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import os
import sys
import traceback
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
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 = 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
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()
shared.state.job = 'interrogate'
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
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:
print("Error interrogating", file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)
res += "<error>"
self.unload()
shared.state.end()
return res