make CLIP interrogator download original text files if the directory does not exist

remove random artist built-in extension (to re-added as a normal extension on demand)
remove artists.csv (but what does it mean????????????????????)
make interrogate buttons show Loading... when you click them

modules/interrogate.py

@@ -5,12 +5,13 @@ from collections import namedtuple
 import re
 
 import torch
+import torch.hub
 
 from torchvision import transforms
 from torchvision.transforms.functional import InterpolationMode
 
 import modules.shared as shared
-from modules import devices, paths, lowvram, modelloader
+from modules import devices, paths, lowvram, modelloader, errors
 
 blip_image_eval_size = 384
 clip_model_name = 'ViT-L/14'
@@ -20,27 +21,59 @@ Category = namedtuple("Category", ["name", "topn", "items"])
 re_topn = re.compile(r"\.top(\d+)\.")
 
 
+def download_default_clip_interrogate_categories(content_dir):
+    print("Downloading CLIP categories...")
+
+    tmpdir = content_dir + "_tmp"
+    try:
+        os.makedirs(tmpdir)
+
+        torch.hub.download_url_to_file("https://raw.githubusercontent.com/pharmapsychotic/clip-interrogator/main/clip_interrogator/data/artists.txt", os.path.join(tmpdir, "artists.txt"))
+        torch.hub.download_url_to_file("https://raw.githubusercontent.com/pharmapsychotic/clip-interrogator/main/clip_interrogator/data/flavors.txt", os.path.join(tmpdir, "flavors.top3.txt"))
+        torch.hub.download_url_to_file("https://raw.githubusercontent.com/pharmapsychotic/clip-interrogator/main/clip_interrogator/data/mediums.txt", os.path.join(tmpdir, "mediums.txt"))
+        torch.hub.download_url_to_file("https://raw.githubusercontent.com/pharmapsychotic/clip-interrogator/main/clip_interrogator/data/movements.txt", os.path.join(tmpdir, "movements.txt"))
+
+        os.rename(tmpdir, content_dir)
+
+    except Exception as e:
+        errors.display(e, "downloading default CLIP interrogate categories")
+    finally:
+        if os.path.exists(tmpdir):
+            os.remove(tmpdir)
+
+
 class InterrogateModels:
     blip_model = None
     clip_model = None
     clip_preprocess = None
-    categories = None
     dtype = None
     running_on_cpu = None
 
     def __init__(self, content_dir):
-        self.categories = []
+        self.loaded_categories = None
+        self.content_dir = content_dir
         self.running_on_cpu = devices.device_interrogate == torch.device("cpu")
 
-        if os.path.exists(content_dir):
-            for filename in os.listdir(content_dir):
+    def categories(self):
+        if self.loaded_categories is not None:
+            return self.loaded_categories
+
+        self.loaded_categories = []
+
+        if not os.path.exists(self.content_dir):
+            download_default_clip_interrogate_categories(self.content_dir)
+
+        if os.path.exists(self.content_dir):
+            for filename in os.listdir(self.content_dir):
                 m = re_topn.search(filename)
                 topn = 1 if m is None else int(m.group(1))
 
-                with open(os.path.join(content_dir, filename), "r", encoding="utf8") as file:
+                with open(os.path.join(self.content_dir, filename), "r", encoding="utf8") as file:
                     lines = [x.strip() for x in file.readlines()]
 
-                self.categories.append(Category(name=filename, topn=topn, items=lines))
+                self.loaded_categories.append(Category(name=filename, topn=topn, items=lines))
+
+        return self.loaded_categories
 
     def load_blip_model(self):
         import models.blip
@@ -139,7 +172,6 @@ class InterrogateModels:
         shared.state.begin()
         shared.state.job = 'interrogate'
         try:
-
             if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:
                 lowvram.send_everything_to_cpu()
                 devices.torch_gc()
@@ -159,12 +191,7 @@ class InterrogateModels:
 
                 image_features /= image_features.norm(dim=-1, keepdim=True)
 
-                if shared.opts.interrogate_use_builtin_artists:
-                    artist = self.rank(image_features, ["by " + artist.name for artist in shared.artist_db.artists])[0]
-
-                    res += ", " + artist[0]
-
-                for name, topn, items in self.categories:
+                for name, topn, items in self.categories():
                     matches = self.rank(image_features, items, top_count=topn)
                     for match, score in matches:
                         if shared.opts.interrogate_return_ranks: