import os import sys from collections import namedtuple from pathlib import Path import re import torch import torch.hub from torchvision import transforms from torchvision.transforms.functional import InterpolationMode from modules import devices, paths, shared, lowvram, modelloader, errors from modules.torch_utils import get_param blip_image_eval_size = 384 clip_model_name = 'ViT-L/14' Category = namedtuple("Category", ["name", "topn", "items"]) re_topn = re.compile(r"\.top(\d+)\.") def category_types(): return [f.stem for f in Path(shared.interrogator.content_dir).glob('*.txt')] def download_default_clip_interrogate_categories(content_dir): print("Downloading CLIP categories...") tmpdir = f"{content_dir}_tmp" category_types = ["artists", "flavors", "mediums", "movements"] try: os.makedirs(tmpdir, exist_ok=True) for category_type in category_types: torch.hub.download_url_to_file(f"https://raw.githubusercontent.com/pharmapsychotic/clip-interrogator/main/clip_interrogator/data/{category_type}.txt", os.path.join(tmpdir, f"{category_type}.txt")) os.rename(tmpdir, content_dir) except Exception as e: errors.display(e, "downloading default CLIP interrogate categories") finally: if os.path.exists(tmpdir): os.removedirs(tmpdir) class InterrogateModels: blip_model = None clip_model = None clip_preprocess = None dtype = None running_on_cpu = None def __init__(self, content_dir): self.loaded_categories = None self.skip_categories = [] self.content_dir = content_dir self.running_on_cpu = devices.device_interrogate == torch.device("cpu") def categories(self): if not os.path.exists(self.content_dir): download_default_clip_interrogate_categories(self.content_dir) if self.loaded_categories is not None and self.skip_categories == shared.opts.interrogate_clip_skip_categories: return self.loaded_categories self.loaded_categories = [] if os.path.exists(self.content_dir): self.skip_categories = shared.opts.interrogate_clip_skip_categories category_types = [] for filename in Path(self.content_dir).glob('*.txt'): category_types.append(filename.stem) if filename.stem in self.skip_categories: continue m = re_topn.search(filename.stem) topn = 1 if m is None else int(m.group(1)) with open(filename, "r", encoding="utf8") as file: lines = [x.strip() for x in file.readlines()] self.loaded_categories.append(Category(name=filename.stem, topn=topn, items=lines)) return self.loaded_categories def create_fake_fairscale(self): class FakeFairscale: def checkpoint_wrapper(self): pass sys.modules["fairscale.nn.checkpoint.checkpoint_activations"] = FakeFairscale def load_blip_model(self): self.create_fake_fairscale() import models.blip files = modelloader.load_models( model_path=os.path.join(paths.models_path, "BLIP"), model_url='https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_caption_capfilt_large.pth', ext_filter=[".pth"], download_name='model_base_caption_capfilt_large.pth', ) blip_model = models.blip.blip_decoder(pretrained=files[0], 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 if self.running_on_cpu: model, preprocess = clip.load(clip_model_name, device="cpu", download_root=shared.cmd_opts.clip_models_path) else: model, preprocess = clip.load(clip_model_name, download_root=shared.cmd_opts.clip_models_path) model.eval() model = model.to(devices.device_interrogate) 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 and not self.running_on_cpu: self.blip_model = self.blip_model.half() self.blip_model = self.blip_model.to(devices.device_interrogate) if self.clip_model is None: self.clip_model, self.clip_preprocess = self.load_clip_model() if not shared.cmd_opts.no_half and not self.running_on_cpu: self.clip_model = self.clip_model.half() self.clip_model = self.clip_model.to(devices.device_interrogate) self.dtype = get_param(self.clip_model).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 devices.torch_gc() 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(list(text_array), truncate=True).to(devices.device_interrogate) 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(devices.device_interrogate) 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(devices.device_interrogate) 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 = "" shared.state.begin(job="interrogate") try: 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 clip_image = self.clip_preprocess(pil_image).unsqueeze(0).type(self.dtype).to(devices.device_interrogate) with torch.no_grad(), devices.autocast(): image_features = self.clip_model.encode_image(clip_image).type(self.dtype) image_features /= image_features.norm(dim=-1, keepdim=True) for cat in self.categories(): matches = self.rank(image_features, cat.items, top_count=cat.topn) for match, score in matches: if shared.opts.interrogate_return_ranks: res += f", ({match}:{score/100:.3f})" else: res += f", {match}" except Exception: errors.report("Error interrogating", exc_info=True) res += "" self.unload() shared.state.end() return res