commit is contained in:
rookiestar28
2026-06-12 16:35:44 +08:00
parent a99ec20fa5
commit c6cfbdb606
9 changed files with 235 additions and 4 deletions
Download Patch File Download Diff File Expand all files Collapse all files
docs/release/api_contract.md
+3 -2
View File
@@ -140,8 +140,9 @@ Connector diagnostics contract notes:
Model-manager contract notes:
- `/models/downloads` supports `since_seq` cursor polling and may return deterministic delta metadata (`requested_since_seq`, `effective_since_seq`, `next_since_seq`, truncation/reset hints) alongside the task list
- `model_type` values SHOULD use current ComfyUI folder keys where applicable, including `text_encoders`, `diffusion_models`, `clip_vision`, `style_models`, `upscale_models`, `vae_approx`, `audio_encoders`, `background_removal`, `frame_interpolation`, `geometry_estimation`, `optical_flow`, and `detection`
- legacy aliases such as `ckpt`, `checkpoints`, `loras`, `controlnets`, `clip`, `text_encoder`, `unet`, `diffusion_model`, `upscale_model`, and `audio_encoder` are normalized before filtering or import destination resolution
- `model_type` values SHOULD use current ComfyUI folder keys where applicable, including `text_encoders`, `diffusion_models`, `clip_vision`, `style_models`, `upscale_models`, `vae_approx`, `gligen`, `latent_upscale_models`, `hypernetworks`, `photomaker`, `model_patches`, `audio_encoders`, `background_removal`, `frame_interpolation`, `geometry_estimation`, `optical_flow`, and `detection`
- legacy aliases such as `ckpt`, `checkpoints`, `loras`, `controlnets`, `clip`, `text_encoder`, `unet`, `diffusion_model`, `upscale_model`, `latent_upscale_model`, `hypernetwork`, `model_patch`, and `audio_encoder` are normalized before filtering or import destination resolution
- current ComfyUI folder keys that are not managed model-file destinations fail closed for download creation: `configs` (configuration YAML), `diffusers` (folder-valued trees), `classifiers` (extensionless classifier artifacts), and `custom_nodes` (executable plugin code)
- download creation requires structured provenance metadata (`publisher`, `license`, `source_url`) and a 64-char `expected_sha256`
- import keeps fail-closed destination/filename validation and re-checks the staged file hash before activation
services/model_manager.py
+34 -2
View File
@@ -97,6 +97,11 @@ MODEL_TYPE_TO_SUBDIR = {
"style_models": "style_models",
"upscale_models": "upscale_models",
"vae_approx": "vae_approx",
"gligen": "gligen",
"latent_upscale_models": "latent_upscale_models",
"hypernetworks": "hypernetworks",
"photomaker": "photomaker",
"model_patches": "model_patches",
"audio_encoders": "audio_encoders",
"background_removal": "background_removal",
"frame_interpolation": "frame_interpolation",
@@ -104,17 +109,30 @@ MODEL_TYPE_TO_SUBDIR = {
"optical_flow": "optical_flow",
"detection": "detection",
}
MODEL_TYPE_EXCLUSION_REASONS = {
"configs": "configuration YAML is not a managed model-weight destination",
"diffusers": "diffusers is folder-valued and needs a directory-tree install design",
"classifiers": "classifiers are extensionless and need a dedicated content policy",
"custom_nodes": "custom_nodes are executable plugin code, not managed model files",
}
MODEL_TYPE_ALIASES = {
"config": "configs",
"ckpt": "checkpoint",
"checkpoints": "checkpoint",
"loras": "lora",
"controlnets": "controlnet",
"control_net": "controlnet",
"diffuser": "diffusers",
"clip": "text_encoders",
"text_encoder": "text_encoders",
"unet": "diffusion_models",
"diffusion_model": "diffusion_models",
"upscale_model": "upscale_models",
"latent_upscale_model": "latent_upscale_models",
"hypernetwork": "hypernetworks",
"photo_maker": "photomaker",
"model_patch": "model_patches",
"classifier": "classifiers",
"audio_encoder": "audio_encoders",
"geometry": "geometry_estimation",
"detector": "detection",
@@ -249,11 +267,21 @@ def _truthy(value: str) -> bool:
return str(value or "").strip().lower() in {"1", "true", "yes", "on"}
def _norm_model_type(model_type: str) -> str:
def _resolve_model_type_token(model_type: str) -> str:
text = str(model_type or "").strip().lower()
if not text:
return ""
return MODEL_TYPE_ALIASES.get(text, text)
def _model_type_exclusion_reason(model_type: str) -> str:
return MODEL_TYPE_EXCLUSION_REASONS.get(_resolve_model_type_token(model_type), "")
def _norm_model_type(model_type: str) -> str:
text = _resolve_model_type_token(model_type)
if not text:
return DEFAULT_MODEL_TYPE
text = MODEL_TYPE_ALIASES.get(text, text)
return text if text in MODEL_TYPE_TO_SUBDIR else "other"
@@ -507,6 +535,10 @@ class ModelManager:
def _norm_model_type(value: str) -> str:
return _norm_model_type(value)
@staticmethod
def _model_type_exclusion_reason(value: str) -> str:
return _model_type_exclusion_reason(value)
@staticmethod
def _norm_source(value: str) -> str:
return _norm_source(value)
services/model_manager_transfer.py
+7
View File
@@ -105,6 +105,13 @@ def create_download_task(
raise manager._error(
"validation_error", "expected_sha256 must be a 64-char hex string"
)
exclusion_reason = manager._model_type_exclusion_reason(model_type)
if exclusion_reason:
raise manager._error(
"unsupported_model_type",
f"model_type '{str(model_type or '').strip()}' is not supported "
f"for managed install/import: {exclusion_reason}",
)
manager._validate_url_policy(download_url)
provenance = manager._validate_provenance(provenance)
mtype = manager._norm_model_type(model_type)
services/preflight.py
+9
View File
@@ -48,6 +48,7 @@ _LEGACY_INVENTORY_CACHE_KEY = "inventory"
_INVENTORY_LOCK = threading.RLock()
_INVENTORY_SCAN_THREAD: threading.Thread | None = None
_INVENTORY_ERROR_RETRY_SEC = 5
_INVENTORY_EXCLUDED_MODEL_TYPES = {"custom_nodes"}
# Heuristic mapping: input_key -> folder_paths type
_INPUT_KEY_MAP = {
@@ -83,6 +84,7 @@ def _get_node_class_mappings() -> Dict[str, Any]:
def _resolve_inventory_model_types() -> List[str]:
model_types = [
"checkpoints",
"configs",
"loras",
"vae",
"embeddings",
@@ -94,7 +96,12 @@ def _resolve_inventory_model_types() -> List[str]:
"style_models",
"diffusers",
"vae_approx",
"gligen",
"latent_upscale_models",
"hypernetworks",
"photomaker",
"classifiers",
"model_patches",
"audio_encoders",
"background_removal",
"frame_interpolation",
@@ -106,6 +113,8 @@ def _resolve_inventory_model_types() -> List[str]:
]
if hasattr(folder_paths, "folder_names_and_paths"):
for key in folder_paths.folder_names_and_paths.keys():
if key in _INVENTORY_EXCLUDED_MODEL_TYPES:
continue
if key not in model_types:
model_types.append(key)
return model_types
tests/e2e/specs/model_manager.spec.js
+16
View File
@@ -145,6 +145,22 @@ test.describe('Model Manager Tab', () => {
await waitForOpenClawReady(page);
await clickTab(page, 'Model Manager');
const modelTypeOptions = await page.locator('#mm-type option').evaluateAll((options) => options.map((option) => option.value));
expect(modelTypeOptions).toEqual(expect.arrayContaining([
'gligen',
'latent_upscale_models',
'hypernetworks',
'photomaker',
'model_patches',
'geometry_estimation',
'optical_flow',
'detection',
]));
expect(modelTypeOptions).not.toContain('configs');
expect(modelTypeOptions).not.toContain('diffusers');
expect(modelTypeOptions).not.toContain('classifiers');
expect(modelTypeOptions).not.toContain('custom_nodes');
await expect(page.locator('#mm-search-results')).toContainText('Flux Test Model');
const queueButton = page.locator('#mm-search-results').getByRole('button', { name: 'Queue Download' }).first();
tests/test_model_manager_service.py
+83
View File
@@ -10,10 +10,13 @@ from pathlib import Path, PurePosixPath
from unittest.mock import patch
from services.model_manager import (
MODEL_TYPE_EXCLUSION_REASONS,
MODEL_TYPE_TO_SUBDIR,
DownloadCancelled,
DownloadTask,
ModelManager,
ModelManagerError,
_model_type_exclusion_reason,
_norm_model_type,
)
from services.model_manager_transfer import (
@@ -138,15 +141,95 @@ class TestModelManagerService(unittest.TestCase):
self.assertEqual(_norm_model_type("audio_encoders"), "audio_encoders")
self.assertEqual(_norm_model_type("background_removal"), "background_removal")
self.assertEqual(_norm_model_type("frame_interpolation"), "frame_interpolation")
self.assertEqual(_norm_model_type("gligen"), "gligen")
self.assertEqual(
_norm_model_type("latent_upscale_models"), "latent_upscale_models"
)
self.assertEqual(_norm_model_type("hypernetworks"), "hypernetworks")
self.assertEqual(_norm_model_type("photomaker"), "photomaker")
self.assertEqual(_norm_model_type("model_patches"), "model_patches")
self.assertEqual(_norm_model_type("geometry_estimation"), "geometry_estimation")
self.assertEqual(_norm_model_type("optical_flow"), "optical_flow")
self.assertEqual(_norm_model_type("detection"), "detection")
self.assertEqual(_norm_model_type("unet"), "diffusion_models")
self.assertEqual(_norm_model_type("clip"), "text_encoders")
self.assertEqual(
_norm_model_type("latent_upscale_model"), "latent_upscale_models"
)
self.assertEqual(_norm_model_type("hypernetwork"), "hypernetworks")
self.assertEqual(_norm_model_type("model_patch"), "model_patches")
self.assertEqual(_norm_model_type("geometry"), "geometry_estimation")
self.assertEqual(_norm_model_type("detector"), "detection")
self.assertEqual(_norm_model_type("diffusers"), "other")
def test_current_comfyui_model_type_support_and_exclusions_are_explicit(self):
supported = {
"checkpoint": "checkpoints",
"lora": "loras",
"vae": "vae",
"controlnet": "controlnet",
"embedding": "embeddings",
"text_encoders": "text_encoders",
"diffusion_models": "diffusion_models",
"clip_vision": "clip_vision",
"style_models": "style_models",
"upscale_models": "upscale_models",
"vae_approx": "vae_approx",
"gligen": "gligen",
"latent_upscale_models": "latent_upscale_models",
"hypernetworks": "hypernetworks",
"photomaker": "photomaker",
"model_patches": "model_patches",
"audio_encoders": "audio_encoders",
"background_removal": "background_removal",
"frame_interpolation": "frame_interpolation",
"geometry_estimation": "geometry_estimation",
"optical_flow": "optical_flow",
"detection": "detection",
}
for model_type, subdir in supported.items():
with self.subTest(model_type=model_type):
self.assertEqual(MODEL_TYPE_TO_SUBDIR[model_type], subdir)
self.assertEqual(_norm_model_type(model_type), model_type)
for excluded in ("configs", "diffusers", "classifiers", "custom_nodes"):
with self.subTest(excluded=excluded):
self.assertIn(excluded, MODEL_TYPE_EXCLUSION_REASONS)
self.assertEqual(_norm_model_type(excluded), "other")
self.assertTrue(_model_type_exclusion_reason(excluded))
@patch("services.model_manager.validate_outbound_url")
def test_create_download_task_rejects_known_excluded_current_folder_keys(
self, mock_validate
):
payload = {
"model_id": "excluded-model",
"name": "Excluded Model",
"source": "catalog",
"source_label": "Catalog",
"download_url": "https://example.com/excluded.safetensors",
"expected_sha256": "a" * 64,
"provenance": {
"publisher": "OpenClaw",
"license": "OpenRAIL",
"source_url": "https://example.com/excluded",
},
}
for model_type in ("configs", "diffusers", "classifiers", "custom_nodes"):
with self.subTest(model_type=model_type):
with self.assertRaises(ModelManagerError) as ctx:
self.manager.create_download_task(
model_type=model_type,
**payload,
)
self.assertEqual(ctx.exception.code, "unsupported_model_type")
self.assertIn(model_type, ctx.exception.detail)
self.assertFalse(mock_validate.called)
self.assertEqual(self.manager._tasks, {})
@patch(
"services.model_manager.validate_outbound_url",
return_value=("https", "example.com", 443, ["1.1.1.1"]),
tests/test_preflight.py
+36
View File
@@ -161,6 +161,42 @@ class TestPreflightBackend(AioHTTPTestCase):
"detection",
)
def test_inventory_model_types_track_current_comfyui_keys_and_exclude_custom_nodes(
self,
):
with patch.object(
services.preflight, "folder_paths", MagicMock(), create=True
) as mock_folder_paths:
mock_folder_paths.folder_names_and_paths = {
"configs": [],
"gligen": [],
"latent_upscale_models": [],
"hypernetworks": [],
"photomaker": [],
"classifiers": [],
"model_patches": [],
"custom_nodes": [],
}
model_types = services.preflight._resolve_inventory_model_types()
for model_type in (
"configs",
"diffusers",
"gligen",
"latent_upscale_models",
"hypernetworks",
"photomaker",
"classifiers",
"model_patches",
"geometry_estimation",
"optical_flow",
"detection",
):
with self.subTest(model_type=model_type):
self.assertIn(model_type, model_types)
self.assertNotIn("custom_nodes", model_types)
@patch("api.preflight_handler.check_rate_limit")
@patch("api.preflight_handler.require_admin_token")
@unittest_run_loop
web/tabs/model_manager_tab.js
+5
View File
@@ -188,6 +188,11 @@ export const ModelManagerTab = {
<option value="clip_vision">clip_vision</option>
<option value="style_models">style_models</option>
<option value="upscale_models">upscale_models</option>
<option value="gligen">gligen</option>
<option value="latent_upscale_models">latent_upscale_models</option>
<option value="hypernetworks">hypernetworks</option>
<option value="photomaker">photomaker</option>
<option value="model_patches">model_patches</option>
<option value="audio_encoders">audio_encoders</option>
<option value="background_removal">background_removal</option>
<option value="frame_interpolation">frame_interpolation</option>
web/tests/unit/model_manager_tab.test.js
+42
View File
@@ -88,4 +88,46 @@ describe("model_manager_tab", () => {
expect.objectContaining({ task_id: "task-1", state: "completed" }),
]);
});
it("lists only managed-install supported current ComfyUI folder keys", async () => {
apiMock.searchModels.mockResolvedValue({
ok: true,
data: { items: [] },
});
apiMock.listModelDownloadTasks.mockResolvedValue({
ok: true,
data: { tasks: [] },
});
apiMock.listModelInstallations.mockResolvedValue({
ok: true,
data: { installations: [] },
});
const container = document.createElement("div");
ModelManagerTab.render(container);
await vi.waitFor(() => {
expect(apiMock.searchModels).toHaveBeenCalled();
});
const options = Array.from(container.querySelectorAll("#mm-type option")).map(
(option) => option.value
);
expect(options).toEqual(expect.arrayContaining([
"text_encoders",
"diffusion_models",
"gligen",
"latent_upscale_models",
"hypernetworks",
"photomaker",
"model_patches",
"geometry_estimation",
"optical_flow",
"detection",
]));
expect(options).not.toContain("configs");
expect(options).not.toContain("diffusers");
expect(options).not.toContain("classifiers");
expect(options).not.toContain("custom_nodes");
});
});