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import torch
import torch.nn.functional as F
from typing import Optional
from diffusers.models.attention_processor import Attention
class WanAttnProcessor2_0:
def __init__(self, scale=4, attn_mask=None, neg_prompt_length=0):
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.attn_mask = attn_mask
self.neg_prompt_length = neg_prompt_length
self.scale = scale
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:
encoder_hidden_states_img = None
if attn.add_k_proj is not None:
# 512 is the context length of the text encoder, hardcoded for now
image_context_length = encoder_hidden_states.shape[1] - 512
encoder_hidden_states_img = encoder_hidden_states[:, :image_context_length]
encoder_hidden_states = encoder_hidden_states[:, image_context_length:]
cross_attn = False
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)
else:
query = attn.to_q(hidden_states)
key = attn.to_k(encoder_hidden_states)
value = attn.to_v(encoder_hidden_states)
cross_attn = True
if cross_attn and self.pos:
# print(value.shape, self.neg_prompt_length)
value[:,-self.neg_prompt_length:] *= -self.scale # should we flip before
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)
# print(self.pos)
if rotary_emb is not None:
def apply_rotary_emb(
hidden_states: torch.Tensor,
freqs_cos: torch.Tensor,
freqs_sin: torch.Tensor,
):
x = hidden_states.view(*hidden_states.shape[:-1], -1, 2)
x1, x2 = x[..., 0], x[..., 1]
cos = freqs_cos[..., 0::2]
sin = freqs_sin[..., 1::2]
out = torch.empty_like(hidden_states)
out[..., 0::2] = x1 * cos - x2 * sin
out[..., 1::2] = x1 * sin + x2 * cos
return out.type_as(hidden_states)
query = apply_rotary_emb(query, *rotary_emb)
key = apply_rotary_emb(key, *rotary_emb)
# I2V task
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)
print(query.shape, key_img.shape, value_img.shape)
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 self.attn_mask is not None:
self.attn_mask = self.attn_mask.to(query.dtype)
if not self.pos:
hidden_states = F.scaled_dot_product_attention(
query, key, value, dropout_p=0.0, is_causal=False
)
else:
hidden_states = F.scaled_dot_product_attention(
query, key, value, attn_mask=self.attn_mask, dropout_p=0.0, is_causal=False
)
# if cross_attn:
# # print(hidden_states.shape)
# hidden_states_norm = torch.norm(hidden_states, dim=-1, keepdim=True)
# new_norm = torch.where(hidden_states_norm > max_norm * 2, max_norm * 2, hidden_states_norm)
# hidden_states = hidden_states * (new_norm / hidden_states_norm)
hidden_states = hidden_states.transpose(1, 2).flatten(2, 3)
hidden_states = hidden_states.type_as(query)
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 |