Align downloaded loader names

README.md

@@ -53,11 +53,11 @@ python main.py
 
 - `Model Download Batch`: 追加・削除ボタンのある一覧UI。内部ではcanonical JSONを保持します。
 - `Model Download Batch (JSON)`: 同じコアを使うJSON直接入力版です。
-- `Downloaded Checkpoint Loader`: Illustriousなどの一体型checkpointをロードします。
-- `Downloaded Diffusion Model Loader`: AnimaやKrea 2のdiffusion modelをロードします。
-- `Downloaded Text Encoder Loader`: Animaでは`auto`、Krea 2では`krea2`を選びます。
-- `Downloaded VAE Loader`: Qwen Image VAEなどをロードします。
-- `Downloaded LoRA Loader`: 対応LoRAをロードします。model-only LoRAでは`strength_clip`を0にできます。
+- `Load Checkpoint (Downloaded)`: Illustriousなどの一体型checkpointをロードします。
+- `Load Diffusion Model (Downloaded)`: AnimaやKrea 2のdiffusion modelをロードします。
+- `Load CLIP (Downloaded)`: ComfyUI標準の`Load CLIP`と同じ`type`の選択肢を使用します。`krea2`、`ideogram4`、`flux2`などを選択できます。
+- `Load VAE (Downloaded)`: Qwen Image VAEなどをロードします。
+- `Load LoRA (Downloaded)`: 対応LoRAをロードします。model-only LoRAでは`strength_clip`を0にできます。
 
 2種類のdownload nodeは、任意型の`passthrough`を入力と出力に持ちます。既存の接続途中に挿入できるほか、未接続でもoutput nodeとして実行できます。取得結果を使う場合は`download_result`を対応するDownloaded Loaderへ接続し、manifestの`id`を指定します。
 

model_batch_downloader/loaders.py

@@ -62,17 +62,18 @@ class ModelBatchDownloaderDiffusionModelLoader:
         return UNETLoader().load_unet(record.relative_path, weight_dtype)
 
 
-class ModelBatchDownloaderTextEncoderLoader:
+class ModelBatchDownloaderCLIPLoader:
     @classmethod
     def INPUT_TYPES(cls):
+        standard = CLIPLoader.INPUT_TYPES()
         return {
             "required": {
                 "download_result": ("DOWNLOAD_RESULT",),
                 "id": ("STRING", {"default": ""}),
-                "type": (["auto", "krea2"],),
+                "type": standard["required"]["type"],
             },
             "optional": {
-                "device": (["default", "cpu"], {"advanced": True}),
+                "device": standard["optional"]["device"],
             },
         }
 
@@ -82,8 +83,7 @@ class ModelBatchDownloaderTextEncoderLoader:
 
     def load(self, download_result, id, type, device="default"):
         record = _record(download_result, id, "text_encoders")
-        clip_type = "stable_diffusion" if type == "auto" else type
-        return CLIPLoader().load_clip(record.relative_path, clip_type, device)
+        return CLIPLoader().load_clip(record.relative_path, type, device)
 
 
 class ModelBatchDownloaderVAELoader:
@@ -151,15 +151,15 @@ class ModelBatchDownloaderLoRALoader:
 LOADER_CLASS_MAPPINGS = {
     "ModelBatchDownloaderCheckpointLoader": ModelBatchDownloaderCheckpointLoader,
     "ModelBatchDownloaderDiffusionModelLoader": ModelBatchDownloaderDiffusionModelLoader,
-    "ModelBatchDownloaderTextEncoderLoader": ModelBatchDownloaderTextEncoderLoader,
+    "ModelBatchDownloaderCLIPLoader": ModelBatchDownloaderCLIPLoader,
     "ModelBatchDownloaderVAELoader": ModelBatchDownloaderVAELoader,
     "ModelBatchDownloaderLoRALoader": ModelBatchDownloaderLoRALoader,
 }
 
 LOADER_DISPLAY_NAME_MAPPINGS = {
-    "ModelBatchDownloaderCheckpointLoader": "Downloaded Checkpoint Loader",
-    "ModelBatchDownloaderDiffusionModelLoader": "Downloaded Diffusion Model Loader",
-    "ModelBatchDownloaderTextEncoderLoader": "Downloaded Text Encoder Loader",
-    "ModelBatchDownloaderVAELoader": "Downloaded VAE Loader",
-    "ModelBatchDownloaderLoRALoader": "Downloaded LoRA Loader",
+    "ModelBatchDownloaderCheckpointLoader": "Load Checkpoint (Downloaded)",
+    "ModelBatchDownloaderDiffusionModelLoader": "Load Diffusion Model (Downloaded)",
+    "ModelBatchDownloaderCLIPLoader": "Load CLIP (Downloaded)",
+    "ModelBatchDownloaderVAELoader": "Load VAE (Downloaded)",
+    "ModelBatchDownloaderLoRALoader": "Load LoRA (Downloaded)",
 }

tests/test_loaders.py

@@ -2,6 +2,17 @@ from model_batch_downloader.aria2_runner import DownloadRecord, DownloadResult
 import model_batch_downloader.loaders as subject
 
 
+subject.CLIPLoader.INPUT_TYPES = classmethod(
+    lambda cls: {
+        "required": {
+            "clip_name": (["model.safetensors"],),
+            "type": (["stable_diffusion", "krea2", "ideogram4", "flux2"],),
+        },
+        "optional": {"device": (["default", "cpu"], {"advanced": True})},
+    }
+)
+
+
 def result(model_type, relative_path="folder/model.safetensors", item_id="asset"):
     return DownloadResult(
         {
@@ -44,20 +55,26 @@ def test_diffusion_loader_passes_weight_dtype(monkeypatch):
     assert seen == [("folder/model.safetensors", "fp8_e4m3fn")]
 
 
-def test_text_encoder_maps_auto_and_krea2(monkeypatch):
+def test_clip_loader_reuses_standard_type_and_device_inputs():
+    standard = subject.CLIPLoader.INPUT_TYPES()
+    custom = subject.ModelBatchDownloaderCLIPLoader.INPUT_TYPES()
+    assert custom["required"]["type"] == standard["required"]["type"]
+    assert custom["optional"]["device"] == standard["optional"]["device"]
+    assert "auto" not in custom["required"]["type"][0]
+
+
+def test_clip_loader_delegates_type_and_device(monkeypatch):
     seen = []
     monkeypatch.setattr(
         subject.CLIPLoader,
         "load_clip",
         lambda self, name, type, device: seen.append((name, type, device)) or ("clip",),
     )
-    loader = subject.ModelBatchDownloaderTextEncoderLoader()
-    loader.load(result("text_encoders"), "asset", "auto", "default")
-    loader.load(result("text_encoders"), "asset", "krea2", "cpu")
-    assert seen == [
-        ("folder/model.safetensors", "stable_diffusion", "default"),
-        ("folder/model.safetensors", "krea2", "cpu"),
-    ]
+    output = subject.ModelBatchDownloaderCLIPLoader().load(
+        result("text_encoders"), "asset", "ideogram4", "cpu"
+    )
+    assert output == ("clip",)
+    assert seen == [("folder/model.safetensors", "ideogram4", "cpu")]
 
 
 def test_wrong_category_fails_before_delegation():

tests/test_registration.py

@@ -17,11 +17,22 @@ def test_plugin_registers_seven_namespaced_nodes():
 
     assert sorted(module.NODE_CLASS_MAPPINGS) == [
         "ModelBatchDownloader",
+        "ModelBatchDownloaderCLIPLoader",
         "ModelBatchDownloaderCheckpointLoader",
         "ModelBatchDownloaderDiffusionModelLoader",
         "ModelBatchDownloaderJSON",
         "ModelBatchDownloaderLoRALoader",
-        "ModelBatchDownloaderTextEncoderLoader",
         "ModelBatchDownloaderVAELoader",
     ]
+    assert "ModelBatchDownloaderCLIPLoader" in module.NODE_CLASS_MAPPINGS
+    assert "ModelBatchDownloaderTextEncoderLoader" not in module.NODE_CLASS_MAPPINGS
+    assert module.NODE_DISPLAY_NAME_MAPPINGS == {
+        "ModelBatchDownloader": "Model Download Batch",
+        "ModelBatchDownloaderJSON": "Model Download Batch (JSON)",
+        "ModelBatchDownloaderCheckpointLoader": "Load Checkpoint (Downloaded)",
+        "ModelBatchDownloaderDiffusionModelLoader": "Load Diffusion Model (Downloaded)",
+        "ModelBatchDownloaderCLIPLoader": "Load CLIP (Downloaded)",
+        "ModelBatchDownloaderVAELoader": "Load VAE (Downloaded)",
+        "ModelBatchDownloaderLoRALoader": "Load LoRA (Downloaded)",
+    }
     assert module.WEB_DIRECTORY == "./web"