# Adapted from OpenSora and DiT # This source code is licensed under the license found in the # LICENSE file in the root directory of this source tree. # -------------------------------------------------------- # References: # DiT: https://github.com/facebookresearch/DiT # OpenSora: https://github.com/hpcaitech/Open-Sora # -------------------------------------------------------- import html import math import re import ftfy import numpy import numpy as np import torch import torch.nn as nn import torch.nn.functional as F import transformers from timm.models.vision_transformer import Mlp from transformers import AutoTokenizer, CLIPTextModel, CLIPTokenizer, T5EncoderModel from videosys.modules.embed import get_1d_sincos_pos_embed_from_grid, get_2d_sincos_pos_embed_from_grid transformers.logging.set_verbosity_error() # =============================================== # Text Embed # =============================================== class AbstractEncoder(nn.Module): def __init__(self): super().__init__() def encode(self, *args, **kwargs): raise NotImplementedError class FrozenCLIPEmbedder(AbstractEncoder): """Uses the CLIP transformer encoder for text (from Hugging Face)""" def __init__(self, path="openai/clip-vit-huge-patch14", device="cuda", max_length=77): super().__init__() self.tokenizer = CLIPTokenizer.from_pretrained(path) self.transformer = CLIPTextModel.from_pretrained(path) self.device = device self.max_length = max_length self._freeze() def _freeze(self): self.transformer = self.transformer.eval() for param in self.parameters(): param.requires_grad = False def forward(self, text): batch_encoding = self.tokenizer( text, truncation=True, max_length=self.max_length, return_length=True, return_overflowing_tokens=False, padding="max_length", return_tensors="pt", ) tokens = batch_encoding["input_ids"].to(self.device) outputs = self.transformer(input_ids=tokens) z = outputs.last_hidden_state pooled_z = outputs.pooler_output return z, pooled_z def encode(self, text): return self(text) class TextEmbedder(nn.Module): """ Embeds text prompt into vector representations. Also handles text dropout for classifier-free guidance. """ def __init__(self, path, hidden_size, dropout_prob=0.1): super().__init__() self.text_encoder = FrozenCLIPEmbedder(path=path) self.dropout_prob = dropout_prob output_dim = self.text_encoder.transformer.config.hidden_size self.output_projection = nn.Linear(output_dim, hidden_size) def token_drop(self, text_prompts, force_drop_ids=None): """ Drops text to enable classifier-free guidance. """ if force_drop_ids is None: drop_ids = numpy.random.uniform(0, 1, len(text_prompts)) < self.dropout_prob else: # TODO drop_ids = force_drop_ids == 1 labels = list(numpy.where(drop_ids, "", text_prompts)) # print(labels) return labels def forward(self, text_prompts, train, force_drop_ids=None): use_dropout = self.dropout_prob > 0 if (train and use_dropout) or (force_drop_ids is not None): text_prompts = self.token_drop(text_prompts, force_drop_ids) embeddings, pooled_embeddings = self.text_encoder(text_prompts) # return embeddings, pooled_embeddings text_embeddings = self.output_projection(pooled_embeddings) return text_embeddings class CaptionEmbedder(nn.Module): """ copied from https://github.com/hpcaitech/Open-Sora Embeds class labels into vector representations. Also handles label dropout for classifier-free guidance. """ def __init__(self, in_channels, hidden_size, uncond_prob, act_layer=nn.GELU(approximate="tanh"), token_num=120): super().__init__() self.y_proj = Mlp( in_features=in_channels, hidden_features=hidden_size, out_features=hidden_size, act_layer=act_layer, drop=0 ) self.register_buffer("y_embedding", nn.Parameter(torch.randn(token_num, in_channels) / in_channels**0.5)) self.uncond_prob = uncond_prob def token_drop(self, caption, force_drop_ids=None): """ Drops labels to enable classifier-free guidance. """ if force_drop_ids is None: drop_ids = torch.rand(caption.shape[0]).cuda() < self.uncond_prob else: drop_ids = force_drop_ids == 1 caption = torch.where(drop_ids[:, None, None, None], self.y_embedding, caption) return caption def forward(self, caption, train, force_drop_ids=None): if train: assert caption.shape[2:] == self.y_embedding.shape use_dropout = self.uncond_prob > 0 if (train and use_dropout) or (force_drop_ids is not None): caption = self.token_drop(caption, force_drop_ids) caption = self.y_proj(caption) return caption class T5Embedder: available_models = ["DeepFloyd/t5-v1_1-xxl"] def __init__( self, device, from_pretrained=None, *, cache_dir=None, hf_token=None, use_text_preprocessing=True, t5_model_kwargs=None, torch_dtype=None, use_offload_folder=None, model_max_length=120, local_files_only=False, ): self.device = torch.device(device) self.torch_dtype = torch_dtype or torch.bfloat16 self.cache_dir = cache_dir if t5_model_kwargs is None: t5_model_kwargs = { "low_cpu_mem_usage": True, "torch_dtype": self.torch_dtype, } if use_offload_folder is not None: t5_model_kwargs["offload_folder"] = use_offload_folder t5_model_kwargs["device_map"] = { "shared": self.device, "encoder.embed_tokens": self.device, "encoder.block.0": self.device, "encoder.block.1": self.device, "encoder.block.2": self.device, "encoder.block.3": self.device, "encoder.block.4": self.device, "encoder.block.5": self.device, "encoder.block.6": self.device, "encoder.block.7": self.device, "encoder.block.8": self.device, "encoder.block.9": self.device, "encoder.block.10": self.device, "encoder.block.11": self.device, "encoder.block.12": "disk", "encoder.block.13": "disk", "encoder.block.14": "disk", "encoder.block.15": "disk", "encoder.block.16": "disk", "encoder.block.17": "disk", "encoder.block.18": "disk", "encoder.block.19": "disk", "encoder.block.20": "disk", "encoder.block.21": "disk", "encoder.block.22": "disk", "encoder.block.23": "disk", "encoder.final_layer_norm": "disk", "encoder.dropout": "disk", } else: t5_model_kwargs["device_map"] = { "shared": self.device, "encoder": self.device, } self.use_text_preprocessing = use_text_preprocessing self.hf_token = hf_token assert from_pretrained in self.available_models self.tokenizer = AutoTokenizer.from_pretrained( from_pretrained, cache_dir=cache_dir, local_files_only=local_files_only, ) self.model = T5EncoderModel.from_pretrained( from_pretrained, cache_dir=cache_dir, local_files_only=local_files_only, **t5_model_kwargs, ).eval() self.model_max_length = model_max_length def get_text_embeddings(self, texts): text_tokens_and_mask = self.tokenizer( texts, max_length=self.model_max_length, padding="max_length", truncation=True, return_attention_mask=True, add_special_tokens=True, return_tensors="pt", ) input_ids = text_tokens_and_mask["input_ids"].to(self.device) attention_mask = text_tokens_and_mask["attention_mask"].to(self.device) with torch.no_grad(): text_encoder_embs = self.model( input_ids=input_ids, attention_mask=attention_mask, )["last_hidden_state"].detach() return text_encoder_embs, attention_mask class T5Encoder: def __init__( self, from_pretrained="DeepFloyd/t5-v1_1-xxl", model_max_length=120, device="cuda", dtype=torch.float, shardformer=False, ): assert from_pretrained is not None, "Please specify the path to the T5 model" self.t5 = T5Embedder( device=device, torch_dtype=dtype, from_pretrained=from_pretrained, model_max_length=model_max_length, ) self.t5.model.to(dtype=dtype) self.y_embedder = None self.model_max_length = model_max_length self.output_dim = self.t5.model.config.d_model if shardformer: self.shardformer_t5() def shardformer_t5(self): from colossalai.shardformer import ShardConfig, ShardFormer from videosys.core.shardformer.t5.policy import T5EncoderPolicy from videosys.utils.utils import requires_grad shard_config = ShardConfig( tensor_parallel_process_group=None, pipeline_stage_manager=None, enable_tensor_parallelism=False, enable_fused_normalization=False, enable_flash_attention=False, enable_jit_fused=True, enable_sequence_parallelism=False, enable_sequence_overlap=False, ) shard_former = ShardFormer(shard_config=shard_config) optim_model, _ = shard_former.optimize(self.t5.model, policy=T5EncoderPolicy()) self.t5.model = optim_model.half() # ensure the weights are frozen requires_grad(self.t5.model, False) def encode(self, text): caption_embs, emb_masks = self.t5.get_text_embeddings(text) caption_embs = caption_embs[:, None] return dict(y=caption_embs, mask=emb_masks) def null(self, n): null_y = self.y_embedder.y_embedding[None].repeat(n, 1, 1)[:, None] return null_y def basic_clean(text): text = ftfy.fix_text(text) text = html.unescape(html.unescape(text)) return text.strip() BAD_PUNCT_REGEX = re.compile( r"[" + "#®•©™&@·º½¾¿¡§~" + "\)" + "\(" + "\]" + "\[" + "\}" + "\{" + "\|" + "\\" + "\/" + "\*" + r"]{1,}" ) # noqa def clean_caption(caption): import urllib.parse as ul from bs4 import BeautifulSoup caption = str(caption) caption = ul.unquote_plus(caption) caption = caption.strip().lower() caption = re.sub("", "person", caption) # urls: caption = re.sub( r"\b((?:https?:(?:\/{1,3}|[a-zA-Z0-9%])|[a-zA-Z0-9.\-]+[.](?:com|co|ru|net|org|edu|gov|it)[\w/-]*\b\/?(?!@)))", # noqa "", caption, ) # regex for urls caption = re.sub( r"\b((?:www:(?:\/{1,3}|[a-zA-Z0-9%])|[a-zA-Z0-9.\-]+[.](?:com|co|ru|net|org|edu|gov|it)[\w/-]*\b\/?(?!@)))", # noqa "", caption, ) # regex for urls # html: caption = BeautifulSoup(caption, features="html.parser").text # @ caption = re.sub(r"@[\w\d]+\b", "", caption) # 31C0—31EF CJK Strokes # 31F0—31FF Katakana Phonetic Extensions # 3200—32FF Enclosed CJK Letters and Months # 3300—33FF CJK Compatibility # 3400—4DBF CJK Unified Ideographs Extension A # 4DC0—4DFF Yijing Hexagram Symbols # 4E00—9FFF CJK Unified Ideographs caption = re.sub(r"[\u31c0-\u31ef]+", "", caption) caption = re.sub(r"[\u31f0-\u31ff]+", "", caption) caption = re.sub(r"[\u3200-\u32ff]+", "", caption) caption = re.sub(r"[\u3300-\u33ff]+", "", caption) caption = re.sub(r"[\u3400-\u4dbf]+", "", caption) caption = re.sub(r"[\u4dc0-\u4dff]+", "", caption) caption = re.sub(r"[\u4e00-\u9fff]+", "", caption) ####################################################### # все виды тире / all types of dash --> "-" caption = re.sub( r"[\u002D\u058A\u05BE\u1400\u1806\u2010-\u2015\u2E17\u2E1A\u2E3A\u2E3B\u2E40\u301C\u3030\u30A0\uFE31\uFE32\uFE58\uFE63\uFF0D]+", # noqa "-", caption, ) # кавычки к одному стандарту caption = re.sub(r"[`´«»“”¨]", '"', caption) caption = re.sub(r"[‘’]", "'", caption) # " caption = re.sub(r""?", "", caption) # & caption = re.sub(r"&", "", caption) # ip adresses: caption = re.sub(r"\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}", " ", caption) # article ids: caption = re.sub(r"\d:\d\d\s+$", "", caption) # \n caption = re.sub(r"\\n", " ", caption) # "#123" caption = re.sub(r"#\d{1,3}\b", "", caption) # "#12345.." caption = re.sub(r"#\d{5,}\b", "", caption) # "123456.." caption = re.sub(r"\b\d{6,}\b", "", caption) # filenames: caption = re.sub(r"[\S]+\.(?:png|jpg|jpeg|bmp|webp|eps|pdf|apk|mp4)", "", caption) # caption = re.sub(r"[\"\']{2,}", r'"', caption) # """AUSVERKAUFT""" caption = re.sub(r"[\.]{2,}", r" ", caption) # """AUSVERKAUFT""" caption = re.sub(BAD_PUNCT_REGEX, r" ", caption) # ***AUSVERKAUFT***, #AUSVERKAUFT caption = re.sub(r"\s+\.\s+", r" ", caption) # " . " # this-is-my-cute-cat / this_is_my_cute_cat regex2 = re.compile(r"(?:\-|\_)") if len(re.findall(regex2, caption)) > 3: caption = re.sub(regex2, " ", caption) caption = basic_clean(caption) caption = re.sub(r"\b[a-zA-Z]{1,3}\d{3,15}\b", "", caption) # jc6640 caption = re.sub(r"\b[a-zA-Z]+\d+[a-zA-Z]+\b", "", caption) # jc6640vc caption = re.sub(r"\b\d+[a-zA-Z]+\d+\b", "", caption) # 6640vc231 caption = re.sub(r"(worldwide\s+)?(free\s+)?shipping", "", caption) caption = re.sub(r"(free\s)?download(\sfree)?", "", caption) caption = re.sub(r"\bclick\b\s(?:for|on)\s\w+", "", caption) caption = re.sub(r"\b(?:png|jpg|jpeg|bmp|webp|eps|pdf|apk|mp4)(\simage[s]?)?", "", caption) caption = re.sub(r"\bpage\s+\d+\b", "", caption) caption = re.sub(r"\b\d*[a-zA-Z]+\d+[a-zA-Z]+\d+[a-zA-Z\d]*\b", r" ", caption) # j2d1a2a... caption = re.sub(r"\b\d+\.?\d*[xх×]\d+\.?\d*\b", "", caption) caption = re.sub(r"\b\s+\:\s+", r": ", caption) caption = re.sub(r"(\D[,\./])\b", r"\1 ", caption) caption = re.sub(r"\s+", " ", caption) caption.strip() caption = re.sub(r"^[\"\']([\w\W]+)[\"\']$", r"\1", caption) caption = re.sub(r"^[\'\_,\-\:;]", r"", caption) caption = re.sub(r"[\'\_,\-\:\-\+]$", r"", caption) caption = re.sub(r"^\.\S+$", "", caption) return caption.strip() def text_preprocessing(text, use_text_preprocessing: bool = True): if use_text_preprocessing: # The exact text cleaning as was in the training stage: text = clean_caption(text) text = clean_caption(text) return text else: return text.lower().strip() class TimestepEmbedder(nn.Module): """ Embeds scalar timesteps into vector representations. """ def __init__(self, hidden_size, frequency_embedding_size=256): super().__init__() self.mlp = nn.Sequential( nn.Linear(frequency_embedding_size, hidden_size, bias=True), nn.SiLU(), nn.Linear(hidden_size, hidden_size, bias=True), ) self.frequency_embedding_size = frequency_embedding_size @staticmethod def timestep_embedding(t, dim, max_period=10000): """ Create sinusoidal timestep embeddings. :param t: a 1-D Tensor of N indices, one per batch element. These may be fractional. :param dim: the dimension of the output. :param max_period: controls the minimum frequency of the embeddings. :return: an (N, D) Tensor of positional embeddings. """ # https://github.com/openai/glide-text2im/blob/main/glide_text2im/nn.py half = dim // 2 freqs = torch.exp(-math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half) freqs = freqs.to(device=t.device) args = t[:, None].float() * freqs[None] embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1) if dim % 2: embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1) return embedding def forward(self, t, dtype): t_freq = self.timestep_embedding(t, self.frequency_embedding_size) if t_freq.dtype != dtype: t_freq = t_freq.to(dtype) t_emb = self.mlp(t_freq) return t_emb # =============================================== # Sine/Cosine Positional Embedding Functions # =============================================== def get_2d_sincos_pos_embed(embed_dim, grid_size, cls_token=False, extra_tokens=0, scale=1.0, base_size=None): """ grid_size: int of the grid height and width return: pos_embed: [grid_size*grid_size, embed_dim] or [1+grid_size*grid_size, embed_dim] (w/ or w/o cls_token) """ if not isinstance(grid_size, tuple): grid_size = (grid_size, grid_size) grid_h = np.arange(grid_size[0], dtype=np.float32) / scale grid_w = np.arange(grid_size[1], dtype=np.float32) / scale if base_size is not None: grid_h *= base_size / grid_size[0] grid_w *= base_size / grid_size[1] grid = np.meshgrid(grid_w, grid_h) # here w goes first grid = np.stack(grid, axis=0) grid = grid.reshape([2, 1, grid_size[1], grid_size[0]]) pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid) if cls_token and extra_tokens > 0: pos_embed = np.concatenate([np.zeros([extra_tokens, embed_dim]), pos_embed], axis=0) return pos_embed def get_1d_sincos_pos_embed(embed_dim, length, scale=1.0): pos = np.arange(0, length)[..., None] / scale return get_1d_sincos_pos_embed_from_grid(embed_dim, pos) # =============================================== # Patch Embed # =============================================== class PatchEmbed3D(nn.Module): """Video to Patch Embedding. Args: patch_size (int): Patch token size. Default: (2,4,4). in_chans (int): Number of input video channels. Default: 3. embed_dim (int): Number of linear projection output channels. Default: 96. norm_layer (nn.Module, optional): Normalization layer. Default: None """ def __init__( self, patch_size=(2, 4, 4), in_chans=3, embed_dim=96, norm_layer=None, flatten=True, ): super().__init__() self.patch_size = patch_size self.flatten = flatten self.in_chans = in_chans self.embed_dim = embed_dim self.proj = nn.Conv3d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size) if norm_layer is not None: self.norm = norm_layer(embed_dim) else: self.norm = None def forward(self, x): """Forward function.""" # padding _, _, D, H, W = x.size() if W % self.patch_size[2] != 0: x = F.pad(x, (0, self.patch_size[2] - W % self.patch_size[2])) if H % self.patch_size[1] != 0: x = F.pad(x, (0, 0, 0, self.patch_size[1] - H % self.patch_size[1])) if D % self.patch_size[0] != 0: x = F.pad(x, (0, 0, 0, 0, 0, self.patch_size[0] - D % self.patch_size[0])) x = self.proj(x) # (B C T H W) if self.norm is not None: D, Wh, Ww = x.size(2), x.size(3), x.size(4) x = x.flatten(2).transpose(1, 2) x = self.norm(x) x = x.transpose(1, 2).view(-1, self.embed_dim, D, Wh, Ww) if self.flatten: x = x.flatten(2).transpose(1, 2) # BCTHW -> BNC return x