290 lines
12 KiB
Python
290 lines
12 KiB
Python
import os
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import glob
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from typing import Any, List, Optional, Tuple, Union
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import torch
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import numpy as np
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from transformers import CLIPTokenizer, T5TokenizerFast
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from . import sd3_utils, train_util
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from . import sd3_models
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from .strategy_base import LatentsCachingStrategy, TextEncodingStrategy, TokenizeStrategy, TextEncoderOutputsCachingStrategy
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from .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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CLIP_L_TOKENIZER_ID = "openai/clip-vit-large-patch14"
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CLIP_G_TOKENIZER_ID = "laion/CLIP-ViT-bigG-14-laion2B-39B-b160k"
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T5_XXL_TOKENIZER_ID = "google/t5-v1_1-xxl"
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class Sd3TokenizeStrategy(TokenizeStrategy):
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def __init__(self, t5xxl_max_length: int = 256, tokenizer_cache_dir: Optional[str] = None) -> None:
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self.t5xxl_max_length = t5xxl_max_length
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self.clip_l = self._load_tokenizer(CLIPTokenizer, CLIP_L_TOKENIZER_ID, tokenizer_cache_dir=tokenizer_cache_dir)
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self.clip_g = self._load_tokenizer(CLIPTokenizer, CLIP_G_TOKENIZER_ID, tokenizer_cache_dir=tokenizer_cache_dir)
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self.t5xxl = self._load_tokenizer(T5TokenizerFast, T5_XXL_TOKENIZER_ID, tokenizer_cache_dir=tokenizer_cache_dir)
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self.clip_g.pad_token_id = 0 # use 0 as pad token for clip_g
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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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l_tokens = self.clip_l(text, max_length=77, padding="max_length", truncation=True, return_tensors="pt")
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g_tokens = self.clip_g(text, max_length=77, padding="max_length", truncation=True, return_tensors="pt")
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t5_tokens = self.t5xxl(text, max_length=self.t5xxl_max_length, padding="max_length", truncation=True, return_tensors="pt")
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l_attn_mask = l_tokens["attention_mask"]
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g_attn_mask = g_tokens["attention_mask"]
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t5_attn_mask = t5_tokens["attention_mask"]
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l_tokens = l_tokens["input_ids"]
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g_tokens = g_tokens["input_ids"]
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t5_tokens = t5_tokens["input_ids"]
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return [l_tokens, g_tokens, t5_tokens, l_attn_mask, g_attn_mask, t5_attn_mask]
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class Sd3TextEncodingStrategy(TextEncodingStrategy):
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def __init__(self) -> None:
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pass
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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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apply_lg_attn_mask: bool = False,
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apply_t5_attn_mask: bool = False,
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) -> List[torch.Tensor]:
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"""
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returned embeddings are not masked
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"""
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clip_l, clip_g, t5xxl = models
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l_tokens, g_tokens, t5_tokens = tokens[:3]
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l_attn_mask, g_attn_mask, t5_attn_mask = tokens[3:] if len(tokens) > 3 else [None, None, None]
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if l_tokens is None:
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assert g_tokens is None, "g_tokens must be None if l_tokens is None"
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lg_out = None
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else:
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assert g_tokens is not None, "g_tokens must not be None if l_tokens is not None"
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l_out, l_pooled = clip_l(l_tokens)
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g_out, g_pooled = clip_g(g_tokens)
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if apply_lg_attn_mask:
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l_out = l_out * l_attn_mask.to(l_out.device).unsqueeze(-1)
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g_out = g_out * g_attn_mask.to(g_out.device).unsqueeze(-1)
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lg_out = torch.cat([l_out, g_out], dim=-1)
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if t5xxl is not None and t5_tokens is not None:
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t5_out, _ = t5xxl(t5_tokens) # t5_out is [1, max length, 4096]
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if apply_t5_attn_mask:
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t5_out = t5_out * t5_attn_mask.to(t5_out.device).unsqueeze(-1)
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else:
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t5_out = None
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lg_pooled = torch.cat((l_pooled, g_pooled), dim=-1) if l_tokens is not None else None
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return [lg_out, t5_out, lg_pooled]
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def concat_encodings(
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self, lg_out: torch.Tensor, t5_out: Optional[torch.Tensor], lg_pooled: torch.Tensor
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) -> Tuple[torch.Tensor, torch.Tensor]:
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lg_out = torch.nn.functional.pad(lg_out, (0, 4096 - lg_out.shape[-1]))
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if t5_out is None:
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t5_out = torch.zeros((lg_out.shape[0], 77, 4096), device=lg_out.device, dtype=lg_out.dtype)
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return torch.cat([lg_out, t5_out], dim=-2), lg_pooled
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class Sd3TextEncoderOutputsCachingStrategy(TextEncoderOutputsCachingStrategy):
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SD3_TEXT_ENCODER_OUTPUTS_NPZ_SUFFIX = "_sd3_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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apply_lg_attn_mask: bool = False,
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apply_t5_attn_mask: bool = False,
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) -> None:
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super().__init__(cache_to_disk, batch_size, skip_disk_cache_validity_check, is_partial)
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self.apply_lg_attn_mask = apply_lg_attn_mask
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self.apply_t5_attn_mask = apply_t5_attn_mask
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def get_outputs_npz_path(self, image_abs_path: str) -> str:
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return os.path.splitext(image_abs_path)[0] + Sd3TextEncoderOutputsCachingStrategy.SD3_TEXT_ENCODER_OUTPUTS_NPZ_SUFFIX
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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 "lg_out" not in npz:
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return False
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if "lg_pooled" not in npz:
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return False
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if "clip_l_attn_mask" not in npz or "clip_g_attn_mask" not in npz: # necessary even if not used
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return False
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# t5xxl is optional
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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 mask_lg_attn(self, lg_out: np.ndarray, l_attn_mask: np.ndarray, g_attn_mask: np.ndarray) -> np.ndarray:
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l_out = lg_out[..., :768]
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g_out = lg_out[..., 768:] # 1280
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l_out = l_out * np.expand_dims(l_attn_mask, -1) # l_out = l_out * l_attn_mask.
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g_out = g_out * np.expand_dims(g_attn_mask, -1) # g_out = g_out * g_attn_mask.
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return np.concatenate([l_out, g_out], axis=-1)
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def mask_t5_attn(self, t5_out: np.ndarray, t5_attn_mask: np.ndarray) -> np.ndarray:
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return t5_out * np.expand_dims(t5_attn_mask, -1)
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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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lg_out = data["lg_out"]
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lg_pooled = data["lg_pooled"]
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t5_out = data["t5_out"] if "t5_out" in data else None
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if self.apply_lg_attn_mask:
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l_attn_mask = data["clip_l_attn_mask"]
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g_attn_mask = data["clip_g_attn_mask"]
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lg_out = self.mask_lg_attn(lg_out, l_attn_mask, g_attn_mask)
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if self.apply_t5_attn_mask and t5_out is not None:
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t5_attn_mask = data["t5_attn_mask"]
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t5_out = self.mask_t5_attn(t5_out, t5_attn_mask)
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return [lg_out, t5_out, lg_pooled]
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def cache_batch_outputs(
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self, tokenize_strategy: TokenizeStrategy, models: List[Any], text_encoding_strategy: TextEncodingStrategy, infos: List
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):
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sd3_text_encoding_strategy: Sd3TextEncodingStrategy = text_encoding_strategy
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captions = [info.caption for info in infos]
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tokens_and_masks = tokenize_strategy.tokenize(captions)
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with torch.no_grad():
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lg_out, t5_out, lg_pooled = sd3_text_encoding_strategy.encode_tokens(
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tokenize_strategy, models, tokens_and_masks, self.apply_lg_attn_mask, self.apply_t5_attn_mask
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)
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if lg_out.dtype == torch.bfloat16:
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lg_out = lg_out.float()
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if lg_pooled.dtype == torch.bfloat16:
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lg_pooled = lg_pooled.float()
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if t5_out is not None and t5_out.dtype == torch.bfloat16:
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t5_out = t5_out.float()
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lg_out = lg_out.cpu().numpy()
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lg_pooled = lg_pooled.cpu().numpy()
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if t5_out is not None:
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t5_out = t5_out.cpu().numpy()
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for i, info in enumerate(infos):
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lg_out_i = lg_out[i]
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t5_out_i = t5_out[i] if t5_out is not None else None
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lg_pooled_i = lg_pooled[i]
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if self.cache_to_disk:
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clip_l_attn_mask, clip_g_attn_mask, t5_attn_mask = tokens_and_masks[3:6]
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clip_l_attn_mask_i = clip_l_attn_mask[i].cpu().numpy()
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clip_g_attn_mask_i = clip_g_attn_mask[i].cpu().numpy()
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t5_attn_mask_i = t5_attn_mask[i].cpu().numpy() if t5_attn_mask is not None else None # shouldn't be None
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kwargs = {}
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if t5_out is not None:
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kwargs["t5_out"] = t5_out_i
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np.savez(
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info.text_encoder_outputs_npz,
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lg_out=lg_out_i,
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lg_pooled=lg_pooled_i,
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clip_l_attn_mask=clip_l_attn_mask_i,
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clip_g_attn_mask=clip_g_attn_mask_i,
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t5_attn_mask=t5_attn_mask_i,
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**kwargs,
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)
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else:
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info.text_encoder_outputs = (lg_out_i, t5_out_i, lg_pooled_i)
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class Sd3LatentsCachingStrategy(LatentsCachingStrategy):
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SD3_LATENTS_NPZ_SUFFIX = "_sd3.npz"
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def __init__(self, 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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def get_image_size_from_disk_cache_path(self, absolute_path: str) -> Tuple[Optional[int], Optional[int]]:
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npz_file = glob.glob(os.path.splitext(absolute_path)[0] + "_*" + Sd3LatentsCachingStrategy.SD3_LATENTS_NPZ_SUFFIX)
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if len(npz_file) == 0:
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return None, None
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w, h = os.path.splitext(npz_file[0])[0].split("_")[-2].split("x")
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return int(w), int(h)
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def get_latents_npz_path(self, absolute_path: str, image_size: Tuple[int, int]) -> 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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+ Sd3LatentsCachingStrategy.SD3_LATENTS_NPZ_SUFFIX
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)
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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)
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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).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(encode_by_vae, vae_device, vae_dtype, image_infos, flip_aug, alpha_mask, random_crop)
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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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if __name__ == "__main__":
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# test code for Sd3TokenizeStrategy
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# tokenizer = sd3_models.SD3Tokenizer()
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strategy = Sd3TokenizeStrategy(256)
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text = "hello world"
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l_tokens, g_tokens, t5_tokens = strategy.tokenize(text)
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# print(l_tokens.shape)
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print(l_tokens)
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print(g_tokens)
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print(t5_tokens)
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texts = ["hello world", "the quick brown fox jumps over the lazy dog"]
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l_tokens_2 = strategy.clip_l(texts, max_length=77, padding="max_length", truncation=True, return_tensors="pt")
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g_tokens_2 = strategy.clip_g(texts, max_length=77, padding="max_length", truncation=True, return_tensors="pt")
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t5_tokens_2 = strategy.t5xxl(
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texts, max_length=strategy.t5xxl_max_length, padding="max_length", truncation=True, return_tensors="pt"
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)
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print(l_tokens_2)
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print(g_tokens_2)
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print(t5_tokens_2)
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# compare
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print(torch.allclose(l_tokens, l_tokens_2["input_ids"][0]))
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print(torch.allclose(g_tokens, g_tokens_2["input_ids"][0]))
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print(torch.allclose(t5_tokens, t5_tokens_2["input_ids"][0]))
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text = ",".join(["hello world! this is long text"] * 50)
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l_tokens, g_tokens, t5_tokens = strategy.tokenize(text)
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print(l_tokens)
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print(g_tokens)
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print(t5_tokens)
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print(f"model max length l: {strategy.clip_l.model_max_length}")
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print(f"model max length g: {strategy.clip_g.model_max_length}")
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print(f"model max length t5: {strategy.t5xxl.model_max_length}")
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