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import json
import faiss
import flax
import gradio as gr
import jax
import numpy as np
import pandas as pd
import requests
from Models.CLIP import CLIP
def danbooru_id_to_url(image_id, selected_ratings, api_username="", api_key=""):
headers = {"User-Agent": "image_similarity_tool"}
ratings_to_letters = {
"General": "g",
"Sensitive": "s",
"Questionable": "q",
"Explicit": "e",
}
acceptable_ratings = [ratings_to_letters[x] for x in selected_ratings]
image_url = f"https://danbooru.donmai.us/posts/{image_id}.json"
if api_username != "" and api_key != "":
image_url = f"{image_url}?api_key={api_key}&login={api_username}"
r = requests.get(image_url, headers=headers)
if r.status_code != 200:
return None
content = json.loads(r.text)
image_url = content["large_file_url"] if "large_file_url" in content else None
image_url = image_url if content["rating"] in acceptable_ratings else None
return image_url
class Predictor:
def __init__(self):
self.base_model = "wd-v1-4-convnext-tagger-v2"
with open(f"data/{self.base_model}/clip.msgpack", "rb") as f:
data = f.read()
self.params = flax.serialization.msgpack_restore(data)["model"]
self.model = CLIP()
self.tags_df = pd.read_csv("data/selected_tags.csv")
self.images_ids = np.load("index/cosine_ids.npy")
self.knn_index = faiss.read_index("index/cosine_knn.index")
config = json.loads(open("index/cosine_infos.json").read())["index_param"]
faiss.ParameterSpace().set_index_parameters(self.knn_index, config)
def predict(
self,
positive_tags,
negative_tags,
selected_ratings,
n_neighbours,
api_username,
api_key,
):
tags_df = self.tags_df
model = self.model
num_classes = len(tags_df)
positive_tags = positive_tags.split(",")
negative_tags = negative_tags.split(",")
positive_tags_idxs = tags_df.index[tags_df["name"].isin(positive_tags)].tolist()
negative_tags_idxs = tags_df.index[tags_df["name"].isin(negative_tags)].tolist()
tags = np.zeros((1, num_classes), dtype=np.float32)
tags[0][positive_tags_idxs] = 1
emb_from_logits = model.apply(
{"params": self.params},
tags,
method=model.encode_text,
)
emb_from_logits = jax.device_get(emb_from_logits)
faiss.normalize_L2(emb_from_logits)
if len(negative_tags_idxs) > 0:
tags = np.zeros((1, num_classes), dtype=np.float32)
tags[0][negative_tags_idxs] = 1
neg_emb_from_logits = model.apply(
{"params": self.params},
tags,
method=model.encode_text,
)
neg_emb_from_logits = jax.device_get(neg_emb_from_logits)
faiss.normalize_L2(neg_emb_from_logits)
emb_from_logits = emb_from_logits - neg_emb_from_logits
faiss.normalize_L2(emb_from_logits)
dists, indexes = self.knn_index.search(emb_from_logits, k=n_neighbours)
neighbours_ids = self.images_ids[indexes][0]
neighbours_ids = [int(x) for x in neighbours_ids]
captions = []
image_urls = []
for image_id, dist in zip(neighbours_ids, dists[0]):
current_url = danbooru_id_to_url(
image_id,
selected_ratings,
api_username,
api_key,
)
if current_url is not None:
image_urls.append(current_url)
captions.append(f"{image_id}/{dist:.2f}")
return list(zip(image_urls, captions))
def main():
predictor = Predictor()
with gr.Blocks() as demo:
with gr.Row():
with gr.Column():
positive_tags = gr.Textbox(label="Positive tags")
negative_tags = gr.Textbox(label="Negative tags")
n_neighbours = gr.Slider(
minimum=1,
maximum=20,
value=5,
step=1,
label="# of images",
)
with gr.Column():
api_username = gr.Textbox(label="Danbooru API Username")
api_key = gr.Textbox(label="Danbooru API Key")
selected_ratings = gr.CheckboxGroup(
choices=["General", "Sensitive", "Questionable", "Explicit"],
value=["General", "Sensitive"],
label="Ratings",
)
find_btn = gr.Button("Find similar images")
similar_images = gr.Gallery(label="Similar images", columns=[5])
examples = gr.Examples(
[
[
"marcille_donato",
"",
["General", "Sensitive"],
5,
"",
"",
],
[
"yellow_eyes,red_horns",
"",
["General", "Sensitive"],
5,
"",
"",
],
[
"artoria_pendragon_(fate),solo",
"excalibur_(fate/stay_night),green_eyes,monochrome,blonde_hair",
["General", "Sensitive"],
5,
"",
"",
],
],
inputs=[
positive_tags,
negative_tags,
selected_ratings,
n_neighbours,
api_username,
api_key,
],
outputs=[similar_images],
fn=predictor.predict,
run_on_click=True,
cache_examples=False,
)
find_btn.click(
fn=predictor.predict,
inputs=[
positive_tags,
negative_tags,
selected_ratings,
n_neighbours,
api_username,
api_key,
],
outputs=[similar_images],
)
demo.queue()
demo.launch()
if __name__ == "__main__":
main()
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