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
This commit is contained in:
AUTOMATIC
parent
40ff6db532
commit
6d805b669e
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@ -49,7 +49,6 @@ A browser interface based on Gradio library for Stable Diffusion.
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- Running arbitrary python code from UI (must run with --allow-code to enable)
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- Mouseover hints for most UI elements
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- Possible to change defaults/mix/max/step values for UI elements via text config
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- Random artist button
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- Tiling support, a checkbox to create images that can be tiled like textures
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- Progress bar and live image generation preview
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- Negative prompt, an extra text field that allows you to list what you don't want to see in generated image
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3041
artists.csv
3041
artists.csv
File diff suppressed because it is too large
Load Diff
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@ -1,50 +0,0 @@
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import random
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from modules import script_callbacks, shared
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import gradio as gr
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art_symbol = '\U0001f3a8' # 🎨
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global_prompt = None
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related_ids = {"txt2img_prompt", "txt2img_clear_prompt", "img2img_prompt", "img2img_clear_prompt" }
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def roll_artist(prompt):
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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])
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artist = random.choice([x for x in shared.artist_db.artists if x.category in allowed_cats])
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return prompt + ", " + artist.name if prompt != '' else artist.name
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def add_roll_button(prompt):
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roll = gr.Button(value=art_symbol, elem_id="roll", visible=len(shared.artist_db.artists) > 0)
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roll.click(
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fn=roll_artist,
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_js="update_txt2img_tokens",
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inputs=[
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prompt,
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],
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outputs=[
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prompt,
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]
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)
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def after_component(component, **kwargs):
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global global_prompt
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elem_id = kwargs.get('elem_id', None)
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if elem_id not in related_ids:
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return
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if elem_id == "txt2img_prompt":
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global_prompt = component
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elif elem_id == "txt2img_clear_prompt":
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add_roll_button(global_prompt)
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elif elem_id == "img2img_prompt":
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global_prompt = component
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elif elem_id == "img2img_clear_prompt":
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add_roll_button(global_prompt)
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script_callbacks.on_after_component(after_component)
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@ -14,7 +14,6 @@ titles = {
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"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",
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"\u{1f3b2}\ufe0f": "Set seed to -1, which will cause a new random number to be used every time",
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"\u267b\ufe0f": "Reuse seed from last generation, mostly useful if it was randomed",
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"\u{1f3a8}": "Add a random artist to the prompt.",
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"\u2199\ufe0f": "Read generation parameters from prompt or last generation if prompt is empty into user interface.",
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"\u{1f4c2}": "Open images output directory",
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"\u{1f4be}": "Save style",
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@ -126,8 +126,6 @@ class Api:
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self.add_api_route("/sdapi/v1/face-restorers", self.get_face_restorers, methods=["GET"], response_model=List[FaceRestorerItem])
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self.add_api_route("/sdapi/v1/realesrgan-models", self.get_realesrgan_models, methods=["GET"], response_model=List[RealesrganItem])
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self.add_api_route("/sdapi/v1/prompt-styles", self.get_prompt_styles, methods=["GET"], response_model=List[PromptStyleItem])
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self.add_api_route("/sdapi/v1/artist-categories", self.get_artists_categories, methods=["GET"], response_model=List[str])
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self.add_api_route("/sdapi/v1/artists", self.get_artists, methods=["GET"], response_model=List[ArtistItem])
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self.add_api_route("/sdapi/v1/embeddings", self.get_embeddings, methods=["GET"], response_model=EmbeddingsResponse)
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self.add_api_route("/sdapi/v1/refresh-checkpoints", self.refresh_checkpoints, methods=["POST"])
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self.add_api_route("/sdapi/v1/create/embedding", self.create_embedding, methods=["POST"], response_model=CreateResponse)
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@ -390,12 +388,6 @@ class Api:
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return styleList
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def get_artists_categories(self):
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return shared.artist_db.cats
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def get_artists(self):
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return [{"name":x[0], "score":x[1], "category":x[2]} for x in shared.artist_db.artists]
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def get_embeddings(self):
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db = sd_hijack.model_hijack.embedding_db
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@ -1,25 +0,0 @@
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import os.path
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import csv
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from collections import namedtuple
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Artist = namedtuple("Artist", ['name', 'weight', 'category'])
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class ArtistsDatabase:
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def __init__(self, filename):
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self.cats = set()
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self.artists = []
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if not os.path.exists(filename):
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return
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with open(filename, "r", newline='', encoding="utf8") as file:
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reader = csv.DictReader(file)
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for row in reader:
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artist = Artist(row["artist"], float(row["score"]), row["category"])
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self.artists.append(artist)
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self.cats.add(artist.category)
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def categories(self):
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return sorted(self.cats)
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@ -5,12 +5,13 @@ from collections import namedtuple
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import re
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import torch
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import torch.hub
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from torchvision import transforms
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from torchvision.transforms.functional import InterpolationMode
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import modules.shared as shared
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from modules import devices, paths, lowvram, modelloader
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from modules import devices, paths, lowvram, modelloader, errors
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blip_image_eval_size = 384
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clip_model_name = 'ViT-L/14'
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@ -20,27 +21,59 @@ Category = namedtuple("Category", ["name", "topn", "items"])
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re_topn = re.compile(r"\.top(\d+)\.")
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def download_default_clip_interrogate_categories(content_dir):
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print("Downloading CLIP categories...")
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tmpdir = content_dir + "_tmp"
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try:
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os.makedirs(tmpdir)
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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"))
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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"))
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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"))
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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"))
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os.rename(tmpdir, content_dir)
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except Exception as e:
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errors.display(e, "downloading default CLIP interrogate categories")
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finally:
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if os.path.exists(tmpdir):
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os.remove(tmpdir)
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class InterrogateModels:
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blip_model = None
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clip_model = None
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clip_preprocess = None
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categories = None
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dtype = None
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running_on_cpu = None
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def __init__(self, content_dir):
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self.categories = []
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self.loaded_categories = None
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self.content_dir = content_dir
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self.running_on_cpu = devices.device_interrogate == torch.device("cpu")
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if os.path.exists(content_dir):
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for filename in os.listdir(content_dir):
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def categories(self):
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if self.loaded_categories is not None:
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return self.loaded_categories
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self.loaded_categories = []
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if not os.path.exists(self.content_dir):
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download_default_clip_interrogate_categories(self.content_dir)
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if os.path.exists(self.content_dir):
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for filename in os.listdir(self.content_dir):
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m = re_topn.search(filename)
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topn = 1 if m is None else int(m.group(1))
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with open(os.path.join(content_dir, filename), "r", encoding="utf8") as file:
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with open(os.path.join(self.content_dir, filename), "r", encoding="utf8") as file:
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lines = [x.strip() for x in file.readlines()]
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self.categories.append(Category(name=filename, topn=topn, items=lines))
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self.loaded_categories.append(Category(name=filename, topn=topn, items=lines))
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return self.loaded_categories
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def load_blip_model(self):
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import models.blip
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@ -139,7 +172,6 @@ class InterrogateModels:
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shared.state.begin()
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shared.state.job = 'interrogate'
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try:
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if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:
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lowvram.send_everything_to_cpu()
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devices.torch_gc()
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@ -159,12 +191,7 @@ class InterrogateModels:
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image_features /= image_features.norm(dim=-1, keepdim=True)
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if shared.opts.interrogate_use_builtin_artists:
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artist = self.rank(image_features, ["by " + artist.name for artist in shared.artist_db.artists])[0]
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res += ", " + artist[0]
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for name, topn, items in self.categories:
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for name, topn, items in self.categories():
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matches = self.rank(image_features, items, top_count=topn)
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for match, score in matches:
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if shared.opts.interrogate_return_ranks:
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@ -9,7 +9,6 @@ from PIL import Image
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import gradio as gr
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import tqdm
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import modules.artists
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import modules.interrogate
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import modules.memmon
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import modules.styles
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@ -254,8 +253,6 @@ class State:
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state = State()
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state.server_start = time.time()
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artist_db = modules.artists.ArtistsDatabase(os.path.join(script_path, 'artists.csv'))
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styles_filename = cmd_opts.styles_file
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prompt_styles = modules.styles.StyleDatabase(styles_filename)
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@ -408,7 +405,6 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), {
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"enable_batch_seeds": OptionInfo(True, "Make K-diffusion samplers produce same images in a batch as when making a single image"),
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"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 }),
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'CLIP_stop_at_last_layers': OptionInfo(1, "Clip skip", gr.Slider, {"minimum": 1, "maximum": 12, "step": 1}),
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"random_artist_categories": OptionInfo([], "Allowed categories for random artists selection when using the Roll button", gr.CheckboxGroup, {"choices": artist_db.categories()}),
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}))
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options_templates.update(options_section(('compatibility', "Compatibility"), {
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@ -419,7 +415,6 @@ options_templates.update(options_section(('compatibility', "Compatibility"), {
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options_templates.update(options_section(('interrogate', "Interrogate Options"), {
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"interrogate_keep_models_in_memory": OptionInfo(False, "Interrogate: keep models in VRAM"),
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"interrogate_use_builtin_artists": OptionInfo(True, "Interrogate: use artists from artists.csv"),
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"interrogate_return_ranks": OptionInfo(False, "Interrogate: include ranks of model tags matches in results (Has no effect on caption-based interrogators)."),
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"interrogate_clip_num_beams": OptionInfo(1, "Interrogate: num_beams for BLIP", gr.Slider, {"minimum": 1, "maximum": 16, "step": 1}),
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"interrogate_clip_min_length": OptionInfo(24, "Interrogate: minimum description length (excluding artists, etc..)", gr.Slider, {"minimum": 1, "maximum": 128, "step": 1}),
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@ -228,17 +228,17 @@ def process_interrogate(interrogation_function, mode, ii_input_dir, ii_output_di
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left, _ = os.path.splitext(filename)
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print(interrogation_function(img), file=open(os.path.join(ii_output_dir, left + ".txt"), 'a'))
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return [gr_show(True), None]
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return [gr.update(), None]
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def interrogate(image):
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prompt = shared.interrogator.interrogate(image.convert("RGB"))
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return gr_show(True) if prompt is None else prompt
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return gr.update() if prompt is None else prompt
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def interrogate_deepbooru(image):
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prompt = deepbooru.model.tag(image)
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return gr_show(True) if prompt is None else prompt
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return gr.update() if prompt is None else prompt
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def create_seed_inputs(target_interface):
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@ -1039,19 +1039,18 @@ def create_ui():
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init_img_inpaint,
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],
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outputs=[img2img_prompt, dummy_component],
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show_progress=False,
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)
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img2img_prompt.submit(**img2img_args)
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submit.click(**img2img_args)
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img2img_interrogate.click(
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fn=lambda *args : process_interrogate(interrogate, *args),
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fn=lambda *args: process_interrogate(interrogate, *args),
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**interrogate_args,
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)
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img2img_deepbooru.click(
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fn=lambda *args : process_interrogate(interrogate_deepbooru, *args),
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fn=lambda *args: process_interrogate(interrogate_deepbooru, *args),
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**interrogate_args,
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)
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