all attentions collected

src/diffusers/models/attention2d.py

+# unet_grad_tts.py
+class LinearAttention(torch.nn.Module):
+    def __init__(self, dim, heads=4, dim_head=32):
+        super(LinearAttention, self).__init__()
+        self.heads = heads
+        self.dim_head = dim_head
+        hidden_dim = dim_head * heads
+        self.to_qkv = torch.nn.Conv2d(dim, hidden_dim * 3, 1, bias=False)
+        self.to_out = torch.nn.Conv2d(hidden_dim, dim, 1)
+
+    def forward(self, x):
+        b, c, h, w = x.shape
+        qkv = self.to_qkv(x)
+        #        q, k, v = rearrange(qkv, "b (qkv heads c) h w -> qkv b heads c (h w)", heads=self.heads, qkv=3)
+        q, k, v = (
+            qkv.reshape(b, 3, self.heads, self.dim_head, h, w)
+            .permute(1, 0, 2, 3, 4, 5)
+            .reshape(3, b, self.heads, self.dim_head, -1)
+        )
+        k = k.softmax(dim=-1)
+        context = torch.einsum("bhdn,bhen->bhde", k, v)
+        out = torch.einsum("bhde,bhdn->bhen", context, q)
+        #        out = rearrange(out, "b heads c (h w) -> b (heads c) h w", heads=self.heads, h=h, w=w)
+        out = out.reshape(b, self.heads, self.dim_head, h, w).reshape(b, self.heads * self.dim_head, h, w)
+        return self.to_out(out)
+
+# unet.py
+class AttnBlock(nn.Module):
+    def __init__(self, in_channels):
+        super().__init__()
+        self.in_channels = in_channels
+
+        self.norm = Normalize(in_channels)
+        self.q = torch.nn.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0)
+        self.k = torch.nn.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0)
+        self.v = torch.nn.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0)
+        self.proj_out = torch.nn.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0)
+
+    def forward(self, x):
+        h_ = x
+        h_ = self.norm(h_)
+        q = self.q(h_)
+        k = self.k(h_)
+        v = self.v(h_)
+
+        # compute attention
+        b, c, h, w = q.shape
+        q = q.reshape(b, c, h * w)
+        q = q.permute(0, 2, 1)  # b,hw,c
+        k = k.reshape(b, c, h * w)  # b,c,hw
+        w_ = torch.bmm(q, k)  # b,hw,hw    w[b,i,j]=sum_c q[b,i,c]k[b,c,j]
+        w_ = w_ * (int(c) ** (-0.5))
+        w_ = torch.nn.functional.softmax(w_, dim=2)
+
+        # attend to values
+        v = v.reshape(b, c, h * w)
+        w_ = w_.permute(0, 2, 1)  # b,hw,hw (first hw of k, second of q)
+        h_ = torch.bmm(v, w_)  # b, c,hw (hw of q) h_[b,c,j] = sum_i v[b,c,i] w_[b,i,j]
+        h_ = h_.reshape(b, c, h, w)
+
+        h_ = self.proj_out(h_)
+
+        return x + h_
+
+# unet_glide.py
+class AttentionBlock(nn.Module):
+    """
+    An attention block that allows spatial positions to attend to each other.
+
+    Originally ported from here, but adapted to the N-d case.
+    https://github.com/hojonathanho/diffusion/blob/1e0dceb3b3495bbe19116a5e1b3596cd0706c543/diffusion_tf/models/unet.py#L66.
+    """
+
+    def __init__(
+        self,
+        channels,
+        num_heads=1,
+        num_head_channels=-1,
+        use_checkpoint=False,
+        encoder_channels=None,
+    ):
+        super().__init__()
+        self.channels = channels
+        if num_head_channels == -1:
+            self.num_heads = num_heads
+        else:
+            assert (
+                channels % num_head_channels == 0
+            ), f"q,k,v channels {channels} is not divisible by num_head_channels {num_head_channels}"
+            self.num_heads = channels // num_head_channels
+        self.use_checkpoint = use_checkpoint
+        self.norm = normalization(channels, swish=0.0)
+        self.qkv = conv_nd(1, channels, channels * 3, 1)
+        self.attention = QKVAttention(self.num_heads)
+
+        if encoder_channels is not None:
+            self.encoder_kv = conv_nd(1, encoder_channels, channels * 2, 1)
+        self.proj_out = zero_module(conv_nd(1, channels, channels, 1))
+
+    def forward(self, x, encoder_out=None):
+        b, c, *spatial = x.shape
+        qkv = self.qkv(self.norm(x).view(b, c, -1))
+        if encoder_out is not None:
+            encoder_out = self.encoder_kv(encoder_out)
+            h = self.attention(qkv, encoder_out)
+        else:
+            h = self.attention(qkv)
+        h = self.proj_out(h)
+        return x + h.reshape(b, c, *spatial)
+
+class QKVAttention(nn.Module):
+    """
+    A module which performs QKV attention. Matches legacy QKVAttention + input/ouput heads shaping
+    """
+
+    def __init__(self, n_heads):
+        super().__init__()
+        self.n_heads = n_heads
+
+    def forward(self, qkv, encoder_kv=None):
+        """
+        Apply QKV attention.
+
+        :param qkv: an [N x (H * 3 * C) x T] tensor of Qs, Ks, and Vs. :return: an [N x (H * C) x T] tensor after
+        attention.
+        """
+        bs, width, length = qkv.shape
+        assert width % (3 * self.n_heads) == 0
+        ch = width // (3 * self.n_heads)
+        q, k, v = qkv.reshape(bs * self.n_heads, ch * 3, length).split(ch, dim=1)
+        if encoder_kv is not None:
+            assert encoder_kv.shape[1] == self.n_heads * ch * 2
+            ek, ev = encoder_kv.reshape(bs * self.n_heads, ch * 2, -1).split(ch, dim=1)
+            k = torch.cat([ek, k], dim=-1)
+            v = torch.cat([ev, v], dim=-1)
+        scale = 1 / math.sqrt(math.sqrt(ch))
+        weight = torch.einsum("bct,bcs->bts", q * scale, k * scale)  # More stable with f16 than dividing afterwards
+        weight = torch.softmax(weight.float(), dim=-1).type(weight.dtype)
+        a = torch.einsum("bts,bcs->bct", weight, v)
+        return a.reshape(bs, -1, length)
+
+
+# unet_ldm.py
+class AttentionBlock(nn.Module):
+    """
+    An attention block that allows spatial positions to attend to each other. Originally ported from here, but adapted
+    to the N-d case.
+    https://github.com/hojonathanho/diffusion/blob/1e0dceb3b3495bbe19116a5e1b3596cd0706c543/diffusion_tf/models/unet.py#L66.
+    """
+
+    def __init__(
+        self,
+        channels,
+        num_heads=1,
+        num_head_channels=-1,
+        use_checkpoint=False,
+        use_new_attention_order=False,
+    ):
+        super().__init__()
+        self.channels = channels
+        if num_head_channels == -1:
+            self.num_heads = num_heads
+        else:
+            assert (
+                channels % num_head_channels == 0
+            ), f"q,k,v channels {channels} is not divisible by num_head_channels {num_head_channels}"
+            self.num_heads = channels // num_head_channels
+        self.use_checkpoint = use_checkpoint
+        self.norm = normalization(channels)
+        self.qkv = conv_nd(1, channels, channels * 3, 1)
+        # split heads before split qkv
+        self.attention = QKVAttentionLegacy(self.num_heads)
+
+        self.proj_out = zero_module(conv_nd(1, channels, channels, 1))
+
+    def forward(self, x):
+        b, c, *spatial = x.shape
+        x = x.reshape(b, c, -1)
+        qkv = self.qkv(self.norm(x))
+        h = self.attention(qkv)
+        h = self.proj_out(h)
+        return (x + h).reshape(b, c, *spatial)
+
+class QKVAttention(nn.Module):
+    """
+    A module which performs QKV attention and splits in a different order.
+    """
+
+    def __init__(self, n_heads):
+        super().__init__()
+        self.n_heads = n_heads
+
+    def forward(self, qkv):
+        """
+        Apply QKV attention. :param qkv: an [N x (3 * H * C) x T] tensor of Qs, Ks, and Vs. :return: an [N x (H * C) x
+        T] tensor after attention.
+        """
+        bs, width, length = qkv.shape
+        assert width % (3 * self.n_heads) == 0
+        ch = width // (3 * self.n_heads)
+        q, k, v = qkv.chunk(3, dim=1)
+        scale = 1 / math.sqrt(math.sqrt(ch))
+        weight = torch.einsum(
+            "bct,bcs->bts",
+            (q * scale).view(bs * self.n_heads, ch, length),
+            (k * scale).view(bs * self.n_heads, ch, length),
+        )  # More stable with f16 than dividing afterwards
+        weight = torch.softmax(weight.float(), dim=-1).type(weight.dtype)
+        a = torch.einsum("bts,bcs->bct", weight, v.reshape(bs * self.n_heads, ch, length))
+        return a.reshape(bs, -1, length)
+
+    @staticmethod
+    def count_flops(model, _x, y):
+        return count_flops_attn(model, _x, y)
+
+# unet_score_estimation.py
+class AttnBlockpp(nn.Module):
+    """Channel-wise self-attention block. Modified from DDPM."""
+
+    def __init__(self, channels, skip_rescale=False, init_scale=0.0):
+        super().__init__()
+        self.GroupNorm_0 = nn.GroupNorm(num_groups=min(channels // 4, 32), num_channels=channels, eps=1e-6)
+        self.NIN_0 = NIN(channels, channels)
+        self.NIN_1 = NIN(channels, channels)
+        self.NIN_2 = NIN(channels, channels)
+        self.NIN_3 = NIN(channels, channels, init_scale=init_scale)
+        self.skip_rescale = skip_rescale
+
+    def forward(self, x):
+        B, C, H, W = x.shape
+        h = self.GroupNorm_0(x)
+        q = self.NIN_0(h)
+        k = self.NIN_1(h)
+        v = self.NIN_2(h)
+
+        w = torch.einsum("bchw,bcij->bhwij", q, k) * (int(C) ** (-0.5))
+        w = torch.reshape(w, (B, H, W, H * W))
+        w = F.softmax(w, dim=-1)
+        w = torch.reshape(w, (B, H, W, H, W))
+        h = torch.einsum("bhwij,bcij->bchw", w, v)
+        h = self.NIN_3(h)
+        if not self.skip_rescale:
+            return x + h
+        else:
+            return (x + h) / np.sqrt(2.0)