import os from typing import Any, List, Optional, Tuple, Union import torch import numpy as np from transformers import AutoTokenizer, Qwen2TokenizerFast from library import hunyuan_image_text_encoder, hunyuan_image_vae, train_util from library.strategy_base import LatentsCachingStrategy, TextEncodingStrategy, TokenizeStrategy, TextEncoderOutputsCachingStrategy from library.utils import setup_logging setup_logging() import logging logger = logging.getLogger(__name__) class HunyuanImageTokenizeStrategy(TokenizeStrategy): def __init__(self, tokenizer_cache_dir: Optional[str] = None) -> None: self.vlm_tokenizer = self._load_tokenizer( Qwen2TokenizerFast, hunyuan_image_text_encoder.QWEN_2_5_VL_IMAGE_ID, tokenizer_cache_dir=tokenizer_cache_dir ) self.byt5_tokenizer = self._load_tokenizer( AutoTokenizer, hunyuan_image_text_encoder.BYT5_TOKENIZER_PATH, subfolder="", 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 vlm_tokens, vlm_mask = hunyuan_image_text_encoder.get_qwen_tokens(self.vlm_tokenizer, text) # byt5_tokens, byt5_mask = hunyuan_image_text_encoder.get_byt5_text_tokens(self.byt5_tokenizer, text) byt5_tokens = [] byt5_mask = [] for t in text: tokens, mask = hunyuan_image_text_encoder.get_byt5_text_tokens(self.byt5_tokenizer, t) if tokens is None: tokens = torch.zeros((1, 1), dtype=torch.long) mask = torch.zeros((1, 1), dtype=torch.long) byt5_tokens.append(tokens) byt5_mask.append(mask) max_len = max([m.shape[1] for m in byt5_mask]) byt5_tokens = torch.cat([torch.nn.functional.pad(t, (0, max_len - t.shape[1]), value=0) for t in byt5_tokens], dim=0) byt5_mask = torch.cat([torch.nn.functional.pad(m, (0, max_len - m.shape[1]), value=0) for m in byt5_mask], dim=0) return [vlm_tokens, vlm_mask, byt5_tokens, byt5_mask] class HunyuanImageTextEncodingStrategy(TextEncodingStrategy): def __init__(self) -> None: pass def encode_tokens( self, tokenize_strategy: TokenizeStrategy, models: List[Any], tokens: List[torch.Tensor] ) -> List[torch.Tensor]: vlm_tokens, vlm_mask, byt5_tokens, byt5_mask = tokens qwen2vlm, byt5 = models # autocast and no_grad are handled in hunyuan_image_text_encoder vlm_embed, vlm_mask = hunyuan_image_text_encoder.get_qwen_prompt_embeds_from_tokens(qwen2vlm, vlm_tokens, vlm_mask) # ocr_mask, byt5_embed, byt5_mask = hunyuan_image_text_encoder.get_byt5_prompt_embeds_from_tokens( # byt5, byt5_tokens, byt5_mask # ) ocr_mask, byt5_embed, byt5_updated_mask = [], [], [] for i in range(byt5_tokens.shape[0]): ocr_m, byt5_e, byt5_m = hunyuan_image_text_encoder.get_byt5_prompt_embeds_from_tokens( byt5, byt5_tokens[i : i + 1], byt5_mask[i : i + 1] ) ocr_mask.append(torch.zeros((1,), dtype=torch.long) + (1 if ocr_m[0] else 0)) # 1 or 0 byt5_embed.append(byt5_e) byt5_updated_mask.append(byt5_m) ocr_mask = torch.cat(ocr_mask, dim=0).to(torch.bool) # [B] byt5_embed = torch.cat(byt5_embed, dim=0) byt5_updated_mask = torch.cat(byt5_updated_mask, dim=0) return [vlm_embed, vlm_mask, byt5_embed, byt5_updated_mask, ocr_mask] class HunyuanImageTextEncoderOutputsCachingStrategy(TextEncoderOutputsCachingStrategy): HUNYUAN_IMAGE_TEXT_ENCODER_OUTPUTS_NPZ_SUFFIX = "_hi_te.npz" def __init__( self, cache_to_disk: bool, batch_size: int, skip_disk_cache_validity_check: bool, is_partial: bool = False ) -> None: super().__init__(cache_to_disk, batch_size, skip_disk_cache_validity_check, is_partial) def get_outputs_npz_path(self, image_abs_path: str) -> str: return ( os.path.splitext(image_abs_path)[0] + HunyuanImageTextEncoderOutputsCachingStrategy.HUNYUAN_IMAGE_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 "vlm_embed" not in npz: return False if "vlm_mask" not in npz: return False if "byt5_embed" not in npz: return False if "byt5_mask" not in npz: return False if "ocr_mask" not in npz: return False except Exception as e: logger.error(f"Error loading file: {npz_path}") raise e return True def load_outputs_npz(self, npz_path: str) -> List[np.ndarray]: data = np.load(npz_path) vln_embed = data["vlm_embed"] vlm_mask = data["vlm_mask"] byt5_embed = data["byt5_embed"] byt5_mask = data["byt5_mask"] ocr_mask = data["ocr_mask"] return [vln_embed, vlm_mask, byt5_embed, byt5_mask, ocr_mask] def cache_batch_outputs( self, tokenize_strategy: TokenizeStrategy, models: List[Any], text_encoding_strategy: TextEncodingStrategy, infos: List ): huyuan_image_text_encoding_strategy: HunyuanImageTextEncodingStrategy = text_encoding_strategy captions = [info.caption for info in infos] tokens_and_masks = tokenize_strategy.tokenize(captions) with torch.no_grad(): vlm_embed, vlm_mask, byt5_embed, byt5_mask, ocr_mask = huyuan_image_text_encoding_strategy.encode_tokens( tokenize_strategy, models, tokens_and_masks ) if vlm_embed.dtype == torch.bfloat16: vlm_embed = vlm_embed.float() if byt5_embed.dtype == torch.bfloat16: byt5_embed = byt5_embed.float() vlm_embed = vlm_embed.cpu().numpy() vlm_mask = vlm_mask.cpu().numpy() byt5_embed = byt5_embed.cpu().numpy() byt5_mask = byt5_mask.cpu().numpy() ocr_mask = ocr_mask.cpu().numpy() for i, info in enumerate(infos): vlm_embed_i = vlm_embed[i] vlm_mask_i = vlm_mask[i] byt5_embed_i = byt5_embed[i] byt5_mask_i = byt5_mask[i] ocr_mask_i = ocr_mask[i] if self.cache_to_disk: np.savez( info.text_encoder_outputs_npz, vlm_embed=vlm_embed_i, vlm_mask=vlm_mask_i, byt5_embed=byt5_embed_i, byt5_mask=byt5_mask_i, ocr_mask=ocr_mask_i, ) else: info.text_encoder_outputs = (vlm_embed_i, vlm_mask_i, byt5_embed_i, byt5_mask_i, ocr_mask_i) class HunyuanImageLatentsCachingStrategy(LatentsCachingStrategy): HUNYUAN_IMAGE_LATENTS_NPZ_SUFFIX = "_hi.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) @property def cache_suffix(self) -> str: return HunyuanImageLatentsCachingStrategy.HUNYUAN_IMAGE_LATENTS_NPZ_SUFFIX 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}" + HunyuanImageLatentsCachingStrategy.HUNYUAN_IMAGE_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(32, bucket_reso, npz_path, flip_aug, alpha_mask, multi_resolution=True) def load_latents_from_disk( self, npz_path: str, bucket_reso: Tuple[int, int] ) -> Tuple[Optional[np.ndarray], Optional[List[int]], Optional[List[int]], Optional[np.ndarray], Optional[np.ndarray]]: return self._default_load_latents_from_disk(32, npz_path, bucket_reso) # support multi-resolution # TODO remove circular dependency for ImageInfo def cache_batch_latents( self, vae: hunyuan_image_vae.HunyuanVAE2D, image_infos: List, flip_aug: bool, alpha_mask: bool, random_crop: bool ): # encode_by_vae = lambda img_tensor: vae.encode(img_tensor).sample() def encode_by_vae(img_tensor): # no_grad is handled in _default_cache_batch_latents nonlocal vae with torch.autocast(device_type=vae.device.type, dtype=vae.dtype): return vae.encode(img_tensor).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, multi_resolution=True ) if not train_util.HIGH_VRAM: train_util.clean_memory_on_device(vae.device)