mirror of https://github.com/vladmandic/automatic
411 lines
17 KiB
Python
411 lines
17 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 threading
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import re
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import torch
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import torch.hub # pylint: disable=ungrouped-imports
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import gradio as gr
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from PIL import Image
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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, errors, sd_models
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caption_models = {
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'blip-base': 'Salesforce/blip-image-captioning-base',
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'blip-large': 'Salesforce/blip-image-captioning-large',
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'blip2-opt-2.7b': 'Salesforce/blip2-opt-2.7b-coco',
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'blip2-opt-6.7b': 'Salesforce/blip2-opt-6.7b',
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'blip2-flip-t5-xl': 'Salesforce/blip2-flan-t5-xl',
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'blip2-flip-t5-xxl': 'Salesforce/blip2-flan-t5-xxl',
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}
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caption_types = [
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'best',
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'fast',
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'classic',
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'caption',
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'negative',
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]
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clip_models = []
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ci = None
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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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load_lock = threading.Lock()
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def category_types():
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return [f.stem for f in Path(interrogator.content_dir).glob('*.txt')]
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def download_default_clip_interrogate_categories(content_dir):
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shared.log.info("Interrogate: downloading CLIP categories...")
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tmpdir = f"{content_dir}_tmp"
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cat_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 cat_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: str = None):
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self.loaded_categories = None
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self.skip_categories = []
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self.content_dir = content_dir or os.path.join(paths.models_path, "interrogate")
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self.running_on_cpu = False
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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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cat_types = []
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for filename in Path(self.content_dir).glob('*.txt'):
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cat_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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with load_lock:
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self.create_fake_fairscale()
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from repositories.blip import models # pylint: disable=unused-import
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from repositories.blip.models import blip
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import modules.modelloader as modelloader
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model_path = os.path.join(paths.models_path, "BLIP")
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download_name='model_base_caption_capfilt_large.pth'
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shared.log.debug(f'Interrogate load: module=BLiP model="{download_name}" path="{model_path}"')
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files = modelloader.load_models(
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model_path=model_path,
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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=download_name,
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)
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blip_model = 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")) # pylint: disable=c-extension-no-member
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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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with load_lock:
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shared.log.debug(f'Interrogate load: module=CLiP model="{clip_model_name}" path="{shared.opts.clip_models_path}"')
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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.opts.clip_models_path)
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else:
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model, preprocess = clip.load(clip_model_name, download_root=shared.opts.clip_models_path)
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model.eval()
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model = model.to(devices.device)
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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.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)
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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.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)
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self.dtype = next(self.clip_model.parameters()).dtype
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def send_clip_to_ram(self):
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if shared.opts.interrogate_offload:
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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 shared.opts.interrogate_offload:
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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)
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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)
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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)
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with devices.inference_context():
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min_length = min(shared.opts.interrogate_clip_min_length, shared.opts.interrogate_clip_max_length)
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max_length = max(shared.opts.interrogate_clip_min_length, shared.opts.interrogate_clip_max_length)
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caption = self.blip_model.generate(gpu_image, sample=False, num_beams=shared.opts.interrogate_clip_num_beams, min_length=min_length, max_length=max_length)
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return caption[0]
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def interrogate(self, image):
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res = ""
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shared.state.begin('Interrogate')
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try:
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self.load()
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if isinstance(image, list):
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image = image[0] if len(image) > 0 else None
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if isinstance(image, dict) and 'name' in image:
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image = Image.open(image['name'])
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if image is None:
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return ''
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image = image.convert("RGB")
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caption = self.generate_caption(image)
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res = caption
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clip_image = self.clip_preprocess(image).unsqueeze(0).type(self.dtype).to(devices.device)
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with devices.inference_context(), 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 _name, topn, items in self.categories():
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matches = self.rank(image_features, items, top_count=topn)
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for match, score in matches:
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if shared.opts.interrogate_score:
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res += f", ({match}:{score/100:.2f})"
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else:
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res += f", {match}"
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except Exception as e:
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errors.display(e, 'interrogate')
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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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# --------- interrrogate ui
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class BatchWriter:
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def __init__(self, folder, mode='w'):
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self.folder = folder
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self.csv = None
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self.file = None
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self.mode = mode
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def add(self, file, prompt):
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txt_file = os.path.splitext(file)[0] + ".txt"
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if self.mode == 'a':
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prompt = '\n' + prompt
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with open(os.path.join(self.folder, txt_file), self.mode, encoding='utf-8') as f:
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f.write(prompt)
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def close(self):
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if self.file is not None:
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self.file.close()
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def update_interrogate_params():
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if ci is not None:
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ci.caption_max_length=shared.opts.interrogate_clip_max_length,
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ci.chunk_size=shared.opts.interrogate_clip_chunk_size,
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ci.flavor_intermediate_count=shared.opts.interrogate_clip_flavor_count,
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ci.clip_offload=shared.opts.interrogate_offload,
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ci.caption_offload=shared.opts.interrogate_offload,
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def get_clip_models():
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return clip_models
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def refresh_clip_models():
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global clip_models # pylint: disable=global-statement
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import open_clip
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models = sorted(open_clip.list_pretrained())
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shared.log.debug(f'Interrogate: pkg=openclip version={open_clip.__version__} models={len(models)}')
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clip_models = ['/'.join(x) for x in models]
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return clip_models
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def load_interrogator(clip_model, blip_model):
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from installer import install
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install('clip_interrogator==0.6.0')
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import clip_interrogator
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clip_interrogator.clip_interrogator.CAPTION_MODELS = caption_models
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global ci # pylint: disable=global-statement
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if ci is None:
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shared.log.debug(f'Interrogate load: clip="{clip_model}" blip="{blip_model}"')
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interrogator_config = clip_interrogator.Config(
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device=devices.get_optimal_device(),
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cache_path=os.path.join(paths.models_path, 'Interrogator'),
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clip_model_name=clip_model,
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caption_model_name=blip_model,
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quiet=True,
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caption_max_length=shared.opts.interrogate_clip_max_length,
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chunk_size=shared.opts.interrogate_clip_chunk_size,
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flavor_intermediate_count=shared.opts.interrogate_clip_flavor_count,
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clip_offload=shared.opts.interrogate_offload,
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caption_offload=shared.opts.interrogate_offload,
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)
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ci = clip_interrogator.Interrogator(interrogator_config)
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elif clip_model != ci.config.clip_model_name or blip_model != ci.config.caption_model_name:
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ci.config.clip_model_name = clip_model
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ci.config.clip_model = None
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ci.load_clip_model()
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ci.config.caption_model_name = blip_model
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ci.config.caption_model = None
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ci.load_caption_model()
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def unload_clip_model():
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if ci is not None and shared.opts.interrogate_offload:
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ci.caption_model = ci.caption_model.to(devices.cpu)
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ci.clip_model = ci.clip_model.to(devices.cpu)
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ci.caption_offloaded = True
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ci.clip_offloaded = True
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devices.torch_gc()
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def interrogate(image, mode, caption=None):
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if isinstance(image, list):
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image = image[0] if len(image) > 0 else None
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if isinstance(image, dict) and 'name' in image:
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image = Image.open(image['name'])
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if image is None:
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return ''
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image = image.convert("RGB")
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if mode == 'best':
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prompt = ci.interrogate(image, caption=caption, min_flavors=shared.opts.interrogate_clip_min_flavors, max_flavors=shared.opts.interrogate_clip_max_flavors, )
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elif mode == 'caption':
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prompt = ci.generate_caption(image) if caption is None else caption
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elif mode == 'classic':
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prompt = ci.interrogate_classic(image, caption=caption, max_flavors=shared.opts.interrogate_clip_max_flavors)
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elif mode == 'fast':
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prompt = ci.interrogate_fast(image, caption=caption, max_flavors=shared.opts.interrogate_clip_max_flavors)
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elif mode == 'negative':
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prompt = ci.interrogate_negative(image, max_flavors=shared.opts.interrogate_clip_max_flavors)
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else:
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raise RuntimeError(f"Unknown mode {mode}")
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return prompt
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def interrogate_image(image, clip_model, blip_model, mode):
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shared.state.begin('Interrogate')
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try:
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if not shared.native and (shared.cmd_opts.lowvram or shared.cmd_opts.medvram):
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lowvram.send_everything_to_cpu()
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devices.torch_gc()
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if shared.native and shared.sd_loaded:
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sd_models.apply_balanced_offload(shared.sd_model)
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load_interrogator(clip_model, blip_model)
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image = image.convert('RGB')
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prompt = interrogate(image, mode)
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devices.torch_gc()
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except Exception as e:
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prompt = f"Exception {type(e)}"
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shared.log.error(f'Interrogate: {e}')
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errors.display(e, 'Interrogate')
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shared.state.end()
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return prompt
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def interrogate_batch(batch_files, batch_folder, batch_str, clip_model, blip_model, mode, write, append, recursive):
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files = []
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if batch_files is not None:
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files += [f.name for f in batch_files]
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if batch_folder is not None:
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files += [f.name for f in batch_folder]
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if batch_str is not None and len(batch_str) > 0 and os.path.exists(batch_str) and os.path.isdir(batch_str):
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from modules.files_cache import list_files
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files += list(list_files(batch_str, ext_filter=['.png', '.jpg', '.jpeg', '.webp'], recursive=recursive))
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if len(files) == 0:
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shared.log.warning('Interrogate batch: type=clip no images')
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return ''
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shared.state.begin('Interrogate batch')
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prompts = []
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load_interrogator(clip_model, blip_model)
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if write:
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file_mode = 'w' if not append else 'a'
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writer = BatchWriter(os.path.dirname(files[0]), mode=file_mode)
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import rich.progress as rp
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pbar = rp.Progress(rp.TextColumn('[cyan]Caption:'), rp.BarColumn(), rp.MofNCompleteColumn(), rp.TaskProgressColumn(), rp.TimeRemainingColumn(), rp.TimeElapsedColumn(), rp.TextColumn('[cyan]{task.description}'), console=shared.console)
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with pbar:
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task = pbar.add_task(total=len(files), description='starting...')
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for file in files:
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pbar.update(task, advance=1, description=file)
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try:
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if shared.state.interrupted:
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break
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image = Image.open(file).convert('RGB')
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prompt = interrogate(image, mode)
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prompts.append(prompt)
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if write:
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writer.add(file, prompt)
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except OSError as e:
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shared.log.error(f'Interrogate batch: {e}')
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if write:
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writer.close()
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ci.config.quiet = False
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unload_clip_model()
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shared.state.end()
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return '\n\n'.join(prompts)
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def analyze_image(image, clip_model, blip_model):
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load_interrogator(clip_model, blip_model)
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image = image.convert('RGB')
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image_features = ci.image_to_features(image)
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top_mediums = ci.mediums.rank(image_features, 5)
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top_artists = ci.artists.rank(image_features, 5)
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top_movements = ci.movements.rank(image_features, 5)
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top_trendings = ci.trendings.rank(image_features, 5)
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top_flavors = ci.flavors.rank(image_features, 5)
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medium_ranks = dict(zip(top_mediums, ci.similarities(image_features, top_mediums)))
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artist_ranks = dict(zip(top_artists, ci.similarities(image_features, top_artists)))
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movement_ranks = dict(zip(top_movements, ci.similarities(image_features, top_movements)))
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trending_ranks = dict(zip(top_trendings, ci.similarities(image_features, top_trendings)))
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flavor_ranks = dict(zip(top_flavors, ci.similarities(image_features, top_flavors)))
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return [
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gr.update(value=medium_ranks, visible=True),
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gr.update(value=artist_ranks, visible=True),
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gr.update(value=movement_ranks, visible=True),
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gr.update(value=trending_ranks, visible=True),
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gr.update(value=flavor_ranks, visible=True),
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]
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interrogator = InterrogateModels()
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