import os import glob from typing import Any, List, Optional, Tuple, Union import torch import numpy as np from transformers import CLIPTokenizer, T5TokenizerFast from library import sd3_utils, train_util from library import sd3_models from library.strategy_base import LatentsCachingStrategy, TextEncodingStrategy, TokenizeStrategy, TextEncoderOutputsCachingStrategy from library.utils import setup_logging setup_logging() import logging logger = logging.getLogger(__name__) CLIP_L_TOKENIZER_ID = "openai/clip-vit-large-patch14" T5_XXL_TOKENIZER_ID = "google/t5-v1_1-xxl" class FluxTokenizeStrategy(TokenizeStrategy): def __init__(self, t5xxl_max_length: int = 256, tokenizer_cache_dir: Optional[str] = None) -> None: self.t5xxl_max_length = t5xxl_max_length self.clip_l = self._load_tokenizer(CLIPTokenizer, CLIP_L_TOKENIZER_ID, tokenizer_cache_dir=tokenizer_cache_dir) self.t5xxl = self._load_tokenizer(T5TokenizerFast, T5_XXL_TOKENIZER_ID, tokenizer_cache_dir=tokenizer_cache_dir) def tokenize(self, text: Union[str, List[str]]) -> List[torch.Tensor]: text = [text] if isinstance(text, str) else text l_tokens = self.clip_l(text, max_length=77, padding="max_length", truncation=True, return_tensors="pt") t5_tokens = self.t5xxl(text, max_length=self.t5xxl_max_length, padding="max_length", truncation=True, return_tensors="pt") t5_attn_mask = t5_tokens["attention_mask"] l_tokens = l_tokens["input_ids"] t5_tokens = t5_tokens["input_ids"] return [l_tokens, t5_tokens, t5_attn_mask] class FluxTextEncodingStrategy(TextEncodingStrategy): def __init__(self) -> None: pass def encode_tokens( self, tokenize_strategy: TokenizeStrategy, models: List[Any], tokens: List[torch.Tensor], apply_t5_attn_mask: bool = False, ) -> List[torch.Tensor]: # supports single model inference only clip_l, t5xxl = models l_tokens, t5_tokens = tokens[:2] t5_attn_mask = tokens[2] if len(tokens) > 2 else None if clip_l is not None and l_tokens is not None: l_pooled = clip_l(l_tokens.to(clip_l.device))["pooler_output"] else: l_pooled = None if t5xxl is not None and t5_tokens is not None: # t5_out is [1, max length, 4096] t5_out, _ = t5xxl(t5_tokens.to(t5xxl.device), return_dict=False, output_hidden_states=True) if apply_t5_attn_mask: t5_out = t5_out * t5_attn_mask.to(t5_out.device).unsqueeze(-1) txt_ids = torch.zeros(1, t5_out.shape[1], 3, device=t5_out.device) else: t5_out = None txt_ids = None return [l_pooled, t5_out, txt_ids] class FluxTextEncoderOutputsCachingStrategy(TextEncoderOutputsCachingStrategy): FLUX_TEXT_ENCODER_OUTPUTS_NPZ_SUFFIX = "_flux_te.npz" def __init__( self, cache_to_disk: bool, batch_size: int, skip_disk_cache_validity_check: bool, is_partial: bool = False, apply_t5_attn_mask: bool = False, ) -> None: super().__init__(cache_to_disk, batch_size, skip_disk_cache_validity_check, is_partial) self.apply_t5_attn_mask = apply_t5_attn_mask def get_outputs_npz_path(self, image_abs_path: str) -> str: return os.path.splitext(image_abs_path)[0] + FluxTextEncoderOutputsCachingStrategy.FLUX_TEXT_ENCODER_OUTPUTS_NPZ_SUFFIX def is_disk_cached_outputs_expected(self, npz_path: str): if not self.cache_to_disk: return False if not os.path.exists(npz_path): return False if self.skip_disk_cache_validity_check: return True try: npz = np.load(npz_path) if "l_pooled" not in npz: return False if "t5_out" not in npz: return False if "txt_ids" not in npz: return False except Exception as e: logger.error(f"Error loading file: {npz_path}") raise e return True def mask_t5_attn(self, t5_out: np.ndarray, t5_attn_mask: np.ndarray) -> np.ndarray: return t5_out * np.expand_dims(t5_attn_mask, -1) def load_outputs_npz(self, npz_path: str) -> List[np.ndarray]: data = np.load(npz_path) l_pooled = data["l_pooled"] t5_out = data["t5_out"] txt_ids = data["txt_ids"] if self.apply_t5_attn_mask: t5_attn_mask = data["t5_attn_mask"] t5_out = self.mask_t5_attn(t5_out, t5_attn_mask) return [l_pooled, t5_out, txt_ids] def cache_batch_outputs( self, tokenize_strategy: TokenizeStrategy, models: List[Any], text_encoding_strategy: TextEncodingStrategy, infos: List ): flux_text_encoding_strategy: FluxTextEncodingStrategy = text_encoding_strategy captions = [info.caption for info in infos] tokens_and_masks = tokenize_strategy.tokenize(captions) with torch.no_grad(): l_pooled, t5_out, txt_ids = flux_text_encoding_strategy.encode_tokens( tokenize_strategy, models, tokens_and_masks, self.apply_t5_attn_mask ) if l_pooled.dtype == torch.bfloat16: l_pooled = l_pooled.float() if t5_out.dtype == torch.bfloat16: t5_out = t5_out.float() if txt_ids.dtype == torch.bfloat16: txt_ids = txt_ids.float() l_pooled = l_pooled.cpu().numpy() t5_out = t5_out.cpu().numpy() txt_ids = txt_ids.cpu().numpy() for i, info in enumerate(infos): l_pooled_i = l_pooled[i] t5_out_i = t5_out[i] txt_ids_i = txt_ids[i] if self.cache_to_disk: t5_attn_mask = tokens_and_masks[2] t5_attn_mask_i = t5_attn_mask[i].cpu().numpy() np.savez( info.text_encoder_outputs_npz, l_pooled=l_pooled_i, t5_out=t5_out_i, txt_ids=txt_ids_i, t5_attn_mask=t5_attn_mask_i, ) else: info.text_encoder_outputs = (l_pooled_i, t5_out_i, txt_ids_i) class FluxLatentsCachingStrategy(LatentsCachingStrategy): FLUX_LATENTS_NPZ_SUFFIX = "_flux.npz" def __init__(self, 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) def get_image_size_from_disk_cache_path(self, absolute_path: str) -> Tuple[Optional[int], Optional[int]]: npz_file = glob.glob(os.path.splitext(absolute_path)[0] + "_*" + FluxLatentsCachingStrategy.FLUX_LATENTS_NPZ_SUFFIX) if len(npz_file) == 0: return None, None w, h = os.path.splitext(npz_file[0])[0].split("_")[-2].split("x") return int(w), int(h) def get_latents_npz_path(self, absolute_path: str, image_size: Tuple[int, int]) -> str: return ( os.path.splitext(absolute_path)[0] + f"_{image_size[0]:04d}x{image_size[1]:04d}" + FluxLatentsCachingStrategy.FLUX_LATENTS_NPZ_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).to("cpu") 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) if __name__ == "__main__": # test code for FluxTokenizeStrategy # tokenizer = sd3_models.SD3Tokenizer() strategy = FluxTokenizeStrategy(256) text = "hello world" l_tokens, g_tokens, t5_tokens = strategy.tokenize(text) # print(l_tokens.shape) print(l_tokens) print(g_tokens) print(t5_tokens) texts = ["hello world", "the quick brown fox jumps over the lazy dog"] l_tokens_2 = strategy.clip_l(texts, max_length=77, padding="max_length", truncation=True, return_tensors="pt") g_tokens_2 = strategy.clip_g(texts, max_length=77, padding="max_length", truncation=True, return_tensors="pt") t5_tokens_2 = strategy.t5xxl( texts, max_length=strategy.t5xxl_max_length, padding="max_length", truncation=True, return_tensors="pt" ) print(l_tokens_2) print(g_tokens_2) print(t5_tokens_2) # compare print(torch.allclose(l_tokens, l_tokens_2["input_ids"][0])) print(torch.allclose(g_tokens, g_tokens_2["input_ids"][0])) print(torch.allclose(t5_tokens, t5_tokens_2["input_ids"][0])) text = ",".join(["hello world! this is long text"] * 50) l_tokens, g_tokens, t5_tokens = strategy.tokenize(text) print(l_tokens) print(g_tokens) print(t5_tokens) print(f"model max length l: {strategy.clip_l.model_max_length}") print(f"model max length g: {strategy.clip_g.model_max_length}") print(f"model max length t5: {strategy.t5xxl.model_max_length}")