Initial support for the stable audio open model.

comfy/latent_formats.py

@@ -135,3 +135,6 @@ class SD3(LatentFormat):
 
     def process_out(self, latent):
         return (latent / self.scale_factor) + self.shift_factor
+
+class StableAudio1(LatentFormat):
+    latent_channels = 64

comfy/ldm/audio/autoencoder.py

+# 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/embedders.py

+# 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 = (

comfy/model_base.py

@@ -6,12 +6,15 @@ from comfy.ldm.cascade.stage_b import StageB
 from comfy.ldm.modules.encoders.noise_aug_modules import CLIPEmbeddingNoiseAugmentation
 from comfy.ldm.modules.diffusionmodules.upscaling import ImageConcatWithNoiseAugmentation
 from comfy.ldm.modules.diffusionmodules.mmdit import OpenAISignatureMMDITWrapper
+import comfy.ldm.audio.dit
+import comfy.ldm.audio.embedders
 import comfy.model_management
 import comfy.conds
 import comfy.ops
 from enum import Enum
 from . import utils
 import comfy.latent_formats
+import math
 
 class ModelType(Enum):
     EPS = 1
@@ -20,9 +23,10 @@ class ModelType(Enum):
     STABLE_CASCADE = 4
     EDM = 5
     FLOW = 6
+    V_PREDICTION_CONTINUOUS = 7
 
 
-from comfy.model_sampling import EPS, V_PREDICTION, EDM, ModelSamplingDiscrete, ModelSamplingContinuousEDM, StableCascadeSampling
+from comfy.model_sampling import EPS, V_PREDICTION, EDM, ModelSamplingDiscrete, ModelSamplingContinuousEDM, StableCascadeSampling, ModelSamplingContinuousV
 
 
 def model_sampling(model_config, model_type):
@@ -44,6 +48,9 @@ def model_sampling(model_config, model_type):
     elif model_type == ModelType.EDM:
         c = EDM
         s = ModelSamplingContinuousEDM
+    elif model_type == ModelType.V_PREDICTION_CONTINUOUS:
+        c = V_PREDICTION
+        s = ModelSamplingContinuousV
 
     class ModelSampling(s, c):
         pass
@@ -236,11 +243,11 @@ class BaseModel(torch.nn.Module):
             if self.manual_cast_dtype is not None:
                 dtype = self.manual_cast_dtype
             #TODO: this needs to be tweaked
-            area = input_shape[0] * input_shape[2] * input_shape[3]
+            area = input_shape[0] * math.prod(input_shape[2:])
             return (area * comfy.model_management.dtype_size(dtype) / 50) * (1024 * 1024)
         else:
             #TODO: this formula might be too aggressive since I tweaked the sub-quad and split algorithms to use less memory.
-            area = input_shape[0] * input_shape[2] * input_shape[3]
+            area = input_shape[0] * math.prod(input_shape[2:])
             return (((area * 0.6) / 0.9) + 1024) * (1024 * 1024)
 
 
@@ -590,3 +597,33 @@ class SD3(BaseModel):
         else:
             area = input_shape[0] * input_shape[2] * input_shape[3]
             return (area * 0.3) * (1024 * 1024)
+
+
+class StableAudio1(BaseModel):
+    def __init__(self, model_config, seconds_start_embedder_weights, seconds_total_embedder_weights, model_type=ModelType.V_PREDICTION_CONTINUOUS, device=None):
+        super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.audio.dit.AudioDiffusionTransformer)
+        self.seconds_start_embedder = comfy.ldm.audio.embedders.NumberConditioner(768, min_val=0, max_val=512)
+        self.seconds_total_embedder = comfy.ldm.audio.embedders.NumberConditioner(768, min_val=0, max_val=512)
+        self.seconds_start_embedder.load_state_dict(seconds_start_embedder_weights)
+        self.seconds_total_embedder.load_state_dict(seconds_total_embedder_weights)
+
+    def extra_conds(self, **kwargs):
+        out = {}
+
+        noise = kwargs.get("noise", None)
+        device = kwargs["device"]
+
+        seconds_start = kwargs.get("seconds_start", 0)
+        seconds_total = kwargs.get("seconds_total", int(noise.shape[-1] / 21.53))
+
+        seconds_start_embed = self.seconds_start_embedder([seconds_start])[0].to(device)
+        seconds_total_embed = self.seconds_total_embedder([seconds_total])[0].to(device)
+
+        global_embed = torch.cat([seconds_start_embed, seconds_total_embed], dim=-1).reshape((1, -1))
+        out['global_embed'] = comfy.conds.CONDRegular(global_embed)
+
+        cross_attn = kwargs.get("cross_attn", None)
+        if cross_attn is not None:
+            cross_attn = torch.cat([cross_attn.to(device), seconds_start_embed.repeat((cross_attn.shape[0], 1, 1)), seconds_total_embed.repeat((cross_attn.shape[0], 1, 1))], dim=1)
+            out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn)
+        return out

comfy/model_detection.py

@@ -96,6 +96,11 @@ def detect_unet_config(state_dict, key_prefix):
                 unet_config['block_repeat'] = [[1, 1, 1, 1], [2, 2, 2, 2]]
         return unet_config
 
+    if '{}transformer.rotary_pos_emb.inv_freq'.format(key_prefix) in state_dict_keys: #stable audio dit
+        unet_config = {}
+        unet_config["audio_model"] = "dit1.0"
+        return unet_config
+
     unet_config = {
         "use_checkpoint": False,
         "image_size": 32,
@@ -236,6 +241,13 @@ def model_config_from_unet(state_dict, unet_key_prefix, use_base_if_no_match=Fal
     else:
         return model_config
 
+def unet_prefix_from_state_dict(state_dict):
+    if "model.model.postprocess_conv.weight" in state_dict: #audio models
+        unet_key_prefix = "model.model."
+    else:
+        unet_key_prefix = "model.diffusion_model."
+    return unet_key_prefix
+
 def convert_config(unet_config):
     new_config = unet_config.copy()
     num_res_blocks = new_config.get("num_res_blocks", None)

comfy/model_sampling.py

@@ -169,6 +169,14 @@ class ModelSamplingContinuousEDM(torch.nn.Module):
         return math.exp((math.log(self.sigma_max) - log_sigma_min) * percent + log_sigma_min)
 
 
+class ModelSamplingContinuousV(ModelSamplingContinuousEDM):
+    def timestep(self, sigma):
+        return sigma.atan() / math.pi * 2
+
+    def sigma(self, timestep):
+        return (timestep * math.pi / 2).tan()
+
+
 def time_snr_shift(alpha, t):
     if alpha == 1.0:
         return t

comfy/ops.py

@@ -51,6 +51,20 @@ class disable_weight_init:
             else:
                 return super().forward(*args, **kwargs)
 
+    class Conv1d(torch.nn.Conv1d, CastWeightBiasOp):
+        def reset_parameters(self):
+            return None
+
+        def forward_comfy_cast_weights(self, input):
+            weight, bias = cast_bias_weight(self, input)
+            return self._conv_forward(input, weight, bias)
+
+        def forward(self, *args, **kwargs):
+            if self.comfy_cast_weights:
+                return self.forward_comfy_cast_weights(*args, **kwargs)
+            else:
+                return super().forward(*args, **kwargs)
+
     class Conv2d(torch.nn.Conv2d, CastWeightBiasOp):
         def reset_parameters(self):
             return None
@@ -133,6 +147,27 @@ class disable_weight_init:
             else:
                 return super().forward(*args, **kwargs)
 
+    class ConvTranspose1d(torch.nn.ConvTranspose1d, CastWeightBiasOp):
+        def reset_parameters(self):
+            return None
+
+        def forward_comfy_cast_weights(self, input, output_size=None):
+            num_spatial_dims = 1
+            output_padding = self._output_padding(
+                input, output_size, self.stride, self.padding, self.kernel_size,
+                num_spatial_dims, self.dilation)
+
+            weight, bias = cast_bias_weight(self, input)
+            return torch.nn.functional.conv_transpose1d(
+                input, weight, bias, self.stride, self.padding,
+                output_padding, self.groups, self.dilation)
+
+        def forward(self, *args, **kwargs):
+            if self.comfy_cast_weights:
+                return self.forward_comfy_cast_weights(*args, **kwargs)
+            else:
+                return super().forward(*args, **kwargs)
+
     @classmethod
     def conv_nd(s, dims, *args, **kwargs):
         if dims == 2:
@@ -147,6 +182,9 @@ class manual_cast(disable_weight_init):
     class Linear(disable_weight_init.Linear):
         comfy_cast_weights = True
 
+    class Conv1d(disable_weight_init.Conv1d):
+        comfy_cast_weights = True
+
     class Conv2d(disable_weight_init.Conv2d):
         comfy_cast_weights = True
 
@@ -161,3 +199,6 @@ class manual_cast(disable_weight_init):
 
     class ConvTranspose2d(disable_weight_init.ConvTranspose2d):
         comfy_cast_weights = True
+
+    class ConvTranspose1d(disable_weight_init.ConvTranspose1d):
+        comfy_cast_weights = True

comfy/sa_t5.py

+from comfy import sd1_clip
+from transformers import T5TokenizerFast
+import comfy.t5
+import os
+
+class T5BaseModel(sd1_clip.SDClipModel):
+    def __init__(self, device="cpu", layer="last", layer_idx=None, dtype=None):
+        textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_config_base.json")
+        super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, special_tokens={"end": 1, "pad": 0}, model_class=comfy.t5.T5, enable_attention_masks=True, zero_out_masked=True)
+
+class T5BaseTokenizer(sd1_clip.SDTokenizer):
+    def __init__(self, embedding_directory=None):
+        tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_tokenizer")
+        super().__init__(tokenizer_path, pad_with_end=False, embedding_size=768, embedding_key='t5base', tokenizer_class=T5TokenizerFast, has_start_token=False, pad_to_max_length=False, max_length=99999999, min_length=128)
+
+class SAT5Tokenizer(sd1_clip.SD1Tokenizer):
+    def __init__(self, embedding_directory=None):
+        super().__init__(embedding_directory=embedding_directory, clip_name="t5base", tokenizer=T5BaseTokenizer)
+
+class SAT5Model(sd1_clip.SD1ClipModel):
+    def __init__(self, device="cpu", dtype=None, **kwargs):
+        super().__init__(device=device, dtype=dtype, clip_name="t5base", clip_model=T5BaseModel, **kwargs)

comfy/sd.py

@@ -6,7 +6,7 @@ from comfy import model_management
 from .ldm.models.autoencoder import AutoencoderKL, AutoencodingEngine
 from .ldm.cascade.stage_a import StageA
 from .ldm.cascade.stage_c_coder import StageC_coder
-
+from .ldm.audio.autoencoder import AudioOobleckVAE
 import yaml
 
 import comfy.utils
@@ -20,6 +20,7 @@ from . import sd1_clip
 from . import sd2_clip
 from . import sdxl_clip
 from . import sd3_clip
+from . import sa_t5
 
 import comfy.model_patcher
 import comfy.lora
@@ -174,6 +175,7 @@ class VAE:
         self.downscale_ratio = 8
         self.upscale_ratio = 8
         self.latent_channels = 4
+        self.output_channels = 3
         self.process_input = lambda image: image * 2.0 - 1.0
         self.process_output = lambda image: torch.clamp((image + 1.0) / 2.0, min=0.0, max=1.0)
 
@@ -232,6 +234,16 @@ class VAE:
                     self.first_stage_model = AutoencodingEngine(regularizer_config={'target': "comfy.ldm.models.autoencoder.DiagonalGaussianRegularizer"},
                                                                 encoder_config={'target': "comfy.ldm.modules.diffusionmodules.model.Encoder", 'params': ddconfig},
                                                                 decoder_config={'target': "comfy.ldm.modules.diffusionmodules.model.Decoder", 'params': ddconfig})
+            elif "decoder.layers.0.weight_v" in sd:
+                self.first_stage_model = AudioOobleckVAE()
+                self.memory_used_encode = lambda shape, dtype: (1767 * shape[2]) * model_management.dtype_size(dtype) #TODO: tweak for the audio VAE
+                self.memory_used_decode = lambda shape, dtype: (2178 * shape[2] * 64) * model_management.dtype_size(dtype)
+                self.latent_channels = 64
+                self.output_channels = 2
+                self.upscale_ratio = 2048
+                self.downscale_ratio =  2048
+                self.process_output = lambda audio: audio
+                self.process_input = lambda audio: audio
             else:
                 logging.warning("WARNING: No VAE weights detected, VAE not initalized.")
                 self.first_stage_model = None
@@ -260,12 +272,12 @@ class VAE:
         self.patcher = comfy.model_patcher.ModelPatcher(self.first_stage_model, load_device=self.device, offload_device=offload_device)
 
     def vae_encode_crop_pixels(self, pixels):
-        x = (pixels.shape[1] // self.downscale_ratio) * self.downscale_ratio
-        y = (pixels.shape[2] // self.downscale_ratio) * self.downscale_ratio
-        if pixels.shape[1] != x or pixels.shape[2] != y:
-            x_offset = (pixels.shape[1] % self.downscale_ratio) // 2
-            y_offset = (pixels.shape[2] % self.downscale_ratio) // 2
-            pixels = pixels[:, x_offset:x + x_offset, y_offset:y + y_offset, :]
+        dims = pixels.shape[1:-1]
+        for d in range(len(dims)):
+            x = (dims[d] // self.downscale_ratio) * self.downscale_ratio
+            x_offset = (dims[d] % self.downscale_ratio) // 2
+            if x != dims[d]:
+                pixels = pixels.narrow(d + 1, x_offset, x)
         return pixels
 
     def decode_tiled_(self, samples, tile_x=64, tile_y=64, overlap = 16):
@@ -303,7 +315,7 @@ class VAE:
             batch_number = int(free_memory / memory_used)
             batch_number = max(1, batch_number)
 
-            pixel_samples = torch.empty((samples_in.shape[0], 3, round(samples_in.shape[2] * self.upscale_ratio), round(samples_in.shape[3] * self.upscale_ratio)), device=self.output_device)
+            pixel_samples = torch.empty((samples_in.shape[0], self.output_channels) + tuple(map(lambda a: a * self.upscale_ratio, samples_in.shape[2:])), device=self.output_device)
             for x in range(0, samples_in.shape[0], batch_number):
                 samples = samples_in[x:x+batch_number].to(self.vae_dtype).to(self.device)
                 pixel_samples[x:x+batch_number] = self.process_output(self.first_stage_model.decode(samples).to(self.output_device).float())
@@ -328,7 +340,7 @@ class VAE:
             free_memory = model_management.get_free_memory(self.device)
             batch_number = int(free_memory / memory_used)
             batch_number = max(1, batch_number)
-            samples = torch.empty((pixel_samples.shape[0], self.latent_channels, round(pixel_samples.shape[2] // self.downscale_ratio), round(pixel_samples.shape[3] // self.downscale_ratio)), device=self.output_device)
+            samples = torch.empty((pixel_samples.shape[0], self.latent_channels) + tuple(map(lambda a: a // self.downscale_ratio, pixel_samples.shape[2:])), device=self.output_device)
             for x in range(0, pixel_samples.shape[0], batch_number):
                 pixels_in = self.process_input(pixel_samples[x:x+batch_number]).to(self.vae_dtype).to(self.device)
                 samples[x:x+batch_number] = self.first_stage_model.encode(pixels_in).to(self.output_device).float()
@@ -371,6 +383,7 @@ class CLIPType(Enum):
     STABLE_DIFFUSION = 1
     STABLE_CASCADE = 2
     SD3 = 3
+    STABLE_AUDIO = 4
 
 def load_clip(ckpt_paths, embedding_directory=None, clip_type=CLIPType.STABLE_DIFFUSION):
     clip_data = []
@@ -404,6 +417,9 @@ def load_clip(ckpt_paths, embedding_directory=None, clip_type=CLIPType.STABLE_DI
             dtype_t5 = clip_data[0]["encoder.block.23.layer.1.DenseReluDense.wi_1.weight"].dtype
             clip_target.clip = sd3_clip.sd3_clip(clip_l=False, clip_g=False, t5=True, dtype_t5=dtype_t5)
             clip_target.tokenizer = sd3_clip.SD3Tokenizer
+        elif "encoder.block.0.layer.0.SelfAttention.k.weight" in clip_data[0]:
+            clip_target.clip = sa_t5.SAT5Model
+            clip_target.tokenizer = sa_t5.SAT5Tokenizer
         else:
             clip_target.clip = sd1_clip.SD1ClipModel
             clip_target.tokenizer = sd1_clip.SD1Tokenizer
@@ -470,10 +486,11 @@ def load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, o
     model_patcher = None
     clip_target = None
 
-    parameters = comfy.utils.calculate_parameters(sd, "model.diffusion_model.")
+    diffusion_model_prefix = model_detection.unet_prefix_from_state_dict(sd)
+    parameters = comfy.utils.calculate_parameters(sd, diffusion_model_prefix)
     load_device = model_management.get_torch_device()
 
-    model_config = model_detection.model_config_from_unet(sd, "model.diffusion_model.")
+    model_config = model_detection.model_config_from_unet(sd, diffusion_model_prefix)
     unet_dtype = model_management.unet_dtype(model_params=parameters, supported_dtypes=model_config.supported_inference_dtypes)
     manual_cast_dtype = model_management.unet_manual_cast(unet_dtype, load_device, model_config.supported_inference_dtypes)
     model_config.set_inference_dtype(unet_dtype, manual_cast_dtype)
@@ -488,8 +505,8 @@ def load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, o
     if output_model:
         inital_load_device = model_management.unet_inital_load_device(parameters, unet_dtype)
         offload_device = model_management.unet_offload_device()
-        model = model_config.get_model(sd, "model.diffusion_model.", device=inital_load_device)
-        model.load_model_weights(sd, "model.diffusion_model.")
+        model = model_config.get_model(sd, diffusion_model_prefix, device=inital_load_device)
+        model.load_model_weights(sd, diffusion_model_prefix)
 
     if output_vae:
         vae_sd = comfy.utils.state_dict_prefix_replace(sd, {k: "" for k in model_config.vae_key_prefix}, filter_keys=True)

comfy/supported_models.py

@@ -6,6 +6,7 @@ from . import sd1_clip
 from . import sd2_clip
 from . import sdxl_clip
 from . import sd3_clip
+from . import sa_t5
 
 from . import supported_models_base
 from . import latent_formats
@@ -524,7 +525,35 @@ class SD3(supported_models_base.BASE):
 
         return supported_models_base.ClipTarget(sd3_clip.SD3Tokenizer, sd3_clip.sd3_clip(clip_l=clip_l, clip_g=clip_g, t5=t5, dtype_t5=dtype_t5))
 
+class StableAudio(supported_models_base.BASE):
+    unet_config = {
+        "audio_model": "dit1.0",
+    }
+
+    sampling_settings = {"sigma_max": 500.0, "sigma_min": 0.03}
+
+    unet_extra_config = {}
+    latent_format = latent_formats.StableAudio1
+
+    text_encoder_key_prefix = ["text_encoders."]
+    vae_key_prefix = ["pretransform.model."]
+
+    def get_model(self, state_dict, prefix="", device=None):
+        seconds_start_sd = utils.state_dict_prefix_replace(state_dict, {"conditioner.conditioners.seconds_start.": ""}, filter_keys=True)
+        seconds_total_sd = utils.state_dict_prefix_replace(state_dict, {"conditioner.conditioners.seconds_total.": ""}, filter_keys=True)
+        return model_base.StableAudio1(self, seconds_start_embedder_weights=seconds_start_sd, seconds_total_embedder_weights=seconds_total_sd, device=device)
+
+
+    def process_unet_state_dict(self, state_dict):
+        for k in list(state_dict.keys()):
+            if k.endswith(".cross_attend_norm.beta") or k.endswith(".ff_norm.beta") or k.endswith(".pre_norm.beta"): #These weights are all zero
+                state_dict.pop(k)
+        return state_dict
+
+    def clip_target(self, state_dict={}):
+        return supported_models_base.ClipTarget(sa_t5.SAT5Tokenizer, sa_t5.SAT5Model)
+
 
-models = [Stable_Zero123, SD15_instructpix2pix, SD15, SD20, SD21UnclipL, SD21UnclipH, SDXL_instructpix2pix, SDXLRefiner, SDXL, SSD1B, KOALA_700M, KOALA_1B, Segmind_Vega, SD_X4Upscaler, Stable_Cascade_C, Stable_Cascade_B, SV3D_u, SV3D_p, SD3]
+models = [Stable_Zero123, SD15_instructpix2pix, SD15, SD20, SD21UnclipL, SD21UnclipH, SDXL_instructpix2pix, SDXLRefiner, SDXL, SSD1B, KOALA_700M, KOALA_1B, Segmind_Vega, SD_X4Upscaler, Stable_Cascade_C, Stable_Cascade_B, SV3D_u, SV3D_p, SD3, StableAudio]
 
 models += [SVD_img2vid]

comfy_extras/nodes_audio.py

+import torchaudio
+import torch
+import comfy.model_management
+import folder_paths
+import os
+
+class EmptyLatentAudio:
+    def __init__(self):
+        self.device = comfy.model_management.intermediate_device()
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {}}
+    RETURN_TYPES = ("LATENT",)
+    FUNCTION = "generate"
+
+    CATEGORY = "_for_testing/audio"
+
+    def generate(self):
+        batch_size = 1
+        latent = torch.zeros([batch_size, 64, 1024], device=self.device)
+        return ({"samples":latent, "type": "audio"}, )
+
+class VAEEncodeAudio:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "audio": ("AUDIO", ), "vae": ("VAE", )}}
+    RETURN_TYPES = ("LATENT",)
+    FUNCTION = "encode"
+
+    CATEGORY = "_for_testing/audio"
+
+    def encode(self, vae, audio):
+        t = vae.encode(audio["waveform"].movedim(1, -1))
+        return ({"samples":t}, )
+
+class VAEDecodeAudio:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "samples": ("LATENT", ), "vae": ("VAE", )}}
+    RETURN_TYPES = ("AUDIO",)
+    FUNCTION = "decode"
+
+    CATEGORY = "_for_testing/audio"
+
+    def decode(self, vae, samples):
+        audio = vae.decode(samples["samples"]).movedim(-1, 1)
+        return ({"waveform": audio, "sample_rate": 44100}, )
+
+class SaveAudio:
+    def __init__(self):
+        self.output_dir = folder_paths.get_output_directory()
+        self.type = "output"
+        self.prefix_append = ""
+        self.compress_level = 4
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "audio": ("AUDIO", ),
+                              "filename_prefix": ("STRING", {"default": "audio/ComfyUI"})},
+                "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},
+                }
+
+    RETURN_TYPES = ()
+    FUNCTION = "save_audio"
+
+    OUTPUT_NODE = True
+
+    CATEGORY = "_for_testing/audio"
+
+    def save_audio(self, audio, filename_prefix="ComfyUI", prompt=None, extra_pnginfo=None):
+        filename_prefix += self.prefix_append
+        full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir)
+        results = list()
+        for (batch_number, waveform) in enumerate(audio["waveform"]):
+            #TODO: metadata
+            filename_with_batch_num = filename.replace("%batch_num%", str(batch_number))
+            file = f"{filename_with_batch_num}_{counter:05}_.flac"
+            torchaudio.save(os.path.join(full_output_folder, file), waveform, audio["sample_rate"], format="FLAC")
+            results.append({
+                "filename": file,
+                "subfolder": subfolder,
+                "type": self.type
+            })
+            counter += 1
+
+        return { "ui": { "audio": results } }
+
+class LoadAudio:
+    @classmethod
+    def INPUT_TYPES(s):
+        input_dir = folder_paths.get_input_directory()
+        files = [f for f in os.listdir(input_dir) if os.path.isfile(os.path.join(input_dir, f))]
+        return {"required": {"audio": [sorted(files), ]}, }
+
+    CATEGORY = "_for_testing/audio"
+
+    RETURN_TYPES = ("AUDIO", )
+    FUNCTION = "load"
+
+    def load(self, audio):
+        audio_path = folder_paths.get_annotated_filepath(audio)
+        waveform, sample_rate = torchaudio.load(audio_path)
+        multiplier = 1.0
+        audio = {"waveform": waveform.unsqueeze(0), "sample_rate": sample_rate}
+        return (audio, )
+
+    @classmethod
+    def IS_CHANGED(s, audio):
+        image_path = folder_paths.get_annotated_filepath(audio)
+        m = hashlib.sha256()
+        with open(image_path, 'rb') as f:
+            m.update(f.read())
+        return m.digest().hex()
+
+    @classmethod
+    def VALIDATE_INPUTS(s, audio):
+        if not folder_paths.exists_annotated_filepath(audio):
+            return "Invalid audio file: {}".format(audio)
+        return True
+
+NODE_CLASS_MAPPINGS = {
+    "EmptyLatentAudio": EmptyLatentAudio,
+    "VAEEncodeAudio": VAEEncodeAudio,
+    "VAEDecodeAudio": VAEDecodeAudio,
+    "SaveAudio": SaveAudio,
+    "LoadAudio": LoadAudio,
+}

comfy_extras/nodes_model_advanced.py

@@ -196,6 +196,36 @@ class ModelSamplingContinuousEDM:
             m.add_object_patch("latent_format", latent_format)
         return (m, )
 
+class ModelSamplingContinuousV:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "model": ("MODEL",),
+                              "sampling": (["v_prediction"],),
+                              "sigma_max": ("FLOAT", {"default": 500.0, "min": 0.0, "max": 1000.0, "step":0.001, "round": False}),
+                              "sigma_min": ("FLOAT", {"default": 0.03, "min": 0.0, "max": 1000.0, "step":0.001, "round": False}),
+                              }}
+
+    RETURN_TYPES = ("MODEL",)
+    FUNCTION = "patch"
+
+    CATEGORY = "advanced/model"
+
+    def patch(self, model, sampling, sigma_max, sigma_min):
+        m = model.clone()
+
+        latent_format = None
+        sigma_data = 1.0
+        if sampling == "v_prediction":
+            sampling_type = comfy.model_sampling.V_PREDICTION
+
+        class ModelSamplingAdvanced(comfy.model_sampling.ModelSamplingContinuousV, sampling_type):
+            pass
+
+        model_sampling = ModelSamplingAdvanced(model.model.model_config)
+        model_sampling.set_parameters(sigma_min, sigma_max, sigma_data)
+        m.add_object_patch("model_sampling", model_sampling)
+        return (m, )
+
 class RescaleCFG:
     @classmethod
     def INPUT_TYPES(s):
@@ -238,6 +268,7 @@ class RescaleCFG:
 NODE_CLASS_MAPPINGS = {
     "ModelSamplingDiscrete": ModelSamplingDiscrete,
     "ModelSamplingContinuousEDM": ModelSamplingContinuousEDM,
+    "ModelSamplingContinuousV": ModelSamplingContinuousV,
     "ModelSamplingStableCascade": ModelSamplingStableCascade,
     "ModelSamplingSD3": ModelSamplingSD3,
     "RescaleCFG": RescaleCFG,

nodes.py

@@ -818,7 +818,7 @@ class CLIPLoader:
     @classmethod
     def INPUT_TYPES(s):
         return {"required": { "clip_name": (folder_paths.get_filename_list("clip"), ),
-                              "type": (["stable_diffusion", "stable_cascade", "sd3"], ),
+                              "type": (["stable_diffusion", "stable_cascade", "sd3", "stable_audio"], ),
                              }}
     RETURN_TYPES = ("CLIP",)
     FUNCTION = "load_clip"
@@ -826,11 +826,14 @@ class CLIPLoader:
     CATEGORY = "advanced/loaders"
 
     def load_clip(self, clip_name, type="stable_diffusion"):
-        clip_type = comfy.sd.CLIPType.STABLE_DIFFUSION
         if type == "stable_cascade":
             clip_type = comfy.sd.CLIPType.STABLE_CASCADE
         elif type == "sd3":
             clip_type = comfy.sd.CLIPType.SD3
+        elif type == "stable_audio":
+            clip_type = comfy.sd.CLIPType.STABLE_AUDIO
+        else:
+            clip_type = comfy.sd.CLIPType.STABLE_DIFFUSION
 
         clip_path = folder_paths.get_full_path("clip", clip_name)
         clip = comfy.sd.load_clip(ckpt_paths=[clip_path], embedding_directory=folder_paths.get_folder_paths("embeddings"), clip_type=clip_type)
@@ -1973,6 +1976,7 @@ def init_custom_nodes():
         "nodes_attention_multiply.py",
         "nodes_advanced_samplers.py",
         "nodes_webcam.py",
+        "nodes_audio.py",
         "nodes_sd3.py",
     ]
 

requirements.txt

 torch
 torchsde
 torchvision
+torchaudio
 einops
 transformers>=4.25.1
 safetensors>=0.4.2