import contextlib import os import sys import traceback from collections import namedtuple import re import torch from torchvision import transforms from torchvision.transforms.functional import InterpolationMode import modules.shared as shared from modules import devices, paths, lowvram blip_image_eval_size = 384 blip_model_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_caption_capfilt_large.pth' clip_model_name = 'ViT-L/14' Category = namedtuple("Category", ["name", "topn", "items"]) re_topn = re.compile(r"\.top(\d+)\.") class InterrogateModels: blip_model = None clip_model = None clip_preprocess = None categories = None dtype = None def __init__(self, content_dir): self.categories = [] if os.path.exists(content_dir): for filename in os.listdir(content_dir): m = re_topn.search(filename) topn = 1 if m is None else int(m.group(1)) with open(os.path.join(content_dir, filename), "r", encoding="utf8") as file: lines = [x.strip() for x in file.readlines()] self.categories.append(Category(name=filename, topn=topn, items=lines)) def load_blip_model(self): import models.blip blip_model = models.blip.blip_decoder(pretrained=blip_model_url, image_size=blip_image_eval_size, vit='base', med_config=os.path.join(paths.paths["BLIP"], "configs", "med_config.json")) blip_model.eval() return blip_model def load_clip_model(self): import clip model, preprocess = clip.load(clip_model_name) model.eval() model = model.to(shared.device) return model, preprocess def load(self): if self.blip_model is None: self.blip_model = self.load_blip_model() if not shared.cmd_opts.no_half: self.blip_model = self.blip_model.half() self.blip_model = self.blip_model.to(shared.device) if self.clip_model is None: self.clip_model, self.clip_preprocess = self.load_clip_model() if not shared.cmd_opts.no_half: self.clip_model = self.clip_model.half() self.clip_model = self.clip_model.to(shared.device) self.dtype = next(self.clip_model.parameters()).dtype def send_clip_to_ram(self): if not shared.opts.interrogate_keep_models_in_memory: if self.clip_model is not None: self.clip_model = self.clip_model.to(devices.cpu) def send_blip_to_ram(self): if not shared.opts.interrogate_keep_models_in_memory: if self.blip_model is not None: self.blip_model = self.blip_model.to(devices.cpu) def unload(self): self.send_clip_to_ram() self.send_blip_to_ram() devices.torch_gc() def rank(self, image_features, text_array, top_count=1): import clip if shared.opts.interrogate_clip_dict_limit != 0: text_array = text_array[0:int(shared.opts.interrogate_clip_dict_limit)] top_count = min(top_count, len(text_array)) text_tokens = clip.tokenize([text for text in text_array]).to(shared.device) text_features = self.clip_model.encode_text(text_tokens).type(self.dtype) text_features /= text_features.norm(dim=-1, keepdim=True) similarity = torch.zeros((1, len(text_array))).to(shared.device) for i in range(image_features.shape[0]): similarity += (100.0 * image_features[i].unsqueeze(0) @ text_features.T).softmax(dim=-1) similarity /= image_features.shape[0] top_probs, top_labels = similarity.cpu().topk(top_count, dim=-1) return [(text_array[top_labels[0][i].numpy()], (top_probs[0][i].numpy()*100)) for i in range(top_count)] def generate_caption(self, pil_image): gpu_image = transforms.Compose([ transforms.Resize((blip_image_eval_size, blip_image_eval_size), interpolation=InterpolationMode.BICUBIC), transforms.ToTensor(), transforms.Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711)) ])(pil_image).unsqueeze(0).type(self.dtype).to(shared.device) with torch.no_grad(): caption = self.blip_model.generate(gpu_image, sample=False, num_beams=shared.opts.interrogate_clip_num_beams, min_length=shared.opts.interrogate_clip_min_length, max_length=shared.opts.interrogate_clip_max_length) return caption[0] def interrogate(self, pil_image): res = None try: if shared.cmd_opts.lowvram or shared.cmd_opts.medvram: lowvram.send_everything_to_cpu() devices.torch_gc() self.load() caption = self.generate_caption(pil_image) self.send_blip_to_ram() devices.torch_gc() res = caption cilp_image = self.clip_preprocess(pil_image).unsqueeze(0).type(self.dtype).to(shared.device) precision_scope = torch.autocast if shared.cmd_opts.precision == "autocast" else contextlib.nullcontext with torch.no_grad(), precision_scope("cuda"): image_features = self.clip_model.encode_image(cilp_image).type(self.dtype) image_features /= image_features.norm(dim=-1, keepdim=True) if shared.opts.interrogate_use_builtin_artists: artist = self.rank(image_features, ["by " + artist.name for artist in shared.artist_db.artists])[0] res += ", " + artist[0] for name, topn, items in self.categories: matches = self.rank(image_features, items, top_count=topn) for match, score in matches: res += ", " + match except Exception: print(f"Error interrogating", file=sys.stderr) print(traceback.format_exc(), file=sys.stderr) res += "" self.unload() return res