Spaces:
Running
on
Zero
Running
on
Zero
File size: 8,746 Bytes
42d7985 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 |
import math
from typing import Optional
import torch
import torch.nn.functional as F
from diffusers.models.attention_processor import Attention
from diffusers.models.embeddings import apply_rotary_emb
class NAGFluxAttnProcessor2_0:
"""Attention processor used typically in processing the SD3-like self-attention projections."""
def __init__(
self,
nag_scale: float = 1.0,
nag_tau=2.5,
nag_alpha=0.25,
encoder_hidden_states_length: int = None,
):
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.nag_scale = nag_scale
self.nag_tau = nag_tau
self.nag_alpha = nag_alpha
self.encoder_hidden_states_length = encoder_hidden_states_length
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,
) -> torch.FloatTensor:
batch_size, _, _ = hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
if self.nag_scale > 1.:
if encoder_hidden_states is not None:
assert len(hidden_states) == batch_size * 0.5
apply_guidance = True
else:
apply_guidance = False
# `sample` projections.
query = attn.to_q(hidden_states)
key = attn.to_k(hidden_states)
value = attn.to_v(hidden_states)
# attention
if apply_guidance and encoder_hidden_states is not None:
query = query.tile(2, 1, 1)
key = key.tile(2, 1, 1)
value = value.tile(2, 1, 1)
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)
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)
encoder_hidden_states_length = encoder_hidden_states.shape[1]
else:
assert self.encoder_hidden_states_length is not None
encoder_hidden_states_length = self.encoder_hidden_states_length
if image_rotary_emb is not None:
query = apply_rotary_emb(query, image_rotary_emb)
key = apply_rotary_emb(key, image_rotary_emb)
if not apply_guidance:
hidden_states = F.scaled_dot_product_attention(query, key, value, 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)
else:
origin_batch_size = batch_size // 2
query, query_negative = torch.chunk(query, 2, dim=0)
key, key_negative = torch.chunk(key, 2, dim=0)
value, value_negative = torch.chunk(value, 2, dim=0)
hidden_states_negative = F.scaled_dot_product_attention(query_negative, key_negative, value_negative, dropout_p=0.0, is_causal=False)
hidden_states_negative = hidden_states_negative.transpose(1, 2).reshape(origin_batch_size, -1, attn.heads * head_dim)
hidden_states_negative = hidden_states_negative.to(query.dtype)
hidden_states = F.scaled_dot_product_attention(query, key, value, dropout_p=0.0, is_causal=False)
hidden_states = hidden_states.transpose(1, 2).reshape(origin_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 apply_guidance:
encoder_hidden_states_negative, hidden_states_negative = (
hidden_states_negative[:, : encoder_hidden_states.shape[1]],
hidden_states_negative[:, encoder_hidden_states.shape[1]:],
)
hidden_states_positive = hidden_states
hidden_states_guidance = hidden_states_positive * self.nag_scale - hidden_states_negative * (self.nag_scale - 1)
norm_positive = torch.norm(hidden_states_positive, p=2, dim=-1, keepdim=True).expand(*hidden_states_positive.shape)
norm_guidance = torch.norm(hidden_states_guidance, p=2, dim=-1, keepdim=True).expand(*hidden_states_positive.shape)
scale = norm_guidance / norm_positive
hidden_states_guidance = hidden_states_guidance * torch.minimum(scale, scale.new_ones(1) * self.nag_tau) / scale
hidden_states = hidden_states_guidance * self.nag_alpha + hidden_states_positive * (1 - self.nag_alpha)
encoder_hidden_states = torch.cat((encoder_hidden_states, encoder_hidden_states_negative), dim=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 apply_guidance:
image_hidden_states_negative = hidden_states_negative[:, encoder_hidden_states_length:]
image_hidden_states = hidden_states[:, encoder_hidden_states_length:]
image_hidden_states_positive = image_hidden_states
image_hidden_states_guidance = image_hidden_states_positive * self.nag_scale - image_hidden_states_negative * (self.nag_scale - 1)
norm_positive = torch.norm(image_hidden_states_positive, p=2, dim=-1, keepdim=True).expand(*image_hidden_states_positive.shape)
norm_guidance = torch.norm(image_hidden_states_guidance, p=2, dim=-1, keepdim=True).expand(*image_hidden_states_positive.shape)
scale = norm_guidance / norm_positive
image_hidden_states_guidance = image_hidden_states_guidance * torch.minimum(scale, scale.new_ones(1) * self.nag_tau) / scale
# scale = torch.nan_to_num(scale, 10)
# image_hidden_states_guidance[scale > self.nag_tau] = image_hidden_states_guidance[scale > self.nag_tau] / (norm_guidance[scale > self.nag_tau] + 1e-7) * norm_positive[scale > self.nag_tau] * self.nag_tau
image_hidden_states = image_hidden_states_guidance * self.nag_alpha + image_hidden_states_positive * (1 - self.nag_alpha)
hidden_states_negative[:, encoder_hidden_states_length:] = image_hidden_states
hidden_states[:, encoder_hidden_states_length:] = image_hidden_states
hidden_states = torch.cat((hidden_states, hidden_states_negative), dim=0)
return hidden_states
|