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vendor/ComfyUI/tests-unit/comfy_test/folder_path_test.py

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+### 🗻 This file is created through the spirit of Mount Fuji at its peak
+# TODO(yoland): clean up this after I get back down
+import sys
+import pytest
+import os
+import tempfile
+from unittest.mock import patch
+from importlib import reload
+
+import folder_paths
+import comfy.cli_args
+from comfy.options import enable_args_parsing
+enable_args_parsing()
+
+
+@pytest.fixture()
+def clear_folder_paths():
+    # Reload the module after each test to ensure isolation
+    yield
+    reload(folder_paths)
+
+@pytest.fixture
+def temp_dir():
+    with tempfile.TemporaryDirectory() as tmpdirname:
+        yield tmpdirname
+
+
+@pytest.fixture
+def set_base_dir():
+    def _set_base_dir(base_dir):
+        # Mock CLI args
+        with patch.object(sys, 'argv', ["main.py", "--base-directory", base_dir]):
+            reload(comfy.cli_args)
+            reload(folder_paths)
+    yield _set_base_dir
+    # Reload the modules after each test to ensure isolation
+    with patch.object(sys, 'argv', ["main.py"]):
+        reload(comfy.cli_args)
+        reload(folder_paths)
+
+
+def test_get_directory_by_type(clear_folder_paths):
+    test_dir = "/test/dir"
+    folder_paths.set_output_directory(test_dir)
+    assert folder_paths.get_directory_by_type("output") == test_dir
+    assert folder_paths.get_directory_by_type("invalid") is None
+
+def test_annotated_filepath():
+    assert folder_paths.annotated_filepath("test.txt") == ("test.txt", None)
+    assert folder_paths.annotated_filepath("test.txt [output]") == ("test.txt", folder_paths.get_output_directory())
+    assert folder_paths.annotated_filepath("test.txt [input]") == ("test.txt", folder_paths.get_input_directory())
+    assert folder_paths.annotated_filepath("test.txt [temp]") == ("test.txt", folder_paths.get_temp_directory())
+
+def test_get_annotated_filepath():
+    default_dir = "/default/dir"
+    assert folder_paths.get_annotated_filepath("test.txt", default_dir) == os.path.join(default_dir, "test.txt")
+    assert folder_paths.get_annotated_filepath("test.txt [output]") == os.path.join(folder_paths.get_output_directory(), "test.txt")
+
+def test_add_model_folder_path_append(clear_folder_paths):
+    folder_paths.add_model_folder_path("test_folder", "/default/path", is_default=True)
+    folder_paths.add_model_folder_path("test_folder", "/test/path", is_default=False)
+    assert folder_paths.get_folder_paths("test_folder") == ["/default/path", "/test/path"]
+
+
+def test_add_model_folder_path_insert(clear_folder_paths):
+    folder_paths.add_model_folder_path("test_folder", "/test/path", is_default=False)
+    folder_paths.add_model_folder_path("test_folder", "/default/path", is_default=True)
+    assert folder_paths.get_folder_paths("test_folder") == ["/default/path", "/test/path"]
+
+
+def test_add_model_folder_path_re_add_existing_default(clear_folder_paths):
+    folder_paths.add_model_folder_path("test_folder", "/test/path", is_default=False)
+    folder_paths.add_model_folder_path("test_folder", "/old_default/path", is_default=True)
+    assert folder_paths.get_folder_paths("test_folder") == ["/old_default/path", "/test/path"]
+    folder_paths.add_model_folder_path("test_folder", "/test/path", is_default=True)
+    assert folder_paths.get_folder_paths("test_folder") == ["/test/path", "/old_default/path"]
+
+
+def test_add_model_folder_path_re_add_existing_non_default(clear_folder_paths):
+    folder_paths.add_model_folder_path("test_folder", "/test/path", is_default=False)
+    folder_paths.add_model_folder_path("test_folder", "/default/path", is_default=True)
+    assert folder_paths.get_folder_paths("test_folder") == ["/default/path", "/test/path"]
+    folder_paths.add_model_folder_path("test_folder", "/test/path", is_default=False)
+    assert folder_paths.get_folder_paths("test_folder") == ["/default/path", "/test/path"]
+
+
+def test_recursive_search(temp_dir):
+    os.makedirs(os.path.join(temp_dir, "subdir"))
+    open(os.path.join(temp_dir, "file1.txt"), "w").close()
+    open(os.path.join(temp_dir, "subdir", "file2.txt"), "w").close()
+
+    files, dirs = folder_paths.recursive_search(temp_dir)
+    assert set(files) == {"file1.txt", os.path.join("subdir", "file2.txt")}
+    assert len(dirs) == 2  # temp_dir and subdir
+
+def test_filter_files_extensions():
+    files = ["file1.txt", "file2.jpg", "file3.png", "file4.txt"]
+    assert folder_paths.filter_files_extensions(files, [".txt"]) == ["file1.txt", "file4.txt"]
+    assert folder_paths.filter_files_extensions(files, [".jpg", ".png"]) == ["file2.jpg", "file3.png"]
+    assert folder_paths.filter_files_extensions(files, []) == files
+
+@patch("folder_paths.recursive_search")
+@patch("folder_paths.folder_names_and_paths")
+def test_get_filename_list(mock_folder_names_and_paths, mock_recursive_search):
+    mock_folder_names_and_paths.__getitem__.return_value = (["/test/path"], {".txt"})
+    mock_recursive_search.return_value = (["file1.txt", "file2.jpg"], {})
+    assert folder_paths.get_filename_list("test_folder") == ["file1.txt"]
+
+def test_get_save_image_path(temp_dir):
+    with patch("folder_paths.output_directory", temp_dir):
+        full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path("test", temp_dir, 100, 100)
+        assert os.path.samefile(full_output_folder, temp_dir)
+        assert filename == "test"
+        assert counter == 1
+        assert subfolder == ""
+        assert filename_prefix == "test"
+
+
+def test_base_path_changes(set_base_dir):
+    test_dir = os.path.abspath("/test/dir")
+    set_base_dir(test_dir)
+
+    assert folder_paths.base_path == test_dir
+    assert folder_paths.models_dir == os.path.join(test_dir, "models")
+    assert folder_paths.input_directory == os.path.join(test_dir, "input")
+    assert folder_paths.output_directory == os.path.join(test_dir, "output")
+    assert folder_paths.temp_directory == os.path.join(test_dir, "temp")
+    assert folder_paths.user_directory == os.path.join(test_dir, "user")
+
+    assert os.path.join(test_dir, "custom_nodes") in folder_paths.get_folder_paths("custom_nodes")
+
+    for name in ["checkpoints", "loras", "vae", "configs", "embeddings", "controlnet", "classifiers"]:
+        assert folder_paths.get_folder_paths(name)[0] == os.path.join(test_dir, "models", name)
+
+
+def test_base_path_change_clears_old(set_base_dir):
+    test_dir = os.path.abspath("/test/dir")
+    set_base_dir(test_dir)
+
+    assert len(folder_paths.get_folder_paths("custom_nodes")) == 1
+
+    single_model_paths = [
+        "checkpoints",
+        "loras",
+        "vae",
+        "configs",
+        "clip_vision",
+        "style_models",
+        "diffusers",
+        "vae_approx",
+        "gligen",
+        "upscale_models",
+        "embeddings",
+        "hypernetworks",
+        "photomaker",
+        "classifiers",
+    ]
+    for name in single_model_paths:
+        assert len(folder_paths.get_folder_paths(name)) == 1
+
+    for name in ["controlnet", "diffusion_models", "text_encoders"]:
+        assert len(folder_paths.get_folder_paths(name)) == 2

vendor/ComfyUI/tests-unit/comfy_test/model_detection_test.py

@@ -0,0 +1,144 @@
+from collections import defaultdict
+
+import torch
+
+from comfy.model_detection import detect_unet_config, model_config_from_unet_config
+import comfy.supported_models
+
+
+def _freeze(value):
+    """Recursively convert a value to a hashable form so configs can be
+    compared/used as dict keys or set members."""
+    if isinstance(value, dict):
+        return frozenset((k, _freeze(v)) for k, v in value.items())
+    if isinstance(value, (list, tuple)):
+        return tuple(_freeze(v) for v in value)
+    if isinstance(value, set):
+        return frozenset(_freeze(v) for v in value)
+    return value
+
+
+def _make_longcat_comfyui_sd():
+    """Minimal ComfyUI-format state dict for pre-converted LongCat-Image weights."""
+    sd = {}
+    H = 32  # Reduce hidden state dimension to reduce memory usage
+    C_IN = 16
+    C_CTX = 3584
+
+    sd["img_in.weight"] = torch.empty(H, C_IN * 4)
+    sd["img_in.bias"] = torch.empty(H)
+    sd["txt_in.weight"] = torch.empty(H, C_CTX)
+    sd["txt_in.bias"] = torch.empty(H)
+
+    sd["time_in.in_layer.weight"] = torch.empty(H, 256)
+    sd["time_in.in_layer.bias"] = torch.empty(H)
+    sd["time_in.out_layer.weight"] = torch.empty(H, H)
+    sd["time_in.out_layer.bias"] = torch.empty(H)
+
+    sd["final_layer.adaLN_modulation.1.weight"] = torch.empty(2 * H, H)
+    sd["final_layer.adaLN_modulation.1.bias"] = torch.empty(2 * H)
+    sd["final_layer.linear.weight"] = torch.empty(C_IN * 4, H)
+    sd["final_layer.linear.bias"] = torch.empty(C_IN * 4)
+
+    for i in range(19):
+        sd[f"double_blocks.{i}.img_attn.norm.key_norm.weight"] = torch.empty(128)
+        sd[f"double_blocks.{i}.img_attn.qkv.weight"] = torch.empty(3 * H, H)
+        sd[f"double_blocks.{i}.img_mod.lin.weight"] = torch.empty(H, H)
+    for i in range(38):
+        sd[f"single_blocks.{i}.modulation.lin.weight"] = torch.empty(H, H)
+
+    return sd
+
+
+def _make_flux_schnell_comfyui_sd():
+    """Minimal ComfyUI-format state dict for standard Flux Schnell."""
+    sd = {}
+    H = 32  # Reduce hidden state dimension to reduce memory usage
+    C_IN = 16
+
+    sd["img_in.weight"] = torch.empty(H, C_IN * 4)
+    sd["img_in.bias"] = torch.empty(H)
+    sd["txt_in.weight"] = torch.empty(H, 4096)
+    sd["txt_in.bias"] = torch.empty(H)
+
+    sd["double_blocks.0.img_attn.norm.key_norm.weight"] = torch.empty(128)
+    sd["double_blocks.0.img_attn.qkv.weight"] = torch.empty(3 * H, H)
+    sd["double_blocks.0.img_mod.lin.weight"] = torch.empty(H, H)
+
+    for i in range(19):
+        sd[f"double_blocks.{i}.img_attn.norm.key_norm.weight"] = torch.empty(128)
+    for i in range(38):
+        sd[f"single_blocks.{i}.modulation.lin.weight"] = torch.empty(H, H)
+
+    return sd
+
+
+class TestModelDetection:
+    """Verify that first-match model detection selects the correct model
+    based on list ordering and unet_config specificity."""
+
+    def test_longcat_before_schnell_in_models_list(self):
+        """LongCatImage must appear before FluxSchnell in the models list."""
+        models = comfy.supported_models.models
+        longcat_idx = next(i for i, m in enumerate(models) if m.__name__ == "LongCatImage")
+        schnell_idx = next(i for i, m in enumerate(models) if m.__name__ == "FluxSchnell")
+        assert longcat_idx < schnell_idx, (
+            f"LongCatImage (index {longcat_idx}) must come before "
+            f"FluxSchnell (index {schnell_idx}) in the models list"
+        )
+
+    def test_longcat_comfyui_detected_as_longcat(self):
+        sd = _make_longcat_comfyui_sd()
+        unet_config = detect_unet_config(sd, "")
+        assert unet_config is not None
+        assert unet_config["image_model"] == "flux"
+        assert unet_config["context_in_dim"] == 3584
+        assert unet_config["vec_in_dim"] is None
+        assert unet_config["guidance_embed"] is False
+        assert unet_config["txt_ids_dims"] == [1, 2]
+
+        model_config = model_config_from_unet_config(unet_config, sd)
+        assert model_config is not None
+        assert type(model_config).__name__ == "LongCatImage"
+
+    def test_longcat_comfyui_keys_pass_through_unchanged(self):
+        """Pre-converted weights should not be transformed by process_unet_state_dict."""
+        sd = _make_longcat_comfyui_sd()
+        unet_config = detect_unet_config(sd, "")
+        model_config = model_config_from_unet_config(unet_config, sd)
+
+        processed = model_config.process_unet_state_dict(dict(sd))
+        assert "img_in.weight" in processed
+        assert "txt_in.weight" in processed
+        assert "time_in.in_layer.weight" in processed
+        assert "final_layer.linear.weight" in processed
+
+    def test_flux_schnell_comfyui_detected_as_flux_schnell(self):
+        sd = _make_flux_schnell_comfyui_sd()
+        unet_config = detect_unet_config(sd, "")
+        assert unet_config is not None
+        assert unet_config["image_model"] == "flux"
+        assert unet_config["context_in_dim"] == 4096
+        assert unet_config["txt_ids_dims"] == []
+
+        model_config = model_config_from_unet_config(unet_config, sd)
+        assert model_config is not None
+        assert type(model_config).__name__ == "FluxSchnell"
+
+    def test_unet_config_and_required_keys_combination_is_unique(self):
+        """Each model in the registry must have a unique combination of
+        ``unet_config`` and ``required_keys``. If two models share the same
+        combination, ``BASE.matches`` cannot disambiguate between them and the
+        first one in the list will always win."""
+        models = comfy.supported_models.models
+        groups = defaultdict(list)
+        for model in models:
+            key = (_freeze(model.unet_config), _freeze(model.required_keys))
+            groups[key].append(model.__name__)
+
+        duplicates = {k: names for k, names in groups.items() if len(names) > 1}
+        assert not duplicates, (
+            "Found models sharing the same (unet_config, required_keys) "
+            "combination, which makes detection ambiguous: "
+            + "; ".join(", ".join(names) for names in duplicates.values())
+        )