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

README.md

@@ -49,7 +49,6 @@ A browser interface based on Gradio library for Stable Diffusion.
 - Running arbitrary python code from UI (must run with --allow-code to enable)
 - Mouseover hints for most UI elements
 - Possible to change defaults/mix/max/step values for UI elements via text config
-- Random artist button
 - Tiling support, a checkbox to create images that can be tiled like textures
 - Progress bar and live image generation preview
 - Negative prompt, an extra text field that allows you to list what you don't want to see in generated image

extensions-builtin/roll-artist/scripts/roll-artist.py

@@ -1,50 +0,0 @@
-import random
-
-from modules import script_callbacks, shared
-import gradio as gr
-
-art_symbol = '\U0001f3a8'  # 🎨
-global_prompt = None
-related_ids = {"txt2img_prompt", "txt2img_clear_prompt", "img2img_prompt", "img2img_clear_prompt" }
-
-
-def roll_artist(prompt):
-    allowed_cats = set([x for x in shared.artist_db.categories() if len(shared.opts.random_artist_categories)==0 or x in shared.opts.random_artist_categories])
-    artist = random.choice([x for x in shared.artist_db.artists if x.category in allowed_cats])
-
-    return prompt + ", " + artist.name if prompt != '' else artist.name
-
-
-def add_roll_button(prompt):
-    roll = gr.Button(value=art_symbol, elem_id="roll", visible=len(shared.artist_db.artists) > 0)
-
-    roll.click(
-        fn=roll_artist,
-        _js="update_txt2img_tokens",
-        inputs=[
-            prompt,
-        ],
-        outputs=[
-            prompt,
-        ]
-    )
-
-
-def after_component(component, **kwargs):
-    global global_prompt
-
-    elem_id = kwargs.get('elem_id', None)
-    if elem_id not in related_ids:
-        return
-
-    if elem_id == "txt2img_prompt":
-        global_prompt = component
-    elif elem_id == "txt2img_clear_prompt":
-        add_roll_button(global_prompt)
-    elif elem_id == "img2img_prompt":
-        global_prompt = component
-    elif elem_id == "img2img_clear_prompt":
-        add_roll_button(global_prompt)
-
-
-script_callbacks.on_after_component(after_component)

javascript/hints.js

@@ -14,7 +14,6 @@ titles = {
     "Seed": "A value that determines the output of random number generator - if you create an image with same parameters and seed as another image, you'll get the same result",
     "\u{1f3b2}\ufe0f": "Set seed to -1, which will cause a new random number to be used every time",
     "\u267b\ufe0f": "Reuse seed from last generation, mostly useful if it was randomed",
-    "\u{1f3a8}": "Add a random artist to the prompt.",
     "\u2199\ufe0f": "Read generation parameters from prompt or last generation if prompt is empty into user interface.",
     "\u{1f4c2}": "Open images output directory",
     "\u{1f4be}": "Save style",

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
 

modules/artists.py

@@ -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)

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):
+    def categories(self):
-            for filename in os.listdir(content_dir):
+        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:
+                for name, topn, items in self.categories():
-                    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:
                     matches = self.rank(image_features, items, top_count=topn)
                     for match, score in matches:
                         if shared.opts.interrogate_return_ranks:

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}),

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,
             )