Merge pull request #7113 from vladmandic/interrogate

Add selector to interrogate categories

1 files changed, 25 insertions, 16 deletions

diff --git a/modules/interrogate.py b/modules/interrogate.py
index 19938cbb..c252b148 100644
--- a/modules/interrogate.py
+++ b/modules/interrogate.py
@@ -2,6 +2,7 @@ import os
 import sys

 import traceback

 from collections import namedtuple

+from pathlib import Path

 import re

 

 import torch

@@ -20,19 +21,20 @@ Category = namedtuple("Category", ["name", "topn", "items"])
 

 re_topn = re.compile(r"\.top(\d+)\.")

 

+def category_types():

+    return [f.stem for f in Path(shared.interrogator.content_dir).glob('*.txt')]

+

 

 def download_default_clip_interrogate_categories(content_dir):

     print("Downloading CLIP categories...")

 

     tmpdir = content_dir + "_tmp"

+    category_types = ["artists", "flavors", "mediums", "movements"]

+

     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"))

-

+        for category_type in category_types:

+            torch.hub.download_url_to_file(f"https://raw.githubusercontent.com/pharmapsychotic/clip-interrogator/main/clip_interrogator/data/{category_type}.txt", os.path.join(tmpdir, f"{category_type}.txt"))

         os.rename(tmpdir, content_dir)

 

     except Exception as e:

@@ -51,27 +53,32 @@ class InterrogateModels:
 

     def __init__(self, content_dir):

         self.loaded_categories = None

+        self.skip_categories = []

         self.content_dir = content_dir

         self.running_on_cpu = devices.device_interrogate == torch.device("cpu")

 

     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 self.loaded_categories is not None and self.skip_categories == shared.opts.interrogate_clip_skip_categories:

+           return self.loaded_categories

+

+        self.loaded_categories = []

+

         if os.path.exists(self.content_dir):

-            for filename in os.listdir(self.content_dir):

-                m = re_topn.search(filename)

+            self.skip_categories = shared.opts.interrogate_clip_skip_categories

+            category_types = []

+            for filename in Path(self.content_dir).glob('*.txt'):

+                category_types.append(filename.stem)

+                if filename.stem in self.skip_categories:

+                    continue

+                m = re_topn.search(filename.stem)

                 topn = 1 if m is None else int(m.group(1))

-

-                with open(os.path.join(self.content_dir, filename), "r", encoding="utf8") as file:

+                with open(filename, "r", encoding="utf8") as file:

                     lines = [x.strip() for x in file.readlines()]

 

-                self.loaded_categories.append(Category(name=filename, topn=topn, items=lines))

+                self.loaded_categories.append(Category(name=filename.stem, topn=topn, items=lines))

 

         return self.loaded_categories

 

@@ -139,6 +146,8 @@ class InterrogateModels:
     def rank(self, image_features, text_array, top_count=1):

         import clip

 

+        devices.torch_gc()

+

         if shared.opts.interrogate_clip_dict_limit != 0:

             text_array = text_array[0:int(shared.opts.interrogate_clip_dict_limit)]