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8b27df3 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 | """Minimal self-contained inference model for the Booru prompt generator release."""
import math
import re
from typing import Dict, List, Optional, Set
import numpy as np
import torch
import torch.nn as nn
PAD_ID = 0
BOS_ID = 1
EOS_ID = 2
RATING_G = 3
RATING_S = 4
RATING_Q = 5
RATING_E = 6
OFFSET = 7
_RATING_TOKENS = {"g": RATING_G, "s": RATING_S, "q": RATING_Q, "e": RATING_E}
def _apply_top_k_top_p(probs: torch.Tensor, top_k: int, top_p: float) -> torch.Tensor:
"""Filter a probability distribution with top-k and/or nucleus (top-p) sampling."""
if top_k > 0:
k = min(top_k, probs.size(0))
threshold = torch.topk(probs, k).values[-1]
probs = probs.where(probs >= threshold, torch.zeros_like(probs))
if top_p < 1.0:
sorted_probs, sorted_idx = torch.sort(probs, descending=True)
cumsum = torch.cumsum(sorted_probs, dim=0)
nucleus_mask = cumsum <= top_p
if nucleus_mask.any():
nucleus_mask[0] = True
kept = torch.zeros_like(probs, dtype=torch.bool)
kept.scatter_(0, sorted_idx, nucleus_mask)
probs = probs.where(kept, torch.zeros_like(probs))
return probs
class Vocab:
def __init__(self, tags: List[str], counts: List[int]):
self.tags = tags
self.tag_to_idx = {tag: i for i, tag in enumerate(tags)}
self.counts = np.array(counts, dtype=np.int64)
self.total = int(self.counts.sum())
self.freqs = self.counts.astype(np.float64) / max(self.total, 1)
def __len__(self) -> int:
return len(self.tags)
class SimpleGraph:
def __init__(self, vocab: Vocab, mutex: List[List[int]]):
self.vocab = vocab
self.mutex = mutex
class TagTransformer(nn.Module):
"""Permutation-equivariant Transformer for tag-set generation."""
def __init__(
self,
vocab_size: int,
d_model: int = 256,
nhead: int = 4,
num_layers: int = 4,
dim_feedforward: int = 1024,
dropout: float = 0.1,
max_len: int = 256,
):
super().__init__()
self.vocab_size = vocab_size
self.d_model = d_model
self.max_len = max_len
self.embedding = nn.Embedding(vocab_size + OFFSET, d_model, padding_idx=PAD_ID)
layer = nn.TransformerEncoderLayer(
d_model=d_model,
nhead=nhead,
dim_feedforward=dim_feedforward,
dropout=dropout,
batch_first=True,
norm_first=True,
)
self.transformer = nn.TransformerEncoder(layer, num_layers=num_layers, enable_nested_tensor=False)
self.output = nn.Linear(d_model, vocab_size + OFFSET)
self._init_weights()
def _init_weights(self):
for p in self.parameters():
if p.dim() > 1:
nn.init.xavier_uniform_(p)
def forward(self, input_ids: torch.Tensor) -> torch.Tensor:
seq_len = input_ids.size(1)
mask = nn.Transformer.generate_square_subsequent_mask(seq_len, device=input_ids.device).bool()
x = self.embedding(input_ids)
padding_mask = input_ids == PAD_ID
x = self.transformer(
x,
mask=mask,
src_key_padding_mask=padding_mask,
is_causal=True,
)
return self.output(x)
class NeuralPromptGenerator:
GROUP_SUFFIXES = ["_hair", "_eyes"]
def __init__(
self,
model: TagTransformer,
graph: SimpleGraph,
device: Optional[torch.device] = None,
seed: Optional[int] = None,
distribution_weight: float = 0.75,
fallback_top_k: int = 20,
):
self.model = model
self.graph = graph
self.vocab = graph.vocab
self.device = device or torch.device("cpu")
self.model.to(self.device)
self.model.eval()
self.rng = np.random.default_rng(seed)
self.distribution_weight = distribution_weight
self.fallback_top_k = fallback_top_k
self._n_real = len(self.vocab)
self._real_token_ids = torch.arange(self._n_real, device=self.device) + OFFSET
self._log_uniform = -np.log(max(self._n_real, 1))
self._special_ids = {PAD_ID, BOS_ID, EOS_ID}
self._group_ids = np.full(self._n_real, -1, dtype=np.int32)
for i, tag in enumerate(self.vocab.tags):
for gid, suffix in enumerate(self.GROUP_SUFFIXES):
if tag.endswith(suffix):
self._group_ids[i] = gid
break
self._group_ids_tensor = torch.from_numpy(self._group_ids).to(self.device)
def _target_log_prob(self, idx: int, alpha: float) -> float:
log_emp = np.log(max(self.vocab.freqs[idx], 1e-12))
return (1.0 - 2.0 * alpha) * log_emp
def _num_people(self, tag_idxs: Set[int]) -> int:
n = 0
for idx in tag_idxs:
tag = self.vocab.tags[idx]
if tag == "solo":
n = max(n, 1)
elif tag in ("1girl", "1boy", "1other"):
n = max(n, 1)
elif tag in ("2girls", "2boys", "2others"):
n = max(n, 2)
elif tag in ("3girls", "3boys", "3others"):
n = max(n, 3)
elif tag in ("4girls", "4boys", "4others"):
n = max(n, 4)
elif tag in ("5girls", "5boys", "5others"):
n = max(n, 5)
elif tag in (
"6+girls", "6+boys", "6+others",
"multiple_girls", "multiple_boys", "multiple_others",
):
n = max(n, 2)
return max(n, 1)
def generate(
self,
alpha: float,
count: int,
length: int = 30,
anchor: Optional[List[str]] = None,
blacklist: Optional[List[str]] = None,
min_prob: float = 0.0,
temperature: float = 1.0,
top_k: int = 0,
top_p: float = 1.0,
rating: Optional[str] = None,
) -> List[List[str]]:
anchor = anchor or []
anchor_indices = [self.vocab.tag_to_idx[t] for t in anchor if t in self.vocab.tag_to_idx]
blacklist = blacklist or []
blacklist_indices = {self.vocab.tag_to_idx[t] for t in blacklist if t in self.vocab.tag_to_idx}
rating_token = _RATING_TOKENS.get((rating or "g").lower(), RATING_G)
target_bias = torch.tensor(
[self._target_log_prob(i, alpha) for i in range(self._n_real)],
dtype=torch.float32,
device=self.device,
)
results: List[List[str]] = []
with torch.no_grad():
for _ in range(count):
prompt_tokens: List[int] = [BOS_ID, rating_token] + [idx + OFFSET for idx in anchor_indices]
present_tag_idxs: Set[int] = set(anchor_indices)
excluded_tag_idxs: Set[int] = set(blacklist_indices)
group_counts: Dict[int, int] = {}
for idx in anchor_indices:
excluded_tag_idxs.update(self.graph.mutex[idx])
gid = self._group_ids[idx]
if gid >= 0:
group_counts[gid] = group_counts.get(gid, 0) + 1
target_len = max(length, len(anchor_indices))
max_len = getattr(self.model, "max_len", 256)
if target_len > max_len - 2:
target_len = max_len - 2
while len(prompt_tokens) - 1 < target_len:
input_ids = torch.tensor([prompt_tokens], dtype=torch.long, device=self.device)
logits = self.model(input_ids)[:, -1, :]
model_probs = torch.softmax(logits / max(temperature, 1e-6), dim=-1).squeeze(0)
allowed = model_probs >= min_prob
real_allowed = allowed[self._real_token_ids]
if not real_allowed.any():
k = min(self.fallback_top_k, self._n_real)
topk = torch.topk(model_probs[self._real_token_ids], k=k).indices
real_allowed = torch.zeros(self._n_real, dtype=torch.bool, device=self.device)
real_allowed[topk] = True
allowed = allowed.clone()
allowed[self._real_token_ids] = real_allowed
max_people = self._num_people(present_tag_idxs)
full_group_ids = [gid for gid, c in group_counts.items() if c >= max_people]
biased_logits = logits.squeeze(0).clone()
biased_logits[self._real_token_ids] += self.distribution_weight * target_bias
biased_logits[~allowed] = -float("inf")
for tid in self._special_ids:
biased_logits[tid] = -float("inf")
for idx in present_tag_idxs:
biased_logits[idx + OFFSET] = -float("inf")
for idx in excluded_tag_idxs:
biased_logits[idx + OFFSET] = -float("inf")
if full_group_ids:
full_groups_tensor = torch.tensor(full_group_ids, dtype=torch.int32, device=self.device)
group_full_mask = torch.isin(self._group_ids_tensor, full_groups_tensor)
biased_logits[self._real_token_ids[group_full_mask]] = -float("inf")
probs = torch.softmax(biased_logits / max(temperature, 1e-6), dim=0)
probs = _apply_top_k_top_p(probs, top_k, top_p)
if not torch.isfinite(probs).all() or probs.sum() <= 0:
break
probs = probs / probs.sum()
probs = probs.cpu().numpy()
probs = probs / probs.sum()
token_id = int(self.rng.choice(len(probs), p=probs))
if token_id in self._special_ids:
break
tag_idx = token_id - OFFSET
if tag_idx < 0 or tag_idx >= self._n_real:
break
if tag_idx in present_tag_idxs or tag_idx in excluded_tag_idxs:
break
prompt_tokens.append(token_id)
present_tag_idxs.add(tag_idx)
excluded_tag_idxs.update(self.graph.mutex[tag_idx])
gid = self._group_ids[tag_idx]
if gid >= 0:
group_counts[gid] = group_counts.get(gid, 0) + 1
results.append([
self.vocab.tags[i - OFFSET]
for i in prompt_tokens
if i >= OFFSET
])
return results
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