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Running
on
Zero
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from typing import Optional
import torch.nn as nn
import torch
import torch.nn.functional as F
from diffusers.models.embeddings import apply_rotary_emb
from einops import rearrange
from .norm_layer import RMSNorm
class FluxIPAttnProcessor(nn.Module):
"""Attention processor used typically in processing the SD3-like self-attention projections."""
def __init__(
self,
hidden_size=None,
ip_hidden_states_dim=None,
):
super().__init__()
self.norm_ip_q = RMSNorm(128, eps=1e-6)
self.to_k_ip = nn.Linear(ip_hidden_states_dim, hidden_size)
self.norm_ip_k = RMSNorm(128, eps=1e-6)
self.to_v_ip = nn.Linear(ip_hidden_states_dim, hidden_size)
def __call__(
self,
attn,
hidden_states: torch.FloatTensor,
encoder_hidden_states: torch.FloatTensor = None,
attention_mask: Optional[torch.FloatTensor] = None,
image_rotary_emb: Optional[torch.Tensor] = None,
emb_dict={},
subject_emb_dict={},
*args,
**kwargs,
) -> torch.FloatTensor:
batch_size, _, _ = hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
# `sample` projections.
query = attn.to_q(hidden_states)
key = attn.to_k(hidden_states)
value = attn.to_v(hidden_states)
# IPadapter
ip_hidden_states = self._get_ip_hidden_states(
attn,
query if encoder_hidden_states is not None else query[:, emb_dict['length_encoder_hidden_states']:],
subject_emb_dict.get('ip_hidden_states', None)
)
inner_dim = key.shape[-1]
head_dim = inner_dim // attn.heads
query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
if attn.norm_q is not None:
query = attn.norm_q(query)
if attn.norm_k is not None:
key = attn.norm_k(key)
# the attention in FluxSingleTransformerBlock does not use `encoder_hidden_states`
if encoder_hidden_states is not None:
# `context` projections.
encoder_hidden_states_query_proj = attn.add_q_proj(encoder_hidden_states)
encoder_hidden_states_key_proj = attn.add_k_proj(encoder_hidden_states)
encoder_hidden_states_value_proj = attn.add_v_proj(encoder_hidden_states)
encoder_hidden_states_query_proj = encoder_hidden_states_query_proj.view(
batch_size, -1, attn.heads, head_dim
).transpose(1, 2)
encoder_hidden_states_key_proj = encoder_hidden_states_key_proj.view(
batch_size, -1, attn.heads, head_dim
).transpose(1, 2)
encoder_hidden_states_value_proj = encoder_hidden_states_value_proj.view(
batch_size, -1, attn.heads, head_dim
).transpose(1, 2)
if attn.norm_added_q is not None:
encoder_hidden_states_query_proj = attn.norm_added_q(encoder_hidden_states_query_proj)
if attn.norm_added_k is not None:
encoder_hidden_states_key_proj = attn.norm_added_k(encoder_hidden_states_key_proj)
# attention
query = torch.cat([encoder_hidden_states_query_proj, query], dim=2)
key = torch.cat([encoder_hidden_states_key_proj, key], dim=2)
value = torch.cat([encoder_hidden_states_value_proj, value], dim=2)
if image_rotary_emb is not None:
query = apply_rotary_emb(query, image_rotary_emb)
key = apply_rotary_emb(key, image_rotary_emb)
hidden_states = F.scaled_dot_product_attention(
query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
)
hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
hidden_states = hidden_states.to(query.dtype)
if encoder_hidden_states is not None:
encoder_hidden_states, hidden_states = (
hidden_states[:, : encoder_hidden_states.shape[1]],
hidden_states[:, encoder_hidden_states.shape[1] :],
)
if ip_hidden_states is not None:
hidden_states = hidden_states + ip_hidden_states * subject_emb_dict.get('scale', 1.0)
# linear proj
hidden_states = attn.to_out[0](hidden_states)
# dropout
hidden_states = attn.to_out[1](hidden_states)
encoder_hidden_states = attn.to_add_out(encoder_hidden_states)
return hidden_states, encoder_hidden_states
else:
if ip_hidden_states is not None:
hidden_states[:, emb_dict['length_encoder_hidden_states']:] = \
hidden_states[:, emb_dict['length_encoder_hidden_states']:] + \
ip_hidden_states * subject_emb_dict.get('scale', 1.0)
return hidden_states
def _scaled_dot_product_attention(self, query, key, value, attention_mask=None, heads=None):
query = rearrange(query, '(b h) l c -> b h l c', h=heads)
key = rearrange(key, '(b h) l c -> b h l c', h=heads)
value = rearrange(value, '(b h) l c -> b h l c', h=heads)
hidden_states = F.scaled_dot_product_attention(query, key, value, dropout_p=0.0, is_causal=False, attn_mask=None)
hidden_states = rearrange(hidden_states, 'b h l c -> (b h) l c', h=heads)
hidden_states = hidden_states.to(query)
return hidden_states
def _get_ip_hidden_states(
self,
attn,
img_query,
ip_hidden_states,
):
if ip_hidden_states is None:
return None
if not hasattr(self, 'to_k_ip') or not hasattr(self, 'to_v_ip'):
return None
ip_query = self.norm_ip_q(rearrange(img_query, 'b l (h d) -> b h l d', h=attn.heads))
ip_query = rearrange(ip_query, 'b h l d -> (b h) l d')
ip_key = self.to_k_ip(ip_hidden_states)
ip_key = self.norm_ip_k(rearrange(ip_key, 'b l (h d) -> b h l d', h=attn.heads))
ip_key = rearrange(ip_key, 'b h l d -> (b h) l d')
ip_value = self.to_v_ip(ip_hidden_states)
ip_value = attn.head_to_batch_dim(ip_value)
ip_hidden_states = self._scaled_dot_product_attention(
ip_query.to(ip_value.dtype), ip_key.to(ip_value.dtype), ip_value, None, attn.heads)
ip_hidden_states = ip_hidden_states.to(img_query.dtype)
ip_hidden_states = attn.batch_to_head_dim(ip_hidden_states)
return ip_hidden_states
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