223 lines
8.4 KiB
Python
223 lines
8.4 KiB
Python
import os
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import sys
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from collections import namedtuple
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from pathlib import Path
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import re
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import torch
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import torch.hub
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from torchvision import transforms
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from torchvision.transforms.functional import InterpolationMode
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from modules import devices, paths, shared, lowvram, modelloader, errors, torch_utils
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blip_image_eval_size = 384
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clip_model_name = 'ViT-L/14'
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Category = namedtuple("Category", ["name", "topn", "items"])
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re_topn = re.compile(r"\.top(\d+)$")
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def category_types():
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return [f.stem for f in Path(shared.interrogator.content_dir).glob('*.txt')]
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def download_default_clip_interrogate_categories(content_dir):
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print("Downloading CLIP categories...")
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tmpdir = f"{content_dir}_tmp"
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category_types = ["artists", "flavors", "mediums", "movements"]
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try:
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os.makedirs(tmpdir, exist_ok=True)
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for category_type in category_types:
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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"))
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os.rename(tmpdir, content_dir)
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except Exception as e:
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errors.display(e, "downloading default CLIP interrogate categories")
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finally:
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if os.path.exists(tmpdir):
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os.removedirs(tmpdir)
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class InterrogateModels:
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blip_model = None
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clip_model = None
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clip_preprocess = None
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dtype = None
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running_on_cpu = None
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def __init__(self, content_dir):
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self.loaded_categories = None
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self.skip_categories = []
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self.content_dir = content_dir
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self.running_on_cpu = devices.device_interrogate == torch.device("cpu")
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def categories(self):
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if not os.path.exists(self.content_dir):
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download_default_clip_interrogate_categories(self.content_dir)
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if self.loaded_categories is not None and self.skip_categories == shared.opts.interrogate_clip_skip_categories:
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return self.loaded_categories
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self.loaded_categories = []
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if os.path.exists(self.content_dir):
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self.skip_categories = shared.opts.interrogate_clip_skip_categories
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category_types = []
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for filename in Path(self.content_dir).glob('*.txt'):
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category_types.append(filename.stem)
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if filename.stem in self.skip_categories:
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continue
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m = re_topn.search(filename.stem)
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topn = 1 if m is None else int(m.group(1))
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with open(filename, "r", encoding="utf8") as file:
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lines = [x.strip() for x in file.readlines()]
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self.loaded_categories.append(Category(name=filename.stem, topn=topn, items=lines))
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return self.loaded_categories
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def create_fake_fairscale(self):
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class FakeFairscale:
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def checkpoint_wrapper(self):
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pass
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sys.modules["fairscale.nn.checkpoint.checkpoint_activations"] = FakeFairscale
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def load_blip_model(self):
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self.create_fake_fairscale()
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import models.blip
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files = modelloader.load_models(
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model_path=os.path.join(paths.models_path, "BLIP"),
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model_url='https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_caption_capfilt_large.pth',
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ext_filter=[".pth"],
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download_name='model_base_caption_capfilt_large.pth',
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)
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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"))
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blip_model.eval()
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return blip_model
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def load_clip_model(self):
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import clip
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if self.running_on_cpu:
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model, preprocess = clip.load(clip_model_name, device="cpu", download_root=shared.cmd_opts.clip_models_path)
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else:
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model, preprocess = clip.load(clip_model_name, download_root=shared.cmd_opts.clip_models_path)
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model.eval()
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model = model.to(devices.device_interrogate)
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return model, preprocess
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def load(self):
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if self.blip_model is None:
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self.blip_model = self.load_blip_model()
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if not shared.cmd_opts.no_half and not self.running_on_cpu:
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self.blip_model = self.blip_model.half()
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self.blip_model = self.blip_model.to(devices.device_interrogate)
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if self.clip_model is None:
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self.clip_model, self.clip_preprocess = self.load_clip_model()
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if not shared.cmd_opts.no_half and not self.running_on_cpu:
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self.clip_model = self.clip_model.half()
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self.clip_model = self.clip_model.to(devices.device_interrogate)
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self.dtype = torch_utils.get_param(self.clip_model).dtype
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def send_clip_to_ram(self):
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if not shared.opts.interrogate_keep_models_in_memory:
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if self.clip_model is not None:
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self.clip_model = self.clip_model.to(devices.cpu)
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def send_blip_to_ram(self):
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if not shared.opts.interrogate_keep_models_in_memory:
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if self.blip_model is not None:
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self.blip_model = self.blip_model.to(devices.cpu)
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def unload(self):
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self.send_clip_to_ram()
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self.send_blip_to_ram()
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devices.torch_gc()
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def rank(self, image_features, text_array, top_count=1):
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import clip
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devices.torch_gc()
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if shared.opts.interrogate_clip_dict_limit != 0:
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text_array = text_array[0:int(shared.opts.interrogate_clip_dict_limit)]
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top_count = min(top_count, len(text_array))
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text_tokens = clip.tokenize(list(text_array), truncate=True).to(devices.device_interrogate)
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text_features = self.clip_model.encode_text(text_tokens).type(self.dtype)
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text_features /= text_features.norm(dim=-1, keepdim=True)
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similarity = torch.zeros((1, len(text_array))).to(devices.device_interrogate)
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for i in range(image_features.shape[0]):
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similarity += (100.0 * image_features[i].unsqueeze(0) @ text_features.T).softmax(dim=-1)
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similarity /= image_features.shape[0]
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top_probs, top_labels = similarity.cpu().topk(top_count, dim=-1)
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return [(text_array[top_labels[0][i].numpy()], (top_probs[0][i].numpy()*100)) for i in range(top_count)]
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def generate_caption(self, pil_image):
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gpu_image = transforms.Compose([
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transforms.Resize((blip_image_eval_size, blip_image_eval_size), interpolation=InterpolationMode.BICUBIC),
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transforms.ToTensor(),
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transforms.Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711))
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])(pil_image).unsqueeze(0).type(self.dtype).to(devices.device_interrogate)
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with torch.no_grad():
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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)
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return caption[0]
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def interrogate(self, pil_image):
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res = ""
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shared.state.begin(job="interrogate")
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try:
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lowvram.send_everything_to_cpu()
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devices.torch_gc()
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self.load()
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caption = self.generate_caption(pil_image)
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self.send_blip_to_ram()
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devices.torch_gc()
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res = caption
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clip_image = self.clip_preprocess(pil_image).unsqueeze(0).type(self.dtype).to(devices.device_interrogate)
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with torch.no_grad(), devices.autocast():
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image_features = self.clip_model.encode_image(clip_image).type(self.dtype)
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image_features /= image_features.norm(dim=-1, keepdim=True)
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for cat in self.categories():
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matches = self.rank(image_features, cat.items, top_count=cat.topn)
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for match, score in matches:
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if shared.opts.interrogate_return_ranks:
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res += f", ({match}:{score/100:.3f})"
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else:
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res += f", {match}"
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except Exception:
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errors.report("Error interrogating", exc_info=True)
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res += "<error>"
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self.unload()
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shared.state.end()
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return res
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