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import torch
import torch.nn as nn
import torch.nn.functional as F
import math
from diffusers.models.attention_processor import Attention
from typing import Optional
from diffusers.models.embeddings import apply_rotary_emb
class FluxAttnProcessor2_0:
"""Attention processor used typically in processing the SD3-like self-attention projections."""
def __init__(self, train_seq_len=512 + 64 * 64):
if not hasattr(F, "scaled_dot_product_attention"):
raise ImportError(
"FluxAttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0."
)
self.train_seq_len = train_seq_len
def __call__(
self,
attn: Attention,
hidden_states: torch.FloatTensor,
encoder_hidden_states: torch.FloatTensor = None,
attention_mask: Optional[torch.FloatTensor] = None,
image_rotary_emb: Optional[torch.Tensor] = None,
proportional_attention=False,
) -> 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)
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)
if proportional_attention:
attention_scale = math.sqrt(
math.log(key.size(2), self.train_seq_len) / head_dim
)
else:
attention_scale = math.sqrt(1 / head_dim)
hidden_states = F.scaled_dot_product_attention(
query, key, value, dropout_p=0.0, is_causal=False, scale=attention_scale
)
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] :],
)
# 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:
return hidden_states
class FluxAttnAdaptationProcessor2_0(nn.Module):
"""Attention processor used typically in processing the SD3-like self-attention projections."""
def __init__(self, rank=16, dim=3072, to_out=False, train_seq_len=512 + 64 * 64):
super().__init__()
if not hasattr(F, "scaled_dot_product_attention"):
raise ImportError(
"FluxAttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0."
)
self.to_q_a = nn.Linear(dim, rank, bias=False)
self.to_q_b = nn.Linear(rank, dim, bias=False)
self.to_q_b.weight.data = torch.zeros_like(self.to_q_b.weight.data)
self.to_k_a = nn.Linear(dim, rank, bias=False)
self.to_k_b = nn.Linear(rank, dim, bias=False)
self.to_k_b.weight.data = torch.zeros_like(self.to_k_b.weight.data)
self.to_v_a = nn.Linear(dim, rank, bias=False)
self.to_v_b = nn.Linear(rank, dim, bias=False)
self.to_v_b.weight.data = torch.zeros_like(self.to_v_b.weight.data)
if to_out:
self.to_out_a = nn.Linear(dim, rank, bias=False)
self.to_out_b = nn.Linear(rank, dim, bias=False)
self.to_out_b.weight.data = torch.zeros_like(self.to_out_b.weight.data)
self.train_seq_len = train_seq_len
def __call__(
self,
attn: Attention,
hidden_states: torch.FloatTensor,
encoder_hidden_states: torch.FloatTensor = None,
attention_mask: Optional[torch.FloatTensor] = None,
image_rotary_emb: Optional[torch.Tensor] = None,
proportional_attention=False,
) -> torch.FloatTensor:
batch_size, _, _ = (
hidden_states.shape
if encoder_hidden_states is None
else encoder_hidden_states.shape
)
use_adaptation = True
# `sample` projections.
query = attn.to_q(hidden_states)
key = attn.to_k(hidden_states)
value = attn.to_v(hidden_states)
if use_adaptation:
query += self.to_q_b(self.to_q_a(hidden_states))
key += self.to_k_b(self.to_k_a(hidden_states))
value += self.to_v_b(self.to_v_a(hidden_states))
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)
if proportional_attention:
attention_scale = math.sqrt(
math.log(key.size(2), self.train_seq_len) / head_dim
)
else:
attention_scale = math.sqrt(1 / head_dim)
hidden_states = F.scaled_dot_product_attention(
query, key, value, dropout_p=0.0, is_causal=False, scale=attention_scale
)
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] :],
)
# linear proj
hidden_states = (
(
attn.to_out[0](hidden_states)
+ self.to_out_b(self.to_out_a(hidden_states))
)
if use_adaptation
else 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:
return hidden_states
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