1667 lines
75 KiB
Python
1667 lines
75 KiB
Python
from importlib import import_module
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import numpy as np
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from typing import Any, Dict, Optional, Tuple, Callable
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from diffusers.utils import USE_PEFT_BACKEND, BaseOutput, deprecate, is_xformers_available
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from diffusers.models.lora import LoRACompatibleConv, LoRACompatibleLinear
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import torch
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import torch.nn.functional as F
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from torch import nn
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from diffusers.utils.torch_utils import maybe_allow_in_graph
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from diffusers.models.embeddings import SinusoidalPositionalEmbedding, TimestepEmbedding, Timesteps
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from diffusers.models.normalization import AdaLayerNorm, AdaLayerNormZero
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from diffusers.models.attention_processor import SpatialNorm, LORA_ATTENTION_PROCESSORS, \
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CustomDiffusionAttnProcessor, CustomDiffusionXFormersAttnProcessor, CustomDiffusionAttnProcessor2_0, \
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AttnAddedKVProcessor, AttnAddedKVProcessor2_0, SlicedAttnAddedKVProcessor, XFormersAttnAddedKVProcessor, \
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LoRAAttnAddedKVProcessor, LoRAXFormersAttnProcessor, XFormersAttnProcessor, LoRAAttnProcessor2_0, LoRAAttnProcessor, \
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AttnProcessor, SlicedAttnProcessor, logger
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from diffusers.models.activations import GEGLU, GELU, ApproximateGELU
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from dataclasses import dataclass
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from opensora.models.diffusion.utils.pos_embed import get_2d_sincos_pos_embed, RoPE1D, RoPE2D
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if is_xformers_available():
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import xformers
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import xformers.ops
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else:
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xformers = None
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class CombinedTimestepSizeEmbeddings(nn.Module):
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"""
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For PixArt-Alpha.
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Reference:
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https://github.com/PixArt-alpha/PixArt-alpha/blob/0f55e922376d8b797edd44d25d0e7464b260dcab/diffusion/model/nets/PixArtMS.py#L164C9-L168C29
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"""
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def __init__(self, embedding_dim, size_emb_dim, use_additional_conditions: bool = False):
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super().__init__()
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self.outdim = size_emb_dim
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self.time_proj = Timesteps(num_channels=256, flip_sin_to_cos=True, downscale_freq_shift=0)
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self.timestep_embedder = TimestepEmbedding(in_channels=256, time_embed_dim=embedding_dim)
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self.use_additional_conditions = use_additional_conditions
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if use_additional_conditions:
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self.use_additional_conditions = True
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self.additional_condition_proj = Timesteps(num_channels=256, flip_sin_to_cos=True, downscale_freq_shift=0)
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self.resolution_embedder = TimestepEmbedding(in_channels=256, time_embed_dim=size_emb_dim)
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self.aspect_ratio_embedder = TimestepEmbedding(in_channels=256, time_embed_dim=size_emb_dim)
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def apply_condition(self, size: torch.Tensor, batch_size: int, embedder: nn.Module):
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if size.ndim == 1:
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size = size[:, None]
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if size.shape[0] != batch_size:
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size = size.repeat(batch_size // size.shape[0], 1)
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if size.shape[0] != batch_size:
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raise ValueError(f"`batch_size` should be {size.shape[0]} but found {batch_size}.")
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current_batch_size, dims = size.shape[0], size.shape[1]
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size = size.reshape(-1)
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size_freq = self.additional_condition_proj(size).to(size.dtype)
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size_emb = embedder(size_freq)
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size_emb = size_emb.reshape(current_batch_size, dims * self.outdim)
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return size_emb
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def forward(self, timestep, resolution, aspect_ratio, batch_size, hidden_dtype):
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timesteps_proj = self.time_proj(timestep)
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timesteps_emb = self.timestep_embedder(timesteps_proj.to(dtype=hidden_dtype)) # (N, D)
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if self.use_additional_conditions:
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resolution = self.apply_condition(resolution, batch_size=batch_size, embedder=self.resolution_embedder)
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aspect_ratio = self.apply_condition(
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aspect_ratio, batch_size=batch_size, embedder=self.aspect_ratio_embedder
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)
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conditioning = timesteps_emb + torch.cat([resolution, aspect_ratio], dim=1)
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else:
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conditioning = timesteps_emb
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return conditioning
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class CaptionProjection(nn.Module):
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"""
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Projects caption embeddings. Also handles dropout for classifier-free guidance.
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Adapted from https://github.com/PixArt-alpha/PixArt-alpha/blob/master/diffusion/model/nets/PixArt_blocks.py
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"""
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def __init__(self, in_features, hidden_size, num_tokens=120):
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super().__init__()
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self.linear_1 = nn.Linear(in_features=in_features, out_features=hidden_size, bias=True)
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self.act_1 = nn.GELU(approximate="tanh")
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self.linear_2 = nn.Linear(in_features=hidden_size, out_features=hidden_size, bias=True)
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self.register_buffer("y_embedding", nn.Parameter(torch.randn(num_tokens, in_features) / in_features**0.5))
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def forward(self, caption, force_drop_ids=None):
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hidden_states = self.linear_1(caption)
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hidden_states = self.act_1(hidden_states)
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hidden_states = self.linear_2(hidden_states)
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return hidden_states
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class PatchEmbed(nn.Module):
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"""2D Image to Patch Embedding"""
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def __init__(
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self,
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height=224,
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width=224,
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patch_size=16,
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in_channels=3,
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embed_dim=768,
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layer_norm=False,
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flatten=True,
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bias=True,
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interpolation_scale=1,
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use_rope=False,
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):
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super().__init__()
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num_patches = (height // patch_size) * (width // patch_size)
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self.flatten = flatten
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self.layer_norm = layer_norm
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self.proj = nn.Conv2d(
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in_channels, embed_dim, kernel_size=(patch_size, patch_size), stride=patch_size, bias=bias
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)
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if layer_norm:
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self.norm = nn.LayerNorm(embed_dim, elementwise_affine=False, eps=1e-6)
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else:
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self.norm = None
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self.patch_size = patch_size
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# See:
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# https://github.com/PixArt-alpha/PixArt-alpha/blob/0f55e922376d8b797edd44d25d0e7464b260dcab/diffusion/model/nets/PixArtMS.py#L161
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self.height, self.width = height // patch_size, width // patch_size
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self.use_rope = use_rope
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if not self.use_rope:
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self.base_size = height // patch_size
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self.interpolation_scale = interpolation_scale
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pos_embed = get_2d_sincos_pos_embed(
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embed_dim, int(num_patches**0.5), base_size=self.base_size, interpolation_scale=self.interpolation_scale
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)
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self.register_buffer("pos_embed", torch.from_numpy(pos_embed).float().unsqueeze(0), persistent=False)
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def forward(self, latent):
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height, width = latent.shape[-2] // self.patch_size, latent.shape[-1] // self.patch_size
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latent = self.proj(latent)
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if self.flatten:
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latent = latent.flatten(2).transpose(1, 2) # BCHW -> BNC
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if self.layer_norm:
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latent = self.norm(latent)
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if self.use_rope:
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return latent.to(latent.dtype)
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# Interpolate positional embeddings if needed.
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# (For PixArt-Alpha: https://github.com/PixArt-alpha/PixArt-alpha/blob/0f55e922376d8b797edd44d25d0e7464b260dcab/diffusion/model/nets/PixArtMS.py#L162C151-L162C160)
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if self.height != height or self.width != width:
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raise ValueError
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pos_embed = get_2d_sincos_pos_embed(
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embed_dim=self.pos_embed.shape[-1],
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grid_size=(height, width),
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base_size=self.base_size,
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interpolation_scale=self.interpolation_scale,
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)
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pos_embed = torch.from_numpy(pos_embed)
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pos_embed = pos_embed.float().unsqueeze(0).to(latent.device)
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else:
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pos_embed = self.pos_embed
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return (latent + pos_embed).to(latent.dtype)
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@maybe_allow_in_graph
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class Attention(nn.Module):
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r"""
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A cross attention layer.
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Parameters:
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query_dim (`int`):
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The number of channels in the query.
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cross_attention_dim (`int`, *optional*):
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The number of channels in the encoder_hidden_states. If not given, defaults to `query_dim`.
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heads (`int`, *optional*, defaults to 8):
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The number of heads to use for multi-head attention.
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dim_head (`int`, *optional*, defaults to 64):
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The number of channels in each head.
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dropout (`float`, *optional*, defaults to 0.0):
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The dropout probability to use.
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bias (`bool`, *optional*, defaults to False):
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Set to `True` for the query, key, and value linear layers to contain a bias parameter.
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upcast_attention (`bool`, *optional*, defaults to False):
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Set to `True` to upcast the attention computation to `float32`.
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upcast_softmax (`bool`, *optional*, defaults to False):
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Set to `True` to upcast the softmax computation to `float32`.
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cross_attention_norm (`str`, *optional*, defaults to `None`):
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The type of normalization to use for the cross attention. Can be `None`, `layer_norm`, or `group_norm`.
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cross_attention_norm_num_groups (`int`, *optional*, defaults to 32):
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The number of groups to use for the group norm in the cross attention.
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added_kv_proj_dim (`int`, *optional*, defaults to `None`):
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The number of channels to use for the added key and value projections. If `None`, no projection is used.
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norm_num_groups (`int`, *optional*, defaults to `None`):
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The number of groups to use for the group norm in the attention.
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spatial_norm_dim (`int`, *optional*, defaults to `None`):
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The number of channels to use for the spatial normalization.
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out_bias (`bool`, *optional*, defaults to `True`):
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Set to `True` to use a bias in the output linear layer.
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scale_qk (`bool`, *optional*, defaults to `True`):
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Set to `True` to scale the query and key by `1 / sqrt(dim_head)`.
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only_cross_attention (`bool`, *optional*, defaults to `False`):
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Set to `True` to only use cross attention and not added_kv_proj_dim. Can only be set to `True` if
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`added_kv_proj_dim` is not `None`.
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eps (`float`, *optional*, defaults to 1e-5):
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An additional value added to the denominator in group normalization that is used for numerical stability.
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rescale_output_factor (`float`, *optional*, defaults to 1.0):
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A factor to rescale the output by dividing it with this value.
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residual_connection (`bool`, *optional*, defaults to `False`):
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Set to `True` to add the residual connection to the output.
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_from_deprecated_attn_block (`bool`, *optional*, defaults to `False`):
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Set to `True` if the attention block is loaded from a deprecated state dict.
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processor (`AttnProcessor`, *optional*, defaults to `None`):
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The attention processor to use. If `None`, defaults to `AttnProcessor2_0` if `torch 2.x` is used and
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`AttnProcessor` otherwise.
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"""
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def __init__(
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self,
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query_dim: int,
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cross_attention_dim: Optional[int] = None,
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heads: int = 8,
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dim_head: int = 64,
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dropout: float = 0.0,
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bias: bool = False,
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upcast_attention: bool = False,
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upcast_softmax: bool = False,
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cross_attention_norm: Optional[str] = None,
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cross_attention_norm_num_groups: int = 32,
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added_kv_proj_dim: Optional[int] = None,
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norm_num_groups: Optional[int] = None,
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spatial_norm_dim: Optional[int] = None,
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out_bias: bool = True,
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scale_qk: bool = True,
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only_cross_attention: bool = False,
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eps: float = 1e-5,
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rescale_output_factor: float = 1.0,
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residual_connection: bool = False,
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_from_deprecated_attn_block: bool = False,
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processor: Optional["AttnProcessor"] = None,
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attention_mode: str = 'xformers',
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use_rope: bool = False,
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):
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super().__init__()
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self.inner_dim = dim_head * heads
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self.cross_attention_dim = cross_attention_dim if cross_attention_dim is not None else query_dim
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self.upcast_attention = upcast_attention
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self.upcast_softmax = upcast_softmax
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self.rescale_output_factor = rescale_output_factor
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self.residual_connection = residual_connection
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self.dropout = dropout
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self.use_rope = use_rope
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# we make use of this private variable to know whether this class is loaded
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# with an deprecated state dict so that we can convert it on the fly
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self._from_deprecated_attn_block = _from_deprecated_attn_block
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self.scale_qk = scale_qk
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self.scale = dim_head**-0.5 if self.scale_qk else 1.0
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self.heads = heads
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# for slice_size > 0 the attention score computation
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# is split across the batch axis to save memory
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# You can set slice_size with `set_attention_slice`
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self.sliceable_head_dim = heads
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self.added_kv_proj_dim = added_kv_proj_dim
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self.only_cross_attention = only_cross_attention
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if self.added_kv_proj_dim is None and self.only_cross_attention:
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raise ValueError(
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"`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`."
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)
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if norm_num_groups is not None:
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self.group_norm = nn.GroupNorm(num_channels=query_dim, num_groups=norm_num_groups, eps=eps, affine=True)
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else:
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self.group_norm = None
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if spatial_norm_dim is not None:
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self.spatial_norm = SpatialNorm(f_channels=query_dim, zq_channels=spatial_norm_dim)
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else:
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self.spatial_norm = None
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if cross_attention_norm is None:
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self.norm_cross = None
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elif cross_attention_norm == "layer_norm":
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self.norm_cross = nn.LayerNorm(self.cross_attention_dim)
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elif cross_attention_norm == "group_norm":
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if self.added_kv_proj_dim is not None:
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# The given `encoder_hidden_states` are initially of shape
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# (batch_size, seq_len, added_kv_proj_dim) before being projected
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# to (batch_size, seq_len, cross_attention_dim). The norm is applied
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# before the projection, so we need to use `added_kv_proj_dim` as
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# the number of channels for the group norm.
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norm_cross_num_channels = added_kv_proj_dim
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else:
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norm_cross_num_channels = self.cross_attention_dim
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self.norm_cross = nn.GroupNorm(
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num_channels=norm_cross_num_channels, num_groups=cross_attention_norm_num_groups, eps=1e-5, affine=True
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)
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else:
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raise ValueError(
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f"unknown cross_attention_norm: {cross_attention_norm}. Should be None, 'layer_norm' or 'group_norm'"
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)
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if USE_PEFT_BACKEND:
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linear_cls = nn.Linear
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else:
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linear_cls = LoRACompatibleLinear
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self.to_q = linear_cls(query_dim, self.inner_dim, bias=bias)
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if not self.only_cross_attention:
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# only relevant for the `AddedKVProcessor` classes
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self.to_k = linear_cls(self.cross_attention_dim, self.inner_dim, bias=bias)
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self.to_v = linear_cls(self.cross_attention_dim, self.inner_dim, bias=bias)
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else:
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self.to_k = None
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self.to_v = None
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if self.added_kv_proj_dim is not None:
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self.add_k_proj = linear_cls(added_kv_proj_dim, self.inner_dim)
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self.add_v_proj = linear_cls(added_kv_proj_dim, self.inner_dim)
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self.to_out = nn.ModuleList([])
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self.to_out.append(linear_cls(self.inner_dim, query_dim, bias=out_bias))
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self.to_out.append(nn.Dropout(dropout))
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# set attention processor
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# We use the AttnProcessor2_0 by default when torch 2.x is used which uses
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# torch.nn.functional.scaled_dot_product_attention for native Flash/memory_efficient_attention
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# but only if it has the default `scale` argument. TODO remove scale_qk check when we move to torch 2.1
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if processor is None:
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processor = (
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AttnProcessor2_0(attention_mode, use_rope) if hasattr(F, "scaled_dot_product_attention") and self.scale_qk else AttnProcessor()
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)
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self.set_processor(processor)
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def set_use_memory_efficient_attention_xformers(
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self, use_memory_efficient_attention_xformers: bool, attention_op: Optional[Callable] = None
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) -> None:
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r"""
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Set whether to use memory efficient attention from `xformers` or not.
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Args:
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use_memory_efficient_attention_xformers (`bool`):
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Whether to use memory efficient attention from `xformers` or not.
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attention_op (`Callable`, *optional*):
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The attention operation to use. Defaults to `None` which uses the default attention operation from
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`xformers`.
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"""
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is_lora = hasattr(self, "processor") and isinstance(
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self.processor,
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LORA_ATTENTION_PROCESSORS,
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)
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is_custom_diffusion = hasattr(self, "processor") and isinstance(
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self.processor,
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(CustomDiffusionAttnProcessor, CustomDiffusionXFormersAttnProcessor, CustomDiffusionAttnProcessor2_0),
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)
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is_added_kv_processor = hasattr(self, "processor") and isinstance(
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self.processor,
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(
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AttnAddedKVProcessor,
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AttnAddedKVProcessor2_0,
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SlicedAttnAddedKVProcessor,
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XFormersAttnAddedKVProcessor,
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LoRAAttnAddedKVProcessor,
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),
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)
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if use_memory_efficient_attention_xformers:
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if is_added_kv_processor and (is_lora or is_custom_diffusion):
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raise NotImplementedError(
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f"Memory efficient attention is currently not supported for LoRA or custom diffusion for attention processor type {self.processor}"
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)
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if not is_xformers_available():
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raise ModuleNotFoundError(
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(
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"Refer to https://github.com/facebookresearch/xformers for more information on how to install"
|
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" xformers"
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),
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name="xformers",
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)
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elif not torch.cuda.is_available():
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raise ValueError(
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"torch.cuda.is_available() should be True but is False. xformers' memory efficient attention is"
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" only available for GPU "
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)
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else:
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try:
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# Make sure we can run the memory efficient attention
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_ = xformers.ops.memory_efficient_attention(
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torch.randn((1, 2, 40), device="cuda"),
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torch.randn((1, 2, 40), device="cuda"),
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torch.randn((1, 2, 40), device="cuda"),
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)
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except Exception as e:
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raise e
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if is_lora:
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# TODO (sayakpaul): should we throw a warning if someone wants to use the xformers
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# variant when using PT 2.0 now that we have LoRAAttnProcessor2_0?
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processor = LoRAXFormersAttnProcessor(
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hidden_size=self.processor.hidden_size,
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cross_attention_dim=self.processor.cross_attention_dim,
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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) -> "AttentionProcessor":
|
|
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
|
|
|
|
class AttnProcessor2_0:
|
|
r"""
|
|
Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0).
|
|
"""
|
|
|
|
def __init__(self, attention_mode='xformers', use_rope=False):
|
|
self.attention_mode = attention_mode
|
|
self.use_rope = use_rope
|
|
|
|
if self.use_rope:
|
|
self.rope2d = RoPE2D(freq=100)
|
|
self.rope1d = RoPE1D(freq=100)
|
|
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 __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,
|
|
) -> 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)
|
|
|
|
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:
|
|
print('use rope')
|
|
print('use rope')
|
|
print('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,
|
|
):
|
|
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
|
|
)
|
|
|
|
# # 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
|
|
|
|
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_temporal: Optional[torch.LongTensor] = 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]
|
|
|
|
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)
|
|
|
|
# 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)
|
|
|
|
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_temporal,
|
|
position_k=position_temporal,
|
|
**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
|
|
|
|
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 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
|
|
|
|
hidden_states = ff_output + hidden_states
|
|
if hidden_states.ndim == 4:
|
|
hidden_states = hidden_states.squeeze(1)
|
|
|
|
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,
|
|
):
|
|
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
|
|
)
|
|
|
|
# 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=use_rope,
|
|
) # 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
|
|
|
|
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_spatial: Optional[torch.LongTensor] = None,
|
|
position_condition: Optional[torch.LongTensor] = 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]
|
|
|
|
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)
|
|
|
|
# 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)
|
|
|
|
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_spatial,
|
|
position_k=position_spatial,
|
|
**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
|
|
|
|
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,
|
|
position_q=position_spatial,
|
|
position_k=position_condition,
|
|
**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 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
|
|
|
|
if self._chunk_size is not None:
|
|
# "feed_forward_chunk_size" can be used to save memory
|
|
ff_output = _chunked_feed_forward(
|
|
self.ff, norm_hidden_states, self._chunk_dim, self._chunk_size, lora_scale=lora_scale
|
|
)
|
|
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
|
|
|
|
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
|