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iopaint/model/anytext/ldm/modules/attention.py
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360
iopaint/model/anytext/ldm/modules/attention.py
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from inspect import isfunction
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import math
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import torch
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import torch.nn.functional as F
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from torch import nn, einsum
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from einops import rearrange, repeat
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from typing import Optional, Any
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from iopaint.model.anytext.ldm.modules.diffusionmodules.util import checkpoint
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# CrossAttn precision handling
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import os
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_ATTN_PRECISION = os.environ.get("ATTN_PRECISION", "fp32")
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def exists(val):
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return val is not None
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def uniq(arr):
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return {el: True for el in arr}.keys()
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def default(val, d):
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if exists(val):
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return val
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return d() if isfunction(d) else d
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def max_neg_value(t):
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return -torch.finfo(t.dtype).max
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def init_(tensor):
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dim = tensor.shape[-1]
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std = 1 / math.sqrt(dim)
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tensor.uniform_(-std, std)
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return tensor
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# feedforward
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class GEGLU(nn.Module):
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def __init__(self, dim_in, dim_out):
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super().__init__()
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self.proj = nn.Linear(dim_in, dim_out * 2)
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def forward(self, x):
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x, gate = self.proj(x).chunk(2, dim=-1)
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return x * F.gelu(gate)
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class FeedForward(nn.Module):
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def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.0):
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super().__init__()
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inner_dim = int(dim * mult)
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dim_out = default(dim_out, dim)
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project_in = (
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nn.Sequential(nn.Linear(dim, inner_dim), nn.GELU())
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if not glu
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else GEGLU(dim, inner_dim)
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)
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self.net = nn.Sequential(
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project_in, nn.Dropout(dropout), nn.Linear(inner_dim, dim_out)
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)
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def forward(self, x):
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return self.net(x)
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def zero_module(module):
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"""
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Zero out the parameters of a module and return it.
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"""
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for p in module.parameters():
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p.detach().zero_()
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return module
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def Normalize(in_channels):
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return torch.nn.GroupNorm(
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num_groups=32, num_channels=in_channels, eps=1e-6, affine=True
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)
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class SpatialSelfAttention(nn.Module):
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def __init__(self, in_channels):
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super().__init__()
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self.in_channels = in_channels
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self.norm = Normalize(in_channels)
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self.q = torch.nn.Conv2d(
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in_channels, in_channels, kernel_size=1, stride=1, padding=0
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)
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self.k = torch.nn.Conv2d(
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in_channels, in_channels, kernel_size=1, stride=1, padding=0
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)
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self.v = torch.nn.Conv2d(
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in_channels, in_channels, kernel_size=1, stride=1, padding=0
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)
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self.proj_out = torch.nn.Conv2d(
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in_channels, in_channels, kernel_size=1, stride=1, padding=0
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)
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def forward(self, x):
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h_ = x
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h_ = self.norm(h_)
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q = self.q(h_)
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k = self.k(h_)
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v = self.v(h_)
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# compute attention
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b, c, h, w = q.shape
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q = rearrange(q, "b c h w -> b (h w) c")
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k = rearrange(k, "b c h w -> b c (h w)")
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w_ = torch.einsum("bij,bjk->bik", q, k)
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w_ = w_ * (int(c) ** (-0.5))
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w_ = torch.nn.functional.softmax(w_, dim=2)
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# attend to values
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v = rearrange(v, "b c h w -> b c (h w)")
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w_ = rearrange(w_, "b i j -> b j i")
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h_ = torch.einsum("bij,bjk->bik", v, w_)
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h_ = rearrange(h_, "b c (h w) -> b c h w", h=h)
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h_ = self.proj_out(h_)
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return x + h_
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class CrossAttention(nn.Module):
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def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0.0):
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super().__init__()
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inner_dim = dim_head * heads
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context_dim = default(context_dim, query_dim)
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self.scale = dim_head**-0.5
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self.heads = heads
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self.to_q = nn.Linear(query_dim, inner_dim, bias=False)
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self.to_k = nn.Linear(context_dim, inner_dim, bias=False)
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self.to_v = nn.Linear(context_dim, inner_dim, bias=False)
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self.to_out = nn.Sequential(
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nn.Linear(inner_dim, query_dim), nn.Dropout(dropout)
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)
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def forward(self, x, context=None, mask=None):
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h = self.heads
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q = self.to_q(x)
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context = default(context, x)
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k = self.to_k(context)
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v = self.to_v(context)
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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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# force cast to fp32 to avoid overflowing
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if _ATTN_PRECISION == "fp32":
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with torch.autocast(enabled=False, device_type="cuda"):
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q, k = q.float(), k.float()
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sim = einsum("b i d, b j d -> b i j", q, k) * self.scale
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else:
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sim = einsum("b i d, b j d -> b i j", q, k) * self.scale
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del q, k
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if exists(mask):
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mask = rearrange(mask, "b ... -> b (...)")
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max_neg_value = -torch.finfo(sim.dtype).max
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mask = repeat(mask, "b j -> (b h) () j", h=h)
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sim.masked_fill_(~mask, max_neg_value)
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# attention, what we cannot get enough of
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sim = sim.softmax(dim=-1)
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out = einsum("b i j, b j d -> b i d", sim, v)
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out = rearrange(out, "(b h) n d -> b n (h d)", h=h)
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return self.to_out(out)
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class SDPACrossAttention(CrossAttention):
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def forward(self, x, context=None, mask=None):
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batch_size, sequence_length, inner_dim = x.shape
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if mask is not None:
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mask = self.prepare_attention_mask(mask, sequence_length, batch_size)
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mask = mask.view(batch_size, self.heads, -1, mask.shape[-1])
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h = self.heads
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q_in = self.to_q(x)
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context = default(context, x)
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k_in = self.to_k(context)
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v_in = self.to_v(context)
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head_dim = inner_dim // h
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q = q_in.view(batch_size, -1, h, head_dim).transpose(1, 2)
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k = k_in.view(batch_size, -1, h, head_dim).transpose(1, 2)
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v = v_in.view(batch_size, -1, h, head_dim).transpose(1, 2)
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del q_in, k_in, v_in
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dtype = q.dtype
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if _ATTN_PRECISION == "fp32":
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q, k, v = q.float(), k.float(), v.float()
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# the output of sdp = (batch, num_heads, seq_len, head_dim)
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hidden_states = torch.nn.functional.scaled_dot_product_attention(
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q, k, v, attn_mask=mask, dropout_p=0.0, is_causal=False
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)
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hidden_states = hidden_states.transpose(1, 2).reshape(
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batch_size, -1, h * head_dim
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)
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hidden_states = hidden_states.to(dtype)
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# linear proj
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hidden_states = self.to_out[0](hidden_states)
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# dropout
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hidden_states = self.to_out[1](hidden_states)
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return hidden_states
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class BasicTransformerBlock(nn.Module):
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def __init__(
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self,
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dim,
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n_heads,
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d_head,
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dropout=0.0,
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context_dim=None,
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gated_ff=True,
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checkpoint=True,
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disable_self_attn=False,
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):
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super().__init__()
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if hasattr(torch.nn.functional, "scaled_dot_product_attention"):
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attn_cls = SDPACrossAttention
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else:
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attn_cls = CrossAttention
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self.disable_self_attn = disable_self_attn
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self.attn1 = attn_cls(
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query_dim=dim,
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heads=n_heads,
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dim_head=d_head,
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dropout=dropout,
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context_dim=context_dim if self.disable_self_attn else None,
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) # is a self-attention if not self.disable_self_attn
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self.ff = FeedForward(dim, dropout=dropout, glu=gated_ff)
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self.attn2 = attn_cls(
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query_dim=dim,
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context_dim=context_dim,
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heads=n_heads,
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dim_head=d_head,
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dropout=dropout,
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) # is self-attn if context is none
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self.norm1 = nn.LayerNorm(dim)
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self.norm2 = nn.LayerNorm(dim)
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self.norm3 = nn.LayerNorm(dim)
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self.checkpoint = checkpoint
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def forward(self, x, context=None):
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return checkpoint(
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self._forward, (x, context), self.parameters(), self.checkpoint
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)
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def _forward(self, x, context=None):
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x = (
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self.attn1(
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self.norm1(x), context=context if self.disable_self_attn else None
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)
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+ x
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)
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x = self.attn2(self.norm2(x), context=context) + x
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x = self.ff(self.norm3(x)) + x
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return x
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class SpatialTransformer(nn.Module):
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"""
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Transformer block for image-like data.
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First, project the input (aka embedding)
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and reshape to b, t, d.
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Then apply standard transformer action.
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Finally, reshape to image
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NEW: use_linear for more efficiency instead of the 1x1 convs
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"""
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def __init__(
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self,
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in_channels,
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n_heads,
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d_head,
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depth=1,
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dropout=0.0,
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context_dim=None,
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disable_self_attn=False,
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use_linear=False,
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use_checkpoint=True,
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):
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super().__init__()
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if exists(context_dim) and not isinstance(context_dim, list):
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context_dim = [context_dim]
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self.in_channels = in_channels
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inner_dim = n_heads * d_head
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self.norm = Normalize(in_channels)
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if not use_linear:
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self.proj_in = nn.Conv2d(
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in_channels, inner_dim, kernel_size=1, stride=1, padding=0
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)
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else:
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self.proj_in = nn.Linear(in_channels, inner_dim)
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self.transformer_blocks = nn.ModuleList(
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[
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BasicTransformerBlock(
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inner_dim,
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n_heads,
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d_head,
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dropout=dropout,
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context_dim=context_dim[d],
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disable_self_attn=disable_self_attn,
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checkpoint=use_checkpoint,
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)
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for d in range(depth)
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]
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)
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if not use_linear:
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self.proj_out = zero_module(
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nn.Conv2d(inner_dim, in_channels, kernel_size=1, stride=1, padding=0)
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)
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else:
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self.proj_out = zero_module(nn.Linear(in_channels, inner_dim))
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self.use_linear = use_linear
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def forward(self, x, context=None):
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# note: if no context is given, cross-attention defaults to self-attention
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if not isinstance(context, list):
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context = [context]
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b, c, h, w = x.shape
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x_in = x
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x = self.norm(x)
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if not self.use_linear:
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x = self.proj_in(x)
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x = rearrange(x, "b c h w -> b (h w) c").contiguous()
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if self.use_linear:
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x = self.proj_in(x)
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for i, block in enumerate(self.transformer_blocks):
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x = block(x, context=context[i])
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if self.use_linear:
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x = self.proj_out(x)
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x = rearrange(x, "b (h w) c -> b c h w", h=h, w=w).contiguous()
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if not self.use_linear:
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x = self.proj_out(x)
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return x + x_in
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