DemonGatanjieuandClaude Opus 5.5
c41af60cd9
- model_kind: Anima LLLite, Qwen-Image block-wise and Z-Image Fun ControlNets (and other ModelPatchLoader files) go to model_patches, not controlnet; import and Tidy follow. - api/mcp_model_use.py: search_models / get_model say which node types load each model, or where a misfiled one belongs; move_model moves it there behind a new "files" gate (asks by default, never overwrites). - An empty drop-down in add_node says ComfyUI found no file; the task tab switch is no longer logged as the user's canvas change. - The folded task list stays while the pointer is in the box around its button and the list. - The message box's picture button picks gallery pictures; "@" offers the gallery's newest and searches it as you type. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
166 lines
9.4 KiB
Python
166 lines
9.4 KiB
Python
"""The models page's Tidy check (api/model_placement.py): models in the wrong folder, identical
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copies, and moving a model with its covers and info. Files go to temporary folders."""
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import asyncio
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import hashlib
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import json
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import os
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import struct
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import sys
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import tempfile
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import unittest
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from pathlib import Path
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from unittest import mock
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from aiohttp import web
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from aiohttp.test_utils import TestClient, TestServer
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PLUGIN = Path(__file__).resolve().parents[1]
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sys.path[:0] = [str(PLUGIN), str(PLUGIN.parents[1])]
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from api import model_placement # noqa: E402
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def safetensors(*names):
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raw = json.dumps({name: {"dtype": "F16", "shape": [4, 4], "data_offsets": [0, 0]} for name in names}).encode()
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return struct.pack("<Q", len(raw)) + raw
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ANIMA = safetensors("net.blocks.0.self_attn.q_proj.weight", "net.llm_adapter.blocks.0.mlp.0.weight")
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FLUX_WHOLE = safetensors("model.diffusion_model.double_blocks.0.img_attn.qkv.weight", "first_stage_model.encoder.down.0.block.0.conv1.weight")
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LORA = safetensors("lora_unet_double_blocks_0_img_attn_proj.lora_up.weight")
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LLLITE = safetensors("lllite_conditioning1.conv1.weight", "lllite_modules.0.down.weight")
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class PlacementTests(unittest.TestCase):
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def setUp(self):
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self.temp = tempfile.TemporaryDirectory()
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base = os.path.realpath(self.temp.name)
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self.roots = {kind: [os.path.join(base, kind)] for kind in ("checkpoints", "loras", "vae", "controlnet", "model_patches")}
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self.roots["diffusion_models"] = [os.path.join(base, "unet"), os.path.join(base, "diffusion_models")]
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for paths in self.roots.values():
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for path in paths:
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os.makedirs(path, exist_ok=True)
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self.hashes = {}
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self.patches = [
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mock.patch.object(model_placement.folder_paths, "folder_names_and_paths", {k: (v, set()) for k, v in self.roots.items()}),
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mock.patch.object(model_placement.folder_paths, "get_folder_paths", side_effect=lambda t: self.roots.get(t, [])),
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mock.patch("api.model_import.folder_paths.get_folder_paths", side_effect=lambda t: self.roots.get(t, [])),
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mock.patch.object(model_placement, "add_entry"),
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mock.patch.object(model_placement, "forget_file_lists"),
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mock.patch.object(model_placement, "get_metadata", side_effect=lambda path: {"hash": self.hashes.get(path, "")}),
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]
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for patch in self.patches:
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patch.start()
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model_placement._heads.clear()
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model_placement._hashes.clear()
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def tearDown(self):
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for patch in reversed(self.patches):
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patch.stop()
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self.temp.cleanup()
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def put(self, kind, rel, data, index=0):
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path = os.path.join(self.roots[kind][index], *rel.split("/"))
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os.makedirs(os.path.dirname(path), exist_ok=True)
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Path(path).write_bytes(data)
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return path
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def test_misplaced_models_and_where_they_go(self):
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self.put("checkpoints", "Anima/realskin.safetensors", ANIMA)
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self.put("diffusion_models", "Flux/flux-fp8.safetensors", FLUX_WHOLE)
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self.put("loras", "ok.safetensors", LORA)
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self.put("checkpoints", "unknown.safetensors", safetensors("something.weight"))
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data = model_placement.check()
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self.assertEqual(data["checked"], 4)
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found = {item["rel"]: item for item in data["misplaced"]}
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self.assertEqual(set(found), {"Anima/realskin.safetensors", "Flux/flux-fp8.safetensors"})
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anima = found["Anima/realskin.safetensors"]
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self.assertEqual((anima["kind"], anima["base"], anima["works"]), ("diffusion_models", "Anima", False))
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self.assertEqual(anima["to"], {"type": "diffusion_models", "root": 0, "rel": "Anima/realskin.safetensors"})
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self.assertEqual((anima["name"], anima["version"], anima["preview_url"]), ("realskin", "", ""))
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# A whole checkpoint in the diffusion models folder still loads there.
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self.assertTrue(found["Flux/flux-fp8.safetensors"]["works"])
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def test_a_model_patch_in_the_controlnet_folder(self):
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path = self.put("controlnet", "anima-lllite-depth.safetensors", LLLITE)
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home = model_placement.belongs(model_placement.one("controlnet", 0, "anima-lllite-depth.safetensors", path))
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self.assertEqual(home["to"], {"type": "model_patches", "root": 0, "rel": "anima-lllite-depth.safetensors"})
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self.assertFalse(home["works"])
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moved = self.put("model_patches", "ok.safetensors", LLLITE)
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self.assertIsNone(model_placement.belongs(model_placement.one("model_patches", 0, "ok.safetensors", moved)))
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def test_identical_copies(self):
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first = self.put("loras", "a.safetensors", LORA)
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self.put("vae", "copy.safetensors", LORA)
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self.put("loras", "other.safetensors", LORA + b"x")
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# Large files never scanned are only counted until a deep check reads them.
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with mock.patch.object(model_placement, "AUTO_HASH", 10):
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data = model_placement.check()
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self.assertEqual(data["duplicates"], [])
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self.assertEqual(data["unchecked"], {"groups": 1, "bytes": 2 * len(LORA)})
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model_placement._hashes.clear()
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self.assertEqual(len(model_placement.check()["duplicates"]), 1) # small ones are read at once
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deep = model_placement.check(deep=True)["duplicates"]
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self.assertEqual(len(deep), 1)
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self.assertEqual({item["rel"] for item in deep[0]["files"]}, {"a.safetensors", "copy.safetensors"})
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self.assertEqual(deep[0]["sha256"], hashlib.sha256(LORA).hexdigest())
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# The copy in the folder that fits what it is is the one to keep.
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self.assertEqual({item["rel"]: item["fits"] for item in deep[0]["files"]}, {"a.safetensors": True, "copy.safetensors": False})
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# Hashes from scans count without reading.
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model_placement._hashes.clear()
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self.hashes[first] = hashlib.sha256(LORA).hexdigest()
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with mock.patch.object(model_placement, "AUTO_HASH", 10):
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self.assertEqual(model_placement.check()["unchecked"]["groups"], 1) # the copy is still unknown
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def test_move_takes_covers_and_info_along(self):
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source = self.put("checkpoints", "Anima/realskin.safetensors", ANIMA)
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for suffix in (".info", ".preview.png", ".civitai_bak.png", ".anomalous.json"):
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Path(source[:-len(".safetensors")] + suffix).write_text("x")
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self.put("diffusion_models", "Anima/realskin.safetensors", b"another model")
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moved = model_placement.move({"type": "checkpoints", "root": 0, "rel": "Anima/realskin.safetensors",
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"to_type": "diffusion_models", "to_root": 0, "to_rel": "Anima/realskin.safetensors"})
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self.assertEqual(moved["rel"], "Anima/realskin (2).safetensors")
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self.assertEqual(moved["left"], [])
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target = Path(self.roots["diffusion_models"][0], "Anima")
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self.assertEqual(sorted(os.listdir(target)), sorted([
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"realskin.safetensors", "realskin (2).safetensors", "realskin (2).info", "realskin (2).preview.png",
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"realskin (2).civitai_bak.png", "realskin (2).anomalous.json"]))
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self.assertEqual(os.listdir(os.path.dirname(source)), [])
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self.assertEqual(Path(target, "realskin.safetensors").read_bytes(), b"another model")
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def test_shared_covers_stay(self):
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source = self.put("checkpoints", "m.safetensors", ANIMA)
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self.put("checkpoints", "m.ckpt", b"older format, same name")
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Path(source[:-len(".safetensors")] + ".info").write_text("x")
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model_placement.move({"type": "checkpoints", "root": 0, "rel": "m.safetensors",
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"to_type": "diffusion_models", "to_root": 1, "to_rel": "m.safetensors"})
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self.assertEqual(sorted(os.listdir(self.roots["checkpoints"][0])), ["m.ckpt", "m.info"])
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def test_refused_moves(self):
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self.put("checkpoints", "m.safetensors", ANIMA)
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for data, code in (({"type": "checkpoints", "rel": "gone.safetensors", "to_type": "loras", "to_rel": "x.safetensors"}, "gone"),
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({"type": "checkpoints", "rel": "../m.safetensors", "to_type": "loras", "to_rel": "x.safetensors"}, "bad_path"),
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({"type": "checkpoints", "rel": "m.safetensors", "to_type": "loras", "to_rel": "../x.safetensors"}, "bad_path"),
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({"type": "nope", "rel": "m.safetensors", "to_type": "loras", "to_rel": "x.safetensors"}, "bad_type")):
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with self.assertRaises(model_placement.Refused) as refused:
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model_placement.move(data)
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self.assertEqual(refused.exception.code, code)
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self.assertTrue(Path(self.roots["checkpoints"][0], "m.safetensors").exists())
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def test_move_only_from_this_computer(self):
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self.put("checkpoints", "m.safetensors", ANIMA)
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move = {"type": "checkpoints", "root": 0, "rel": "m.safetensors", "to_type": "loras", "to_root": 0, "to_rel": "m.safetensors"}
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async def run():
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app = web.Application()
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model_placement.register_routes(app)
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async with TestClient(TestServer(app)) as client: # through a proxy: not this computer
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resp = await client.post("/anomalous/placement/move", json=move, headers={"X-Forwarded-For": "192.168.1.20"})
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return resp.status, await resp.json()
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self.assertEqual(asyncio.run(run()), (403, {"error": "not_local"}))
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self.assertTrue(Path(self.roots["checkpoints"][0], "m.safetensors").exists())
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if __name__ == "__main__":
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unittest.main()
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