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import sys
sys.path.append('src/blip')
sys.path.append('src/clip')
import clip
import gradio as gr
import hashlib
import math
import numpy as np
import os
import pickle
import torch
import torchvision.transforms as T
import torchvision.transforms.functional as TF
from models.blip import blip_decoder
from PIL import Image
from torch import nn
from torch.nn import functional as F
from tqdm import tqdm
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
print("Loading BLIP model...")
blip_image_eval_size = 384
blip_model_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_large_caption.pth'
blip_model = blip_decoder(pretrained=blip_model_url, image_size=blip_image_eval_size, vit='large', med_config='./src/blip/configs/med_config.json')
blip_model.eval()
blip_model = blip_model.to(device)
print("Loading CLIP model...")
clip_model_name = 'ViT-L/14' # https://huggingface.co/openai/clip-vit-large-patch14
clip_model, clip_preprocess = clip.load(clip_model_name, device=device)
clip_model.to(device).eval()
chunk_size = 2048
flavor_intermediate_count = 2048
class LabelTable():
def __init__(self, labels, desc):
self.labels = labels
self.embeds = []
hash = hashlib.sha256(",".join(labels).encode()).hexdigest()
os.makedirs('./cache', exist_ok=True)
cache_filepath = f"./cache/{desc}.pkl"
if desc is not None and os.path.exists(cache_filepath):
with open(cache_filepath, 'rb') as f:
data = pickle.load(f)
if data['hash'] == hash:
self.labels = data['labels']
self.embeds = data['embeds']
if len(self.labels) != len(self.embeds):
self.embeds = []
chunks = np.array_split(self.labels, max(1, len(self.labels)/chunk_size))
for chunk in tqdm(chunks, desc=f"Preprocessing {desc}" if desc else None):
text_tokens = clip.tokenize(chunk).to(device)
with torch.no_grad():
text_features = clip_model.encode_text(text_tokens).float()
text_features /= text_features.norm(dim=-1, keepdim=True)
text_features = text_features.half().cpu().numpy()
for i in range(text_features.shape[0]):
self.embeds.append(text_features[i])
with open(cache_filepath, 'wb') as f:
pickle.dump({"labels":self.labels, "embeds":self.embeds, "hash":hash}, f)
def _rank(self, image_features, text_embeds, top_count=1):
top_count = min(top_count, len(text_embeds))
similarity = torch.zeros((1, len(text_embeds))).to(device)
text_embeds = torch.stack([torch.from_numpy(t) for t in text_embeds]).float().to(device)
for i in range(image_features.shape[0]):
similarity += (image_features[i].unsqueeze(0) @ text_embeds.T).softmax(dim=-1)
_, top_labels = similarity.cpu().topk(top_count, dim=-1)
return [top_labels[0][i].numpy() for i in range(top_count)]
def rank(self, image_features, top_count=1):
if len(self.labels) <= chunk_size:
tops = self._rank(image_features, self.embeds, top_count=top_count)
return [self.labels[i] for i in tops]
num_chunks = int(math.ceil(len(self.labels)/chunk_size))
keep_per_chunk = int(chunk_size / num_chunks)
top_labels, top_embeds = [], []
for chunk_idx in tqdm(range(num_chunks)):
start = chunk_idx*chunk_size
stop = min(start+chunk_size, len(self.embeds))
tops = self._rank(image_features, self.embeds[start:stop], top_count=keep_per_chunk)
top_labels.extend([self.labels[start+i] for i in tops])
top_embeds.extend([self.embeds[start+i] for i in tops])
tops = self._rank(image_features, top_embeds, top_count=top_count)
return [top_labels[i] for i in tops]
def generate_caption(pil_image):
gpu_image = T.Compose([
T.Resize((blip_image_eval_size, blip_image_eval_size), interpolation=TF.InterpolationMode.BICUBIC),
T.ToTensor(),
T.Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711))
])(pil_image).unsqueeze(0).to(device)
with torch.no_grad():
caption = blip_model.generate(gpu_image, sample=False, num_beams=3, max_length=20, min_length=5)
return caption[0]
def load_list(filename):
with open(filename, 'r', encoding='utf-8', errors='replace') as f:
items = [line.strip() for line in f.readlines()]
return items
def rank_top(image_features, text_array):
text_tokens = clip.tokenize([text for text in text_array]).to(device)
with torch.no_grad():
text_features = clip_model.encode_text(text_tokens).float()
text_features /= text_features.norm(dim=-1, keepdim=True)
similarity = torch.zeros((1, len(text_array)), device=device)
for i in range(image_features.shape[0]):
similarity += (image_features[i].unsqueeze(0) @ text_features.T).softmax(dim=-1)
_, top_labels = similarity.cpu().topk(1, dim=-1)
return text_array[top_labels[0][0].numpy()]
def similarity(image_features, text):
text_tokens = clip.tokenize([text]).to(device)
with torch.no_grad():
text_features = clip_model.encode_text(text_tokens).float()
text_features /= text_features.norm(dim=-1, keepdim=True)
similarity = text_features.cpu().numpy() @ image_features.cpu().numpy().T
return similarity[0][0]
def interrogate(image):
caption = generate_caption(image)
images = clip_preprocess(image).unsqueeze(0).to(device)
with torch.no_grad():
image_features = clip_model.encode_image(images).float()
image_features /= image_features.norm(dim=-1, keepdim=True)
flaves = flavors.rank(image_features, flavor_intermediate_count)
best_medium = mediums.rank(image_features, 1)[0]
best_artist = artists.rank(image_features, 1)[0]
best_trending = trendings.rank(image_features, 1)[0]
best_movement = movements.rank(image_features, 1)[0]
best_prompt = caption
best_sim = similarity(image_features, best_prompt)
def check(addition):
nonlocal best_prompt, best_sim
prompt = best_prompt + ", " + addition
sim = similarity(image_features, prompt)
if sim > best_sim:
best_sim = sim
best_prompt = prompt
return True
return False
def check_multi_batch(opts):
nonlocal best_prompt, best_sim
prompts = []
for i in range(2**len(opts)):
prompt = best_prompt
for bit in range(len(opts)):
if i & (1 << bit):
prompt += ", " + opts[bit]
prompts.append(prompt)
prompt = rank_top(image_features, prompts)
sim = similarity(image_features, prompt)
if sim > best_sim:
best_sim = sim
best_prompt = prompt
check_multi_batch([best_medium, best_artist, best_trending, best_movement])
extended_flavors = set(flaves)
for _ in tqdm(range(25), desc="Flavor chain"):
try:
best = rank_top(image_features, [f"{best_prompt}, {f}" for f in extended_flavors])
flave = best[len(best_prompt)+2:]
if not check(flave):
break
extended_flavors.remove(flave)
except:
# exceeded max prompt length
break
return best_prompt
sites = ['Artstation', 'behance', 'cg society', 'cgsociety', 'deviantart', 'dribble', 'flickr', 'instagram', 'pexels', 'pinterest', 'pixabay', 'pixiv', 'polycount', 'reddit', 'shutterstock', 'tumblr', 'unsplash', 'zbrush central']
trending_list = [site for site in sites]
trending_list.extend(["trending on "+site for site in sites])
trending_list.extend(["featured on "+site for site in sites])
trending_list.extend([site+" contest winner" for site in sites])
raw_artists = load_list('data/artists.txt')
artists = [f"by {a}" for a in raw_artists]
artists.extend([f"inspired by {a}" for a in raw_artists])
artists = LabelTable(artists, "artists")
flavors = LabelTable(load_list('data/flavors.txt'), "flavors")
mediums = LabelTable(load_list('data/mediums.txt'), "mediums")
movements = LabelTable(load_list('data/movements.txt'), "movements")
trendings = LabelTable(trending_list, "trendings")
def inference(image):
return interrogate(image)
inputs = [gr.inputs.Image(type='pil')]
outputs = gr.outputs.Textbox(label="Output")
title = "CLIP Interrogator"
description = "Want to figure out what a good prompt might be to create new images like an existing one? The CLIP Interrogator is here to get you answers!"
article = """
<p>
Example art by <a href="https://pixabay.com/illustrations/watercolour-painting-art-effect-4799014/">Layers</a>
and <a href="https://pixabay.com/illustrations/animal-painting-cat-feline-pet-7154059/">Lin Tong</a>
from pixabay.com
</p>
<p>
Server busy? You can also run on <a href="https://colab.research.google.com/github/pharmapsychotic/clip-interrogator/blob/main/clip_interrogator.ipynb">Google Colab</a>
</p>
<p>
Has this been helpful to you? Follow me on twitter
<a href="https://twitter.com/pharmapsychotic">@pharmapsychotic</a>
and check out more tools at my
<a href="https://pharmapsychotic.com/tools.html">Ai generative art tools list</a>
</p>
"""
io = gr.Interface(
inference,
inputs,
outputs,
title=title, description=description,
article=article,
examples=[['example01.jpg'], ['example02.jpg']]
)
io.queue(max_size=32)
io.launch()
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