mirror of
https://github.com/kohya-ss/sd-scripts.git
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274 lines
8.6 KiB
Python
274 lines
8.6 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 torch
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from transformers import AutoTokenizer, AutoModel, GemmaTokenizerFast
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from library import train_util
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from library.strategy_base import (
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LatentsCachingStrategy,
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TokenizeStrategy,
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TextEncodingStrategy,
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TextEncoderOutputsCachingStrategy
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)
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import numpy as np
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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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GEMMA_ID = "google/gemma-2-2b"
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class LuminaTokenizeStrategy(TokenizeStrategy):
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def __init__(
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self, max_length: Optional[int], tokenizer_cache_dir: Optional[str] = None
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) -> None:
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self.tokenizer: GemmaTokenizerFast = AutoTokenizer.from_pretrained(
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GEMMA_ID, cache_dir=tokenizer_cache_dir
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)
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self.tokenizer.padding_side = "right"
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if max_length is None:
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self.max_length = 256
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else:
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self.max_length = max_length
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def tokenize(self, text: Union[str, List[str]]) -> Tuple[torch.Tensor, torch.Tensor]:
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text = [text] if isinstance(text, str) else text
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encodings = self.tokenizer(
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text,
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max_length=self.max_length,
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return_tensors="pt",
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padding=True,
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pad_to_multiple_of=8,
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truncation=True,
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)
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return [encodings.input_ids, encodings.attention_mask]
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def tokenize_with_weights(
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self, text: str | List[str]
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) -> Tuple[torch.Tensor, torch.Tensor, List[torch.Tensor]]:
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# Gemma doesn't support weighted prompts, return uniform weights
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tokens, attention_masks = self.tokenize(text)
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weights = [torch.ones_like(t) for t in tokens]
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return tokens, attention_masks, weights
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class LuminaTextEncodingStrategy(TextEncodingStrategy):
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def __init__(self) -> None:
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super().__init__()
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def encode_tokens(
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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[torch.Tensor],
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) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
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text_encoder = models[0]
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input_ids, attention_masks = tokens
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outputs = text_encoder(
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input_ids=input_ids.to(text_encoder.device),
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attention_mask=attention_masks.to(text_encoder.device),
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output_hidden_states=True,
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return_dict=True,
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)
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return outputs.hidden_states[-2], input_ids, attention_masks
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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[torch.Tensor],
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weights_list: List[torch.Tensor],
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) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
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# For simplicity, use uniform weighting
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return self.encode_tokens(tokenize_strategy, models, tokens)
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class LuminaTextEncoderOutputsCachingStrategy(TextEncoderOutputsCachingStrategy):
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LUMINA_TEXT_ENCODER_OUTPUTS_NPZ_SUFFIX = "_lumina_te.npz"
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def __init__(
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self,
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cache_to_disk: bool,
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batch_size: int,
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skip_disk_cache_validity_check: bool,
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is_partial: bool = False,
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) -> None:
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super().__init__(
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cache_to_disk,
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batch_size,
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skip_disk_cache_validity_check,
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is_partial,
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)
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def get_outputs_npz_path(self, image_abs_path: str) -> str:
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return (
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os.path.splitext(image_abs_path)[0]
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+ LuminaTextEncoderOutputsCachingStrategy.LUMINA_TEXT_ENCODER_OUTPUTS_NPZ_SUFFIX
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)
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def is_disk_cached_outputs_expected(self, npz_path: str):
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if not self.cache_to_disk:
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return False
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if not os.path.exists(npz_path):
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return False
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if self.skip_disk_cache_validity_check:
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return True
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try:
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npz = np.load(npz_path)
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if "hidden_state" not in npz:
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return False
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if "attention_mask" not in npz:
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return False
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if "input_ids" not in npz:
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return False
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if "apply_gemma2_attn_mask" not in npz:
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return False
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npz_apply_gemma2_attn_mask = npz["apply_gemma2_attn_mask"]
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if not npz_apply_gemma2_attn_mask:
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return False
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except Exception as e:
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logger.error(f"Error loading file: {npz_path}")
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raise e
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return True
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def load_outputs_npz(self, npz_path: str) -> List[np.ndarray]:
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data = np.load(npz_path)
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hidden_state = data["hidden_state"]
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attention_mask = data["attention_mask"]
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input_ids = data["input_ids"]
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return [hidden_state, attention_mask, input_ids]
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def cache_batch_outputs(
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self,
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tokenize_strategy: LuminaTokenizeStrategy,
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models: List[Any],
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text_encoding_strategy: LuminaTextEncodingStrategy,
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infos: List,
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):
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lumina_text_encoding_strategy: LuminaTextEncodingStrategy = (
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text_encoding_strategy
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)
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captions = [info.caption for info in infos]
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if self.is_weighted:
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tokens, weights_list = tokenize_strategy.tokenize_with_weights(
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captions
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)
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with torch.no_grad():
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hidden_state, input_ids, attention_masks = lumina_text_encoding_strategy.encode_tokens_with_weights(
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tokenize_strategy, models, tokens, weights_list
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)
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else:
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tokens = tokenize_strategy.tokenize(captions)
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with torch.no_grad():
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hidden_state, input_ids, attention_masks = lumina_text_encoding_strategy.encode_tokens(
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tokenize_strategy, models, tokens
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)
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if hidden_state.dtype != torch.float32:
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hidden_state = hidden_state.float()
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hidden_state = hidden_state.cpu().numpy()
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attention_mask = attention_masks.cpu().numpy()
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input_ids = tokens.cpu().numpy()
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for i, info in enumerate(infos):
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hidden_state_i = hidden_state[i]
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attention_mask_i = attention_mask[i]
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input_ids_i = input_ids[i]
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if self.cache_to_disk:
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np.savez(
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info.text_encoder_outputs_npz,
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hidden_state=hidden_state_i,
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attention_mask=attention_mask_i,
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input_ids=input_ids_i,
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apply_gemma2_attn_mask=True
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)
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else:
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info.text_encoder_outputs = [hidden_state_i, attention_mask_i, input_ids_i]
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class LuminaLatentsCachingStrategy(LatentsCachingStrategy):
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LUMINA_LATENTS_NPZ_SUFFIX = "_lumina.npz"
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def __init__(
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self, cache_to_disk: bool, batch_size: int, skip_disk_cache_validity_check: bool
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) -> None:
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super().__init__(cache_to_disk, batch_size, skip_disk_cache_validity_check)
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@property
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def cache_suffix(self) -> str:
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return LuminaLatentsCachingStrategy.LUMINA_LATENTS_NPZ_SUFFIX
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def get_latents_npz_path(
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self, absolute_path: str, image_size: Tuple[int, int]
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) -> str:
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return (
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os.path.splitext(absolute_path)[0]
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+ f"_{image_size[0]:04d}x{image_size[1]:04d}"
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+ LuminaLatentsCachingStrategy.LUMINA_LATENTS_NPZ_SUFFIX
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)
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def is_disk_cached_latents_expected(
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self,
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bucket_reso: Tuple[int, int],
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npz_path: str,
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flip_aug: bool,
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alpha_mask: bool,
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):
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return self._default_is_disk_cached_latents_expected(
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8, bucket_reso, npz_path, flip_aug, alpha_mask, multi_resolution=True
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)
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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[
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Optional[np.ndarray],
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Optional[List[int]],
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Optional[List[int]],
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Optional[np.ndarray],
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Optional[np.ndarray],
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]:
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return self._default_load_latents_from_disk(
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8, npz_path, bucket_reso
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) # support multi-resolution
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# TODO remove circular dependency for ImageInfo
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def cache_batch_latents(
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self,
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vae,
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image_infos: List,
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flip_aug: bool,
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alpha_mask: bool,
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random_crop: bool,
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):
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encode_by_vae = lambda img_tensor: vae.encode(img_tensor).to("cpu")
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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,
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vae_device,
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vae_dtype,
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image_infos,
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flip_aug,
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alpha_mask,
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random_crop,
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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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