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| # 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>", "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 | |
| # @<nickname> | |
| 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 | |
| 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 | |