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import os
import sys
import traceback
from collections import namedtuple
import re
import torch
import torch.hub
from torchvision import transforms
from torchvision.transforms.functional import InterpolationMode
import modules.shared as shared
from modules import devices, paths, 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+)\.")
category_types = ["artists", "flavors", "mediums", "movements"]
def download_default_clip_interrogate_categories(content_dir):
print("Downloading CLIP categories...")
tmpdir = content_dir + "_tmp"
try:
os.makedirs(tmpdir)
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.remove(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.selected_categories = []
self.content_dir = content_dir
self.running_on_cpu = devices.device_interrogate == torch.device("cpu")
def categories(self):
if self.loaded_categories is not None and self.selected_categories == shared.opts.interrogate_clip_categories:
return self.loaded_categories
self.loaded_categories = []
if not os.path.exists(self.content_dir):
download_default_clip_interrogate_categories(self.content_dir)
if os.path.exists(self.content_dir):
self.selected_categories = shared.opts.interrogate_clip_categories
for category_type in category_types:
if 'all' not in self.selected_categories and category_type not in self.selected_categories:
continue
filename = os.path.join(self.content_dir, f"{category_type}.txt")
if not os.path.isfile(filename):
continue
m = re_topn.search(filename)
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=category_type, topn=topn, items=lines))
return self.loaded_categories
def load_blip_model(self):
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([text for text in 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 name, topn, items in self.categories():
matches = self.rank(image_features, items, top_count=topn)
for match, score in matches:
if shared.opts.interrogate_return_ranks:
res += f", ({match}:{score/100:.3f})"
else:
res += ", " + 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
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