# Adapted from Open-Sora-Plan # This source code is licensed under the license found in the # LICENSE file in the root directory of this source tree. # -------------------------------------------------------- # References: # Open-Sora-Plan: https://github.com/PKU-YuanGroup/Open-Sora-Plan # -------------------------------------------------------- import json import os from dataclasses import dataclass from functools import partial from importlib import import_module from typing import Any, Callable, Dict, Optional, Tuple import numpy as np import torch import torch.nn.functional as F from diffusers.configuration_utils import ConfigMixin, register_to_config from diffusers.models.activations import GEGLU, GELU, ApproximateGELU from diffusers.models.attention_processor import ( AttnAddedKVProcessor, AttnAddedKVProcessor2_0, AttnProcessor, CustomDiffusionAttnProcessor, CustomDiffusionAttnProcessor2_0, CustomDiffusionXFormersAttnProcessor, LoRAAttnAddedKVProcessor, LoRAAttnProcessor, LoRAAttnProcessor2_0, LoRAXFormersAttnProcessor, SlicedAttnAddedKVProcessor, SlicedAttnProcessor, SpatialNorm, XFormersAttnAddedKVProcessor, XFormersAttnProcessor, ) from diffusers.models.embeddings import SinusoidalPositionalEmbedding, TimestepEmbedding, Timesteps from diffusers.models.lora import LoRACompatibleConv, LoRACompatibleLinear from diffusers.models.modeling_utils import ModelMixin from diffusers.models.normalization import AdaLayerNorm, AdaLayerNormZero from diffusers.utils import USE_PEFT_BACKEND, BaseOutput, deprecate, is_xformers_available from diffusers.utils.torch_utils import maybe_allow_in_graph from einops import rearrange, repeat from torch import nn from videosys.core.comm import ( all_to_all_with_pad, gather_sequence, get_spatial_pad, get_temporal_pad, set_spatial_pad, set_temporal_pad, split_sequence, ) from videosys.core.pab_mgr import ( enable_pab, get_mlp_output, if_broadcast_cross, if_broadcast_mlp, if_broadcast_spatial, if_broadcast_temporal, save_mlp_output, ) from videosys.core.parallel_mgr import ( enable_sequence_parallel, get_cfg_parallel_group, get_cfg_parallel_size, get_sequence_parallel_group, ) from videosys.utils.logging import logger from videosys.utils.utils import batch_func if is_xformers_available(): import xformers import xformers.ops else: xformers = None SPATIAL_LIST = [] TEMPROAL_LIST = [] CROSS_LIST = [] def get_2d_sincos_pos_embed( embed_dim, grid_size, cls_token=False, extra_tokens=0, interpolation_scale=1.0, base_size=16 ): """ 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 isinstance(grid_size, int): grid_size = (grid_size, grid_size) grid_h = np.arange(grid_size[0], dtype=np.float32) / (grid_size[0] / base_size) / interpolation_scale grid_w = np.arange(grid_size[1], dtype=np.float32) / (grid_size[1] / base_size) / interpolation_scale 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_2d_sincos_pos_embed_from_grid(embed_dim, grid): if embed_dim % 2 != 0: raise ValueError("embed_dim must be divisible by 2") # use half of dimensions to encode grid_h emb_h = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[0]) # (H*W, D/2) emb_w = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[1]) # (H*W, D/2) emb = np.concatenate([emb_h, emb_w], axis=1) # (H*W, D) return emb def get_1d_sincos_pos_embed(embed_dim, length, interpolation_scale=1.0, base_size=16): pos = torch.arange(0, length).unsqueeze(1) / interpolation_scale pos_embed = get_1d_sincos_pos_embed_from_grid(embed_dim, pos) return pos_embed def get_1d_sincos_pos_embed_from_grid(embed_dim, pos): """ embed_dim: output dimension for each position pos: a list of positions to be encoded: size (M,) out: (M, D) """ if embed_dim % 2 != 0: raise ValueError("embed_dim must be divisible by 2") omega = np.arange(embed_dim // 2, dtype=np.float64) omega /= embed_dim / 2.0 omega = 1.0 / 10000**omega # (D/2,) pos = pos.reshape(-1) # (M,) out = np.einsum("m,d->md", pos, omega) # (M, D/2), outer product emb_sin = np.sin(out) # (M, D/2) emb_cos = np.cos(out) # (M, D/2) emb = np.concatenate([emb_sin, emb_cos], axis=1) # (M, D) return emb class RoPE2D(torch.nn.Module): def __init__(self, freq=10000.0, F0=1.0, scaling_factor=1.0): super().__init__() self.base = freq self.F0 = F0 self.scaling_factor = scaling_factor self.cache = {} def get_cos_sin(self, D, seq_len, device, dtype): if (D, seq_len, device, dtype) not in self.cache: inv_freq = 1.0 / (self.base ** (torch.arange(0, D, 2).float().to(device) / D)) t = torch.arange(seq_len, device=device, dtype=inv_freq.dtype) freqs = torch.einsum("i,j->ij", t, inv_freq).to(dtype) freqs = torch.cat((freqs, freqs), dim=-1) cos = freqs.cos() # (Seq, Dim) sin = freqs.sin() self.cache[D, seq_len, device, dtype] = (cos, sin) return self.cache[D, seq_len, device, dtype] @staticmethod def rotate_half(x): x1, x2 = x[..., : x.shape[-1] // 2], x[..., x.shape[-1] // 2 :] return torch.cat((-x2, x1), dim=-1) def apply_rope1d(self, tokens, pos1d, cos, sin): assert pos1d.ndim == 2 cos = torch.nn.functional.embedding(pos1d, cos)[:, None, :, :] sin = torch.nn.functional.embedding(pos1d, sin)[:, None, :, :] return (tokens * cos) + (self.rotate_half(tokens) * sin) def forward(self, tokens, positions): """ input: * tokens: batch_size x nheads x ntokens x dim * positions: batch_size x ntokens x 2 (y and x position of each token) output: * tokens after appplying RoPE2D (batch_size x nheads x ntokens x dim) """ assert tokens.size(3) % 2 == 0, "number of dimensions should be a multiple of two" D = tokens.size(3) // 2 assert positions.ndim == 3 and positions.shape[-1] == 2 # Batch, Seq, 2 cos, sin = self.get_cos_sin(D, int(positions.max()) + 1, tokens.device, tokens.dtype) # split features into two along the feature dimension, and apply rope1d on each half y, x = tokens.chunk(2, dim=-1) y = self.apply_rope1d(y, positions[:, :, 0], cos, sin) x = self.apply_rope1d(x, positions[:, :, 1], cos, sin) tokens = torch.cat((y, x), dim=-1) return tokens class LinearScalingRoPE2D(RoPE2D): """Code from https://github.com/huggingface/transformers/blob/main/src/transformers/models/llama/modeling_llama.py#L148""" def forward(self, tokens, positions): # difference to the original RoPE: a scaling factor is aplied to the position ids dtype = positions.dtype positions = positions.float() / self.scaling_factor positions = positions.to(dtype) tokens = super().forward(tokens, positions) return tokens class RoPE1D(torch.nn.Module): def __init__(self, freq=10000.0, F0=1.0, scaling_factor=1.0): super().__init__() self.base = freq self.F0 = F0 self.scaling_factor = scaling_factor self.cache = {} def get_cos_sin(self, D, seq_len, device, dtype): if (D, seq_len, device, dtype) not in self.cache: inv_freq = 1.0 / (self.base ** (torch.arange(0, D, 2).float().to(device) / D)) t = torch.arange(seq_len, device=device, dtype=inv_freq.dtype) freqs = torch.einsum("i,j->ij", t, inv_freq).to(dtype) freqs = torch.cat((freqs, freqs), dim=-1) cos = freqs.cos() # (Seq, Dim) sin = freqs.sin() self.cache[D, seq_len, device, dtype] = (cos, sin) return self.cache[D, seq_len, device, dtype] @staticmethod def rotate_half(x): x1, x2 = x[..., : x.shape[-1] // 2], x[..., x.shape[-1] // 2 :] return torch.cat((-x2, x1), dim=-1) def apply_rope1d(self, tokens, pos1d, cos, sin): assert pos1d.ndim == 2 cos = torch.nn.functional.embedding(pos1d, cos)[:, None, :, :] sin = torch.nn.functional.embedding(pos1d, sin)[:, None, :, :] return (tokens * cos) + (self.rotate_half(tokens) * sin) def forward(self, tokens, positions): """ input: * tokens: batch_size x nheads x ntokens x dim * positions: batch_size x ntokens (t position of each token) output: * tokens after appplying RoPE2D (batch_size x nheads x ntokens x dim) """ D = tokens.size(3) assert positions.ndim == 2 # Batch, Seq cos, sin = self.get_cos_sin(D, int(positions.max()) + 1, tokens.device, tokens.dtype) tokens = self.apply_rope1d(tokens, positions, cos, sin) return tokens class LinearScalingRoPE1D(RoPE1D): """Code from https://github.com/huggingface/transformers/blob/main/src/transformers/models/llama/modeling_llama.py#L148""" def forward(self, tokens, positions): # difference to the original RoPE: a scaling factor is aplied to the position ids dtype = positions.dtype positions = positions.float() / self.scaling_factor positions = positions.to(dtype) tokens = super().forward(tokens, positions) return tokens class PositionGetter2D(object): """return positions of patches""" def __init__(self): self.cache_positions = {} def __call__(self, b, h, w, device): if not (h, w) in self.cache_positions: x = torch.arange(w, device=device) y = torch.arange(h, device=device) self.cache_positions[h, w] = torch.cartesian_prod(y, x) # (h, w, 2) pos = self.cache_positions[h, w].view(1, h * w, 2).expand(b, -1, 2).clone() return pos class PositionGetter1D(object): """return positions of patches""" def __init__(self): self.cache_positions = {} def __call__(self, b, l, device): if not (l) in self.cache_positions: x = torch.arange(l, device=device) self.cache_positions[l] = x # (l, ) pos = self.cache_positions[l].view(1, l).expand(b, -1).clone() return pos class CombinedTimestepSizeEmbeddings(nn.Module): """ For PixArt-Alpha. Reference: https://github.com/PixArt-alpha/PixArt-alpha/blob/0f55e922376d8b797edd44d25d0e7464b260dcab/diffusion/model/nets/PixArtMS.py#L164C9-L168C29 """ def __init__(self, embedding_dim, size_emb_dim, use_additional_conditions: bool = False): super().__init__() self.outdim = size_emb_dim self.time_proj = Timesteps(num_channels=256, flip_sin_to_cos=True, downscale_freq_shift=0) self.timestep_embedder = TimestepEmbedding(in_channels=256, time_embed_dim=embedding_dim) self.use_additional_conditions = use_additional_conditions if use_additional_conditions: self.use_additional_conditions = True self.additional_condition_proj = Timesteps(num_channels=256, flip_sin_to_cos=True, downscale_freq_shift=0) self.resolution_embedder = TimestepEmbedding(in_channels=256, time_embed_dim=size_emb_dim) self.aspect_ratio_embedder = TimestepEmbedding(in_channels=256, time_embed_dim=size_emb_dim) def apply_condition(self, size: torch.Tensor, batch_size: int, embedder: nn.Module): if size.ndim == 1: size = size[:, None] if size.shape[0] != batch_size: size = size.repeat(batch_size // size.shape[0], 1) if size.shape[0] != batch_size: raise ValueError(f"`batch_size` should be {size.shape[0]} but found {batch_size}.") current_batch_size, dims = size.shape[0], size.shape[1] size = size.reshape(-1) size_freq = self.additional_condition_proj(size).to(size.dtype) size_emb = embedder(size_freq) size_emb = size_emb.reshape(current_batch_size, dims * self.outdim) return size_emb def forward(self, timestep, resolution, aspect_ratio, batch_size, hidden_dtype): timesteps_proj = self.time_proj(timestep) timesteps_emb = self.timestep_embedder(timesteps_proj.to(dtype=hidden_dtype)) # (N, D) if self.use_additional_conditions: resolution = self.apply_condition(resolution, batch_size=batch_size, embedder=self.resolution_embedder) aspect_ratio = self.apply_condition( aspect_ratio, batch_size=batch_size, embedder=self.aspect_ratio_embedder ) conditioning = timesteps_emb + torch.cat([resolution, aspect_ratio], dim=1) else: conditioning = timesteps_emb return conditioning class CaptionProjection(nn.Module): """ Projects caption embeddings. Also handles dropout for classifier-free guidance. Adapted from https://github.com/PixArt-alpha/PixArt-alpha/blob/master/diffusion/model/nets/PixArt_blocks.py """ def __init__(self, in_features, hidden_size, num_tokens=120): super().__init__() self.linear_1 = nn.Linear(in_features=in_features, out_features=hidden_size, bias=True) self.act_1 = nn.GELU(approximate="tanh") self.linear_2 = nn.Linear(in_features=hidden_size, out_features=hidden_size, bias=True) self.register_buffer("y_embedding", nn.Parameter(torch.randn(num_tokens, in_features) / in_features**0.5)) def forward(self, caption, force_drop_ids=None): hidden_states = self.linear_1(caption) hidden_states = self.act_1(hidden_states) hidden_states = self.linear_2(hidden_states) return hidden_states class PatchEmbed(nn.Module): """2D Image to Patch Embedding""" def __init__( self, height=224, width=224, patch_size=16, in_channels=3, embed_dim=768, layer_norm=False, flatten=True, bias=True, interpolation_scale=1, ): super().__init__() num_patches = (height // patch_size) * (width // patch_size) self.flatten = flatten self.layer_norm = layer_norm self.proj = nn.Conv2d( in_channels, embed_dim, kernel_size=(patch_size, patch_size), stride=patch_size, bias=bias ) if layer_norm: self.norm = nn.LayerNorm(embed_dim, elementwise_affine=False, eps=1e-6) else: self.norm = None self.patch_size = patch_size # See: # https://github.com/PixArt-alpha/PixArt-alpha/blob/0f55e922376d8b797edd44d25d0e7464b260dcab/diffusion/model/nets/PixArtMS.py#L161 self.height, self.width = height // patch_size, width // patch_size self.base_size = height // patch_size self.interpolation_scale = interpolation_scale pos_embed = get_2d_sincos_pos_embed( embed_dim, int(num_patches**0.5), base_size=self.base_size, interpolation_scale=self.interpolation_scale ) self.register_buffer("pos_embed", torch.from_numpy(pos_embed).float().unsqueeze(0), persistent=False) def forward(self, latent): height, width = latent.shape[-2] // self.patch_size, latent.shape[-1] // self.patch_size latent = self.proj(latent) if self.flatten: latent = latent.flatten(2).transpose(1, 2) # BCHW -> BNC if self.layer_norm: latent = self.norm(latent) # Interpolate positional embeddings if needed. # (For PixArt-Alpha: https://github.com/PixArt-alpha/PixArt-alpha/blob/0f55e922376d8b797edd44d25d0e7464b260dcab/diffusion/model/nets/PixArtMS.py#L162C151-L162C160) if self.height != height or self.width != width: # raise ValueError pos_embed = get_2d_sincos_pos_embed( embed_dim=self.pos_embed.shape[-1], grid_size=(height, width), base_size=self.base_size, interpolation_scale=self.interpolation_scale, ) pos_embed = torch.from_numpy(pos_embed) pos_embed = pos_embed.float().unsqueeze(0).to(latent.device) else: pos_embed = self.pos_embed return (latent + pos_embed).to(latent.dtype) @maybe_allow_in_graph class Attention(nn.Module): r""" A cross attention layer. Parameters: query_dim (`int`): The number of channels in the query. cross_attention_dim (`int`, *optional*): The number of channels in the encoder_hidden_states. If not given, defaults to `query_dim`. heads (`int`, *optional*, defaults to 8): The number of heads to use for multi-head attention. dim_head (`int`, *optional*, defaults to 64): The number of channels in each head. dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use. bias (`bool`, *optional*, defaults to False): Set to `True` for the query, key, and value linear layers to contain a bias parameter. upcast_attention (`bool`, *optional*, defaults to False): Set to `True` to upcast the attention computation to `float32`. upcast_softmax (`bool`, *optional*, defaults to False): Set to `True` to upcast the softmax computation to `float32`. cross_attention_norm (`str`, *optional*, defaults to `None`): The type of normalization to use for the cross attention. Can be `None`, `layer_norm`, or `group_norm`. cross_attention_norm_num_groups (`int`, *optional*, defaults to 32): The number of groups to use for the group norm in the cross attention. added_kv_proj_dim (`int`, *optional*, defaults to `None`): The number of channels to use for the added key and value projections. If `None`, no projection is used. norm_num_groups (`int`, *optional*, defaults to `None`): The number of groups to use for the group norm in the attention. spatial_norm_dim (`int`, *optional*, defaults to `None`): The number of channels to use for the spatial normalization. out_bias (`bool`, *optional*, defaults to `True`): Set to `True` to use a bias in the output linear layer. scale_qk (`bool`, *optional*, defaults to `True`): Set to `True` to scale the query and key by `1 / sqrt(dim_head)`. only_cross_attention (`bool`, *optional*, defaults to `False`): Set to `True` to only use cross attention and not added_kv_proj_dim. Can only be set to `True` if `added_kv_proj_dim` is not `None`. eps (`float`, *optional*, defaults to 1e-5): An additional value added to the denominator in group normalization that is used for numerical stability. rescale_output_factor (`float`, *optional*, defaults to 1.0): A factor to rescale the output by dividing it with this value. residual_connection (`bool`, *optional*, defaults to `False`): Set to `True` to add the residual connection to the output. _from_deprecated_attn_block (`bool`, *optional*, defaults to `False`): Set to `True` if the attention block is loaded from a deprecated state dict. processor (`AttnProcessor`, *optional*, defaults to `None`): The attention processor to use. If `None`, defaults to `AttnProcessor2_0` if `torch 2.x` is used and `AttnProcessor` otherwise. """ def __init__( self, query_dim: int, cross_attention_dim: Optional[int] = None, heads: int = 8, dim_head: int = 64, dropout: float = 0.0, bias: bool = False, upcast_attention: bool = False, upcast_softmax: bool = False, cross_attention_norm: Optional[str] = None, cross_attention_norm_num_groups: int = 32, added_kv_proj_dim: Optional[int] = None, norm_num_groups: Optional[int] = None, spatial_norm_dim: Optional[int] = None, out_bias: bool = True, scale_qk: bool = True, only_cross_attention: bool = False, eps: float = 1e-5, rescale_output_factor: float = 1.0, residual_connection: bool = False, _from_deprecated_attn_block: bool = False, processor: Optional["AttnProcessor"] = None, attention_mode: str = "xformers", use_rope: bool = False, rope_scaling: Optional[Dict] = None, compress_kv_factor: Optional[Tuple] = None, ): super().__init__() self.inner_dim = dim_head * heads self.cross_attention_dim = cross_attention_dim if cross_attention_dim is not None else query_dim self.upcast_attention = upcast_attention self.upcast_softmax = upcast_softmax self.rescale_output_factor = rescale_output_factor self.residual_connection = residual_connection self.dropout = dropout self.use_rope = use_rope self.rope_scaling = rope_scaling self.compress_kv_factor = compress_kv_factor # we make use of this private variable to know whether this class is loaded # with an deprecated state dict so that we can convert it on the fly self._from_deprecated_attn_block = _from_deprecated_attn_block self.scale_qk = scale_qk self.scale = dim_head**-0.5 if self.scale_qk else 1.0 self.heads = heads # for slice_size > 0 the attention score computation # is split across the batch axis to save memory # You can set slice_size with `set_attention_slice` self.sliceable_head_dim = heads self.added_kv_proj_dim = added_kv_proj_dim self.only_cross_attention = only_cross_attention if self.added_kv_proj_dim is None and self.only_cross_attention: raise ValueError( "`only_cross_attention` can only be set to True if `added_kv_proj_dim` is not None. Make sure to set either `only_cross_attention=False` or define `added_kv_proj_dim`." ) if norm_num_groups is not None: self.group_norm = nn.GroupNorm(num_channels=query_dim, num_groups=norm_num_groups, eps=eps, affine=True) else: self.group_norm = None if spatial_norm_dim is not None: self.spatial_norm = SpatialNorm(f_channels=query_dim, zq_channels=spatial_norm_dim) else: self.spatial_norm = None if cross_attention_norm is None: self.norm_cross = None elif cross_attention_norm == "layer_norm": self.norm_cross = nn.LayerNorm(self.cross_attention_dim) elif cross_attention_norm == "group_norm": if self.added_kv_proj_dim is not None: # The given `encoder_hidden_states` are initially of shape # (batch_size, seq_len, added_kv_proj_dim) before being projected # to (batch_size, seq_len, cross_attention_dim). The norm is applied # before the projection, so we need to use `added_kv_proj_dim` as # the number of channels for the group norm. norm_cross_num_channels = added_kv_proj_dim else: norm_cross_num_channels = self.cross_attention_dim self.norm_cross = nn.GroupNorm( num_channels=norm_cross_num_channels, num_groups=cross_attention_norm_num_groups, eps=1e-5, affine=True ) else: raise ValueError( f"unknown cross_attention_norm: {cross_attention_norm}. Should be None, 'layer_norm' or 'group_norm'" ) if USE_PEFT_BACKEND: linear_cls = nn.Linear else: linear_cls = LoRACompatibleLinear assert not ( self.use_rope and (self.compress_kv_factor is not None) ), "Can not both enable compressing kv and using rope" if self.compress_kv_factor is not None: self._init_compress() self.to_q = linear_cls(query_dim, self.inner_dim, bias=bias) if not self.only_cross_attention: # only relevant for the `AddedKVProcessor` classes self.to_k = linear_cls(self.cross_attention_dim, self.inner_dim, bias=bias) self.to_v = linear_cls(self.cross_attention_dim, self.inner_dim, bias=bias) else: self.to_k = None self.to_v = None if self.added_kv_proj_dim is not None: self.add_k_proj = linear_cls(added_kv_proj_dim, self.inner_dim) self.add_v_proj = linear_cls(added_kv_proj_dim, self.inner_dim) self.to_out = nn.ModuleList([]) self.to_out.append(linear_cls(self.inner_dim, query_dim, bias=out_bias)) self.to_out.append(nn.Dropout(dropout)) # set attention processor # We use the AttnProcessor2_0 by default when torch 2.x is used which uses # torch.nn.functional.scaled_dot_product_attention for native Flash/memory_efficient_attention # but only if it has the default `scale` argument. TODO remove scale_qk check when we move to torch 2.1 if processor is None: processor = ( AttnProcessor2_0( self.inner_dim, attention_mode, use_rope, rope_scaling=rope_scaling, compress_kv_factor=compress_kv_factor, ) if hasattr(F, "scaled_dot_product_attention") and self.scale_qk else AttnProcessor() ) self.set_processor(processor) def set_use_memory_efficient_attention_xformers( self, use_memory_efficient_attention_xformers: bool, attention_op: Optional[Callable] = None ) -> None: r""" Set whether to use memory efficient attention from `xformers` or not. Args: use_memory_efficient_attention_xformers (`bool`): Whether to use memory efficient attention from `xformers` or not. attention_op (`Callable`, *optional*): The attention operation to use. Defaults to `None` which uses the default attention operation from `xformers`. """ is_lora = hasattr(self, "processor") is_custom_diffusion = hasattr(self, "processor") and isinstance( self.processor, (CustomDiffusionAttnProcessor, CustomDiffusionXFormersAttnProcessor, CustomDiffusionAttnProcessor2_0), ) is_added_kv_processor = hasattr(self, "processor") and isinstance( self.processor, ( AttnAddedKVProcessor, AttnAddedKVProcessor2_0, SlicedAttnAddedKVProcessor, XFormersAttnAddedKVProcessor, LoRAAttnAddedKVProcessor, ), ) if use_memory_efficient_attention_xformers: if is_added_kv_processor and (is_lora or is_custom_diffusion): raise NotImplementedError( f"Memory efficient attention is currently not supported for LoRA or custom diffusion for attention processor type {self.processor}" ) if not is_xformers_available(): raise ModuleNotFoundError( ( "Refer to https://github.com/facebookresearch/xformers for more information on how to install" " xformers" ), name="xformers", ) elif not torch.cuda.is_available(): raise ValueError( "torch.cuda.is_available() should be True but is False. xformers' memory efficient attention is" " only available for GPU " ) else: try: # Make sure we can run the memory efficient attention _ = xformers.ops.memory_efficient_attention( torch.randn((1, 2, 40), device="cuda"), torch.randn((1, 2, 40), device="cuda"), torch.randn((1, 2, 40), device="cuda"), ) except Exception as e: raise e if is_lora: # TODO (sayakpaul): should we throw a warning if someone wants to use the xformers # variant when using PT 2.0 now that we have LoRAAttnProcessor2_0? processor = LoRAXFormersAttnProcessor( hidden_size=self.processor.hidden_size, cross_attention_dim=self.processor.cross_attention_dim, rank=self.processor.rank, attention_op=attention_op, ) processor.load_state_dict(self.processor.state_dict()) processor.to(self.processor.to_q_lora.up.weight.device) elif is_custom_diffusion: processor = CustomDiffusionXFormersAttnProcessor( train_kv=self.processor.train_kv, train_q_out=self.processor.train_q_out, hidden_size=self.processor.hidden_size, cross_attention_dim=self.processor.cross_attention_dim, attention_op=attention_op, ) processor.load_state_dict(self.processor.state_dict()) if hasattr(self.processor, "to_k_custom_diffusion"): processor.to(self.processor.to_k_custom_diffusion.weight.device) elif is_added_kv_processor: # TODO(Patrick, Suraj, William) - currently xformers doesn't work for UnCLIP # which uses this type of cross attention ONLY because the attention mask of format # [0, ..., -10.000, ..., 0, ...,] is not supported # throw warning logger.info( "Memory efficient attention with `xformers` might currently not work correctly if an attention mask is required for the attention operation." ) processor = XFormersAttnAddedKVProcessor(attention_op=attention_op) else: processor = XFormersAttnProcessor(attention_op=attention_op) else: if is_lora: attn_processor_class = ( LoRAAttnProcessor2_0 if hasattr(F, "scaled_dot_product_attention") else LoRAAttnProcessor ) processor = attn_processor_class( hidden_size=self.processor.hidden_size, cross_attention_dim=self.processor.cross_attention_dim, rank=self.processor.rank, ) processor.load_state_dict(self.processor.state_dict()) processor.to(self.processor.to_q_lora.up.weight.device) elif is_custom_diffusion: attn_processor_class = ( CustomDiffusionAttnProcessor2_0 if hasattr(F, "scaled_dot_product_attention") else CustomDiffusionAttnProcessor ) processor = attn_processor_class( train_kv=self.processor.train_kv, train_q_out=self.processor.train_q_out, hidden_size=self.processor.hidden_size, cross_attention_dim=self.processor.cross_attention_dim, ) processor.load_state_dict(self.processor.state_dict()) if hasattr(self.processor, "to_k_custom_diffusion"): processor.to(self.processor.to_k_custom_diffusion.weight.device) else: # set attention processor # We use the AttnProcessor2_0 by default when torch 2.x is used which uses # torch.nn.functional.scaled_dot_product_attention for native Flash/memory_efficient_attention # but only if it has the default `scale` argument. TODO remove scale_qk check when we move to torch 2.1 processor = ( AttnProcessor2_0() if hasattr(F, "scaled_dot_product_attention") and self.scale_qk else AttnProcessor() ) self.set_processor(processor) def set_attention_slice(self, slice_size: int) -> None: r""" Set the slice size for attention computation. Args: slice_size (`int`): The slice size for attention computation. """ if slice_size is not None and slice_size > self.sliceable_head_dim: raise ValueError(f"slice_size {slice_size} has to be smaller or equal to {self.sliceable_head_dim}.") if slice_size is not None and self.added_kv_proj_dim is not None: processor = SlicedAttnAddedKVProcessor(slice_size) elif slice_size is not None: processor = SlicedAttnProcessor(slice_size) elif self.added_kv_proj_dim is not None: processor = AttnAddedKVProcessor() else: # set attention processor # We use the AttnProcessor2_0 by default when torch 2.x is used which uses # torch.nn.functional.scaled_dot_product_attention for native Flash/memory_efficient_attention # but only if it has the default `scale` argument. TODO remove scale_qk check when we move to torch 2.1 processor = ( AttnProcessor2_0() if hasattr(F, "scaled_dot_product_attention") and self.scale_qk else AttnProcessor() ) self.set_processor(processor) def set_processor(self, processor: "AttnProcessor", _remove_lora: bool = False) -> None: r""" Set the attention processor to use. Args: processor (`AttnProcessor`): The attention processor to use. _remove_lora (`bool`, *optional*, defaults to `False`): Set to `True` to remove LoRA layers from the model. """ if not USE_PEFT_BACKEND and hasattr(self, "processor") and _remove_lora and self.to_q.lora_layer is not None: deprecate( "set_processor to offload LoRA", "0.26.0", "In detail, removing LoRA layers via calling `set_default_attn_processor` is deprecated. Please make sure to call `pipe.unload_lora_weights()` instead.", ) # TODO(Patrick, Sayak) - this can be deprecated once PEFT LoRA integration is complete # We need to remove all LoRA layers # Don't forget to remove ALL `_remove_lora` from the codebase for module in self.modules(): if hasattr(module, "set_lora_layer"): module.set_lora_layer(None) # if current processor is in `self._modules` and if passed `processor` is not, we need to # pop `processor` from `self._modules` if ( hasattr(self, "processor") and isinstance(self.processor, torch.nn.Module) and not isinstance(processor, torch.nn.Module) ): logger.info(f"You are removing possibly trained weights of {self.processor} with {processor}") self._modules.pop("processor") self.processor = processor def get_processor(self, return_deprecated_lora: bool = False): r""" Get the attention processor in use. Args: return_deprecated_lora (`bool`, *optional*, defaults to `False`): Set to `True` to return the deprecated LoRA attention processor. Returns: "AttentionProcessor": The attention processor in use. """ if not return_deprecated_lora: return self.processor # TODO(Sayak, Patrick). The rest of the function is needed to ensure backwards compatible # serialization format for LoRA Attention Processors. It should be deleted once the integration # with PEFT is completed. is_lora_activated = { name: module.lora_layer is not None for name, module in self.named_modules() if hasattr(module, "lora_layer") } # 1. if no layer has a LoRA activated we can return the processor as usual if not any(is_lora_activated.values()): return self.processor # If doesn't apply LoRA do `add_k_proj` or `add_v_proj` is_lora_activated.pop("add_k_proj", None) is_lora_activated.pop("add_v_proj", None) # 2. else it is not posssible that only some layers have LoRA activated if not all(is_lora_activated.values()): raise ValueError( f"Make sure that either all layers or no layers have LoRA activated, but have {is_lora_activated}" ) # 3. And we need to merge the current LoRA layers into the corresponding LoRA attention processor non_lora_processor_cls_name = self.processor.__class__.__name__ lora_processor_cls = getattr(import_module(__name__), "LoRA" + non_lora_processor_cls_name) hidden_size = self.inner_dim # now create a LoRA attention processor from the LoRA layers if lora_processor_cls in [LoRAAttnProcessor, LoRAAttnProcessor2_0, LoRAXFormersAttnProcessor]: kwargs = { "cross_attention_dim": self.cross_attention_dim, "rank": self.to_q.lora_layer.rank, "network_alpha": self.to_q.lora_layer.network_alpha, "q_rank": self.to_q.lora_layer.rank, "q_hidden_size": self.to_q.lora_layer.out_features, "k_rank": self.to_k.lora_layer.rank, "k_hidden_size": self.to_k.lora_layer.out_features, "v_rank": self.to_v.lora_layer.rank, "v_hidden_size": self.to_v.lora_layer.out_features, "out_rank": self.to_out[0].lora_layer.rank, "out_hidden_size": self.to_out[0].lora_layer.out_features, } if hasattr(self.processor, "attention_op"): kwargs["attention_op"] = self.processor.attention_op lora_processor = lora_processor_cls(hidden_size, **kwargs) lora_processor.to_q_lora.load_state_dict(self.to_q.lora_layer.state_dict()) lora_processor.to_k_lora.load_state_dict(self.to_k.lora_layer.state_dict()) lora_processor.to_v_lora.load_state_dict(self.to_v.lora_layer.state_dict()) lora_processor.to_out_lora.load_state_dict(self.to_out[0].lora_layer.state_dict()) elif lora_processor_cls == LoRAAttnAddedKVProcessor: lora_processor = lora_processor_cls( hidden_size, cross_attention_dim=self.add_k_proj.weight.shape[0], rank=self.to_q.lora_layer.rank, network_alpha=self.to_q.lora_layer.network_alpha, ) lora_processor.to_q_lora.load_state_dict(self.to_q.lora_layer.state_dict()) lora_processor.to_k_lora.load_state_dict(self.to_k.lora_layer.state_dict()) lora_processor.to_v_lora.load_state_dict(self.to_v.lora_layer.state_dict()) lora_processor.to_out_lora.load_state_dict(self.to_out[0].lora_layer.state_dict()) # only save if used if self.add_k_proj.lora_layer is not None: lora_processor.add_k_proj_lora.load_state_dict(self.add_k_proj.lora_layer.state_dict()) lora_processor.add_v_proj_lora.load_state_dict(self.add_v_proj.lora_layer.state_dict()) else: lora_processor.add_k_proj_lora = None lora_processor.add_v_proj_lora = None else: raise ValueError(f"{lora_processor_cls} does not exist.") return lora_processor def forward( self, hidden_states: torch.FloatTensor, encoder_hidden_states: Optional[torch.FloatTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, **cross_attention_kwargs, ) -> torch.Tensor: r""" The forward method of the `Attention` class. Args: hidden_states (`torch.Tensor`): The hidden states of the query. encoder_hidden_states (`torch.Tensor`, *optional*): The hidden states of the encoder. attention_mask (`torch.Tensor`, *optional*): The attention mask to use. If `None`, no mask is applied. **cross_attention_kwargs: Additional keyword arguments to pass along to the cross attention. Returns: `torch.Tensor`: The output of the attention layer. """ # The `Attention` class can call different attention processors / attention functions # here we simply pass along all tensors to the selected processor class # For standard processors that are defined here, `**cross_attention_kwargs` is empty return self.processor( self, hidden_states, encoder_hidden_states=encoder_hidden_states, attention_mask=attention_mask, **cross_attention_kwargs, ) def batch_to_head_dim(self, tensor: torch.Tensor) -> torch.Tensor: r""" Reshape the tensor from `[batch_size, seq_len, dim]` to `[batch_size // heads, seq_len, dim * heads]`. `heads` is the number of heads initialized while constructing the `Attention` class. Args: tensor (`torch.Tensor`): The tensor to reshape. Returns: `torch.Tensor`: The reshaped tensor. """ head_size = self.heads batch_size, seq_len, dim = tensor.shape tensor = tensor.reshape(batch_size // head_size, head_size, seq_len, dim) tensor = tensor.permute(0, 2, 1, 3).reshape(batch_size // head_size, seq_len, dim * head_size) return tensor def head_to_batch_dim(self, tensor: torch.Tensor, out_dim: int = 3) -> torch.Tensor: r""" Reshape the tensor from `[batch_size, seq_len, dim]` to `[batch_size, seq_len, heads, dim // heads]` `heads` is the number of heads initialized while constructing the `Attention` class. Args: tensor (`torch.Tensor`): The tensor to reshape. out_dim (`int`, *optional*, defaults to `3`): The output dimension of the tensor. If `3`, the tensor is reshaped to `[batch_size * heads, seq_len, dim // heads]`. Returns: `torch.Tensor`: The reshaped tensor. """ head_size = self.heads batch_size, seq_len, dim = tensor.shape tensor = tensor.reshape(batch_size, seq_len, head_size, dim // head_size) tensor = tensor.permute(0, 2, 1, 3) if out_dim == 3: tensor = tensor.reshape(batch_size * head_size, seq_len, dim // head_size) return tensor def get_attention_scores( self, query: torch.Tensor, key: torch.Tensor, attention_mask: torch.Tensor = None ) -> torch.Tensor: r""" Compute the attention scores. Args: query (`torch.Tensor`): The query tensor. key (`torch.Tensor`): The key tensor. attention_mask (`torch.Tensor`, *optional*): The attention mask to use. If `None`, no mask is applied. Returns: `torch.Tensor`: The attention probabilities/scores. """ dtype = query.dtype if self.upcast_attention: query = query.float() key = key.float() if attention_mask is None: baddbmm_input = torch.empty( query.shape[0], query.shape[1], key.shape[1], dtype=query.dtype, device=query.device ) beta = 0 else: baddbmm_input = attention_mask beta = 1 attention_scores = torch.baddbmm( baddbmm_input, query, key.transpose(-1, -2), beta=beta, alpha=self.scale, ) del baddbmm_input if self.upcast_softmax: attention_scores = attention_scores.float() attention_probs = attention_scores.softmax(dim=-1) del attention_scores attention_probs = attention_probs.to(dtype) return attention_probs def prepare_attention_mask( self, attention_mask: torch.Tensor, target_length: int, batch_size: int, out_dim: int = 3 ) -> torch.Tensor: r""" Prepare the attention mask for the attention computation. Args: attention_mask (`torch.Tensor`): The attention mask to prepare. target_length (`int`): The target length of the attention mask. This is the length of the attention mask after padding. batch_size (`int`): The batch size, which is used to repeat the attention mask. out_dim (`int`, *optional*, defaults to `3`): The output dimension of the attention mask. Can be either `3` or `4`. Returns: `torch.Tensor`: The prepared attention mask. """ head_size = self.heads if attention_mask is None: return attention_mask current_length: int = attention_mask.shape[-1] if current_length != target_length: if attention_mask.device.type == "mps": # HACK: MPS: Does not support padding by greater than dimension of input tensor. # Instead, we can manually construct the padding tensor. padding_shape = (attention_mask.shape[0], attention_mask.shape[1], target_length) padding = torch.zeros(padding_shape, dtype=attention_mask.dtype, device=attention_mask.device) attention_mask = torch.cat([attention_mask, padding], dim=2) else: # TODO: for pipelines such as stable-diffusion, padding cross-attn mask: # we want to instead pad by (0, remaining_length), where remaining_length is: # remaining_length: int = target_length - current_length # TODO: re-enable tests/models/test_models_unet_2d_condition.py#test_model_xattn_padding attention_mask = F.pad(attention_mask, (0, target_length), value=0.0) if out_dim == 3: if attention_mask.shape[0] < batch_size * head_size: attention_mask = attention_mask.repeat_interleave(head_size, dim=0) elif out_dim == 4: attention_mask = attention_mask.unsqueeze(1) attention_mask = attention_mask.repeat_interleave(head_size, dim=1) return attention_mask def norm_encoder_hidden_states(self, encoder_hidden_states: torch.Tensor) -> torch.Tensor: r""" Normalize the encoder hidden states. Requires `self.norm_cross` to be specified when constructing the `Attention` class. Args: encoder_hidden_states (`torch.Tensor`): Hidden states of the encoder. Returns: `torch.Tensor`: The normalized encoder hidden states. """ assert self.norm_cross is not None, "self.norm_cross must be defined to call self.norm_encoder_hidden_states" if isinstance(self.norm_cross, nn.LayerNorm): encoder_hidden_states = self.norm_cross(encoder_hidden_states) elif isinstance(self.norm_cross, nn.GroupNorm): # Group norm norms along the channels dimension and expects # input to be in the shape of (N, C, *). In this case, we want # to norm along the hidden dimension, so we need to move # (batch_size, sequence_length, hidden_size) -> # (batch_size, hidden_size, sequence_length) encoder_hidden_states = encoder_hidden_states.transpose(1, 2) encoder_hidden_states = self.norm_cross(encoder_hidden_states) encoder_hidden_states = encoder_hidden_states.transpose(1, 2) else: assert False return encoder_hidden_states def _init_compress(self): if len(self.compress_kv_factor) == 2: self.sr = nn.Conv2d( self.inner_dim, self.inner_dim, groups=self.inner_dim, kernel_size=self.compress_kv_factor, stride=self.compress_kv_factor, ) self.sr.weight.data.fill_(1 / self.compress_kv_factor[0] ** 2) elif len(self.compress_kv_factor) == 1: self.kernel_size = self.compress_kv_factor[0] self.sr = nn.Conv1d( self.inner_dim, self.inner_dim, groups=self.inner_dim, kernel_size=self.compress_kv_factor[0], stride=self.compress_kv_factor[0], ) self.sr.weight.data.fill_(1 / self.compress_kv_factor[0]) self.sr.bias.data.zero_() self.norm = nn.LayerNorm(self.inner_dim) class AttnProcessor2_0: r""" Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0). """ def __init__(self, dim=1152, attention_mode="xformers", use_rope=False, rope_scaling=None, compress_kv_factor=None): self.dim = dim self.attention_mode = attention_mode self.use_rope = use_rope self.rope_scaling = rope_scaling self.compress_kv_factor = compress_kv_factor if self.use_rope: self._init_rope() if not hasattr(F, "scaled_dot_product_attention"): raise ImportError("AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.") def _init_rope(self): if self.rope_scaling is None: self.rope2d = RoPE2D() self.rope1d = RoPE1D() else: scaling_type = self.rope_scaling["type"] scaling_factor_2d = self.rope_scaling["factor_2d"] scaling_factor_1d = self.rope_scaling["factor_1d"] if scaling_type == "linear": self.rope2d = LinearScalingRoPE2D(scaling_factor=scaling_factor_2d) self.rope1d = LinearScalingRoPE1D(scaling_factor=scaling_factor_1d) else: raise ValueError(f"Unknown RoPE scaling type {scaling_type}") def __call__( self, attn: Attention, hidden_states: torch.FloatTensor, encoder_hidden_states: Optional[torch.FloatTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, temb: Optional[torch.FloatTensor] = None, scale: float = 1.0, position_q: Optional[torch.LongTensor] = None, position_k: Optional[torch.LongTensor] = None, last_shape: Tuple[int] = None, ) -> torch.FloatTensor: residual = hidden_states args = () if USE_PEFT_BACKEND else (scale,) if attn.spatial_norm is not None: hidden_states = attn.spatial_norm(hidden_states, temb) input_ndim = hidden_states.ndim if input_ndim == 4: batch_size, channel, height, width = hidden_states.shape hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2) if self.compress_kv_factor is not None: batch_size = hidden_states.shape[0] if len(last_shape) == 2: encoder_hidden_states = hidden_states.permute(0, 2, 1).reshape(batch_size, self.dim, *last_shape) encoder_hidden_states = ( attn.sr(encoder_hidden_states).reshape(batch_size, self.dim, -1).permute(0, 2, 1) ) elif len(last_shape) == 1: encoder_hidden_states = hidden_states.permute(0, 2, 1) if last_shape[0] % 2 == 1: first_frame_pad = encoder_hidden_states[:, :, :1].repeat((1, 1, attn.kernel_size - 1)) encoder_hidden_states = torch.concatenate((first_frame_pad, encoder_hidden_states), dim=2) encoder_hidden_states = attn.sr(encoder_hidden_states).permute(0, 2, 1) else: raise NotImplementedError(f"NotImplementedError with last_shape {last_shape}") encoder_hidden_states = attn.norm(encoder_hidden_states) batch_size, sequence_length, _ = ( hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape ) if attention_mask is not None: attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size) # scaled_dot_product_attention expects attention_mask shape to be # (batch, heads, source_length, target_length) attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1]) if attn.group_norm is not None: hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2) args = () if USE_PEFT_BACKEND else (scale,) query = attn.to_q(hidden_states, *args) if encoder_hidden_states is None: encoder_hidden_states = hidden_states elif attn.norm_cross: encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states) key = attn.to_k(encoder_hidden_states, *args) value = attn.to_v(encoder_hidden_states, *args) inner_dim = key.shape[-1] head_dim = inner_dim // attn.heads query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) if self.use_rope: # require the shape of (batch_size x nheads x ntokens x dim) if position_q.ndim == 3: query = self.rope2d(query, position_q) elif position_q.ndim == 2: query = self.rope1d(query, position_q) else: raise NotImplementedError if position_k.ndim == 3: key = self.rope2d(key, position_k) elif position_k.ndim == 2: key = self.rope1d(key, position_k) else: raise NotImplementedError # the output of sdp = (batch, num_heads, seq_len, head_dim) # TODO: add support for attn.scale when we move to Torch 2.1 if self.attention_mode == "flash": assert attention_mask is None or torch.all( attention_mask.bool() ), "flash-attn do not support attention_mask" with torch.backends.cuda.sdp_kernel(enable_math=False, enable_flash=True, enable_mem_efficient=False): hidden_states = F.scaled_dot_product_attention(query, key, value, dropout_p=0.0, is_causal=False) elif self.attention_mode == "xformers": with torch.backends.cuda.sdp_kernel(enable_math=False, enable_flash=False, enable_mem_efficient=True): hidden_states = F.scaled_dot_product_attention( query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False ) elif self.attention_mode == "math": hidden_states = F.scaled_dot_product_attention( query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False ) else: raise NotImplementedError(f"Found attention_mode: {self.attention_mode}") hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim) hidden_states = hidden_states.to(query.dtype) # linear proj hidden_states = attn.to_out[0](hidden_states, *args) # dropout hidden_states = attn.to_out[1](hidden_states) if input_ndim == 4: hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width) if attn.residual_connection: hidden_states = hidden_states + residual hidden_states = hidden_states / attn.rescale_output_factor return hidden_states @maybe_allow_in_graph class GatedSelfAttentionDense(nn.Module): r""" A gated self-attention dense layer that combines visual features and object features. Parameters: query_dim (`int`): The number of channels in the query. context_dim (`int`): The number of channels in the context. n_heads (`int`): The number of heads to use for attention. d_head (`int`): The number of channels in each head. """ def __init__(self, query_dim: int, context_dim: int, n_heads: int, d_head: int): super().__init__() # we need a linear projection since we need cat visual feature and obj feature self.linear = nn.Linear(context_dim, query_dim) self.attn = Attention(query_dim=query_dim, heads=n_heads, dim_head=d_head) self.ff = FeedForward(query_dim, activation_fn="geglu") self.norm1 = nn.LayerNorm(query_dim) self.norm2 = nn.LayerNorm(query_dim) self.register_parameter("alpha_attn", nn.Parameter(torch.tensor(0.0))) self.register_parameter("alpha_dense", nn.Parameter(torch.tensor(0.0))) self.enabled = True def forward(self, x: torch.Tensor, objs: torch.Tensor) -> torch.Tensor: if not self.enabled: return x n_visual = x.shape[1] objs = self.linear(objs) x = x + self.alpha_attn.tanh() * self.attn(self.norm1(torch.cat([x, objs], dim=1)))[:, :n_visual, :] x = x + self.alpha_dense.tanh() * self.ff(self.norm2(x)) return x class FeedForward(nn.Module): r""" A feed-forward layer. Parameters: dim (`int`): The number of channels in the input. dim_out (`int`, *optional*): The number of channels in the output. If not given, defaults to `dim`. mult (`int`, *optional*, defaults to 4): The multiplier to use for the hidden dimension. dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use. activation_fn (`str`, *optional*, defaults to `"geglu"`): Activation function to be used in feed-forward. final_dropout (`bool` *optional*, defaults to False): Apply a final dropout. """ def __init__( self, dim: int, dim_out: Optional[int] = None, mult: int = 4, dropout: float = 0.0, activation_fn: str = "geglu", final_dropout: bool = False, ): super().__init__() inner_dim = int(dim * mult) dim_out = dim_out if dim_out is not None else dim linear_cls = LoRACompatibleLinear if not USE_PEFT_BACKEND else nn.Linear if activation_fn == "gelu": act_fn = GELU(dim, inner_dim) if activation_fn == "gelu-approximate": act_fn = GELU(dim, inner_dim, approximate="tanh") elif activation_fn == "geglu": act_fn = GEGLU(dim, inner_dim) elif activation_fn == "geglu-approximate": act_fn = ApproximateGELU(dim, inner_dim) self.net = nn.ModuleList([]) # project in self.net.append(act_fn) # project dropout self.net.append(nn.Dropout(dropout)) # project out self.net.append(linear_cls(inner_dim, dim_out)) # FF as used in Vision Transformer, MLP-Mixer, etc. have a final dropout if final_dropout: self.net.append(nn.Dropout(dropout)) def forward(self, hidden_states: torch.Tensor, scale: float = 1.0) -> torch.Tensor: compatible_cls = (GEGLU,) if USE_PEFT_BACKEND else (GEGLU, LoRACompatibleLinear) for module in self.net: if isinstance(module, compatible_cls): hidden_states = module(hidden_states, scale) else: hidden_states = module(hidden_states) return hidden_states @maybe_allow_in_graph class BasicTransformerBlock_(nn.Module): r""" A basic Transformer block. Parameters: dim (`int`): The number of channels in the input and output. num_attention_heads (`int`): The number of heads to use for multi-head attention. attention_head_dim (`int`): The number of channels in each head. dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use. cross_attention_dim (`int`, *optional*): The size of the encoder_hidden_states vector for cross attention. activation_fn (`str`, *optional*, defaults to `"geglu"`): Activation function to be used in feed-forward. num_embeds_ada_norm (: obj: `int`, *optional*): The number of diffusion steps used during training. See `Transformer2DModel`. attention_bias (: obj: `bool`, *optional*, defaults to `False`): Configure if the attentions should contain a bias parameter. only_cross_attention (`bool`, *optional*): Whether to use only cross-attention layers. In this case two cross attention layers are used. double_self_attention (`bool`, *optional*): Whether to use two self-attention layers. In this case no cross attention layers are used. upcast_attention (`bool`, *optional*): Whether to upcast the attention computation to float32. This is useful for mixed precision training. norm_elementwise_affine (`bool`, *optional*, defaults to `True`): Whether to use learnable elementwise affine parameters for normalization. norm_type (`str`, *optional*, defaults to `"layer_norm"`): The normalization layer to use. Can be `"layer_norm"`, `"ada_norm"` or `"ada_norm_zero"`. final_dropout (`bool` *optional*, defaults to False): Whether to apply a final dropout after the last feed-forward layer. attention_type (`str`, *optional*, defaults to `"default"`): The type of attention to use. Can be `"default"` or `"gated"` or `"gated-text-image"`. positional_embeddings (`str`, *optional*, defaults to `None`): The type of positional embeddings to apply to. num_positional_embeddings (`int`, *optional*, defaults to `None`): The maximum number of positional embeddings to apply. """ def __init__( self, dim: int, num_attention_heads: int, attention_head_dim: int, dropout=0.0, cross_attention_dim: Optional[int] = None, activation_fn: str = "geglu", num_embeds_ada_norm: Optional[int] = None, attention_bias: bool = False, only_cross_attention: bool = False, double_self_attention: bool = False, upcast_attention: bool = False, norm_elementwise_affine: bool = True, norm_type: str = "layer_norm", # 'layer_norm', 'ada_norm', 'ada_norm_zero', 'ada_norm_single' norm_eps: float = 1e-5, final_dropout: bool = False, attention_type: str = "default", positional_embeddings: Optional[str] = None, num_positional_embeddings: Optional[int] = None, attention_mode: str = "xformers", use_rope: bool = False, rope_scaling: Optional[Dict] = None, compress_kv_factor: Optional[Tuple] = None, block_idx: Optional[int] = None, ): super().__init__() self.only_cross_attention = only_cross_attention self.use_ada_layer_norm_zero = (num_embeds_ada_norm is not None) and norm_type == "ada_norm_zero" self.use_ada_layer_norm = (num_embeds_ada_norm is not None) and norm_type == "ada_norm" self.use_ada_layer_norm_single = norm_type == "ada_norm_single" self.use_layer_norm = norm_type == "layer_norm" if norm_type in ("ada_norm", "ada_norm_zero") and num_embeds_ada_norm is None: raise ValueError( f"`norm_type` is set to {norm_type}, but `num_embeds_ada_norm` is not defined. Please make sure to" f" define `num_embeds_ada_norm` if setting `norm_type` to {norm_type}." ) if positional_embeddings and (num_positional_embeddings is None): raise ValueError( "If `positional_embedding` type is defined, `num_positition_embeddings` must also be defined." ) if positional_embeddings == "sinusoidal": self.pos_embed = SinusoidalPositionalEmbedding(dim, max_seq_length=num_positional_embeddings) else: self.pos_embed = None # Define 3 blocks. Each block has its own normalization layer. # 1. Self-Attn if self.use_ada_layer_norm: self.norm1 = AdaLayerNorm(dim, num_embeds_ada_norm) elif self.use_ada_layer_norm_zero: self.norm1 = AdaLayerNormZero(dim, num_embeds_ada_norm) else: self.norm1 = nn.LayerNorm(dim, elementwise_affine=norm_elementwise_affine, eps=norm_eps) self.attn1 = Attention( query_dim=dim, heads=num_attention_heads, dim_head=attention_head_dim, dropout=dropout, bias=attention_bias, cross_attention_dim=cross_attention_dim if only_cross_attention else None, upcast_attention=upcast_attention, attention_mode=attention_mode, use_rope=use_rope, rope_scaling=rope_scaling, compress_kv_factor=compress_kv_factor, ) # # 2. Cross-Attn # if cross_attention_dim is not None or double_self_attention: # # We currently only use AdaLayerNormZero for self attention where there will only be one attention block. # # I.e. the number of returned modulation chunks from AdaLayerZero would not make sense if returned during # # the second cross attention block. # self.norm2 = ( # AdaLayerNorm(dim, num_embeds_ada_norm) # if self.use_ada_layer_norm # else nn.LayerNorm(dim, elementwise_affine=norm_elementwise_affine, eps=norm_eps) # ) # self.attn2 = Attention( # query_dim=dim, # cross_attention_dim=cross_attention_dim if not double_self_attention else None, # heads=num_attention_heads, # dim_head=attention_head_dim, # dropout=dropout, # bias=attention_bias, # upcast_attention=upcast_attention, # ) # is self-attn if encoder_hidden_states is none # else: # self.norm2 = None # self.attn2 = None # 3. Feed-forward # if not self.use_ada_layer_norm_single: # self.norm3 = nn.LayerNorm(dim, elementwise_affine=norm_elementwise_affine, eps=norm_eps) self.norm3 = nn.LayerNorm(dim, elementwise_affine=norm_elementwise_affine, eps=norm_eps) self.ff = FeedForward(dim, dropout=dropout, activation_fn=activation_fn, final_dropout=final_dropout) # 4. Fuser if attention_type == "gated" or attention_type == "gated-text-image": self.fuser = GatedSelfAttentionDense(dim, cross_attention_dim, num_attention_heads, attention_head_dim) # 5. Scale-shift for PixArt-Alpha. if self.use_ada_layer_norm_single: self.scale_shift_table = nn.Parameter(torch.randn(6, dim) / dim**0.5) # let chunk size default to None self._chunk_size = None self._chunk_dim = 0 # pab self.last_out = None self.count = 0 self.block_idx = block_idx self.temp_mlp_count = 0 def set_last_out(self, last_out: torch.Tensor): self.last_out = last_out def set_chunk_feed_forward(self, chunk_size: Optional[int], dim: int): # Sets chunk feed-forward self._chunk_size = chunk_size self._chunk_dim = dim def forward( self, hidden_states: torch.FloatTensor, attention_mask: Optional[torch.FloatTensor] = None, encoder_hidden_states: Optional[torch.FloatTensor] = None, encoder_attention_mask: Optional[torch.FloatTensor] = None, timestep: Optional[torch.LongTensor] = None, cross_attention_kwargs: Dict[str, Any] = None, class_labels: Optional[torch.LongTensor] = None, position_q: Optional[torch.LongTensor] = None, position_k: Optional[torch.LongTensor] = None, frame: int = None, org_timestep: Optional[torch.LongTensor] = None, all_timesteps=None, ) -> torch.FloatTensor: # Notice that normalization is always applied before the real computation in the following blocks. # 0. Self-Attention batch_size = hidden_states.shape[0] # 1. Retrieve lora scale. lora_scale = cross_attention_kwargs.get("scale", 1.0) if cross_attention_kwargs is not None else 1.0 # 2. Prepare GLIGEN inputs cross_attention_kwargs = cross_attention_kwargs.copy() if cross_attention_kwargs is not None else {} gligen_kwargs = cross_attention_kwargs.pop("gligen", None) broadcast_temporal, self.count = if_broadcast_temporal(int(org_timestep[0]), self.count) if broadcast_temporal: attn_output = self.last_out assert self.use_ada_layer_norm_single shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = ( self.scale_shift_table[None] + timestep.reshape(batch_size, 6, -1) ).chunk(6, dim=1) else: if self.use_ada_layer_norm: norm_hidden_states = self.norm1(hidden_states, timestep) elif self.use_ada_layer_norm_zero: norm_hidden_states, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.norm1( hidden_states, timestep, class_labels, hidden_dtype=hidden_states.dtype ) elif self.use_layer_norm: norm_hidden_states = self.norm1(hidden_states) elif self.use_ada_layer_norm_single: shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = ( self.scale_shift_table[None] + timestep.reshape(batch_size, 6, -1) ).chunk(6, dim=1) norm_hidden_states = self.norm1(hidden_states) norm_hidden_states = norm_hidden_states * (1 + scale_msa) + shift_msa norm_hidden_states = norm_hidden_states.squeeze(1) else: raise ValueError("Incorrect norm used") if self.pos_embed is not None: norm_hidden_states = self.pos_embed(norm_hidden_states) if enable_sequence_parallel(): norm_hidden_states = self.dynamic_switch(norm_hidden_states, to_spatial_shard=True) attn_output = self.attn1( norm_hidden_states, encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None, attention_mask=attention_mask, position_q=position_q, position_k=position_k, last_shape=frame, **cross_attention_kwargs, ) if enable_sequence_parallel(): attn_output = self.dynamic_switch(attn_output, to_spatial_shard=False) if self.use_ada_layer_norm_zero: attn_output = gate_msa.unsqueeze(1) * attn_output elif self.use_ada_layer_norm_single: attn_output = gate_msa * attn_output if enable_pab(): self.set_last_out(attn_output) hidden_states = attn_output + hidden_states if hidden_states.ndim == 4: hidden_states = hidden_states.squeeze(1) # 2.5 GLIGEN Control if gligen_kwargs is not None: hidden_states = self.fuser(hidden_states, gligen_kwargs["objs"]) # # 3. Cross-Attention # if self.attn2 is not None: # if self.use_ada_layer_norm: # norm_hidden_states = self.norm2(hidden_states, timestep) # elif self.use_ada_layer_norm_zero or self.use_layer_norm: # norm_hidden_states = self.norm2(hidden_states) # elif self.use_ada_layer_norm_single: # # For PixArt norm2 isn't applied here: # # https://github.com/PixArt-alpha/PixArt-alpha/blob/0f55e922376d8b797edd44d25d0e7464b260dcab/diffusion/model/nets/PixArtMS.py#L70C1-L76C103 # norm_hidden_states = hidden_states # else: # raise ValueError("Incorrect norm") # if self.pos_embed is not None and self.use_ada_layer_norm_single is False: # norm_hidden_states = self.pos_embed(norm_hidden_states) # attn_output = self.attn2( # norm_hidden_states, # encoder_hidden_states=encoder_hidden_states, # attention_mask=encoder_attention_mask, # **cross_attention_kwargs, # ) # hidden_states = attn_output + hidden_states # 4. Feed-forward # if not self.use_ada_layer_norm_single: # norm_hidden_states = self.norm3(hidden_states) if enable_pab(): broadcast_mlp, self.temp_mlp_count, broadcast_next, broadcast_range = if_broadcast_mlp( int(org_timestep[0]), self.temp_mlp_count, self.block_idx, all_timesteps.tolist(), is_temporal=True, ) if enable_pab() and broadcast_mlp: ff_output = get_mlp_output( broadcast_range, timestep=int(org_timestep[0]), block_idx=self.block_idx, is_temporal=True, ) else: if self.use_ada_layer_norm_zero: norm_hidden_states = norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None] if self.use_ada_layer_norm_single: # norm_hidden_states = self.norm2(hidden_states) norm_hidden_states = self.norm3(hidden_states) norm_hidden_states = norm_hidden_states * (1 + scale_mlp) + shift_mlp if self._chunk_size is not None: # "feed_forward_chunk_size" can be used to save memory if norm_hidden_states.shape[self._chunk_dim] % self._chunk_size != 0: raise ValueError( f"`hidden_states` dimension to be chunked: {norm_hidden_states.shape[self._chunk_dim]} has to be divisible by chunk size: {self._chunk_size}. Make sure to set an appropriate `chunk_size` when calling `unet.enable_forward_chunking`." ) num_chunks = norm_hidden_states.shape[self._chunk_dim] // self._chunk_size ff_output = torch.cat( [ self.ff(hid_slice, scale=lora_scale) for hid_slice in norm_hidden_states.chunk(num_chunks, dim=self._chunk_dim) ], dim=self._chunk_dim, ) else: ff_output = self.ff(norm_hidden_states, scale=lora_scale) if self.use_ada_layer_norm_zero: ff_output = gate_mlp.unsqueeze(1) * ff_output elif self.use_ada_layer_norm_single: ff_output = gate_mlp * ff_output if enable_pab() and broadcast_next: save_mlp_output( timestep=int(org_timestep[0]), block_idx=self.block_idx, ff_output=ff_output, is_temporal=True, ) hidden_states = ff_output + hidden_states if hidden_states.ndim == 4: hidden_states = hidden_states.squeeze(1) return hidden_states def dynamic_switch(self, x, to_spatial_shard: bool): if to_spatial_shard: scatter_dim, gather_dim = 0, 1 scatter_pad = get_spatial_pad() gather_pad = get_temporal_pad() else: scatter_dim, gather_dim = 1, 0 scatter_pad = get_temporal_pad() gather_pad = get_spatial_pad() x = all_to_all_with_pad( x, get_sequence_parallel_group(), scatter_dim=scatter_dim, gather_dim=gather_dim, scatter_pad=scatter_pad, gather_pad=gather_pad, ) return x @maybe_allow_in_graph class BasicTransformerBlock(nn.Module): r""" A basic Transformer block. Parameters: dim (`int`): The number of channels in the input and output. num_attention_heads (`int`): The number of heads to use for multi-head attention. attention_head_dim (`int`): The number of channels in each head. dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use. cross_attention_dim (`int`, *optional*): The size of the encoder_hidden_states vector for cross attention. activation_fn (`str`, *optional*, defaults to `"geglu"`): Activation function to be used in feed-forward. num_embeds_ada_norm (: obj: `int`, *optional*): The number of diffusion steps used during training. See `Transformer2DModel`. attention_bias (: obj: `bool`, *optional*, defaults to `False`): Configure if the attentions should contain a bias parameter. only_cross_attention (`bool`, *optional*): Whether to use only cross-attention layers. In this case two cross attention layers are used. double_self_attention (`bool`, *optional*): Whether to use two self-attention layers. In this case no cross attention layers are used. upcast_attention (`bool`, *optional*): Whether to upcast the attention computation to float32. This is useful for mixed precision training. norm_elementwise_affine (`bool`, *optional*, defaults to `True`): Whether to use learnable elementwise affine parameters for normalization. norm_type (`str`, *optional*, defaults to `"layer_norm"`): The normalization layer to use. Can be `"layer_norm"`, `"ada_norm"` or `"ada_norm_zero"`. final_dropout (`bool` *optional*, defaults to False): Whether to apply a final dropout after the last feed-forward layer. attention_type (`str`, *optional*, defaults to `"default"`): The type of attention to use. Can be `"default"` or `"gated"` or `"gated-text-image"`. positional_embeddings (`str`, *optional*, defaults to `None`): The type of positional embeddings to apply to. num_positional_embeddings (`int`, *optional*, defaults to `None`): The maximum number of positional embeddings to apply. """ def __init__( self, dim: int, num_attention_heads: int, attention_head_dim: int, dropout=0.0, cross_attention_dim: Optional[int] = None, activation_fn: str = "geglu", num_embeds_ada_norm: Optional[int] = None, attention_bias: bool = False, only_cross_attention: bool = False, double_self_attention: bool = False, upcast_attention: bool = False, norm_elementwise_affine: bool = True, norm_type: str = "layer_norm", # 'layer_norm', 'ada_norm', 'ada_norm_zero', 'ada_norm_single' norm_eps: float = 1e-5, final_dropout: bool = False, attention_type: str = "default", positional_embeddings: Optional[str] = None, num_positional_embeddings: Optional[int] = None, attention_mode: str = "xformers", use_rope: bool = False, rope_scaling: Optional[Dict] = None, compress_kv_factor: Optional[Tuple] = None, block_idx: Optional[int] = None, ): super().__init__() self.only_cross_attention = only_cross_attention self.use_ada_layer_norm_zero = (num_embeds_ada_norm is not None) and norm_type == "ada_norm_zero" self.use_ada_layer_norm = (num_embeds_ada_norm is not None) and norm_type == "ada_norm" self.use_ada_layer_norm_single = norm_type == "ada_norm_single" self.use_layer_norm = norm_type == "layer_norm" if norm_type in ("ada_norm", "ada_norm_zero") and num_embeds_ada_norm is None: raise ValueError( f"`norm_type` is set to {norm_type}, but `num_embeds_ada_norm` is not defined. Please make sure to" f" define `num_embeds_ada_norm` if setting `norm_type` to {norm_type}." ) if positional_embeddings and (num_positional_embeddings is None): raise ValueError( "If `positional_embedding` type is defined, `num_positition_embeddings` must also be defined." ) if positional_embeddings == "sinusoidal": self.pos_embed = SinusoidalPositionalEmbedding(dim, max_seq_length=num_positional_embeddings) else: self.pos_embed = None # Define 3 blocks. Each block has its own normalization layer. # 1. Self-Attn if self.use_ada_layer_norm: self.norm1 = AdaLayerNorm(dim, num_embeds_ada_norm) elif self.use_ada_layer_norm_zero: self.norm1 = AdaLayerNormZero(dim, num_embeds_ada_norm) else: self.norm1 = nn.LayerNorm(dim, elementwise_affine=norm_elementwise_affine, eps=norm_eps) self.attn1 = Attention( query_dim=dim, heads=num_attention_heads, dim_head=attention_head_dim, dropout=dropout, bias=attention_bias, cross_attention_dim=cross_attention_dim if only_cross_attention else None, upcast_attention=upcast_attention, attention_mode=attention_mode, use_rope=use_rope, rope_scaling=rope_scaling, compress_kv_factor=compress_kv_factor, ) # 2. Cross-Attn if cross_attention_dim is not None or double_self_attention: # We currently only use AdaLayerNormZero for self attention where there will only be one attention block. # I.e. the number of returned modulation chunks from AdaLayerZero would not make sense if returned during # the second cross attention block. self.norm2 = ( AdaLayerNorm(dim, num_embeds_ada_norm) if self.use_ada_layer_norm else nn.LayerNorm(dim, elementwise_affine=norm_elementwise_affine, eps=norm_eps) ) self.attn2 = Attention( query_dim=dim, cross_attention_dim=cross_attention_dim if not double_self_attention else None, heads=num_attention_heads, dim_head=attention_head_dim, dropout=dropout, bias=attention_bias, upcast_attention=upcast_attention, attention_mode=attention_mode, # only xformers support attention_mask use_rope=False, # do not position in cross attention compress_kv_factor=None, ) # is self-attn if encoder_hidden_states is none else: self.norm2 = None self.attn2 = None # 3. Feed-forward if not self.use_ada_layer_norm_single: self.norm3 = nn.LayerNorm(dim, elementwise_affine=norm_elementwise_affine, eps=norm_eps) self.ff = FeedForward( dim, dropout=dropout, activation_fn=activation_fn, final_dropout=final_dropout, ) # 4. Fuser if attention_type == "gated" or attention_type == "gated-text-image": self.fuser = GatedSelfAttentionDense(dim, cross_attention_dim, num_attention_heads, attention_head_dim) # 5. Scale-shift for PixArt-Alpha. if self.use_ada_layer_norm_single: self.scale_shift_table = nn.Parameter(torch.randn(6, dim) / dim**0.5) # let chunk size default to None self._chunk_size = None self._chunk_dim = 0 # pab self.cross_last = None self.cross_count = 0 self.spatial_last = None self.spatial_count = 0 self.block_idx = block_idx self.spatila_mlp_count = 0 def set_cross_last(self, last_out: torch.Tensor): self.cross_last = last_out def set_spatial_last(self, last_out: torch.Tensor): self.spatial_last = last_out def set_chunk_feed_forward(self, chunk_size: Optional[int], dim: int = 0): # Sets chunk feed-forward self._chunk_size = chunk_size self._chunk_dim = dim def forward( self, hidden_states: torch.FloatTensor, attention_mask: Optional[torch.FloatTensor] = None, encoder_hidden_states: Optional[torch.FloatTensor] = None, encoder_attention_mask: Optional[torch.FloatTensor] = None, timestep: Optional[torch.LongTensor] = None, cross_attention_kwargs: Dict[str, Any] = None, class_labels: Optional[torch.LongTensor] = None, position_q: Optional[torch.LongTensor] = None, position_k: Optional[torch.LongTensor] = None, hw: Tuple[int, int] = None, org_timestep: Optional[torch.LongTensor] = None, all_timesteps=None, ) -> torch.FloatTensor: # Notice that normalization is always applied before the real computation in the following blocks. # 0. Self-Attention batch_size = hidden_states.shape[0] # 1. Retrieve lora scale. lora_scale = cross_attention_kwargs.get("scale", 1.0) if cross_attention_kwargs is not None else 1.0 # 2. Prepare GLIGEN inputs cross_attention_kwargs = cross_attention_kwargs.copy() if cross_attention_kwargs is not None else {} gligen_kwargs = cross_attention_kwargs.pop("gligen", None) broadcast_spatial, self.spatial_count = if_broadcast_spatial( int(org_timestep[0]), self.spatial_count, self.block_idx ) if broadcast_spatial: attn_output = self.spatial_last assert self.use_ada_layer_norm_single shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = ( self.scale_shift_table[None] + timestep.reshape(batch_size, 6, -1) ).chunk(6, dim=1) else: if self.use_ada_layer_norm: norm_hidden_states = self.norm1(hidden_states, timestep) elif self.use_ada_layer_norm_zero: norm_hidden_states, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.norm1( hidden_states, timestep, class_labels, hidden_dtype=hidden_states.dtype ) elif self.use_layer_norm: norm_hidden_states = self.norm1(hidden_states) elif self.use_ada_layer_norm_single: shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = ( self.scale_shift_table[None] + timestep.reshape(batch_size, 6, -1) ).chunk(6, dim=1) norm_hidden_states = self.norm1(hidden_states) norm_hidden_states = norm_hidden_states * (1 + scale_msa) + shift_msa norm_hidden_states = norm_hidden_states.squeeze(1) else: raise ValueError("Incorrect norm used") if self.pos_embed is not None: norm_hidden_states = self.pos_embed(norm_hidden_states) attn_output = self.attn1( norm_hidden_states, encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None, attention_mask=attention_mask, position_q=position_q, position_k=position_k, last_shape=hw, **cross_attention_kwargs, ) if self.use_ada_layer_norm_zero: attn_output = gate_msa.unsqueeze(1) * attn_output elif self.use_ada_layer_norm_single: attn_output = gate_msa * attn_output if enable_pab(): self.set_spatial_last(attn_output) hidden_states = attn_output + hidden_states if hidden_states.ndim == 4: hidden_states = hidden_states.squeeze(1) # 2.5 GLIGEN Control if gligen_kwargs is not None: hidden_states = self.fuser(hidden_states, gligen_kwargs["objs"]) # 3. Cross-Attention if self.attn2 is not None: broadcast_cross, self.cross_count = if_broadcast_cross(int(org_timestep[0]), self.cross_count) if broadcast_cross: hidden_states = hidden_states + self.cross_last else: if self.use_ada_layer_norm: norm_hidden_states = self.norm2(hidden_states, timestep) elif self.use_ada_layer_norm_zero or self.use_layer_norm: norm_hidden_states = self.norm2(hidden_states) elif self.use_ada_layer_norm_single: # For PixArt norm2 isn't applied here: # https://github.com/PixArt-alpha/PixArt-alpha/blob/0f55e922376d8b797edd44d25d0e7464b260dcab/diffusion/model/nets/PixArtMS.py#L70C1-L76C103 norm_hidden_states = hidden_states else: raise ValueError("Incorrect norm") if self.pos_embed is not None and self.use_ada_layer_norm_single is False: norm_hidden_states = self.pos_embed(norm_hidden_states) attn_output = self.attn2( norm_hidden_states, encoder_hidden_states=encoder_hidden_states, attention_mask=encoder_attention_mask, position_q=None, # cross attn do not need relative position position_k=None, last_shape=None, **cross_attention_kwargs, ) hidden_states = attn_output + hidden_states if enable_pab(): self.set_cross_last(attn_output) if enable_pab(): broadcast_mlp, self.spatila_mlp_count, broadcast_next, broadcast_range = if_broadcast_mlp( int(org_timestep[0]), self.spatila_mlp_count, self.block_idx, all_timesteps.tolist(), is_temporal=False, ) if enable_pab() and broadcast_mlp: ff_output = get_mlp_output( broadcast_range, timestep=int(org_timestep[0]), block_idx=self.block_idx, is_temporal=False, ) else: # 4. Feed-forward if not self.use_ada_layer_norm_single: norm_hidden_states = self.norm3(hidden_states) if self.use_ada_layer_norm_zero: norm_hidden_states = norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None] if self.use_ada_layer_norm_single: norm_hidden_states = self.norm2(hidden_states) norm_hidden_states = norm_hidden_states * (1 + scale_mlp) + shift_mlp ff_output = self.ff(norm_hidden_states, scale=lora_scale) if self.use_ada_layer_norm_zero: ff_output = gate_mlp.unsqueeze(1) * ff_output elif self.use_ada_layer_norm_single: ff_output = gate_mlp * ff_output if enable_pab() and broadcast_next: save_mlp_output( timestep=int(org_timestep[0]), block_idx=self.block_idx, ff_output=ff_output, is_temporal=False, ) hidden_states = ff_output + hidden_states if hidden_states.ndim == 4: hidden_states = hidden_states.squeeze(1) return hidden_states class AdaLayerNormSingle(nn.Module): r""" Norm layer adaptive layer norm single (adaLN-single). As proposed in PixArt-Alpha (see: https://arxiv.org/abs/2310.00426; Section 2.3). Parameters: embedding_dim (`int`): The size of each embedding vector. use_additional_conditions (`bool`): To use additional conditions for normalization or not. """ def __init__(self, embedding_dim: int, use_additional_conditions: bool = False): super().__init__() self.emb = CombinedTimestepSizeEmbeddings( embedding_dim, size_emb_dim=embedding_dim // 3, use_additional_conditions=use_additional_conditions ) self.silu = nn.SiLU() self.linear = nn.Linear(embedding_dim, 6 * embedding_dim, bias=True) def forward( self, timestep: torch.Tensor, added_cond_kwargs: Dict[str, torch.Tensor] = None, batch_size: int = None, hidden_dtype: Optional[torch.dtype] = None, ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: # No modulation happening here. embedded_timestep = self.emb( timestep, batch_size=batch_size, hidden_dtype=hidden_dtype, resolution=None, aspect_ratio=None ) return self.linear(self.silu(embedded_timestep)), embedded_timestep @dataclass class Transformer3DModelOutput(BaseOutput): """ The output of [`Transformer2DModel`]. Args: sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` or `(batch size, num_vector_embeds - 1, num_latent_pixels)` if [`Transformer2DModel`] is discrete): The hidden states output conditioned on the `encoder_hidden_states` input. If discrete, returns probability distributions for the unnoised latent pixels. """ sample: torch.FloatTensor class LatteT2V(ModelMixin, ConfigMixin): _supports_gradient_checkpointing = True """ A 2D Transformer model for image-like data. Parameters: num_attention_heads (`int`, *optional*, defaults to 16): The number of heads to use for multi-head attention. attention_head_dim (`int`, *optional*, defaults to 88): The number of channels in each head. in_channels (`int`, *optional*): The number of channels in the input and output (specify if the input is **continuous**). num_layers (`int`, *optional*, defaults to 1): The number of layers of Transformer blocks to use. dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use. cross_attention_dim (`int`, *optional*): The number of `encoder_hidden_states` dimensions to use. sample_size (`int`, *optional*): The width of the latent images (specify if the input is **discrete**). This is fixed during training since it is used to learn a number of position embeddings. num_vector_embeds (`int`, *optional*): The number of classes of the vector embeddings of the latent pixels (specify if the input is **discrete**). Includes the class for the masked latent pixel. activation_fn (`str`, *optional*, defaults to `"geglu"`): Activation function to use in feed-forward. num_embeds_ada_norm ( `int`, *optional*): The number of diffusion steps used during training. Pass if at least one of the norm_layers is `AdaLayerNorm`. This is fixed during training since it is used to learn a number of embeddings that are added to the hidden states. During inference, you can denoise for up to but not more steps than `num_embeds_ada_norm`. attention_bias (`bool`, *optional*): Configure if the `TransformerBlocks` attention should contain a bias parameter. """ @register_to_config def __init__( self, num_attention_heads: int = 16, patch_size_t: int = 1, attention_head_dim: int = 88, in_channels: Optional[int] = None, out_channels: Optional[int] = None, num_layers: int = 1, dropout: float = 0.0, norm_num_groups: int = 32, cross_attention_dim: Optional[int] = None, attention_bias: bool = False, sample_size: Optional[int] = None, num_vector_embeds: Optional[int] = None, patch_size: Optional[int] = None, activation_fn: str = "geglu", num_embeds_ada_norm: Optional[int] = None, use_linear_projection: bool = False, only_cross_attention: bool = False, double_self_attention: bool = False, upcast_attention: bool = False, norm_type: str = "layer_norm", norm_elementwise_affine: bool = True, norm_eps: float = 1e-5, attention_type: str = "default", caption_channels: int = None, video_length: int = 16, attention_mode: str = "flash", use_rope: bool = False, model_max_length: int = 300, rope_scaling_type: str = "linear", compress_kv_factor: int = 1, interpolation_scale_1d: float = None, ): super().__init__() self.use_linear_projection = use_linear_projection self.num_attention_heads = num_attention_heads self.attention_head_dim = attention_head_dim inner_dim = num_attention_heads * attention_head_dim self.video_length = video_length self.use_rope = use_rope self.model_max_length = model_max_length self.compress_kv_factor = compress_kv_factor self.num_layers = num_layers self.config.hidden_size = model_max_length assert not (self.compress_kv_factor != 1 and use_rope), "Can not both enable compressing kv and using rope" conv_cls = nn.Conv2d if USE_PEFT_BACKEND else LoRACompatibleConv linear_cls = nn.Linear if USE_PEFT_BACKEND else LoRACompatibleLinear # 1. Transformer2DModel can process both standard continuous images of shape `(batch_size, num_channels, width, height)` as well as quantized image embeddings of shape `(batch_size, num_image_vectors)` # Define whether input is continuous or discrete depending on configuration self.is_input_continuous = (in_channels is not None) and (patch_size is None) self.is_input_vectorized = num_vector_embeds is not None # self.is_input_patches = in_channels is not None and patch_size is not None self.is_input_patches = True if norm_type == "layer_norm" and num_embeds_ada_norm is not None: deprecation_message = ( f"The configuration file of this model: {self.__class__} is outdated. `norm_type` is either not set or" " incorrectly set to `'layer_norm'`.Make sure to set `norm_type` to `'ada_norm'` in the config." " Please make sure to update the config accordingly as leaving `norm_type` might led to incorrect" " results in future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it" " would be very nice if you could open a Pull request for the `transformer/config.json` file" ) deprecate("norm_type!=num_embeds_ada_norm", "1.0.0", deprecation_message, standard_warn=False) norm_type = "ada_norm" # 2. Define input layers assert sample_size is not None, "Transformer2DModel over patched input must provide sample_size" self.height = sample_size[0] self.width = sample_size[1] self.patch_size = patch_size interpolation_scale_2d = self.config.sample_size[0] // 64 # => 64 (= 512 pixart) has interpolation scale 1 interpolation_scale_2d = max(interpolation_scale_2d, 1) self.pos_embed = PatchEmbed( height=sample_size[0], width=sample_size[1], patch_size=patch_size, in_channels=in_channels, embed_dim=inner_dim, interpolation_scale=interpolation_scale_2d, ) # define temporal positional embedding if interpolation_scale_1d is None: if self.config.video_length % 2 == 1: interpolation_scale_1d = ( self.config.video_length - 1 ) // 16 # => 16 (= 16 Latte) has interpolation scale 1 else: interpolation_scale_1d = self.config.video_length // 16 # => 16 (= 16 Latte) has interpolation scale 1 # interpolation_scale_1d = self.config.video_length // 5 # interpolation_scale_1d = max(interpolation_scale_1d, 1) temp_pos_embed = get_1d_sincos_pos_embed( inner_dim, video_length, interpolation_scale=interpolation_scale_1d ) # 1152 hidden size self.register_buffer("temp_pos_embed", torch.from_numpy(temp_pos_embed).float().unsqueeze(0), persistent=False) rope_scaling = None if self.use_rope: self.position_getter_2d = PositionGetter2D() self.position_getter_1d = PositionGetter1D() rope_scaling = dict( type=rope_scaling_type, factor_2d=interpolation_scale_2d, factor_1d=interpolation_scale_1d ) # 3. Define transformers blocks, spatial attention self.transformer_blocks = nn.ModuleList( [ BasicTransformerBlock( inner_dim, num_attention_heads, attention_head_dim, dropout=dropout, cross_attention_dim=cross_attention_dim, activation_fn=activation_fn, num_embeds_ada_norm=num_embeds_ada_norm, attention_bias=attention_bias, only_cross_attention=only_cross_attention, double_self_attention=double_self_attention, upcast_attention=upcast_attention, norm_type=norm_type, norm_elementwise_affine=norm_elementwise_affine, norm_eps=norm_eps, attention_type=attention_type, attention_mode=attention_mode, use_rope=use_rope, rope_scaling=rope_scaling, compress_kv_factor=(compress_kv_factor, compress_kv_factor) if d >= num_layers // 2 and compress_kv_factor != 1 else None, # follow pixart-sigma, apply in second-half layers block_idx=d, ) for d in range(num_layers) ] ) # Define temporal transformers blocks self.temporal_transformer_blocks = nn.ModuleList( [ BasicTransformerBlock_( # one attention inner_dim, num_attention_heads, # num_attention_heads attention_head_dim, # attention_head_dim 72 dropout=dropout, cross_attention_dim=None, activation_fn=activation_fn, num_embeds_ada_norm=num_embeds_ada_norm, attention_bias=attention_bias, only_cross_attention=only_cross_attention, double_self_attention=False, upcast_attention=upcast_attention, norm_type=norm_type, norm_elementwise_affine=norm_elementwise_affine, norm_eps=norm_eps, attention_type=attention_type, attention_mode=attention_mode, use_rope=use_rope, rope_scaling=rope_scaling, compress_kv_factor=(compress_kv_factor,) if d >= num_layers // 2 and compress_kv_factor != 1 else None, # follow pixart-sigma, apply in second-half layers block_idx=d, ) for d in range(num_layers) ] ) # 4. Define output layers self.out_channels = in_channels if out_channels is None else out_channels if self.is_input_continuous: # TODO: should use out_channels for continuous projections if use_linear_projection: self.proj_out = linear_cls(inner_dim, in_channels) else: self.proj_out = conv_cls(inner_dim, in_channels, kernel_size=1, stride=1, padding=0) elif self.is_input_vectorized: self.norm_out = nn.LayerNorm(inner_dim) self.out = nn.Linear(inner_dim, self.num_vector_embeds - 1) elif self.is_input_patches and norm_type != "ada_norm_single": self.norm_out = nn.LayerNorm(inner_dim, elementwise_affine=False, eps=1e-6) self.proj_out_1 = nn.Linear(inner_dim, 2 * inner_dim) self.proj_out_2 = nn.Linear(inner_dim, patch_size * patch_size * self.out_channels) elif self.is_input_patches and norm_type == "ada_norm_single": self.norm_out = nn.LayerNorm(inner_dim, elementwise_affine=False, eps=1e-6) self.scale_shift_table = nn.Parameter(torch.randn(2, inner_dim) / inner_dim**0.5) self.proj_out = nn.Linear(inner_dim, patch_size * patch_size * self.out_channels) # 5. PixArt-Alpha blocks. self.adaln_single = None self.use_additional_conditions = False if norm_type == "ada_norm_single": # self.use_additional_conditions = self.config.sample_size[0] == 128 # False, 128 -> 1024 # TODO(Sayak, PVP) clean this, for now we use sample size to determine whether to use # additional conditions until we find better name self.adaln_single = AdaLayerNormSingle(inner_dim, use_additional_conditions=self.use_additional_conditions) self.caption_projection = None if caption_channels is not None: self.caption_projection = CaptionProjection(in_features=caption_channels, hidden_size=inner_dim) self.gradient_checkpointing = False def _set_gradient_checkpointing(self, module, value=False): self.gradient_checkpointing = value def make_position(self, b, t, use_image_num, h, w, device): pos_hw = self.position_getter_2d(b * (t + use_image_num), h, w, device) # fake_b = b*(t+use_image_num) pos_t = self.position_getter_1d(b * h * w, t, device) # fake_b = b*h*w return pos_hw, pos_t def make_attn_mask(self, attention_mask, frame, dtype): attention_mask = rearrange(attention_mask, "b t h w -> (b t) 1 (h w)") # assume that mask is expressed as: # (1 = keep, 0 = discard) # convert mask into a bias that can be added to attention scores: # (keep = +0, discard = -10000.0) attention_mask = (1 - attention_mask.to(dtype)) * -10000.0 attention_mask = attention_mask.to(self.dtype) return attention_mask def vae_to_diff_mask(self, attention_mask, use_image_num): dtype = attention_mask.dtype # b, t+use_image_num, h, w, assume t as channel # this version do not use 3d patch embedding attention_mask = F.max_pool2d( attention_mask, kernel_size=(self.patch_size, self.patch_size), stride=(self.patch_size, self.patch_size) ) attention_mask = attention_mask.bool().to(dtype) return attention_mask def forward( self, hidden_states: torch.Tensor, timestep: Optional[torch.LongTensor] = None, all_timesteps=None, encoder_hidden_states: Optional[torch.Tensor] = None, added_cond_kwargs: Dict[str, torch.Tensor] = None, class_labels: Optional[torch.LongTensor] = None, cross_attention_kwargs: Dict[str, Any] = None, attention_mask: Optional[torch.Tensor] = None, encoder_attention_mask: Optional[torch.Tensor] = None, use_image_num: int = 0, enable_temporal_attentions: bool = True, return_dict: bool = True, ): """ The [`Transformer2DModel`] forward method. Args: hidden_states (`torch.LongTensor` of shape `(batch size, num latent pixels)` if discrete, `torch.FloatTensor` of shape `(batch size, frame, channel, height, width)` if continuous): Input `hidden_states`. encoder_hidden_states ( `torch.FloatTensor` of shape `(batch size, sequence len, embed dims)`, *optional*): Conditional embeddings for cross attention layer. If not given, cross-attention defaults to self-attention. timestep ( `torch.LongTensor`, *optional*): Used to indicate denoising step. Optional timestep to be applied as an embedding in `AdaLayerNorm`. class_labels ( `torch.LongTensor` of shape `(batch size, num classes)`, *optional*): Used to indicate class labels conditioning. Optional class labels to be applied as an embedding in `AdaLayerZeroNorm`. cross_attention_kwargs ( `Dict[str, Any]`, *optional*): A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under `self.processor` in [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py). attention_mask ( `torch.Tensor`, *optional*): An attention mask of shape `(batch, key_tokens)` is applied to `encoder_hidden_states`. If `1` the mask is kept, otherwise if `0` it is discarded. Mask will be converted into a bias, which adds large negative values to the attention scores corresponding to "discard" tokens. encoder_attention_mask ( `torch.Tensor`, *optional*): Cross-attention mask applied to `encoder_hidden_states`. Two formats supported: * Mask `(batch, sequence_length)` True = keep, False = discard. * Bias `(batch, 1, sequence_length)` 0 = keep, -10000 = discard. If `ndim == 2`: will be interpreted as a mask, then converted into a bias consistent with the format above. This bias will be added to the cross-attention scores. return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`~models.unet_2d_condition.UNet2DConditionOutput`] instead of a plain tuple. Returns: If `return_dict` is True, an [`~models.transformer_2d.Transformer2DModelOutput`] is returned, otherwise a `tuple` where the first element is the sample tensor. """ # 0. Split batch if get_cfg_parallel_size() > 1: ( hidden_states, timestep, encoder_hidden_states, class_labels, attention_mask, encoder_attention_mask, ) = batch_func( partial(split_sequence, process_group=get_cfg_parallel_group(), dim=0), hidden_states, timestep, encoder_hidden_states, class_labels, attention_mask, encoder_attention_mask, ) input_batch_size, c, frame, h, w = hidden_states.shape frame = frame - use_image_num # 20-4=16 hidden_states = rearrange(hidden_states, "b c f h w -> (b f) c h w").contiguous() org_timestep = timestep # ensure attention_mask is a bias, and give it a singleton query_tokens dimension. # we may have done this conversion already, e.g. if we came here via UNet2DConditionModel#forward. # we can tell by counting dims; if ndim == 2: it's a mask rather than a bias. # expects mask of shape: # [batch, key_tokens] # adds singleton query_tokens dimension: # [batch, 1, key_tokens] # this helps to broadcast it as a bias over attention scores, which will be in one of the following shapes: # [batch, heads, query_tokens, key_tokens] (e.g. torch sdp attn) # [batch * heads, query_tokens, key_tokens] (e.g. xformers or classic attn) if attention_mask is None: attention_mask = torch.ones( (input_batch_size, frame + use_image_num, h, w), device=hidden_states.device, dtype=hidden_states.dtype ) attention_mask = self.vae_to_diff_mask(attention_mask, use_image_num) dtype = attention_mask.dtype attention_mask_compress = F.max_pool2d( attention_mask.float(), kernel_size=self.compress_kv_factor, stride=self.compress_kv_factor ) attention_mask_compress = attention_mask_compress.to(dtype) attention_mask = self.make_attn_mask(attention_mask, frame, hidden_states.dtype) attention_mask_compress = self.make_attn_mask(attention_mask_compress, frame, hidden_states.dtype) # 1 + 4, 1 -> video condition, 4 -> image condition # convert encoder_attention_mask to a bias the same way we do for attention_mask if encoder_attention_mask is not None and encoder_attention_mask.ndim == 2: # ndim == 2 means no image joint encoder_attention_mask = (1 - encoder_attention_mask.to(hidden_states.dtype)) * -10000.0 encoder_attention_mask = encoder_attention_mask.unsqueeze(1) encoder_attention_mask = repeat(encoder_attention_mask, "b 1 l -> (b f) 1 l", f=frame).contiguous() encoder_attention_mask = encoder_attention_mask.to(self.dtype) elif encoder_attention_mask is not None and encoder_attention_mask.ndim == 3: # ndim == 3 means image joint encoder_attention_mask = (1 - encoder_attention_mask.to(hidden_states.dtype)) * -10000.0 encoder_attention_mask_video = encoder_attention_mask[:, :1, ...] encoder_attention_mask_video = repeat( encoder_attention_mask_video, "b 1 l -> b (1 f) l", f=frame ).contiguous() encoder_attention_mask_image = encoder_attention_mask[:, 1:, ...] encoder_attention_mask = torch.cat([encoder_attention_mask_video, encoder_attention_mask_image], dim=1) encoder_attention_mask = rearrange(encoder_attention_mask, "b n l -> (b n) l").contiguous().unsqueeze(1) encoder_attention_mask = encoder_attention_mask.to(self.dtype) # Retrieve lora scale. cross_attention_kwargs.get("scale", 1.0) if cross_attention_kwargs is not None else 1.0 # 1. Input if self.is_input_patches: # here height, width = hidden_states.shape[-2] // self.patch_size, hidden_states.shape[-1] // self.patch_size hw = (height, width) num_patches = height * width hidden_states = self.pos_embed(hidden_states.to(self.dtype)) # alrady add positional embeddings if self.adaln_single is not None: if self.use_additional_conditions and added_cond_kwargs is None: raise ValueError( "`added_cond_kwargs` cannot be None when using additional conditions for `adaln_single`." ) # batch_size = hidden_states.shape[0] batch_size = input_batch_size timestep, embedded_timestep = self.adaln_single( timestep, added_cond_kwargs, batch_size=batch_size, hidden_dtype=hidden_states.dtype ) # 2. Blocks if self.caption_projection is not None: batch_size = hidden_states.shape[0] encoder_hidden_states = self.caption_projection(encoder_hidden_states.to(self.dtype)) # 3 120 1152 if use_image_num != 0 and self.training: encoder_hidden_states_video = encoder_hidden_states[:, :1, ...] encoder_hidden_states_video = repeat( encoder_hidden_states_video, "b 1 t d -> b (1 f) t d", f=frame ).contiguous() encoder_hidden_states_image = encoder_hidden_states[:, 1:, ...] encoder_hidden_states = torch.cat([encoder_hidden_states_video, encoder_hidden_states_image], dim=1) encoder_hidden_states_spatial = rearrange(encoder_hidden_states, "b f t d -> (b f) t d").contiguous() else: encoder_hidden_states_spatial = repeat( encoder_hidden_states, "b 1 t d -> (b f) t d", f=frame ).contiguous() # prepare timesteps for spatial and temporal block timestep_spatial = repeat(timestep, "b d -> (b f) d", f=frame + use_image_num).contiguous() timestep_temp = repeat(timestep, "b d -> (b p) d", p=num_patches).contiguous() pos_hw, pos_t = None, None if self.use_rope: pos_hw, pos_t = self.make_position( input_batch_size, frame, use_image_num, height, width, hidden_states.device ) if enable_sequence_parallel(): set_temporal_pad(frame + use_image_num) set_spatial_pad(num_patches) hidden_states = self.split_from_second_dim(hidden_states, input_batch_size) encoder_hidden_states_spatial = self.split_from_second_dim(encoder_hidden_states_spatial, input_batch_size) timestep_spatial = self.split_from_second_dim(timestep_spatial, input_batch_size) attention_mask = self.split_from_second_dim(attention_mask, input_batch_size) attention_mask_compress = self.split_from_second_dim(attention_mask_compress, input_batch_size) temp_pos_embed = split_sequence( self.temp_pos_embed, get_sequence_parallel_group(), dim=1, grad_scale="down", pad=get_temporal_pad() ) else: temp_pos_embed = self.temp_pos_embed for i, (spatial_block, temp_block) in enumerate(zip(self.transformer_blocks, self.temporal_transformer_blocks)): if self.training and self.gradient_checkpointing: hidden_states = torch.utils.checkpoint.checkpoint( spatial_block, hidden_states, attention_mask_compress if i >= self.num_layers // 2 else attention_mask, encoder_hidden_states_spatial, encoder_attention_mask, timestep_spatial, cross_attention_kwargs, class_labels, pos_hw, pos_hw, hw, use_reentrant=False, ) if enable_temporal_attentions: hidden_states = rearrange(hidden_states, "(b f) t d -> (b t) f d", b=input_batch_size).contiguous() if use_image_num != 0: # image-video joitn training hidden_states_video = hidden_states[:, :frame, ...] hidden_states_image = hidden_states[:, frame:, ...] # if i == 0 and not self.use_rope: if i == 0: hidden_states_video = hidden_states_video + temp_pos_embed hidden_states_video = torch.utils.checkpoint.checkpoint( temp_block, hidden_states_video, None, # attention_mask None, # encoder_hidden_states None, # encoder_attention_mask timestep_temp, cross_attention_kwargs, class_labels, pos_t, pos_t, (frame,), use_reentrant=False, ) hidden_states = torch.cat([hidden_states_video, hidden_states_image], dim=1) hidden_states = rearrange( hidden_states, "(b t) f d -> (b f) t d", b=input_batch_size ).contiguous() else: # if i == 0 and not self.use_rope: if i == 0: hidden_states = hidden_states + temp_pos_embed hidden_states = torch.utils.checkpoint.checkpoint( temp_block, hidden_states, None, # attention_mask None, # encoder_hidden_states None, # encoder_attention_mask timestep_temp, cross_attention_kwargs, class_labels, pos_t, pos_t, (frame,), use_reentrant=False, ) hidden_states = rearrange( hidden_states, "(b t) f d -> (b f) t d", b=input_batch_size ).contiguous() else: hidden_states = spatial_block( hidden_states, attention_mask_compress if i >= self.num_layers // 2 else attention_mask, encoder_hidden_states_spatial, encoder_attention_mask, timestep_spatial, cross_attention_kwargs, class_labels, pos_hw, pos_hw, hw, org_timestep, all_timesteps=all_timesteps, ) if enable_temporal_attentions: # b c f h w, f = 16 + 4 hidden_states = rearrange(hidden_states, "(b f) t d -> (b t) f d", b=input_batch_size).contiguous() if use_image_num != 0 and self.training: hidden_states_video = hidden_states[:, :frame, ...] hidden_states_image = hidden_states[:, frame:, ...] # if i == 0 and not self.use_rope: # hidden_states_video = hidden_states_video + temp_pos_embed hidden_states_video = temp_block( hidden_states_video, None, # attention_mask None, # encoder_hidden_states None, # encoder_attention_mask timestep_temp, cross_attention_kwargs, class_labels, pos_t, pos_t, (frame,), org_timestep, ) hidden_states = torch.cat([hidden_states_video, hidden_states_image], dim=1) hidden_states = rearrange( hidden_states, "(b t) f d -> (b f) t d", b=input_batch_size ).contiguous() else: # if i == 0 and not self.use_rope: if i == 0: hidden_states = hidden_states + temp_pos_embed hidden_states = temp_block( hidden_states, None, # attention_mask None, # encoder_hidden_states None, # encoder_attention_mask timestep_temp, cross_attention_kwargs, class_labels, pos_t, pos_t, (frame,), org_timestep, all_timesteps=all_timesteps, ) hidden_states = rearrange( hidden_states, "(b t) f d -> (b f) t d", b=input_batch_size ).contiguous() if enable_sequence_parallel(): hidden_states = self.gather_from_second_dim(hidden_states, input_batch_size) if self.is_input_patches: if self.config.norm_type != "ada_norm_single": conditioning = self.transformer_blocks[0].norm1.emb( timestep, class_labels, hidden_dtype=hidden_states.dtype ) shift, scale = self.proj_out_1(F.silu(conditioning)).chunk(2, dim=1) hidden_states = self.norm_out(hidden_states) * (1 + scale[:, None]) + shift[:, None] hidden_states = self.proj_out_2(hidden_states) elif self.config.norm_type == "ada_norm_single": embedded_timestep = repeat(embedded_timestep, "b d -> (b f) d", f=frame + use_image_num).contiguous() shift, scale = (self.scale_shift_table[None] + embedded_timestep[:, None]).chunk(2, dim=1) hidden_states = self.norm_out(hidden_states) # Modulation hidden_states = hidden_states * (1 + scale) + shift hidden_states = self.proj_out(hidden_states) # unpatchify if self.adaln_single is None: height = width = int(hidden_states.shape[1] ** 0.5) hidden_states = hidden_states.reshape( shape=(-1, height, width, self.patch_size, self.patch_size, self.out_channels) ) hidden_states = torch.einsum("nhwpqc->nchpwq", hidden_states) output = hidden_states.reshape( shape=(-1, self.out_channels, height * self.patch_size, width * self.patch_size) ) output = rearrange(output, "(b f) c h w -> b c f h w", b=input_batch_size).contiguous() # 3. Gather batch for data parallelism if get_cfg_parallel_size() > 1: output = gather_sequence(output, get_cfg_parallel_group(), dim=0) if not return_dict: return (output,) return Transformer3DModelOutput(sample=output) @classmethod def from_pretrained_2d(cls, pretrained_model_path, subfolder=None, **kwargs): if subfolder is not None: pretrained_model_path = os.path.join(pretrained_model_path, subfolder) config_file = os.path.join(pretrained_model_path, "config.json") if not os.path.isfile(config_file): raise RuntimeError(f"{config_file} does not exist") with open(config_file, "r") as f: config = json.load(f) model = cls.from_config(config, **kwargs) return model def split_from_second_dim(self, x, batch_size): x = x.view(batch_size, -1, *x.shape[1:]) x = split_sequence(x, get_sequence_parallel_group(), dim=1, grad_scale="down", pad=get_temporal_pad()) x = x.reshape(-1, *x.shape[2:]) return x def gather_from_second_dim(self, x, batch_size): x = x.view(batch_size, -1, *x.shape[1:]) x = gather_sequence(x, get_sequence_parallel_group(), dim=1, grad_scale="up", pad=get_temporal_pad()) x = x.reshape(-1, *x.shape[2:]) return x # depth = num_layers * 2 def LatteT2V_XL_122(**kwargs): return LatteT2V( num_layers=28, attention_head_dim=72, num_attention_heads=16, patch_size_t=1, patch_size=2, norm_type="ada_norm_single", caption_channels=4096, cross_attention_dim=1152, **kwargs, ) def LatteT2V_D64_XL_122(**kwargs): return LatteT2V( num_layers=28, attention_head_dim=64, num_attention_heads=18, patch_size_t=1, patch_size=2, norm_type="ada_norm_single", caption_channels=4096, cross_attention_dim=1152, **kwargs, ) Latte_models = { "LatteT2V-XL/122": LatteT2V_XL_122, "LatteT2V-D64-XL/122": LatteT2V_D64_XL_122, }