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

5 files changed, 46 insertions, 58 deletions

diff --git a/modules/api/api.py b/modules/api/api.py
index 2c371e6e..f2e9e884 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -126,8 +126,6 @@ class Api:
         self.add_api_route("/sdapi/v1/face-restorers", self.get_face_restorers, methods=["GET"], response_model=List[FaceRestorerItem])
         self.add_api_route("/sdapi/v1/realesrgan-models", self.get_realesrgan_models, methods=["GET"], response_model=List[RealesrganItem])
         self.add_api_route("/sdapi/v1/prompt-styles", self.get_prompt_styles, methods=["GET"], response_model=List[PromptStyleItem])
-        self.add_api_route("/sdapi/v1/artist-categories", self.get_artists_categories, methods=["GET"], response_model=List[str])
-        self.add_api_route("/sdapi/v1/artists", self.get_artists, methods=["GET"], response_model=List[ArtistItem])
         self.add_api_route("/sdapi/v1/embeddings", self.get_embeddings, methods=["GET"], response_model=EmbeddingsResponse)
         self.add_api_route("/sdapi/v1/refresh-checkpoints", self.refresh_checkpoints, methods=["POST"])
         self.add_api_route("/sdapi/v1/create/embedding", self.create_embedding, methods=["POST"], response_model=CreateResponse)
@@ -390,12 +388,6 @@ class Api:
 
         return styleList
 
-    def get_artists_categories(self):
-        return shared.artist_db.cats
-
-    def get_artists(self):
-        return [{"name":x[0], "score":x[1], "category":x[2]} for x in shared.artist_db.artists]
-
     def get_embeddings(self):
         db = sd_hijack.model_hijack.embedding_db
 
diff --git a/modules/artists.py b/modules/artists.py
deleted file mode 100644
index 3612758b..00000000
--- a/modules/artists.py
+++ /dev/null
@@ -1,25 +0,0 @@
-import os.path

-import csv

-from collections import namedtuple

-

-Artist = namedtuple("Artist", ['name', 'weight', 'category'])

-

-

-class ArtistsDatabase:

-    def __init__(self, filename):

-        self.cats = set()

-        self.artists = []

-

-        if not os.path.exists(filename):

-            return

-

-        with open(filename, "r", newline='', encoding="utf8") as file:

-            reader = csv.DictReader(file)

-

-            for row in reader:

-                artist = Artist(row["artist"], float(row["score"]), row["category"])

-                self.artists.append(artist)

-                self.cats.add(artist.category)

-

-    def categories(self):

-        return sorted(self.cats)

diff --git a/modules/interrogate.py b/modules/interrogate.py
index 738d8ff7..19938cbb 100644
--- a/modules/interrogate.py
+++ b/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:

diff --git a/modules/shared.py b/modules/shared.py
index c0e11f18..72fb1934 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -9,7 +9,6 @@ from PIL import Image
 import gradio as gr

 import tqdm

 

-import modules.artists

 import modules.interrogate

 import modules.memmon

 import modules.styles

@@ -254,8 +253,6 @@ class State:
 state = State()

 state.server_start = time.time()

 

-artist_db = modules.artists.ArtistsDatabase(os.path.join(script_path, 'artists.csv'))

-

 styles_filename = cmd_opts.styles_file

 prompt_styles = modules.styles.StyleDatabase(styles_filename)

 

@@ -408,7 +405,6 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), {
     "enable_batch_seeds": OptionInfo(True, "Make K-diffusion samplers produce same images in a batch as when making a single image"),

     "comma_padding_backtrack": OptionInfo(20, "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens", gr.Slider, {"minimum": 0, "maximum": 74, "step": 1 }),

     'CLIP_stop_at_last_layers': OptionInfo(1, "Clip skip", gr.Slider, {"minimum": 1, "maximum": 12, "step": 1}),

-    "random_artist_categories": OptionInfo([], "Allowed categories for random artists selection when using the Roll button", gr.CheckboxGroup, {"choices": artist_db.categories()}),

 }))

 

 options_templates.update(options_section(('compatibility', "Compatibility"), {

@@ -419,7 +415,6 @@ options_templates.update(options_section(('compatibility', "Compatibility"), {
 

 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}),

diff --git a/modules/ui.py b/modules/ui.py
index d23b2b8e..164e0e93 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -228,17 +228,17 @@ def process_interrogate(interrogation_function, mode, ii_input_dir, ii_output_di
             left, _ = os.path.splitext(filename)

             print(interrogation_function(img), file=open(os.path.join(ii_output_dir, left + ".txt"), 'a'))

 

-        return [gr_show(True), None]

+        return [gr.update(), None]

 

 

 def interrogate(image):

     prompt = shared.interrogator.interrogate(image.convert("RGB"))

-    return gr_show(True) if prompt is None else prompt

+    return gr.update() if prompt is None else prompt

 

 

 def interrogate_deepbooru(image):

     prompt = deepbooru.model.tag(image)

-    return gr_show(True) if prompt is None else prompt

+    return gr.update() if prompt is None else prompt

 

 

 def create_seed_inputs(target_interface):

@@ -1039,19 +1039,18 @@ def create_ui():
                     init_img_inpaint,

                 ],

                 outputs=[img2img_prompt, dummy_component],

-                show_progress=False,

             )

 

             img2img_prompt.submit(**img2img_args)

             submit.click(**img2img_args)

 

             img2img_interrogate.click(

-                fn=lambda *args : process_interrogate(interrogate, *args),

+                fn=lambda *args: process_interrogate(interrogate, *args),

                 **interrogate_args,

             )

 

             img2img_deepbooru.click(

-                fn=lambda *args : process_interrogate(interrogate_deepbooru, *args),

+                fn=lambda *args: process_interrogate(interrogate_deepbooru, *args),

                 **interrogate_args,

             )