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from __future__ import annotations
import PIL.Image
import huggingface_hub
import numpy as np
import onnxruntime as rt
import pandas as pd
import torch
from transformers import AutoModelForCausalLM
from transformers import AutoProcessor
from . import dbimutils
from .singleton import Singleton
import torch
from clip_interrogator import Config, Interrogator
device = "cuda" if torch.cuda.is_available() else "cpu"
@Singleton
class Models(object):
# WD14 models
SWIN_MODEL_REPO = "SmilingWolf/wd-v1-4-swinv2-tagger-v2"
CONV_MODEL_REPO = "SmilingWolf/wd-v1-4-convnext-tagger-v2"
CONV2_MODEL_REPO = "SmilingWolf/wd-v1-4-convnextv2-tagger-v2"
VIT_MODEL_REPO = "SmilingWolf/wd-v1-4-vit-tagger-v2"
MODEL_FILENAME = "model.onnx"
LABEL_FILENAME = "selected_tags.csv"
# CLIP models
VIT_H_14_MODEL_REPO = "ViT-H-14/laion2b_s32b_b79k" # Stable Diffusion 2.X
VIT_L_14_MODEL_REPO = "ViT-L-14/openai" # Stable Diffusion 1.X
def __init__(self):
pass
@classmethod
def load_clip_model(cls, model_repo):
config = Config()
config.device = 'cuda' if torch.cuda.is_available() else 'cpu'
config.blip_offload = False if torch.cuda.is_available() else True
config.chunk_size = 2048
config.flavor_intermediate_count = 512
config.blip_num_beams = 64
config.clip_model_name = model_repo
ci = Interrogator(config)
return ci
def __getattr__(self, item):
if item in self.__dict__:
return getattr(self, item)
print(f"Loading {item}...")
if item in ('clip_vit_h_14_model',):
self.clip_vit_h_14_model = self.load_clip_model(self.VIT_H_14_MODEL_REPO)
if item in ('clip_vit_l_14_model',):
self.clip_vit_l_14_model = self.load_clip_model(self.VIT_L_14_MODEL_REPO)
if item in ('swinv2_model',):
self.swinv2_model = self.load_model(self.SWIN_MODEL_REPO, self.MODEL_FILENAME)
if item in ('convnext_model',):
self.convnext_model = self.load_model(self.CONV_MODEL_REPO, self.MODEL_FILENAME)
if item in ('vit_model',):
self.vit_model = self.load_model(self.VIT_MODEL_REPO, self.MODEL_FILENAME)
if item in ('convnextv2_model',):
self.convnextv2_model = self.load_model(self.CONV2_MODEL_REPO, self.MODEL_FILENAME)
if item in ('git_model', 'git_processor'):
self.git_model, self.git_processor = self.load_git_model()
if item in ('tag_names', 'rating_indexes', 'general_indexes', 'character_indexes'):
self.tag_names, self.rating_indexes, self.general_indexes, self.character_indexes = self.load_w14_labels()
return getattr(self, item)
@classmethod
def load_git_model(cls):
model = AutoModelForCausalLM.from_pretrained("microsoft/git-large-coco")
processor = AutoProcessor.from_pretrained("microsoft/git-large-coco")
return model, processor
@staticmethod
def load_model(model_repo: str, model_filename: str) -> rt.InferenceSession:
path = huggingface_hub.hf_hub_download(
model_repo, model_filename,
)
model = rt.InferenceSession(path)
return model
@classmethod
def load_w14_labels(cls) -> list[str]:
path = huggingface_hub.hf_hub_download(
cls.CONV2_MODEL_REPO, cls.LABEL_FILENAME
)
df = pd.read_csv(path)
tag_names = df["name"].tolist()
rating_indexes = list(np.where(df["category"] == 9)[0])
general_indexes = list(np.where(df["category"] == 0)[0])
character_indexes = list(np.where(df["category"] == 4)[0])
return [tag_names, rating_indexes, general_indexes, character_indexes]
models = Models.instance()
def clip_image2text(image, mode_type='best', model_name='vit_h_14'):
image = image.convert('RGB')
model = getattr(models, f'clip_{model_name}_model')
if mode_type == 'classic':
prompt = model.interrogate_classic(image)
elif mode_type == 'fast':
prompt = model.interrogate_fast(image)
elif mode_type == 'negative':
prompt = model.interrogate_negative(image)
else:
prompt = model.interrogate(image) # default to best
return prompt
def git_image2text(input_image, max_length=50):
image = input_image.convert('RGB')
pixel_values = models.git_processor(images=image, return_tensors="pt").to(device).pixel_values
generated_ids = models.git_model.to(device).generate(pixel_values=pixel_values, max_length=max_length)
generated_caption = models.git_processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
return generated_caption
def w14_image2text(
image: PIL.Image.Image,
model_name: str,
general_threshold: float,
character_threshold: float,
):
tag_names: list[str] = models.tag_names
rating_indexes: list[np.int64] = models.rating_indexes
general_indexes: list[np.int64] = models.general_indexes
character_indexes: list[np.int64] = models.character_indexes
model_name = "{}_model".format(model_name.lower())
model = getattr(models, model_name)
_, height, width, _ = model.get_inputs()[0].shape
# Alpha to white
image = image.convert("RGBA")
new_image = PIL.Image.new("RGBA", image.size, "WHITE")
new_image.paste(image, mask=image)
image = new_image.convert("RGB")
image = np.asarray(image)
# PIL RGB to OpenCV BGR
image = image[:, :, ::-1]
image = dbimutils.make_square(image, height)
image = dbimutils.smart_resize(image, height)
image = image.astype(np.float32)
image = np.expand_dims(image, 0)
input_name = model.get_inputs()[0].name
label_name = model.get_outputs()[0].name
probs = model.run([label_name], {input_name: image})[0]
labels = list(zip(tag_names, probs[0].astype(float)))
# First 4 labels are actually ratings: pick one with argmax
ratings_names = [labels[i] for i in rating_indexes]
rating = dict(ratings_names)
# Then we have general tags: pick any where prediction confidence > threshold
general_names = [labels[i] for i in general_indexes]
general_res = [x for x in general_names if x[1] > general_threshold]
general_res = dict(general_res)
# Everything else is characters: pick any where prediction confidence > threshold
character_names = [labels[i] for i in character_indexes]
character_res = [x for x in character_names if x[1] > character_threshold]
character_res = dict(character_res)
b = dict(sorted(general_res.items(), key=lambda item: item[1], reverse=True))
a = (
", ".join(list(b.keys()))
.replace("_", " ")
.replace("(", "\(")
.replace(")", "\)")
)
c = ", ".join(list(b.keys()))
d = " ".join(list(b.keys()))
return a, c, d, rating, character_res, general_res
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