InvokeAI/invokeai/backend/ip_adapter/attention_processor.py

# copied from https://github.com/tencent-ailab/IP-Adapter (Apache License 2.0)
#   and modified as needed

# tencent-ailab comment:
# 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
from diffusers.models.attention_processor import AttnProcessor as DiffusersAttnProcessor
from diffusers.models.attention_processor import (
    AttnProcessor2_0 as DiffusersAttnProcessor2_0,
)


# Create versions of AttnProcessor and AttnProcessor2_0 that are sub-classes of nn.Module. This is required for
# IP-Adapter state_dict loading.
class AttnProcessor(DiffusersAttnProcessor, nn.Module):
    def __init__(self):
        DiffusersAttnProcessor.__init__(self)
        nn.Module.__init__(self)

    def __call__(
        self,
        attn,
        hidden_states,
        encoder_hidden_states=None,
        attention_mask=None,
        temb=None,
        ip_adapter_image_prompt_embeds=None,
    ):
        """Re-definition of DiffusersAttnProcessor.__call__(...) that accepts and ignores the
        ip_adapter_image_prompt_embeds parameter.
        """
        return DiffusersAttnProcessor.__call__(self, attn, hidden_states, encoder_hidden_states, attention_mask, temb)


class AttnProcessor2_0(DiffusersAttnProcessor2_0, nn.Module):
    def __init__(self):
        DiffusersAttnProcessor2_0.__init__(self)
        nn.Module.__init__(self)

    def __call__(
        self,
        attn,
        hidden_states,
        encoder_hidden_states=None,
        attention_mask=None,
        temb=None,
        ip_adapter_image_prompt_embeds=None,
    ):
        """Re-definition of DiffusersAttnProcessor2_0.__call__(...) that accepts and ignores the
        ip_adapter_image_prompt_embeds parameter.
        """
        return DiffusersAttnProcessor2_0.__call__(
            self, attn, hidden_states, encoder_hidden_states, attention_mask, temb
        )


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.
    """

    def __init__(self, hidden_size, cross_attention_dim=None, scale=1.0):
        super().__init__()

        self.hidden_size = hidden_size
        self.cross_attention_dim = cross_attention_dim
        self.scale = scale

        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,
        ip_adapter_image_prompt_embeds=None,
    ):
        if encoder_hidden_states is not None:
            # If encoder_hidden_states is not None, then we are doing cross-attention, not self-attention. In this case,
            # we will apply IP-Adapter conditioning. We validate the inputs for IP-Adapter conditioning here.
            assert ip_adapter_image_prompt_embeds is not None
            # The batch dimensions should match.
            assert ip_adapter_image_prompt_embeds.shape[0] == encoder_hidden_states.shape[0]
            # The channel dimensions should match.
            assert ip_adapter_image_prompt_embeds.shape[2] == encoder_hidden_states.shape[2]
            ip_hidden_states = ip_adapter_image_prompt_embeds

        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)

        if ip_hidden_states is not None:
            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 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.
    """

    def __init__(self, hidden_size, cross_attention_dim=None, scale=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.hidden_size = hidden_size
        self.cross_attention_dim = cross_attention_dim
        self.scale = scale

        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,
        ip_adapter_image_prompt_embeds=None,
    ):
        if encoder_hidden_states is not None:
            # If encoder_hidden_states is not None, then we are doing cross-attention, not self-attention. In this case,
            # we will apply IP-Adapter conditioning. We validate the inputs for IP-Adapter conditioning here.
            assert ip_adapter_image_prompt_embeds is not None
            # The batch dimensions should match.
            assert ip_adapter_image_prompt_embeds.shape[0] == encoder_hidden_states.shape[0]
            # The channel dimensions should match.
            assert ip_adapter_image_prompt_embeds.shape[2] == encoder_hidden_states.shape[2]
            ip_hidden_states = ip_adapter_image_prompt_embeds

        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)
        hidden_states = hidden_states.to(query.dtype)

        if ip_hidden_states:
            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