import glob import os from typing import Any, List, Optional, Tuple, Union import torch from transformers import CLIPTokenizer from library import train_util from library.strategy_base import LatentsCachingStrategy, TokenizeStrategy, TextEncodingStrategy from library.utils import setup_logging setup_logging() import logging logger = logging.getLogger(__name__) TOKENIZER_ID = "openai/clip-vit-large-patch14" 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 class SdTokenizeStrategy(TokenizeStrategy): def __init__(self, v2: bool, max_length: Optional[int], tokenizer_cache_dir: Optional[str] = None) -> None: """ max_length does not include and (None, 75, 150, 225) """ logger.info(f"Using {'v2' if v2 else 'v1'} tokenizer") if v2: self.tokenizer = self._load_tokenizer( CLIPTokenizer, V2_STABLE_DIFFUSION_ID, subfolder="tokenizer", tokenizer_cache_dir=tokenizer_cache_dir ) else: self.tokenizer = self._load_tokenizer(CLIPTokenizer, TOKENIZER_ID, tokenizer_cache_dir=tokenizer_cache_dir) if max_length is None: self.max_length = self.tokenizer.model_max_length else: self.max_length = max_length + 2 def tokenize(self, text: Union[str, List[str]]) -> List[torch.Tensor]: text = [text] if isinstance(text, str) else text return [torch.stack([self._get_input_ids(self.tokenizer, t, self.max_length) for t in text], dim=0)] def tokenize_with_weights(self, text: str | List[str]) -> Tuple[List[torch.Tensor]]: text = [text] if isinstance(text, str) else text tokens_list = [] weights_list = [] for t in text: tokens, weights = self._get_input_ids(self.tokenizer, t, self.max_length, weighted=True) tokens_list.append(tokens) weights_list.append(weights) return [torch.stack(tokens_list, dim=0)], [torch.stack(weights_list, dim=0)] class SdTextEncodingStrategy(TextEncodingStrategy): def __init__(self, clip_skip: Optional[int] = None) -> None: self.clip_skip = clip_skip def encode_tokens( self, tokenize_strategy: TokenizeStrategy, models: List[Any], tokens: List[torch.Tensor] ) -> List[torch.Tensor]: text_encoder = models[0] tokens = tokens[0] sd_tokenize_strategy = tokenize_strategy # type: SdTokenizeStrategy # tokens: b,n,77 b_size = tokens.size()[0] max_token_length = tokens.size()[1] * tokens.size()[2] model_max_length = sd_tokenize_strategy.tokenizer.model_max_length tokens = tokens.reshape((-1, model_max_length)) # batch_size*3, 77 tokens = tokens.to(text_encoder.device) if self.clip_skip is None: encoder_hidden_states = text_encoder(tokens)[0] else: enc_out = text_encoder(tokens, output_hidden_states=True, return_dict=True) encoder_hidden_states = enc_out["hidden_states"][-self.clip_skip] encoder_hidden_states = text_encoder.text_model.final_layer_norm(encoder_hidden_states) # bs*3, 77, 768 or 1024 encoder_hidden_states = encoder_hidden_states.reshape((b_size, -1, encoder_hidden_states.shape[-1])) if max_token_length != model_max_length: v1 = sd_tokenize_strategy.tokenizer.pad_token_id == sd_tokenize_strategy.tokenizer.eos_token_id if not v1: # v2: ... ... の三連を ... ... へ戻す　正直この実装でいいのかわからん states_list = [encoder_hidden_states[:, 0].unsqueeze(1)] # for i in range(1, max_token_length, model_max_length): chunk = encoder_hidden_states[:, i : i + model_max_length - 2] # の後から 最後の前まで if i > 0: for j in range(len(chunk)): if tokens[j, 1] == sd_tokenize_strategy.tokenizer.eos_token: # 空、つまり ...のパターン chunk[j, 0] = chunk[j, 1] # 次の の値をコピーする states_list.append(chunk) # の後から の前まで states_list.append(encoder_hidden_states[:, -1].unsqueeze(1)) # か のどちらか encoder_hidden_states = torch.cat(states_list, dim=1) else: # v1: ... の三連を ... へ戻す states_list = [encoder_hidden_states[:, 0].unsqueeze(1)] # for i in range(1, max_token_length, model_max_length): states_list.append(encoder_hidden_states[:, i : i + model_max_length - 2]) # の後から の前まで states_list.append(encoder_hidden_states[:, -1].unsqueeze(1)) # encoder_hidden_states = torch.cat(states_list, dim=1) return [encoder_hidden_states] def encode_tokens_with_weights( self, tokenize_strategy: TokenizeStrategy, models: List[Any], tokens_list: List[torch.Tensor], weights_list: List[torch.Tensor], ) -> List[torch.Tensor]: encoder_hidden_states = self.encode_tokens(tokenize_strategy, models, tokens_list)[0] weights = weights_list[0].to(encoder_hidden_states.device) # apply weights if weights.shape[1] == 1: # no max_token_length # weights: ((b, 1, 77), (b, 1, 77)), hidden_states: (b, 77, 768), (b, 77, 768) encoder_hidden_states = encoder_hidden_states * weights.squeeze(1).unsqueeze(2) else: # weights: ((b, n, 77), (b, n, 77)), hidden_states: (b, n*75+2, 768), (b, n*75+2, 768) for i in range(weights.shape[1]): encoder_hidden_states[:, i * 75 + 1 : i * 75 + 76] = encoder_hidden_states[:, i * 75 + 1 : i * 75 + 76] * weights[ :, i, 1:-1 ].unsqueeze(-1) return [encoder_hidden_states] class SdSdxlLatentsCachingStrategy(LatentsCachingStrategy): # sd and sdxl share the same strategy. we can make them separate, but the difference is only the suffix. # and we keep the old npz for the backward compatibility. SD_OLD_LATENTS_NPZ_SUFFIX = ".npz" SD_LATENTS_NPZ_SUFFIX = "_sd.npz" SDXL_LATENTS_NPZ_SUFFIX = "_sdxl.npz" def __init__(self, sd: bool, cache_to_disk: bool, batch_size: int, skip_disk_cache_validity_check: bool) -> None: super().__init__(cache_to_disk, batch_size, skip_disk_cache_validity_check) self.sd = sd self.suffix = ( SdSdxlLatentsCachingStrategy.SD_LATENTS_NPZ_SUFFIX if sd else SdSdxlLatentsCachingStrategy.SDXL_LATENTS_NPZ_SUFFIX ) @property def cache_suffix(self) -> str: return self.suffix def get_latents_npz_path(self, absolute_path: str, image_size: Tuple[int, int]) -> str: # support old .npz old_npz_file = os.path.splitext(absolute_path)[0] + SdSdxlLatentsCachingStrategy.SD_OLD_LATENTS_NPZ_SUFFIX if os.path.exists(old_npz_file): return old_npz_file return os.path.splitext(absolute_path)[0] + f"_{image_size[0]:04d}x{image_size[1]:04d}" + self.suffix def is_disk_cached_latents_expected(self, bucket_reso: Tuple[int, int], npz_path: str, flip_aug: bool, alpha_mask: bool): return self._default_is_disk_cached_latents_expected(8, bucket_reso, npz_path, flip_aug, alpha_mask) # TODO remove circular dependency for ImageInfo def cache_batch_latents(self, vae, image_infos: List, flip_aug: bool, alpha_mask: bool, random_crop: bool): encode_by_vae = lambda img_tensor: vae.encode(img_tensor).latent_dist.sample() vae_device = vae.device vae_dtype = vae.dtype self._default_cache_batch_latents(encode_by_vae, vae_device, vae_dtype, image_infos, flip_aug, alpha_mask, random_crop) if not train_util.HIGH_VRAM: train_util.clean_memory_on_device(vae.device)