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from typing import Optional
import torch
import torch.nn.functional as F
from diffusers.models.attention import Attention
from diffusers.models.embeddings import apply_rotary_emb
from einops import rearrange, repeat
class HunyuanAttnProcessor2_0:
r"""
Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0). This is
used in the HunyuanDiT model. It applies a s normalization layer and rotary embedding on query and key vector.
"""
def __init__(self):
if not hasattr(F, "scaled_dot_product_attention"):
raise ImportError("AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.")
def __call__(
self,
attn: Attention,
hidden_states: torch.Tensor,
encoder_hidden_states: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
temb: Optional[torch.Tensor] = None,
image_rotary_emb: Optional[torch.Tensor] = None,
) -> torch.Tensor:
residual = hidden_states
if attn.spatial_norm is not None:
hidden_states = attn.spatial_norm(hidden_states, temb)
input_ndim = hidden_states.ndim
if input_ndim == 4:
batch_size, channel, height, width = hidden_states.shape
hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
batch_size, sequence_length, _ = (
hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
)
if attention_mask is not None:
attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
# scaled_dot_product_attention expects attention_mask shape to be
# (batch, heads, source_length, target_length)
attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])
if attn.group_norm is not None:
hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
query = attn.to_q(hidden_states)
if encoder_hidden_states is None:
encoder_hidden_states = hidden_states
elif attn.norm_cross:
encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
key = attn.to_k(encoder_hidden_states)
value = attn.to_v(encoder_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)
# Apply RoPE if needed
if image_rotary_emb is not None:
query = apply_rotary_emb(query, image_rotary_emb)
if not attn.is_cross_attention:
key = apply_rotary_emb(key, image_rotary_emb)
# the output of sdp = (batch, num_heads, seq_len, head_dim)
# TODO: add support for attn.scale when we move to Torch 2.1
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)
# linear proj
hidden_states = attn.to_out[0](hidden_states)
# dropout
hidden_states = attn.to_out[1](hidden_states)
if input_ndim == 4:
hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
if attn.residual_connection:
hidden_states = hidden_states + residual
hidden_states = hidden_states / attn.rescale_output_factor
return hidden_states
class LazyKVCompressionProcessor2_0:
r"""
Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0). This is
used in the KVCompression model. It applies a s normalization layer and rotary embedding on query and key vector.
"""
def __init__(self):
if not hasattr(F, "scaled_dot_product_attention"):
raise ImportError("AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.")
def __call__(
self,
attn: Attention,
hidden_states: torch.Tensor,
encoder_hidden_states: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
temb: Optional[torch.Tensor] = None,
image_rotary_emb: Optional[torch.Tensor] = None,
) -> torch.Tensor:
residual = hidden_states
if attn.spatial_norm is not None:
hidden_states = attn.spatial_norm(hidden_states, temb)
input_ndim = hidden_states.ndim
batch_size, channel, num_frames, height, width = hidden_states.shape
hidden_states = rearrange(hidden_states, "b c f h w -> b (f h w) c", f=num_frames, h=height, w=width)
batch_size, sequence_length, _ = (
hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
)
if attention_mask is not None:
attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
# scaled_dot_product_attention expects attention_mask shape to be
# (batch, heads, source_length, target_length)
attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])
if attn.group_norm is not None:
hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
query = attn.to_q(hidden_states)
if encoder_hidden_states is None:
encoder_hidden_states = hidden_states
elif attn.norm_cross:
encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
key = attn.to_k(encoder_hidden_states)
value = attn.to_v(encoder_hidden_states)
key = rearrange(key, "b (f h w) c -> (b f) c h w", f=num_frames, h=height, w=width)
key = attn.k_compression(key)
key_shape = key.size()
key = rearrange(key, "(b f) c h w -> b (f h w) c", f=num_frames)
value = rearrange(value, "b (f h w) c -> (b f) c h w", f=num_frames, h=height, w=width)
value = attn.v_compression(value)
value = rearrange(value, "(b f) c h w -> b (f h w) c", f=num_frames)
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)
# Apply RoPE if needed
if image_rotary_emb is not None:
compression_image_rotary_emb = (
rearrange(image_rotary_emb[0], "(f h w) c -> f c h w", f=num_frames, h=height, w=width),
rearrange(image_rotary_emb[1], "(f h w) c -> f c h w", f=num_frames, h=height, w=width),
)
compression_image_rotary_emb = (
F.interpolate(compression_image_rotary_emb[0], size=key_shape[-2:], mode='bilinear'),
F.interpolate(compression_image_rotary_emb[1], size=key_shape[-2:], mode='bilinear')
)
compression_image_rotary_emb = (
rearrange(compression_image_rotary_emb[0], "f c h w -> (f h w) c"),
rearrange(compression_image_rotary_emb[1], "f c h w -> (f h w) c"),
)
query = apply_rotary_emb(query, image_rotary_emb)
if not attn.is_cross_attention:
key = apply_rotary_emb(key, compression_image_rotary_emb)
# the output of sdp = (batch, num_heads, seq_len, head_dim)
# TODO: add support for attn.scale when we move to Torch 2.1
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)
# linear proj
hidden_states = attn.to_out[0](hidden_states)
# dropout
hidden_states = attn.to_out[1](hidden_states)
if attn.residual_connection:
hidden_states = hidden_states + residual
hidden_states = hidden_states / attn.rescale_output_factor
return hidden_states
class EasyAnimateAttnProcessor2_0:
def __init__(self):
pass
def __call__(
self,
attn: Attention,
hidden_states: torch.Tensor,
encoder_hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
image_rotary_emb: Optional[torch.Tensor] = None,
attn2: Attention = None,
) -> torch.Tensor:
text_seq_length = encoder_hidden_states.size(1)
batch_size, sequence_length, _ = (
hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
)
if attention_mask is not None:
attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])
if attn2 is None:
hidden_states = torch.cat([encoder_hidden_states, hidden_states], dim=1)
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)
if attn2 is not None:
query_txt = attn2.to_q(encoder_hidden_states)
key_txt = attn2.to_k(encoder_hidden_states)
value_txt = attn2.to_v(encoder_hidden_states)
inner_dim = key_txt.shape[-1]
head_dim = inner_dim // attn.heads
query_txt = query_txt.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
key_txt = key_txt.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
value_txt = value_txt.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
if attn2.norm_q is not None:
query_txt = attn2.norm_q(query_txt)
if attn2.norm_k is not None:
key_txt = attn2.norm_k(key_txt)
query = torch.cat([query_txt, query], dim=2)
key = torch.cat([key_txt, key], dim=2)
value = torch.cat([value_txt, value], dim=2)
# Apply RoPE if needed
if image_rotary_emb is not None:
query[:, :, text_seq_length:] = apply_rotary_emb(query[:, :, text_seq_length:], image_rotary_emb)
if not attn.is_cross_attention:
key[:, :, text_seq_length:] = apply_rotary_emb(key[:, :, text_seq_length:], 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)
if attn2 is None:
# linear proj
hidden_states = attn.to_out[0](hidden_states)
# dropout
hidden_states = attn.to_out[1](hidden_states)
encoder_hidden_states, hidden_states = hidden_states.split(
[text_seq_length, hidden_states.size(1) - text_seq_length], dim=1
)
else:
encoder_hidden_states, hidden_states = hidden_states.split(
[text_seq_length, hidden_states.size(1) - text_seq_length], dim=1
)
# linear proj
hidden_states = attn.to_out[0](hidden_states)
encoder_hidden_states = attn2.to_out[0](encoder_hidden_states)
# dropout
hidden_states = attn.to_out[1](hidden_states)
encoder_hidden_states = attn2.to_out[1](encoder_hidden_states)
return hidden_states, encoder_hidden_states
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