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 = """

Example art by Layers and Lin Tong from pixabay.com

Server busy? You can also run on Google Colab

Has this been helpful to you? Follow me on twitter @pharmapsychotic and check out more tools at my Ai generative art tools list

""" io = gr.Interface( inference, inputs, outputs, title=title, description=description, article=article, examples=[['example01.jpg'], ['example02.jpg']] ) io.queue(max_size=32) io.launch(show_api=False)