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