feat: extract prismaudio_core model modules (DiT, conditioners, VAE, diffusion)

Fetch and adapt inference-critical model modules from upstream PrismAudio repo:
- dit.py: DiffusionTransformer with debug prints removed
- diffusion.py: ConditionedDiffusionModelWrapper, DiTWrapper, MMDiTWrapper
- conditioners.py: Cond_MLP, Sync_MLP, MultiConditioner with stubbed training imports
- autoencoders.py: AudioAutoencoder, OobleckEncoder/Decoder
- transformer.py: ContinuousTransformer, Attention with flash_attn fallback to SDPA
- blocks.py, utils.py, bottleneck.py, pretransforms.py, local_attention.py, pqmf.py
- adp.py: UNetCFG1d, UNet1d, NumberEmbedder
- mmmodules/model/low_level.py: MLP, ChannelLastConv1d, ConvMLP

All internal imports rewritten from PrismAudio.* to prismaudio_core.*,
training-only imports stubbed, flash_attn made optional with HAS_FLASH_ATTN flag.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

prismaudio_core/models/__init__.py

@@ -0,0 +1,9 @@
+"""
+PrismAudio model modules for inference.
+
+Re-exports create_model_from_config from the factory module.
+"""
+
+from prismaudio_core.factory import create_model_from_config
+
+__all__ = ["create_model_from_config"]

prismaudio_core/models/autoencoders.py

@@ -0,0 +1,830 @@
+import torch
+import math
+import numpy as np
+
+from torch import nn
+from torch.nn import functional as F
+from torchaudio import transforms as T
+from alias_free_torch import Activation1d
+from dac.nn.layers import WNConv1d, WNConvTranspose1d
+from typing import Literal, Dict, Any
+
+from .blocks import SnakeBeta
+from .bottleneck import Bottleneck, DiscreteBottleneck
+from .diffusion import ConditionedDiffusionModel, DAU1DCondWrapper, UNet1DCondWrapper, DiTWrapper
+from .pretransforms import Pretransform
+
+
+def prepare_audio(audio, in_sr, target_sr, target_length, target_channels, device):
+    """Minimal stub for inference.utils.prepare_audio used by autoencoders."""
+    import torchaudio.transforms as T
+    import torch
+
+    if in_sr != target_sr:
+        resample_tf = T.Resample(in_sr, target_sr).to(device)
+        audio = resample_tf(audio)
+
+    if audio.shape[0] > target_channels:
+        audio = audio[:target_channels]
+    elif audio.shape[0] < target_channels:
+        audio = audio.repeat(target_channels // audio.shape[0] + 1, 1)[:target_channels]
+
+    if audio.shape[-1] < target_length:
+        audio = torch.nn.functional.pad(audio, (0, target_length - audio.shape[-1]))
+    elif audio.shape[-1] > target_length:
+        audio = audio[..., :target_length]
+
+    return audio.unsqueeze(0)
+
+
+def _lazy_create_pretransform_from_config(pretransform, sample_rate):
+    from prismaudio_core.factory import create_pretransform_from_config
+    return create_pretransform_from_config(pretransform, sample_rate)
+
+
+def _lazy_create_bottleneck_from_config(bottleneck):
+    from prismaudio_core.factory import create_bottleneck_from_config
+    return create_bottleneck_from_config(bottleneck)
+
+def checkpoint(function, *args, **kwargs):
+    kwargs.setdefault("use_reentrant", False)
+    return torch.utils.checkpoint.checkpoint(function, *args, **kwargs)
+
+def get_activation(activation: Literal["elu", "snake", "none"], antialias=False, channels=None) -> nn.Module:
+    if activation == "elu":
+        act = nn.ELU()
+    elif activation == "snake":
+        act = SnakeBeta(channels)
+    elif activation == "none":
+        act = 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 DACEncoderWrapper(nn.Module):
+    def __init__(self, in_channels=1, **kwargs):
+        super().__init__()
+
+        from dac.model.dac import Encoder as DACEncoder
+
+        latent_dim = kwargs.pop("latent_dim", None)
+
+        encoder_out_dim = kwargs["d_model"] * (2 ** len(kwargs["strides"]))
+        self.encoder = DACEncoder(d_latent=encoder_out_dim, **kwargs)
+        self.latent_dim = latent_dim
+
+        # Latent-dim support was added to DAC after this was first written, and implemented differently, so this is for backwards compatibility
+        self.proj_out = nn.Conv1d(self.encoder.enc_dim, latent_dim, kernel_size=1) if latent_dim is not None else nn.Identity()
+
+        if in_channels != 1:
+            self.encoder.block[0] = WNConv1d(in_channels, kwargs.get("d_model", 64), kernel_size=7, padding=3)
+
+    def forward(self, x):
+        x = self.encoder(x)
+        x = self.proj_out(x)
+        return x
+
+class DACDecoderWrapper(nn.Module):
+    def __init__(self, latent_dim, out_channels=1, **kwargs):
+        super().__init__()
+
+        from dac.model.dac import Decoder as DACDecoder
+
+        self.decoder = DACDecoder(**kwargs, input_channel = latent_dim, d_out=out_channels)
+
+        self.latent_dim = latent_dim
+
+    def forward(self, x):
+        return self.decoder(x)
+
+class AudioAutoencoder(nn.Module):
+    def __init__(
+        self,
+        encoder,
+        decoder,
+        latent_dim,
+        downsampling_ratio,
+        sample_rate,
+        io_channels=2,
+        bottleneck: Bottleneck = None,
+        pretransform: Pretransform = None,
+        in_channels = None,
+        out_channels = None,
+        soft_clip = False
+    ):
+        super().__init__()
+
+        self.downsampling_ratio = downsampling_ratio
+        self.sample_rate = sample_rate
+
+        self.latent_dim = latent_dim
+        self.io_channels = io_channels
+        self.in_channels = io_channels
+        self.out_channels = io_channels
+
+        self.min_length = self.downsampling_ratio
+
+        if in_channels is not None:
+            self.in_channels = in_channels
+
+        if out_channels is not None:
+            self.out_channels = out_channels
+
+        self.bottleneck = bottleneck
+
+        self.encoder = encoder
+
+        self.decoder = decoder
+
+        self.pretransform = pretransform
+
+        self.soft_clip = soft_clip
+ 
+        self.is_discrete = self.bottleneck is not None and self.bottleneck.is_discrete
+
+    def encode(self, audio, return_info=False, skip_pretransform=False, iterate_batch=False, **kwargs):
+
+        info = {}
+        # import ipdb
+        # ipdb.set_trace()
+        if self.pretransform is not None and not skip_pretransform:
+            if self.pretransform.enable_grad:
+                if iterate_batch:
+                    audios = []
+                    for i in range(audio.shape[0]):
+                        audios.append(self.pretransform.encode(audio[i:i+1]))
+                    audio = torch.cat(audios, dim=0)
+                else:
+                    audio = self.pretransform.encode(audio)
+            else:
+                with torch.no_grad():
+                    if iterate_batch:
+                        audios = []
+                        for i in range(audio.shape[0]):
+                            audios.append(self.pretransform.encode(audio[i:i+1]))
+                        audio = torch.cat(audios, dim=0)
+                    else:
+                        audio = self.pretransform.encode(audio)
+
+        if self.encoder is not None:
+            if iterate_batch:
+                latents = []
+                for i in range(audio.shape[0]):
+                    latents.append(self.encoder(audio[i:i+1]))
+                latents = torch.cat(latents, dim=0)
+            else:
+                latents = self.encoder(audio)
+        else:
+            latents = audio
+
+        if self.bottleneck is not None:
+            # TODO: Add iterate batch logic, needs to merge the info dicts
+            latents, bottleneck_info = self.bottleneck.encode(latents, return_info=True, **kwargs)
+
+            info.update(bottleneck_info)
+        
+        if return_info:
+            return latents, info
+
+        return latents
+
+    def decode(self, latents, iterate_batch=False, **kwargs):
+
+        if self.bottleneck is not None:
+            if iterate_batch:
+                decoded = []
+                for i in range(latents.shape[0]):
+                    decoded.append(self.bottleneck.decode(latents[i:i+1]))
+                latents = torch.cat(decoded, dim=0)
+            else:
+                latents = self.bottleneck.decode(latents)
+
+        if iterate_batch:
+            decoded = []
+            for i in range(latents.shape[0]):
+                decoded.append(self.decoder(latents[i:i+1]))
+            decoded = torch.cat(decoded, dim=0)
+        else:
+            decoded = self.decoder(latents, **kwargs)
+
+        if self.pretransform is not None:
+            if self.pretransform.enable_grad:
+                if iterate_batch:
+                    decodeds = []
+                    for i in range(decoded.shape[0]):
+                        decodeds.append(self.pretransform.decode(decoded[i:i+1]))
+                    decoded = torch.cat(decodeds, dim=0)
+                else:
+                    decoded = self.pretransform.decode(decoded)
+            else:
+                with torch.no_grad():
+                    if iterate_batch:
+                        decodeds = []
+                        for i in range(latents.shape[0]):
+                            decodeds.append(self.pretransform.decode(decoded[i:i+1]))
+                        decoded = torch.cat(decodeds, dim=0)
+                    else:
+                        decoded = self.pretransform.decode(decoded)
+
+        if self.soft_clip:
+            decoded = torch.tanh(decoded)
+        
+        return decoded
+          
+    def decode_tokens(self, tokens, **kwargs):
+        '''
+        Decode discrete tokens to audio
+        Only works with discrete autoencoders
+        '''
+
+        assert isinstance(self.bottleneck, DiscreteBottleneck), "decode_tokens only works with discrete autoencoders"
+
+        latents = self.bottleneck.decode_tokens(tokens, **kwargs)
+
+        return self.decode(latents, **kwargs)
+        
+    
+    def preprocess_audio_for_encoder(self, audio, in_sr):
+        '''
+        Preprocess single audio tensor (Channels x Length) to be compatible with the encoder.
+        If the model is mono, stereo audio will be converted to mono.
+        Audio will be silence-padded to be a multiple of the model's downsampling ratio.
+        Audio will be resampled to the model's sample rate. 
+        The output will have batch size 1 and be shape (1 x Channels x Length)
+        '''
+        return self.preprocess_audio_list_for_encoder([audio], [in_sr])
+
+    def preprocess_audio_list_for_encoder(self, audio_list, in_sr_list):
+        '''
+        Preprocess a [list] of audio (Channels x Length) into a batch tensor to be compatable with the encoder. 
+        The audio in that list can be of different lengths and channels. 
+        in_sr can be an integer or list. If it's an integer it will be assumed it is the input sample_rate for every audio.
+        All audio will be resampled to the model's sample rate. 
+        Audio will be silence-padded to the longest length, and further padded to be a multiple of the model's downsampling ratio. 
+        If the model is mono, all audio will be converted to mono. 
+        The output will be a tensor of shape (Batch x Channels x Length)
+        '''
+        batch_size = len(audio_list)
+        if isinstance(in_sr_list, int):
+            in_sr_list = [in_sr_list]*batch_size
+        assert len(in_sr_list) == batch_size, "list of sample rates must be the same length of audio_list"
+        new_audio = []
+        max_length = 0
+        # resample & find the max length
+        for i in range(batch_size):
+            audio = audio_list[i]
+            in_sr = in_sr_list[i]
+            if len(audio.shape) == 3 and audio.shape[0] == 1:
+                # batchsize 1 was given by accident. Just squeeze it.
+                audio = audio.squeeze(0)
+            elif len(audio.shape) == 1:
+                # Mono signal, channel dimension is missing, unsqueeze it in
+                audio = audio.unsqueeze(0)
+            assert len(audio.shape)==2, "Audio should be shape (Channels x Length) with no batch dimension" 
+            # Resample audio
+            if in_sr != self.sample_rate:
+                resample_tf = T.Resample(in_sr, self.sample_rate).to(audio.device)
+                audio = resample_tf(audio)
+            new_audio.append(audio)
+            if audio.shape[-1] > max_length:
+                max_length = audio.shape[-1]
+        # Pad every audio to the same length, multiple of model's downsampling ratio
+        padded_audio_length = max_length + (self.min_length - (max_length % self.min_length)) % self.min_length
+        for i in range(batch_size):
+            # Pad it & if necessary, mixdown/duplicate stereo/mono channels to support model
+            new_audio[i] = prepare_audio(new_audio[i], in_sr=in_sr, target_sr=in_sr, target_length=padded_audio_length, 
+                target_channels=self.in_channels, device=new_audio[i].device).squeeze(0)
+        # convert to tensor 
+        return torch.stack(new_audio) 
+
+    def encode_audio(self, audio, chunked=False, overlap=32, chunk_size=128, **kwargs):
+        '''
+        Encode audios into latents. Audios should already be preprocesed by preprocess_audio_for_encoder.
+        If chunked is True, split the audio into chunks of a given maximum size chunk_size, with given overlap.
+        Overlap and chunk_size params are both measured in number of latents (not audio samples) 
+        # and therefore you likely could use the same values with decode_audio. 
+        A overlap of zero will cause discontinuity artefacts. Overlap should be => receptive field size. 
+        Every autoencoder will have a different receptive field size, and thus ideal overlap.
+        You can determine it empirically by diffing unchunked vs chunked output and looking at maximum diff.
+        The final chunk may have a longer overlap in order to keep chunk_size consistent for all chunks.
+        Smaller chunk_size uses less memory, but more compute.
+        The chunk_size vs memory tradeoff isn't linear, and possibly depends on the GPU and CUDA version
+        For example, on a A6000 chunk_size 128 is overall faster than 256 and 512 even though it has more chunks
+        '''
+        if not chunked:
+            # default behavior. Encode the entire audio in parallel
+            return self.encode(audio, **kwargs)
+        else:
+            # CHUNKED ENCODING
+            # samples_per_latent is just the downsampling ratio (which is also the upsampling ratio)
+            # import ipdb
+            # ipdb.set_trace()
+            samples_per_latent = self.downsampling_ratio
+            total_size = audio.shape[2] # in samples
+            print(f'audio shape: {audio.shape}')
+            batch_size = audio.shape[0]
+            chunk_size *= samples_per_latent # converting metric in latents to samples
+            overlap *= samples_per_latent # converting metric in latents to samples
+            hop_size = chunk_size - overlap
+            chunks = []
+            for i in range(0, total_size - chunk_size + 1, hop_size):
+                chunk = audio[:,:,i:i+chunk_size]
+                chunks.append(chunk)
+            if i+chunk_size != total_size:
+                # Final chunk
+                chunk = audio[:,:,-chunk_size:]
+                chunks.append(chunk)
+            chunks = torch.stack(chunks)
+            num_chunks = chunks.shape[0]
+            # Note: y_size might be a different value from the latent length used in diffusion training
+            # because we can encode audio of varying lengths
+            # However, the audio should've been padded to a multiple of samples_per_latent by now.
+            y_size = total_size // samples_per_latent
+            # Create an empty latent, we will populate it with chunks as we encode them
+            y_final = torch.zeros((batch_size,self.latent_dim,y_size)).to(audio.device)
+            print(f'y_final shape: {y_final.shape}')
+            for i in range(num_chunks):
+                x_chunk = chunks[i,:]
+                # encode the chunk
+                y_chunk = self.encode(x_chunk)
+                print(f'y_chunk shape: {y_chunk.shape}')
+                # figure out where to put the audio along the time domain
+                if i == num_chunks-1:
+                    # final chunk always goes at the end
+                    t_end = y_size
+                    t_start = t_end - y_chunk.shape[2]
+                else:
+                    t_start = i * hop_size // samples_per_latent
+                    t_end = t_start + chunk_size // samples_per_latent
+                #  remove the edges of the overlaps
+                ol = overlap//samples_per_latent//2
+                chunk_start = 0
+                chunk_end = y_chunk.shape[2]
+                if i > 0:
+                    # no overlap for the start of the first chunk
+                    t_start += ol
+                    chunk_start += ol
+                if i < num_chunks-1:
+                    # no overlap for the end of the last chunk
+                    t_end -= ol
+                    chunk_end -= ol
+                # paste the chunked audio into our y_final output audio
+                y_final[:,:,t_start:t_end] = y_chunk[:,:,chunk_start:chunk_end]
+            return y_final
+    
+    def decode_audio(self, latents, chunked=False, overlap=32, chunk_size=128, **kwargs):
+        '''
+        Decode latents to audio. 
+        If chunked is True, split the latents into chunks of a given maximum size chunk_size, with given overlap, both of which are measured in number of latents. 
+        A overlap of zero will cause discontinuity artefacts. Overlap should be => receptive field size. 
+        Every autoencoder will have a different receptive field size, and thus ideal overlap.
+        You can determine it empirically by diffing unchunked vs chunked audio and looking at maximum diff.
+        The final chunk may have a longer overlap in order to keep chunk_size consistent for all chunks.
+        Smaller chunk_size uses less memory, but more compute.
+        The chunk_size vs memory tradeoff isn't linear, and possibly depends on the GPU and CUDA version
+        For example, on a A6000 chunk_size 128 is overall faster than 256 and 512 even though it has more chunks
+        '''
+        if not chunked:
+            # default behavior. Decode the entire latent in parallel
+            return self.decode(latents, **kwargs)
+        else:
+            # chunked decoding
+            hop_size = chunk_size - overlap
+            total_size = latents.shape[2]
+            batch_size = latents.shape[0]
+            chunks = []
+            for i in range(0, total_size - chunk_size + 1, hop_size):
+                chunk = latents[:,:,i:i+chunk_size]
+                chunks.append(chunk)
+            if i+chunk_size != total_size:
+                # Final chunk
+                chunk = latents[:,:,-chunk_size:]
+                chunks.append(chunk)
+            chunks = torch.stack(chunks)
+            num_chunks = chunks.shape[0]
+            # samples_per_latent is just the downsampling ratio
+            samples_per_latent = self.downsampling_ratio
+            # Create an empty waveform, we will populate it with chunks as decode them
+            y_size = total_size * samples_per_latent
+            y_final = torch.zeros((batch_size,self.out_channels,y_size)).to(latents.device)
+            for i in range(num_chunks):
+                x_chunk = chunks[i,:]
+                # decode the chunk
+                y_chunk = self.decode(x_chunk)
+                # figure out where to put the audio along the time domain
+                if i == num_chunks-1:
+                    # final chunk always goes at the end
+                    t_end = y_size
+                    t_start = t_end - y_chunk.shape[2]
+                else:
+                    t_start = i * hop_size * samples_per_latent
+                    t_end = t_start + chunk_size * samples_per_latent
+                #  remove the edges of the overlaps
+                ol = (overlap//2) * samples_per_latent
+                chunk_start = 0
+                chunk_end = y_chunk.shape[2]
+                if i > 0:
+                    # no overlap for the start of the first chunk
+                    t_start += ol
+                    chunk_start += ol
+                if i < num_chunks-1:
+                    # no overlap for the end of the last chunk
+                    t_end -= ol
+                    chunk_end -= ol
+                # paste the chunked audio into our y_final output audio
+                y_final[:,:,t_start:t_end] = y_chunk[:,:,chunk_start:chunk_end]
+            return y_final
+
+    
+class DiffusionAutoencoder(AudioAutoencoder):
+    def __init__(
+        self,
+        diffusion: ConditionedDiffusionModel,
+        diffusion_downsampling_ratio,
+        *args,
+        **kwargs
+    ):
+        super().__init__(*args, **kwargs)
+
+        self.diffusion = diffusion
+
+        self.min_length = self.downsampling_ratio * diffusion_downsampling_ratio
+
+        if self.encoder is not None:
+            # Shrink the initial encoder parameters to avoid saturated latents
+            with torch.no_grad():
+                for param in self.encoder.parameters():
+                    param *= 0.5
+
+    def decode(self, latents, steps=100):
+
+        upsampled_length = latents.shape[2] * self.downsampling_ratio
+
+        if self.bottleneck is not None:
+            latents = self.bottleneck.decode(latents)
+
+        if self.decoder is not None:
+            latents = self.decode(latents)
+    
+        # Upsample latents to match diffusion length
+        if latents.shape[2] != upsampled_length:
+            latents = F.interpolate(latents, size=upsampled_length, mode='nearest')
+
+        noise = torch.randn(latents.shape[0], self.io_channels, upsampled_length, device=latents.device)
+        from prismaudio_core.inference.sampling import sample
+        decoded = sample(self.diffusion, noise, steps, 0, input_concat_cond=latents)
+
+        if self.pretransform is not None:
+            if self.pretransform.enable_grad:
+                decoded = self.pretransform.decode(decoded)
+            else:
+                with torch.no_grad():
+                    decoded = self.pretransform.decode(decoded)
+
+        return decoded
+        
+# AE factories
+
+def create_encoder_from_config(encoder_config: Dict[str, Any]):
+    encoder_type = encoder_config.get("type", None)
+    assert encoder_type is not None, "Encoder type must be specified"
+
+    if encoder_type == "oobleck":
+        encoder = OobleckEncoder(
+            **encoder_config["config"]
+        )
+    
+    elif encoder_type == "seanet":
+        from encodec.modules import SEANetEncoder
+        seanet_encoder_config = encoder_config["config"]
+
+        #SEANet encoder expects strides in reverse order
+        seanet_encoder_config["ratios"] = list(reversed(seanet_encoder_config.get("ratios", [2, 2, 2, 2, 2])))
+        encoder = SEANetEncoder(
+            **seanet_encoder_config
+        )
+    elif encoder_type == "dac":
+        dac_config = encoder_config["config"]
+
+        encoder = DACEncoderWrapper(**dac_config)
+    elif encoder_type == "local_attn":
+        from .local_attention import TransformerEncoder1D
+
+        local_attn_config = encoder_config["config"]
+
+        encoder = TransformerEncoder1D(
+            **local_attn_config
+        )
+    else:
+        raise ValueError(f"Unknown encoder type {encoder_type}")
+    
+    requires_grad = encoder_config.get("requires_grad", True)
+    if not requires_grad:
+        for param in encoder.parameters():
+            param.requires_grad = False
+
+    return encoder
+
+def create_decoder_from_config(decoder_config: Dict[str, Any]):
+    decoder_type = decoder_config.get("type", None)
+    assert decoder_type is not None, "Decoder type must be specified"
+
+    if decoder_type == "oobleck":
+        decoder = OobleckDecoder(
+            **decoder_config["config"]
+        )
+    elif decoder_type == "seanet":
+        from encodec.modules import SEANetDecoder
+
+        decoder = SEANetDecoder(
+            **decoder_config["config"]
+        )
+    elif decoder_type == "dac":
+        dac_config = decoder_config["config"]
+
+        decoder = DACDecoderWrapper(**dac_config)
+    elif decoder_type == "local_attn":
+        from .local_attention import TransformerDecoder1D
+
+        local_attn_config = decoder_config["config"]
+
+        decoder = TransformerDecoder1D(
+            **local_attn_config
+        )
+    else:
+        raise ValueError(f"Unknown decoder type {decoder_type}")
+    
+    requires_grad = decoder_config.get("requires_grad", True)
+    if not requires_grad:
+        for param in decoder.parameters():
+            param.requires_grad = False
+
+    return decoder
+
+def create_autoencoder_from_config(config: Dict[str, Any]):
+    
+    ae_config = config["model"]
+
+    encoder = create_encoder_from_config(ae_config["encoder"])
+    decoder = create_decoder_from_config(ae_config["decoder"])
+
+    bottleneck = ae_config.get("bottleneck", None)
+
+    latent_dim = ae_config.get("latent_dim", None)
+    assert latent_dim is not None, "latent_dim must be specified in model config"
+    downsampling_ratio = ae_config.get("downsampling_ratio", None)
+    assert downsampling_ratio is not None, "downsampling_ratio must be specified in model config"
+    io_channels = ae_config.get("io_channels", None)
+    assert io_channels is not None, "io_channels must be specified in model config"
+    sample_rate = config.get("sample_rate", None)
+    assert sample_rate is not None, "sample_rate must be specified in model config"
+
+    in_channels = ae_config.get("in_channels", None)
+    out_channels = ae_config.get("out_channels", None)
+
+    pretransform = ae_config.get("pretransform", None)
+
+    if pretransform is not None:
+        pretransform = _lazy_create_pretransform_from_config(pretransform, sample_rate)
+
+    if bottleneck is not None:
+        bottleneck = _lazy_create_bottleneck_from_config(bottleneck)
+
+    soft_clip = ae_config["decoder"].get("soft_clip", False)
+
+    return AudioAutoencoder(
+        encoder,
+        decoder,
+        io_channels=io_channels,
+        latent_dim=latent_dim,
+        downsampling_ratio=downsampling_ratio,
+        sample_rate=sample_rate,
+        bottleneck=bottleneck,
+        pretransform=pretransform,
+        in_channels=in_channels,
+        out_channels=out_channels,
+        soft_clip=soft_clip
+    )
+
+def create_diffAE_from_config(config: Dict[str, Any]):
+    
+    diffae_config = config["model"]
+
+    if "encoder" in diffae_config:
+        encoder = create_encoder_from_config(diffae_config["encoder"])
+    else:
+        encoder = None
+
+    if "decoder" in diffae_config:
+        decoder = create_decoder_from_config(diffae_config["decoder"])
+    else:
+        decoder = None
+
+    diffusion_model_type = diffae_config["diffusion"]["type"]
+
+    if diffusion_model_type == "DAU1d":
+        diffusion = DAU1DCondWrapper(**diffae_config["diffusion"]["config"])
+    elif diffusion_model_type == "adp_1d":
+        diffusion = UNet1DCondWrapper(**diffae_config["diffusion"]["config"])
+    elif diffusion_model_type == "dit":
+        diffusion = DiTWrapper(**diffae_config["diffusion"]["config"])
+
+    latent_dim = diffae_config.get("latent_dim", None)
+    assert latent_dim is not None, "latent_dim must be specified in model config"
+    downsampling_ratio = diffae_config.get("downsampling_ratio", None)
+    assert downsampling_ratio is not None, "downsampling_ratio must be specified in model config"
+    io_channels = diffae_config.get("io_channels", None)
+    assert io_channels is not None, "io_channels must be specified in model config"
+    sample_rate = config.get("sample_rate", None)
+    assert sample_rate is not None, "sample_rate must be specified in model config"
+
+    bottleneck = diffae_config.get("bottleneck", None)
+
+    pretransform = diffae_config.get("pretransform", None)
+
+    if pretransform is not None:
+        pretransform = _lazy_create_pretransform_from_config(pretransform, sample_rate)
+
+    if bottleneck is not None:
+        bottleneck = _lazy_create_bottleneck_from_config(bottleneck)
+
+    diffusion_downsampling_ratio = None,
+
+    if diffusion_model_type == "DAU1d":
+        diffusion_downsampling_ratio = np.prod(diffae_config["diffusion"]["config"]["strides"])
+    elif diffusion_model_type == "adp_1d":
+        diffusion_downsampling_ratio = np.prod(diffae_config["diffusion"]["config"]["factors"])
+    elif diffusion_model_type == "dit":
+        diffusion_downsampling_ratio = 1
+
+    return DiffusionAutoencoder(
+        encoder=encoder,
+        decoder=decoder,
+        diffusion=diffusion,
+        io_channels=io_channels,
+        sample_rate=sample_rate,
+        latent_dim=latent_dim,
+        downsampling_ratio=downsampling_ratio,
+        diffusion_downsampling_ratio=diffusion_downsampling_ratio,
+        bottleneck=bottleneck,
+        pretransform=pretransform
+    )

prismaudio_core/models/blocks.py

@@ -0,0 +1,339 @@
+from functools import reduce
+import math
+import numpy as np
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+from torch.backends.cuda import sdp_kernel
+from packaging import version
+
+from dac.nn.layers import Snake1d
+
+class ResidualBlock(nn.Module):
+    def __init__(self, main, skip=None):
+        super().__init__()
+        self.main = nn.Sequential(*main)
+        self.skip = skip if skip else nn.Identity()
+
+    def forward(self, input):
+        return self.main(input) + self.skip(input)
+
+class ResConvBlock(ResidualBlock):
+    def __init__(self, c_in, c_mid, c_out, is_last=False, kernel_size=5, conv_bias=True, use_snake=False):
+        skip = None if c_in == c_out else nn.Conv1d(c_in, c_out, 1, bias=False)
+        super().__init__([
+            nn.Conv1d(c_in, c_mid, kernel_size, padding=kernel_size//2, bias=conv_bias),
+            nn.GroupNorm(1, c_mid),
+            Snake1d(c_mid) if use_snake else nn.GELU(),
+            nn.Conv1d(c_mid, c_out, kernel_size, padding=kernel_size//2, bias=conv_bias),
+            nn.GroupNorm(1, c_out) if not is_last else nn.Identity(),
+            (Snake1d(c_out) if use_snake else nn.GELU()) if not is_last else nn.Identity(),
+        ], skip)
+
+class SelfAttention1d(nn.Module):
+    def __init__(self, c_in, n_head=1, dropout_rate=0.):
+        super().__init__()
+        assert c_in % n_head == 0
+        self.norm = nn.GroupNorm(1, c_in)
+        self.n_head = n_head
+        self.qkv_proj = nn.Conv1d(c_in, c_in * 3, 1)
+        self.out_proj = nn.Conv1d(c_in, c_in, 1)
+        self.dropout = nn.Dropout(dropout_rate, inplace=True)
+
+        self.use_flash = torch.cuda.is_available() and version.parse(torch.__version__) >= version.parse('2.0.0')
+
+        if not self.use_flash:
+            return
+
+        device_properties = torch.cuda.get_device_properties(torch.device('cuda'))
+
+        if device_properties.major == 8 and device_properties.minor == 0:
+            # Use flash attention for A100 GPUs
+            self.sdp_kernel_config = (True, False, False)
+        else:
+            # Don't use flash attention for other GPUs
+            self.sdp_kernel_config = (False, True, True)
+
+    def forward(self, input):
+        n, c, s = input.shape
+        qkv = self.qkv_proj(self.norm(input))
+        qkv = qkv.view(
+            [n, self.n_head * 3, c // self.n_head, s]).transpose(2, 3)
+        q, k, v = qkv.chunk(3, dim=1)
+        scale = k.shape[3]**-0.25
+
+        if self.use_flash:
+            with sdp_kernel(*self.sdp_kernel_config):
+                y = F.scaled_dot_product_attention(q, k, v, is_causal=False).contiguous().view([n, c, s])
+        else:
+            att = ((q * scale) @ (k.transpose(2, 3) * scale)).softmax(3)
+            y = (att @ v).transpose(2, 3).contiguous().view([n, c, s])
+
+
+        return input + self.dropout(self.out_proj(y))
+
+class SkipBlock(nn.Module):
+    def __init__(self, *main):
+        super().__init__()
+        self.main = nn.Sequential(*main)
+
+    def forward(self, input):
+        return torch.cat([self.main(input), input], dim=1)
+
+class FourierFeatures(nn.Module):
+    def __init__(self, in_features, out_features, std=1.):
+        super().__init__()
+        assert out_features % 2 == 0
+        self.weight = nn.Parameter(torch.randn(
+            [out_features // 2, in_features]) * std)
+
+    def forward(self, input):
+        f = 2 * math.pi * input @ self.weight.T
+        return torch.cat([f.cos(), f.sin()], dim=-1)
+
+def expand_to_planes(input, shape):
+    return input[..., None].repeat([1, 1, shape[2]])
+
+_kernels = {
+    'linear':
+        [1 / 8, 3 / 8, 3 / 8, 1 / 8],
+    'cubic': 
+        [-0.01171875, -0.03515625, 0.11328125, 0.43359375,
+        0.43359375, 0.11328125, -0.03515625, -0.01171875],
+    'lanczos3': 
+        [0.003689131001010537, 0.015056144446134567, -0.03399861603975296,
+        -0.066637322306633, 0.13550527393817902, 0.44638532400131226,
+        0.44638532400131226, 0.13550527393817902, -0.066637322306633,
+        -0.03399861603975296, 0.015056144446134567, 0.003689131001010537]
+}
+
+class Downsample1d(nn.Module):
+    def __init__(self, kernel='linear', pad_mode='reflect', channels_last=False):
+        super().__init__()
+        self.pad_mode = pad_mode
+        kernel_1d = torch.tensor(_kernels[kernel])
+        self.pad = kernel_1d.shape[0] // 2 - 1
+        self.register_buffer('kernel', kernel_1d)
+        self.channels_last = channels_last
+    
+    def forward(self, x):
+        if self.channels_last:
+            x = x.permute(0, 2, 1)
+        x = F.pad(x, (self.pad,) * 2, self.pad_mode)
+        weight = x.new_zeros([x.shape[1], x.shape[1], self.kernel.shape[0]])
+        indices = torch.arange(x.shape[1], device=x.device)
+        weight[indices, indices] = self.kernel.to(weight)
+        x = F.conv1d(x, weight, stride=2)
+        if self.channels_last:
+            x = x.permute(0, 2, 1)
+        return x
+
+
+class Upsample1d(nn.Module):
+    def __init__(self, kernel='linear', pad_mode='reflect', channels_last=False):
+        super().__init__()
+        self.pad_mode = pad_mode
+        kernel_1d = torch.tensor(_kernels[kernel]) * 2
+        self.pad = kernel_1d.shape[0] // 2 - 1
+        self.register_buffer('kernel', kernel_1d)
+        self.channels_last = channels_last
+    
+    def forward(self, x):
+        if self.channels_last:
+            x = x.permute(0, 2, 1)
+        x = F.pad(x, ((self.pad + 1) // 2,) * 2, self.pad_mode)
+        weight = x.new_zeros([x.shape[1], x.shape[1], self.kernel.shape[0]])
+        indices = torch.arange(x.shape[1], device=x.device)
+        weight[indices, indices] = self.kernel.to(weight)
+        x = F.conv_transpose1d(x, weight, stride=2, padding=self.pad * 2 + 1)
+        if self.channels_last:
+            x = x.permute(0, 2, 1)
+        return x
+        
+def Downsample1d_2(
+    in_channels: int, out_channels: int, factor: int, kernel_multiplier: int = 2
+) -> nn.Module:
+    assert kernel_multiplier % 2 == 0, "Kernel multiplier must be even"
+
+    return nn.Conv1d(
+        in_channels=in_channels,
+        out_channels=out_channels,
+        kernel_size=factor * kernel_multiplier + 1,
+        stride=factor,
+        padding=factor * (kernel_multiplier // 2),
+    )
+
+
+def Upsample1d_2(
+    in_channels: int, out_channels: int, factor: int, use_nearest: bool = False
+) -> nn.Module:
+
+    if factor == 1:
+        return nn.Conv1d(
+            in_channels=in_channels, out_channels=out_channels, kernel_size=3, padding=1
+        )
+
+    if use_nearest:
+        return nn.Sequential(
+            nn.Upsample(scale_factor=factor, mode="nearest"),
+            nn.Conv1d(
+                in_channels=in_channels,
+                out_channels=out_channels,
+                kernel_size=3,
+                padding=1,
+            ),
+        )
+    else:
+        return nn.ConvTranspose1d(
+            in_channels=in_channels,
+            out_channels=out_channels,
+            kernel_size=factor * 2,
+            stride=factor,
+            padding=factor // 2 + factor % 2,
+            output_padding=factor % 2,
+        )
+
+def zero_init(layer):
+    nn.init.zeros_(layer.weight)
+    if layer.bias is not None:
+        nn.init.zeros_(layer.bias)
+    return layer
+
+def rms_norm(x, scale, eps):
+    dtype = reduce(torch.promote_types, (x.dtype, scale.dtype, torch.float32))
+    mean_sq = torch.mean(x.to(dtype)**2, dim=-1, keepdim=True)
+    scale = scale.to(dtype) * torch.rsqrt(mean_sq + eps)
+    return x * scale.to(x.dtype)
+
+#rms_norm = torch.compile(rms_norm)
+
+class AdaRMSNorm(nn.Module):
+    def __init__(self, features, cond_features, eps=1e-6):
+        super().__init__()
+        self.eps = eps
+        self.linear = zero_init(nn.Linear(cond_features, features, bias=False))
+  
+    def extra_repr(self):
+        return f"eps={self.eps},"
+
+    def forward(self, x, cond):
+        return rms_norm(x, self.linear(cond)[:, None, :] + 1, self.eps)
+    
+def normalize(x, eps=1e-4):
+    dim = list(range(1, x.ndim))
+    n = torch.linalg.vector_norm(x, dim=dim, keepdim=True)
+    alpha = np.sqrt(n.numel() / x.numel())
+    return x / torch.add(eps, n, alpha=alpha)
+
+class ForcedWNConv1d(nn.Module):
+    def __init__(self, in_channels, out_channels, kernel_size=1):
+        super().__init__()
+        self.weight = nn.Parameter(torch.randn([out_channels, in_channels, kernel_size]))
+
+    def forward(self, x):
+        if self.training:
+            with torch.no_grad():
+                self.weight.copy_(normalize(self.weight))
+        
+        fan_in = self.weight[0].numel()
+
+        w = normalize(self.weight) / math.sqrt(fan_in)
+
+        return F.conv1d(x, w, padding='same')
+        
+# Kernels
+
+use_compile = True
+
+def compile(function, *args, **kwargs):
+    if not use_compile:
+        return function
+    try:
+        return torch.compile(function, *args, **kwargs)
+    except RuntimeError:
+        return function
+
+
+@compile
+def linear_geglu(x, weight, bias=None):
+    x = x @ weight.mT
+    if bias is not None:
+        x = x + bias
+    x, gate = x.chunk(2, dim=-1)
+    return x * F.gelu(gate)
+
+
+@compile
+def rms_norm(x, scale, eps):
+    dtype = reduce(torch.promote_types, (x.dtype, scale.dtype, torch.float32))
+    mean_sq = torch.mean(x.to(dtype)**2, dim=-1, keepdim=True)
+    scale = scale.to(dtype) * torch.rsqrt(mean_sq + eps)
+    return x * scale.to(x.dtype)
+
+# Layers
+
+class LinearGEGLU(nn.Linear):
+    def __init__(self, in_features, out_features, bias=True):
+        super().__init__(in_features, out_features * 2, bias=bias)
+        self.out_features = out_features
+
+    def forward(self, x):
+        return linear_geglu(x, self.weight, self.bias)
+
+
+class RMSNorm(nn.Module):
+    def __init__(self, shape, fix_scale = False, eps=1e-6):
+        super().__init__()
+        self.eps = eps
+
+        if fix_scale:
+            self.register_buffer("scale", torch.ones(shape))
+        else:
+            self.scale = nn.Parameter(torch.ones(shape))
+
+    def extra_repr(self):
+        return f"shape={tuple(self.scale.shape)}, eps={self.eps}"
+
+    def forward(self, x):
+        return rms_norm(x, self.scale, self.eps)    
+
+def snake_beta(x, alpha, beta):
+    return x + (1.0 / (beta + 0.000000001)) * pow(torch.sin(x * alpha), 2)
+
+# try:
+#     snake_beta = torch.compile(snake_beta)
+# except RuntimeError:
+#     pass
+
+# Adapted from https://github.com/NVIDIA/BigVGAN/blob/main/activations.py under MIT license
+# License available in LICENSES/LICENSE_NVIDIA.txt
+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) # line up with x to [B, C, T]
+        beta = self.beta.unsqueeze(0).unsqueeze(-1)
+        if self.alpha_logscale:
+            alpha = torch.exp(alpha)
+            beta = torch.exp(beta)
+        x = snake_beta(x, alpha, beta)
+
+        return x

prismaudio_core/models/bottleneck.py

@@ -0,0 +1,355 @@
+import numpy as np
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+from einops import rearrange
+from vector_quantize_pytorch import ResidualVQ, FSQ
+from dac.nn.quantize import ResidualVectorQuantize as DACResidualVQ
+
+class Bottleneck(nn.Module):
+    def __init__(self, is_discrete: bool = False):
+        super().__init__()
+
+        self.is_discrete = is_discrete
+
+    def encode(self, x, return_info=False, **kwargs):
+        raise NotImplementedError
+
+    def decode(self, x):
+        raise NotImplementedError
+
+class DiscreteBottleneck(Bottleneck):
+    def __init__(self, num_quantizers, codebook_size, tokens_id):
+        super().__init__(is_discrete=True)
+
+        self.num_quantizers = num_quantizers
+        self.codebook_size = codebook_size
+        self.tokens_id = tokens_id
+
+    def decode_tokens(self, codes, **kwargs):
+        raise NotImplementedError
+    
+class TanhBottleneck(Bottleneck):
+    def __init__(self):
+        super().__init__(is_discrete=False)
+        self.tanh = nn.Tanh()
+
+    def encode(self, x, return_info=False):
+        info = {}
+
+        x = torch.tanh(x)
+
+        if return_info:
+            return x, info
+        else:
+            return x
+
+    def decode(self, x):
+        return x
+
+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(Bottleneck):
+    def __init__(self):
+        super().__init__(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 compute_mean_kernel(x, y):
+        kernel_input = (x[:, None] - y[None]).pow(2).mean(2) / x.shape[-1]
+        return torch.exp(-kernel_input).mean()
+
+def compute_mmd(latents):
+    latents_reshaped = latents.permute(0, 2, 1).reshape(-1, latents.shape[1])
+    noise = torch.randn_like(latents_reshaped)
+
+    latents_kernel = compute_mean_kernel(latents_reshaped, latents_reshaped)
+    noise_kernel = compute_mean_kernel(noise, noise)
+    latents_noise_kernel = compute_mean_kernel(latents_reshaped, noise)
+    
+    mmd = latents_kernel + noise_kernel - 2 * latents_noise_kernel
+    return mmd.mean()
+
+class WassersteinBottleneck(Bottleneck):
+    def __init__(self, noise_augment_dim: int = 0, bypass_mmd: bool = False):
+        super().__init__(is_discrete=False)
+
+        self.noise_augment_dim = noise_augment_dim
+        self.bypass_mmd = bypass_mmd
+    
+    def encode(self, x, return_info=False):
+        info = {}
+
+        if self.training and return_info:
+            if self.bypass_mmd:
+                mmd = torch.tensor(0.0)
+            else:
+                mmd = compute_mmd(x)
+                
+            info["mmd"] = mmd
+        
+        if return_info:
+            return x, info
+        
+        return x
+
+    def decode(self, x):
+
+        if self.noise_augment_dim > 0:
+            noise = torch.randn(x.shape[0], self.noise_augment_dim,
+                                x.shape[-1]).type_as(x)
+            x = torch.cat([x, noise], dim=1)
+
+        return x
+
+class L2Bottleneck(Bottleneck):
+    def __init__(self):
+        super().__init__(is_discrete=False)
+    
+    def encode(self, x, return_info=False):
+        info = {}
+
+        x = F.normalize(x, dim=1)
+
+        if return_info:
+            return x, info
+        else:
+            return x
+        
+    def decode(self, x):
+        return F.normalize(x, dim=1)
+        
+class RVQBottleneck(DiscreteBottleneck):
+    def __init__(self, **quantizer_kwargs):
+        super().__init__(num_quantizers = quantizer_kwargs["num_quantizers"], codebook_size = quantizer_kwargs["codebook_size"], tokens_id = "quantizer_indices")
+        self.quantizer = ResidualVQ(**quantizer_kwargs)
+        self.num_quantizers = quantizer_kwargs["num_quantizers"]
+
+    def encode(self, x, return_info=False, **kwargs):
+        info = {}
+
+        x = rearrange(x, "b c n -> b n c")
+        x, indices, loss = self.quantizer(x)
+        x = rearrange(x, "b n c -> b c n")
+
+        info["quantizer_indices"] = indices
+        info["quantizer_loss"] = loss.mean()
+
+        if return_info:
+            return x, info
+        else:
+            return x
+        
+    def decode(self, x):
+        return x
+    
+    def decode_tokens(self, codes, **kwargs):
+        latents = self.quantizer.get_outputs_from_indices(codes)
+
+        return self.decode(latents, **kwargs)
+    
+class RVQVAEBottleneck(DiscreteBottleneck):
+    def __init__(self, **quantizer_kwargs):
+        super().__init__(num_quantizers = quantizer_kwargs["num_quantizers"], codebook_size = quantizer_kwargs["codebook_size"], tokens_id = "quantizer_indices")
+        self.quantizer = ResidualVQ(**quantizer_kwargs)
+        self.num_quantizers = quantizer_kwargs["num_quantizers"]
+
+    def encode(self, x, return_info=False):
+        info = {}
+
+        x, kl = vae_sample(*x.chunk(2, dim=1))
+
+        info["kl"] = kl
+
+        x = rearrange(x, "b c n -> b n c")
+        x, indices, loss = self.quantizer(x)
+        x = rearrange(x, "b n c -> b c n")
+
+        info["quantizer_indices"] = indices
+        info["quantizer_loss"] = loss.mean()
+
+        if return_info:
+            return x, info
+        else:
+            return x
+        
+    def decode(self, x):
+        return x
+    
+    def decode_tokens(self, codes, **kwargs):
+        latents = self.quantizer.get_outputs_from_indices(codes)
+
+        return self.decode(latents, **kwargs)
+
+class DACRVQBottleneck(DiscreteBottleneck):
+    def __init__(self, quantize_on_decode=False, noise_augment_dim=0, **quantizer_kwargs):
+        super().__init__(num_quantizers = quantizer_kwargs["n_codebooks"], codebook_size = quantizer_kwargs["codebook_size"], tokens_id = "codes")
+        self.quantizer = DACResidualVQ(**quantizer_kwargs)
+        self.num_quantizers = quantizer_kwargs["n_codebooks"]
+        self.quantize_on_decode = quantize_on_decode
+        self.noise_augment_dim = noise_augment_dim
+
+    def encode(self, x, return_info=False, **kwargs):
+        info = {}
+
+        info["pre_quantizer"] = x
+
+        if self.quantize_on_decode:
+            return x, info if return_info else x
+
+        z, codes, latents, commitment_loss, codebook_loss = self.quantizer(x, **kwargs)
+
+        output = {
+            "z": z,
+            "codes": codes,
+            "latents": latents,
+            "vq/commitment_loss": commitment_loss,
+            "vq/codebook_loss": codebook_loss,
+        }
+
+        output["vq/commitment_loss"] /= self.num_quantizers
+        output["vq/codebook_loss"] /= self.num_quantizers
+
+        info.update(output)
+
+        if return_info:
+            return output["z"], info
+        
+        return output["z"]
+    
+    def decode(self, x):
+
+        if self.quantize_on_decode:
+            x = self.quantizer(x)[0]
+
+        if self.noise_augment_dim > 0:
+            noise = torch.randn(x.shape[0], self.noise_augment_dim,
+                                x.shape[-1]).type_as(x)
+            x = torch.cat([x, noise], dim=1)
+
+        return x
+    
+    def decode_tokens(self, codes, **kwargs):
+        latents, _, _ = self.quantizer.from_codes(codes)
+
+        return self.decode(latents, **kwargs)
+
+class DACRVQVAEBottleneck(DiscreteBottleneck):
+    def __init__(self, quantize_on_decode=False, **quantizer_kwargs):
+        super().__init__(num_quantizers = quantizer_kwargs["n_codebooks"], codebook_size = quantizer_kwargs["codebook_size"], tokens_id = "codes")
+        self.quantizer = DACResidualVQ(**quantizer_kwargs)
+        self.num_quantizers = quantizer_kwargs["n_codebooks"]
+        self.quantize_on_decode = quantize_on_decode
+
+    def encode(self, x, return_info=False, n_quantizers: int = None):
+        info = {}
+
+        mean, scale = x.chunk(2, dim=1)
+
+        x, kl = vae_sample(mean, scale)
+
+        info["pre_quantizer"] = x
+        info["kl"] = kl
+
+        if self.quantize_on_decode:
+            return x, info if return_info else x
+
+        z, codes, latents, commitment_loss, codebook_loss = self.quantizer(x, n_quantizers=n_quantizers)
+
+        output = {
+            "z": z,
+            "codes": codes,
+            "latents": latents,
+            "vq/commitment_loss": commitment_loss,
+            "vq/codebook_loss": codebook_loss,
+        }
+
+        output["vq/commitment_loss"] /= self.num_quantizers
+        output["vq/codebook_loss"] /= self.num_quantizers
+
+        info.update(output)
+
+        if return_info:
+            return output["z"], info
+        
+        return output["z"]
+    
+    def decode(self, x):
+
+        if self.quantize_on_decode:
+            x = self.quantizer(x)[0]
+
+        return x
+
+    def decode_tokens(self, codes, **kwargs):
+        latents, _, _ = self.quantizer.from_codes(codes)
+
+        return self.decode(latents, **kwargs)
+    
+class FSQBottleneck(DiscreteBottleneck):
+    def __init__(self, noise_augment_dim=0, **kwargs):
+        super().__init__(num_quantizers = kwargs.get("num_codebooks", 1), codebook_size = np.prod(kwargs["levels"]), tokens_id = "quantizer_indices")
+
+        self.noise_augment_dim = noise_augment_dim
+
+        self.quantizer = FSQ(**kwargs, allowed_dtypes=[torch.float16, torch.float32, torch.float64])
+
+    def encode(self, x, return_info=False):
+        info = {}
+
+        orig_dtype = x.dtype
+        x = x.float()
+
+        x = rearrange(x, "b c n -> b n c")
+        x, indices = self.quantizer(x)
+        x = rearrange(x, "b n c -> b c n")
+
+        x = x.to(orig_dtype)
+
+        # Reorder indices to match the expected format
+        indices = rearrange(indices, "b n q -> b q n")
+
+        info["quantizer_indices"] = indices
+
+        if return_info:
+            return x, info
+        else:
+            return x
+        
+    def decode(self, x):
+
+        if self.noise_augment_dim > 0:
+            noise = torch.randn(x.shape[0], self.noise_augment_dim,
+                                x.shape[-1]).type_as(x)
+            x = torch.cat([x, noise], dim=1)
+
+        return x
+    
+    def decode_tokens(self, tokens, **kwargs):
+        latents = self.quantizer.indices_to_codes(tokens)
+
+        return self.decode(latents, **kwargs)

prismaudio_core/models/diffusion.py

@@ -0,0 +1,965 @@
+import torch
+from torch import nn
+from torch.nn import functional as F
+from functools import partial
+import numpy as np
+import typing as tp
+
+from .blocks import ResConvBlock, FourierFeatures, Upsample1d, Upsample1d_2, Downsample1d, Downsample1d_2, SelfAttention1d, SkipBlock, expand_to_planes
+from .conditioners import MultiConditioner
+from .dit import DiffusionTransformer
+from .pretransforms import Pretransform
+
+from .adp import UNetCFG1d, UNet1d
+
+# Lazy imports for factory functions to avoid circular imports
+def _get_create_pretransform_from_config():
+    from prismaudio_core.factory import create_pretransform_from_config
+    return create_pretransform_from_config
+
+def _get_create_multi_conditioner_from_conditioning_config():
+    from prismaudio_core.factory import create_multi_conditioner_from_conditioning_config
+    return create_multi_conditioner_from_conditioning_config
+
+from time import time
+
+class Profiler:
+
+    def __init__(self):
+        self.ticks = [[time(), None]]
+
+    def tick(self, msg):
+        self.ticks.append([time(), msg])
+
+    def __repr__(self):
+        rep = 80 * "=" + "\n"
+        for i in range(1, len(self.ticks)):
+            msg = self.ticks[i][1]
+            ellapsed = self.ticks[i][0] - self.ticks[i - 1][0]
+            rep += msg + f": {ellapsed*1000:.2f}ms\n"
+        rep += 80 * "=" + "\n\n\n"
+        return rep
+
+class DiffusionModel(nn.Module):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+    def forward(self, x, t, **kwargs):
+        raise NotImplementedError()
+
+class DiffusionModelWrapper(nn.Module):
+    def __init__(
+                self,
+                model: DiffusionModel,
+                io_channels,
+                sample_size,
+                sample_rate,
+                min_input_length,
+                pretransform: tp.Optional[Pretransform] = None,
+    ):
+        super().__init__()
+        self.io_channels = io_channels
+        self.sample_size = sample_size
+        self.sample_rate = sample_rate
+        self.min_input_length = min_input_length
+
+        self.model = model
+
+        if pretransform is not None:
+            self.pretransform = pretransform
+        else:
+            self.pretransform = None
+
+    def forward(self, x, t, **kwargs):
+        return self.model(x, t, **kwargs)
+
+class ConditionedDiffusionModel(nn.Module):
+    def __init__(self,
+                *args,
+                supports_cross_attention: bool = False,
+                supports_input_concat: bool = False,
+                supports_global_cond: bool = False,
+                supports_prepend_cond: bool = False,
+                **kwargs):
+        super().__init__(*args, **kwargs)
+        self.supports_cross_attention = supports_cross_attention
+        self.supports_input_concat = supports_input_concat
+        self.supports_global_cond = supports_global_cond
+        self.supports_prepend_cond = supports_prepend_cond
+
+    def forward(self,
+                x: torch.Tensor,
+                t: torch.Tensor,
+                cross_attn_cond: torch.Tensor = None,
+                cross_attn_mask: torch.Tensor = None,
+                input_concat_cond: torch.Tensor = None,
+                global_embed: torch.Tensor = None,
+                prepend_cond: torch.Tensor = None,
+                prepend_cond_mask: torch.Tensor = None,
+                cfg_scale: float = 1.0,
+                cfg_dropout_prob: float = 0.0,
+                batch_cfg: bool = False,
+                rescale_cfg: bool = False,
+                **kwargs):
+        raise NotImplementedError()
+
+class ConditionedDiffusionModelWrapper(nn.Module):
+    """
+    A diffusion model that takes in conditioning
+    """
+    def __init__(
+            self,
+            model: ConditionedDiffusionModel,
+            conditioner: MultiConditioner,
+            io_channels,
+            sample_rate,
+            min_input_length: int,
+            diffusion_objective: tp.Literal["v", "rectified_flow"] = "v",
+            zero_init: bool = False,
+            pretransform: tp.Optional[Pretransform] = None,
+            cross_attn_cond_ids: tp.List[str] = [],
+            global_cond_ids: tp.List[str] = [],
+            input_concat_ids: tp.List[str] = [],
+            prepend_cond_ids: tp.List[str] = [],
+            add_cond_ids: tp.List[str] = [],
+            sync_cond_ids: tp.List[str] = [],
+            ):
+        super().__init__()
+
+        self.model = model
+        self.conditioner = conditioner
+        self.io_channels = io_channels
+        self.sample_rate = sample_rate
+        self.diffusion_objective = diffusion_objective
+        self.pretransform = pretransform
+        self.cross_attn_cond_ids = cross_attn_cond_ids
+        self.global_cond_ids = global_cond_ids
+        self.input_concat_ids = input_concat_ids
+        self.prepend_cond_ids = prepend_cond_ids
+        self.add_cond_ids = add_cond_ids
+        self.sync_cond_ids = sync_cond_ids
+        self.min_input_length = min_input_length
+        def _basic_init(module):
+            if isinstance(module, nn.Linear):
+                torch.nn.init.xavier_uniform_(module.weight)
+                if module.bias is not None:
+                    nn.init.constant_(module.bias, 0)
+        
+        if zero_init is True:
+            self.conditioner.apply(_basic_init)
+            self.model.model.initialize_weights()
+
+
+    def get_conditioning_inputs(self, conditioning_tensors: tp.Dict[str, tp.Any], negative=False):
+        cross_attention_input = None
+        cross_attention_masks = None
+        global_cond = None
+        input_concat_cond = None
+        prepend_cond = None
+        prepend_cond_mask = None
+        add_input = None
+        sync_input = None
+
+        if len(self.cross_attn_cond_ids) > 0:
+            # Concatenate all cross-attention inputs over the sequence dimension
+            # Assumes that the cross-attention inputs are of shape (batch, seq, channels)
+            cross_attention_input = []
+            cross_attention_masks = []
+
+            for key in self.cross_attn_cond_ids:
+                cross_attn_in, cross_attn_mask = conditioning_tensors[key]
+
+                # Add sequence dimension if it's not there
+                if len(cross_attn_in.shape) == 2:
+                    cross_attn_in = cross_attn_in.unsqueeze(1)
+                    # cross_attn_mask = cross_attn_mask.unsqueeze(1)
+
+                cross_attention_input.append(cross_attn_in)
+                cross_attention_masks.append(cross_attn_mask)
+            # import ipdb
+            # ipdb.set_trace()
+            cross_attention_input = torch.cat(cross_attention_input, dim=1)
+            cross_attention_masks = torch.cat(cross_attention_masks, dim=1)
+
+        if len(self.add_cond_ids) > 0:
+            # Concatenate all cross-attention inputs over the sequence dimension
+            # Assumes that the cross-attention inputs are of shape (batch, seq, channels)
+            add_input = []
+
+            for key in self.add_cond_ids:
+                add_in = conditioning_tensors[key][0]
+
+                # Add sequence dimension if it's not there
+                if len(add_in.shape) == 2:
+                    add_in = add_in.unsqueeze(1)
+                # add_in = add_in.transpose(1,2)
+                # add_in = F.interpolate(add_in, (194, ), mode='linear', align_corners=False)
+                # add_in = add_in.transpose(1,2)
+                add_input.append(add_in)
+
+            add_input = torch.cat(add_input, dim=2)
+
+        if len(self.sync_cond_ids) > 0:
+            # Concatenate all cross-attention inputs over the sequence dimension
+            # Assumes that the cross-attention inputs are of shape (batch, seq, channels)
+            sync_input = []
+
+            for key in self.sync_cond_ids:
+                sync_in = conditioning_tensors[key][0]
+
+                # Add sequence dimension if it's not there
+                if len(sync_in.shape) == 2:
+                    sync_in = sync_in.unsqueeze(1)
+                sync_input.append(sync_in)
+
+            sync_input = torch.cat(sync_input, dim=2)
+
+        if len(self.global_cond_ids) > 0:
+            # Concatenate all global conditioning inputs over the channel dimension
+            # Assumes that the global conditioning inputs are of shape (batch, channels)
+            global_conds = []
+            for key in self.global_cond_ids:
+                global_cond_input = conditioning_tensors[key][0]
+                if len(global_cond_input.shape) == 2:
+                    global_cond_input = global_cond_input.unsqueeze(1)
+                global_conds.append(global_cond_input)
+
+            # # Concatenate over the channel dimension
+            # if global_conds[0].shape[-1] == 768:
+            #     global_cond = torch.cat(global_conds, dim=-1)
+            # else:
+            #     global_cond = sum(global_conds)
+            global_cond = sum(global_conds)
+            # global_cond = torch.cat(global_conds, dim=-1)
+
+            if len(global_cond.shape) == 3:
+                global_cond = global_cond.squeeze(1)
+
+        if len(self.input_concat_ids) > 0:
+            # Concatenate all input concat conditioning inputs over the channel dimension
+            # Assumes that the input concat conditioning inputs are of shape (batch, channels, seq)
+            input_concat_cond = torch.cat([conditioning_tensors[key][0] for key in self.input_concat_ids], dim=1)
+
+        if len(self.prepend_cond_ids) > 0:
+            # Concatenate all prepend conditioning inputs over the sequence dimension
+            # Assumes that the prepend conditioning inputs are of shape (batch, seq, channels)
+            prepend_conds = []
+            prepend_cond_masks = []
+
+            for key in self.prepend_cond_ids:
+                prepend_cond_input, prepend_cond_mask = conditioning_tensors[key]
+                if len(prepend_cond_input.shape) == 2:
+                    prepend_cond_input = prepend_cond_input.unsqueeze(1)
+                prepend_conds.append(prepend_cond_input)
+                prepend_cond_masks.append(prepend_cond_mask)
+
+            prepend_cond = torch.cat(prepend_conds, dim=1)
+            prepend_cond_mask = torch.cat(prepend_cond_masks, dim=1)
+
+        if negative:
+            return {
+                "negative_cross_attn_cond": cross_attention_input,
+                "negative_cross_attn_mask": cross_attention_masks,
+                "negative_global_cond": global_cond,
+                "negative_input_concat_cond": input_concat_cond
+            }
+        else:
+            return {
+                "cross_attn_cond": cross_attention_input,
+                "cross_attn_mask": cross_attention_masks,
+                "global_cond": global_cond,
+                "input_concat_cond": input_concat_cond,
+                "prepend_cond": prepend_cond,
+                "prepend_cond_mask": prepend_cond_mask,
+                "add_cond": add_input,
+                "sync_cond": sync_input
+                }
+
+    def forward(self, x: torch.Tensor, t: torch.Tensor, cond: tp.Dict[str, tp.Any], **kwargs):
+        return self.model(x, t, **self.get_conditioning_inputs(cond), **kwargs)
+
+    def generate(self, *args, **kwargs):
+        from prismaudio_core.inference.generation import generate_diffusion_cond
+        return generate_diffusion_cond(self, *args, **kwargs)
+
+class UNetCFG1DWrapper(ConditionedDiffusionModel):
+    def __init__(
+        self,
+        *args,
+        **kwargs
+    ):
+        super().__init__(supports_cross_attention=True, supports_global_cond=True, supports_input_concat=True)
+
+        self.model = UNetCFG1d(*args, **kwargs)
+
+        with torch.no_grad():
+            for param in self.model.parameters():
+                param *= 0.5
+
+    def forward(self,
+                x,
+                t,
+                cross_attn_cond=None,
+                cross_attn_mask=None,
+                input_concat_cond=None,
+                global_cond=None,
+                cfg_scale=1.0,
+                cfg_dropout_prob: float = 0.0,
+                batch_cfg: bool = False,
+                rescale_cfg: bool = False,
+                negative_cross_attn_cond=None,
+                negative_cross_attn_mask=None,
+                negative_global_cond=None,
+                negative_input_concat_cond=None,
+                prepend_cond=None,
+                prepend_cond_mask=None,
+                **kwargs):
+        p = Profiler()
+
+        p.tick("start")
+
+        channels_list = None
+        if input_concat_cond is not None:
+            channels_list = [input_concat_cond]
+
+        outputs = self.model(
+            x,
+            t,
+            embedding=cross_attn_cond,
+            embedding_mask=cross_attn_mask,
+            features=global_cond,
+            channels_list=channels_list,
+            embedding_scale=cfg_scale,
+            embedding_mask_proba=cfg_dropout_prob,
+            batch_cfg=batch_cfg,
+            rescale_cfg=rescale_cfg,
+            negative_embedding=negative_cross_attn_cond,
+            negative_embedding_mask=negative_cross_attn_mask,
+            **kwargs)
+
+        p.tick("UNetCFG1D forward")
+
+        #print(f"Profiler: {p}")
+        return outputs
+
+class UNet1DCondWrapper(ConditionedDiffusionModel):
+    def __init__(
+        self,
+        *args,
+        **kwargs
+    ):
+        super().__init__(supports_cross_attention=False, supports_global_cond=True, supports_input_concat=True)
+
+        self.model = UNet1d(*args, **kwargs)
+
+        with torch.no_grad():
+            for param in self.model.parameters():
+                param *= 0.5
+
+    def forward(self,
+                x,
+                t,
+                input_concat_cond=None,
+                global_cond=None,
+                cross_attn_cond=None,
+                cross_attn_mask=None,
+                prepend_cond=None,
+                prepend_cond_mask=None,
+                cfg_scale=1.0,
+                cfg_dropout_prob: float = 0.0,
+                batch_cfg: bool = False,
+                rescale_cfg: bool = False,
+                negative_cross_attn_cond=None,
+                negative_cross_attn_mask=None,
+                negative_global_cond=None,
+                negative_input_concat_cond=None,
+                **kwargs):
+
+        channels_list = None
+        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')
+
+            channels_list = [input_concat_cond]
+
+        outputs = self.model(
+            x,
+            t,
+            features=global_cond,
+            channels_list=channels_list,
+            **kwargs)
+
+        return outputs
+
+class UNet1DUncondWrapper(DiffusionModel):
+    def __init__(
+        self,
+        in_channels,
+        *args,
+        **kwargs
+    ):
+        super().__init__()
+
+        self.model = UNet1d(in_channels=in_channels, *args, **kwargs)
+
+        self.io_channels = in_channels
+
+        with torch.no_grad():
+            for param in self.model.parameters():
+                param *= 0.5
+
+    def forward(self, x, t, **kwargs):
+        return self.model(x, t, **kwargs)
+
+class DAU1DCondWrapper(ConditionedDiffusionModel):
+    def __init__(
+        self,
+        *args,
+        **kwargs
+    ):
+        super().__init__(supports_cross_attention=False, supports_global_cond=False, supports_input_concat=True)
+
+        self.model = DiffusionAttnUnet1D(*args, **kwargs)
+
+        with torch.no_grad():
+            for param in self.model.parameters():
+                param *= 0.5
+
+    def forward(self,
+                x,
+                t,
+                input_concat_cond=None,
+                cross_attn_cond=None,
+                cross_attn_mask=None,
+                global_cond=None,
+                cfg_scale=1.0,
+                cfg_dropout_prob: float = 0.0,
+                batch_cfg: bool = False,
+                rescale_cfg: bool = False,
+                negative_cross_attn_cond=None,
+                negative_cross_attn_mask=None,
+                negative_global_cond=None,
+                negative_input_concat_cond=None,
+                prepend_cond=None,
+                **kwargs):
+
+        return self.model(x, t, cond = input_concat_cond)
+
+class DiffusionAttnUnet1D(nn.Module):
+    def __init__(
+        self,
+        io_channels = 2,
+        depth=14,
+        n_attn_layers = 6,
+        channels = [128, 128, 256, 256] + [512] * 10,
+        cond_dim = 0,
+        cond_noise_aug = False,
+        kernel_size = 5,
+        learned_resample = False,
+        strides = [2] * 13,
+        conv_bias = True,
+        use_snake = False
+    ):
+        super().__init__()
+
+        self.cond_noise_aug = cond_noise_aug
+
+        self.io_channels = io_channels
+
+        if self.cond_noise_aug:
+            self.rng = torch.quasirandom.SobolEngine(1, scramble=True)
+
+        self.timestep_embed = FourierFeatures(1, 16)
+
+        attn_layer = depth - n_attn_layers
+
+        strides = [1] + strides
+
+        block = nn.Identity()
+
+        conv_block = partial(ResConvBlock, kernel_size=kernel_size, conv_bias = conv_bias, use_snake=use_snake)
+
+        for i in range(depth, 0, -1):
+            c = channels[i - 1]
+            stride = strides[i-1]
+            if stride > 2 and not learned_resample:
+                raise ValueError("Must have stride 2 without learned resampling")
+
+            if i > 1:
+                c_prev = channels[i - 2]
+                add_attn = i >= attn_layer and n_attn_layers > 0
+                block = SkipBlock(
+                    Downsample1d_2(c_prev, c_prev, stride) if (learned_resample or stride == 1) else Downsample1d("cubic"),
+                    conv_block(c_prev, c, c),
+                    SelfAttention1d(
+                        c, c // 32) if add_attn else nn.Identity(),
+                    conv_block(c, c, c),
+                    SelfAttention1d(
+                        c, c // 32) if add_attn else nn.Identity(),
+                    conv_block(c, c, c),
+                    SelfAttention1d(
+                        c, c // 32) if add_attn else nn.Identity(),
+                    block,
+                    conv_block(c * 2 if i != depth else c, c, c),
+                    SelfAttention1d(
+                        c, c // 32) if add_attn else nn.Identity(),
+                    conv_block(c, c, c),
+                    SelfAttention1d(
+                        c, c // 32) if add_attn else nn.Identity(),
+                    conv_block(c, c, c_prev),
+                    SelfAttention1d(c_prev, c_prev //
+                                    32) if add_attn else nn.Identity(),
+                    Upsample1d_2(c_prev, c_prev, stride) if learned_resample else Upsample1d(kernel="cubic")
+                )
+            else:
+                cond_embed_dim = 16 if not self.cond_noise_aug else 32
+                block = nn.Sequential(
+                    conv_block((io_channels + cond_dim) + cond_embed_dim, c, c),
+                    conv_block(c, c, c),
+                    conv_block(c, c, c),
+                    block,
+                    conv_block(c * 2, c, c),
+                    conv_block(c, c, c),
+                    conv_block(c, c, io_channels, is_last=True),
+                )
+        self.net = block
+
+        with torch.no_grad():
+            for param in self.net.parameters():
+                param *= 0.5
+
+    def forward(self, x, t, cond=None, cond_aug_scale=None):
+
+        timestep_embed = expand_to_planes(self.timestep_embed(t[:, None]), x.shape)
+
+        inputs = [x, timestep_embed]
+
+        if cond is not None:
+            if cond.shape[2] != x.shape[2]:
+                cond = F.interpolate(cond, (x.shape[2], ), mode='linear', align_corners=False)
+
+            if self.cond_noise_aug:
+                # Get a random number between 0 and 1, uniformly sampled
+                if cond_aug_scale is None:
+                    aug_level = self.rng.draw(cond.shape[0])[:, 0].to(cond)
+                else:
+                    aug_level = torch.tensor([cond_aug_scale]).repeat([cond.shape[0]]).to(cond)
+
+                # Add noise to the conditioning signal
+                cond = cond + torch.randn_like(cond) * aug_level[:, None, None]
+
+                # Get embedding for noise cond level, reusing timestamp_embed
+                aug_level_embed = expand_to_planes(self.timestep_embed(aug_level[:, None]), x.shape)
+
+                inputs.append(aug_level_embed)
+
+            inputs.append(cond)
+
+        outputs = self.net(torch.cat(inputs, dim=1))
+
+        return outputs
+
+class DiTWrapper(ConditionedDiffusionModel):
+    def __init__(
+        self,
+        *args,
+        **kwargs
+    ):
+        super().__init__(supports_cross_attention=True, supports_global_cond=False, supports_input_concat=False)
+
+        self.model = DiffusionTransformer(*args, **kwargs)
+        # with torch.no_grad():
+        #     for param in self.model.parameters():
+        #         param *= 0.5
+
+    def forward(self,
+                x,
+                t,
+                cross_attn_cond=None,
+                cross_attn_mask=None,
+                negative_cross_attn_cond=None,
+                negative_cross_attn_mask=None,
+                input_concat_cond=None,
+                negative_input_concat_cond=None,
+                global_cond=None,
+                negative_global_cond=None,
+                prepend_cond=None,
+                prepend_cond_mask=None,
+                cfg_scale=1.0,
+                cfg_dropout_prob: float = 0.0,
+                batch_cfg: bool = True,
+                rescale_cfg: bool = False,
+                scale_phi: float = 0.0,
+                **kwargs):
+
+        assert batch_cfg, "batch_cfg must be True for DiTWrapper"
+        #assert negative_input_concat_cond is None, "negative_input_concat_cond is not supported for DiTWrapper"
+
+        return self.model(
+            x,
+            t,
+            cross_attn_cond=cross_attn_cond,
+            cross_attn_cond_mask=cross_attn_mask,
+            negative_cross_attn_cond=negative_cross_attn_cond,
+            negative_cross_attn_mask=negative_cross_attn_mask,
+            input_concat_cond=input_concat_cond,
+            prepend_cond=prepend_cond,
+            prepend_cond_mask=prepend_cond_mask,
+            cfg_scale=cfg_scale,
+            cfg_dropout_prob=cfg_dropout_prob,
+            scale_phi=scale_phi,
+            global_embed=global_cond,
+            **kwargs)
+
+class MMDiTWrapper(ConditionedDiffusionModel):
+    def __init__(
+        self,
+        *args,
+        **kwargs
+    ):
+        super().__init__(supports_cross_attention=True, supports_global_cond=False, supports_input_concat=False)
+
+        self.model = MMAudio(*args, **kwargs)
+
+        # with torch.no_grad():
+        #     for param in self.model.parameters():
+        #         param *= 0.5
+
+    def forward(self,
+                x,
+                t,
+                clip_f,
+                sync_f,
+                text_f,
+                inpaint_masked_input=None,
+                t5_features=None,
+                metaclip_global_text_features=None,
+                cfg_scale=1.0,
+                cfg_dropout_prob: float = 0.0,
+                batch_cfg: bool = True,
+                rescale_cfg: bool = False,
+                scale_phi: float = 0.0,
+                **kwargs):
+
+        # breakpoint()
+        assert batch_cfg, "batch_cfg must be True for DiTWrapper"
+        #assert negative_input_concat_cond is None, "negative_input_concat_cond is not supported for DiTWrapper"
+
+        return self.model(
+            latent=x,
+            t=t,
+            clip_f=clip_f,
+            sync_f=sync_f,
+            text_f=text_f,
+            inpaint_masked_input=inpaint_masked_input,
+            t5_features=t5_features,
+            metaclip_global_text_features=metaclip_global_text_features,
+            cfg_scale=cfg_scale,
+            cfg_dropout_prob=cfg_dropout_prob,
+            scale_phi=scale_phi,
+            **kwargs)
+
+class MMConditionedDiffusionModelWrapper(ConditionedDiffusionModel):
+    """
+    A diffusion model that takes in conditioning
+    """
+    def __init__(
+            self,
+            model,
+            conditioner: MultiConditioner,
+            io_channels,
+            sample_rate,
+            min_input_length: int,
+            diffusion_objective: tp.Literal["v", "rectified_flow"] = "v",
+            pretransform: tp.Optional[Pretransform] = None,
+            cross_attn_cond_ids: tp.List[str] = [],
+            global_cond_ids: tp.List[str] = [],
+            input_concat_ids: tp.List[str] = [],
+            prepend_cond_ids: tp.List[str] = [],
+            add_cond_ids: tp.List[str] = [],
+            mm_cond_ids: tp.List[str] = [],
+            ):
+        super().__init__()
+
+        self.model = model
+        self.conditioner = conditioner
+        self.io_channels = io_channels
+        self.sample_rate = sample_rate
+        self.diffusion_objective = diffusion_objective
+        self.pretransform = pretransform
+        self.cross_attn_cond_ids = cross_attn_cond_ids
+        self.global_cond_ids = global_cond_ids
+        self.input_concat_ids = input_concat_ids
+        self.prepend_cond_ids = prepend_cond_ids
+        self.add_cond_ids = add_cond_ids
+        self.min_input_length = min_input_length
+        self.mm_cond_ids = mm_cond_ids
+        
+        assert len(self.cross_attn_cond_ids) == 0, "cross_attn_cond_ids is not supported for MMDiTWrapper"
+        assert len(self.global_cond_ids) == 0, "global_cond_ids is not supported for MMDiTWrapper"
+        assert len(self.input_concat_ids) == 0, "input_concat_ids is not supported for MMDiTWrapper"
+        assert len(self.prepend_cond_ids) == 0, "prepend_cond_ids is not supported for MMDiTWrapper"
+        assert len(self.add_cond_ids) == 0, "add_cond_ids is not supported for MMDiTWrapper"
+        assert len(self.mm_cond_ids) > 0, "mm_cond_ids must be specified for MMDiTWrapper"
+        assert "metaclip_features" in self.mm_cond_ids, "clip_f must be specified in mm_cond_ids for MMDiTWrapper"
+        assert "sync_features" in self.mm_cond_ids, "sync_features must be specified in mm_cond_ids for MMDiTWrapper"
+        assert "metaclip_text_features" in self.mm_cond_ids, "metaclip_text_features must be specified in mm_cond_ids for MMDiTWrapper"
+        # assert len(self.mm_cond_ids) == 3, "mm_cond_ids must be clip_f sync_f text_f for MMDiTWrapper"
+
+    def get_conditioning_inputs(self, conditioning_tensors: tp.Dict[str, tp.Any], negative=False):
+        assert negative == False, "negative conditioning is not supported for MMDiTWrapper"
+        cross_attention_input = None
+        cross_attention_masks = None
+        global_cond = None
+        input_concat_cond = None
+        prepend_cond = None
+        prepend_cond_mask = None
+        add_input = None
+        inpaint_masked_input = None
+        t5_features = None
+        metaclip_global_text_features = None
+        clip_f = conditioning_tensors["metaclip_features"]
+        sync_f = conditioning_tensors["sync_features"]
+        text_f = conditioning_tensors["metaclip_text_features"]
+        if 'inpaint_masked_input' in conditioning_tensors.keys():
+            inpaint_masked_input = conditioning_tensors["inpaint_masked_input"]
+        if 't5_features' in conditioning_tensors.keys():
+            t5_features = conditioning_tensors["t5_features"]
+        if 'metaclip_global_text_features' in conditioning_tensors.keys():
+            metaclip_global_text_features = conditioning_tensors["metaclip_global_text_features"]
+        return {
+            "clip_f": clip_f,
+            "sync_f": sync_f,
+            "text_f": text_f,
+            "inpaint_masked_input": inpaint_masked_input,
+            "t5_features": t5_features,
+            "metaclip_global_text_features": metaclip_global_text_features
+            }
+
+    def forward(self, x: torch.Tensor, t: torch.Tensor, cond: tp.Dict[str, tp.Any], **kwargs):
+        # breakpoint()
+        # print(kwargs)
+        return self.model(x=x, t=t, **self.get_conditioning_inputs(cond), **kwargs)
+
+    def generate(self, *args, **kwargs):
+        from prismaudio_core.inference.generation import generate_diffusion_cond
+        return generate_diffusion_cond(self, *args, **kwargs)
+
+class DiTUncondWrapper(DiffusionModel):
+    def __init__(
+        self,
+        io_channels,
+        *args,
+        **kwargs
+    ):
+        super().__init__()
+
+        self.model = DiffusionTransformer(io_channels=io_channels, *args, **kwargs)
+
+        self.io_channels = io_channels
+
+        with torch.no_grad():
+            for param in self.model.parameters():
+                param *= 0.5
+
+    def forward(self, x, t, **kwargs):
+        return self.model(x, t, **kwargs)
+
+def create_diffusion_uncond_from_config(config: tp.Dict[str, tp.Any]):
+    diffusion_uncond_config = config["model"]
+
+    model_type = diffusion_uncond_config.get('type', None)
+
+    diffusion_config = diffusion_uncond_config.get('config', {})
+
+    assert model_type is not None, "Must specify model type in config"
+
+    pretransform = diffusion_uncond_config.get("pretransform", None)
+
+    sample_size = config.get("sample_size", None)
+    assert sample_size is not None, "Must specify sample size in config"
+
+    sample_rate = config.get("sample_rate", None)
+    assert sample_rate is not None, "Must specify sample rate in config"
+
+    if pretransform is not None:
+        pretransform = _get_create_pretransform_from_config()(pretransform, sample_rate)
+        min_input_length = pretransform.downsampling_ratio
+    else:
+        min_input_length = 1
+
+    if model_type == 'DAU1d':
+
+        model = DiffusionAttnUnet1D(
+            **diffusion_config
+        )
+    
+    elif model_type == "adp_uncond_1d":
+
+        model = UNet1DUncondWrapper(
+            **diffusion_config
+        )
+
+    elif model_type == "dit":
+        model = DiTUncondWrapper(
+            **diffusion_config
+        )
+
+    else:
+        raise NotImplementedError(f'Unknown model type: {model_type}')
+
+    return DiffusionModelWrapper(model,
+                                io_channels=model.io_channels,
+                                sample_size=sample_size,
+                                sample_rate=sample_rate,
+                                pretransform=pretransform,
+                                min_input_length=min_input_length)
+
+def create_diffusion_infill_from_config(config: tp.Dict[str, tp.Any]):
+    diffusion_uncond_config = config["model"]
+
+
+    diffusion_config = diffusion_uncond_config.get('diffusion', {})
+    model_type = diffusion_config.get('type', None)
+    model_config = diffusion_config.get("config",{})
+    assert model_type is not None, "Must specify model type in config"
+
+    pretransform = diffusion_uncond_config.get("pretransform", None)
+
+    sample_size = config.get("sample_size", None)
+    assert sample_size is not None, "Must specify sample size in config"
+
+    sample_rate = config.get("sample_rate", None)
+    assert sample_rate is not None, "Must specify sample rate in config"
+
+    if pretransform is not None:
+        pretransform = _get_create_pretransform_from_config()(pretransform, sample_rate)
+        min_input_length = pretransform.downsampling_ratio
+    else:
+        min_input_length = 1
+
+    if model_type == 'DAU1d':
+
+        model = DiffusionAttnUnet1D(
+            **model_config
+        )
+    
+    elif model_type == "adp_uncond_1d":
+
+        model = UNet1DUncondWrapper(
+            io_channels = io_channels,
+            **model_config
+        )
+    elif model_type == "dit":
+        model = DiTUncondWrapper(
+            **model_config
+        )
+
+    else:
+        raise NotImplementedError(f'Unknown model type: {model_type}')
+
+    return DiffusionModelWrapper(model,
+                                io_channels=model.io_channels,
+                                sample_size=sample_size,
+                                sample_rate=sample_rate,
+                                pretransform=pretransform,
+                                min_input_length=min_input_length)
+
+def create_diffusion_cond_from_config(config: tp.Dict[str, tp.Any]):
+
+    model_config = config["model"]
+
+    model_type = config["model_type"]
+
+    diffusion_config = model_config.get('diffusion', None)
+    assert diffusion_config is not None, "Must specify diffusion config"
+
+    diffusion_model_type = diffusion_config.get('type', None)
+    assert diffusion_model_type is not None, "Must specify diffusion model type"
+
+    diffusion_model_config = diffusion_config.get('config', None)
+    assert diffusion_model_config is not None, "Must specify diffusion model config"
+
+    if diffusion_model_type == 'adp_cfg_1d':
+        diffusion_model = UNetCFG1DWrapper(**diffusion_model_config)
+    elif diffusion_model_type == 'adp_1d':
+        diffusion_model = UNet1DCondWrapper(**diffusion_model_config)
+    elif diffusion_model_type == 'dit':
+        diffusion_model = DiTWrapper(**diffusion_model_config)
+    elif diffusion_model_type == 'mmdit':
+        diffusion_model = MMDiTWrapper(**diffusion_model_config)
+
+    io_channels = model_config.get('io_channels', None)
+    assert io_channels is not None, "Must specify io_channels in model config"
+
+    sample_rate = config.get('sample_rate', None)
+    assert sample_rate is not None, "Must specify sample_rate in config"
+
+    diffusion_objective = diffusion_config.get('diffusion_objective', 'v')
+
+    conditioning_config = model_config.get('conditioning', None)
+
+    conditioner = None
+    if conditioning_config is not None:
+        conditioner = _get_create_multi_conditioner_from_conditioning_config()(conditioning_config)
+
+    cross_attention_ids = diffusion_config.get('cross_attention_cond_ids', [])
+    add_cond_ids = diffusion_config.get('add_cond_ids', [])
+    sync_cond_ids = diffusion_config.get('sync_cond_ids', [])
+    global_cond_ids = diffusion_config.get('global_cond_ids', [])
+    input_concat_ids = diffusion_config.get('input_concat_ids', [])
+    prepend_cond_ids = diffusion_config.get('prepend_cond_ids', [])
+    mm_cond_ids = diffusion_config.get('mm_cond_ids', [])
+    zero_init = diffusion_config.get('zero_init', False)
+    pretransform = model_config.get("pretransform", None)
+
+    if pretransform is not None:
+        pretransform = _get_create_pretransform_from_config()(pretransform, sample_rate)
+        min_input_length = pretransform.downsampling_ratio
+    else:
+        min_input_length = 1
+
+    if diffusion_model_type == "adp_cfg_1d" or diffusion_model_type == "adp_1d":
+        min_input_length *= np.prod(diffusion_model_config["factors"])
+    elif diffusion_model_type == "dit":
+        min_input_length *= diffusion_model.model.patch_size
+
+    # Get the proper wrapper class
+
+    extra_kwargs = {}
+
+    if model_type == "mm_diffusion_cond":
+        wrapper_fn = MMConditionedDiffusionModelWrapper
+        extra_kwargs["diffusion_objective"] = diffusion_objective
+        extra_kwargs["mm_cond_ids"] = mm_cond_ids
+
+    if model_type == "diffusion_cond" or model_type == "diffusion_cond_inpaint" or model_type == 'diffusion_infill':
+        wrapper_fn = ConditionedDiffusionModelWrapper
+        extra_kwargs["diffusion_objective"] = diffusion_objective
+
+    elif model_type == "diffusion_prior":
+        prior_type = model_config.get("prior_type", None)
+        assert prior_type is not None, "Must specify prior_type in diffusion prior model config"
+
+        if prior_type == "mono_stereo":
+            from .diffusion_prior import MonoToStereoDiffusionPrior
+            wrapper_fn = MonoToStereoDiffusionPrior
+            
+    return wrapper_fn(
+        diffusion_model,
+        conditioner,
+        min_input_length=min_input_length,
+        sample_rate=sample_rate,
+        cross_attn_cond_ids=cross_attention_ids,
+        global_cond_ids=global_cond_ids,
+        input_concat_ids=input_concat_ids,
+        prepend_cond_ids=prepend_cond_ids,
+        add_cond_ids=add_cond_ids,
+        sync_cond_ids=sync_cond_ids,
+        pretransform=pretransform,
+        io_channels=io_channels,
+        zero_init=zero_init,
+        **extra_kwargs
+    )

prismaudio_core/models/dit.py

@@ -0,0 +1,541 @@
+import typing as tp
+import math
+import torch
+# from beartype.typing import Tuple
+from einops import rearrange
+from torch import nn
+from torch.nn import functional as F
+from .mmmodules.model.low_level import MLP, ChannelLastConv1d, ConvMLP
+from .blocks import FourierFeatures
+from .transformer import ContinuousTransformer
+from .utils import mask_from_frac_lengths, resample
+
+class DiffusionTransformer(nn.Module):
+    def __init__(self, 
+        io_channels=32, 
+        patch_size=1,
+        embed_dim=768,
+        cond_token_dim=0,
+        project_cond_tokens=True,
+        global_cond_dim=0,
+        project_global_cond=True,
+        input_concat_dim=0,
+        prepend_cond_dim=0,
+        cond_ctx_dim=0,
+        depth=12,
+        num_heads=8,
+        transformer_type: tp.Literal["continuous_transformer"] = "continuous_transformer",
+        global_cond_type: tp.Literal["prepend", "adaLN"] = "prepend",
+        timestep_cond_type: tp.Literal["global", "input_concat"] = "global",
+        add_token_dim=0,
+        sync_token_dim=0,
+        use_mlp=False,
+        use_zero_init=False,
+        **kwargs):
+
+        super().__init__()
+        
+        self.cond_token_dim = cond_token_dim
+
+        # Timestep embeddings
+        timestep_features_dim = 256
+        # Timestep embeddings
+        self.timestep_cond_type = timestep_cond_type
+        self.timestep_features = FourierFeatures(1, timestep_features_dim)
+
+        if timestep_cond_type == "global":
+            timestep_embed_dim = embed_dim
+        elif timestep_cond_type == "input_concat":
+            assert timestep_embed_dim is not None, "timestep_embed_dim must be specified if timestep_cond_type is input_concat"
+            input_concat_dim += timestep_embed_dim
+
+        self.to_timestep_embed = nn.Sequential(
+            nn.Linear(timestep_features_dim, embed_dim, bias=True),
+            nn.SiLU(),
+            nn.Linear(embed_dim, embed_dim, bias=True),
+        )
+        self.use_mlp = use_mlp
+        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(
+                nn.Linear(cond_token_dim, cond_embed_dim, bias=False),
+                nn.SiLU(),
+                nn.Linear(cond_embed_dim, cond_embed_dim, bias=False)
+            )
+        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(
+                nn.Linear(global_cond_dim, global_embed_dim, bias=False),
+                nn.SiLU(),
+                nn.Linear(global_embed_dim, global_embed_dim, bias=False)
+            )
+        if add_token_dim > 0:
+            # Conditioning tokens
+            add_embed_dim = add_token_dim if not project_cond_tokens else embed_dim
+            self.to_add_embed = nn.Sequential(
+                nn.Linear(add_token_dim, add_embed_dim, bias=False),
+                nn.SiLU(),
+                nn.Linear(add_embed_dim, add_embed_dim, bias=False)
+            )
+        else:
+            add_embed_dim = 0
+        
+        if sync_token_dim > 0:
+            # Conditioning tokens
+            sync_embed_dim = sync_token_dim if not project_cond_tokens else embed_dim
+            self.to_sync_embed = nn.Sequential(
+                nn.Linear(sync_token_dim, sync_embed_dim, bias=False),
+                nn.SiLU(),
+                nn.Linear(sync_embed_dim, sync_embed_dim, bias=False)
+            )
+        else:
+            sync_embed_dim = 0
+
+
+        if prepend_cond_dim > 0:
+            # Prepend conditioning
+            self.to_prepend_embed = nn.Sequential(
+                nn.Linear(prepend_cond_dim, embed_dim, bias=False),
+                nn.SiLU(),
+                nn.Linear(embed_dim, embed_dim, bias=False)
+            )
+
+        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.empty_clip_feat = nn.Parameter(torch.zeros(1, embed_dim), requires_grad=True)
+        self.empty_sync_feat = nn.Parameter(torch.zeros(1, embed_dim), requires_grad=True)
+        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,
+                **kwargs
+            )
+        else:
+            raise ValueError(f"Unknown transformer type: {self.transformer_type}")
+
+        self.preprocess_conv = nn.Conv1d(dim_in, dim_in, 1, bias=False)
+        self.postprocess_conv = nn.Conv1d(io_channels, io_channels, 1, bias=False)
+        nn.init.zeros_(self.preprocess_conv.weight)
+        nn.init.zeros_(self.postprocess_conv.weight)
+
+
+    def initialize_weights(self):
+        def _basic_init(module):
+            if isinstance(module, nn.Linear):
+                torch.nn.init.xavier_uniform_(module.weight)
+                if module.bias is not None:
+                    nn.init.constant_(module.bias, 0)
+
+            # if isinstance(module, nn.Conv1d):
+            #     if module.bias is not None:
+            #         nn.init.constant_(module.bias, 0)
+
+        self.apply(_basic_init)
+
+        # Initialize timestep embedding MLP:
+        nn.init.normal_(self.to_timestep_embed[0].weight, std=0.02)
+        nn.init.normal_(self.to_timestep_embed[2].weight, std=0.02)
+
+        # Zero-out output layers:
+        if self.global_cond_type == "adaLN":
+            for block in self.transformer.layers:
+                nn.init.constant_(block.adaLN_modulation[-1].weight, 0)
+                nn.init.constant_(block.adaLN_modulation[-1].bias, 0)
+
+        nn.init.constant_(self.empty_clip_feat, 0)
+        nn.init.constant_(self.empty_sync_feat, 0)
+
+    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,
+        add_cond=None,
+        add_masks=None,
+        sync_cond=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:
+            # reshape from (b, n, c) to (b, c, n)
+            if input_concat_cond.shape[1] != x.shape[1]:
+                input_concat_cond = input_concat_cond.transpose(1,2)
+            # Interpolate input_concat_cond to the same length as x
+            # if input_concat_cond.shape[1] != x.shape[2]:
+            # input_concat_cond = input_concat_cond.transpose(1,2)
+            input_concat_cond = F.interpolate(input_concat_cond, (x.shape[2], ), mode='nearest')
+            # input_concat_cond = input_concat_cond.transpose(1,2)
+            # if len(global_embed.shape) == 2:
+            #     global_embed = global_embed.unsqueeze(1)
+            # global_embed = global_embed + input_concat_cond
+            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])) # (b, embed_dim)
+        # import ipdb
+        # ipdb.set_trace()
+        # Timestep embedding is considered a global embedding. Add to the global conditioning if it exists
+        if self.timestep_cond_type == "global":
+            if global_embed is not None:
+                if len(global_embed.shape) == 3:
+                    timestep_embed = timestep_embed.unsqueeze(1)
+                global_embed = global_embed + timestep_embed
+            else:
+                global_embed = timestep_embed
+        elif self.timestep_cond_type == "input_concat":
+            x = torch.cat([x, timestep_embed.unsqueeze(1).expand(-1, -1, x.shape[2])], dim=1)
+
+        # Add the global_embed to the prepend inputs if there is no global conditioning support in the transformer
+        if self.global_cond_type == "prepend" and global_embed is not None:
+            if prepend_inputs is None:
+                # Prepend inputs are just the global embed, and the mask is all ones
+                if len(global_embed.shape) == 2:
+                    prepend_inputs = global_embed.unsqueeze(1)
+                else:
+                    prepend_inputs = global_embed
+                prepend_mask = torch.ones((x.shape[0], 1), device=x.device, dtype=torch.bool)
+            else:
+                # Prepend inputs are the prepend conditioning + the global embed
+                if len(global_embed.shape) == 2:
+                    prepend_inputs = torch.cat([prepend_inputs, global_embed.unsqueeze(1)], dim=1)
+                else:
+                    prepend_inputs = torch.cat([prepend_inputs, global_embed], 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:
+            b, seq_len, c = x.shape
+            
+            # 计算需要填充的数量
+            pad_amount = (self.patch_size - seq_len % self.patch_size) % self.patch_size
+            
+            if pad_amount > 0:
+                # 在时间维度上进行填充
+                x = F.pad(x, (0, 0, 0, pad_amount), mode='constant', value=0)
+            x = rearrange(x, "b (t p) c -> b t (c p)", p=self.patch_size)
+
+        if add_cond is not None:
+            # Interpolate add_cond to the same length as x
+            # if self.use_mlp:
+            add_cond = self.to_add_embed(add_cond)
+            if add_cond.shape[1] != x.shape[1]:
+                add_cond = add_cond.transpose(1,2)
+                add_cond = F.interpolate(add_cond, (x.shape[1], ), mode='linear', align_corners=False)
+                add_cond = add_cond.transpose(1,2)
+                # add_cond = resample(add_cond, x)
+
+        if sync_cond is not None:
+            sync_cond = self.to_sync_embed(sync_cond)
+
+        if self.transformer_type == "continuous_transformer":
+            output = self.transformer(x, prepend_embeds=prepend_inputs, context=cross_attn_cond, add_cond=add_cond, sync_cond=sync_cond, mask=mask, prepend_mask=prepend_mask, return_info=return_info, **extra_args, **kwargs)
+
+            if return_info:
+                output, info = output
+
+        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)
+            # 移除之前添加的填充
+            if pad_amount > 0:
+                output = output[:, :, :seq_len]
+
+        output = self.postprocess_conv(output) + output
+
+        if return_info:
+            return output, info
+
+        return output
+
+    def forward(
+        self, 
+        x, 
+        t,
+        cross_attn_cond=None,
+        cross_attn_cond_mask=None,
+        negative_cross_attn_cond=None,
+        negative_cross_attn_mask=None,
+        input_concat_cond=None,
+        global_embed=None,
+        negative_global_embed=None,
+        prepend_cond=None,
+        prepend_cond_mask=None,
+        add_cond=None,
+        sync_cond=None,
+        cfg_scale=1.0,
+        cfg_dropout_prob=0.0,
+        causal=False,
+        scale_phi=0.0,
+        mask=None,
+        return_info=False,
+        **kwargs):
+
+        assert causal == False, "Causal mode is not supported for DiffusionTransformer"
+        bsz, a, b = x.shape
+        model_dtype = next(self.parameters()).dtype
+        x = x.to(model_dtype)
+        t = t.to(model_dtype)
+
+        if cross_attn_cond is not None:
+            cross_attn_cond = cross_attn_cond.to(model_dtype)
+
+        if negative_cross_attn_cond is not None:
+            negative_cross_attn_cond = negative_cross_attn_cond.to(model_dtype)
+
+        if input_concat_cond is not None:
+            input_concat_cond = input_concat_cond.to(model_dtype)
+
+        if global_embed is not None:
+            global_embed = global_embed.to(model_dtype)
+
+        if negative_global_embed is not None:
+            negative_global_embed = negative_global_embed.to(model_dtype)
+
+        if prepend_cond is not None:
+            prepend_cond = prepend_cond.to(model_dtype)
+
+        if add_cond is not None:
+            add_cond = add_cond.to(model_dtype)
+        
+        if sync_cond is not None:
+            sync_cond = sync_cond.to(model_dtype)
+
+        if cross_attn_cond_mask is not None:
+            cross_attn_cond_mask = cross_attn_cond_mask.bool()
+
+            cross_attn_cond_mask = None # Temporarily disabling conditioning masks due to kernel issue for flash attention
+
+        if prepend_cond_mask is not None:
+            prepend_cond_mask = prepend_cond_mask.bool()
+        
+
+        # CFG dropout
+        if cfg_dropout_prob > 0.0 and cfg_scale == 1.0:
+            if cross_attn_cond is not None:
+                null_embed = torch.zeros_like(cross_attn_cond, device=cross_attn_cond.device)
+                dropout_mask = torch.bernoulli(torch.full((cross_attn_cond.shape[0], 1, 1), cfg_dropout_prob, device=cross_attn_cond.device)).to(torch.bool)
+                cross_attn_cond = torch.where(dropout_mask, null_embed, cross_attn_cond)
+
+            if prepend_cond is not None:
+                null_embed = torch.zeros_like(prepend_cond, device=prepend_cond.device)
+                dropout_mask = torch.bernoulli(torch.full((prepend_cond.shape[0], 1, 1), cfg_dropout_prob, device=prepend_cond.device)).to(torch.bool)
+                prepend_cond = torch.where(dropout_mask, null_embed, prepend_cond)
+            
+            if add_cond is not None:
+                null_embed = torch.zeros_like(add_cond, device=add_cond.device)
+                dropout_mask = torch.bernoulli(torch.full((add_cond.shape[0], 1, 1), cfg_dropout_prob, device=add_cond.device)).to(torch.bool)
+                add_cond = torch.where(dropout_mask, null_embed, add_cond)
+            
+            if sync_cond is not None:
+                null_embed = torch.zeros_like(sync_cond, device=sync_cond.device)
+                dropout_mask = torch.bernoulli(torch.full((sync_cond.shape[0], 1, 1), cfg_dropout_prob, device=sync_cond.device)).to(torch.bool)
+                sync_cond = torch.where(dropout_mask, null_embed, sync_cond)
+
+        if cfg_scale != 1.0 and (cross_attn_cond is not None or prepend_cond is not None or add_cond is not None):
+            # Classifier-free guidance
+            # Concatenate conditioned and unconditioned inputs on the batch dimension            
+            batch_inputs = torch.cat([x, x], dim=0)
+            batch_timestep = torch.cat([t, t], dim=0)
+            if global_embed is not None and global_embed.shape[0] == bsz:
+                batch_global_cond = torch.cat([global_embed, global_embed], dim=0)
+            elif global_embed is not None:
+                batch_global_cond = global_embed
+            else:
+                batch_global_cond = None
+
+            if input_concat_cond is not None and input_concat_cond.shape[0] == bsz:
+                batch_input_concat_cond = torch.cat([input_concat_cond, input_concat_cond], dim=0)
+            elif input_concat_cond is not None:
+                batch_input_concat_cond = input_concat_cond
+            else:
+                batch_input_concat_cond = None
+
+            batch_cond = None
+            batch_cond_masks = None
+            
+            # Handle CFG for cross-attention conditioning
+            if cross_attn_cond is not None and cross_attn_cond.shape[0] == bsz:
+
+                null_embed = torch.zeros_like(cross_attn_cond, device=cross_attn_cond.device)
+
+                # For negative cross-attention conditioning, replace the null embed with the negative cross-attention conditioning
+                if negative_cross_attn_cond is not None:
+
+                    # If there's a negative cross-attention mask, set the masked tokens to the null embed
+                    if negative_cross_attn_mask is not None:
+                        negative_cross_attn_mask = negative_cross_attn_mask.to(torch.bool).unsqueeze(2)
+
+                        negative_cross_attn_cond = torch.where(negative_cross_attn_mask, negative_cross_attn_cond, null_embed)
+                    
+                    batch_cond = torch.cat([cross_attn_cond, negative_cross_attn_cond], dim=0)
+
+                else:
+                    batch_cond = torch.cat([cross_attn_cond, null_embed], dim=0)
+
+                if cross_attn_cond_mask is not None:
+                    batch_cond_masks = torch.cat([cross_attn_cond_mask, cross_attn_cond_mask], dim=0)
+            elif cross_attn_cond is not None:
+                batch_cond = cross_attn_cond
+            else:
+                batch_cond = None
+
+            batch_prepend_cond = None
+            batch_prepend_cond_mask = None
+
+            if prepend_cond is not None and prepend_cond.shape[0] == bsz:
+
+                null_embed = torch.zeros_like(prepend_cond, device=prepend_cond.device)
+
+                batch_prepend_cond = torch.cat([prepend_cond, null_embed], dim=0)
+                           
+                if prepend_cond_mask is not None:
+                    batch_prepend_cond_mask = torch.cat([prepend_cond_mask, prepend_cond_mask], dim=0)
+            elif prepend_cond is not None:
+                batch_prepend_cond = prepend_cond
+            else:
+                batch_prepend_cond = None
+
+            batch_add_cond = None
+            
+            # Handle CFG for cross-attention conditioning
+            if add_cond is not None and add_cond.shape[0] == bsz:
+
+                null_embed = torch.zeros_like(add_cond, device=add_cond.device)
+
+                
+                batch_add_cond = torch.cat([add_cond, null_embed], dim=0)
+            elif add_cond is not None:
+                batch_add_cond = add_cond
+            else:
+                batch_add_cond = None
+            
+            batch_sync_cond = None
+            
+            # Handle CFG for cross-attention conditioning
+            if sync_cond is not None and sync_cond.shape[0] == bsz:
+
+                null_embed = torch.zeros_like(sync_cond, device=sync_cond.device)
+
+                
+                batch_sync_cond = torch.cat([sync_cond, null_embed], dim=0)
+            elif sync_cond is not None:
+                batch_sync_cond = sync_cond
+            else:
+                batch_sync_cond = None
+
+            if mask is not None:
+                batch_masks = torch.cat([mask, mask], dim=0)
+            else:
+                batch_masks = None
+            
+            batch_output = self._forward(
+                batch_inputs, 
+                batch_timestep, 
+                cross_attn_cond=batch_cond, 
+                cross_attn_cond_mask=batch_cond_masks, 
+                mask = batch_masks, 
+                input_concat_cond=batch_input_concat_cond, 
+                global_embed = batch_global_cond,
+                prepend_cond = batch_prepend_cond,
+                prepend_cond_mask = batch_prepend_cond_mask,
+                add_cond = batch_add_cond,
+                sync_cond = batch_sync_cond,
+                return_info = return_info,
+                **kwargs)
+
+            if return_info:
+                batch_output, info = batch_output
+
+            cond_output, uncond_output = torch.chunk(batch_output, 2, dim=0)
+            cfg_output = uncond_output + (cond_output - uncond_output) * cfg_scale
+
+            # CFG Rescale
+            if scale_phi != 0.0:
+                cond_out_std = cond_output.std(dim=1, keepdim=True)
+                out_cfg_std = cfg_output.std(dim=1, keepdim=True)
+                output = scale_phi * (cfg_output * (cond_out_std/out_cfg_std)) + (1-scale_phi) * cfg_output
+            else:
+                output = cfg_output
+            
+            if return_info:
+                return output, info
+
+            return output
+            
+        else:
+            return self._forward(
+                x,
+                t,
+                cross_attn_cond=cross_attn_cond, 
+                cross_attn_cond_mask=cross_attn_cond_mask, 
+                input_concat_cond=input_concat_cond, 
+                global_embed=global_embed, 
+                prepend_cond=prepend_cond, 
+                prepend_cond_mask=prepend_cond_mask,
+                add_cond=add_cond,
+                sync_cond=sync_cond,
+                mask=mask,
+                return_info=return_info,
+                **kwargs
+            )

prismaudio_core/models/local_attention.py

@@ -0,0 +1,278 @@
+import torch
+
+from einops import rearrange
+from torch import nn
+
+from .blocks import AdaRMSNorm
+from .transformer import Attention, FeedForward, RotaryEmbedding, LayerNorm
+
+def checkpoint(function, *args, **kwargs):
+    kwargs.setdefault("use_reentrant", False)
+    return torch.utils.checkpoint.checkpoint(function, *args, **kwargs)
+
+# Adapted from https://github.com/lucidrains/local-attention/blob/master/local_attention/transformer.py
+class ContinuousLocalTransformer(nn.Module):
+    def __init__(
+        self,
+        *,
+        dim,
+        depth,
+        dim_in = None,
+        dim_out = None,
+        causal = False,
+        local_attn_window_size = 64,
+        heads = 8,
+        ff_mult = 2,
+        cond_dim = 0,
+        cross_attn_cond_dim = 0,
+        **kwargs
+    ):
+        super().__init__()
+        
+        dim_head = dim//heads
+
+        self.layers = nn.ModuleList([])
+
+        self.project_in = nn.Linear(dim_in, dim) if dim_in is not None else nn.Identity()
+
+        self.project_out = nn.Linear(dim, dim_out) if dim_out is not None else nn.Identity()
+
+        self.local_attn_window_size = local_attn_window_size
+
+        self.cond_dim = cond_dim
+
+        self.cross_attn_cond_dim = cross_attn_cond_dim
+
+        self.rotary_pos_emb = RotaryEmbedding(max(dim_head // 2, 32))
+       
+        for _ in range(depth):
+
+            self.layers.append(nn.ModuleList([
+                AdaRMSNorm(dim, cond_dim, eps=1e-8) if cond_dim > 0 else LayerNorm(dim),
+                Attention(
+                    dim=dim,
+                    dim_heads=dim_head,
+                    causal=causal,
+                    zero_init_output=True,
+                    natten_kernel_size=local_attn_window_size,
+                ),
+                Attention(
+                    dim=dim,
+                    dim_heads=dim_head,
+                    dim_context = cross_attn_cond_dim,
+                    zero_init_output=True
+                ) if self.cross_attn_cond_dim > 0 else nn.Identity(),
+                AdaRMSNorm(dim, cond_dim, eps=1e-8) if cond_dim > 0 else LayerNorm(dim),
+                FeedForward(dim = dim, mult = ff_mult, no_bias=True)
+            ]))
+
+    def forward(self, x, mask = None, cond = None, cross_attn_cond = None, cross_attn_cond_mask = None, prepend_cond = None):
+ 
+        x = checkpoint(self.project_in, x)
+
+        if prepend_cond is not None:
+            x = torch.cat([prepend_cond, x], dim=1)
+
+        pos_emb = self.rotary_pos_emb.forward_from_seq_len(x.shape[1])
+
+        for attn_norm, attn, xattn, ff_norm, ff in self.layers:
+
+            residual = x
+            if cond is not None:
+                x = checkpoint(attn_norm, x, cond)
+            else:
+                x = checkpoint(attn_norm, x)
+
+            x = checkpoint(attn, x, mask = mask, rotary_pos_emb=pos_emb) + residual
+
+            if cross_attn_cond is not None:
+                x = checkpoint(xattn, x, context=cross_attn_cond, context_mask=cross_attn_cond_mask) + x
+
+            residual = x
+
+            if cond is not None:
+                x = checkpoint(ff_norm, x, cond)
+            else:
+                x = checkpoint(ff_norm, x)
+
+            x = checkpoint(ff, x) + residual
+
+        return checkpoint(self.project_out, x)
+
+class TransformerDownsampleBlock1D(nn.Module):
+    def __init__(
+        self, 
+        in_channels,
+        embed_dim = 768,
+        depth = 3,
+        heads = 12,
+        downsample_ratio = 2,
+        local_attn_window_size = 64,
+        **kwargs
+    ):
+        super().__init__()
+
+        self.downsample_ratio = downsample_ratio
+
+        self.transformer = ContinuousLocalTransformer(
+            dim=embed_dim,
+            depth=depth,
+            heads=heads,
+            local_attn_window_size=local_attn_window_size,
+            **kwargs
+        )
+
+        self.project_in = nn.Linear(in_channels, embed_dim, bias=False) if in_channels != embed_dim else nn.Identity()
+
+        self.project_down = nn.Linear(embed_dim * self.downsample_ratio, embed_dim, bias=False)
+        
+    
+    def forward(self, x):
+
+        x = checkpoint(self.project_in, x)
+
+        # Compute
+        x = self.transformer(x)
+
+        # Trade sequence length for channels
+        x = rearrange(x, "b (n r) c -> b n (c r)", r=self.downsample_ratio)
+
+        # Project back to embed dim
+        x = checkpoint(self.project_down, x)
+
+        return x
+
+class TransformerUpsampleBlock1D(nn.Module):
+    def __init__(
+        self, 
+        in_channels,
+        embed_dim,
+        depth = 3,
+        heads = 12,
+        upsample_ratio = 2,
+        local_attn_window_size = 64,
+        **kwargs
+    ):
+        super().__init__()
+
+        self.upsample_ratio = upsample_ratio
+
+        self.transformer = ContinuousLocalTransformer(
+            dim=embed_dim,
+            depth=depth,
+            heads=heads,
+            local_attn_window_size = local_attn_window_size,
+            **kwargs
+        )
+
+        self.project_in = nn.Linear(in_channels, embed_dim, bias=False) if in_channels != embed_dim else nn.Identity()
+
+        self.project_up = nn.Linear(embed_dim, embed_dim * self.upsample_ratio, bias=False)
+        
+    def forward(self, x):
+
+        # Project to embed dim
+        x = checkpoint(self.project_in, x)
+
+        # Project to increase channel dim
+        x = checkpoint(self.project_up, x)
+
+        # Trade channels for sequence length
+        x = rearrange(x, "b n (c r) -> b (n r) c", r=self.upsample_ratio)
+
+        # Compute
+        x = self.transformer(x)        
+
+        return x
+   
+
+class TransformerEncoder1D(nn.Module):
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        embed_dims = [96, 192, 384, 768],
+        heads = [12, 12, 12, 12],
+        depths = [3, 3, 3, 3],
+        ratios = [2, 2, 2, 2],
+        local_attn_window_size = 64,
+        **kwargs
+    ):
+        super().__init__()
+        
+        layers = []
+       
+        for layer in range(len(depths)):
+            prev_dim = embed_dims[layer - 1] if layer > 0 else embed_dims[0]
+
+            layers.append(
+                TransformerDownsampleBlock1D(
+                    in_channels = prev_dim,
+                    embed_dim = embed_dims[layer],
+                    heads = heads[layer],
+                    depth = depths[layer],
+                    downsample_ratio = ratios[layer],
+                    local_attn_window_size = local_attn_window_size,
+                    **kwargs
+                )
+            )
+        
+        self.layers = nn.Sequential(*layers)
+
+        self.project_in = nn.Linear(in_channels, embed_dims[0], bias=False)
+        self.project_out = nn.Linear(embed_dims[-1], out_channels, bias=False)
+
+    def forward(self, x):
+        x = rearrange(x, "b c n -> b n c")
+        x = checkpoint(self.project_in, x)
+        x = self.layers(x)
+        x = checkpoint(self.project_out, x)
+        x = rearrange(x, "b n c -> b c n")
+
+        return x
+
+
+class TransformerDecoder1D(nn.Module):
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        embed_dims = [768, 384, 192, 96],
+        heads = [12, 12, 12, 12],
+        depths = [3, 3, 3, 3],
+        ratios = [2, 2, 2, 2],
+        local_attn_window_size = 64,
+        **kwargs
+    ):
+
+        super().__init__()
+
+        layers = []
+       
+        for layer in range(len(depths)):
+            prev_dim = embed_dims[layer - 1] if layer > 0 else embed_dims[0]
+
+            layers.append(
+                TransformerUpsampleBlock1D(
+                    in_channels = prev_dim,
+                    embed_dim = embed_dims[layer],
+                    heads = heads[layer],
+                    depth = depths[layer],
+                    upsample_ratio = ratios[layer],
+                    local_attn_window_size = local_attn_window_size,
+                    **kwargs
+                )
+            )
+        
+        self.layers = nn.Sequential(*layers)
+
+        self.project_in = nn.Linear(in_channels, embed_dims[0], bias=False)
+        self.project_out = nn.Linear(embed_dims[-1], out_channels, bias=False)
+
+    def forward(self, x):
+        x = rearrange(x, "b c n -> b n c")
+        x = checkpoint(self.project_in, x)
+        x = self.layers(x)
+        x = checkpoint(self.project_out, x)
+        x = rearrange(x, "b n c -> b c n")
+        return x

prismaudio_core/models/mmmodules/__init__.py

@@ -0,0 +1 @@
+# mmmodules package

prismaudio_core/models/mmmodules/model/__init__.py

@@ -0,0 +1 @@
+# mmmodules.model package

prismaudio_core/models/mmmodules/model/low_level.py

@@ -0,0 +1,95 @@
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+
+class ChannelLastConv1d(nn.Conv1d):
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = x.permute(0, 2, 1)
+        x = super().forward(x)
+        x = x.permute(0, 2, 1)
+        return x
+
+
+# https://github.com/Stability-AI/sd3-ref
+class MLP(nn.Module):
+
+    def __init__(
+        self,
+        dim: int,
+        hidden_dim: int,
+        multiple_of: int = 256,
+    ):
+        """
+        Initialize the FeedForward module.
+
+        Args:
+            dim (int): Input dimension.
+            hidden_dim (int): Hidden dimension of the feedforward layer.
+            multiple_of (int): Value to ensure hidden dimension is a multiple of this value.
+
+        Attributes:
+            w1 (ColumnParallelLinear): Linear transformation for the first layer.
+            w2 (RowParallelLinear): Linear transformation for the second layer.
+            w3 (ColumnParallelLinear): Linear transformation for the third layer.
+
+        """
+        super().__init__()
+        hidden_dim = int(2 * hidden_dim / 3)
+        hidden_dim = multiple_of * ((hidden_dim + multiple_of - 1) // multiple_of)
+
+        self.w1 = nn.Linear(dim, hidden_dim, bias=False)
+        self.w2 = nn.Linear(hidden_dim, dim, bias=False)
+        self.w3 = nn.Linear(dim, hidden_dim, bias=False)
+
+    def forward(self, x):
+        return self.w2(F.silu(self.w1(x)) * self.w3(x))
+
+
+class ConvMLP(nn.Module):
+
+    def __init__(
+        self,
+        dim: int,
+        hidden_dim: int,
+        multiple_of: int = 256,
+        kernel_size: int = 3,
+        padding: int = 1,
+    ):
+        """
+        Initialize the FeedForward module.
+
+        Args:
+            dim (int): Input dimension.
+            hidden_dim (int): Hidden dimension of the feedforward layer.
+            multiple_of (int): Value to ensure hidden dimension is a multiple of this value.
+
+        Attributes:
+            w1 (ColumnParallelLinear): Linear transformation for the first layer.
+            w2 (RowParallelLinear): Linear transformation for the second layer.
+            w3 (ColumnParallelLinear): Linear transformation for the third layer.
+
+        """
+        super().__init__()
+        hidden_dim = int(2 * hidden_dim / 3)
+        hidden_dim = multiple_of * ((hidden_dim + multiple_of - 1) // multiple_of)
+
+        self.w1 = ChannelLastConv1d(dim,
+                                    hidden_dim,
+                                    bias=False,
+                                    kernel_size=kernel_size,
+                                    padding=padding)
+        self.w2 = ChannelLastConv1d(hidden_dim,
+                                    dim,
+                                    bias=False,
+                                    kernel_size=kernel_size,
+                                    padding=padding)
+        self.w3 = ChannelLastConv1d(dim,
+                                    hidden_dim,
+                                    bias=False,
+                                    kernel_size=kernel_size,
+                                    padding=padding)
+
+    def forward(self, x):
+        return self.w2(F.silu(self.w1(x)) * self.w3(x))

prismaudio_core/models/pqmf.py

@@ -0,0 +1,393 @@
+import math
+import numpy as np
+import torch
+import torch.nn as nn
+from einops import rearrange
+from scipy.optimize import fmin
+from scipy.signal import firwin, kaiser, kaiser_beta, kaiserord
+
+class PQMF(nn.Module):
+    """
+    Pseudo Quadrature Mirror Filter (PQMF) for multiband signal decomposition and reconstruction.
+    Uses polyphase representation which is computationally more efficient for real-time. 
+    
+    Parameters:
+    - attenuation (int): Desired attenuation of the rejected frequency bands, usually between 80 and 120 dB.
+    - num_bands (int): Number of desired frequency bands. It must be a power of 2.
+    """
+
+    def __init__(self, attenuation, num_bands):
+        super(PQMF, self).__init__()
+        
+        # Ensure num_bands is a power of 2 
+        is_power_of_2 = (math.log2(num_bands) == int(math.log2(num_bands)))
+        assert is_power_of_2, "'num_bands' must be a power of 2."
+        
+        # Create the prototype filter
+        prototype_filter = design_prototype_filter(attenuation, num_bands)
+        filter_bank = generate_modulated_filter_bank(prototype_filter, num_bands)
+        padded_filter_bank = pad_to_nearest_power_of_two(filter_bank)
+        
+        # Register filters and settings
+        self.register_buffer("filter_bank", padded_filter_bank)
+        self.register_buffer("prototype", prototype_filter)
+        self.num_bands = num_bands
+
+    def forward(self, signal):
+        """Decompose the signal into multiple frequency bands."""
+        # If signal is not a pytorch tensor of Batch x Channels x Length, convert it 
+        signal = prepare_signal_dimensions(signal)
+        # The signal length must be a multiple of num_bands. Pad it with zeros.
+        signal = pad_signal(signal, self.num_bands)
+        # run it
+        signal = polyphase_analysis(signal, self.filter_bank)
+        return apply_alias_cancellation(signal)
+
+    def inverse(self, bands):
+        """Reconstruct the original signal from the frequency bands."""
+        bands = apply_alias_cancellation(bands)
+        return polyphase_synthesis(bands, self.filter_bank)
+
+
+def prepare_signal_dimensions(signal):
+    """
+    Rearrange signal into Batch x Channels x Length. 
+    
+    Parameters
+    ----------
+    signal : torch.Tensor or numpy.ndarray
+        The input signal.
+        
+    Returns
+    -------
+    torch.Tensor
+        Preprocessed signal tensor.
+    """
+    # Convert numpy to torch tensor
+    if isinstance(signal, np.ndarray):
+        signal = torch.from_numpy(signal)
+        
+    # Ensure tensor
+    if not isinstance(signal, torch.Tensor):
+        raise ValueError("Input should be either a numpy array or a PyTorch tensor.")
+    
+    # Modify dimension of signal to Batch x Channels x Length
+    if signal.dim() == 1:
+        # This is just a mono signal. Unsqueeze to 1 x 1 x Length
+        signal = signal.unsqueeze(0).unsqueeze(0)
+    elif signal.dim() == 2:
+        # This is a multi-channel signal (e.g. stereo)
+        # Rearrange so that larger dimension (Length) is last
+        if signal.shape[0] > signal.shape[1]:
+            signal = signal.T
+        # Unsqueeze to 1 x Channels x Length 
+        signal = signal.unsqueeze(0)
+    return signal
+    
+def pad_signal(signal, num_bands):
+    """
+    Pads the signal to make its length divisible by the given number of bands.
+
+    Parameters
+    ----------
+    signal : torch.Tensor
+        The input signal tensor, where the last dimension represents the signal length.
+
+    num_bands : int
+        The number of bands by which the signal length should be divisible.
+
+    Returns
+    -------
+    torch.Tensor
+        The padded signal tensor. If the original signal length was already divisible
+        by num_bands, returns the original signal unchanged.
+    """
+    remainder = signal.shape[-1] % num_bands
+    if remainder > 0:
+        padding_size = num_bands - remainder
+        signal = nn.functional.pad(signal, (0, padding_size))
+    return signal
+
+def generate_modulated_filter_bank(prototype_filter, num_bands):
+    """
+    Generate a QMF bank of cosine modulated filters based on a given prototype filter. 
+    
+    Parameters
+    ----------
+    prototype_filter : torch.Tensor
+        The prototype filter used as the basis for modulation.
+    num_bands : int
+        The number of desired subbands or filters.
+    
+    Returns
+    -------
+    torch.Tensor
+        A bank of cosine modulated filters.
+    """
+    
+    # Initialize indices for modulation.
+    subband_indices = torch.arange(num_bands).reshape(-1, 1)
+    
+    # Calculate the length of the prototype filter.
+    filter_length = prototype_filter.shape[-1]
+    
+    # Generate symmetric time indices centered around zero.
+    time_indices = torch.arange(-(filter_length // 2), (filter_length // 2) + 1)
+    
+    # Calculate phase offsets to ensure orthogonality between subbands.
+    phase_offsets = (-1)**subband_indices * np.pi / 4
+    
+    # Compute the cosine modulation function.
+    modulation = torch.cos(
+        (2 * subband_indices + 1) * np.pi / (2 * num_bands) * time_indices + phase_offsets
+    )
+    
+    # Apply modulation to the prototype filter.
+    modulated_filters = 2 * prototype_filter * modulation
+    
+    return modulated_filters
+
+
+def design_kaiser_lowpass(angular_cutoff, attenuation, filter_length=None):
+    """
+    Design a lowpass filter using the Kaiser window.
+    
+    Parameters
+    ----------
+    angular_cutoff : float
+        The angular frequency cutoff of the filter.
+    attenuation : float
+        The desired stopband attenuation in decibels (dB).
+    filter_length : int, optional
+        Desired length of the filter. If not provided, it's computed based on the given specs.
+    
+    Returns
+    -------
+    ndarray
+        The designed lowpass filter coefficients.
+    """
+    
+    estimated_length, beta = kaiserord(attenuation, angular_cutoff / np.pi)
+    
+    # Ensure the estimated length is odd.
+    estimated_length = 2 * (estimated_length // 2) + 1
+    
+    if filter_length is None:
+        filter_length = estimated_length
+    
+    return firwin(filter_length, angular_cutoff, window=('kaiser', beta), scale=False, nyq=np.pi)
+
+
+def evaluate_filter_objective(angular_cutoff, attenuation, num_bands, filter_length):
+    """
+    Evaluate the filter's objective value based on the criteria from https://ieeexplore.ieee.org/document/681427
+    
+    Parameters
+    ----------
+    angular_cutoff : float
+        Angular frequency cutoff of the filter.
+    attenuation : float
+        Desired stopband attenuation in dB.
+    num_bands : int
+        Number of bands for the multiband filter system.
+    filter_length : int, optional
+        Desired length of the filter.
+    
+    Returns
+    -------
+    float
+        The computed objective (loss) value for the given filter specs.
+    """
+    
+    filter_coeffs = design_kaiser_lowpass(angular_cutoff, attenuation, filter_length)
+    convolved_filter = np.convolve(filter_coeffs, filter_coeffs[::-1], "full")
+    
+    return np.max(np.abs(convolved_filter[convolved_filter.shape[-1] // 2::2 * num_bands][1:]))
+
+
+def design_prototype_filter(attenuation, num_bands, filter_length=None):
+    """
+    Design the optimal prototype filter for a multiband system given the desired specs.
+    
+    Parameters
+    ----------
+    attenuation : float
+        The desired stopband attenuation in dB.
+    num_bands : int
+        Number of bands for the multiband filter system.
+    filter_length : int, optional
+        Desired length of the filter. If not provided, it's computed based on the given specs.
+    
+    Returns
+    -------
+    ndarray
+        The optimal prototype filter coefficients.
+    """
+    
+    optimal_angular_cutoff = fmin(lambda angular_cutoff: evaluate_filter_objective(angular_cutoff, attenuation, num_bands, filter_length), 
+                                  1 / num_bands, disp=0)[0]
+    
+    prototype_filter = design_kaiser_lowpass(optimal_angular_cutoff, attenuation, filter_length)
+    return torch.tensor(prototype_filter, dtype=torch.float32)
+
+def pad_to_nearest_power_of_two(x):
+    """
+    Pads the input tensor 'x' on both sides such that its last dimension 
+    becomes the nearest larger power of two.
+    
+    Parameters:
+    -----------
+    x : torch.Tensor
+        The input tensor to be padded.
+        
+    Returns:
+    --------
+    torch.Tensor
+        The padded tensor.
+    """
+    current_length = x.shape[-1]
+    target_length = 2**math.ceil(math.log2(current_length))
+    
+    total_padding = target_length - current_length
+    left_padding = total_padding // 2
+    right_padding = total_padding - left_padding
+    
+    return nn.functional.pad(x, (left_padding, right_padding))
+
+def apply_alias_cancellation(x):
+    """
+    Applies alias cancellation by inverting the sign of every 
+    second element of every second row, starting from the second 
+    row's first element in a tensor.
+    
+    This operation helps ensure that the aliasing introduced in 
+    each band during the decomposition will be counteracted during 
+    the reconstruction.
+    
+    Parameters:
+    -----------
+    x : torch.Tensor
+        The input tensor.
+        
+    Returns:
+    --------
+    torch.Tensor
+        Tensor with specific elements' sign inverted for alias cancellation.
+    """
+    
+    # Create a mask of the same shape as 'x', initialized with all ones
+    mask = torch.ones_like(x)
+    
+    # Update specific elements in the mask to -1 to perform inversion
+    mask[..., 1::2, ::2] = -1
+    
+    # Apply the mask to the input tensor 'x'
+    return x * mask
+
+def ensure_odd_length(tensor):
+    """
+    Pads the last dimension of a tensor to ensure its size is odd.
+    
+    Parameters:
+    -----------
+    tensor : torch.Tensor
+        Input tensor whose last dimension might need padding.
+        
+    Returns:
+    --------
+    torch.Tensor
+        The original tensor if its last dimension was already odd, 
+        or the padded tensor with an odd-sized last dimension.
+    """
+    
+    last_dim_size = tensor.shape[-1]
+    
+    if last_dim_size % 2 == 0:
+        tensor = nn.functional.pad(tensor, (0, 1))
+    
+    return tensor
+
+def polyphase_analysis(signal, filter_bank):
+    """
+    Applies the polyphase method to efficiently analyze the signal using a filter bank.
+
+    Parameters:
+    -----------
+    signal : torch.Tensor
+        Input signal tensor with shape (Batch x Channels x Length).
+    
+    filter_bank : torch.Tensor
+        Filter bank tensor with shape (Bands x Length).
+    
+    Returns:
+    --------
+    torch.Tensor
+        Signal split into sub-bands. (Batch x Channels x Bands x Length)
+    """
+    
+    num_bands = filter_bank.shape[0]
+    num_channels = signal.shape[1]
+    
+    # Rearrange signal for polyphase processing. 
+    # Also combine Batch x Channel into one dimension for now. 
+    #signal = rearrange(signal, "b c (t n) -> b (c n) t", n=num_bands)
+    signal = rearrange(signal, "b c (t n) -> (b c) n t", n=num_bands)
+    
+    # Rearrange the filter bank for matching signal shape
+    filter_bank = rearrange(filter_bank, "c (t n) -> c n t", n=num_bands)
+    
+    # Apply convolution with appropriate padding to maintain spatial dimensions
+    padding = filter_bank.shape[-1] // 2
+    filtered_signal = nn.functional.conv1d(signal, filter_bank, padding=padding)
+    
+    # Truncate the last dimension post-convolution to adjust the output shape
+    filtered_signal = filtered_signal[..., :-1]
+    # Rearrange the first dimension back into Batch x Channels
+    filtered_signal = rearrange(filtered_signal, "(b c) n t -> b c n t", c=num_channels)
+
+    return filtered_signal
+
+def polyphase_synthesis(signal, filter_bank):
+    """
+    Polyphase Inverse: Apply polyphase filter bank synthesis to reconstruct a signal. 
+    
+    Parameters
+    ----------
+    signal : torch.Tensor
+        Decomposed signal to be reconstructed (shape: Batch x Channels x Bands x Length).
+    
+    filter_bank : torch.Tensor
+        Analysis filter bank (shape: Bands x Length).
+    
+    should_rearrange : bool, optional
+        Flag to determine if the filters should be rearranged for polyphase synthesis. Default is True.
+
+    Returns
+    -------
+    torch.Tensor
+        Reconstructed signal (shape: Batch x Channels X Length)
+    """
+    
+    num_bands = filter_bank.shape[0]
+    num_channels = signal.shape[1]
+
+    # Rearrange the filter bank 
+    filter_bank = filter_bank.flip(-1)
+    filter_bank = rearrange(filter_bank, "c (t n) -> n c t", n=num_bands)
+
+    # Combine Batch x Channels into one dimension for now.
+    signal = rearrange(signal, "b c n t -> (b c) n t")
+
+    # Apply convolution with appropriate padding
+    padding_amount = filter_bank.shape[-1] // 2 + 1
+    reconstructed_signal = nn.functional.conv1d(signal, filter_bank, padding=int(padding_amount))
+    
+    # Scale the result
+    reconstructed_signal = reconstructed_signal[..., :-1] * num_bands
+
+    # Reorganize the output and truncate
+    reconstructed_signal = reconstructed_signal.flip(1)
+    reconstructed_signal = rearrange(reconstructed_signal, "(b c) n t -> b c (t n)", c=num_channels, n=num_bands)
+    reconstructed_signal = reconstructed_signal[..., 2 * filter_bank.shape[1]:]
+    
+    return reconstructed_signal

prismaudio_core/models/pretransforms.py

@@ -0,0 +1,258 @@
+import torch
+from einops import rearrange
+from torch import nn
+
+class Pretransform(nn.Module):
+    def __init__(self, enable_grad, io_channels, is_discrete):
+        super().__init__()
+
+        self.is_discrete = is_discrete
+        self.io_channels = io_channels
+        self.encoded_channels = None
+        self.downsampling_ratio = None
+
+        self.enable_grad = enable_grad
+
+    def encode(self, x):
+        raise NotImplementedError
+
+    def decode(self, z):
+        raise NotImplementedError
+    
+    def tokenize(self, x):
+        raise NotImplementedError
+    
+    def decode_tokens(self, tokens):
+        raise NotImplementedError
+
+class AutoencoderPretransform(Pretransform):
+    def __init__(self, model, scale=1.0, model_half=False, iterate_batch=False, chunked=False):
+        super().__init__(enable_grad=False, io_channels=model.io_channels, is_discrete=model.bottleneck is not None and model.bottleneck.is_discrete)
+        self.model = model
+        self.model.requires_grad_(False).eval()
+        self.scale=scale
+        self.downsampling_ratio = model.downsampling_ratio
+        self.io_channels = model.io_channels
+        self.sample_rate = model.sample_rate
+        
+        self.model_half = model_half
+        self.iterate_batch = iterate_batch
+
+        self.encoded_channels = model.latent_dim
+
+        self.chunked = chunked
+        self.num_quantizers = model.bottleneck.num_quantizers if model.bottleneck is not None and model.bottleneck.is_discrete else None
+        self.codebook_size = model.bottleneck.codebook_size if model.bottleneck is not None and model.bottleneck.is_discrete else None
+
+        if self.model_half:
+            self.model.half()
+    
+    def encode(self, x, **kwargs):
+        
+        if self.model_half:
+            x = x.half()
+            self.model.to(torch.float16)
+
+        encoded = self.model.encode_audio(x, chunked=self.chunked, iterate_batch=self.iterate_batch, **kwargs)
+
+        if self.model_half:
+            encoded = encoded.float()
+
+        return encoded / self.scale
+
+    def decode(self, z, **kwargs):
+        z = z * self.scale
+
+        if self.model_half:
+            z = z.half()
+            self.model.to(torch.float16)
+
+        decoded = self.model.decode_audio(z, chunked=self.chunked, iterate_batch=self.iterate_batch, **kwargs)
+
+        if self.model_half:
+            decoded = decoded.float()
+
+        return decoded
+    
+    def tokenize(self, x, **kwargs):
+        assert self.model.is_discrete, "Cannot tokenize with a continuous model"
+
+        _, info = self.model.encode(x, return_info = True, **kwargs)
+
+        return info[self.model.bottleneck.tokens_id]
+    
+    def decode_tokens(self, tokens, **kwargs):
+        assert self.model.is_discrete, "Cannot decode tokens with a continuous model"
+
+        return self.model.decode_tokens(tokens, **kwargs)
+    
+    def load_state_dict(self, state_dict, strict=True):
+        self.model.load_state_dict(state_dict, strict=strict)
+
+class WaveletPretransform(Pretransform):
+    def __init__(self, channels, levels, wavelet):
+        super().__init__(enable_grad=False, io_channels=channels, is_discrete=False)
+
+        from .wavelets import WaveletEncode1d, WaveletDecode1d
+
+        self.encoder = WaveletEncode1d(channels, levels, wavelet)
+        self.decoder = WaveletDecode1d(channels, levels, wavelet)
+
+        self.downsampling_ratio = 2 ** levels
+        self.io_channels = channels
+        self.encoded_channels = channels * self.downsampling_ratio
+    
+    def encode(self, x):
+        return self.encoder(x)
+    
+    def decode(self, z):
+        return self.decoder(z)
+    
+class PQMFPretransform(Pretransform):
+    def __init__(self, attenuation=100, num_bands=16):
+        # TODO: Fix PQMF to take in in-channels
+        super().__init__(enable_grad=False, io_channels=1, is_discrete=False)
+        from .pqmf import PQMF
+        self.pqmf = PQMF(attenuation, num_bands)
+
+
+    def encode(self, x):
+        # x is (Batch x Channels x Time)
+        x = self.pqmf.forward(x)
+        # pqmf.forward returns (Batch x Channels x Bands x Time)
+        # but Pretransform needs Batch x Channels x Time
+        # so concatenate channels and bands into one axis
+        return rearrange(x, "b c n t -> b (c n) t")
+
+    def decode(self, x):
+        # x is (Batch x (Channels Bands) x Time), convert back to (Batch x Channels x Bands x Time) 
+        x = rearrange(x, "b (c n) t -> b c n t", n=self.pqmf.num_bands)
+        # returns (Batch x Channels x Time) 
+        return self.pqmf.inverse(x)
+        
+class PretrainedDACPretransform(Pretransform):
+    def __init__(self, model_type="44khz", model_bitrate="8kbps", scale=1.0, quantize_on_decode: bool = True, chunked=True):
+        super().__init__(enable_grad=False, io_channels=1, is_discrete=True)
+        
+        import dac
+        
+        model_path = dac.utils.download(model_type=model_type, model_bitrate=model_bitrate)
+        
+        self.model = dac.DAC.load(model_path)
+
+        self.quantize_on_decode = quantize_on_decode
+
+        if model_type == "44khz":
+            self.downsampling_ratio = 512
+        else:
+            self.downsampling_ratio = 320
+
+        self.io_channels = 1
+
+        self.scale = scale
+
+        self.chunked = chunked
+
+        self.encoded_channels = self.model.latent_dim
+
+        self.num_quantizers = self.model.n_codebooks
+
+        self.codebook_size = self.model.codebook_size
+
+    def encode(self, x):
+
+        latents = self.model.encoder(x)
+
+        if self.quantize_on_decode:
+            output = latents
+        else:
+            z, _, _, _, _ = self.model.quantizer(latents, n_quantizers=self.model.n_codebooks)
+            output = z
+        
+        if self.scale != 1.0:
+            output = output / self.scale
+        
+        return output
+
+    def decode(self, z):
+        
+        if self.scale != 1.0:
+            z = z * self.scale
+
+        if self.quantize_on_decode:
+            z, _, _, _, _ = self.model.quantizer(z, n_quantizers=self.model.n_codebooks)
+
+        return self.model.decode(z)
+
+    def tokenize(self, x):
+        return self.model.encode(x)[1]
+    
+    def decode_tokens(self, tokens):
+        latents = self.model.quantizer.from_codes(tokens)
+        return self.model.decode(latents)
+    
+class AudiocraftCompressionPretransform(Pretransform):
+    def __init__(self, model_type="facebook/encodec_32khz", scale=1.0, quantize_on_decode: bool = True):
+        super().__init__(enable_grad=False, io_channels=1, is_discrete=True)
+        
+        try:
+            from audiocraft.models import CompressionModel
+        except ImportError:
+            raise ImportError("Audiocraft is not installed. Please install audiocraft to use Audiocraft models.")
+               
+        self.model = CompressionModel.get_pretrained(model_type)
+
+        self.quantize_on_decode = quantize_on_decode
+
+        self.downsampling_ratio = round(self.model.sample_rate / self.model.frame_rate)
+
+        self.sample_rate = self.model.sample_rate
+
+        self.io_channels = self.model.channels
+
+        self.scale = scale
+
+        #self.encoded_channels = self.model.latent_dim
+
+        self.num_quantizers = self.model.num_codebooks
+
+        self.codebook_size = self.model.cardinality
+
+        self.model.to(torch.float16).eval().requires_grad_(False)
+
+    def encode(self, x):
+
+        assert False, "Audiocraft compression models do not support continuous encoding"
+
+        # latents = self.model.encoder(x)
+
+        # if self.quantize_on_decode:
+        #     output = latents
+        # else:
+        #     z, _, _, _, _ = self.model.quantizer(latents, n_quantizers=self.model.n_codebooks)
+        #     output = z
+        
+        # if self.scale != 1.0:
+        #     output = output / self.scale
+        
+        # return output
+
+    def decode(self, z):
+        
+        assert False, "Audiocraft compression models do not support continuous decoding"
+
+        # if self.scale != 1.0:
+        #     z = z * self.scale
+
+        # if self.quantize_on_decode:
+        #     z, _, _, _, _ = self.model.quantizer(z, n_quantizers=self.model.n_codebooks)
+
+        # return self.model.decode(z)
+
+    def tokenize(self, x):
+        with torch.cuda.amp.autocast(enabled=False):
+            return self.model.encode(x.to(torch.float16))[0]
+    
+    def decode_tokens(self, tokens):
+        with torch.cuda.amp.autocast(enabled=False):
+            return self.model.decode(tokens)

prismaudio_core/models/transformer.py

@@ -0,0 +1,993 @@
+from functools import reduce, partial
+from packaging import version
+
+from einops import rearrange, repeat
+from einops.layers.torch import Rearrange
+import torch
+import torch.nn.functional as F
+from torch import nn, einsum
+from torch.cuda.amp import autocast
+from .mmmodules.model.low_level import MLP, ChannelLastConv1d, ConvMLP
+from typing import Callable, Literal
+
+try:
+    from flash_attn import flash_attn_func, flash_attn_kvpacked_func
+    HAS_FLASH_ATTN = True
+except ImportError:
+    HAS_FLASH_ATTN = False
+    flash_attn_kvpacked_func = None
+    flash_attn_func = None
+
+from .utils import compile
+try:
+    import natten
+except ImportError:
+    natten = None
+
+def checkpoint(function, *args, **kwargs):
+    kwargs.setdefault("use_reentrant", False)
+    return torch.utils.checkpoint.checkpoint(function, *args, **kwargs)
+
+def modulate(x: torch.Tensor, shift: torch.Tensor, scale: torch.Tensor):
+    return x * (1 + scale) + shift
+
+# Copied and modified from https://github.com/lucidrains/x-transformers/blob/main/x_transformers/attend.py under MIT License
+# License can be found in LICENSES/LICENSE_XTRANSFORMERS.txt
+
+def create_causal_mask(i, j, device):
+    return torch.ones((i, j), device = device, dtype = torch.bool).triu(j - i + 1)
+
+def or_reduce(masks):
+    head, *body = masks
+    for rest in body:
+        head = head | rest
+    return head
+
+# positional embeddings
+
+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 = 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 = self.inv_freq.device
+
+        t = torch.arange(seq_len, device = device)
+        return self.forward(t)
+
+    @autocast(enabled = False)
+    def forward(self, t):
+        device = self.inv_freq.device
+
+        t = t.to(torch.float32)
+
+        t = t / self.interpolation_factor
+
+        freqs = torch.einsum('i , j -> i j', t, self.inv_freq)
+        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 ** 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)
+
+@autocast(enabled = False)
+def apply_rotary_pos_emb(t, freqs, scale = 1):
+    out_dtype = t.dtype
+
+    # cast to float32 if necessary for numerical stability
+    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)
+
+# norms
+class DynamicTanh(nn.Module):
+    def __init__(self, dim, init_alpha=10.0):
+        super().__init__()
+        self.alpha = nn.Parameter(torch.ones(1) * init_alpha)
+        self.gamma = nn.Parameter(torch.ones(dim))
+        self.beta = nn.Parameter(torch.zeros(dim))
+
+    def forward(self, x):
+        x = F.tanh(self.alpha * x)
+        return self.gamma * x + self.beta
+
+class RunningInstanceNorm(nn.Module):
+    def __init__(self, dim, momentum = 0.99, eps = 1e-4, saturate = True, trainable_gain = True):
+        super().__init__()
+        self.register_buffer("running_mean", torch.zeros(1,1,dim))
+        self.register_buffer("running_std", torch.ones(1,1,dim))
+        self.saturate = saturate
+        self.eps = eps
+        self.momentum = momentum
+        self.dim = dim
+        self.trainable_gain = trainable_gain
+        if self.trainable_gain:
+            self.gain = nn.Parameter(torch.ones(1))
+    
+    def _update_stats(self, x):
+        self.running_mean = self.running_mean * self.momentum + x.detach().mean(dim = [0,1]).view(1, 1, self.dim) * (1 - self.momentum)
+        self.running_std  = (self.running_std * self.momentum + x.detach().std(dim = [0,1]).view(1, 1, self.dim) * (1 - self.momentum)).clip(min = self.eps)
+
+    def forward(self, x):
+        if self.training:
+            self._update_stats(x)
+        x = (x - self.running_mean) / self.running_std
+        if self.saturate:
+            x = torch.asinh(x)
+        if self.trainable_gain:
+            x = x * self.gain
+        return x
+
+class LayerNorm(nn.Module):
+    def __init__(self, dim, bias = False, fix_scale=False, force_fp32=False, eps=1e-5):
+        """
+        bias-less layernorm has been shown to be more stable. most newer models have moved towards rmsnorm, also bias-less
+        """
+        super().__init__()
+
+        if fix_scale:
+            self.register_buffer("gamma", torch.ones(dim))
+        else:
+            self.gamma = nn.Parameter(torch.ones(dim))
+
+        if bias:
+            self.beta = nn.Parameter(torch.zeros(dim))
+        else:
+            self.register_buffer("beta", torch.zeros(dim))
+
+        self.eps = eps
+
+        self.force_fp32 = force_fp32
+
+    def forward(self, x):
+        if not self.force_fp32:
+            return F.layer_norm(x, x.shape[-1:], weight=self.gamma, bias=self.beta, eps=self.eps)
+        else:
+            output = F.layer_norm(x.float(), x.shape[-1:], weight=self.gamma.float(), bias=self.beta.float(), eps=self.eps)
+            return output.to(x.dtype)
+
+class LayerScale(nn.Module):
+    def __init__(self, dim, init_val = 1e-5):
+        super().__init__()
+        self.scale = nn.Parameter(torch.full([dim], init_val))
+    def forward(self, x):
+        return x * self.scale
+
+class GLU(nn.Module):
+    def __init__(
+        self,
+        dim_in,
+        dim_out,
+        activation: Callable,
+        use_conv = False,
+        conv_kernel_size = 3,
+    ):
+        super().__init__()
+        self.act = activation
+        self.proj = nn.Linear(dim_in, dim_out * 2) if not use_conv else nn.Conv1d(dim_in, dim_out * 2, conv_kernel_size, padding = (conv_kernel_size // 2))
+        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 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,
+    ):
+        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)
+        else:
+            linear_in = nn.Sequential(
+                Rearrange('b n d -> b d n') if use_conv else nn.Identity(),
+                nn.Linear(dim, inner_dim, bias = not no_bias) if not use_conv else nn.Conv1d(dim, inner_dim, conv_kernel_size, padding = (conv_kernel_size // 2), bias = not no_bias),
+                Rearrange('b n d -> b d n') if use_conv else nn.Identity(),
+                activation
+            )
+
+        linear_out = nn.Linear(inner_dim, dim_out, bias = not no_bias) if not use_conv else nn.Conv1d(inner_dim, dim_out, conv_kernel_size, padding = (conv_kernel_size // 2), bias = not no_bias)
+
+        # 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: Literal['l2', 'ln', 'rns', 'dyt', 'none'] = 'none',
+        differential = False,
+        feat_scale = False
+    ):
+        super().__init__()
+        self.dim = dim
+        self.dim_heads = dim_heads
+        self.differential = differential
+
+        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:
+            if differential:
+                self.to_q = nn.Linear(dim, dim * 2, bias=False)
+                self.to_kv = nn.Linear(dim_kv, dim_kv * 3, bias=False)
+            else:
+                self.to_q = nn.Linear(dim, dim, bias=False)
+                self.to_kv = nn.Linear(dim_kv, dim_kv * 2, bias=False)
+        else:
+            if differential:
+                self.to_qkv = nn.Linear(dim, dim * 5, bias=False)
+            else:
+                self.to_qkv = nn.Linear(dim, dim * 3, bias=False)
+
+        self.to_out = nn.Linear(dim, dim, bias=False)
+
+        if zero_init_output:
+            nn.init.zeros_(self.to_out.weight)
+
+        if qk_norm not in ['l2', 'ln', 'rns', 'dyt','none']:
+            raise ValueError(f'qk_norm must be one of ["l2", "ln", "none"], got {qk_norm}')
+
+        self.qk_norm = qk_norm
+        if self.qk_norm == "ln":
+            self.q_norm = nn.LayerNorm(dim_heads, elementwise_affine=True, eps=1.0e-6)
+            self.k_norm = nn.LayerNorm(dim_heads, elementwise_affine=True, eps=1.0e-6)
+        elif self.qk_norm == 'rns':
+            self.q_norm = nn.RMSNorm(dim_heads)
+            self.k_norm = nn.RMSNorm(dim_heads)
+        elif self.qk_norm == 'dyt':
+            self.q_norm = DynamicTanh(dim_heads)
+            self.k_norm = DynamicTanh(dim_heads)
+
+        self.sdp_kwargs = dict(
+            enable_flash = True,
+            enable_math = True,
+            enable_mem_efficient = True
+        )
+
+        self.feat_scale = feat_scale
+
+        if self.feat_scale:
+            self.lambda_dc = nn.Parameter(torch.zeros(dim))
+            self.lambda_hf = nn.Parameter(torch.zeros(dim))
+
+        self.causal = causal
+        if causal:
+            print('Using `causal` argument disables FlexAttention. If you want to use them together, incorporate causal masking into `flex_attention_block_mask`.')
+
+    @compile
+    def apply_qk_layernorm(self, q, k):
+        q_type = q.dtype
+        k_type = k.dtype
+        q = self.q_norm(q).to(q_type)
+        k = self.k_norm(k).to(k_type)
+        return q, k
+
+
+    def apply_attn(self, q, k, v, causal = None, flex_attention_block_mask = None, flex_attention_score_mod = None, flash_attn_sliding_window = None):
+
+        if self.num_heads != self.kv_heads:
+             # Repeat interleave kv_heads to match q_heads for grouped query attention
+             heads_per_kv_head = self.num_heads // self.kv_heads
+             k, v = map(lambda t: t.repeat_interleave(heads_per_kv_head, dim = 1), (k, v))
+
+        flash_attn_available = HAS_FLASH_ATTN
+        if flash_attn_sliding_window is not None and (not flash_attn_available):
+            print(f"Cannot use FlashAttention sliding window as FlashAttention is disabled or not available")
+
+        if (flex_attention_block_mask is not None or flex_attention_score_mod is not None) and flash_attn_sliding_window is not None:
+            print(f"cannot use both FlashAttention and FlexAttention, favouring FlexAttention")
+
+        if causal and (flex_attention_block_mask is not None or flex_attention_score_mod is not None):
+            print(f"Disabling FlexAttention because causal is set")
+            flex_attention_block_mask = None
+            flex_attention_score_mod = None
+
+        if flex_attention_block_mask is not None or flex_attention_score_mod is not None:
+            out = flex_attention_compiled(q,k,v,
+                block_mask = flex_attention_block_mask,
+                score_mod = flex_attention_score_mod)        
+        elif flash_attn_available:
+            fa_dtype_in = q.dtype
+            q, k, v = map(lambda t: rearrange(t, 'b h n d -> b n h d'), (q, k, v))
+
+            if fa_dtype_in != torch.float16 and fa_dtype_in != torch.bfloat16:
+                q, k, v = map(lambda t: t.to(torch.bfloat16), (q, k, v))
+            
+            out = flash_attn_func(q, k, v, causal = causal, window_size=flash_attn_sliding_window if (flash_attn_sliding_window is not None) else [-1,-1])
+            
+            out = rearrange(out.to(fa_dtype_in), 'b n h d -> b h n d')
+        else:
+            out = F.scaled_dot_product_attention(q, k, v, is_causal = causal)
+        return out
+
+
+    #@compile
+    def forward(
+        self,
+        x,
+        context = None,
+        rotary_pos_emb = None,
+        causal = None, 
+        flex_attention_block_mask = None,
+        flex_attention_score_mod = None,
+        flash_attn_sliding_window = 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
+            if self.differential:
+                q, q_diff = self.to_q(x).chunk(2, dim=-1)
+                q, q_diff = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h = h), (q, q_diff))
+                q = torch.stack([q, q_diff], dim = 1)
+                k, k_diff, v = self.to_kv(kv_input).chunk(3, dim=-1)
+                k, k_diff, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h = kv_h), (k, k_diff, v))
+                k = torch.stack([k, k_diff], dim = 1)
+            else:
+                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
+            if self.differential:
+                q, k, v, q_diff, k_diff = self.to_qkv(x).chunk(5, dim=-1)
+                q, k, v, q_diff, k_diff  = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h = h), (q, k, v, q_diff, k_diff))
+                q = torch.stack([q, q_diff], dim = 1)
+                k = torch.stack([k, k_diff], dim = 1)
+            else:
+                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 == "l2":
+            q = F.normalize(q, dim=-1)
+            k = F.normalize(k, dim=-1)
+        elif self.qk_norm != "none":
+            q, k = self.apply_qk_layernorm(q, k)
+
+        if rotary_pos_emb is not None:
+            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)
+            if q.shape[-2] >= k.shape[-2]:
+                ratio = q.shape[-2] / k.shape[-2]
+                q_freqs, k_freqs = freqs, ratio * freqs
+            else:
+                ratio = k.shape[-2] / q.shape[-2]
+                q_freqs, k_freqs = ratio * freqs, freqs
+            q = apply_rotary_pos_emb(q, q_freqs)
+            k = apply_rotary_pos_emb(k, k_freqs)
+            q = q.to(v.dtype)
+            k = k.to(v.dtype)
+        
+        n, device = q.shape[-2], q.device
+
+        causal = self.causal if causal is None else causal
+
+        if n == 1 and causal:
+            causal = False
+
+        if self.differential:
+            q, q_diff = q.unbind(dim = 1)
+            k, k_diff = k.unbind(dim = 1)
+            out = self.apply_attn(q, k, v,  causal = causal, flex_attention_block_mask = flex_attention_block_mask, flex_attention_score_mod = flex_attention_score_mod, flash_attn_sliding_window = flash_attn_sliding_window)
+            out_diff = self.apply_attn(q_diff, k_diff, v, causal = causal, flex_attention_block_mask = flex_attention_block_mask, flex_attention_score_mod = flex_attention_score_mod, flash_attn_sliding_window = flash_attn_sliding_window)
+            out = out - out_diff
+        else:
+            out = self.apply_attn(q, k, v, causal = causal, flex_attention_block_mask = flex_attention_block_mask, flex_attention_score_mod = flex_attention_score_mod, flash_attn_sliding_window = flash_attn_sliding_window)
+
+        # merge heads
+        out = rearrange(out, ' b h n d -> b n (h d)')
+
+        # Communicate between heads
+        
+        # with autocast(enabled = False):
+        #     out_dtype = out.dtype
+        #     out = out.to(torch.float32)
+        #     out = self.to_out(out).to(out_dtype)
+        out = self.to_out(out)
+
+        if self.feat_scale:
+            out_dc = out.mean(dim=-2, keepdim=True)
+            out_hf = out - out_dc
+
+            # Selectively modulate DC and high frequency components
+            out = out + self.lambda_dc * out_dc + self.lambda_hf * out_hf
+
+        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)
+
+    #@compile
+    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,
+            add_rope = False,
+            layer_scale = False,
+            use_sync_block_film = False,
+            attn_kwargs = {},
+            ff_kwargs = {},
+            norm_kwargs = {}
+    ):
+        
+        super().__init__()
+        self.dim = dim
+        self.dim_heads = min(dim_heads,dim)
+        self.cross_attend = cross_attend
+        self.dim_context = dim_context
+        self.causal = causal
+        if layer_scale and zero_init_branch_outputs:
+            print('zero_init_branch_outputs is redundant with layer_scale, setting zero_init_branch_outputs to False')
+            zero_init_branch_outputs = False
+
+        self.pre_norm = LayerNorm(dim,**norm_kwargs) if not remove_norms else DynamicTanh(dim)
+
+        self.add_rope = add_rope
+
+        self.self_attn = Attention(
+            dim,
+            dim_heads = self.dim_heads,
+            causal = causal,
+            zero_init_output=zero_init_branch_outputs,
+            **attn_kwargs
+        )
+
+        self.self_attn_scale = LayerScale(dim) if layer_scale else nn.Identity()
+
+        self.cross_attend = cross_attend
+        if cross_attend:
+            self.cross_attend_norm = LayerNorm(dim, **norm_kwargs) if not remove_norms else DynamicTanh(dim)
+            self.cross_attn = Attention(
+                dim,
+                dim_heads = self.dim_heads,
+                dim_context=dim_context,
+                causal = causal,
+                zero_init_output=zero_init_branch_outputs,
+                **attn_kwargs
+            )
+            self.cross_attn_scale = LayerScale(dim) if layer_scale else nn.Identity()
+        
+        self.ff_norm = LayerNorm(dim, **norm_kwargs) if not remove_norms else DynamicTanh(dim)
+        self.ff = FeedForward(dim, zero_init_output=zero_init_branch_outputs, **ff_kwargs)
+        self.ff_scale = LayerScale(dim) if layer_scale else nn.Identity()
+
+        self.layer_ix = layer_ix
+
+        self.conformer = None
+        if conformer:
+            self.conformer = ConformerModule(dim, norm_kwargs=norm_kwargs)
+            self.conformer_scale = LayerScale(dim) if layer_scale else nn.Identity()
+
+        self.global_cond_dim = global_cond_dim
+        if global_cond_dim is not None:
+            self.to_scale_shift_gate = nn.Parameter(torch.randn(6*dim)/dim**0.5)
+
+        self.rope = RotaryEmbedding(self.dim_heads // 2) if add_rope else None
+
+        if use_sync_block_film:
+            self.sync_film_generator = nn.Sequential(
+                nn.Linear(dim, dim, bias=False),
+                nn.SiLU(),
+                nn.Linear(dim, dim * 2, bias=False) # 从sync_cond生成scale和shift
+            )
+        
+    @compile
+    def forward(
+        self,
+        x,
+        context = None,
+        global_cond=None,
+        rotary_pos_emb = None,
+        self_attention_block_mask = None,
+        self_attention_score_mod = None,
+        cross_attention_block_mask = None,
+        cross_attention_score_mod = None,
+        self_attention_flash_sliding_window = None,
+        cross_attention_flash_sliding_window = None,
+        sync_cond = None,
+        prepend_length=0
+    ):
+        if rotary_pos_emb is None and self.add_rope:
+            rotary_pos_emb = self.rope.forward_from_seq_len(x.shape[-2])
+
+        if self.global_cond_dim is not None and self.global_cond_dim > 0 and global_cond is not None:
+            if len(global_cond.shape) == 2:
+                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)
+            else:
+                
+                scale_self, shift_self, gate_self, scale_ff, shift_ff, gate_ff = (self.to_scale_shift_gate + global_cond).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, rotary_pos_emb = rotary_pos_emb, flex_attention_block_mask = self_attention_block_mask, flex_attention_score_mod = self_attention_score_mod, flash_attn_sliding_window = self_attention_flash_sliding_window)
+            x = x * torch.sigmoid(1 - gate_self)
+            x = self.self_attn_scale(x)
+            x = x + residual
+
+            if context is not None and self.cross_attend:
+                x = x + self.cross_attn_scale(self.cross_attn(self.cross_attend_norm(x), context = context, flex_attention_block_mask = cross_attention_block_mask, flex_attention_score_mod = cross_attention_score_mod, flash_attn_sliding_window = cross_attention_flash_sliding_window))
+            
+            if self.conformer is not None:
+                x = x + self.conformer_scale(self.conformer(x))
+
+            if sync_cond is not None and hasattr(self, 'sync_film_generator'):
+                scale, shift = self.sync_film_generator(sync_cond).chunk(2, dim=-1)
+                x = x * (1 + scale) + shift
+
+            # 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 = self.ff_scale(x)
+            x = x + residual
+
+        else:
+            x = x + self.self_attn_scale(self.self_attn(self.pre_norm(x), rotary_pos_emb = rotary_pos_emb, flex_attention_block_mask = self_attention_block_mask, flex_attention_score_mod = self_attention_score_mod, flash_attn_sliding_window = self_attention_flash_sliding_window))
+
+            if context is not None and self.cross_attend:
+                x = x + self.cross_attn_scale(self.cross_attn(self.cross_attend_norm(x), context = context, flex_attention_block_mask = cross_attention_block_mask, flex_attention_score_mod = cross_attention_score_mod, flash_attn_sliding_window = cross_attention_flash_sliding_window))
+                    
+            if self.conformer is not None:
+                x = x + self.conformer_scale(self.conformer(x))
+
+            if sync_cond is not None and hasattr(self, 'sync_film_generator'):
+                prepend_part = x[:, :prepend_length, :]
+                audio_part = x[:, prepend_length:, :]
+                scale, shift = self.sync_film_generator(sync_cond).chunk(2, dim=-1)
+                modulated_audio_part = audio_part * (1 + scale) + shift
+                x = torch.cat([prepend_part, modulated_audio_part], dim=1)
+
+            x = x + self.ff_scale(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,
+        pre_cross_attn_ix=-1,
+        final_cross_attn_ix=-1,
+        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,
+        num_memory_tokens=0,
+        sliding_window=None,
+        use_mlp=False,
+        use_add_norm=False,
+        use_gated=False,
+        use_final_layer=False,
+        use_zeros=False,
+        use_conv=False,
+        use_fusion_mlp=False,
+        use_film=False,
+        use_sync_film=False,
+        use_sync_gated=False,
+        **kwargs
+        ):
+
+        super().__init__()
+
+        self.dim = dim
+        self.depth = depth
+        self.causal = causal
+        self.layers = nn.ModuleList([])
+        if use_mlp:
+            self.project_in = nn.Sequential(
+                nn.Linear(dim_in, dim, bias=False),
+                nn.SiLU(),
+                nn.Linear(dim, dim, bias=False)
+            )
+        else:
+            self.project_in = nn.Linear(dim_in, dim, bias=False) if dim_in is not None else nn.Identity()
+        self.project_out = nn.Linear(dim, dim_out, bias=False) if dim_out is not None else nn.Identity()
+        self.video_temporal_conv = None
+        self.audio_temporal_conv = None
+        self.fusion_mlp = None
+        if use_conv:
+            self.video_temporal_conv = nn.Conv1d(dim, dim, kernel_size=3, padding=1)
+            self.audio_temporal_conv = nn.Conv1d(dim, dim, kernel_size=3, padding=1)
+        if use_fusion_mlp:
+            self.fusion_mlp = nn.Sequential(
+                nn.Linear(dim, dim),
+                nn.SiLU(),
+                nn.Linear(dim, dim)
+            )
+
+        if rotary_pos_emb:
+            self.rotary_pos_emb = RotaryEmbedding(max(dim_heads // 2, 32))
+        else:
+            self.rotary_pos_emb = None
+        self.num_memory_tokens = num_memory_tokens
+        if num_memory_tokens > 0:
+            self.memory_tokens = nn.Parameter(torch.randn(num_memory_tokens, dim))
+
+        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 + self.num_memory_tokens)
+
+        self.adaLN_modulation = None
+        if global_cond_dim is not None:
+            if use_final_layer:
+                self.norm_final = LayerNorm(dim)
+                self.adaLN_modulation = nn.Sequential(
+                    nn.SiLU(),
+                    nn.Linear(
+                        dim, 2 * dim, bias=True
+                    ),
+                )
+
+                if use_zeros:
+                    nn.init.constant_(self.adaLN_modulation[-1].weight, 0)
+                    nn.init.constant_(self.adaLN_modulation[-1].bias, 0)
+                    nn.init.constant_(self.project_out.weight, 0)
+            self.global_cond_embedder = nn.Sequential(
+                nn.Linear(global_cond_dim, dim),
+                nn.SiLU(),
+                nn.Linear(dim, dim * 6)
+            )
+            if use_zeros:
+                nn.init.constant_(self.global_cond_embedder[-1].weight, 0)
+                nn.init.constant_(self.global_cond_embedder[-1].bias, 0)
+                nn.init.constant_(self.global_cond_embedder[0].weight, 0)
+                nn.init.constant_(self.global_cond_embedder[0].bias, 0)
+
+        self.final_cross_attn_ix = final_cross_attn_ix
+        self.use_gated = use_gated
+        self.use_film = use_film
+        self.use_add_norm = use_add_norm
+        if self.use_add_norm:
+            self.add_norm = nn.LayerNorm(dim)
+        if use_gated:
+            self.gate = nn.Parameter(torch.ones(1, 1, dim))
+        
+        if use_film:
+            self.film_generator = nn.Sequential(
+                nn.Linear(dim, dim, bias=False),
+                nn.SiLU(),
+                nn.Linear(dim, dim * 2, bias=False) # 从sync_cond生成scale和shift
+            )
+        else:
+            self.film_generator = None
+
+        if use_sync_film:
+            self.sync_film_generator = nn.Sequential(
+                nn.Linear(dim, dim, bias=False),
+                nn.SiLU(),
+                nn.Linear(dim, dim * 2, bias=False) # 从sync_cond生成scale和shift
+            )
+        else:
+            self.sync_film_generator = None
+        if use_sync_gated:
+            self.sync_gate = nn.Parameter(torch.zeros(1, 1, dim))
+        else:
+            self.sync_gate = None
+
+        self.sliding_window = sliding_window
+
+        for i in range(depth):
+            should_cross_attend = cross_attend and (self.final_cross_attn_ix == -1 or i < (self.final_cross_attn_ix)) and (pre_cross_attn_ix == -1 or i >= (pre_cross_attn_ix))
+            # print(f"Layer {i} cross attends: {should_cross_attend}")
+            self.layers.append(
+                TransformerBlock(
+                    dim,
+                    dim_heads = dim_heads,
+                    cross_attend = should_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,
+                    **kwargs
+                )
+            )
+
+    def forward(
+        self,
+        x,
+        mask = None,
+        prepend_embeds = None,
+        prepend_mask = None,
+        add_cond = None,
+        sync_cond = None,
+        global_cond = None,
+        return_info = False,
+        use_checkpointing = True,
+        exit_layer_ix = None,
+        video_dropout_prob = 0.0,
+        **kwargs
+    ):
+        batch, seq, device = *x.shape[:2], x.device
+        model_dtype = next(self.parameters()).dtype
+        x = x.to(model_dtype)
+
+        info = {
+            "hidden_states": [],
+        }
+
+        x = self.project_in(x)
+        if add_cond is not None:
+            if self.use_gated:
+                gate = torch.sigmoid(self.gate)
+                x = x + gate * add_cond
+            elif self.use_film:
+                scale, shift = self.film_generator(add_cond).chunk(2, dim=-1)
+                x = x * (1 + scale) + shift
+            else:
+                x = x + add_cond
+
+            if self.use_add_norm:
+                x = self.add_norm(x)
+            if self.fusion_mlp is not None:
+                x = self.fusion_mlp(x)
+
+            if sync_cond is not None:
+                if self.sync_film_generator is not None:
+                    scale, shift = self.sync_film_generator(sync_cond).chunk(2, dim=-1)
+                    x = x * (1 + scale) + shift
+                elif self.sync_gate is not None:
+                    gate_value = torch.sigmoid(self.sync_gate)
+                    x = x + gate_value * sync_cond 
+                # else:
+                #     x = x + sync_cond
+
+        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 self.num_memory_tokens > 0:
+            memory_tokens = self.memory_tokens.expand(batch, -1, -1)
+            x = torch.cat((memory_tokens, x), dim=1)
+
+        if self.rotary_pos_emb is not None:
+            rotary_pos_emb = self.rotary_pos_emb.forward_from_seq_len(x.shape[1])
+        else:
+            rotary_pos_emb = None
+
+        if self.use_sinusoidal_emb or self.use_abs_pos_emb:
+            x = x + self.pos_emb(x)
+
+        if global_cond is not None and self.global_cond_embedder is not None:
+            global_cond_embed = self.global_cond_embedder(global_cond)
+        else:
+            global_cond_embed = global_cond
+        # Iterate over the transformer layers
+        for layer_ix, layer in enumerate(self.layers):
+            if use_checkpointing:
+                x = checkpoint(layer, x, rotary_pos_emb = rotary_pos_emb, global_cond=global_cond_embed, self_attention_flash_sliding_window = self.sliding_window, sync_cond=sync_cond, prepend_length=prepend_length, **kwargs)
+            else:
+                x = layer(x, rotary_pos_emb = rotary_pos_emb, global_cond=global_cond_embed, self_attention_flash_sliding_window = self.sliding_window, sync_cond=sync_cond, prepend_length=prepend_length, **kwargs)
+
+            if return_info:
+                info["hidden_states"].append(x)
+
+            if exit_layer_ix is not None and layer_ix == exit_layer_ix:
+                x = x[:, self.num_memory_tokens:, :]
+
+                if return_info:
+                    return x, info
+                
+                return x
+
+        x = x[:, self.num_memory_tokens:, :]
+        if global_cond is not None and self.adaLN_modulation is not None:
+            if len(global_cond.shape) == 2:
+                global_cond = global_cond.unsqueeze(1)
+            shift, scale = self.adaLN_modulation(global_cond).chunk(2, dim=-1)
+            x = modulate(self.norm_final(x), shift, scale)
+        x = self.project_out(x)
+
+        if return_info:
+            return x, info
+        
+        return x

prismaudio_core/models/utils.py

@@ -0,0 +1,177 @@
+import torch
+from safetensors.torch import load_file
+from torch import nn, Tensor, einsum, IntTensor, FloatTensor, BoolTensor
+#from torchcubicspline import natural_cubic_spline_coeffs, NaturalCubicSpline
+from torch.nn.utils import remove_weight_norm
+
+def load_ckpt_state_dict(ckpt_path, prefix=None):
+    if ckpt_path.endswith(".safetensors"):
+        state_dict = load_file(ckpt_path)
+    else:
+        state_dict = torch.load(ckpt_path, map_location="cpu")["state_dict"]
+
+    # 过滤特定前缀的state_dict
+    filtered_state_dict = {k.replace(f'{prefix}',''): v for k, v in state_dict.items() if k.startswith(prefix)} if prefix is not None else state_dict
+
+    return filtered_state_dict
+
+def remove_weight_norm_from_model(model):
+    for module in model.modules():
+        if hasattr(module, "weight"):
+            print(f"Removing weight norm from {module}")
+            remove_weight_norm(module)
+
+    return model
+
+# Sampling functions copied from https://github.com/facebookresearch/audiocraft/blob/main/audiocraft/utils/utils.py under MIT license
+# License can be found in LICENSES/LICENSE_META.txt
+
+def multinomial(input: torch.Tensor, num_samples: int, replacement=False, *, generator=None):
+    """torch.multinomial with arbitrary number of dimensions, and number of candidates on the last dimension.
+
+    Args:
+        input (torch.Tensor): The input tensor containing probabilities.
+        num_samples (int): Number of samples to draw.
+        replacement (bool): Whether to draw with replacement or not.
+    Keywords args:
+        generator (torch.Generator): A pseudorandom number generator for sampling.
+    Returns:
+        torch.Tensor: Last dimension contains num_samples indices
+            sampled from the multinomial probability distribution
+            located in the last dimension of tensor input.
+    """
+
+    if num_samples == 1:
+        q = torch.empty_like(input).exponential_(1, generator=generator)
+        return torch.argmax(input / q, dim=-1, keepdim=True).to(torch.int64)
+
+    input_ = input.reshape(-1, input.shape[-1])
+    output_ = torch.multinomial(input_, num_samples=num_samples, replacement=replacement, generator=generator)
+    output = output_.reshape(*list(input.shape[:-1]), -1)
+    return output
+
+
+def sample_top_k(probs: torch.Tensor, k: int) -> torch.Tensor:
+    """Sample next token from top K values along the last dimension of the input probs tensor.
+
+    Args:
+        probs (torch.Tensor): Input probabilities with token candidates on the last dimension.
+        k (int): The k in “top-k”.
+    Returns:
+        torch.Tensor: Sampled tokens.
+    """
+    top_k_value, _ = torch.topk(probs, k, dim=-1)
+    min_value_top_k = top_k_value[..., [-1]]
+    probs *= (probs >= min_value_top_k).float()
+    probs.div_(probs.sum(dim=-1, keepdim=True))
+    next_token = multinomial(probs, num_samples=1)
+    return next_token
+
+
+def sample_top_p(probs: torch.Tensor, p: float) -> torch.Tensor:
+    """Sample next token from top P probabilities along the last dimension of the input probs tensor.
+
+    Args:
+        probs (torch.Tensor): Input probabilities with token candidates on the last dimension.
+        p (int): The p in “top-p”.
+    Returns:
+        torch.Tensor: Sampled tokens.
+    """
+    probs_sort, probs_idx = torch.sort(probs, dim=-1, descending=True)
+    probs_sum = torch.cumsum(probs_sort, dim=-1)
+    mask = probs_sum - probs_sort > p
+    probs_sort *= (~mask).float()
+    probs_sort.div_(probs_sort.sum(dim=-1, keepdim=True))
+    next_token = multinomial(probs_sort, num_samples=1)
+    next_token = torch.gather(probs_idx, -1, next_token)
+    return next_token
+
+def next_power_of_two(n):
+    return 2 ** (n - 1).bit_length()
+
+def next_multiple_of_64(n):
+    return ((n + 63) // 64) * 64
+
+
+# mask construction helpers
+
+def mask_from_start_end_indices(
+    seq_len: int,
+    start: Tensor,
+    end: Tensor
+):
+    assert start.shape == end.shape
+    device = start.device
+
+    seq = torch.arange(seq_len, device = device, dtype = torch.long)
+    seq = seq.reshape(*((-1,) * start.ndim), seq_len)
+    seq = seq.expand(*start.shape, seq_len)
+
+    mask = seq >= start[..., None].long()
+    mask &= seq < end[..., None].long()
+    return mask
+
+def mask_from_frac_lengths(
+    seq_len: int,
+    frac_lengths: Tensor
+):
+    device = frac_lengths.device
+
+    lengths = (frac_lengths * seq_len).long()
+    max_start = seq_len - lengths
+
+    rand = torch.zeros_like(frac_lengths, device = device).float().uniform_(0, 1)
+    start = (max_start * rand).clamp(min = 0)
+    end = start + lengths
+
+    return mask_from_start_end_indices(seq_len, start, end)
+
+def _build_spline(video_feat, video_t, target_t):
+    # 三次样条插值核心实现
+    coeffs = natural_cubic_spline_coeffs(video_t, video_feat.permute(0,2,1))
+    spline = NaturalCubicSpline(coeffs)
+    return spline.evaluate(target_t).permute(0,2,1)
+
+def resample(video_feat, audio_latent):
+    """
+    9s
+    video_feat: [B, 72, D]
+    audio_latent: [B, D', 194] or int
+    """
+    B, Tv, D = video_feat.shape
+    
+    if isinstance(audio_latent, torch.Tensor):
+        # audio_latent is a tensor
+        if audio_latent.shape[1] != 64:
+            Ta = audio_latent.shape[1]
+        else:
+            Ta = audio_latent.shape[2]
+    elif isinstance(audio_latent, int):
+        # audio_latent is an int
+        Ta = audio_latent
+    else:
+        raise TypeError("audio_latent must be either a tensor or an int")
+    
+    # 构建时间戳 (关键改进点)
+    video_time = torch.linspace(0, 9, Tv, device=video_feat.device)
+    audio_time = torch.linspace(0, 9, Ta, device=video_feat.device)
+    
+    # 三维化处理 (Batch, Feature, Time)
+    video_feat = video_feat.permute(0, 2, 1)  # [B, D, Tv]
+    
+    # 三次样条插值
+    aligned_video = _build_spline(video_feat, video_time, audio_time)  # [B, D, Ta]
+    return aligned_video.permute(0, 2, 1)  # [B, Ta, D]
+
+import os
+enable_torch_compile = os.environ.get("ENABLE_TORCH_COMPILE", "0") == "1"
+
+def compile(function, *args, **kwargs):
+    
+    if enable_torch_compile:
+        try:
+            return torch.compile(function, *args, **kwargs)
+        except RuntimeError:
+            return function
+
+    return function