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9c98083 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 | """Pluggable local captioners for LoRA dataset building.
All captioners expose the same interface: ``caption(images: list[PIL.Image]) -> str``.
``images`` is a list so video callers can pass multiple sampled frames; single-image
captioners just use the first one.
- JoyCaptionCaptioner: fancyfeast/llama-joycaption-beta-one-hf-llava (AutoProcessor +
LlavaForConditionalGeneration, confirmed against the model card). The exact prompt
wording for a dedicated "training caption" mode is not documented on the model card,
so the instruction text here is our own default and is overridable via --caption-prompt.
- Florence2Captioner: microsoft/Florence-2-large, <MORE_DETAILED_CAPTION> task.
- WD14Captioner: SmilingWolf/wd-vit-tagger-v3 onnx tagger (booru-style tags), preprocessing
confirmed against the reference wd-tagger Space source (white-pad-to-square, RGB->BGR,
no normalization, category 9/4/0 = rating/character/general).
"""
import csv
import numpy as np
import torch
from PIL import Image
class JoyCaptionCaptioner:
MODEL_ID = "fancyfeast/llama-joycaption-beta-one-hf-llava"
DEFAULT_PROMPT = (
"Write a detailed but concise caption for this image, suitable for training "
"an image/video LoRA. Describe the subject's appearance, pose, clothing, "
"setting, and lighting in plain descriptive sentences. Do not guess names."
)
def __init__(self, device="cuda", load_in_4bit=False, prompt=None, system_prompt=None):
from transformers import AutoProcessor, LlavaForConditionalGeneration
self.processor = AutoProcessor.from_pretrained(self.MODEL_ID)
load_kwargs = {}
if load_in_4bit:
from transformers import BitsAndBytesConfig
load_kwargs["quantization_config"] = BitsAndBytesConfig(load_in_4bit=True)
load_kwargs["device_map"] = "auto"
else:
load_kwargs["torch_dtype"] = torch.bfloat16
load_kwargs["device_map"] = device
self.model = LlavaForConditionalGeneration.from_pretrained(self.MODEL_ID, **load_kwargs)
self.model.eval()
self.system_prompt = system_prompt or "You are a helpful image captioner."
self.prompt = prompt or self.DEFAULT_PROMPT
def caption(self, images):
image = images[0].convert("RGB")
convo = [
{"role": "system", "content": self.system_prompt},
{"role": "user", "content": self.prompt},
]
convo_string = self.processor.apply_chat_template(
convo, tokenize=False, add_generation_prompt=True
)
inputs = self.processor(text=[convo_string], images=[image], return_tensors="pt")
inputs = {k: v.to(self.model.device) for k, v in inputs.items()}
if "pixel_values" in inputs:
inputs["pixel_values"] = inputs["pixel_values"].to(self.model.dtype)
with torch.no_grad():
generate_ids = self.model.generate(
**inputs,
max_new_tokens=300,
do_sample=True,
suppress_tokens=None,
use_cache=True,
temperature=0.6,
top_k=None,
top_p=0.9,
)[0]
generate_ids = generate_ids[inputs["input_ids"].shape[1]:]
text = self.processor.tokenizer.decode(
generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False
)
return text.strip()
class Florence2Captioner:
MODEL_ID = "microsoft/Florence-2-large"
TASK = "<MORE_DETAILED_CAPTION>"
def __init__(self, device="cuda"):
from transformers import AutoModelForCausalLM, AutoProcessor
dtype = torch.float16 if device.startswith("cuda") else torch.float32
self.device = device
self.model = (
AutoModelForCausalLM.from_pretrained(self.MODEL_ID, trust_remote_code=True, torch_dtype=dtype)
.to(device)
.eval()
)
self.processor = AutoProcessor.from_pretrained(self.MODEL_ID, trust_remote_code=True)
def caption(self, images):
image = images[0].convert("RGB")
inputs = self.processor(text=self.TASK, images=image, return_tensors="pt")
inputs = {k: v.to(self.device, self.model.dtype) if v.is_floating_point() else v.to(self.device)
for k, v in inputs.items()}
with torch.no_grad():
generated_ids = self.model.generate(
input_ids=inputs["input_ids"],
pixel_values=inputs["pixel_values"],
max_new_tokens=200,
num_beams=3,
do_sample=False,
)
text = self.processor.batch_decode(generated_ids, skip_special_tokens=False)[0]
parsed = self.processor.post_process_generation(
text, task=self.TASK, image_size=(image.width, image.height)
)
return parsed[self.TASK].strip()
class WD14Captioner:
REPO_ID = "SmilingWolf/wd-vit-tagger-v3"
MODEL_FILE = "model.onnx"
TAGS_FILE = "selected_tags.csv"
def __init__(self, general_thresh=0.35, character_thresh=0.75, use_gpu=True):
import onnxruntime as ort
from huggingface_hub import hf_hub_download
model_path = hf_hub_download(self.REPO_ID, self.MODEL_FILE)
tags_path = hf_hub_download(self.REPO_ID, self.TAGS_FILE)
providers = ["CPUExecutionProvider"]
if use_gpu:
providers = ["CUDAExecutionProvider", "CPUExecutionProvider"]
self.session = ort.InferenceSession(model_path, providers=providers)
self.input_name = self.session.get_inputs()[0].name
self.output_name = self.session.get_outputs()[0].name
self.target_size = int(self.session.get_inputs()[0].shape[1])
self.general_thresh = general_thresh
self.character_thresh = character_thresh
self.tag_names, self.general_idx, self.character_idx = self._load_tags(tags_path)
@staticmethod
def _load_tags(path):
names, general, character = [], [], []
with open(path, newline="", encoding="utf-8") as f:
for i, row in enumerate(csv.DictReader(f)):
names.append(row["name"])
cat = int(row["category"])
if cat == 4:
character.append(i)
elif cat != 9: # skip rating (9); keep general (0) and any others
general.append(i)
return names, general, character
def _preprocess(self, image: Image.Image):
image = image.convert("RGBA")
canvas = Image.new("RGBA", image.size, (255, 255, 255, 255))
canvas.alpha_composite(image)
image = canvas.convert("RGB")
w, h = image.size
size = max(w, h)
padded = Image.new("RGB", (size, size), (255, 255, 255))
padded.paste(image, ((size - w) // 2, (size - h) // 2))
padded = padded.resize((self.target_size, self.target_size), Image.BICUBIC)
arr = np.asarray(padded, dtype=np.float32)
arr = arr[:, :, ::-1] # RGB -> BGR
return np.expand_dims(arr, axis=0)
def caption(self, images):
image = images[0]
arr = self._preprocess(image)
preds = self.session.run([self.output_name], {self.input_name: arr})[0][0]
general = [(self.tag_names[i], preds[i]) for i in self.general_idx if preds[i] >= self.general_thresh]
character = [(self.tag_names[i], preds[i]) for i in self.character_idx if preds[i] >= self.character_thresh]
general.sort(key=lambda t: -t[1])
character.sort(key=lambda t: -t[1])
tags = [t.replace("_", " ") for t, _ in character] + [t.replace("_", " ") for t, _ in general]
return ", ".join(tags)
def build_captioner(args):
name = args.captioner
if name == "joycaption":
return JoyCaptionCaptioner(
device=args.device,
load_in_4bit=args.load_in_4bit,
prompt=args.caption_prompt,
)
if name == "florence2":
return Florence2Captioner(device=args.device)
if name == "wd14":
return WD14Captioner(
general_thresh=args.wd14_general_thresh,
character_thresh=args.wd14_character_thresh,
use_gpu=args.device.startswith("cuda"),
)
raise ValueError(f"Unknown captioner: {name}")
def with_trigger(caption, trigger):
caption = (caption or "").strip()
if not trigger:
return caption
return f"{trigger}, {caption}" if caption else trigger
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