889 lines
30 KiB
Python
889 lines
30 KiB
Python
# code adapted from: https://github.com/Stability-AI/stable-audio-tools
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from comfy.ldm.modules.attention import optimized_attention
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import typing as tp
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import torch
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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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import math
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class FourierFeatures(nn.Module):
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def __init__(self, in_features, out_features, std=1., dtype=None, device=None):
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super().__init__()
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assert out_features % 2 == 0
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self.weight = nn.Parameter(torch.empty(
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[out_features // 2, in_features], dtype=dtype, device=device))
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def forward(self, input):
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f = 2 * math.pi * input @ self.weight.T.to(dtype=input.dtype, device=input.device)
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return torch.cat([f.cos(), f.sin()], dim=-1)
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# norms
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class LayerNorm(nn.Module):
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def __init__(self, dim, bias=False, fix_scale=False, dtype=None, device=None):
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"""
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bias-less layernorm has been shown to be more stable. most newer models have moved towards rmsnorm, also bias-less
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"""
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super().__init__()
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self.gamma = nn.Parameter(torch.empty(dim, dtype=dtype, device=device))
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if bias:
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self.beta = nn.Parameter(torch.empty(dim, dtype=dtype, device=device))
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else:
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self.beta = None
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def forward(self, x):
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beta = self.beta
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if self.beta is not None:
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beta = beta.to(dtype=x.dtype, device=x.device)
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return F.layer_norm(x, x.shape[-1:], weight=self.gamma.to(dtype=x.dtype, device=x.device), bias=beta)
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class GLU(nn.Module):
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def __init__(
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self,
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dim_in,
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dim_out,
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activation,
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use_conv = False,
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conv_kernel_size = 3,
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dtype=None,
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device=None,
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operations=None,
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):
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super().__init__()
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self.act = activation
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self.proj = operations.Linear(dim_in, dim_out * 2, dtype=dtype, device=device) if not use_conv else operations.Conv1d(dim_in, dim_out * 2, conv_kernel_size, padding = (conv_kernel_size // 2), dtype=dtype, device=device)
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self.use_conv = use_conv
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def forward(self, x):
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if self.use_conv:
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x = rearrange(x, 'b n d -> b d n')
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x = self.proj(x)
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x = rearrange(x, 'b d n -> b n d')
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else:
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x = self.proj(x)
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x, gate = x.chunk(2, dim = -1)
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return x * self.act(gate)
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class AbsolutePositionalEmbedding(nn.Module):
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def __init__(self, dim, max_seq_len):
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super().__init__()
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self.scale = dim ** -0.5
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self.max_seq_len = max_seq_len
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self.emb = nn.Embedding(max_seq_len, dim)
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def forward(self, x, pos = None, seq_start_pos = None):
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seq_len, device = x.shape[1], x.device
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assert seq_len <= self.max_seq_len, f'you are passing in a sequence length of {seq_len} but your absolute positional embedding has a max sequence length of {self.max_seq_len}'
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if pos is None:
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pos = torch.arange(seq_len, device = device)
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if seq_start_pos is not None:
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pos = (pos - seq_start_pos[..., None]).clamp(min = 0)
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pos_emb = self.emb(pos)
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pos_emb = pos_emb * self.scale
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return pos_emb
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class ScaledSinusoidalEmbedding(nn.Module):
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def __init__(self, dim, theta = 10000):
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super().__init__()
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assert (dim % 2) == 0, 'dimension must be divisible by 2'
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self.scale = nn.Parameter(torch.ones(1) * dim ** -0.5)
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half_dim = dim // 2
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freq_seq = torch.arange(half_dim).float() / half_dim
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inv_freq = theta ** -freq_seq
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self.register_buffer('inv_freq', inv_freq, persistent = False)
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def forward(self, x, pos = None, seq_start_pos = None):
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seq_len, device = x.shape[1], x.device
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if pos is None:
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pos = torch.arange(seq_len, device = device)
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if seq_start_pos is not None:
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pos = pos - seq_start_pos[..., None]
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emb = torch.einsum('i, j -> i j', pos, self.inv_freq)
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emb = torch.cat((emb.sin(), emb.cos()), dim = -1)
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return emb * self.scale
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class RotaryEmbedding(nn.Module):
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def __init__(
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self,
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dim,
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use_xpos = False,
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scale_base = 512,
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interpolation_factor = 1.,
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base = 10000,
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base_rescale_factor = 1.
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):
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super().__init__()
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# proposed by reddit user bloc97, to rescale rotary embeddings to longer sequence length without fine-tuning
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# has some connection to NTK literature
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# https://www.reddit.com/r/LocalLLaMA/comments/14lz7j5/ntkaware_scaled_rope_allows_llama_models_to_have/
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base *= base_rescale_factor ** (dim / (dim - 2))
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inv_freq = 1. / (base ** (torch.arange(0, dim, 2).float() / dim))
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self.register_buffer('inv_freq', inv_freq)
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assert interpolation_factor >= 1.
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self.interpolation_factor = interpolation_factor
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if not use_xpos:
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self.register_buffer('scale', None)
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return
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scale = (torch.arange(0, dim, 2) + 0.4 * dim) / (1.4 * dim)
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self.scale_base = scale_base
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self.register_buffer('scale', scale)
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def forward_from_seq_len(self, seq_len, device, dtype):
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# device = self.inv_freq.device
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t = torch.arange(seq_len, device=device, dtype=dtype)
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return self.forward(t)
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def forward(self, t):
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# device = self.inv_freq.device
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device = t.device
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dtype = t.dtype
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# t = t.to(torch.float32)
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t = t / self.interpolation_factor
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freqs = torch.einsum('i , j -> i j', t, self.inv_freq.to(dtype=dtype, device=device))
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freqs = torch.cat((freqs, freqs), dim = -1)
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if self.scale is None:
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return freqs, 1.
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power = (torch.arange(seq_len, device = device) - (seq_len // 2)) / self.scale_base
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scale = self.scale.to(dtype=dtype, device=device) ** rearrange(power, 'n -> n 1')
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scale = torch.cat((scale, scale), dim = -1)
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return freqs, scale
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def rotate_half(x):
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x = rearrange(x, '... (j d) -> ... j d', j = 2)
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x1, x2 = x.unbind(dim = -2)
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return torch.cat((-x2, x1), dim = -1)
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def apply_rotary_pos_emb(t, freqs, scale = 1):
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out_dtype = t.dtype
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# cast to float32 if necessary for numerical stability
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dtype = t.dtype #reduce(torch.promote_types, (t.dtype, freqs.dtype, torch.float32))
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rot_dim, seq_len = freqs.shape[-1], t.shape[-2]
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freqs, t = freqs.to(dtype), t.to(dtype)
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freqs = freqs[-seq_len:, :]
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if t.ndim == 4 and freqs.ndim == 3:
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freqs = rearrange(freqs, 'b n d -> b 1 n d')
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# partial rotary embeddings, Wang et al. GPT-J
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t, t_unrotated = t[..., :rot_dim], t[..., rot_dim:]
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t = (t * freqs.cos() * scale) + (rotate_half(t) * freqs.sin() * scale)
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t, t_unrotated = t.to(out_dtype), t_unrotated.to(out_dtype)
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return torch.cat((t, t_unrotated), dim = -1)
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class FeedForward(nn.Module):
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def __init__(
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self,
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dim,
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dim_out = None,
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mult = 4,
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no_bias = False,
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glu = True,
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use_conv = False,
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conv_kernel_size = 3,
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zero_init_output = True,
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dtype=None,
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device=None,
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operations=None,
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):
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super().__init__()
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inner_dim = int(dim * mult)
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# Default to SwiGLU
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activation = nn.SiLU()
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dim_out = dim if dim_out is None else dim_out
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if glu:
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linear_in = GLU(dim, inner_dim, activation, dtype=dtype, device=device, operations=operations)
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else:
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linear_in = nn.Sequential(
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Rearrange('b n d -> b d n') if use_conv else nn.Identity(),
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operations.Linear(dim, inner_dim, bias = not no_bias, dtype=dtype, device=device) if not use_conv else operations.Conv1d(dim, inner_dim, conv_kernel_size, padding = (conv_kernel_size // 2), bias = not no_bias, dtype=dtype, device=device),
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Rearrange('b n d -> b d n') if use_conv else nn.Identity(),
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activation
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)
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linear_out = operations.Linear(inner_dim, dim_out, bias = not no_bias, dtype=dtype, device=device) if not use_conv else operations.Conv1d(inner_dim, dim_out, conv_kernel_size, padding = (conv_kernel_size // 2), bias = not no_bias, dtype=dtype, device=device)
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# # init last linear layer to 0
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# if zero_init_output:
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# nn.init.zeros_(linear_out.weight)
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# if not no_bias:
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# nn.init.zeros_(linear_out.bias)
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self.ff = nn.Sequential(
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linear_in,
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Rearrange('b d n -> b n d') if use_conv else nn.Identity(),
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linear_out,
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Rearrange('b n d -> b d n') if use_conv else nn.Identity(),
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)
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def forward(self, x):
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return self.ff(x)
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class Attention(nn.Module):
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def __init__(
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self,
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dim,
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dim_heads = 64,
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dim_context = None,
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causal = False,
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zero_init_output=True,
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qk_norm = False,
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natten_kernel_size = None,
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dtype=None,
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device=None,
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operations=None,
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):
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super().__init__()
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self.dim = dim
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self.dim_heads = dim_heads
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self.causal = causal
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dim_kv = dim_context if dim_context is not None else dim
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self.num_heads = dim // dim_heads
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self.kv_heads = dim_kv // dim_heads
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if dim_context is not None:
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self.to_q = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device)
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self.to_kv = operations.Linear(dim_kv, dim_kv * 2, bias=False, dtype=dtype, device=device)
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else:
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self.to_qkv = operations.Linear(dim, dim * 3, bias=False, dtype=dtype, device=device)
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self.to_out = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device)
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# if zero_init_output:
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# nn.init.zeros_(self.to_out.weight)
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self.qk_norm = qk_norm
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def forward(
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self,
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x,
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context = None,
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mask = None,
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context_mask = None,
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rotary_pos_emb = None,
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causal = None
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):
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h, kv_h, has_context = self.num_heads, self.kv_heads, context is not None
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kv_input = context if has_context else x
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if hasattr(self, 'to_q'):
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# Use separate linear projections for q and k/v
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q = self.to_q(x)
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q = rearrange(q, 'b n (h d) -> b h n d', h = h)
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k, v = self.to_kv(kv_input).chunk(2, dim=-1)
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k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h = kv_h), (k, v))
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else:
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# Use fused linear projection
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q, k, v = self.to_qkv(x).chunk(3, dim=-1)
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q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h = h), (q, k, v))
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# Normalize q and k for cosine sim attention
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if self.qk_norm:
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q = F.normalize(q, dim=-1)
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k = F.normalize(k, dim=-1)
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if rotary_pos_emb is not None and not has_context:
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freqs, _ = rotary_pos_emb
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q_dtype = q.dtype
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k_dtype = k.dtype
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q = q.to(torch.float32)
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k = k.to(torch.float32)
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freqs = freqs.to(torch.float32)
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q = apply_rotary_pos_emb(q, freqs)
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k = apply_rotary_pos_emb(k, freqs)
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q = q.to(q_dtype)
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k = k.to(k_dtype)
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input_mask = context_mask
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if input_mask is None and not has_context:
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input_mask = mask
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# determine masking
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masks = []
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final_attn_mask = None # The mask that will be applied to the attention matrix, taking all masks into account
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if input_mask is not None:
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input_mask = rearrange(input_mask, 'b j -> b 1 1 j')
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masks.append(~input_mask)
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# Other masks will be added here later
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if len(masks) > 0:
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final_attn_mask = ~or_reduce(masks)
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n, device = q.shape[-2], q.device
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causal = self.causal if causal is None else causal
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if n == 1 and causal:
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causal = False
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if h != kv_h:
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# Repeat interleave kv_heads to match q_heads
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heads_per_kv_head = h // kv_h
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k, v = map(lambda t: t.repeat_interleave(heads_per_kv_head, dim = 1), (k, v))
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out = optimized_attention(q, k, v, h, skip_reshape=True)
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out = self.to_out(out)
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if mask is not None:
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mask = rearrange(mask, 'b n -> b n 1')
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out = out.masked_fill(~mask, 0.)
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return out
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class ConformerModule(nn.Module):
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def __init__(
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self,
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dim,
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norm_kwargs = {},
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):
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super().__init__()
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self.dim = dim
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self.in_norm = LayerNorm(dim, **norm_kwargs)
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self.pointwise_conv = nn.Conv1d(dim, dim, kernel_size=1, bias=False)
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self.glu = GLU(dim, dim, nn.SiLU())
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self.depthwise_conv = nn.Conv1d(dim, dim, kernel_size=17, groups=dim, padding=8, bias=False)
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self.mid_norm = LayerNorm(dim, **norm_kwargs) # This is a batch norm in the original but I don't like batch norm
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self.swish = nn.SiLU()
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self.pointwise_conv_2 = nn.Conv1d(dim, dim, kernel_size=1, bias=False)
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def forward(self, x):
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x = self.in_norm(x)
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x = rearrange(x, 'b n d -> b d n')
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x = self.pointwise_conv(x)
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x = rearrange(x, 'b d n -> b n d')
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x = self.glu(x)
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x = rearrange(x, 'b n d -> b d n')
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x = self.depthwise_conv(x)
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x = rearrange(x, 'b d n -> b n d')
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x = self.mid_norm(x)
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x = self.swish(x)
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x = rearrange(x, 'b n d -> b d n')
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x = self.pointwise_conv_2(x)
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x = rearrange(x, 'b d n -> b n d')
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return x
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class TransformerBlock(nn.Module):
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def __init__(
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self,
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dim,
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dim_heads = 64,
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cross_attend = False,
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dim_context = None,
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global_cond_dim = None,
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causal = False,
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zero_init_branch_outputs = True,
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conformer = False,
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layer_ix = -1,
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remove_norms = False,
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attn_kwargs = {},
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ff_kwargs = {},
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norm_kwargs = {},
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dtype=None,
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device=None,
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operations=None,
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):
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super().__init__()
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self.dim = dim
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self.dim_heads = dim_heads
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self.cross_attend = cross_attend
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self.dim_context = dim_context
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self.causal = causal
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self.pre_norm = LayerNorm(dim, dtype=dtype, device=device, **norm_kwargs) if not remove_norms else nn.Identity()
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self.self_attn = Attention(
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dim,
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dim_heads = dim_heads,
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causal = causal,
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zero_init_output=zero_init_branch_outputs,
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dtype=dtype,
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device=device,
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operations=operations,
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**attn_kwargs
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)
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if cross_attend:
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self.cross_attend_norm = LayerNorm(dim, dtype=dtype, device=device, **norm_kwargs) if not remove_norms else nn.Identity()
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self.cross_attn = Attention(
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dim,
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dim_heads = dim_heads,
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dim_context=dim_context,
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causal = causal,
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zero_init_output=zero_init_branch_outputs,
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dtype=dtype,
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device=device,
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operations=operations,
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**attn_kwargs
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)
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self.ff_norm = LayerNorm(dim, dtype=dtype, device=device, **norm_kwargs) if not remove_norms else nn.Identity()
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self.ff = FeedForward(dim, zero_init_output=zero_init_branch_outputs, dtype=dtype, device=device, operations=operations,**ff_kwargs)
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self.layer_ix = layer_ix
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self.conformer = ConformerModule(dim, norm_kwargs=norm_kwargs) if conformer else None
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self.global_cond_dim = global_cond_dim
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if global_cond_dim is not None:
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self.to_scale_shift_gate = nn.Sequential(
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nn.SiLU(),
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nn.Linear(global_cond_dim, dim * 6, bias=False)
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)
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nn.init.zeros_(self.to_scale_shift_gate[1].weight)
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#nn.init.zeros_(self.to_scale_shift_gate_self[1].bias)
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def forward(
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self,
|
|
x,
|
|
context = None,
|
|
global_cond=None,
|
|
mask = None,
|
|
context_mask = None,
|
|
rotary_pos_emb = None
|
|
):
|
|
if self.global_cond_dim is not None and self.global_cond_dim > 0 and global_cond is not None:
|
|
|
|
scale_self, shift_self, gate_self, scale_ff, shift_ff, gate_ff = self.to_scale_shift_gate(global_cond).unsqueeze(1).chunk(6, dim = -1)
|
|
|
|
# self-attention with adaLN
|
|
residual = x
|
|
x = self.pre_norm(x)
|
|
x = x * (1 + scale_self) + shift_self
|
|
x = self.self_attn(x, mask = mask, rotary_pos_emb = rotary_pos_emb)
|
|
x = x * torch.sigmoid(1 - gate_self)
|
|
x = x + residual
|
|
|
|
if context is not None:
|
|
x = x + self.cross_attn(self.cross_attend_norm(x), context = context, context_mask = context_mask)
|
|
|
|
if self.conformer is not None:
|
|
x = x + self.conformer(x)
|
|
|
|
# feedforward with adaLN
|
|
residual = x
|
|
x = self.ff_norm(x)
|
|
x = x * (1 + scale_ff) + shift_ff
|
|
x = self.ff(x)
|
|
x = x * torch.sigmoid(1 - gate_ff)
|
|
x = x + residual
|
|
|
|
else:
|
|
x = x + self.self_attn(self.pre_norm(x), mask = mask, rotary_pos_emb = rotary_pos_emb)
|
|
|
|
if context is not None:
|
|
x = x + self.cross_attn(self.cross_attend_norm(x), context = context, context_mask = context_mask)
|
|
|
|
if self.conformer is not None:
|
|
x = x + self.conformer(x)
|
|
|
|
x = x + self.ff(self.ff_norm(x))
|
|
|
|
return x
|
|
|
|
class ContinuousTransformer(nn.Module):
|
|
def __init__(
|
|
self,
|
|
dim,
|
|
depth,
|
|
*,
|
|
dim_in = None,
|
|
dim_out = None,
|
|
dim_heads = 64,
|
|
cross_attend=False,
|
|
cond_token_dim=None,
|
|
global_cond_dim=None,
|
|
causal=False,
|
|
rotary_pos_emb=True,
|
|
zero_init_branch_outputs=True,
|
|
conformer=False,
|
|
use_sinusoidal_emb=False,
|
|
use_abs_pos_emb=False,
|
|
abs_pos_emb_max_length=10000,
|
|
dtype=None,
|
|
device=None,
|
|
operations=None,
|
|
**kwargs
|
|
):
|
|
|
|
super().__init__()
|
|
|
|
self.dim = dim
|
|
self.depth = depth
|
|
self.causal = causal
|
|
self.layers = nn.ModuleList([])
|
|
|
|
self.project_in = operations.Linear(dim_in, dim, bias=False, dtype=dtype, device=device) if dim_in is not None else nn.Identity()
|
|
self.project_out = operations.Linear(dim, dim_out, bias=False, dtype=dtype, device=device) if dim_out is not None else nn.Identity()
|
|
|
|
if rotary_pos_emb:
|
|
self.rotary_pos_emb = RotaryEmbedding(max(dim_heads // 2, 32))
|
|
else:
|
|
self.rotary_pos_emb = None
|
|
|
|
self.use_sinusoidal_emb = use_sinusoidal_emb
|
|
if use_sinusoidal_emb:
|
|
self.pos_emb = ScaledSinusoidalEmbedding(dim)
|
|
|
|
self.use_abs_pos_emb = use_abs_pos_emb
|
|
if use_abs_pos_emb:
|
|
self.pos_emb = AbsolutePositionalEmbedding(dim, abs_pos_emb_max_length)
|
|
|
|
for i in range(depth):
|
|
self.layers.append(
|
|
TransformerBlock(
|
|
dim,
|
|
dim_heads = dim_heads,
|
|
cross_attend = cross_attend,
|
|
dim_context = cond_token_dim,
|
|
global_cond_dim = global_cond_dim,
|
|
causal = causal,
|
|
zero_init_branch_outputs = zero_init_branch_outputs,
|
|
conformer=conformer,
|
|
layer_ix=i,
|
|
dtype=dtype,
|
|
device=device,
|
|
operations=operations,
|
|
**kwargs
|
|
)
|
|
)
|
|
|
|
def forward(
|
|
self,
|
|
x,
|
|
mask = None,
|
|
prepend_embeds = None,
|
|
prepend_mask = None,
|
|
global_cond = None,
|
|
return_info = False,
|
|
**kwargs
|
|
):
|
|
batch, seq, device = *x.shape[:2], x.device
|
|
|
|
info = {
|
|
"hidden_states": [],
|
|
}
|
|
|
|
x = self.project_in(x)
|
|
|
|
if prepend_embeds is not None:
|
|
prepend_length, prepend_dim = prepend_embeds.shape[1:]
|
|
|
|
assert prepend_dim == x.shape[-1], 'prepend dimension must match sequence dimension'
|
|
|
|
x = torch.cat((prepend_embeds, x), dim = -2)
|
|
|
|
if prepend_mask is not None or mask is not None:
|
|
mask = mask if mask is not None else torch.ones((batch, seq), device = device, dtype = torch.bool)
|
|
prepend_mask = prepend_mask if prepend_mask is not None else torch.ones((batch, prepend_length), device = device, dtype = torch.bool)
|
|
|
|
mask = torch.cat((prepend_mask, mask), dim = -1)
|
|
|
|
# Attention layers
|
|
|
|
if self.rotary_pos_emb is not None:
|
|
rotary_pos_emb = self.rotary_pos_emb.forward_from_seq_len(x.shape[1], dtype=x.dtype, device=x.device)
|
|
else:
|
|
rotary_pos_emb = None
|
|
|
|
if self.use_sinusoidal_emb or self.use_abs_pos_emb:
|
|
x = x + self.pos_emb(x)
|
|
|
|
# Iterate over the transformer layers
|
|
for layer in self.layers:
|
|
x = layer(x, rotary_pos_emb = rotary_pos_emb, global_cond=global_cond, **kwargs)
|
|
# x = checkpoint(layer, x, rotary_pos_emb = rotary_pos_emb, global_cond=global_cond, **kwargs)
|
|
|
|
if return_info:
|
|
info["hidden_states"].append(x)
|
|
|
|
x = self.project_out(x)
|
|
|
|
if return_info:
|
|
return x, info
|
|
|
|
return x
|
|
|
|
class AudioDiffusionTransformer(nn.Module):
|
|
def __init__(self,
|
|
io_channels=64,
|
|
patch_size=1,
|
|
embed_dim=1536,
|
|
cond_token_dim=768,
|
|
project_cond_tokens=False,
|
|
global_cond_dim=1536,
|
|
project_global_cond=True,
|
|
input_concat_dim=0,
|
|
prepend_cond_dim=0,
|
|
depth=24,
|
|
num_heads=24,
|
|
transformer_type: tp.Literal["continuous_transformer"] = "continuous_transformer",
|
|
global_cond_type: tp.Literal["prepend", "adaLN"] = "prepend",
|
|
audio_model="",
|
|
dtype=None,
|
|
device=None,
|
|
operations=None,
|
|
**kwargs):
|
|
|
|
super().__init__()
|
|
|
|
self.dtype = dtype
|
|
self.cond_token_dim = cond_token_dim
|
|
|
|
# Timestep embeddings
|
|
timestep_features_dim = 256
|
|
|
|
self.timestep_features = FourierFeatures(1, timestep_features_dim, dtype=dtype, device=device)
|
|
|
|
self.to_timestep_embed = nn.Sequential(
|
|
operations.Linear(timestep_features_dim, embed_dim, bias=True, dtype=dtype, device=device),
|
|
nn.SiLU(),
|
|
operations.Linear(embed_dim, embed_dim, bias=True, dtype=dtype, device=device),
|
|
)
|
|
|
|
if cond_token_dim > 0:
|
|
# Conditioning tokens
|
|
|
|
cond_embed_dim = cond_token_dim if not project_cond_tokens else embed_dim
|
|
self.to_cond_embed = nn.Sequential(
|
|
operations.Linear(cond_token_dim, cond_embed_dim, bias=False, dtype=dtype, device=device),
|
|
nn.SiLU(),
|
|
operations.Linear(cond_embed_dim, cond_embed_dim, bias=False, dtype=dtype, device=device)
|
|
)
|
|
else:
|
|
cond_embed_dim = 0
|
|
|
|
if global_cond_dim > 0:
|
|
# Global conditioning
|
|
global_embed_dim = global_cond_dim if not project_global_cond else embed_dim
|
|
self.to_global_embed = nn.Sequential(
|
|
operations.Linear(global_cond_dim, global_embed_dim, bias=False, dtype=dtype, device=device),
|
|
nn.SiLU(),
|
|
operations.Linear(global_embed_dim, global_embed_dim, bias=False, dtype=dtype, device=device)
|
|
)
|
|
|
|
if prepend_cond_dim > 0:
|
|
# Prepend conditioning
|
|
self.to_prepend_embed = nn.Sequential(
|
|
operations.Linear(prepend_cond_dim, embed_dim, bias=False, dtype=dtype, device=device),
|
|
nn.SiLU(),
|
|
operations.Linear(embed_dim, embed_dim, bias=False, dtype=dtype, device=device)
|
|
)
|
|
|
|
self.input_concat_dim = input_concat_dim
|
|
|
|
dim_in = io_channels + self.input_concat_dim
|
|
|
|
self.patch_size = patch_size
|
|
|
|
# Transformer
|
|
|
|
self.transformer_type = transformer_type
|
|
|
|
self.global_cond_type = global_cond_type
|
|
|
|
if self.transformer_type == "continuous_transformer":
|
|
|
|
global_dim = None
|
|
|
|
if self.global_cond_type == "adaLN":
|
|
# The global conditioning is projected to the embed_dim already at this point
|
|
global_dim = embed_dim
|
|
|
|
self.transformer = ContinuousTransformer(
|
|
dim=embed_dim,
|
|
depth=depth,
|
|
dim_heads=embed_dim // num_heads,
|
|
dim_in=dim_in * patch_size,
|
|
dim_out=io_channels * patch_size,
|
|
cross_attend = cond_token_dim > 0,
|
|
cond_token_dim = cond_embed_dim,
|
|
global_cond_dim=global_dim,
|
|
dtype=dtype,
|
|
device=device,
|
|
operations=operations,
|
|
**kwargs
|
|
)
|
|
else:
|
|
raise ValueError(f"Unknown transformer type: {self.transformer_type}")
|
|
|
|
self.preprocess_conv = operations.Conv1d(dim_in, dim_in, 1, bias=False, dtype=dtype, device=device)
|
|
self.postprocess_conv = operations.Conv1d(io_channels, io_channels, 1, bias=False, dtype=dtype, device=device)
|
|
|
|
def _forward(
|
|
self,
|
|
x,
|
|
t,
|
|
mask=None,
|
|
cross_attn_cond=None,
|
|
cross_attn_cond_mask=None,
|
|
input_concat_cond=None,
|
|
global_embed=None,
|
|
prepend_cond=None,
|
|
prepend_cond_mask=None,
|
|
return_info=False,
|
|
**kwargs):
|
|
|
|
if cross_attn_cond is not None:
|
|
cross_attn_cond = self.to_cond_embed(cross_attn_cond)
|
|
|
|
if global_embed is not None:
|
|
# Project the global conditioning to the embedding dimension
|
|
global_embed = self.to_global_embed(global_embed)
|
|
|
|
prepend_inputs = None
|
|
prepend_mask = None
|
|
prepend_length = 0
|
|
if prepend_cond is not None:
|
|
# Project the prepend conditioning to the embedding dimension
|
|
prepend_cond = self.to_prepend_embed(prepend_cond)
|
|
|
|
prepend_inputs = prepend_cond
|
|
if prepend_cond_mask is not None:
|
|
prepend_mask = prepend_cond_mask
|
|
|
|
if input_concat_cond is not None:
|
|
|
|
# Interpolate input_concat_cond to the same length as x
|
|
if input_concat_cond.shape[2] != x.shape[2]:
|
|
input_concat_cond = F.interpolate(input_concat_cond, (x.shape[2], ), mode='nearest')
|
|
|
|
x = torch.cat([x, input_concat_cond], dim=1)
|
|
|
|
# Get the batch of timestep embeddings
|
|
timestep_embed = self.to_timestep_embed(self.timestep_features(t[:, None]).to(x.dtype)) # (b, embed_dim)
|
|
|
|
# Timestep embedding is considered a global embedding. Add to the global conditioning if it exists
|
|
if global_embed is not None:
|
|
global_embed = global_embed + timestep_embed
|
|
else:
|
|
global_embed = timestep_embed
|
|
|
|
# Add the global_embed to the prepend inputs if there is no global conditioning support in the transformer
|
|
if self.global_cond_type == "prepend":
|
|
if prepend_inputs is None:
|
|
# Prepend inputs are just the global embed, and the mask is all ones
|
|
prepend_inputs = global_embed.unsqueeze(1)
|
|
prepend_mask = torch.ones((x.shape[0], 1), device=x.device, dtype=torch.bool)
|
|
else:
|
|
# Prepend inputs are the prepend conditioning + the global embed
|
|
prepend_inputs = torch.cat([prepend_inputs, global_embed.unsqueeze(1)], dim=1)
|
|
prepend_mask = torch.cat([prepend_mask, torch.ones((x.shape[0], 1), device=x.device, dtype=torch.bool)], dim=1)
|
|
|
|
prepend_length = prepend_inputs.shape[1]
|
|
|
|
x = self.preprocess_conv(x) + x
|
|
|
|
x = rearrange(x, "b c t -> b t c")
|
|
|
|
extra_args = {}
|
|
|
|
if self.global_cond_type == "adaLN":
|
|
extra_args["global_cond"] = global_embed
|
|
|
|
if self.patch_size > 1:
|
|
x = rearrange(x, "b (t p) c -> b t (c p)", p=self.patch_size)
|
|
|
|
if self.transformer_type == "x-transformers":
|
|
output = self.transformer(x, prepend_embeds=prepend_inputs, context=cross_attn_cond, context_mask=cross_attn_cond_mask, mask=mask, prepend_mask=prepend_mask, **extra_args, **kwargs)
|
|
elif self.transformer_type == "continuous_transformer":
|
|
output = self.transformer(x, prepend_embeds=prepend_inputs, context=cross_attn_cond, context_mask=cross_attn_cond_mask, mask=mask, prepend_mask=prepend_mask, return_info=return_info, **extra_args, **kwargs)
|
|
|
|
if return_info:
|
|
output, info = output
|
|
elif self.transformer_type == "mm_transformer":
|
|
output = self.transformer(x, context=cross_attn_cond, mask=mask, context_mask=cross_attn_cond_mask, **extra_args, **kwargs)
|
|
|
|
output = rearrange(output, "b t c -> b c t")[:,:,prepend_length:]
|
|
|
|
if self.patch_size > 1:
|
|
output = rearrange(output, "b (c p) t -> b c (t p)", p=self.patch_size)
|
|
|
|
output = self.postprocess_conv(output) + output
|
|
|
|
if return_info:
|
|
return output, info
|
|
|
|
return output
|
|
|
|
def forward(
|
|
self,
|
|
x,
|
|
timestep,
|
|
context=None,
|
|
context_mask=None,
|
|
input_concat_cond=None,
|
|
global_embed=None,
|
|
negative_global_embed=None,
|
|
prepend_cond=None,
|
|
prepend_cond_mask=None,
|
|
mask=None,
|
|
return_info=False,
|
|
control=None,
|
|
transformer_options={},
|
|
**kwargs):
|
|
return self._forward(
|
|
x,
|
|
timestep,
|
|
cross_attn_cond=context,
|
|
cross_attn_cond_mask=context_mask,
|
|
input_concat_cond=input_concat_cond,
|
|
global_embed=global_embed,
|
|
prepend_cond=prepend_cond,
|
|
prepend_cond_mask=prepend_cond_mask,
|
|
mask=mask,
|
|
return_info=return_info,
|
|
**kwargs
|
|
)
|