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| import torch | |
| from torch import nn, einsum | |
| from einops import rearrange, repeat | |
| from ... import share | |
| from ..attentionpatch import painta | |
| use_grad = True | |
| def forward(self, x, context=None): | |
| # Todo: add batch inference support | |
| if use_grad: | |
| y, self_v, self_sim = self.attn1(self.norm1(x), None) # Self Attn. | |
| x_uncond, x_cond = x.chunk(2) | |
| context_uncond, context_cond = context.chunk(2) | |
| y_uncond, y_cond = y.chunk(2) | |
| self_sim_uncond, self_sim_cond = self_sim.chunk(2) | |
| self_v_uncond, self_v_cond = self_v.chunk(2) | |
| # Calculate CA similarities with conditional context | |
| cross_h = self.attn2.heads | |
| cross_q = self.attn2.to_q(self.norm2(x_cond+y_cond)) | |
| cross_k = self.attn2.to_k(context_cond) | |
| cross_v = self.attn2.to_v(context_cond) | |
| cross_q, cross_k, cross_v = map(lambda t: rearrange(t, "b n (h d) -> (b h) n d", h=cross_h), (cross_q, cross_k, cross_v)) | |
| with torch.autocast(enabled=False, device_type = 'cuda'): | |
| cross_q, cross_k = cross_q.float(), cross_k.float() | |
| cross_sim = einsum('b i d, b j d -> b i j', cross_q, cross_k) * self.attn2.scale | |
| del cross_q, cross_k | |
| cross_sim = cross_sim.softmax(dim=-1) # Up to this point cross_sim is regular cross_sim in CA layer | |
| cross_sim = cross_sim.mean(dim=0) # Calculate mean across heads | |
| # PAIntA rescale | |
| y_cond = painta.painta_rescale( | |
| y_cond, self_v_cond, self_sim_cond, cross_sim, self.attn1.heads, self.attn1.to_out) # Rescale cond | |
| y_uncond = painta.painta_rescale( | |
| y_uncond, self_v_uncond, self_sim_uncond, cross_sim, self.attn1.heads, self.attn1.to_out) # Rescale uncond | |
| y = torch.cat([y_uncond, y_cond], dim=0) | |
| x = x + y | |
| x = x + self.attn2(self.norm2(x), context=context) # Cross Attn. | |
| x = x + self.ff(self.norm3(x)) | |
| return x |