import clip import hashlib import inspect import math import numpy as np import os import pickle import torch from dataclasses import dataclass from models.blip import blip_decoder from PIL import Image from torchvision import transforms from torchvision.transforms.functional import InterpolationMode from tqdm import tqdm from typing import List @dataclass class Config: # models can optionally be passed in directly blip_model = None clip_model = None clip_preprocess = None # blip settings blip_image_eval_size: int = 384 blip_max_length: int = 32 blip_model_url: str = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_large_caption.pth' blip_num_beams: int = 8 # clip settings clip_model_name: str = 'ViT-L/14' # interrogator settings cache_path: str = 'cache' chunk_size: int = 2048 data_path: str = os.path.join(os.path.dirname(__file__), 'data') device: str = 'cuda' if torch.cuda.is_available() else 'cpu' flavor_intermediate_count: int = 2048 class Interrogator(): def __init__(self, config: Config): self.config = config self.device = config.device if config.blip_model is None: print("Loading BLIP model...") blip_path = os.path.dirname(inspect.getfile(blip_decoder)) configs_path = os.path.join(os.path.dirname(blip_path), 'configs') med_config = os.path.join(configs_path, 'med_config.json') blip_model = blip_decoder( pretrained=config.blip_model_url, image_size=config.blip_image_eval_size, vit='large', med_config=med_config ) blip_model.eval() blip_model = blip_model.to(config.device) self.blip_model = blip_model else: self.blip_model = config.blip_model if config.clip_model is None: print("Loading CLIP model...") self.clip_model, self.clip_preprocess = clip.load(config.clip_model_name, device=config.device) self.clip_model.to(config.device).eval() else: self.clip_model = config.clip_model self.clip_preprocess = config.clip_preprocess 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(config.data_path, 'artists.txt') artists = [f"by {a}" for a in raw_artists] artists.extend([f"inspired by {a}" for a in raw_artists]) self.artists = LabelTable(artists, "artists", self.clip_model, config) self.flavors = LabelTable(_load_list(config.data_path, 'flavors.txt'), "flavors", self.clip_model, config) self.mediums = LabelTable(_load_list(config.data_path, 'mediums.txt'), "mediums", self.clip_model, config) self.movements = LabelTable(_load_list(config.data_path, 'movements.txt'), "movements", self.clip_model, config) self.trendings = LabelTable(trending_list, "trendings", self.clip_model, config) def generate_caption(self, pil_image: Image) -> str: size = self.config.blip_image_eval_size gpu_image = transforms.Compose([ transforms.Resize((size, size), interpolation=InterpolationMode.BICUBIC), transforms.ToTensor(), transforms.Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711)) ])(pil_image).unsqueeze(0).to(self.device) with torch.no_grad(): caption = self.blip_model.generate( gpu_image, sample=False, num_beams=self.config.blip_num_beams, max_length=self.config.blip_max_length, min_length=5 ) return caption[0] def image_to_features(self, image: Image) -> torch.Tensor: images = self.clip_preprocess(image).unsqueeze(0).to(self.device) with torch.no_grad(): image_features = self.clip_model.encode_image(images).float() image_features /= image_features.norm(dim=-1, keepdim=True) return image_features def interrogate_classic(self, image: Image, max_flaves: int=3) -> str: caption = self.generate_caption(image) image_features = self.image_to_features(image) medium = self.mediums.rank(image_features, 1)[0] artist = self.artists.rank(image_features, 1)[0] trending = self.trendings.rank(image_features, 1)[0] movement = self.movements.rank(image_features, 1)[0] flaves = ", ".join(self.flavors.rank(image_features, max_flaves)) if caption.startswith(medium): prompt = f"{caption} {artist}, {trending}, {movement}, {flaves}" else: prompt = f"{caption}, {medium} {artist}, {trending}, {movement}, {flaves}" return _truncate_to_fit(prompt) def interrogate_fast(self, image: Image) -> str: caption = self.generate_caption(image) image_features = self.image_to_features(image) merged = _merge_tables([self.artists, self.flavors, self.mediums, self.movements, self.trendings], self.config) tops = merged.rank(image_features, 32) return _truncate_to_fit(caption + ", " + ", ".join(tops)) def interrogate(self, image: Image) -> str: caption = self.generate_caption(image) image_features = self.image_to_features(image) flaves = self.flavors.rank(image_features, self.config.flavor_intermediate_count) best_medium = self.mediums.rank(image_features, 1)[0] best_artist = self.artists.rank(image_features, 1)[0] best_trending = self.trendings.rank(image_features, 1)[0] best_movement = self.movements.rank(image_features, 1)[0] best_prompt = caption best_sim = self.similarity(image_features, best_prompt) def check(addition: str) -> bool: nonlocal best_prompt, best_sim prompt = best_prompt + ", " + addition sim = self.similarity(image_features, prompt) if sim > best_sim: best_sim = sim best_prompt = prompt return True return False def check_multi_batch(opts: List[str]): 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) t = LabelTable(prompts, None, self.clip_model, self.config) best_prompt = t.rank(image_features, 1)[0] best_sim = self.similarity(image_features, best_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 = self.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 def rank_top(self, image_features, text_array: List[str]) -> str: text_tokens = clip.tokenize([text for text in text_array]).to(self.device) with torch.no_grad(): text_features = self.clip_model.encode_text(text_tokens).float() text_features /= text_features.norm(dim=-1, keepdim=True) similarity = torch.zeros((1, len(text_array)), device=self.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(self, image_features, text) -> np.float32: text_tokens = clip.tokenize([text]).to(self.device) with torch.no_grad(): text_features = self.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] class LabelTable(): def __init__(self, labels:List[str], desc:str, clip_model, config: Config): self.chunk_size = config.chunk_size self.device = config.device self.labels = labels self.embeds = [] hash = hashlib.sha256(",".join(labels).encode()).hexdigest() cache_filepath = None if config.cache_path is not None and desc is not None: os.makedirs(config.cache_path, exist_ok=True) sanitized_name = config.clip_model_name.replace('/', '_').replace('@', '_') cache_filepath = os.path.join(config.cache_path, f"{sanitized_name}_{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.get('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)/config.chunk_size)) for chunk in tqdm(chunks, desc=f"Preprocessing {desc}" if desc else None): text_tokens = clip.tokenize(chunk).to(self.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]) if cache_filepath is not None: with open(cache_filepath, 'wb') as f: pickle.dump({ "labels": self.labels, "embeds": self.embeds, "hash": hash, "model": config.clip_model_name }, 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(self.device) text_embeds = torch.stack([torch.from_numpy(t) for t in text_embeds]).float().to(self.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) -> List[str]: if len(self.labels) <= self.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)/self.chunk_size)) keep_per_chunk = int(self.chunk_size / num_chunks) top_labels, top_embeds = [], [] for chunk_idx in tqdm(range(num_chunks)): start = chunk_idx*self.chunk_size stop = min(start+self.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 _load_list(data_path, filename) -> List[str]: with open(os.path.join(data_path, filename), 'r', encoding='utf-8', errors='replace') as f: items = [line.strip() for line in f.readlines()] return items def _merge_tables(tables: List[LabelTable], config: Config) -> LabelTable: m = LabelTable([], None, None, config) for table in tables: m.labels.extend(table.labels) m.embeds.extend(table.embeds) return m def _truncate_to_fit(text: str) -> str: while True: try: _ = clip.tokenize([text]) return text except: text = ",".join(text.split(",")[:-1])