mirror of
https://github.com/AUTOMATIC1111/stable-diffusion-webui.git
synced 2024-06-07 21:20:49 +00:00
100 lines
3.0 KiB
Python
100 lines
3.0 KiB
Python
import os
|
|
import re
|
|
|
|
import torch
|
|
from PIL import Image
|
|
import numpy as np
|
|
|
|
from modules import modelloader, paths, deepbooru_model, devices, images, shared
|
|
|
|
re_special = re.compile(r'([\\()])')
|
|
|
|
|
|
class DeepDanbooru:
|
|
def __init__(self):
|
|
self.model = None
|
|
|
|
def load(self):
|
|
if self.model is not None:
|
|
return
|
|
|
|
files = modelloader.load_models(
|
|
model_path=os.path.join(paths.models_path, "torch_deepdanbooru"),
|
|
model_url='https://github.com/AUTOMATIC1111/TorchDeepDanbooru/releases/download/v1/model-resnet_custom_v3.pt',
|
|
ext_filter=[".pt"],
|
|
download_name='model-resnet_custom_v3.pt',
|
|
)
|
|
|
|
self.model = deepbooru_model.DeepDanbooruModel()
|
|
self.model.load_state_dict(torch.load(files[0], map_location="cpu"))
|
|
|
|
self.model.eval()
|
|
self.model.to(devices.cpu, devices.dtype)
|
|
|
|
def start(self):
|
|
self.load()
|
|
self.model.to(devices.device)
|
|
|
|
def stop(self):
|
|
if not shared.opts.interrogate_keep_models_in_memory:
|
|
self.model.to(devices.cpu)
|
|
devices.torch_gc()
|
|
|
|
def tag(self, pil_image):
|
|
self.start()
|
|
res = self.tag_multi(pil_image)
|
|
self.stop()
|
|
|
|
return res
|
|
|
|
def tag_multi(self, pil_image, force_disable_ranks=False):
|
|
threshold = shared.opts.interrogate_deepbooru_score_threshold
|
|
use_spaces = shared.opts.deepbooru_use_spaces
|
|
use_escape = shared.opts.deepbooru_escape
|
|
alpha_sort = shared.opts.deepbooru_sort_alpha
|
|
include_ranks = shared.opts.interrogate_return_ranks and not force_disable_ranks
|
|
|
|
pic = images.resize_image(2, pil_image.convert("RGB"), 512, 512)
|
|
a = np.expand_dims(np.array(pic, dtype=np.float32), 0) / 255
|
|
|
|
with torch.no_grad(), devices.autocast():
|
|
x = torch.from_numpy(a).to(devices.device)
|
|
y = self.model(x)[0].detach().cpu().numpy()
|
|
|
|
probability_dict = {}
|
|
|
|
for tag, probability in zip(self.model.tags, y):
|
|
if probability < threshold:
|
|
continue
|
|
|
|
if tag.startswith("rating:"):
|
|
continue
|
|
|
|
probability_dict[tag] = probability
|
|
|
|
if alpha_sort:
|
|
tags = sorted(probability_dict)
|
|
else:
|
|
tags = [tag for tag, _ in sorted(probability_dict.items(), key=lambda x: -x[1])]
|
|
|
|
res = []
|
|
|
|
filtertags = set([x.strip().replace(' ', '_') for x in shared.opts.deepbooru_filter_tags.split(",")])
|
|
|
|
for tag in [x for x in tags if x not in filtertags]:
|
|
probability = probability_dict[tag]
|
|
tag_outformat = tag
|
|
if use_spaces:
|
|
tag_outformat = tag_outformat.replace('_', ' ')
|
|
if use_escape:
|
|
tag_outformat = re.sub(re_special, r'\\\1', tag_outformat)
|
|
if include_ranks:
|
|
tag_outformat = f"({tag_outformat}:{probability:.3f})"
|
|
|
|
res.append(tag_outformat)
|
|
|
|
return ", ".join(res)
|
|
|
|
|
|
model = DeepDanbooru()
|