mirror of
https://github.com/kohya-ss/sd-scripts.git
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262 lines
12 KiB
Python
262 lines
12 KiB
Python
import glob
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import os
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from typing import Any, List, Optional, Tuple, Union
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import numpy as np
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import torch
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from transformers import CLIPTokenizer
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from library import train_util
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from library.strategy_base import LatentsCachingStrategy, TokenizeStrategy, TextEncodingStrategy
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from library.utils import setup_logging
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setup_logging()
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import logging
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logger = logging.getLogger(__name__)
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TOKENIZER_ID = "openai/clip-vit-large-patch14"
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V2_STABLE_DIFFUSION_ID = "stabilityai/stable-diffusion-2" # ここからtokenizerだけ使う v2とv2.1はtokenizer仕様は同じ
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class SdTokenizeStrategy(TokenizeStrategy):
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def __init__(self, v2: bool, max_length: Optional[int], tokenizer_cache_dir: Optional[str] = None) -> None:
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"""
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max_length does not include <BOS> and <EOS> (None, 75, 150, 225)
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"""
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logger.info(f"Using {'v2' if v2 else 'v1'} tokenizer")
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if v2:
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self.tokenizer = self._load_tokenizer(
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CLIPTokenizer, V2_STABLE_DIFFUSION_ID, subfolder="tokenizer", tokenizer_cache_dir=tokenizer_cache_dir
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)
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else:
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self.tokenizer = self._load_tokenizer(CLIPTokenizer, TOKENIZER_ID, tokenizer_cache_dir=tokenizer_cache_dir)
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if max_length is None:
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self.max_length = self.tokenizer.model_max_length
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else:
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self.max_length = max_length + 2
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self.break_separator = "BREAK"
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def _split_on_break(self, text: str) -> List[str]:
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"""Split text on BREAK separator (case-sensitive), filtering empty segments."""
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segments = text.split(self.break_separator)
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# Filter out empty or whitespace-only segments
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filtered = [seg.strip() for seg in segments if seg.strip()]
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# Return at least one segment to maintain consistency
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return filtered if filtered else [""]
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def _tokenize_segments(self, segments: List[str], weighted: bool = False) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
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"""Tokenize multiple segments and concatenate them."""
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if len(segments) == 1:
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# No BREAK present, use existing logic
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if weighted:
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return self._get_input_ids(self.tokenizer, segments[0], self.max_length, weighted=True)
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else:
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tokens = self._get_input_ids(self.tokenizer, segments[0], self.max_length)
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return tokens, None
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# Multiple segments - tokenize each separately
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all_tokens = []
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all_weights = [] if weighted else None
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for segment in segments:
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if weighted:
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seg_tokens, seg_weights = self._get_input_ids(self.tokenizer, segment, self.max_length, weighted=True)
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all_tokens.append(seg_tokens)
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all_weights.append(seg_weights)
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else:
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seg_tokens = self._get_input_ids(self.tokenizer, segment, self.max_length)
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all_tokens.append(seg_tokens)
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# Concatenate along the sequence dimension (dim=1 for tokens that are [batch, seq_len] or [n_chunks, seq_len])
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combined_tokens = torch.cat(all_tokens, dim=1) if all_tokens[0].dim() == 2 else torch.cat(all_tokens, dim=0)
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combined_weights = None
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if weighted:
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combined_weights = torch.cat(all_weights, dim=1) if all_weights[0].dim() == 2 else torch.cat(all_weights, dim=0)
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return combined_tokens, combined_weights
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def tokenize(self, text: Union[str, List[str]]) -> List[torch.Tensor]:
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text = [text] if isinstance(text, str) else text
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tokens_list = []
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for t in text:
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segments = self._split_on_break(t)
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tokens, _ = self._tokenize_segments(segments, weighted=False)
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tokens_list.append(tokens)
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# Pad tokens to same length for stacking
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max_length = max(t.shape[-1] for t in tokens_list)
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padded_tokens = []
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for tokens in tokens_list:
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if tokens.shape[-1] < max_length:
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# Pad with pad_token_id
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pad_size = max_length - tokens.shape[-1]
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if tokens.dim() == 2:
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padding = torch.full((tokens.shape[0], pad_size), self.tokenizer.pad_token_id, dtype=tokens.dtype)
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tokens = torch.cat([tokens, padding], dim=1)
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else:
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padding = torch.full((pad_size,), self.tokenizer.pad_token_id, dtype=tokens.dtype)
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tokens = torch.cat([tokens, padding], dim=0)
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padded_tokens.append(tokens)
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return [torch.stack(padded_tokens, dim=0)]
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def tokenize_with_weights(self, text: str | List[str]) -> Tuple[List[torch.Tensor], List[torch.Tensor]]:
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text = [text] if isinstance(text, str) else text
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tokens_list = []
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weights_list = []
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for t in text:
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segments = self._split_on_break(t)
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tokens, weights = self._tokenize_segments(segments, weighted=True)
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tokens_list.append(tokens)
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weights_list.append(weights)
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return [torch.stack(tokens_list, dim=0)], [torch.stack(weights_list, dim=0)]
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class SdTextEncodingStrategy(TextEncodingStrategy):
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def __init__(self, clip_skip: Optional[int] = None) -> None:
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self.clip_skip = clip_skip
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def _encode_with_clip_skip(self, text_encoder: Any, tokens: torch.Tensor) -> torch.Tensor:
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"""Encode tokens with optional CLIP skip."""
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if self.clip_skip is None:
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return text_encoder(tokens)[0]
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enc_out = text_encoder(tokens, output_hidden_states=True, return_dict=True)
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hidden_states = enc_out["hidden_states"][-self.clip_skip]
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return text_encoder.text_model.final_layer_norm(hidden_states)
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def _reconstruct_embeddings(self, encoder_hidden_states: torch.Tensor, tokens: torch.Tensor,
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max_token_length: int, model_max_length: int,
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tokenizer: Any) -> torch.Tensor:
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"""Reconstruct embeddings from chunked encoding."""
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v1 = tokenizer.pad_token_id == tokenizer.eos_token_id
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states_list = [encoder_hidden_states[:, 0].unsqueeze(1)] # <BOS>
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if not v1:
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# v2: <BOS>...<EOS> <PAD> ... の三連を <BOS>...<EOS> <PAD> ... へ戻す
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for i in range(1, max_token_length, model_max_length):
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chunk = encoder_hidden_states[:, i : i + model_max_length - 2]
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if i > 0:
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for j in range(len(chunk)):
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if tokens[j, 1] == tokenizer.eos_token:
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chunk[j, 0] = chunk[j, 1]
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states_list.append(chunk)
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states_list.append(encoder_hidden_states[:, -1].unsqueeze(1))
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else:
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# v1: <BOS>...<EOS> の三連を <BOS>...<EOS> へ戻す
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for i in range(1, max_token_length, model_max_length):
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states_list.append(encoder_hidden_states[:, i : i + model_max_length - 2])
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states_list.append(encoder_hidden_states[:, -1].unsqueeze(1))
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return torch.cat(states_list, dim=1)
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def _apply_weights_single_chunk(self, encoder_hidden_states: torch.Tensor,
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weights: torch.Tensor) -> torch.Tensor:
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"""Apply weights for single chunk case (no max_token_length)."""
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return encoder_hidden_states * weights.squeeze(1).unsqueeze(2)
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def _apply_weights_multi_chunk(self, encoder_hidden_states: torch.Tensor,
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weights: torch.Tensor) -> torch.Tensor:
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"""Apply weights for multi-chunk case (with max_token_length)."""
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for i in range(weights.shape[1]):
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start_idx = i * 75 + 1
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end_idx = i * 75 + 76
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encoder_hidden_states[:, start_idx:end_idx] = (
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encoder_hidden_states[:, start_idx:end_idx] * weights[:, i, 1:-1].unsqueeze(-1)
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)
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return encoder_hidden_states
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def encode_tokens(
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self, tokenize_strategy: TokenizeStrategy, models: List[Any], tokens: List[torch.Tensor]
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) -> List[torch.Tensor]:
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text_encoder = models[0]
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tokens = tokens[0]
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sd_tokenize_strategy = tokenize_strategy # type: SdTokenizeStrategy
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b_size = tokens.size()[0]
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max_token_length = tokens.size()[1] * tokens.size()[2]
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model_max_length = sd_tokenize_strategy.tokenizer.model_max_length
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tokens = tokens.reshape((-1, model_max_length))
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tokens = tokens.to(text_encoder.device)
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encoder_hidden_states = self._encode_with_clip_skip(text_encoder, tokens)
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encoder_hidden_states = encoder_hidden_states.reshape((b_size, -1, encoder_hidden_states.shape[-1]))
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if max_token_length != model_max_length:
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encoder_hidden_states = self._reconstruct_embeddings(
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encoder_hidden_states, tokens, max_token_length,
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model_max_length, sd_tokenize_strategy.tokenizer
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)
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return [encoder_hidden_states]
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def encode_tokens_with_weights(
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self,
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tokenize_strategy: TokenizeStrategy,
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models: List[Any],
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tokens_list: List[torch.Tensor],
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weights_list: List[torch.Tensor],
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) -> List[torch.Tensor]:
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encoder_hidden_states = self.encode_tokens(tokenize_strategy, models, tokens_list)[0]
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weights = weights_list[0].to(encoder_hidden_states.device)
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if weights.shape[1] == 1:
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encoder_hidden_states = self._apply_weights_single_chunk(encoder_hidden_states, weights)
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else:
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encoder_hidden_states = self._apply_weights_multi_chunk(encoder_hidden_states, weights)
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return [encoder_hidden_states]
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class SdSdxlLatentsCachingStrategy(LatentsCachingStrategy):
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# sd and sdxl share the same strategy. we can make them separate, but the difference is only the suffix.
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# and we keep the old npz for the backward compatibility.
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SD_OLD_LATENTS_NPZ_SUFFIX = ".npz"
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SD_LATENTS_NPZ_SUFFIX = "_sd.npz"
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SDXL_LATENTS_NPZ_SUFFIX = "_sdxl.npz"
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def __init__(self, sd: bool, cache_to_disk: bool, batch_size: int, skip_disk_cache_validity_check: bool) -> None:
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super().__init__(cache_to_disk, batch_size, skip_disk_cache_validity_check)
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self.sd = sd
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self.suffix = (
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SdSdxlLatentsCachingStrategy.SD_LATENTS_NPZ_SUFFIX if sd else SdSdxlLatentsCachingStrategy.SDXL_LATENTS_NPZ_SUFFIX
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)
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@property
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def cache_suffix(self) -> str:
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return self.suffix
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def get_latents_npz_path(self, absolute_path: str, image_size: Tuple[int, int]) -> str:
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# support old .npz
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old_npz_file = os.path.splitext(absolute_path)[0] + SdSdxlLatentsCachingStrategy.SD_OLD_LATENTS_NPZ_SUFFIX
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if os.path.exists(old_npz_file):
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return old_npz_file
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return os.path.splitext(absolute_path)[0] + f"_{image_size[0]:04d}x{image_size[1]:04d}" + self.suffix
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def is_disk_cached_latents_expected(self, bucket_reso: Tuple[int, int], npz_path: str, flip_aug: bool, alpha_mask: bool):
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return self._default_is_disk_cached_latents_expected(8, bucket_reso, npz_path, flip_aug, alpha_mask, multi_resolution=True)
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def load_latents_from_disk(
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self, npz_path: str, bucket_reso: Tuple[int, int]
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) -> Tuple[Optional[np.ndarray], Optional[List[int]], Optional[List[int]], Optional[np.ndarray], Optional[np.ndarray]]:
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return self._default_load_latents_from_disk(8, npz_path, bucket_reso)
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# TODO remove circular dependency for ImageInfo
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def cache_batch_latents(self, vae, image_infos: List, flip_aug: bool, alpha_mask: bool, random_crop: bool):
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encode_by_vae = lambda img_tensor: vae.encode(img_tensor).latent_dist.sample()
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vae_device = vae.device
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vae_dtype = vae.dtype
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self._default_cache_batch_latents(
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encode_by_vae, vae_device, vae_dtype, image_infos, flip_aug, alpha_mask, random_crop, multi_resolution=True
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)
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if not train_util.HIGH_VRAM:
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train_util.clean_memory_on_device(vae.device)
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