539 lines
21 KiB
Python
539 lines
21 KiB
Python
import typing as tp
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import math
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import torch
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# from beartype.typing import Tuple
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from einops import rearrange
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from torch import nn
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from torch.nn import functional as F
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from .mmmodules.model.low_level import MLP, ChannelLastConv1d, ConvMLP
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from .blocks import FourierFeatures
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from .transformer import ContinuousTransformer
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from .utils import mask_from_frac_lengths, resample
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class DiffusionTransformer(nn.Module):
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def __init__(self,
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io_channels=32,
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patch_size=1,
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embed_dim=768,
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cond_token_dim=0,
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project_cond_tokens=True,
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global_cond_dim=0,
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project_global_cond=True,
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input_concat_dim=0,
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prepend_cond_dim=0,
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cond_ctx_dim=0,
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depth=12,
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num_heads=8,
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transformer_type: tp.Literal["continuous_transformer"] = "continuous_transformer",
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global_cond_type: tp.Literal["prepend", "adaLN"] = "prepend",
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timestep_cond_type: tp.Literal["global", "input_concat"] = "global",
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add_token_dim=0,
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sync_token_dim=0,
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use_mlp=False,
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use_zero_init=False,
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**kwargs):
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super().__init__()
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self.cond_token_dim = cond_token_dim
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# Timestep embeddings
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timestep_features_dim = 256
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# Timestep embeddings
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self.timestep_cond_type = timestep_cond_type
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self.timestep_features = FourierFeatures(1, timestep_features_dim)
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if timestep_cond_type == "global":
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timestep_embed_dim = embed_dim
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elif timestep_cond_type == "input_concat":
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assert timestep_embed_dim is not None, "timestep_embed_dim must be specified if timestep_cond_type is input_concat"
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input_concat_dim += timestep_embed_dim
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self.to_timestep_embed = nn.Sequential(
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nn.Linear(timestep_features_dim, embed_dim, bias=True),
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nn.SiLU(),
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nn.Linear(embed_dim, embed_dim, bias=True),
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)
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self.use_mlp = use_mlp
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if cond_token_dim > 0:
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# Conditioning tokens
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cond_embed_dim = cond_token_dim if not project_cond_tokens else embed_dim
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self.to_cond_embed = nn.Sequential(
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nn.Linear(cond_token_dim, cond_embed_dim, bias=False),
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nn.SiLU(),
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nn.Linear(cond_embed_dim, cond_embed_dim, bias=False)
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)
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else:
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cond_embed_dim = 0
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if global_cond_dim > 0:
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# Global conditioning
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global_embed_dim = global_cond_dim if not project_global_cond else embed_dim
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self.to_global_embed = nn.Sequential(
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nn.Linear(global_cond_dim, global_embed_dim, bias=False),
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nn.SiLU(),
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nn.Linear(global_embed_dim, global_embed_dim, bias=False)
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)
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if add_token_dim > 0:
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# Conditioning tokens
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add_embed_dim = add_token_dim if not project_cond_tokens else embed_dim
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self.to_add_embed = nn.Sequential(
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nn.Linear(add_token_dim, add_embed_dim, bias=False),
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nn.SiLU(),
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nn.Linear(add_embed_dim, add_embed_dim, bias=False)
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)
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else:
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add_embed_dim = 0
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if sync_token_dim > 0:
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# Conditioning tokens
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sync_embed_dim = sync_token_dim if not project_cond_tokens else embed_dim
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self.to_sync_embed = nn.Sequential(
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nn.Linear(sync_token_dim, sync_embed_dim, bias=False),
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nn.SiLU(),
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nn.Linear(sync_embed_dim, sync_embed_dim, bias=False)
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)
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else:
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sync_embed_dim = 0
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if prepend_cond_dim > 0:
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# Prepend conditioning
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self.to_prepend_embed = nn.Sequential(
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nn.Linear(prepend_cond_dim, embed_dim, bias=False),
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nn.SiLU(),
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nn.Linear(embed_dim, embed_dim, bias=False)
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)
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self.input_concat_dim = input_concat_dim
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dim_in = io_channels + self.input_concat_dim
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self.patch_size = patch_size
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# Transformer
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self.transformer_type = transformer_type
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self.empty_clip_feat = nn.Parameter(torch.zeros(1, embed_dim), requires_grad=True)
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self.empty_sync_feat = nn.Parameter(torch.zeros(1, embed_dim), requires_grad=True)
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self.global_cond_type = global_cond_type
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if self.transformer_type == "continuous_transformer":
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global_dim = None
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if self.global_cond_type == "adaLN":
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# The global conditioning is projected to the embed_dim already at this point
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global_dim = embed_dim
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self.transformer = ContinuousTransformer(
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dim=embed_dim,
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depth=depth,
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dim_heads=embed_dim // num_heads,
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dim_in=dim_in * patch_size,
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dim_out=io_channels * patch_size,
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cross_attend = cond_token_dim > 0,
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cond_token_dim = cond_embed_dim,
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global_cond_dim=global_dim,
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**kwargs
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)
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else:
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raise ValueError(f"Unknown transformer type: {self.transformer_type}")
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self.preprocess_conv = nn.Conv1d(dim_in, dim_in, 1, bias=False)
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self.postprocess_conv = nn.Conv1d(io_channels, io_channels, 1, bias=False)
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nn.init.zeros_(self.preprocess_conv.weight)
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nn.init.zeros_(self.postprocess_conv.weight)
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def initialize_weights(self):
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def _basic_init(module):
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if isinstance(module, nn.Linear):
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torch.nn.init.xavier_uniform_(module.weight)
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if module.bias is not None:
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nn.init.constant_(module.bias, 0)
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# if isinstance(module, nn.Conv1d):
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# if module.bias is not None:
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# nn.init.constant_(module.bias, 0)
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self.apply(_basic_init)
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# Initialize timestep embedding MLP:
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nn.init.normal_(self.to_timestep_embed[0].weight, std=0.02)
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nn.init.normal_(self.to_timestep_embed[2].weight, std=0.02)
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# Zero-out output layers:
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if self.global_cond_type == "adaLN":
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for block in self.transformer.layers:
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nn.init.constant_(block.adaLN_modulation[-1].weight, 0)
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nn.init.constant_(block.adaLN_modulation[-1].bias, 0)
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nn.init.constant_(self.empty_clip_feat, 0)
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nn.init.constant_(self.empty_sync_feat, 0)
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def _forward(
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self,
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x,
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t,
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mask=None,
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cross_attn_cond=None,
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cross_attn_cond_mask=None,
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input_concat_cond=None,
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global_embed=None,
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prepend_cond=None,
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prepend_cond_mask=None,
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add_cond=None,
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add_masks=None,
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sync_cond=None,
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return_info=False,
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**kwargs):
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if cross_attn_cond is not None:
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cross_attn_cond = self.to_cond_embed(cross_attn_cond)
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if global_embed is not None:
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# Project the global conditioning to the embedding dimension
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global_embed = self.to_global_embed(global_embed)
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prepend_inputs = None
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prepend_mask = None
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prepend_length = 0
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if prepend_cond is not None:
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# Project the prepend conditioning to the embedding dimension
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prepend_cond = self.to_prepend_embed(prepend_cond)
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prepend_inputs = prepend_cond
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if prepend_cond_mask is not None:
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prepend_mask = prepend_cond_mask
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if input_concat_cond is not None:
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# reshape from (b, n, c) to (b, c, n)
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if input_concat_cond.shape[1] != x.shape[1]:
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input_concat_cond = input_concat_cond.transpose(1,2)
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# Interpolate input_concat_cond to the same length as x
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# if input_concat_cond.shape[1] != x.shape[2]:
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# input_concat_cond = input_concat_cond.transpose(1,2)
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input_concat_cond = F.interpolate(input_concat_cond, (x.shape[2], ), mode='nearest')
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# input_concat_cond = input_concat_cond.transpose(1,2)
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# if len(global_embed.shape) == 2:
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# global_embed = global_embed.unsqueeze(1)
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# global_embed = global_embed + input_concat_cond
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x = torch.cat([x, input_concat_cond], dim=1)
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# Get the batch of timestep embeddings
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timestep_embed = self.to_timestep_embed(self.timestep_features(t[:, None])) # (b, embed_dim)
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# Timestep embedding is considered a global embedding. Add to the global conditioning if it exists
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if self.timestep_cond_type == "global":
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if global_embed is not None:
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if len(global_embed.shape) == 3:
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timestep_embed = timestep_embed.unsqueeze(1)
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global_embed = global_embed + timestep_embed
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else:
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global_embed = timestep_embed
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elif self.timestep_cond_type == "input_concat":
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x = torch.cat([x, timestep_embed.unsqueeze(1).expand(-1, -1, x.shape[2])], dim=1)
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# Add the global_embed to the prepend inputs if there is no global conditioning support in the transformer
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if self.global_cond_type == "prepend" and global_embed is not None:
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if prepend_inputs is None:
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# Prepend inputs are just the global embed, and the mask is all ones
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if len(global_embed.shape) == 2:
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prepend_inputs = global_embed.unsqueeze(1)
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else:
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prepend_inputs = global_embed
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prepend_mask = torch.ones((x.shape[0], 1), device=x.device, dtype=torch.bool)
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else:
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# Prepend inputs are the prepend conditioning + the global embed
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if len(global_embed.shape) == 2:
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prepend_inputs = torch.cat([prepend_inputs, global_embed.unsqueeze(1)], dim=1)
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else:
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prepend_inputs = torch.cat([prepend_inputs, global_embed], dim=1)
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prepend_mask = torch.cat([prepend_mask, torch.ones((x.shape[0], 1), device=x.device, dtype=torch.bool)], dim=1)
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prepend_length = prepend_inputs.shape[1]
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x = self.preprocess_conv(x) + x
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x = rearrange(x, "b c t -> b t c")
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extra_args = {}
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if self.global_cond_type == "adaLN":
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extra_args["global_cond"] = global_embed
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if self.patch_size > 1:
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b, seq_len, c = x.shape
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# 计算需要填充的数量
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pad_amount = (self.patch_size - seq_len % self.patch_size) % self.patch_size
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if pad_amount > 0:
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# 在时间维度上进行填充
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x = F.pad(x, (0, 0, 0, pad_amount), mode='constant', value=0)
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x = rearrange(x, "b (t p) c -> b t (c p)", p=self.patch_size)
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if add_cond is not None:
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# Interpolate add_cond to the same length as x
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# if self.use_mlp:
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add_cond = self.to_add_embed(add_cond)
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if add_cond.shape[1] != x.shape[1]:
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add_cond = add_cond.transpose(1,2)
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add_cond = F.interpolate(add_cond, (x.shape[1], ), mode='linear', align_corners=False)
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add_cond = add_cond.transpose(1,2)
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# add_cond = resample(add_cond, x)
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if sync_cond is not None:
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sync_cond = self.to_sync_embed(sync_cond)
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if self.transformer_type == "continuous_transformer":
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output = self.transformer(x, prepend_embeds=prepend_inputs, context=cross_attn_cond, add_cond=add_cond, sync_cond=sync_cond, mask=mask, prepend_mask=prepend_mask, return_info=return_info, **extra_args, **kwargs)
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if return_info:
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output, info = output
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output = rearrange(output, "b t c -> b c t")[:,:,prepend_length:]
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if self.patch_size > 1:
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output = rearrange(output, "b (c p) t -> b c (t p)", p=self.patch_size)
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# 移除之前添加的填充
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if pad_amount > 0:
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output = output[:, :, :seq_len]
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output = self.postprocess_conv(output) + output
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if return_info:
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return output, info
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return output
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def forward(
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self,
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x,
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t,
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cross_attn_cond=None,
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cross_attn_cond_mask=None,
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negative_cross_attn_cond=None,
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negative_cross_attn_mask=None,
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input_concat_cond=None,
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global_embed=None,
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negative_global_embed=None,
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prepend_cond=None,
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prepend_cond_mask=None,
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add_cond=None,
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sync_cond=None,
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cfg_scale=1.0,
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cfg_dropout_prob=0.0,
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causal=False,
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scale_phi=0.0,
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mask=None,
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return_info=False,
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**kwargs):
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assert causal == False, "Causal mode is not supported for DiffusionTransformer"
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bsz, a, b = x.shape
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model_dtype = next(self.parameters()).dtype
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x = x.to(model_dtype)
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t = t.to(model_dtype)
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if cross_attn_cond is not None:
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cross_attn_cond = cross_attn_cond.to(model_dtype)
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if negative_cross_attn_cond is not None:
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negative_cross_attn_cond = negative_cross_attn_cond.to(model_dtype)
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if input_concat_cond is not None:
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input_concat_cond = input_concat_cond.to(model_dtype)
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if global_embed is not None:
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global_embed = global_embed.to(model_dtype)
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if negative_global_embed is not None:
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negative_global_embed = negative_global_embed.to(model_dtype)
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if prepend_cond is not None:
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prepend_cond = prepend_cond.to(model_dtype)
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if add_cond is not None:
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add_cond = add_cond.to(model_dtype)
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if sync_cond is not None:
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sync_cond = sync_cond.to(model_dtype)
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if cross_attn_cond_mask is not None:
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cross_attn_cond_mask = cross_attn_cond_mask.bool()
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cross_attn_cond_mask = None # Temporarily disabling conditioning masks due to kernel issue for flash attention
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if prepend_cond_mask is not None:
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prepend_cond_mask = prepend_cond_mask.bool()
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# CFG dropout
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if cfg_dropout_prob > 0.0 and cfg_scale == 1.0:
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if cross_attn_cond is not None:
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null_embed = torch.zeros_like(cross_attn_cond, device=cross_attn_cond.device)
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dropout_mask = torch.bernoulli(torch.full((cross_attn_cond.shape[0], 1, 1), cfg_dropout_prob, device=cross_attn_cond.device)).to(torch.bool)
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cross_attn_cond = torch.where(dropout_mask, null_embed, cross_attn_cond)
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if prepend_cond is not None:
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null_embed = torch.zeros_like(prepend_cond, device=prepend_cond.device)
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dropout_mask = torch.bernoulli(torch.full((prepend_cond.shape[0], 1, 1), cfg_dropout_prob, device=prepend_cond.device)).to(torch.bool)
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prepend_cond = torch.where(dropout_mask, null_embed, prepend_cond)
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if add_cond is not None:
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null_embed = torch.zeros_like(add_cond, device=add_cond.device)
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dropout_mask = torch.bernoulli(torch.full((add_cond.shape[0], 1, 1), cfg_dropout_prob, device=add_cond.device)).to(torch.bool)
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add_cond = torch.where(dropout_mask, null_embed, add_cond)
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if sync_cond is not None:
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null_embed = torch.zeros_like(sync_cond, device=sync_cond.device)
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dropout_mask = torch.bernoulli(torch.full((sync_cond.shape[0], 1, 1), cfg_dropout_prob, device=sync_cond.device)).to(torch.bool)
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sync_cond = torch.where(dropout_mask, null_embed, sync_cond)
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if cfg_scale != 1.0 and (cross_attn_cond is not None or prepend_cond is not None or add_cond is not None):
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# Classifier-free guidance
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# Concatenate conditioned and unconditioned inputs on the batch dimension
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batch_inputs = torch.cat([x, x], dim=0)
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batch_timestep = torch.cat([t, t], dim=0)
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if global_embed is not None and global_embed.shape[0] == bsz:
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batch_global_cond = torch.cat([global_embed, global_embed], dim=0)
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elif global_embed is not None:
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batch_global_cond = global_embed
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else:
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batch_global_cond = None
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if input_concat_cond is not None and input_concat_cond.shape[0] == bsz:
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batch_input_concat_cond = torch.cat([input_concat_cond, input_concat_cond], dim=0)
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elif input_concat_cond is not None:
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batch_input_concat_cond = input_concat_cond
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else:
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batch_input_concat_cond = None
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batch_cond = None
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batch_cond_masks = None
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# Handle CFG for cross-attention conditioning
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if cross_attn_cond is not None and cross_attn_cond.shape[0] == bsz:
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null_embed = torch.zeros_like(cross_attn_cond, device=cross_attn_cond.device)
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# For negative cross-attention conditioning, replace the null embed with the negative cross-attention conditioning
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if negative_cross_attn_cond is not None:
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# If there's a negative cross-attention mask, set the masked tokens to the null embed
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if negative_cross_attn_mask is not None:
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negative_cross_attn_mask = negative_cross_attn_mask.to(torch.bool).unsqueeze(2)
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negative_cross_attn_cond = torch.where(negative_cross_attn_mask, negative_cross_attn_cond, null_embed)
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batch_cond = torch.cat([cross_attn_cond, negative_cross_attn_cond], dim=0)
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else:
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batch_cond = torch.cat([cross_attn_cond, null_embed], dim=0)
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if cross_attn_cond_mask is not None:
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batch_cond_masks = torch.cat([cross_attn_cond_mask, cross_attn_cond_mask], dim=0)
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elif cross_attn_cond is not None:
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batch_cond = cross_attn_cond
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else:
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batch_cond = None
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batch_prepend_cond = None
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batch_prepend_cond_mask = None
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if prepend_cond is not None and prepend_cond.shape[0] == bsz:
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null_embed = torch.zeros_like(prepend_cond, device=prepend_cond.device)
|
|
|
|
batch_prepend_cond = torch.cat([prepend_cond, null_embed], dim=0)
|
|
|
|
if prepend_cond_mask is not None:
|
|
batch_prepend_cond_mask = torch.cat([prepend_cond_mask, prepend_cond_mask], dim=0)
|
|
elif prepend_cond is not None:
|
|
batch_prepend_cond = prepend_cond
|
|
else:
|
|
batch_prepend_cond = None
|
|
|
|
batch_add_cond = None
|
|
|
|
# Handle CFG for cross-attention conditioning
|
|
if add_cond is not None and add_cond.shape[0] == bsz:
|
|
|
|
null_embed = torch.zeros_like(add_cond, device=add_cond.device)
|
|
|
|
|
|
batch_add_cond = torch.cat([add_cond, null_embed], dim=0)
|
|
elif add_cond is not None:
|
|
batch_add_cond = add_cond
|
|
else:
|
|
batch_add_cond = None
|
|
|
|
batch_sync_cond = None
|
|
|
|
# Handle CFG for cross-attention conditioning
|
|
if sync_cond is not None and sync_cond.shape[0] == bsz:
|
|
|
|
null_embed = torch.zeros_like(sync_cond, device=sync_cond.device)
|
|
|
|
|
|
batch_sync_cond = torch.cat([sync_cond, null_embed], dim=0)
|
|
elif sync_cond is not None:
|
|
batch_sync_cond = sync_cond
|
|
else:
|
|
batch_sync_cond = None
|
|
|
|
if mask is not None:
|
|
batch_masks = torch.cat([mask, mask], dim=0)
|
|
else:
|
|
batch_masks = None
|
|
|
|
batch_output = self._forward(
|
|
batch_inputs,
|
|
batch_timestep,
|
|
cross_attn_cond=batch_cond,
|
|
cross_attn_cond_mask=batch_cond_masks,
|
|
mask = batch_masks,
|
|
input_concat_cond=batch_input_concat_cond,
|
|
global_embed = batch_global_cond,
|
|
prepend_cond = batch_prepend_cond,
|
|
prepend_cond_mask = batch_prepend_cond_mask,
|
|
add_cond = batch_add_cond,
|
|
sync_cond = batch_sync_cond,
|
|
return_info = return_info,
|
|
**kwargs)
|
|
|
|
if return_info:
|
|
batch_output, info = batch_output
|
|
|
|
cond_output, uncond_output = torch.chunk(batch_output, 2, dim=0)
|
|
cfg_output = uncond_output + (cond_output - uncond_output) * cfg_scale
|
|
|
|
# CFG Rescale
|
|
if scale_phi != 0.0:
|
|
cond_out_std = cond_output.std(dim=1, keepdim=True)
|
|
out_cfg_std = cfg_output.std(dim=1, keepdim=True)
|
|
output = scale_phi * (cfg_output * (cond_out_std/out_cfg_std)) + (1-scale_phi) * cfg_output
|
|
else:
|
|
output = cfg_output
|
|
|
|
if return_info:
|
|
return output, info
|
|
|
|
return output
|
|
|
|
else:
|
|
return self._forward(
|
|
x,
|
|
t,
|
|
cross_attn_cond=cross_attn_cond,
|
|
cross_attn_cond_mask=cross_attn_cond_mask,
|
|
input_concat_cond=input_concat_cond,
|
|
global_embed=global_embed,
|
|
prepend_cond=prepend_cond,
|
|
prepend_cond_mask=prepend_cond_mask,
|
|
add_cond=add_cond,
|
|
sync_cond=sync_cond,
|
|
mask=mask,
|
|
return_info=return_info,
|
|
**kwargs
|
|
) |