Add sapiens2 matting as a mask generator. Begin transition to model paths and model folders.

toolkit/models/loaders/comfy.py

@@ -1,15 +0,0 @@
-import os
-from typing import List
-from toolkit.paths import COMFY_MODELS_PATH
-
-
-def get_comfy_path(comfy_files: List[str]) -> str:
-    """
-    Get the path to the first existing file in the COMFY_MODELS_PATH.
-    """
-    if COMFY_MODELS_PATH is not None and comfy_files is not None and len(comfy_files) > 0:
-        for file in comfy_files:
-            file_path = os.path.join(COMFY_MODELS_PATH, file)
-            if os.path.exists(file_path):
-                return file_path
-    return None

toolkit/models/loaders/umt5.py

@@ -1,7 +1,6 @@
 from typing import List
 import torch
 from transformers import T5Tokenizer, UMT5EncoderModel
-from toolkit.models.loaders.comfy import get_comfy_path
 
 class PatchedT5Tokenizer(T5Tokenizer):
     def __init__(
@@ -40,15 +39,8 @@ def get_umt5_encoder(
     Load the UMT5 encoder model from the specified path.
     """
     tokenizer = PatchedT5Tokenizer.from_pretrained(model_path, subfolder=tokenizer_subfolder)
-    comfy_path = get_comfy_path(comfy_files)
-    comfy_path = None
-    if comfy_path is not None:
-        text_encoder = UMT5EncoderModel.from_single_file(
-            comfy_path, torch_dtype=torch_dtype
-        )
-    else:
-        print(f"Using {model_path} for UMT5 encoder.")
-        text_encoder = UMT5EncoderModel.from_pretrained(
-            model_path, subfolder=encoder_subfolder, torch_dtype=torch_dtype
-        )
+    print(f"Using {model_path} for UMT5 encoder.")
+    text_encoder = UMT5EncoderModel.from_pretrained(
+        model_path, subfolder=encoder_subfolder, torch_dtype=torch_dtype
+    )
     return tokenizer, text_encoder