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# modified from https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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class AttnProcessor2_0(torch.nn.Module):
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r"""
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Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0).
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"""
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def __init__(
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self,
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hidden_size=None,
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cross_attention_dim=None,
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):
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super().__init__()
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if not hasattr(F, "scaled_dot_product_attention"):
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raise ImportError("AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.")
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def __call__(
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self,
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attn,
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hidden_states,
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encoder_hidden_states=None,
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attention_mask=None,
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temb=None,
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):
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residual = hidden_states
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if attn.spatial_norm is not None:
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hidden_states = attn.spatial_norm(hidden_states, temb)
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input_ndim = hidden_states.ndim
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if input_ndim == 4:
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batch_size, channel, height, width = hidden_states.shape
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hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
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batch_size, sequence_length, _ = (
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hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
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)
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if attention_mask is not None:
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attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
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# scaled_dot_product_attention expects attention_mask shape to be
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# (batch, heads, source_length, target_length)
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attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])
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if attn.group_norm is not None:
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hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
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query = attn.to_q(hidden_states)
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if encoder_hidden_states is None:
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encoder_hidden_states = hidden_states
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elif attn.norm_cross:
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encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
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key = attn.to_k(encoder_hidden_states)
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value = attn.to_v(encoder_hidden_states)
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inner_dim = key.shape[-1]
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head_dim = inner_dim // attn.heads
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query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
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key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
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value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
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# the output of sdp = (batch, num_heads, seq_len, head_dim)
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# TODO: add support for attn.scale when we move to Torch 2.1
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hidden_states = F.scaled_dot_product_attention(
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query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
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)
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hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
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hidden_states = hidden_states.to(query.dtype)
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# linear proj
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hidden_states = attn.to_out[0](hidden_states)
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# dropout
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hidden_states = attn.to_out[1](hidden_states)
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if input_ndim == 4:
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hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
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if attn.residual_connection:
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hidden_states = hidden_states + residual
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hidden_states = hidden_states / attn.rescale_output_factor
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return hidden_states
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class IPAttnProcessor2_0(torch.nn.Module):
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r"""
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Attention processor for IP-Adapater for PyTorch 2.0.
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Args:
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hidden_size (`int`):
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The hidden size of the attention layer.
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cross_attention_dim (`int`):
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The number of channels in the `encoder_hidden_states`.
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scale (`float`, defaults to 1.0):
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the weight scale of image prompt.
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num_tokens (`int`, defaults to 4 when do ip_adapter_plus it should be 16):
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The context length of the image features.
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"""
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def __init__(self, hidden_size, cross_attention_dim=None, scale=1.0, num_tokens=4):
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super().__init__()
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if not hasattr(F, "scaled_dot_product_attention"):
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raise ImportError("AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.")
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self.hidden_size = hidden_size
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self.cross_attention_dim = cross_attention_dim
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self.scale = scale
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self.num_tokens = num_tokens
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self.to_k_ip = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False)
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self.to_v_ip = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False)
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def __call__(
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self,
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attn,
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hidden_states,
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encoder_hidden_states=None,
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attention_mask=None,
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temb=None,
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):
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residual = hidden_states
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if attn.spatial_norm is not None:
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hidden_states = attn.spatial_norm(hidden_states, temb)
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input_ndim = hidden_states.ndim
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if input_ndim == 4:
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batch_size, channel, height, width = hidden_states.shape
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hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
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batch_size, sequence_length, _ = (
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hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
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)
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if attention_mask is not None:
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attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
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# scaled_dot_product_attention expects attention_mask shape to be
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# (batch, heads, source_length, target_length)
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attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])
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if attn.group_norm is not None:
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hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
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query = attn.to_q(hidden_states)
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if encoder_hidden_states is None:
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encoder_hidden_states = hidden_states
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else:
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# get encoder_hidden_states, ip_hidden_states
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end_pos = encoder_hidden_states.shape[1] - self.num_tokens
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encoder_hidden_states, ip_hidden_states = encoder_hidden_states[:, :end_pos, :], encoder_hidden_states[:, end_pos:, :]
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if attn.norm_cross:
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encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
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key = attn.to_k(encoder_hidden_states)
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value = attn.to_v(encoder_hidden_states)
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inner_dim = key.shape[-1]
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head_dim = inner_dim // attn.heads
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query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
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key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
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value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
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# the output of sdp = (batch, num_heads, seq_len, head_dim)
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# TODO: add support for attn.scale when we move to Torch 2.1
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hidden_states = F.scaled_dot_product_attention(
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query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
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)
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hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
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hidden_states = hidden_states.to(query.dtype)
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# for ip-adapter
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ip_key = self.to_k_ip(ip_hidden_states)
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ip_value = self.to_v_ip(ip_hidden_states)
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ip_key = ip_key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
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ip_value = ip_value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
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# the output of sdp = (batch, num_heads, seq_len, head_dim)
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# TODO: add support for attn.scale when we move to Torch 2.1
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ip_hidden_states = F.scaled_dot_product_attention(
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query, ip_key, ip_value, attn_mask=None, dropout_p=0.0, is_causal=False
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)
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ip_hidden_states = ip_hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
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ip_hidden_states = ip_hidden_states.to(query.dtype)
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hidden_states = hidden_states + self.scale * ip_hidden_states
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# linear proj
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hidden_states = attn.to_out[0](hidden_states)
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# dropout
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hidden_states = attn.to_out[1](hidden_states)
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if input_ndim == 4:
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hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
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if attn.residual_connection:
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hidden_states = hidden_states + residual
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hidden_states = hidden_states / attn.rescale_output_factor
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return hidden_states
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@@ -0,0 +1,137 @@
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import torch
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import torch.nn as nn
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import math
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def reshape_tensor(x, heads):
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bs, length, width = x.shape
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#(bs, length, width) --> (bs, length, n_heads, dim_per_head)
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x = x.view(bs, length, heads, -1)
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# (bs, length, n_heads, dim_per_head) --> (bs, n_heads, length, dim_per_head)
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x = x.transpose(1, 2)
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# (bs, n_heads, length, dim_per_head) --> (bs*n_heads, length, dim_per_head)
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x = x.reshape(bs, heads, length, -1)
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return x
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def FeedForward(dim, mult=4):
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inner_dim = int(dim * mult)
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return nn.Sequential(
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nn.LayerNorm(dim),
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nn.Linear(dim, inner_dim, bias=False),
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nn.GELU(),
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nn.Linear(inner_dim, dim, bias=False),
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)
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class PerceiverAttention(nn.Module):
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def __init__(self, *, dim, dim_head=64, heads=8):
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super().__init__()
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self.scale = dim_head**-0.5
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self.dim_head = dim_head
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self.heads = heads
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inner_dim = dim_head * heads
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self.norm1 = nn.LayerNorm(dim)
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self.norm2 = nn.LayerNorm(dim)
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self.to_q = nn.Linear(dim, inner_dim, bias=False)
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self.to_kv = nn.Linear(dim, inner_dim * 2, bias=False)
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self.to_out = nn.Linear(inner_dim, dim, bias=False)
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def forward(self, x, latents):
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"""
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Args:
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x (torch.Tensor): image features
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shape (b, n1, D)
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latent (torch.Tensor): latent features
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shape (b, n2, D)
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"""
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x = self.norm1(x)
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latents = self.norm2(latents)
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b, l, _ = latents.shape
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q = self.to_q(latents)
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kv_input = torch.cat((x, latents), dim=-2)
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k, v = self.to_kv(kv_input).chunk(2, dim=-1)
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q = reshape_tensor(q, self.heads)
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k = reshape_tensor(k, self.heads)
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v = reshape_tensor(v, self.heads)
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# attention
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scale = 1 / math.sqrt(math.sqrt(self.dim_head))
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weight = (q * scale) @ (k * scale).transpose(-2, -1) # More stable with f16 than dividing afterwards
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weight = torch.softmax(weight.float(), dim=-1).type(weight.dtype)
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out = weight @ v
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out = out.permute(0, 2, 1, 3).reshape(b, l, -1)
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return self.to_out(out)
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class FacePerceiverResampler(torch.nn.Module):
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def __init__(
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self,
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*,
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dim=768,
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depth=4,
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dim_head=64,
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heads=16,
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embedding_dim=1280,
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output_dim=768,
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ff_mult=4,
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):
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super().__init__()
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self.proj_in = torch.nn.Linear(embedding_dim, dim)
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self.proj_out = torch.nn.Linear(dim, output_dim)
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self.norm_out = torch.nn.LayerNorm(output_dim)
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self.layers = torch.nn.ModuleList([])
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for _ in range(depth):
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self.layers.append(
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torch.nn.ModuleList(
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[
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PerceiverAttention(dim=dim, dim_head=dim_head, heads=heads),
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FeedForward(dim=dim, mult=ff_mult),
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]
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)
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)
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def forward(self, latents, x):
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x = self.proj_in(x)
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for attn, ff in self.layers:
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latents = attn(x, latents) + latents
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latents = ff(latents) + latents
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latents = self.proj_out(latents)
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return self.norm_out(latents)
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class ProjPlusModel(torch.nn.Module):
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def __init__(self, cross_attention_dim=768, id_embeddings_dim=512, clip_embeddings_dim=1280, num_tokens=4):
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super().__init__()
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self.cross_attention_dim = cross_attention_dim
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self.num_tokens = num_tokens
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self.proj = torch.nn.Sequential(
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torch.nn.Linear(id_embeddings_dim, id_embeddings_dim*2),
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torch.nn.GELU(),
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torch.nn.Linear(id_embeddings_dim*2, cross_attention_dim*num_tokens),
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)
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self.norm = torch.nn.LayerNorm(cross_attention_dim)
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self.perceiver_resampler = FacePerceiverResampler(
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dim=cross_attention_dim,
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depth=4,
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dim_head=64,
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heads=cross_attention_dim // 64,
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embedding_dim=clip_embeddings_dim,
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output_dim=cross_attention_dim,
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ff_mult=4,
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)
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def forward(self, id_embeds, clip_embeds, shortcut = True, scale = 1.0):
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x = self.proj(id_embeds)
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x = x.reshape(-1, self.num_tokens, self.cross_attention_dim)
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x = self.norm(x)
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out = self.perceiver_resampler(x, clip_embeds)
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if shortcut:
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out = x + scale * out
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return out
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