Model sort selection.

scripts/tag_autocomplete_helper.py

@@ -1,6 +1,7 @@
 # This helper script scans folders for wildcards and embeddings and writes them
 # to a temporary file to expose it to the javascript side
 
+import os
 import glob
 import json
 import urllib.parse
@@ -24,12 +25,48 @@ except Exception as e: # Not supported.
     load_textual_inversion_embeddings = lambda *args, **kwargs: None
     print("Tag Autocomplete: Cannot reload embeddings instantly:", e)
 
+QUO = "\""
+EXTKEY = "tac"
+# EXTNAME = "Tag Autocomplete Helper"
+# Default values, because shared doesn't allocate a value automatically.
+# (id: def)
+DEXTSETV = {
+"sortModels": "default",
+}
+fseti = lambda x: shared.opts.data.get(EXTKEY + "_" + x, DEXTSETV[x])
+
+def sort_models(lmodels, sort_method = None, indwrap = False):
+    """Sorts models according to setting.
+    
+    Input: list of (full_path, display_name, {hash}) models. 
+    Returns models in the standard temp file format (ie name, hash).
+    Default sort is lexicographical, mdate is by file modification date.
+    Hash is optional, can be any textual value written after the name (eg v1/v2 for embeddings).
+    For some reason only loras and lycos are wrapped in quote marks, so it's left to caller.
+    Creep: Requires sort modifications on js side to preserve order during merge.
+    """
+    if len(lmodels) == 0:
+        return lmodels
+    if sort_method is None:
+        sort_method = fseti("sortModels")
+    if sort_method == "modified_date":
+        lsorted = sorted(lmodels, key=lambda x: os.path.getmtime(x[0]), reverse = True)
+    else: 
+        lsorted = sorted(lmodels, key = lambda x: x[1].lower())
+    if len(lsorted[0]) > 2:
+        # lret = [f"\"{name}\",{hash}" for pt, name, hash in lsorted]
+        lret = [f"{name},{hash}" for pt, name, hash in lsorted]
+    else:
+        lret = [name for pt, name in lsorted]
+    return lret
+
 def get_wildcards():
     """Returns a list of all wildcards. Works on nested folders."""
     wildcard_files = list(WILDCARD_PATH.rglob("*.txt"))
-    resolved = [w.relative_to(WILDCARD_PATH).as_posix(
-    ) for w in wildcard_files if w.name != "put wildcards here.txt"]
-    return resolved
+    resolved = [(w, w.relative_to(WILDCARD_PATH).as_posix())
+                for w in wildcard_files
+                if w.name != "put wildcards here.txt"]
+    return sort_models(resolved)
 
 
 def get_ext_wildcards():
@@ -38,7 +75,10 @@ def get_ext_wildcards():
 
     for path in WILDCARD_EXT_PATHS:
         wildcard_files.append(path.as_posix())
-        wildcard_files.extend(p.relative_to(path).as_posix() for p in path.rglob("*.txt") if p.name != "put wildcards here.txt")
+        lfiles = [(w, w.relative_to(path).as_posix())
+                  for w in path.rglob("*.txt")
+                  if w.name != "put wildcards here.txt"]
+        wildcard_files.extend(sort_models(lfiles))
         wildcard_files.append("-----")
 
     return wildcard_files
@@ -136,14 +176,14 @@ def get_embeddings(sd_model):
 
         # Add embeddings to the correct list
         if (emb_a_shape == V1_SHAPE):
-            emb_v1 = list(emb_type_a.keys())
+            emb_v1 = [(v.filename, k, "v1") for (k,v) in emb_type_a.items()]
         elif (emb_a_shape == V2_SHAPE):
-            emb_v2 = list(emb_type_a.keys())
+            emb_v2 = [(v.filename, k, "v2") for (k,v) in emb_type_a.items()]
 
         if (emb_b_shape == V1_SHAPE):
-            emb_v1 = list(emb_type_b.keys())
+            emb_v1 = [(v.filename, k, "v1") for (k,v) in emb_type_b.items()]
         elif (emb_b_shape == V2_SHAPE):
-            emb_v2 = list(emb_type_b.keys())
+            emb_v2 = [(v.filename, k, "v2") for (k,v) in emb_type_b.items()]
 
         # Get shape of current model
         #vec = sd_model.cond_stage_model.encode_embedding_init_text(",", 1)
@@ -155,7 +195,7 @@ def get_embeddings(sd_model):
         #    results = [e + ",v2" for e in emb_v2] + [e + ",v1" for e in emb_v1]
         #else:
         #    raise AttributeError # Fallback to old method
-        results = sorted([e + ",v1" for e in emb_v1] + [e + ",v2" for e in emb_v2], key=lambda x: x.lower())
+        results = sort_models(emb_v1) + sort_models(emb_v2)
     except AttributeError:
         print("tag_autocomplete_helper: Old webui version or unrecognized model shape, using fallback for embedding completion.")
         # Get a list of all embeddings in the folder
@@ -173,9 +213,10 @@ def get_hypernetworks():
 
     # Get a list of all hypernetworks in the folder
     hyp_paths = [Path(h) for h in glob.glob(HYP_PATH.joinpath("**/*").as_posix(), recursive=True)]
-    all_hypernetworks = [str(h.name) for h in hyp_paths if h.suffix in {".pt"}]
+    all_hypernetworks = [h for h in hyp_paths if h.suffix in {".pt"}]
     # Remove file extensions
-    return sorted([h[:h.rfind('.')] for h in all_hypernetworks], key=lambda x: x.lower())
+    lfiles = [(h, os.path.splitext(h.name)[0]) for h in all_hypernetworks]
+    return sort_models(lfiles)
 
 model_keyword_installed = write_model_keyword_path()
 def get_lora():
@@ -186,17 +227,17 @@ def get_lora():
     lora_paths = [Path(l) for l in glob.glob(LORA_PATH.joinpath("**/*").as_posix(), recursive=True)]
     # Get hashes
     valid_loras = [lf for lf in lora_paths if lf.suffix in {".safetensors", ".ckpt", ".pt"}]
-    hashes = {}
+    lhashes = []
     for l in valid_loras:
         name = l.relative_to(LORA_PATH).as_posix()
+        name = QUO + name + QUO # Wrapped in quote marks.
         if model_keyword_installed:
-            hashes[name] = get_lora_simple_hash(l)
+            vhash = get_lora_simple_hash(l)
         else:
-            hashes[name] = ""
+            vhash = ""
+        lhashes.append((l, name, vhash))
     # Sort
-    sorted_loras = dict(sorted(hashes.items()))
-    # Add hashes and return
-    return [f"\"{name}\",{hash}" for name, hash in sorted_loras.items()]
+    return sort_models(lhashes)
 
 
 def get_lyco():
@@ -207,19 +248,18 @@ def get_lyco():
 
     # Get hashes
     valid_lycos = [lyf for lyf in lyco_paths if lyf.suffix in {".safetensors", ".ckpt", ".pt"}]
-    hashes = {}
+    lhashes = []
     for ly in valid_lycos:
         name = ly.relative_to(LYCO_PATH).as_posix()
+        name = QUO + name + QUO
         if model_keyword_installed:
-            hashes[name] = get_lora_simple_hash(ly)
+            vhash = get_lora_simple_hash(ly)
         else:
-            hashes[name] = ""
+            vhash = ""
+        lhashes.append((ly, name, vhash))
 
     # Sort
-    sorted_lycos = dict(sorted(hashes.items()))
-    # Add hashes and return
-    return [f"\"{name}\",{hash}" for name, hash in sorted_lycos.items()]
-
+    return sort_models(lhashes)
 
 def write_tag_base_path():
     """Writes the tag base path to a fixed location temporary file"""
@@ -397,6 +437,7 @@ def on_ui_settings():
         "tac_extra.addMode": shared.OptionInfo("Insert before", "Mode to add the extra tags to the main tag list", gr.Dropdown, lambda: {"choices": ["Insert before","Insert after"]}),
         # Chant settings
         "tac_chantFile": shared.OptionInfo("demo-chants.json", "Chant filename", gr.Dropdown, lambda: {"choices": json_files_withnone}, refresh=update_json_files).info("Chants are longer prompt presets"),
+        "tac_sortModels": shared.OptionInfo("name", "Model sort order", gr.Dropdown, lambda: {"choices": ["name", "modified_date"]}).info("WIP: Order of appearance for models in dropdown"),
     }
 
     # Add normal settings