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# modified from https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py | |
import torch | |
import torch.nn as nn | |
import torch.nn.functional as F | |
class AdaLayerNorm(nn.Module): | |
def __init__(self, embedding_dim: int, time_embedding_dim: int = None): | |
super().__init__() | |
if time_embedding_dim is None: | |
time_embedding_dim = embedding_dim | |
self.silu = nn.SiLU() | |
self.linear = nn.Linear(time_embedding_dim, 2 * embedding_dim, bias=True) | |
nn.init.zeros_(self.linear.weight) | |
nn.init.zeros_(self.linear.bias) | |
self.norm = nn.LayerNorm(embedding_dim, elementwise_affine=False, eps=1e-6) | |
def forward( | |
self, x: torch.Tensor, timestep_embedding: torch.Tensor | |
): | |
emb = self.linear(self.silu(timestep_embedding)) | |
shift, scale = emb.view(len(x), 1, -1).chunk(2, dim=-1) | |
x = self.norm(x) * (1 + scale) + shift | |
return x | |
class AttnProcessor(nn.Module): | |
r""" | |
Default processor for performing attention-related computations. | |
""" | |
def __init__( | |
self, | |
hidden_size=None, | |
cross_attention_dim=None, | |
): | |
super().__init__() | |
def __call__( | |
self, | |
attn, | |
hidden_states, | |
encoder_hidden_states=None, | |
attention_mask=None, | |
temb=None, | |
): | |
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 | |
) | |
attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size) | |
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) | |
query = attn.head_to_batch_dim(query) | |
key = attn.head_to_batch_dim(key) | |
value = attn.head_to_batch_dim(value) | |
attention_probs = attn.get_attention_scores(query, key, attention_mask) | |
hidden_states = torch.bmm(attention_probs, value) | |
hidden_states = attn.batch_to_head_dim(hidden_states) | |
# 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 IPAttnProcessor(nn.Module): | |
r""" | |
Attention processor for IP-Adapater. | |
Args: | |
hidden_size (`int`): | |
The hidden size of the attention layer. | |
cross_attention_dim (`int`): | |
The number of channels in the `encoder_hidden_states`. | |
scale (`float`, defaults to 1.0): | |
the weight scale of image prompt. | |
num_tokens (`int`, defaults to 4 when do ip_adapter_plus it should be 16): | |
The context length of the image features. | |
""" | |
def __init__(self, hidden_size, cross_attention_dim=None, scale=1.0, num_tokens=4): | |
super().__init__() | |
self.hidden_size = hidden_size | |
self.cross_attention_dim = cross_attention_dim | |
self.scale = scale | |
self.num_tokens = num_tokens | |
self.to_k_ip = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False) | |
self.to_v_ip = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False) | |
def __call__( | |
self, | |
attn, | |
hidden_states, | |
encoder_hidden_states=None, | |
attention_mask=None, | |
temb=None, | |
): | |
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 | |
) | |
attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size) | |
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 | |
else: | |
# get encoder_hidden_states, ip_hidden_states | |
end_pos = encoder_hidden_states.shape[1] - self.num_tokens | |
encoder_hidden_states, ip_hidden_states = ( | |
encoder_hidden_states[:, :end_pos, :], | |
encoder_hidden_states[:, end_pos:, :], | |
) | |
if 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) | |
query = attn.head_to_batch_dim(query) | |
key = attn.head_to_batch_dim(key) | |
value = attn.head_to_batch_dim(value) | |
attention_probs = attn.get_attention_scores(query, key, attention_mask) | |
hidden_states = torch.bmm(attention_probs, value) | |
hidden_states = attn.batch_to_head_dim(hidden_states) | |
# for ip-adapter | |
ip_key = self.to_k_ip(ip_hidden_states) | |
ip_value = self.to_v_ip(ip_hidden_states) | |
ip_key = attn.head_to_batch_dim(ip_key) | |
ip_value = attn.head_to_batch_dim(ip_value) | |
ip_attention_probs = attn.get_attention_scores(query, ip_key, None) | |
ip_hidden_states = torch.bmm(ip_attention_probs, ip_value) | |
ip_hidden_states = attn.batch_to_head_dim(ip_hidden_states) | |
hidden_states = hidden_states + self.scale * ip_hidden_states | |
# 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 TA_IPAttnProcessor(nn.Module): | |
r""" | |
Attention processor for IP-Adapater. | |
Args: | |
hidden_size (`int`): | |
The hidden size of the attention layer. | |
cross_attention_dim (`int`): | |
The number of channels in the `encoder_hidden_states`. | |
scale (`float`, defaults to 1.0): | |
the weight scale of image prompt. | |
num_tokens (`int`, defaults to 4 when do ip_adapter_plus it should be 16): | |
The context length of the image features. | |
""" | |
def __init__(self, hidden_size, cross_attention_dim=None, time_embedding_dim: int = None, scale=1.0, num_tokens=4): | |
super().__init__() | |
self.hidden_size = hidden_size | |
self.cross_attention_dim = cross_attention_dim | |
self.scale = scale | |
self.num_tokens = num_tokens | |
self.to_k_ip = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False) | |
self.to_v_ip = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False) | |
self.ln_k_ip = AdaLayerNorm(hidden_size, time_embedding_dim) | |
self.ln_v_ip = AdaLayerNorm(hidden_size, time_embedding_dim) | |
def __call__( | |
self, | |
attn, | |
hidden_states, | |
encoder_hidden_states=None, | |
attention_mask=None, | |
temb=None, | |
): | |
assert temb is not None, "Timestep embedding is needed for a time-aware attention processor." | |
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 | |
) | |
attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size) | |
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 | |
else: | |
# get encoder_hidden_states, ip_hidden_states | |
end_pos = encoder_hidden_states.shape[1] - self.num_tokens | |
encoder_hidden_states, ip_hidden_states = ( | |
encoder_hidden_states[:, :end_pos, :], | |
encoder_hidden_states[:, end_pos:, :], | |
) | |
if 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) | |
query = attn.head_to_batch_dim(query) | |
key = attn.head_to_batch_dim(key) | |
value = attn.head_to_batch_dim(value) | |
attention_probs = attn.get_attention_scores(query, key, attention_mask) | |
hidden_states = torch.bmm(attention_probs, value) | |
hidden_states = attn.batch_to_head_dim(hidden_states) | |
# for ip-adapter | |
ip_key = self.to_k_ip(ip_hidden_states) | |
ip_value = self.to_v_ip(ip_hidden_states) | |
# time-dependent adaLN | |
ip_key = self.ln_k_ip(ip_key, temb) | |
ip_value = self.ln_v_ip(ip_value, temb) | |
ip_key = attn.head_to_batch_dim(ip_key) | |
ip_value = attn.head_to_batch_dim(ip_value) | |
ip_attention_probs = attn.get_attention_scores(query, ip_key, None) | |
ip_hidden_states = torch.bmm(ip_attention_probs, ip_value) | |
ip_hidden_states = attn.batch_to_head_dim(ip_hidden_states) | |
hidden_states = hidden_states + self.scale * ip_hidden_states | |
# 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 AttnProcessor2_0(torch.nn.Module): | |
r""" | |
Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0). | |
""" | |
def __init__( | |
self, | |
hidden_size=None, | |
cross_attention_dim=None, | |
): | |
super().__init__() | |
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, | |
hidden_states, | |
encoder_hidden_states=None, | |
attention_mask=None, | |
external_kv=None, | |
temb=None, | |
): | |
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) | |
if external_kv: | |
key = torch.cat([key, external_kv.k], axis=1) | |
value = torch.cat([value, external_kv.v], axis=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) | |
# 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 split_AttnProcessor2_0(torch.nn.Module): | |
r""" | |
Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0). | |
""" | |
def __init__( | |
self, | |
hidden_size=None, | |
cross_attention_dim=None, | |
time_embedding_dim=None, | |
): | |
super().__init__() | |
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, | |
hidden_states, | |
encoder_hidden_states=None, | |
attention_mask=None, | |
external_kv=None, | |
temb=None, | |
cat_dim=-2, | |
original_shape=None, | |
): | |
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: | |
# 2d to sequence. | |
height, width = hidden_states.shape[-2:] | |
if cat_dim==-2 or cat_dim==2: | |
hidden_states_0 = hidden_states[:, :, :height//2, :] | |
hidden_states_1 = hidden_states[:, :, -(height//2):, :] | |
elif cat_dim==-1 or cat_dim==3: | |
hidden_states_0 = hidden_states[:, :, :, :width//2] | |
hidden_states_1 = hidden_states[:, :, :, -(width//2):] | |
batch_size, channel, height, width = hidden_states_0.shape | |
hidden_states_0 = hidden_states_0.view(batch_size, channel, height * width).transpose(1, 2) | |
hidden_states_1 = hidden_states_1.view(batch_size, channel, height * width).transpose(1, 2) | |
else: | |
# directly split sqeuence according to concat dim. | |
single_dim = original_shape[2] if cat_dim==-2 or cat_dim==2 else original_shape[1] | |
hidden_states_0 = hidden_states[:, :single_dim*single_dim,:] | |
hidden_states_1 = hidden_states[:, single_dim*(single_dim+1):,:] | |
hidden_states = torch.cat([hidden_states_0, hidden_states_1], dim=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) | |
# 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) | |
key = attn.to_k(hidden_states) | |
value = attn.to_v(hidden_states) | |
if external_kv: | |
key = torch.cat([key, external_kv.k], dim=1) | |
value = torch.cat([value, external_kv.v], dim=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) | |
# 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) | |
# spatially split. | |
hidden_states_0, hidden_states_1 = hidden_states.chunk(2, dim=1) | |
if input_ndim == 4: | |
hidden_states_0 = hidden_states_0.transpose(-1, -2).reshape(batch_size, channel, height, width) | |
hidden_states_1 = hidden_states_1.transpose(-1, -2).reshape(batch_size, channel, height, width) | |
if cat_dim==-2 or cat_dim==2: | |
hidden_states_pad = torch.zeros(batch_size, channel, 1, width) | |
elif cat_dim==-1 or cat_dim==3: | |
hidden_states_pad = torch.zeros(batch_size, channel, height, 1) | |
hidden_states_pad = hidden_states_pad.to(hidden_states_0.device, dtype=hidden_states_0.dtype) | |
hidden_states = torch.cat([hidden_states_0, hidden_states_pad, hidden_states_1], dim=cat_dim) | |
assert hidden_states.shape == residual.shape, f"{hidden_states.shape} != {residual.shape}" | |
else: | |
batch_size, sequence_length, inner_dim = hidden_states.shape | |
hidden_states_pad = torch.zeros(batch_size, single_dim, inner_dim) | |
hidden_states_pad = hidden_states_pad.to(hidden_states_0.device, dtype=hidden_states_0.dtype) | |
hidden_states = torch.cat([hidden_states_0, hidden_states_pad, hidden_states_1], dim=1) | |
assert hidden_states.shape == residual.shape, f"{hidden_states.shape} != {residual.shape}" | |
if attn.residual_connection: | |
hidden_states = hidden_states + residual | |
hidden_states = hidden_states / attn.rescale_output_factor | |
return hidden_states | |
class sep_split_AttnProcessor2_0(torch.nn.Module): | |
r""" | |
Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0). | |
""" | |
def __init__( | |
self, | |
hidden_size=None, | |
cross_attention_dim=None, | |
time_embedding_dim=None, | |
): | |
super().__init__() | |
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.") | |
self.ln_k_ref = AdaLayerNorm(hidden_size, time_embedding_dim) | |
self.ln_v_ref = AdaLayerNorm(hidden_size, time_embedding_dim) | |
# self.hidden_size = hidden_size | |
# self.cross_attention_dim = cross_attention_dim | |
# self.scale = scale | |
# self.num_tokens = num_tokens | |
# self.to_q_ref = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False) | |
# self.to_k_ref = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False) | |
# self.to_v_ref = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False) | |
def __call__( | |
self, | |
attn, | |
hidden_states, | |
encoder_hidden_states=None, | |
attention_mask=None, | |
external_kv=None, | |
temb=None, | |
cat_dim=-2, | |
original_shape=None, | |
ref_scale=1.0, | |
): | |
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: | |
# 2d to sequence. | |
height, width = hidden_states.shape[-2:] | |
if cat_dim==-2 or cat_dim==2: | |
hidden_states_0 = hidden_states[:, :, :height//2, :] | |
hidden_states_1 = hidden_states[:, :, -(height//2):, :] | |
elif cat_dim==-1 or cat_dim==3: | |
hidden_states_0 = hidden_states[:, :, :, :width//2] | |
hidden_states_1 = hidden_states[:, :, :, -(width//2):] | |
batch_size, channel, height, width = hidden_states_0.shape | |
hidden_states_0 = hidden_states_0.view(batch_size, channel, height * width).transpose(1, 2) | |
hidden_states_1 = hidden_states_1.view(batch_size, channel, height * width).transpose(1, 2) | |
else: | |
# directly split sqeuence according to concat dim. | |
single_dim = original_shape[2] if cat_dim==-2 or cat_dim==2 else original_shape[1] | |
hidden_states_0 = hidden_states[:, :single_dim*single_dim,:] | |
hidden_states_1 = hidden_states[:, single_dim*(single_dim+1):,:] | |
batch_size, sequence_length, _ = ( | |
hidden_states_0.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_0 = attn.group_norm(hidden_states_0.transpose(1, 2)).transpose(1, 2) | |
hidden_states_1 = attn.group_norm(hidden_states_1.transpose(1, 2)).transpose(1, 2) | |
query_0 = attn.to_q(hidden_states_0) | |
query_1 = attn.to_q(hidden_states_1) | |
key_0 = attn.to_k(hidden_states_0) | |
key_1 = attn.to_k(hidden_states_1) | |
value_0 = attn.to_v(hidden_states_0) | |
value_1 = attn.to_v(hidden_states_1) | |
# time-dependent adaLN | |
key_1 = self.ln_k_ref(key_1, temb) | |
value_1 = self.ln_v_ref(value_1, temb) | |
if external_kv: | |
key_1 = torch.cat([key_1, external_kv.k], dim=1) | |
value_1 = torch.cat([value_1, external_kv.v], dim=1) | |
inner_dim = key_0.shape[-1] | |
head_dim = inner_dim // attn.heads | |
query_0 = query_0.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) | |
query_1 = query_1.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) | |
key_0 = key_0.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) | |
key_1 = key_1.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) | |
value_0 = value_0.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) | |
value_1 = value_1.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) | |
# 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_0 = F.scaled_dot_product_attention( | |
query_0, key_0, value_0, attn_mask=attention_mask, dropout_p=0.0, is_causal=False | |
) | |
hidden_states_1 = F.scaled_dot_product_attention( | |
query_1, key_1, value_1, attn_mask=attention_mask, dropout_p=0.0, is_causal=False | |
) | |
# cross-attn | |
_hidden_states_0 = F.scaled_dot_product_attention( | |
query_0, key_1, value_1, attn_mask=attention_mask, dropout_p=0.0, is_causal=False | |
) | |
hidden_states_0 = hidden_states_0 + ref_scale * _hidden_states_0 * 10 | |
# TODO: drop this cross-attn | |
_hidden_states_1 = F.scaled_dot_product_attention( | |
query_1, key_0, value_0, attn_mask=attention_mask, dropout_p=0.0, is_causal=False | |
) | |
hidden_states_1 = hidden_states_1 + ref_scale * _hidden_states_1 | |
hidden_states_0 = hidden_states_0.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim) | |
hidden_states_1 = hidden_states_1.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim) | |
hidden_states_0 = hidden_states_0.to(query_0.dtype) | |
hidden_states_1 = hidden_states_1.to(query_1.dtype) | |
# linear proj | |
hidden_states_0 = attn.to_out[0](hidden_states_0) | |
hidden_states_1 = attn.to_out[0](hidden_states_1) | |
# dropout | |
hidden_states_0 = attn.to_out[1](hidden_states_0) | |
hidden_states_1 = attn.to_out[1](hidden_states_1) | |
if input_ndim == 4: | |
hidden_states_0 = hidden_states_0.transpose(-1, -2).reshape(batch_size, channel, height, width) | |
hidden_states_1 = hidden_states_1.transpose(-1, -2).reshape(batch_size, channel, height, width) | |
if cat_dim==-2 or cat_dim==2: | |
hidden_states_pad = torch.zeros(batch_size, channel, 1, width) | |
elif cat_dim==-1 or cat_dim==3: | |
hidden_states_pad = torch.zeros(batch_size, channel, height, 1) | |
hidden_states_pad = hidden_states_pad.to(hidden_states_0.device, dtype=hidden_states_0.dtype) | |
hidden_states = torch.cat([hidden_states_0, hidden_states_pad, hidden_states_1], dim=cat_dim) | |
assert hidden_states.shape == residual.shape, f"{hidden_states.shape} != {residual.shape}" | |
else: | |
batch_size, sequence_length, inner_dim = hidden_states.shape | |
hidden_states_pad = torch.zeros(batch_size, single_dim, inner_dim) | |
hidden_states_pad = hidden_states_pad.to(hidden_states_0.device, dtype=hidden_states_0.dtype) | |
hidden_states = torch.cat([hidden_states_0, hidden_states_pad, hidden_states_1], dim=1) | |
assert hidden_states.shape == residual.shape, f"{hidden_states.shape} != {residual.shape}" | |
if attn.residual_connection: | |
hidden_states = hidden_states + residual | |
hidden_states = hidden_states / attn.rescale_output_factor | |
return hidden_states | |
class AdditiveKV_AttnProcessor2_0(torch.nn.Module): | |
r""" | |
Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0). | |
""" | |
def __init__( | |
self, | |
hidden_size: int = None, | |
cross_attention_dim: int = None, | |
time_embedding_dim: int = None, | |
additive_scale: float = 1.0, | |
): | |
super().__init__() | |
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.") | |
self.additive_scale = additive_scale | |
def __call__( | |
self, | |
attn, | |
hidden_states, | |
encoder_hidden_states=None, | |
external_kv=None, | |
attention_mask=None, | |
temb=None, | |
): | |
assert temb is not None, "Timestep embedding is needed for a time-aware attention processor." | |
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) | |
# 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) | |
if external_kv: | |
key = external_kv.k | |
value = external_kv.v | |
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) | |
external_attn_output = F.scaled_dot_product_attention( | |
query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False | |
) | |
external_attn_output = external_attn_output.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim) | |
hidden_states = hidden_states + self.additive_scale * external_attn_output | |
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 TA_AdditiveKV_AttnProcessor2_0(torch.nn.Module): | |
r""" | |
Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0). | |
""" | |
def __init__( | |
self, | |
hidden_size: int = None, | |
cross_attention_dim: int = None, | |
time_embedding_dim: int = None, | |
additive_scale: float = 1.0, | |
): | |
super().__init__() | |
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.") | |
self.ln_k = AdaLayerNorm(hidden_size, time_embedding_dim) | |
self.ln_v = AdaLayerNorm(hidden_size, time_embedding_dim) | |
self.additive_scale = additive_scale | |
def __call__( | |
self, | |
attn, | |
hidden_states, | |
encoder_hidden_states=None, | |
external_kv=None, | |
attention_mask=None, | |
temb=None, | |
): | |
assert temb is not None, "Timestep embedding is needed for a time-aware attention processor." | |
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) | |
# 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) | |
if external_kv: | |
key = external_kv.k | |
value = external_kv.v | |
# time-dependent adaLN | |
key = self.ln_k(key, temb) | |
value = self.ln_v(value, temb) | |
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) | |
external_attn_output = F.scaled_dot_product_attention( | |
query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False | |
) | |
external_attn_output = external_attn_output.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim) | |
hidden_states = hidden_states + self.additive_scale * external_attn_output | |
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 IPAttnProcessor2_0(torch.nn.Module): | |
r""" | |
Attention processor for IP-Adapater for PyTorch 2.0. | |
Args: | |
hidden_size (`int`): | |
The hidden size of the attention layer. | |
cross_attention_dim (`int`): | |
The number of channels in the `encoder_hidden_states`. | |
scale (`float`, defaults to 1.0): | |
the weight scale of image prompt. | |
num_tokens (`int`, defaults to 4 when do ip_adapter_plus it should be 16): | |
The context length of the image features. | |
""" | |
def __init__(self, hidden_size, cross_attention_dim=None, scale=1.0, num_tokens=4): | |
super().__init__() | |
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.") | |
self.hidden_size = hidden_size | |
self.cross_attention_dim = cross_attention_dim | |
self.scale = scale | |
self.num_tokens = num_tokens | |
self.to_k_ip = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False) | |
self.to_v_ip = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False) | |
def __call__( | |
self, | |
attn, | |
hidden_states, | |
encoder_hidden_states=None, | |
attention_mask=None, | |
temb=None, | |
): | |
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) | |
if isinstance(encoder_hidden_states, tuple): | |
# FIXME: now hard coded to single image prompt. | |
batch_size, _, hid_dim = encoder_hidden_states[0].shape | |
ip_tokens = encoder_hidden_states[1][0] | |
encoder_hidden_states = torch.cat([encoder_hidden_states[0], ip_tokens], dim=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) | |
# 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 | |
else: | |
# get encoder_hidden_states, ip_hidden_states | |
end_pos = encoder_hidden_states.shape[1] - self.num_tokens | |
encoder_hidden_states, ip_hidden_states = ( | |
encoder_hidden_states[:, :end_pos, :], | |
encoder_hidden_states[:, end_pos:, :], | |
) | |
if 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) | |
# 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) | |
# for ip-adapter | |
ip_key = self.to_k_ip(ip_hidden_states) | |
ip_value = self.to_v_ip(ip_hidden_states) | |
ip_key = ip_key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) | |
ip_value = ip_value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) | |
# the output of sdp = (batch, num_heads, seq_len, head_dim) | |
# TODO: add support for attn.scale when we move to Torch 2.1 | |
ip_hidden_states = F.scaled_dot_product_attention( | |
query, ip_key, ip_value, attn_mask=None, dropout_p=0.0, is_causal=False | |
) | |
ip_hidden_states = ip_hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim) | |
ip_hidden_states = ip_hidden_states.to(query.dtype) | |
hidden_states = hidden_states + self.scale * ip_hidden_states | |
# 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 TA_IPAttnProcessor2_0(torch.nn.Module): | |
r""" | |
Attention processor for IP-Adapater for PyTorch 2.0. | |
Args: | |
hidden_size (`int`): | |
The hidden size of the attention layer. | |
cross_attention_dim (`int`): | |
The number of channels in the `encoder_hidden_states`. | |
scale (`float`, defaults to 1.0): | |
the weight scale of image prompt. | |
num_tokens (`int`, defaults to 4 when do ip_adapter_plus it should be 16): | |
The context length of the image features. | |
""" | |
def __init__(self, hidden_size, cross_attention_dim=None, time_embedding_dim: int = None, scale=1.0, num_tokens=4): | |
super().__init__() | |
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.") | |
self.hidden_size = hidden_size | |
self.cross_attention_dim = cross_attention_dim | |
self.scale = scale | |
self.num_tokens = num_tokens | |
self.to_k_ip = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False) | |
self.to_v_ip = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False) | |
self.ln_k_ip = AdaLayerNorm(hidden_size, time_embedding_dim) | |
self.ln_v_ip = AdaLayerNorm(hidden_size, time_embedding_dim) | |
def __call__( | |
self, | |
attn, | |
hidden_states, | |
encoder_hidden_states=None, | |
attention_mask=None, | |
external_kv=None, | |
temb=None, | |
): | |
assert temb is not None, "Timestep embedding is needed for a time-aware attention processor." | |
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) | |
if not isinstance(encoder_hidden_states, tuple): | |
# get encoder_hidden_states, ip_hidden_states | |
end_pos = encoder_hidden_states.shape[1] - self.num_tokens | |
encoder_hidden_states, ip_hidden_states = ( | |
encoder_hidden_states[:, :end_pos, :], | |
encoder_hidden_states[:, end_pos:, :], | |
) | |
else: | |
# FIXME: now hard coded to single image prompt. | |
batch_size, _, hid_dim = encoder_hidden_states[0].shape | |
ip_hidden_states = encoder_hidden_states[1][0] | |
encoder_hidden_states = encoder_hidden_states[0] | |
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 | |
else: | |
if 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) | |
if external_kv: | |
key = torch.cat([key, external_kv.k], axis=1) | |
value = torch.cat([value, external_kv.v], axis=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) | |
# 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) | |
# for ip-adapter | |
ip_key = self.to_k_ip(ip_hidden_states) | |
ip_value = self.to_v_ip(ip_hidden_states) | |
# time-dependent adaLN | |
ip_key = self.ln_k_ip(ip_key, temb) | |
ip_value = self.ln_v_ip(ip_value, temb) | |
ip_key = ip_key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) | |
ip_value = ip_value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) | |
# the output of sdp = (batch, num_heads, seq_len, head_dim) | |
# TODO: add support for attn.scale when we move to Torch 2.1 | |
ip_hidden_states = F.scaled_dot_product_attention( | |
query, ip_key, ip_value, attn_mask=None, dropout_p=0.0, is_causal=False | |
) | |
ip_hidden_states = ip_hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim) | |
ip_hidden_states = ip_hidden_states.to(query.dtype) | |
hidden_states = hidden_states + self.scale * ip_hidden_states | |
# 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 | |
## for controlnet | |
class CNAttnProcessor: | |
r""" | |
Default processor for performing attention-related computations. | |
""" | |
def __init__(self, num_tokens=4): | |
self.num_tokens = num_tokens | |
def __call__(self, attn, hidden_states, encoder_hidden_states=None, attention_mask=None, temb=None): | |
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 | |
) | |
attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size) | |
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 | |
else: | |
end_pos = encoder_hidden_states.shape[1] - self.num_tokens | |
encoder_hidden_states = encoder_hidden_states[:, :end_pos] # only use text | |
if 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) | |
query = attn.head_to_batch_dim(query) | |
key = attn.head_to_batch_dim(key) | |
value = attn.head_to_batch_dim(value) | |
attention_probs = attn.get_attention_scores(query, key, attention_mask) | |
hidden_states = torch.bmm(attention_probs, value) | |
hidden_states = attn.batch_to_head_dim(hidden_states) | |
# 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 CNAttnProcessor2_0: | |
r""" | |
Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0). | |
""" | |
def __init__(self, num_tokens=4): | |
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.") | |
self.num_tokens = num_tokens | |
def __call__( | |
self, | |
attn, | |
hidden_states, | |
encoder_hidden_states=None, | |
attention_mask=None, | |
temb=None, | |
): | |
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 | |
else: | |
end_pos = encoder_hidden_states.shape[1] - self.num_tokens | |
encoder_hidden_states = encoder_hidden_states[:, :end_pos] # only use text | |
if 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) | |
# 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 | |
def init_attn_proc(unet, ip_adapter_tokens=16, use_lcm=False, use_adaln=True, use_external_kv=False): | |
attn_procs = {} | |
unet_sd = unet.state_dict() | |
for name in unet.attn_processors.keys(): | |
cross_attention_dim = None if name.endswith("attn1.processor") else unet.config.cross_attention_dim | |
if name.startswith("mid_block"): | |
hidden_size = unet.config.block_out_channels[-1] | |
elif name.startswith("up_blocks"): | |
block_id = int(name[len("up_blocks.")]) | |
hidden_size = list(reversed(unet.config.block_out_channels))[block_id] | |
elif name.startswith("down_blocks"): | |
block_id = int(name[len("down_blocks.")]) | |
hidden_size = unet.config.block_out_channels[block_id] | |
if cross_attention_dim is None: | |
if use_external_kv: | |
attn_procs[name] = AdditiveKV_AttnProcessor2_0( | |
hidden_size=hidden_size, | |
cross_attention_dim=cross_attention_dim, | |
time_embedding_dim=1280, | |
) if hasattr(F, "scaled_dot_product_attention") else AdditiveKV_AttnProcessor() | |
else: | |
attn_procs[name] = AttnProcessor2_0() if hasattr(F, "scaled_dot_product_attention") else AttnProcessor() | |
else: | |
if use_adaln: | |
layer_name = name.split(".processor")[0] | |
if use_lcm: | |
weights = { | |
"to_k_ip.weight": unet_sd[layer_name + ".to_k.base_layer.weight"], | |
"to_v_ip.weight": unet_sd[layer_name + ".to_v.base_layer.weight"], | |
} | |
else: | |
weights = { | |
"to_k_ip.weight": unet_sd[layer_name + ".to_k.weight"], | |
"to_v_ip.weight": unet_sd[layer_name + ".to_v.weight"], | |
} | |
attn_procs[name] = TA_IPAttnProcessor2_0( | |
hidden_size=hidden_size, | |
cross_attention_dim=cross_attention_dim, | |
num_tokens=ip_adapter_tokens, | |
time_embedding_dim=1280, | |
) if hasattr(F, "scaled_dot_product_attention") else \ | |
TA_IPAttnProcessor( | |
hidden_size=hidden_size, | |
cross_attention_dim=cross_attention_dim, | |
num_tokens=ip_adapter_tokens, | |
time_embedding_dim=1280, | |
) | |
attn_procs[name].load_state_dict(weights, strict=False) | |
else: | |
attn_procs[name] = AttnProcessor2_0() if hasattr(F, "scaled_dot_product_attention") else AttnProcessor() | |
return attn_procs | |
def init_aggregator_attn_proc(unet, use_adaln=False, split_attn=False): | |
attn_procs = {} | |
unet_sd = unet.state_dict() | |
for name in unet.attn_processors.keys(): | |
# get layer name and hidden dim | |
cross_attention_dim = None if name.endswith("attn1.processor") else unet.config.cross_attention_dim | |
if name.startswith("mid_block"): | |
hidden_size = unet.config.block_out_channels[-1] | |
elif name.startswith("up_blocks"): | |
block_id = int(name[len("up_blocks.")]) | |
hidden_size = list(reversed(unet.config.block_out_channels))[block_id] | |
elif name.startswith("down_blocks"): | |
block_id = int(name[len("down_blocks.")]) | |
hidden_size = unet.config.block_out_channels[block_id] | |
# init attn proc | |
if split_attn: | |
# layer_name = name.split(".processor")[0] | |
# weights = { | |
# "to_q_ref.weight": unet_sd[layer_name + ".to_q.weight"], | |
# "to_k_ref.weight": unet_sd[layer_name + ".to_k.weight"], | |
# "to_v_ref.weight": unet_sd[layer_name + ".to_v.weight"], | |
# } | |
attn_procs[name] = ( | |
sep_split_AttnProcessor2_0( | |
hidden_size=hidden_size, | |
cross_attention_dim=hidden_size, | |
time_embedding_dim=1280, | |
) | |
if use_adaln | |
else split_AttnProcessor2_0( | |
hidden_size=hidden_size, | |
cross_attention_dim=cross_attention_dim, | |
time_embedding_dim=1280, | |
) | |
) | |
# attn_procs[name].load_state_dict(weights, strict=False) | |
else: | |
attn_procs[name] = ( | |
AttnProcessor2_0( | |
hidden_size=hidden_size, | |
cross_attention_dim=hidden_size, | |
) | |
if hasattr(F, "scaled_dot_product_attention") | |
else AttnProcessor( | |
hidden_size=hidden_size, | |
cross_attention_dim=hidden_size, | |
) | |
) | |
return attn_procs |