|
|
from typing import Optional |
|
|
|
|
|
import torch |
|
|
import torch.nn.functional as F |
|
|
|
|
|
from diffusers.models.attention_processor import Attention |
|
|
from ftfy import apply_plan |
|
|
|
|
|
|
|
|
class NAGWanAttnProcessor2_0: |
|
|
def __init__(self, nag_scale=1.0, nag_tau=2.5, nag_alpha=0.25): |
|
|
if not hasattr(F, "scaled_dot_product_attention"): |
|
|
raise ImportError("WanAttnProcessor2_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 |
|
|
|
|
|
def __call__( |
|
|
self, |
|
|
attn: Attention, |
|
|
hidden_states: torch.Tensor, |
|
|
encoder_hidden_states: Optional[torch.Tensor] = None, |
|
|
attention_mask: Optional[torch.Tensor] = None, |
|
|
rotary_emb: Optional[torch.Tensor] = None, |
|
|
) -> torch.Tensor: |
|
|
apply_guidance = self.nag_scale > 1 and encoder_hidden_states is not None |
|
|
if apply_guidance: |
|
|
if len(encoder_hidden_states) == 2 * len(hidden_states): |
|
|
batch_size = len(hidden_states) |
|
|
else: |
|
|
apply_guidance = False |
|
|
|
|
|
encoder_hidden_states_img = None |
|
|
if attn.add_k_proj is not None: |
|
|
encoder_hidden_states_img = encoder_hidden_states[:, :257] |
|
|
encoder_hidden_states = encoder_hidden_states[:, 257:] |
|
|
if apply_guidance: |
|
|
encoder_hidden_states_img = encoder_hidden_states_img[:batch_size] |
|
|
if encoder_hidden_states is None: |
|
|
encoder_hidden_states = hidden_states |
|
|
|
|
|
query = attn.to_q(hidden_states) |
|
|
key = attn.to_k(encoder_hidden_states) |
|
|
value = attn.to_v(encoder_hidden_states) |
|
|
|
|
|
if attn.norm_q is not None: |
|
|
query = attn.norm_q(query) |
|
|
if attn.norm_k is not None: |
|
|
key = attn.norm_k(key) |
|
|
|
|
|
query = query.unflatten(2, (attn.heads, -1)).transpose(1, 2) |
|
|
key = key.unflatten(2, (attn.heads, -1)).transpose(1, 2) |
|
|
value = value.unflatten(2, (attn.heads, -1)).transpose(1, 2) |
|
|
|
|
|
if rotary_emb is not None: |
|
|
|
|
|
def apply_rotary_emb(hidden_states: torch.Tensor, freqs: torch.Tensor): |
|
|
x_rotated = torch.view_as_complex(hidden_states.to(torch.float64).unflatten(3, (-1, 2))) |
|
|
x_out = torch.view_as_real(x_rotated * freqs).flatten(3, 4) |
|
|
return x_out.type_as(hidden_states) |
|
|
|
|
|
query = apply_rotary_emb(query, rotary_emb) |
|
|
key = apply_rotary_emb(key, rotary_emb) |
|
|
|
|
|
|
|
|
hidden_states_img = None |
|
|
if encoder_hidden_states_img is not None: |
|
|
key_img = attn.add_k_proj(encoder_hidden_states_img) |
|
|
key_img = attn.norm_added_k(key_img) |
|
|
value_img = attn.add_v_proj(encoder_hidden_states_img) |
|
|
|
|
|
key_img = key_img.unflatten(2, (attn.heads, -1)).transpose(1, 2) |
|
|
value_img = value_img.unflatten(2, (attn.heads, -1)).transpose(1, 2) |
|
|
|
|
|
hidden_states_img = F.scaled_dot_product_attention( |
|
|
query, key_img, value_img, attn_mask=None, dropout_p=0.0, is_causal=False |
|
|
) |
|
|
hidden_states_img = hidden_states_img.transpose(1, 2).flatten(2, 3) |
|
|
hidden_states_img = hidden_states_img.type_as(query) |
|
|
|
|
|
if apply_guidance: |
|
|
key, key_negative = torch.chunk(key, 2, dim=0) |
|
|
value, value_negative = torch.chunk(value, 2, dim=0) |
|
|
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).flatten(2, 3) |
|
|
hidden_states = hidden_states.type_as(query) |
|
|
if apply_guidance: |
|
|
hidden_states_negative = F.scaled_dot_product_attention( |
|
|
query, key_negative, value_negative, attn_mask=attention_mask, dropout_p=0.0, is_causal=False |
|
|
) |
|
|
hidden_states_negative = hidden_states_negative.transpose(1, 2).flatten(2, 3) |
|
|
hidden_states_negative = hidden_states_negative.type_as(query) |
|
|
|
|
|
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=1, dim=-1, keepdim=True).expand(*hidden_states_positive.shape) |
|
|
norm_guidance = torch.norm(hidden_states_guidance, p=1, dim=-1, keepdim=True).expand(*hidden_states_guidance.shape) |
|
|
|
|
|
scale = norm_guidance / norm_positive |
|
|
scale = torch.nan_to_num(scale, 10) |
|
|
hidden_states_guidance[scale > self.nag_tau] = \ |
|
|
hidden_states_guidance[scale > self.nag_tau] / (norm_guidance[scale > self.nag_tau] + 1e-7) * norm_positive[scale > self.nag_tau] * self.nag_tau |
|
|
|
|
|
hidden_states = hidden_states_guidance * self.nag_alpha + hidden_states_positive * (1 - self.nag_alpha) |
|
|
|
|
|
if hidden_states_img is not None: |
|
|
hidden_states = hidden_states + hidden_states_img |
|
|
|
|
|
hidden_states = attn.to_out[0](hidden_states) |
|
|
hidden_states = attn.to_out[1](hidden_states) |
|
|
return hidden_states |
|
|
|
