Interrogate: add option to include ranks in output

Since the UI also allows users to specify ranks, it can be useful to show people what ranks are being returned by interrogate

This can also give much better results when feeding the interrogate results back into either img2img or txt2img, especially when trying to generate a specific character or scene for which you have a similar concept image

Testing Steps:

Launch Webui with command line arg: --deepdanbooru
Navigate to img2img tab, use interrogate DeepBooru, verify tags appears as before. Use "Interrogate CLIP", verify prompt appears as before
Navigate to Settings tab, enable new option, click "apply settings"
Navigate to img2img, Interrogate DeepBooru again, verify that weights appear and are properly formatted. Note that "Interrogate CLIP" prompt is still unchanged
In my testing, this change has no effect to "Interrogate CLIP", as it seems to generate a sentence-structured caption, and not a set of tags.

(reproduce changes from 6ed4faac46)

modules/deepbooru.py

@@ -3,7 +3,7 @@ from concurrent.futures import ProcessPoolExecutor
 from multiprocessing import get_context
 
 
-def _load_tf_and_return_tags(pil_image, threshold):
+def _load_tf_and_return_tags(pil_image, threshold, include_ranks):
     import deepdanbooru as dd
     import tensorflow as tf
     import numpy as np
@@ -52,12 +52,16 @@ def _load_tf_and_return_tags(pil_image, threshold):
         if result_dict[tag] >= threshold:
             if tag.startswith("rating:"):
                 continue
-            result_tags_out.append(tag)
+            tag_formatted = tag.replace('_', ' ').replace(':', ' ')
+            if include_ranks:
+                result_tags_out.append(f'({tag_formatted}:{result_dict[tag]})')
+            else:
+                result_tags_out.append(tag_formatted)
             result_tags_print.append(f'{result_dict[tag]} {tag}')
 
     print('\n'.join(sorted(result_tags_print, reverse=True)))
 
-    return ', '.join(result_tags_out).replace('_', ' ').replace(':', ' ')
+    return ', '.join(result_tags_out)
 
 
 def subprocess_init_no_cuda():
@@ -65,9 +69,9 @@ def subprocess_init_no_cuda():
     os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
 
 
-def get_deepbooru_tags(pil_image, threshold=0.5):
+def get_deepbooru_tags(pil_image, threshold=0.5, include_ranks=False):
     context = get_context('spawn')
     with ProcessPoolExecutor(initializer=subprocess_init_no_cuda, mp_context=context) as executor:
-        f = executor.submit(_load_tf_and_return_tags, pil_image, threshold, )
+        f = executor.submit(_load_tf_and_return_tags, pil_image, threshold, include_ranks)
         ret = f.result()  # will rethrow any exceptions
     return ret

modules/interrogate.py

@@ -123,7 +123,7 @@ class InterrogateModels:
 
         return caption[0]
 
-    def interrogate(self, pil_image):
+    def interrogate(self, pil_image, include_ranks=False):
         res = None
 
         try:
@@ -156,7 +156,10 @@ class InterrogateModels:
                 for name, topn, items in self.categories:
                     matches = self.rank(image_features, items, top_count=topn)
                     for match, score in matches:
-                        res += ", " + match
+                        if include_ranks:
+                            res += ", " + match
+                        else:
+                            res += f", ({match}:{score})"
 
         except Exception:
             print(f"Error interrogating", file=sys.stderr)

modules/shared.py

@@ -251,6 +251,7 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), {
 options_templates.update(options_section(('interrogate', "Interrogate Options"), {
     "interrogate_keep_models_in_memory": OptionInfo(False, "Interrogate: keep models in VRAM"),
     "interrogate_use_builtin_artists": OptionInfo(True, "Interrogate: use artists from artists.csv"),
+    "interrogate_return_ranks": OptionInfo(False, "Interrogate: include ranks of model tags matches in results (Has no effect on caption-based interrogators)."),
     "interrogate_clip_num_beams": OptionInfo(1, "Interrogate: num_beams for BLIP", gr.Slider, {"minimum": 1, "maximum": 16, "step": 1}),
     "interrogate_clip_min_length": OptionInfo(24, "Interrogate: minimum description length (excluding artists, etc..)", gr.Slider, {"minimum": 1, "maximum": 128, "step": 1}),
     "interrogate_clip_max_length": OptionInfo(48, "Interrogate: maximum description length", gr.Slider, {"minimum": 1, "maximum": 256, "step": 1}),

modules/ui.py

@@ -311,13 +311,12 @@ def apply_styles(prompt, prompt_neg, style1_name, style2_name):
 
 
 def interrogate(image):
-    prompt = shared.interrogator.interrogate(image)
+    prompt = shared.interrogator.interrogate(image, include_ranks=opts.interrogate_return_ranks)
-
     return gr_show(True) if prompt is None else prompt
 
 
 def interrogate_deepbooru(image):
-    prompt = get_deepbooru_tags(image, opts.interrogate_deepbooru_score_threshold)
+    prompt = get_deepbooru_tags(image, opts.interrogate_deepbooru_score_threshold, opts.interrogate_return_ranks)
     return gr_show(True) if prompt is None else prompt