import hashlib import inspect import math import numpy as np import open_clip import os import pickle import time 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 blip_offload: bool = False # clip settings clip_model_name: str = 'ViT-H-14/laion2b_s32b_b79k' clip_model_path: str = None # 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 quiet: bool = False # when quiet progress bars are not shown class Interrogator(): def __init__(self, config: Config): self.config = config self.device = config.device if config.blip_model is None: if not config.quiet: 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 self.load_clip_model() def load_clip_model(self): start_time = time.time() config = self.config if config.clip_model is None: if not config.quiet: print("Loading CLIP model...") clip_model_name, clip_model_pretrained_name = config.clip_model_name.split('/', 2) self.clip_model, _, self.clip_preprocess = open_clip.create_model_and_transforms( clip_model_name, pretrained=clip_model_pretrained_name, precision='fp16', device=config.device, jit=False, cache_dir=config.clip_model_path ) self.clip_model.half().to(config.device).eval() else: self.clip_model = config.clip_model self.clip_preprocess = config.clip_preprocess self.tokenize = open_clip.get_tokenizer(clip_model_name) 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, self.tokenize, config) self.flavors = LabelTable(_load_list(config.data_path, 'flavors.txt'), "flavors", self.clip_model, self.tokenize, config) self.mediums = LabelTable(_load_list(config.data_path, 'mediums.txt'), "mediums", self.clip_model, self.tokenize, config) self.movements = LabelTable(_load_list(config.data_path, 'movements.txt'), "movements", self.clip_model, self.tokenize, config) self.trendings = LabelTable(trending_list, "trendings", self.clip_model, self.tokenize, config) end_time = time.time() if not config.quiet: print(f"Loaded CLIP model and data in {end_time-start_time:.2f} seconds.") def generate_caption(self, pil_image: Image) -> str: if self.config.blip_offload: self.blip_model = self.blip_model.to(self.device) 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 ) if self.config.blip_offload: self.blip_model = self.blip_model.to("cpu") 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(), torch.cuda.amp.autocast(): image_features = self.clip_model.encode_image(images) image_features /= image_features.norm(dim=-1, keepdim=True) return image_features def interrogate_classic(self, image: Image, max_flavors: 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_flavors)) 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, self.tokenize) def interrogate_fast(self, image: Image, max_flavors: int = 32) -> 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, max_flavors) return _truncate_to_fit(caption + ", " + ", ".join(tops), self.tokenize) def interrogate(self, image: Image, max_flavors: int=32) -> 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.tokenize, 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(max_flavors), desc="Flavor chain", disable=self.config.quiet): 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 if _prompt_at_max_len(best_prompt, self.tokenize): break extended_flavors.remove(flave) return best_prompt def rank_top(self, image_features: torch.Tensor, text_array: List[str]) -> str: text_tokens = self.tokenize([text for text in text_array]).to(self.device) with torch.no_grad(), torch.cuda.amp.autocast(): text_features = self.clip_model.encode_text(text_tokens) text_features /= text_features.norm(dim=-1, keepdim=True) similarity = text_features @ image_features.T return text_array[similarity.argmax().item()] def similarity(self, image_features: torch.Tensor, text: str) -> float: text_tokens = self.tokenize([text]).to(self.device) with torch.no_grad(), torch.cuda.amp.autocast(): text_features = self.clip_model.encode_text(text_tokens) text_features /= text_features.norm(dim=-1, keepdim=True) similarity = text_features @ image_features.T return similarity[0][0].item() class LabelTable(): def __init__(self, labels:List[str], desc:str, clip_model, tokenize, config: Config): self.chunk_size = config.chunk_size self.config = config self.device = config.device self.embeds = [] self.labels = labels self.tokenize = tokenize 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, disable=self.config.quiet): text_tokens = self.tokenize(chunk).to(self.device) with torch.no_grad(), torch.cuda.amp.autocast(): text_features = clip_model.encode_text(text_tokens) 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: torch.Tensor, text_embeds: torch.Tensor, top_count: int=1) -> str: top_count = min(top_count, len(text_embeds)) text_embeds = torch.stack([torch.from_numpy(t) for t in text_embeds]).to(self.device) with torch.cuda.amp.autocast(): similarity = image_features @ text_embeds.T _, top_labels = similarity.float().cpu().topk(top_count, dim=-1) return [top_labels[0][i].numpy() for i in range(top_count)] def rank(self, image_features: torch.Tensor, top_count: int=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), disable=self.config.quiet): 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: str, filename: str) -> 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, None, config) for table in tables: m.labels.extend(table.labels) m.embeds.extend(table.embeds) return m def _prompt_at_max_len(text: str, tokenize) -> bool: tokens = tokenize([text]) return tokens[0][-1] != 0 def _truncate_to_fit(text: str, tokenize) -> str: parts = text.split(', ') new_text = parts[0] for part in parts[1:]: if _prompt_at_max_len(new_text + part, tokenize): break new_text += ', ' + part return new_text