sd-webui-controlnet/scripts/ipadapter/image_proj_models.py

import math

import torch
import torch.nn as nn


class MLPProjModel(torch.nn.Module):
    def __init__(self, cross_attention_dim=1024, clip_embeddings_dim=1024):
        super().__init__()

        self.proj = torch.nn.Sequential(
            torch.nn.Linear(clip_embeddings_dim, clip_embeddings_dim),
            torch.nn.GELU(),
            torch.nn.Linear(clip_embeddings_dim, cross_attention_dim),
            torch.nn.LayerNorm(cross_attention_dim),
        )

    def forward(self, image_embeds):
        clip_extra_context_tokens = self.proj(image_embeds)
        return clip_extra_context_tokens


class MLPProjModelFaceId(torch.nn.Module):
    """MLPProjModel used for FaceId.
    Source: https://github.com/tencent-ailab/IP-Adapter/blob/main/ip_adapter/ip_adapter_faceid.py
    """

    def __init__(self, cross_attention_dim=768, id_embeddings_dim=512, num_tokens=4):
        super().__init__()

        self.cross_attention_dim = cross_attention_dim
        self.num_tokens = num_tokens

        self.proj = torch.nn.Sequential(
            torch.nn.Linear(id_embeddings_dim, id_embeddings_dim * 2),
            torch.nn.GELU(),
            torch.nn.Linear(id_embeddings_dim * 2, cross_attention_dim * num_tokens),
        )
        self.norm = torch.nn.LayerNorm(cross_attention_dim)

    def forward(self, id_embeds):
        clip_extra_context_tokens = self.proj(id_embeds)
        clip_extra_context_tokens = clip_extra_context_tokens.reshape(
            -1, self.num_tokens, self.cross_attention_dim
        )
        clip_extra_context_tokens = self.norm(clip_extra_context_tokens)
        return clip_extra_context_tokens


class FacePerceiverResampler(torch.nn.Module):
    """Source: https://github.com/tencent-ailab/IP-Adapter/blob/main/ip_adapter/ip_adapter_faceid.py"""

    def __init__(
        self,
        *,
        dim=768,
        depth=4,
        dim_head=64,
        heads=16,
        embedding_dim=1280,
        output_dim=768,
        ff_mult=4,
    ):
        super().__init__()

        self.proj_in = torch.nn.Linear(embedding_dim, dim)
        self.proj_out = torch.nn.Linear(dim, output_dim)
        self.norm_out = torch.nn.LayerNorm(output_dim)
        self.layers = torch.nn.ModuleList([])
        for _ in range(depth):
            self.layers.append(
                torch.nn.ModuleList(
                    [
                        PerceiverAttention(dim=dim, dim_head=dim_head, heads=heads),
                        FeedForward(dim=dim, mult=ff_mult),
                    ]
                )
            )

    def forward(self, latents, x):
        x = self.proj_in(x)
        for attn, ff in self.layers:
            latents = attn(x, latents) + latents
            latents = ff(latents) + latents
        latents = self.proj_out(latents)
        return self.norm_out(latents)


class ProjModelFaceIdPlus(torch.nn.Module):
    """Source: https://github.com/tencent-ailab/IP-Adapter/blob/main/ip_adapter/ip_adapter_faceid.py"""

    def __init__(
        self,
        cross_attention_dim=768,
        id_embeddings_dim=512,
        clip_embeddings_dim=1280,
        num_tokens=4,
    ):
        super().__init__()

        self.cross_attention_dim = cross_attention_dim
        self.num_tokens = num_tokens

        self.proj = torch.nn.Sequential(
            torch.nn.Linear(id_embeddings_dim, id_embeddings_dim * 2),
            torch.nn.GELU(),
            torch.nn.Linear(id_embeddings_dim * 2, cross_attention_dim * num_tokens),
        )
        self.norm = torch.nn.LayerNorm(cross_attention_dim)

        self.perceiver_resampler = FacePerceiverResampler(
            dim=cross_attention_dim,
            depth=4,
            dim_head=64,
            heads=cross_attention_dim // 64,
            embedding_dim=clip_embeddings_dim,
            output_dim=cross_attention_dim,
            ff_mult=4,
        )

    def forward(self, id_embeds, clip_embeds, scale=1.0, shortcut=False):
        x = self.proj(id_embeds)
        x = x.reshape(-1, self.num_tokens, self.cross_attention_dim)
        x = self.norm(x)
        out = self.perceiver_resampler(x, clip_embeds)
        if shortcut:
            out = x + scale * out
        return out


class ImageProjModel(torch.nn.Module):
    """Projection Model"""

    def __init__(
        self,
        cross_attention_dim=1024,
        clip_embeddings_dim=1024,
        clip_extra_context_tokens=4,
    ):
        super().__init__()

        self.cross_attention_dim = cross_attention_dim
        self.clip_extra_context_tokens = clip_extra_context_tokens
        self.proj = torch.nn.Linear(
            clip_embeddings_dim, self.clip_extra_context_tokens * cross_attention_dim
        )
        self.norm = torch.nn.LayerNorm(cross_attention_dim)

    def forward(self, image_embeds):
        embeds = image_embeds
        clip_extra_context_tokens = self.proj(embeds).reshape(
            -1, self.clip_extra_context_tokens, self.cross_attention_dim
        )
        clip_extra_context_tokens = self.norm(clip_extra_context_tokens)
        return clip_extra_context_tokens


def FeedForward(dim, mult=4):
    inner_dim = int(dim * mult)
    return nn.Sequential(
        nn.LayerNorm(dim),
        nn.Linear(dim, inner_dim, bias=False),
        nn.GELU(),
        nn.Linear(inner_dim, dim, bias=False),
    )


def reshape_tensor(x, heads):
    bs, length, width = x.shape
    # (bs, length, width) --> (bs, length, n_heads, dim_per_head)
    x = x.view(bs, length, heads, -1)
    # (bs, length, n_heads, dim_per_head) --> (bs, n_heads, length, dim_per_head)
    x = x.transpose(1, 2)
    # (bs, n_heads, length, dim_per_head) --> (bs*n_heads, length, dim_per_head)
    x = x.reshape(bs, heads, length, -1)
    return x


class PerceiverAttention(nn.Module):
    def __init__(self, *, dim, dim_head=64, heads=8):
        super().__init__()
        self.scale = dim_head**-0.5
        self.dim_head = dim_head
        self.heads = heads
        inner_dim = dim_head * heads

        self.norm1 = nn.LayerNorm(dim)
        self.norm2 = nn.LayerNorm(dim)

        self.to_q = nn.Linear(dim, inner_dim, bias=False)
        self.to_kv = nn.Linear(dim, inner_dim * 2, bias=False)
        self.to_out = nn.Linear(inner_dim, dim, bias=False)

    def forward(self, x, latents):
        """
        Args:
            x (torch.Tensor): image features
                shape (b, n1, D)
            latent (torch.Tensor): latent features
                shape (b, n2, D)
        """
        x = self.norm1(x)
        latents = self.norm2(latents)

        b, l, _ = latents.shape  # noqa: E741

        q = self.to_q(latents)
        kv_input = torch.cat((x, latents), dim=-2)
        k, v = self.to_kv(kv_input).chunk(2, dim=-1)

        q = reshape_tensor(q, self.heads)
        k = reshape_tensor(k, self.heads)
        v = reshape_tensor(v, self.heads)

        # attention
        scale = 1 / math.sqrt(math.sqrt(self.dim_head))
        weight = (q * scale) @ (k * scale).transpose(
            -2, -1
        )  # More stable with f16 than dividing afterwards
        weight = torch.softmax(weight.float(), dim=-1).type(weight.dtype)
        out = weight @ v

        out = out.permute(0, 2, 1, 3).reshape(b, l, -1)

        return self.to_out(out)


class Resampler(nn.Module):
    def __init__(
        self,
        dim=1024,
        depth=8,
        dim_head=64,
        heads=16,
        num_queries=8,
        embedding_dim=768,
        output_dim=1024,
        ff_mult=4,
    ):
        super().__init__()

        self.latents = nn.Parameter(torch.randn(1, num_queries, dim) / dim**0.5)

        self.proj_in = nn.Linear(embedding_dim, dim)

        self.proj_out = nn.Linear(dim, output_dim)
        self.norm_out = nn.LayerNorm(output_dim)

        self.layers = nn.ModuleList([])
        for _ in range(depth):
            self.layers.append(
                nn.ModuleList(
                    [
                        PerceiverAttention(dim=dim, dim_head=dim_head, heads=heads),
                        FeedForward(dim=dim, mult=ff_mult),
                    ]
                )
            )

    def forward(self, x):

        latents = self.latents.repeat(x.size(0), 1, 1)

        x = self.proj_in(x)

        for attn, ff in self.layers:
            latents = attn(x, latents) + latents
            latents = ff(latents) + latents

        latents = self.proj_out(latents)
        return self.norm_out(latents)


class PuLIDEncoder(nn.Module):
    def __init__(self, width=1280, context_dim=2048, num_token=5):
        super().__init__()
        self.num_token = num_token
        self.context_dim = context_dim
        h1 = min((context_dim * num_token) // 4, 1024)
        h2 = min((context_dim * num_token) // 2, 1024)
        self.body = nn.Sequential(
            nn.Linear(width, h1),
            nn.LayerNorm(h1),
            nn.LeakyReLU(),
            nn.Linear(h1, h2),
            nn.LayerNorm(h2),
            nn.LeakyReLU(),
            nn.Linear(h2, context_dim * num_token),
        )

        for i in range(5):
            setattr(
                self,
                f"mapping_{i}",
                nn.Sequential(
                    nn.Linear(1024, 1024),
                    nn.LayerNorm(1024),
                    nn.LeakyReLU(),
                    nn.Linear(1024, 1024),
                    nn.LayerNorm(1024),
                    nn.LeakyReLU(),
                    nn.Linear(1024, context_dim),
                ),
            )

            setattr(
                self,
                f"mapping_patch_{i}",
                nn.Sequential(
                    nn.Linear(1024, 1024),
                    nn.LayerNorm(1024),
                    nn.LeakyReLU(),
                    nn.Linear(1024, 1024),
                    nn.LayerNorm(1024),
                    nn.LeakyReLU(),
                    nn.Linear(1024, context_dim),
                ),
            )

    def forward(self, x, y):
        # x shape [N, C]
        x = self.body(x)
        x = x.reshape(-1, self.num_token, self.context_dim)

        hidden_states = ()
        for i, emb in enumerate(y):
            hidden_state = getattr(self, f"mapping_{i}")(emb[:, :1]) + getattr(
                self, f"mapping_patch_{i}"
            )(emb[:, 1:]).mean(dim=1, keepdim=True)
            hidden_states += (hidden_state,)
        hidden_states = torch.cat(hidden_states, dim=1)

        return torch.cat([x, hidden_states], dim=1)