| import torch |
| import torch.nn.functional as F |
| from dataclasses import dataclass |
| from typing import Callable |
|
|
|
|
| @dataclass |
| class PuLIDAttnSetting: |
| num_zero: int = 0 |
| ortho: bool = False |
| ortho_v2: bool = False |
|
|
| def eval( |
| self, |
| hidden_states: torch.Tensor, |
| query: torch.Tensor, |
| id_embedding: torch.Tensor, |
| attn_heads: int, |
| head_dim: int, |
| id_to_k: Callable[[torch.Tensor], torch.Tensor], |
| id_to_v: Callable[[torch.Tensor], torch.Tensor], |
| ): |
| assert hidden_states.ndim == 3 |
| batch_size, sequence_length, inner_dim = hidden_states.shape |
|
|
| if self.num_zero == 0: |
| id_key = id_to_k(id_embedding).to(query.dtype) |
| id_value = id_to_v(id_embedding).to(query.dtype) |
| else: |
| zero_tensor = torch.zeros( |
| (id_embedding.size(0), self.num_zero, id_embedding.size(-1)), |
| dtype=id_embedding.dtype, |
| device=id_embedding.device, |
| ) |
| id_key = id_to_k(torch.cat((id_embedding, zero_tensor), dim=1)).to( |
| query.dtype |
| ) |
| id_value = id_to_v(torch.cat((id_embedding, zero_tensor), dim=1)).to( |
| query.dtype |
| ) |
|
|
| id_key = id_key.view(batch_size, -1, attn_heads, head_dim).transpose(1, 2) |
| id_value = id_value.view(batch_size, -1, attn_heads, head_dim).transpose(1, 2) |
|
|
| |
| id_hidden_states = F.scaled_dot_product_attention( |
| query, id_key, id_value, attn_mask=None, dropout_p=0.0, is_causal=False |
| ) |
|
|
| id_hidden_states = id_hidden_states.transpose(1, 2).reshape( |
| batch_size, -1, attn_heads * head_dim |
| ) |
| id_hidden_states = id_hidden_states.to(query.dtype) |
|
|
| if not self.ortho and not self.ortho_v2: |
| return id_hidden_states |
| elif self.ortho_v2: |
| orig_dtype = hidden_states.dtype |
| hidden_states = hidden_states.to(torch.float32) |
| id_hidden_states = id_hidden_states.to(torch.float32) |
| attn_map = query @ id_key.transpose(-2, -1) |
| attn_mean = attn_map.softmax(dim=-1).mean(dim=1) |
| attn_mean = attn_mean[:, :, :5].sum(dim=-1, keepdim=True) |
| projection = ( |
| torch.sum((hidden_states * id_hidden_states), dim=-2, keepdim=True) |
| / torch.sum((hidden_states * hidden_states), dim=-2, keepdim=True) |
| * hidden_states |
| ) |
| orthogonal = id_hidden_states + (attn_mean - 1) * projection |
| return orthogonal.to(orig_dtype) |
| else: |
| orig_dtype = hidden_states.dtype |
| hidden_states = hidden_states.to(torch.float32) |
| id_hidden_states = id_hidden_states.to(torch.float32) |
| projection = ( |
| torch.sum((hidden_states * id_hidden_states), dim=-2, keepdim=True) |
| / torch.sum((hidden_states * hidden_states), dim=-2, keepdim=True) |
| * hidden_states |
| ) |
| orthogonal = id_hidden_states - projection |
| return orthogonal.to(orig_dtype) |
|
|
|
|
| PULID_SETTING_FIDELITY = PuLIDAttnSetting( |
| num_zero=8, |
| ortho=False, |
| ortho_v2=True, |
| ) |
|
|
| PULID_SETTING_STYLE = PuLIDAttnSetting( |
| num_zero=16, |
| ortho=True, |
| ortho_v2=False, |
| ) |
|
|
