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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 | |