Initial support for the stable audio open model.

comfy/ldm/audio/autoencoder.py

@@ -0,0 +1,276 @@
+# code adapted from: https://github.com/Stability-AI/stable-audio-tools
+
+import torch
+from torch import nn
+from typing import Literal, Dict, Any
+import math
+import comfy.ops
+ops = comfy.ops.disable_weight_init
+
+def vae_sample(mean, scale):
+        stdev = nn.functional.softplus(scale) + 1e-4
+        var = stdev * stdev
+        logvar = torch.log(var)
+        latents = torch.randn_like(mean) * stdev + mean
+
+        kl = (mean * mean + var - logvar - 1).sum(1).mean()
+
+        return latents, kl
+
+class VAEBottleneck(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.is_discrete = False
+
+    def encode(self, x, return_info=False, **kwargs):
+        info = {}
+
+        mean, scale = x.chunk(2, dim=1)
+
+        x, kl = vae_sample(mean, scale)
+
+        info["kl"] = kl
+
+        if return_info:
+            return x, info
+        else:
+            return x
+
+    def decode(self, x):
+        return x
+
+
+def snake_beta(x, alpha, beta):
+    return x + (1.0 / (beta + 0.000000001)) * pow(torch.sin(x * alpha), 2)
+
+# Adapted from https://github.com/NVIDIA/BigVGAN/blob/main/activations.py under MIT license
+class SnakeBeta(nn.Module):
+
+    def __init__(self, in_features, alpha=1.0, alpha_trainable=True, alpha_logscale=True):
+        super(SnakeBeta, self).__init__()
+        self.in_features = in_features
+
+        # initialize alpha
+        self.alpha_logscale = alpha_logscale
+        if self.alpha_logscale: # log scale alphas initialized to zeros
+            self.alpha = nn.Parameter(torch.zeros(in_features) * alpha)
+            self.beta = nn.Parameter(torch.zeros(in_features) * alpha)
+        else: # linear scale alphas initialized to ones
+            self.alpha = nn.Parameter(torch.ones(in_features) * alpha)
+            self.beta = nn.Parameter(torch.ones(in_features) * alpha)
+
+        # self.alpha.requires_grad = alpha_trainable
+        # self.beta.requires_grad = alpha_trainable
+
+        self.no_div_by_zero = 0.000000001
+
+    def forward(self, x):
+        alpha = self.alpha.unsqueeze(0).unsqueeze(-1).to(x.device) # line up with x to [B, C, T]
+        beta = self.beta.unsqueeze(0).unsqueeze(-1).to(x.device)
+        if self.alpha_logscale:
+            alpha = torch.exp(alpha)
+            beta = torch.exp(beta)
+        x = snake_beta(x, alpha, beta)
+
+        return x
+
+def WNConv1d(*args, **kwargs):
+    return torch.nn.utils.weight_norm(ops.Conv1d(*args, **kwargs))
+
+def WNConvTranspose1d(*args, **kwargs):
+    return torch.nn.utils.weight_norm(ops.ConvTranspose1d(*args, **kwargs))
+
+def get_activation(activation: Literal["elu", "snake", "none"], antialias=False, channels=None) -> nn.Module:
+    if activation == "elu":
+        act = torch.nn.ELU()
+    elif activation == "snake":
+        act = SnakeBeta(channels)
+    elif activation == "none":
+        act = torch.nn.Identity()
+    else:
+        raise ValueError(f"Unknown activation {activation}")
+
+    if antialias:
+        act = Activation1d(act)
+
+    return act
+
+
+class ResidualUnit(nn.Module):
+    def __init__(self, in_channels, out_channels, dilation, use_snake=False, antialias_activation=False):
+        super().__init__()
+
+        self.dilation = dilation
+
+        padding = (dilation * (7-1)) // 2
+
+        self.layers = nn.Sequential(
+            get_activation("snake" if use_snake else "elu", antialias=antialias_activation, channels=out_channels),
+            WNConv1d(in_channels=in_channels, out_channels=out_channels,
+                      kernel_size=7, dilation=dilation, padding=padding),
+            get_activation("snake" if use_snake else "elu", antialias=antialias_activation, channels=out_channels),
+            WNConv1d(in_channels=out_channels, out_channels=out_channels,
+                      kernel_size=1)
+        )
+
+    def forward(self, x):
+        res = x
+
+        #x = checkpoint(self.layers, x)
+        x = self.layers(x)
+
+        return x + res
+
+class EncoderBlock(nn.Module):
+    def __init__(self, in_channels, out_channels, stride, use_snake=False, antialias_activation=False):
+        super().__init__()
+
+        self.layers = nn.Sequential(
+            ResidualUnit(in_channels=in_channels,
+                         out_channels=in_channels, dilation=1, use_snake=use_snake),
+            ResidualUnit(in_channels=in_channels,
+                         out_channels=in_channels, dilation=3, use_snake=use_snake),
+            ResidualUnit(in_channels=in_channels,
+                         out_channels=in_channels, dilation=9, use_snake=use_snake),
+            get_activation("snake" if use_snake else "elu", antialias=antialias_activation, channels=in_channels),
+            WNConv1d(in_channels=in_channels, out_channels=out_channels,
+                      kernel_size=2*stride, stride=stride, padding=math.ceil(stride/2)),
+        )
+
+    def forward(self, x):
+        return self.layers(x)
+
+class DecoderBlock(nn.Module):
+    def __init__(self, in_channels, out_channels, stride, use_snake=False, antialias_activation=False, use_nearest_upsample=False):
+        super().__init__()
+
+        if use_nearest_upsample:
+            upsample_layer = nn.Sequential(
+                nn.Upsample(scale_factor=stride, mode="nearest"),
+                WNConv1d(in_channels=in_channels,
+                        out_channels=out_channels,
+                        kernel_size=2*stride,
+                        stride=1,
+                        bias=False,
+                        padding='same')
+            )
+        else:
+            upsample_layer = WNConvTranspose1d(in_channels=in_channels,
+                               out_channels=out_channels,
+                               kernel_size=2*stride, stride=stride, padding=math.ceil(stride/2))
+
+        self.layers = nn.Sequential(
+            get_activation("snake" if use_snake else "elu", antialias=antialias_activation, channels=in_channels),
+            upsample_layer,
+            ResidualUnit(in_channels=out_channels, out_channels=out_channels,
+                         dilation=1, use_snake=use_snake),
+            ResidualUnit(in_channels=out_channels, out_channels=out_channels,
+                         dilation=3, use_snake=use_snake),
+            ResidualUnit(in_channels=out_channels, out_channels=out_channels,
+                         dilation=9, use_snake=use_snake),
+        )
+
+    def forward(self, x):
+        return self.layers(x)
+
+class OobleckEncoder(nn.Module):
+    def __init__(self,
+                 in_channels=2,
+                 channels=128,
+                 latent_dim=32,
+                 c_mults = [1, 2, 4, 8],
+                 strides = [2, 4, 8, 8],
+                 use_snake=False,
+                 antialias_activation=False
+        ):
+        super().__init__()
+
+        c_mults = [1] + c_mults
+
+        self.depth = len(c_mults)
+
+        layers = [
+            WNConv1d(in_channels=in_channels, out_channels=c_mults[0] * channels, kernel_size=7, padding=3)
+        ]
+
+        for i in range(self.depth-1):
+            layers += [EncoderBlock(in_channels=c_mults[i]*channels, out_channels=c_mults[i+1]*channels, stride=strides[i], use_snake=use_snake)]
+
+        layers += [
+            get_activation("snake" if use_snake else "elu", antialias=antialias_activation, channels=c_mults[-1] * channels),
+            WNConv1d(in_channels=c_mults[-1]*channels, out_channels=latent_dim, kernel_size=3, padding=1)
+        ]
+
+        self.layers = nn.Sequential(*layers)
+
+    def forward(self, x):
+        return self.layers(x)
+
+
+class OobleckDecoder(nn.Module):
+    def __init__(self,
+                 out_channels=2,
+                 channels=128,
+                 latent_dim=32,
+                 c_mults = [1, 2, 4, 8],
+                 strides = [2, 4, 8, 8],
+                 use_snake=False,
+                 antialias_activation=False,
+                 use_nearest_upsample=False,
+                 final_tanh=True):
+        super().__init__()
+
+        c_mults = [1] + c_mults
+
+        self.depth = len(c_mults)
+
+        layers = [
+            WNConv1d(in_channels=latent_dim, out_channels=c_mults[-1]*channels, kernel_size=7, padding=3),
+        ]
+
+        for i in range(self.depth-1, 0, -1):
+            layers += [DecoderBlock(
+                in_channels=c_mults[i]*channels,
+                out_channels=c_mults[i-1]*channels,
+                stride=strides[i-1],
+                use_snake=use_snake,
+                antialias_activation=antialias_activation,
+                use_nearest_upsample=use_nearest_upsample
+                )
+            ]
+
+        layers += [
+            get_activation("snake" if use_snake else "elu", antialias=antialias_activation, channels=c_mults[0] * channels),
+            WNConv1d(in_channels=c_mults[0] * channels, out_channels=out_channels, kernel_size=7, padding=3, bias=False),
+            nn.Tanh() if final_tanh else nn.Identity()
+        ]
+
+        self.layers = nn.Sequential(*layers)
+
+    def forward(self, x):
+        return self.layers(x)
+
+
+class AudioOobleckVAE(nn.Module):
+    def __init__(self,
+                 in_channels=2,
+                 channels=128,
+                 latent_dim=64,
+                 c_mults = [1, 2, 4, 8, 16],
+                 strides = [2, 4, 4, 8, 8],
+                 use_snake=True,
+                 antialias_activation=False,
+                 use_nearest_upsample=False,
+                 final_tanh=False):
+        super().__init__()
+        self.encoder = OobleckEncoder(in_channels, channels, latent_dim * 2, c_mults, strides, use_snake, antialias_activation)
+        self.decoder = OobleckDecoder(in_channels, channels, latent_dim, c_mults, strides, use_snake, antialias_activation,
+                                      use_nearest_upsample=use_nearest_upsample, final_tanh=final_tanh)
+        self.bottleneck = VAEBottleneck()
+
+    def encode(self, x):
+        return self.bottleneck.encode(self.encoder(x))
+
+    def decode(self, x):
+        return self.decoder(self.bottleneck.decode(x))
+

comfy/ldm/audio/dit.py

@@ -0,0 +1,888 @@
+# code adapted from: https://github.com/Stability-AI/stable-audio-tools
+
+from comfy.ldm.modules.attention import optimized_attention
+import typing as tp
+
+import torch
+
+from einops import rearrange
+from torch import nn
+from torch.nn import functional as F
+import math
+
+class FourierFeatures(nn.Module):
+    def __init__(self, in_features, out_features, std=1., dtype=None, device=None):
+        super().__init__()
+        assert out_features % 2 == 0
+        self.weight = nn.Parameter(torch.empty(
+            [out_features // 2, in_features], dtype=dtype, device=device))
+
+    def forward(self, input):
+        f = 2 * math.pi * input @ self.weight.T.to(dtype=input.dtype, device=input.device)
+        return torch.cat([f.cos(), f.sin()], dim=-1)
+
+# norms
+class LayerNorm(nn.Module):
+    def __init__(self, dim, bias=False, fix_scale=False, dtype=None, device=None):
+        """
+        bias-less layernorm has been shown to be more stable. most newer models have moved towards rmsnorm, also bias-less
+        """
+        super().__init__()
+
+        self.gamma = nn.Parameter(torch.empty(dim, dtype=dtype, device=device))
+
+        if bias:
+            self.beta = nn.Parameter(torch.empty(dim, dtype=dtype, device=device))
+        else:
+            self.beta = None
+
+    def forward(self, x):
+        beta = self.beta
+        if self.beta is not None:
+            beta = beta.to(dtype=x.dtype, device=x.device)
+        return F.layer_norm(x, x.shape[-1:], weight=self.gamma.to(dtype=x.dtype, device=x.device), bias=beta)
+
+class GLU(nn.Module):
+    def __init__(
+        self,
+        dim_in,
+        dim_out,
+        activation,
+        use_conv = False,
+        conv_kernel_size = 3,
+        dtype=None,
+        device=None,
+        operations=None,
+    ):
+        super().__init__()
+        self.act = activation
+        self.proj = operations.Linear(dim_in, dim_out * 2, dtype=dtype, device=device) if not use_conv else operations.Conv1d(dim_in, dim_out * 2, conv_kernel_size, padding = (conv_kernel_size // 2), dtype=dtype, device=device)
+        self.use_conv = use_conv
+
+    def forward(self, x):
+        if self.use_conv:
+            x = rearrange(x, 'b n d -> b d n')
+            x = self.proj(x)
+            x = rearrange(x, 'b d n -> b n d')
+        else:
+            x = self.proj(x)
+
+        x, gate = x.chunk(2, dim = -1)
+        return x * self.act(gate)
+
+class AbsolutePositionalEmbedding(nn.Module):
+    def __init__(self, dim, max_seq_len):
+        super().__init__()
+        self.scale = dim ** -0.5
+        self.max_seq_len = max_seq_len
+        self.emb = nn.Embedding(max_seq_len, dim)
+
+    def forward(self, x, pos = None, seq_start_pos = None):
+        seq_len, device = x.shape[1], x.device
+        assert seq_len <= self.max_seq_len, f'you are passing in a sequence length of {seq_len} but your absolute positional embedding has a max sequence length of {self.max_seq_len}'
+
+        if pos is None:
+            pos = torch.arange(seq_len, device = device)
+
+        if seq_start_pos is not None:
+            pos = (pos - seq_start_pos[..., None]).clamp(min = 0)
+
+        pos_emb = self.emb(pos)
+        pos_emb = pos_emb * self.scale
+        return pos_emb
+
+class ScaledSinusoidalEmbedding(nn.Module):
+    def __init__(self, dim, theta = 10000):
+        super().__init__()
+        assert (dim % 2) == 0, 'dimension must be divisible by 2'
+        self.scale = nn.Parameter(torch.ones(1) * dim ** -0.5)
+
+        half_dim = dim // 2
+        freq_seq = torch.arange(half_dim).float() / half_dim
+        inv_freq = theta ** -freq_seq
+        self.register_buffer('inv_freq', inv_freq, persistent = False)
+
+    def forward(self, x, pos = None, seq_start_pos = None):
+        seq_len, device = x.shape[1], x.device
+
+        if pos is None:
+            pos = torch.arange(seq_len, device = device)
+
+        if seq_start_pos is not None:
+            pos = pos - seq_start_pos[..., None]
+
+        emb = torch.einsum('i, j -> i j', pos, self.inv_freq)
+        emb = torch.cat((emb.sin(), emb.cos()), dim = -1)
+        return emb * self.scale
+
+class RotaryEmbedding(nn.Module):
+    def __init__(
+        self,
+        dim,
+        use_xpos = False,
+        scale_base = 512,
+        interpolation_factor = 1.,
+        base = 10000,
+        base_rescale_factor = 1.
+    ):
+        super().__init__()
+        # proposed by reddit user bloc97, to rescale rotary embeddings to longer sequence length without fine-tuning
+        # has some connection to NTK literature
+        # https://www.reddit.com/r/LocalLLaMA/comments/14lz7j5/ntkaware_scaled_rope_allows_llama_models_to_have/
+        base *= base_rescale_factor ** (dim / (dim - 2))
+
+        inv_freq = 1. / (base ** (torch.arange(0, dim, 2).float() / dim))
+        self.register_buffer('inv_freq', inv_freq)
+
+        assert interpolation_factor >= 1.
+        self.interpolation_factor = interpolation_factor
+
+        if not use_xpos:
+            self.register_buffer('scale', None)
+            return
+
+        scale = (torch.arange(0, dim, 2) + 0.4 * dim) / (1.4 * dim)
+
+        self.scale_base = scale_base
+        self.register_buffer('scale', scale)
+
+    def forward_from_seq_len(self, seq_len, device, dtype):
+        # device = self.inv_freq.device
+
+        t = torch.arange(seq_len, device=device, dtype=dtype)
+        return self.forward(t)
+
+    def forward(self, t):
+        # device = self.inv_freq.device
+        device = t.device
+        dtype = t.dtype
+
+        # t = t.to(torch.float32)
+
+        t = t / self.interpolation_factor
+
+        freqs = torch.einsum('i , j -> i j', t, self.inv_freq.to(dtype=dtype, device=device))
+        freqs = torch.cat((freqs, freqs), dim = -1)
+
+        if self.scale is None:
+            return freqs, 1.
+
+        power = (torch.arange(seq_len, device = device) - (seq_len // 2)) / self.scale_base
+        scale = self.scale.to(dtype=dtype, device=device) ** rearrange(power, 'n -> n 1')
+        scale = torch.cat((scale, scale), dim = -1)
+
+        return freqs, scale
+
+def rotate_half(x):
+    x = rearrange(x, '... (j d) -> ... j d', j = 2)
+    x1, x2 = x.unbind(dim = -2)
+    return torch.cat((-x2, x1), dim = -1)
+
+def apply_rotary_pos_emb(t, freqs, scale = 1):
+    out_dtype = t.dtype
+
+    # cast to float32 if necessary for numerical stability
+    dtype = t.dtype #reduce(torch.promote_types, (t.dtype, freqs.dtype, torch.float32))
+    rot_dim, seq_len = freqs.shape[-1], t.shape[-2]
+    freqs, t = freqs.to(dtype), t.to(dtype)
+    freqs = freqs[-seq_len:, :]
+
+    if t.ndim == 4 and freqs.ndim == 3:
+        freqs = rearrange(freqs, 'b n d -> b 1 n d')
+
+    # partial rotary embeddings, Wang et al. GPT-J
+    t, t_unrotated = t[..., :rot_dim], t[..., rot_dim:]
+    t = (t * freqs.cos() * scale) + (rotate_half(t) * freqs.sin() * scale)
+
+    t, t_unrotated = t.to(out_dtype), t_unrotated.to(out_dtype)
+
+    return torch.cat((t, t_unrotated), dim = -1)
+
+class FeedForward(nn.Module):
+    def __init__(
+        self,
+        dim,
+        dim_out = None,
+        mult = 4,
+        no_bias = False,
+        glu = True,
+        use_conv = False,
+        conv_kernel_size = 3,
+        zero_init_output = True,
+        dtype=None,
+        device=None,
+        operations=None,
+    ):
+        super().__init__()
+        inner_dim = int(dim * mult)
+
+        # Default to SwiGLU
+
+        activation = nn.SiLU()
+
+        dim_out = dim if dim_out is None else dim_out
+
+        if glu:
+            linear_in = GLU(dim, inner_dim, activation, dtype=dtype, device=device, operations=operations)
+        else:
+            linear_in = nn.Sequential(
+                Rearrange('b n d -> b d n') if use_conv else nn.Identity(),
+                operations.Linear(dim, inner_dim, bias = not no_bias, dtype=dtype, device=device) if not use_conv else operations.Conv1d(dim, inner_dim, conv_kernel_size, padding = (conv_kernel_size // 2), bias = not no_bias, dtype=dtype, device=device),
+                Rearrange('b n d -> b d n') if use_conv else nn.Identity(),
+                activation
+            )
+
+        linear_out = operations.Linear(inner_dim, dim_out, bias = not no_bias, dtype=dtype, device=device) if not use_conv else operations.Conv1d(inner_dim, dim_out, conv_kernel_size, padding = (conv_kernel_size // 2), bias = not no_bias, dtype=dtype, device=device)
+
+        # # init last linear layer to 0
+        # if zero_init_output:
+        #     nn.init.zeros_(linear_out.weight)
+        #     if not no_bias:
+        #         nn.init.zeros_(linear_out.bias)
+
+
+        self.ff = nn.Sequential(
+            linear_in,
+            Rearrange('b d n -> b n d') if use_conv else nn.Identity(),
+            linear_out,
+            Rearrange('b n d -> b d n') if use_conv else nn.Identity(),
+        )
+
+    def forward(self, x):
+        return self.ff(x)
+
+class Attention(nn.Module):
+    def __init__(
+        self,
+        dim,
+        dim_heads = 64,
+        dim_context = None,
+        causal = False,
+        zero_init_output=True,
+        qk_norm = False,
+        natten_kernel_size = None,
+        dtype=None,
+        device=None,
+        operations=None,
+    ):
+        super().__init__()
+        self.dim = dim
+        self.dim_heads = dim_heads
+        self.causal = causal
+
+        dim_kv = dim_context if dim_context is not None else dim
+
+        self.num_heads = dim // dim_heads
+        self.kv_heads = dim_kv // dim_heads
+
+        if dim_context is not None:
+            self.to_q = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device)
+            self.to_kv = operations.Linear(dim_kv, dim_kv * 2, bias=False, dtype=dtype, device=device)
+        else:
+            self.to_qkv = operations.Linear(dim, dim * 3, bias=False, dtype=dtype, device=device)
+
+        self.to_out = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device)
+
+        # if zero_init_output:
+        #     nn.init.zeros_(self.to_out.weight)
+
+        self.qk_norm = qk_norm
+
+
+    def forward(
+        self,
+        x,
+        context = None,
+        mask = None,
+        context_mask = None,
+        rotary_pos_emb = None,
+        causal = None
+    ):
+        h, kv_h, has_context = self.num_heads, self.kv_heads, context is not None
+
+        kv_input = context if has_context else x
+
+        if hasattr(self, 'to_q'):
+            # Use separate linear projections for q and k/v
+            q = self.to_q(x)
+            q = rearrange(q, 'b n (h d) -> b h n d', h = h)
+
+            k, v = self.to_kv(kv_input).chunk(2, dim=-1)
+
+            k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h = kv_h), (k, v))
+        else:
+            # Use fused linear projection
+            q, k, v = self.to_qkv(x).chunk(3, dim=-1)
+            q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h = h), (q, k, v))
+
+        # Normalize q and k for cosine sim attention
+        if self.qk_norm:
+            q = F.normalize(q, dim=-1)
+            k = F.normalize(k, dim=-1)
+
+        if rotary_pos_emb is not None and not has_context:
+            freqs, _ = rotary_pos_emb
+
+            q_dtype = q.dtype
+            k_dtype = k.dtype
+
+            q = q.to(torch.float32)
+            k = k.to(torch.float32)
+            freqs = freqs.to(torch.float32)
+
+            q = apply_rotary_pos_emb(q, freqs)
+            k = apply_rotary_pos_emb(k, freqs)
+
+            q = q.to(q_dtype)
+            k = k.to(k_dtype)
+
+        input_mask = context_mask
+
+        if input_mask is None and not has_context:
+            input_mask = mask
+
+        # determine masking
+        masks = []
+        final_attn_mask = None # The mask that will be applied to the attention matrix, taking all masks into account
+
+        if input_mask is not None:
+            input_mask = rearrange(input_mask, 'b j -> b 1 1 j')
+            masks.append(~input_mask)
+
+        # Other masks will be added here later
+
+        if len(masks) > 0:
+            final_attn_mask = ~or_reduce(masks)
+
+        n, device = q.shape[-2], q.device
+
+        causal = self.causal if causal is None else causal
+
+        if n == 1 and causal:
+            causal = False
+
+        if h != kv_h:
+            # Repeat interleave kv_heads to match q_heads
+            heads_per_kv_head = h // kv_h
+            k, v = map(lambda t: t.repeat_interleave(heads_per_kv_head, dim = 1), (k, v))
+
+        out = optimized_attention(q, k, v, h, skip_reshape=True)
+        out = self.to_out(out)
+
+        if mask is not None:
+            mask = rearrange(mask, 'b n -> b n 1')
+            out = out.masked_fill(~mask, 0.)
+
+        return out
+
+class ConformerModule(nn.Module):
+    def __init__(
+        self,
+        dim,
+        norm_kwargs = {},
+    ):
+
+        super().__init__()
+
+        self.dim = dim
+
+        self.in_norm = LayerNorm(dim, **norm_kwargs)
+        self.pointwise_conv = nn.Conv1d(dim, dim, kernel_size=1, bias=False)
+        self.glu = GLU(dim, dim, nn.SiLU())
+        self.depthwise_conv = nn.Conv1d(dim, dim, kernel_size=17, groups=dim, padding=8, bias=False)
+        self.mid_norm = LayerNorm(dim, **norm_kwargs) # This is a batch norm in the original but I don't like batch norm
+        self.swish = nn.SiLU()
+        self.pointwise_conv_2 = nn.Conv1d(dim, dim, kernel_size=1, bias=False)
+
+    def forward(self, x):
+        x = self.in_norm(x)
+        x = rearrange(x, 'b n d -> b d n')
+        x = self.pointwise_conv(x)
+        x = rearrange(x, 'b d n -> b n d')
+        x = self.glu(x)
+        x = rearrange(x, 'b n d -> b d n')
+        x = self.depthwise_conv(x)
+        x = rearrange(x, 'b d n -> b n d')
+        x = self.mid_norm(x)
+        x = self.swish(x)
+        x = rearrange(x, 'b n d -> b d n')
+        x = self.pointwise_conv_2(x)
+        x = rearrange(x, 'b d n -> b n d')
+
+        return x
+
+class TransformerBlock(nn.Module):
+    def __init__(
+            self,
+            dim,
+            dim_heads = 64,
+            cross_attend = False,
+            dim_context = None,
+            global_cond_dim = None,
+            causal = False,
+            zero_init_branch_outputs = True,
+            conformer = False,
+            layer_ix = -1,
+            remove_norms = False,
+            attn_kwargs = {},
+            ff_kwargs = {},
+            norm_kwargs = {},
+            dtype=None,
+            device=None,
+            operations=None,
+    ):
+
+        super().__init__()
+        self.dim = dim
+        self.dim_heads = dim_heads
+        self.cross_attend = cross_attend
+        self.dim_context = dim_context
+        self.causal = causal
+
+        self.pre_norm = LayerNorm(dim, dtype=dtype, device=device, **norm_kwargs) if not remove_norms else nn.Identity()
+
+        self.self_attn = Attention(
+            dim,
+            dim_heads = dim_heads,
+            causal = causal,
+            zero_init_output=zero_init_branch_outputs,
+            dtype=dtype,
+            device=device,
+            operations=operations,
+            **attn_kwargs
+        )
+
+        if cross_attend:
+            self.cross_attend_norm = LayerNorm(dim, dtype=dtype, device=device, **norm_kwargs) if not remove_norms else nn.Identity()
+            self.cross_attn = Attention(
+                dim,
+                dim_heads = dim_heads,
+                dim_context=dim_context,
+                causal = causal,
+                zero_init_output=zero_init_branch_outputs,
+                dtype=dtype,
+                device=device,
+                operations=operations,
+                **attn_kwargs
+            )
+
+        self.ff_norm = LayerNorm(dim, dtype=dtype, device=device, **norm_kwargs) if not remove_norms else nn.Identity()
+        self.ff = FeedForward(dim, zero_init_output=zero_init_branch_outputs, dtype=dtype, device=device, operations=operations,**ff_kwargs)
+
+        self.layer_ix = layer_ix
+
+        self.conformer = ConformerModule(dim, norm_kwargs=norm_kwargs) if conformer else None
+
+        self.global_cond_dim = global_cond_dim
+
+        if global_cond_dim is not None:
+            self.to_scale_shift_gate = nn.Sequential(
+                nn.SiLU(),
+                nn.Linear(global_cond_dim, dim * 6, bias=False)
+            )
+
+            nn.init.zeros_(self.to_scale_shift_gate[1].weight)
+            #nn.init.zeros_(self.to_scale_shift_gate_self[1].bias)
+
+    def forward(
+        self,
+        x,
+        context = None,
+        global_cond=None,
+        mask = None,
+        context_mask = None,
+        rotary_pos_emb = None
+    ):
+        if self.global_cond_dim is not None and self.global_cond_dim > 0 and global_cond is not None:
+
+            scale_self, shift_self, gate_self, scale_ff, shift_ff, gate_ff = self.to_scale_shift_gate(global_cond).unsqueeze(1).chunk(6, dim = -1)
+
+            # self-attention with adaLN
+            residual = x
+            x = self.pre_norm(x)
+            x = x * (1 + scale_self) + shift_self
+            x = self.self_attn(x, mask = mask, rotary_pos_emb = rotary_pos_emb)
+            x = x * torch.sigmoid(1 - gate_self)
+            x = x + residual
+
+            if context is not None:
+                x = x + self.cross_attn(self.cross_attend_norm(x), context = context, context_mask = context_mask)
+
+            if self.conformer is not None:
+                x = x + self.conformer(x)
+
+            # feedforward with adaLN
+            residual = x
+            x = self.ff_norm(x)
+            x = x * (1 + scale_ff) + shift_ff
+            x = self.ff(x)
+            x = x * torch.sigmoid(1 - gate_ff)
+            x = x + residual
+
+        else:
+            x = x + self.self_attn(self.pre_norm(x), mask = mask, rotary_pos_emb = rotary_pos_emb)
+
+            if context is not None:
+                x = x + self.cross_attn(self.cross_attend_norm(x), context = context, context_mask = context_mask)
+
+            if self.conformer is not None:
+                x = x + self.conformer(x)
+
+            x = x + self.ff(self.ff_norm(x))
+
+        return x
+
+class ContinuousTransformer(nn.Module):
+    def __init__(
+        self,
+        dim,
+        depth,
+        *,
+        dim_in = None,
+        dim_out = None,
+        dim_heads = 64,
+        cross_attend=False,
+        cond_token_dim=None,
+        global_cond_dim=None,
+        causal=False,
+        rotary_pos_emb=True,
+        zero_init_branch_outputs=True,
+        conformer=False,
+        use_sinusoidal_emb=False,
+        use_abs_pos_emb=False,
+        abs_pos_emb_max_length=10000,
+        dtype=None,
+        device=None,
+        operations=None,
+        **kwargs
+        ):
+
+        super().__init__()
+
+        self.dim = dim
+        self.depth = depth
+        self.causal = causal
+        self.layers = nn.ModuleList([])
+
+        self.project_in = operations.Linear(dim_in, dim, bias=False, dtype=dtype, device=device) if dim_in is not None else nn.Identity()
+        self.project_out = operations.Linear(dim, dim_out, bias=False, dtype=dtype, device=device) if dim_out is not None else nn.Identity()
+
+        if rotary_pos_emb:
+            self.rotary_pos_emb = RotaryEmbedding(max(dim_heads // 2, 32))
+        else:
+            self.rotary_pos_emb = None
+
+        self.use_sinusoidal_emb = use_sinusoidal_emb
+        if use_sinusoidal_emb:
+            self.pos_emb = ScaledSinusoidalEmbedding(dim)
+
+        self.use_abs_pos_emb = use_abs_pos_emb
+        if use_abs_pos_emb:
+            self.pos_emb = AbsolutePositionalEmbedding(dim, abs_pos_emb_max_length)
+
+        for i in range(depth):
+            self.layers.append(
+                TransformerBlock(
+                    dim,
+                    dim_heads = dim_heads,
+                    cross_attend = cross_attend,
+                    dim_context = cond_token_dim,
+                    global_cond_dim = global_cond_dim,
+                    causal = causal,
+                    zero_init_branch_outputs = zero_init_branch_outputs,
+                    conformer=conformer,
+                    layer_ix=i,
+                    dtype=dtype,
+                    device=device,
+                    operations=operations,
+                    **kwargs
+                )
+            )
+
+    def forward(
+        self,
+        x,
+        mask = None,
+        prepend_embeds = None,
+        prepend_mask = None,
+        global_cond = None,
+        return_info = False,
+        **kwargs
+    ):
+        batch, seq, device = *x.shape[:2], x.device
+
+        info = {
+            "hidden_states": [],
+        }
+
+        x = self.project_in(x)
+
+        if prepend_embeds is not None:
+            prepend_length, prepend_dim = prepend_embeds.shape[1:]
+
+            assert prepend_dim == x.shape[-1], 'prepend dimension must match sequence dimension'
+
+            x = torch.cat((prepend_embeds, x), dim = -2)
+
+            if prepend_mask is not None or mask is not None:
+                mask = mask if mask is not None else torch.ones((batch, seq), device = device, dtype = torch.bool)
+                prepend_mask = prepend_mask if prepend_mask is not None else torch.ones((batch, prepend_length), device = device, dtype = torch.bool)
+
+                mask = torch.cat((prepend_mask, mask), dim = -1)
+
+        # Attention layers
+
+        if self.rotary_pos_emb is not None:
+            rotary_pos_emb = self.rotary_pos_emb.forward_from_seq_len(x.shape[1], dtype=x.dtype, device=x.device)
+        else:
+            rotary_pos_emb = None
+
+        if self.use_sinusoidal_emb or self.use_abs_pos_emb:
+            x = x + self.pos_emb(x)
+
+        # Iterate over the transformer layers
+        for layer in self.layers:
+            x = layer(x, rotary_pos_emb = rotary_pos_emb, global_cond=global_cond, **kwargs)
+            # x = checkpoint(layer, x, rotary_pos_emb = rotary_pos_emb, global_cond=global_cond, **kwargs)
+
+            if return_info:
+                info["hidden_states"].append(x)
+
+        x = self.project_out(x)
+
+        if return_info:
+            return x, info
+
+        return x
+
+class AudioDiffusionTransformer(nn.Module):
+    def __init__(self,
+        io_channels=64,
+        patch_size=1,
+        embed_dim=1536,
+        cond_token_dim=768,
+        project_cond_tokens=False,
+        global_cond_dim=1536,
+        project_global_cond=True,
+        input_concat_dim=0,
+        prepend_cond_dim=0,
+        depth=24,
+        num_heads=24,
+        transformer_type: tp.Literal["continuous_transformer"] = "continuous_transformer",
+        global_cond_type: tp.Literal["prepend", "adaLN"] = "prepend",
+        audio_model="",
+        dtype=None,
+        device=None,
+        operations=None,
+        **kwargs):
+
+        super().__init__()
+
+        self.dtype = dtype
+        self.cond_token_dim = cond_token_dim
+
+        # Timestep embeddings
+        timestep_features_dim = 256
+
+        self.timestep_features = FourierFeatures(1, timestep_features_dim, dtype=dtype, device=device)
+
+        self.to_timestep_embed = nn.Sequential(
+            operations.Linear(timestep_features_dim, embed_dim, bias=True, dtype=dtype, device=device),
+            nn.SiLU(),
+            operations.Linear(embed_dim, embed_dim, bias=True, dtype=dtype, device=device),
+        )
+
+        if cond_token_dim > 0:
+            # Conditioning tokens
+
+            cond_embed_dim = cond_token_dim if not project_cond_tokens else embed_dim
+            self.to_cond_embed = nn.Sequential(
+                operations.Linear(cond_token_dim, cond_embed_dim, bias=False, dtype=dtype, device=device),
+                nn.SiLU(),
+                operations.Linear(cond_embed_dim, cond_embed_dim, bias=False, dtype=dtype, device=device)
+            )
+        else:
+            cond_embed_dim = 0
+
+        if global_cond_dim > 0:
+            # Global conditioning
+            global_embed_dim = global_cond_dim if not project_global_cond else embed_dim
+            self.to_global_embed = nn.Sequential(
+                operations.Linear(global_cond_dim, global_embed_dim, bias=False, dtype=dtype, device=device),
+                nn.SiLU(),
+                operations.Linear(global_embed_dim, global_embed_dim, bias=False, dtype=dtype, device=device)
+            )
+
+        if prepend_cond_dim > 0:
+            # Prepend conditioning
+            self.to_prepend_embed = nn.Sequential(
+                operations.Linear(prepend_cond_dim, embed_dim, bias=False, dtype=dtype, device=device),
+                nn.SiLU(),
+                operations.Linear(embed_dim, embed_dim, bias=False, dtype=dtype, device=device)
+            )
+
+        self.input_concat_dim = input_concat_dim
+
+        dim_in = io_channels + self.input_concat_dim
+
+        self.patch_size = patch_size
+
+        # Transformer
+
+        self.transformer_type = transformer_type
+
+        self.global_cond_type = global_cond_type
+
+        if self.transformer_type == "continuous_transformer":
+
+            global_dim = None
+
+            if self.global_cond_type == "adaLN":
+                # The global conditioning is projected to the embed_dim already at this point
+                global_dim = embed_dim
+
+            self.transformer = ContinuousTransformer(
+                dim=embed_dim,
+                depth=depth,
+                dim_heads=embed_dim // num_heads,
+                dim_in=dim_in * patch_size,
+                dim_out=io_channels * patch_size,
+                cross_attend = cond_token_dim > 0,
+                cond_token_dim = cond_embed_dim,
+                global_cond_dim=global_dim,
+                dtype=dtype,
+                device=device,
+                operations=operations,
+                **kwargs
+            )
+        else:
+            raise ValueError(f"Unknown transformer type: {self.transformer_type}")
+
+        self.preprocess_conv = operations.Conv1d(dim_in, dim_in, 1, bias=False, dtype=dtype, device=device)
+        self.postprocess_conv = operations.Conv1d(io_channels, io_channels, 1, bias=False, dtype=dtype, device=device)
+
+    def _forward(
+        self,
+        x,
+        t,
+        mask=None,
+        cross_attn_cond=None,
+        cross_attn_cond_mask=None,
+        input_concat_cond=None,
+        global_embed=None,
+        prepend_cond=None,
+        prepend_cond_mask=None,
+        return_info=False,
+        **kwargs):
+
+        if cross_attn_cond is not None:
+            cross_attn_cond = self.to_cond_embed(cross_attn_cond)
+
+        if global_embed is not None:
+            # Project the global conditioning to the embedding dimension
+            global_embed = self.to_global_embed(global_embed)
+
+        prepend_inputs = None
+        prepend_mask = None
+        prepend_length = 0
+        if prepend_cond is not None:
+            # Project the prepend conditioning to the embedding dimension
+            prepend_cond = self.to_prepend_embed(prepend_cond)
+
+            prepend_inputs = prepend_cond
+            if prepend_cond_mask is not None:
+                prepend_mask = prepend_cond_mask
+
+        if input_concat_cond is not None:
+
+            # Interpolate input_concat_cond to the same length as x
+            if input_concat_cond.shape[2] != x.shape[2]:
+                input_concat_cond = F.interpolate(input_concat_cond, (x.shape[2], ), mode='nearest')
+
+            x = torch.cat([x, input_concat_cond], dim=1)
+
+        # Get the batch of timestep embeddings
+        timestep_embed = self.to_timestep_embed(self.timestep_features(t[:, None]).to(x.dtype)) # (b, embed_dim)
+
+        # Timestep embedding is considered a global embedding. Add to the global conditioning if it exists
+        if global_embed is not None:
+            global_embed = global_embed + timestep_embed
+        else:
+            global_embed = timestep_embed
+
+        # Add the global_embed to the prepend inputs if there is no global conditioning support in the transformer
+        if self.global_cond_type == "prepend":
+            if prepend_inputs is None:
+                # Prepend inputs are just the global embed, and the mask is all ones
+                prepend_inputs = global_embed.unsqueeze(1)
+                prepend_mask = torch.ones((x.shape[0], 1), device=x.device, dtype=torch.bool)
+            else:
+                # Prepend inputs are the prepend conditioning + the global embed
+                prepend_inputs = torch.cat([prepend_inputs, global_embed.unsqueeze(1)], dim=1)
+                prepend_mask = torch.cat([prepend_mask, torch.ones((x.shape[0], 1), device=x.device, dtype=torch.bool)], dim=1)
+
+            prepend_length = prepend_inputs.shape[1]
+
+        x = self.preprocess_conv(x) + x
+
+        x = rearrange(x, "b c t -> b t c")
+
+        extra_args = {}
+
+        if self.global_cond_type == "adaLN":
+            extra_args["global_cond"] = global_embed
+
+        if self.patch_size > 1:
+            x = rearrange(x, "b (t p) c -> b t (c p)", p=self.patch_size)
+
+        if self.transformer_type == "x-transformers":
+            output = self.transformer(x, prepend_embeds=prepend_inputs, context=cross_attn_cond, context_mask=cross_attn_cond_mask, mask=mask, prepend_mask=prepend_mask, **extra_args, **kwargs)
+        elif self.transformer_type == "continuous_transformer":
+            output = self.transformer(x, prepend_embeds=prepend_inputs, context=cross_attn_cond, context_mask=cross_attn_cond_mask, mask=mask, prepend_mask=prepend_mask, return_info=return_info, **extra_args, **kwargs)
+
+            if return_info:
+                output, info = output
+        elif self.transformer_type == "mm_transformer":
+            output = self.transformer(x, context=cross_attn_cond, mask=mask, context_mask=cross_attn_cond_mask, **extra_args, **kwargs)
+
+        output = rearrange(output, "b t c -> b c t")[:,:,prepend_length:]
+
+        if self.patch_size > 1:
+            output = rearrange(output, "b (c p) t -> b c (t p)", p=self.patch_size)
+
+        output = self.postprocess_conv(output) + output
+
+        if return_info:
+            return output, info
+
+        return output
+
+    def forward(
+        self,
+        x,
+        timestep,
+        context=None,
+        context_mask=None,
+        input_concat_cond=None,
+        global_embed=None,
+        negative_global_embed=None,
+        prepend_cond=None,
+        prepend_cond_mask=None,
+        mask=None,
+        return_info=False,
+        control=None,
+        transformer_options={},
+        **kwargs):
+            return self._forward(
+                x,
+                timestep,
+                cross_attn_cond=context,
+                cross_attn_cond_mask=context_mask,
+                input_concat_cond=input_concat_cond,
+                global_embed=global_embed,
+                prepend_cond=prepend_cond,
+                prepend_cond_mask=prepend_cond_mask,
+                mask=mask,
+                return_info=return_info,
+                **kwargs
+            )

comfy/ldm/audio/embedders.py

@@ -0,0 +1,108 @@
+# code adapted from: https://github.com/Stability-AI/stable-audio-tools
+
+import torch
+import torch.nn as nn
+from torch import Tensor, einsum
+from typing import Any, Callable, Dict, List, Optional, Sequence, Tuple, TypeVar, Union
+from einops import rearrange
+import math
+import comfy.ops
+
+class LearnedPositionalEmbedding(nn.Module):
+    """Used for continuous time"""
+
+    def __init__(self, dim: int):
+        super().__init__()
+        assert (dim % 2) == 0
+        half_dim = dim // 2
+        self.weights = nn.Parameter(torch.empty(half_dim))
+
+    def forward(self, x: Tensor) -> Tensor:
+        x = rearrange(x, "b -> b 1")
+        freqs = x * rearrange(self.weights, "d -> 1 d") * 2 * math.pi
+        fouriered = torch.cat((freqs.sin(), freqs.cos()), dim=-1)
+        fouriered = torch.cat((x, fouriered), dim=-1)
+        return fouriered
+
+def TimePositionalEmbedding(dim: int, out_features: int) -> nn.Module:
+    return nn.Sequential(
+        LearnedPositionalEmbedding(dim),
+        comfy.ops.manual_cast.Linear(in_features=dim + 1, out_features=out_features),
+    )
+
+
+class NumberEmbedder(nn.Module):
+    def __init__(
+        self,
+        features: int,
+        dim: int = 256,
+    ):
+        super().__init__()
+        self.features = features
+        self.embedding = TimePositionalEmbedding(dim=dim, out_features=features)
+
+    def forward(self, x: Union[List[float], Tensor]) -> Tensor:
+        if not torch.is_tensor(x):
+            device = next(self.embedding.parameters()).device
+            x = torch.tensor(x, device=device)
+        assert isinstance(x, Tensor)
+        shape = x.shape
+        x = rearrange(x, "... -> (...)")
+        embedding = self.embedding(x)
+        x = embedding.view(*shape, self.features)
+        return x  # type: ignore
+
+
+class Conditioner(nn.Module):
+    def __init__(
+            self,
+            dim: int,
+            output_dim: int,
+            project_out: bool = False
+            ):
+
+        super().__init__()
+
+        self.dim = dim
+        self.output_dim = output_dim
+        self.proj_out = nn.Linear(dim, output_dim) if (dim != output_dim or project_out) else nn.Identity()
+
+    def forward(self, x):
+        raise NotImplementedError()
+
+class NumberConditioner(Conditioner):
+    '''
+        Conditioner that takes a list of floats, normalizes them for a given range, and returns a list of embeddings
+    '''
+    def __init__(self,
+                output_dim: int,
+                min_val: float=0,
+                max_val: float=1
+                ):
+        super().__init__(output_dim, output_dim)
+
+        self.min_val = min_val
+        self.max_val = max_val
+
+        self.embedder = NumberEmbedder(features=output_dim)
+
+    def forward(self, floats, device=None):
+            # Cast the inputs to floats
+            floats = [float(x) for x in floats]
+
+            if device is None:
+                device = next(self.embedder.parameters()).device
+
+            floats = torch.tensor(floats).to(device)
+
+            floats = floats.clamp(self.min_val, self.max_val)
+
+            normalized_floats = (floats - self.min_val) / (self.max_val - self.min_val)
+
+            # Cast floats to same type as embedder
+            embedder_dtype = next(self.embedder.parameters()).dtype
+            normalized_floats = normalized_floats.to(embedder_dtype)
+
+            float_embeds = self.embedder(normalized_floats).unsqueeze(1)
+
+            return [float_embeds, torch.ones(float_embeds.shape[0], 1).to(device)]

comfy/ldm/modules/attention.py

@@ -86,22 +86,32 @@ class FeedForward(nn.Module):
 def Normalize(in_channels, dtype=None, device=None):
     return torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True, dtype=dtype, device=device)
 
-def attention_basic(q, k, v, heads, mask=None, attn_precision=None):
+def attention_basic(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False):
     attn_precision = get_attn_precision(attn_precision)
 
-    b, _, dim_head = q.shape
-    dim_head //= heads
+    if skip_reshape:
+        b, _, _, dim_head = q.shape
+    else:
+        b, _, dim_head = q.shape
+        dim_head //= heads
+
     scale = dim_head ** -0.5
 
     h = heads
-    q, k, v = map(
-        lambda t: t.unsqueeze(3)
-        .reshape(b, -1, heads, dim_head)
-        .permute(0, 2, 1, 3)
-        .reshape(b * heads, -1, dim_head)
-        .contiguous(),
-        (q, k, v),
-    )
+    if skip_reshape:
+         q, k, v = map(
+            lambda t: t.reshape(b * heads, -1, dim_head),
+            (q, k, v),
+        )
+    else:
+        q, k, v = map(
+            lambda t: t.unsqueeze(3)
+            .reshape(b, -1, heads, dim_head)
+            .permute(0, 2, 1, 3)
+            .reshape(b * heads, -1, dim_head)
+            .contiguous(),
+            (q, k, v),
+        )
 
     # force cast to fp32 to avoid overflowing
     if attn_precision == torch.float32:
@@ -138,17 +148,26 @@ def attention_basic(q, k, v, heads, mask=None, attn_precision=None):
     return out
 
 
-def attention_sub_quad(query, key, value, heads, mask=None, attn_precision=None):
+def attention_sub_quad(query, key, value, heads, mask=None, attn_precision=None, skip_reshape=False):
     attn_precision = get_attn_precision(attn_precision)
 
-    b, _, dim_head = query.shape
-    dim_head //= heads
+    if skip_reshape:
+        b, _, _, dim_head = query.shape
+    else:
+        b, _, dim_head = query.shape
+        dim_head //= heads
 
     scale = dim_head ** -0.5
-    query = query.unsqueeze(3).reshape(b, -1, heads, dim_head).permute(0, 2, 1, 3).reshape(b * heads, -1, dim_head)
-    value = value.unsqueeze(3).reshape(b, -1, heads, dim_head).permute(0, 2, 1, 3).reshape(b * heads, -1, dim_head)
 
-    key = key.unsqueeze(3).reshape(b, -1, heads, dim_head).permute(0, 2, 3, 1).reshape(b * heads, dim_head, -1)
+    if skip_reshape:
+        query = query.reshape(b * heads, -1, dim_head)
+        value = value.reshape(b * heads, -1, dim_head)
+        key = key.reshape(b * heads, -1, dim_head).movedim(1, 2)
+    else:
+        query = query.unsqueeze(3).reshape(b, -1, heads, dim_head).permute(0, 2, 1, 3).reshape(b * heads, -1, dim_head)
+        value = value.unsqueeze(3).reshape(b, -1, heads, dim_head).permute(0, 2, 1, 3).reshape(b * heads, -1, dim_head)
+        key = key.unsqueeze(3).reshape(b, -1, heads, dim_head).permute(0, 2, 3, 1).reshape(b * heads, dim_head, -1)
+
 
     dtype = query.dtype
     upcast_attention = attn_precision == torch.float32 and query.dtype != torch.float32
@@ -200,22 +219,32 @@ def attention_sub_quad(query, key, value, heads, mask=None, attn_precision=None)
     hidden_states = hidden_states.unflatten(0, (-1, heads)).transpose(1,2).flatten(start_dim=2)
     return hidden_states
 
-def attention_split(q, k, v, heads, mask=None, attn_precision=None):
+def attention_split(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False):
     attn_precision = get_attn_precision(attn_precision)
 
-    b, _, dim_head = q.shape
-    dim_head //= heads
+    if skip_reshape:
+        b, _, _, dim_head = q.shape
+    else:
+        b, _, dim_head = q.shape
+        dim_head //= heads
+
     scale = dim_head ** -0.5
 
     h = heads
-    q, k, v = map(
-        lambda t: t.unsqueeze(3)
-        .reshape(b, -1, heads, dim_head)
-        .permute(0, 2, 1, 3)
-        .reshape(b * heads, -1, dim_head)
-        .contiguous(),
-        (q, k, v),
-    )
+    if skip_reshape:
+         q, k, v = map(
+            lambda t: t.reshape(b * heads, -1, dim_head),
+            (q, k, v),
+        )
+    else:
+        q, k, v = map(
+            lambda t: t.unsqueeze(3)
+            .reshape(b, -1, heads, dim_head)
+            .permute(0, 2, 1, 3)
+            .reshape(b * heads, -1, dim_head)
+            .contiguous(),
+            (q, k, v),
+        )
 
     r1 = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device, dtype=q.dtype)
 
@@ -311,9 +340,12 @@ try:
 except:
     pass
 
-def attention_xformers(q, k, v, heads, mask=None, attn_precision=None):
-    b, _, dim_head = q.shape
-    dim_head //= heads
+def attention_xformers(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False):
+    if skip_reshape:
+        b, _, _, dim_head = q.shape
+    else:
+        b, _, dim_head = q.shape
+        dim_head //= heads
 
     disabled_xformers = False
 
@@ -328,10 +360,16 @@ def attention_xformers(q, k, v, heads, mask=None, attn_precision=None):
     if disabled_xformers:
         return attention_pytorch(q, k, v, heads, mask)
 
-    q, k, v = map(
-        lambda t: t.reshape(b, -1, heads, dim_head),
-        (q, k, v),
-    )
+    if skip_reshape:
+         q, k, v = map(
+            lambda t: t.reshape(b * heads, -1, dim_head),
+            (q, k, v),
+        )
+    else:
+        q, k, v = map(
+            lambda t: t.reshape(b, -1, heads, dim_head),
+            (q, k, v),
+        )
 
     if mask is not None:
         pad = 8 - q.shape[1] % 8
@@ -341,18 +379,30 @@ def attention_xformers(q, k, v, heads, mask=None, attn_precision=None):
 
     out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=mask)
 
-    out = (
-        out.reshape(b, -1, heads * dim_head)
-    )
+    if skip_reshape:
+        out = (
+            out.unsqueeze(0)
+            .reshape(b, heads, -1, dim_head)
+            .permute(0, 2, 1, 3)
+            .reshape(b, -1, heads * dim_head)
+        )
+    else:
+        out = (
+            out.reshape(b, -1, heads * dim_head)
+        )
+
     return out
 
-def attention_pytorch(q, k, v, heads, mask=None, attn_precision=None):
-    b, _, dim_head = q.shape
-    dim_head //= heads
-    q, k, v = map(
-        lambda t: t.view(b, -1, heads, dim_head).transpose(1, 2),
-        (q, k, v),
-    )
+def attention_pytorch(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False):
+    if skip_reshape:
+        b, _, _, dim_head = q.shape
+    else:
+        b, _, dim_head = q.shape
+        dim_head //= heads
+        q, k, v = map(
+            lambda t: t.view(b, -1, heads, dim_head).transpose(1, 2),
+            (q, k, v),
+        )
 
     out = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=mask, dropout_p=0.0, is_causal=False)
     out = (