178 lines
6.7 KiB
Python
178 lines
6.7 KiB
Python
import contextlib
|
|
import os
|
|
import sys
|
|
import traceback
|
|
from collections import namedtuple
|
|
import re
|
|
|
|
import torch
|
|
|
|
from torchvision import transforms
|
|
from torchvision.transforms.functional import InterpolationMode
|
|
|
|
import modules.shared as shared
|
|
from modules import devices, paths, lowvram
|
|
|
|
blip_image_eval_size = 384
|
|
blip_model_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_caption_capfilt_large.pth'
|
|
clip_model_name = 'ViT-L/14'
|
|
|
|
Category = namedtuple("Category", ["name", "topn", "items"])
|
|
|
|
re_topn = re.compile(r"\.top(\d+)\.")
|
|
|
|
|
|
class InterrogateModels:
|
|
blip_model = None
|
|
clip_model = None
|
|
clip_preprocess = None
|
|
categories = None
|
|
dtype = None
|
|
running_on_cpu = None
|
|
|
|
def __init__(self, content_dir):
|
|
self.categories = []
|
|
self.running_on_cpu = devices.device_interrogate == torch.device("cpu")
|
|
|
|
if os.path.exists(content_dir):
|
|
for filename in os.listdir(content_dir):
|
|
m = re_topn.search(filename)
|
|
topn = 1 if m is None else int(m.group(1))
|
|
|
|
with open(os.path.join(content_dir, filename), "r", encoding="utf8") as file:
|
|
lines = [x.strip() for x in file.readlines()]
|
|
|
|
self.categories.append(Category(name=filename, topn=topn, items=lines))
|
|
|
|
def load_blip_model(self):
|
|
import models.blip
|
|
|
|
blip_model = models.blip.blip_decoder(pretrained=blip_model_url, 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
|
|
|
|
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 = None
|
|
|
|
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)
|
|
|
|
precision_scope = torch.autocast if shared.cmd_opts.precision == "autocast" else contextlib.nullcontext
|
|
with torch.no_grad(), precision_scope("cuda"):
|
|
image_features = self.clip_model.encode_image(clip_image).type(self.dtype)
|
|
|
|
image_features /= image_features.norm(dim=-1, keepdim=True)
|
|
|
|
if shared.opts.interrogate_use_builtin_artists:
|
|
artist = self.rank(image_features, ["by " + artist.name for artist in shared.artist_db.artists])[0]
|
|
|
|
res += ", " + artist[0]
|
|
|
|
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(f"Error interrogating", file=sys.stderr)
|
|
print(traceback.format_exc(), file=sys.stderr)
|
|
res += "<error>"
|
|
|
|
self.unload()
|
|
|
|
return res
|