diff --git a/Ovi/audio_model_layers.py b/Ovi/audio_model_layers.py new file mode 100644 index 0000000..e1988d2 --- /dev/null +++ b/Ovi/audio_model_layers.py @@ -0,0 +1,48 @@ +import torch +import torch.nn as nn +import torch.nn.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 + + +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)) + diff --git a/Ovi/bigvgan/LICENSE b/Ovi/bigvgan/LICENSE new file mode 100644 index 0000000..e966359 --- /dev/null +++ b/Ovi/bigvgan/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2022 NVIDIA CORPORATION. + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. \ No newline at end of file diff --git a/Ovi/bigvgan/__init__.py b/Ovi/bigvgan/__init__.py new file mode 100644 index 0000000..00f13e9 --- /dev/null +++ b/Ovi/bigvgan/__init__.py @@ -0,0 +1 @@ +from .bigvgan import BigVGAN diff --git a/Ovi/bigvgan/activations.py b/Ovi/bigvgan/activations.py new file mode 100644 index 0000000..61f2808 --- /dev/null +++ b/Ovi/bigvgan/activations.py @@ -0,0 +1,120 @@ +# Implementation adapted from https://github.com/EdwardDixon/snake under the MIT license. +# LICENSE is in incl_licenses directory. + +import torch +from torch import nn, sin, pow +from torch.nn import Parameter + + +class Snake(nn.Module): + ''' + Implementation of a sine-based periodic activation function + Shape: + - Input: (B, C, T) + - Output: (B, C, T), same shape as the input + Parameters: + - alpha - trainable parameter + References: + - This activation function is from this paper by Liu Ziyin, Tilman Hartwig, Masahito Ueda: + https://arxiv.org/abs/2006.08195 + Examples: + >>> a1 = snake(256) + >>> x = torch.randn(256) + >>> x = a1(x) + ''' + def __init__(self, in_features, alpha=1.0, alpha_trainable=True, alpha_logscale=False): + ''' + Initialization. + INPUT: + - in_features: shape of the input + - alpha: trainable parameter + alpha is initialized to 1 by default, higher values = higher-frequency. + alpha will be trained along with the rest of your model. + ''' + super(Snake, 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 = Parameter(torch.zeros(in_features) * alpha) + else: # linear scale alphas initialized to ones + self.alpha = Parameter(torch.ones(in_features) * alpha) + + self.alpha.requires_grad = alpha_trainable + + self.no_div_by_zero = 0.000000001 + + def forward(self, x): + ''' + Forward pass of the function. + Applies the function to the input elementwise. + Snake ∶= x + 1/a * sin^2 (xa) + ''' + alpha = self.alpha.unsqueeze(0).unsqueeze(-1) # line up with x to [B, C, T] + if self.alpha_logscale: + alpha = torch.exp(alpha) + x = x + (1.0 / (alpha + self.no_div_by_zero)) * pow(sin(x * alpha), 2) + + return x + + +class SnakeBeta(nn.Module): + ''' + A modified Snake function which uses separate parameters for the magnitude of the periodic components + Shape: + - Input: (B, C, T) + - Output: (B, C, T), same shape as the input + Parameters: + - alpha - trainable parameter that controls frequency + - beta - trainable parameter that controls magnitude + References: + - This activation function is a modified version based on this paper by Liu Ziyin, Tilman Hartwig, Masahito Ueda: + https://arxiv.org/abs/2006.08195 + Examples: + >>> a1 = snakebeta(256) + >>> x = torch.randn(256) + >>> x = a1(x) + ''' + def __init__(self, in_features, alpha=1.0, alpha_trainable=True, alpha_logscale=False): + ''' + Initialization. + INPUT: + - in_features: shape of the input + - alpha - trainable parameter that controls frequency + - beta - trainable parameter that controls magnitude + alpha is initialized to 1 by default, higher values = higher-frequency. + beta is initialized to 1 by default, higher values = higher-magnitude. + alpha will be trained along with the rest of your model. + ''' + 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 = Parameter(torch.zeros(in_features) * alpha) + self.beta = Parameter(torch.zeros(in_features) * alpha) + else: # linear scale alphas initialized to ones + self.alpha = Parameter(torch.ones(in_features) * alpha) + self.beta = 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): + ''' + Forward pass of the function. + Applies the function to the input elementwise. + SnakeBeta ∶= x + 1/b * sin^2 (xa) + ''' + 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 = x + (1.0 / (beta + self.no_div_by_zero)) * pow(sin(x * alpha), 2) + + return x \ No newline at end of file diff --git a/Ovi/bigvgan/alias_free_torch/__init__.py b/Ovi/bigvgan/alias_free_torch/__init__.py new file mode 100644 index 0000000..a2318b6 --- /dev/null +++ b/Ovi/bigvgan/alias_free_torch/__init__.py @@ -0,0 +1,6 @@ +# Adapted from https://github.com/junjun3518/alias-free-torch under the Apache License 2.0 +# LICENSE is in incl_licenses directory. + +from .filter import * +from .resample import * +from .act import * \ No newline at end of file diff --git a/Ovi/bigvgan/alias_free_torch/act.py b/Ovi/bigvgan/alias_free_torch/act.py new file mode 100644 index 0000000..028debd --- /dev/null +++ b/Ovi/bigvgan/alias_free_torch/act.py @@ -0,0 +1,28 @@ +# Adapted from https://github.com/junjun3518/alias-free-torch under the Apache License 2.0 +# LICENSE is in incl_licenses directory. + +import torch.nn as nn +from .resample import UpSample1d, DownSample1d + + +class Activation1d(nn.Module): + def __init__(self, + activation, + up_ratio: int = 2, + down_ratio: int = 2, + up_kernel_size: int = 12, + down_kernel_size: int = 12): + super().__init__() + self.up_ratio = up_ratio + self.down_ratio = down_ratio + self.act = activation + self.upsample = UpSample1d(up_ratio, up_kernel_size) + self.downsample = DownSample1d(down_ratio, down_kernel_size) + + # x: [B,C,T] + def forward(self, x): + x = self.upsample(x) + x = self.act(x) + x = self.downsample(x) + + return x \ No newline at end of file diff --git a/Ovi/bigvgan/alias_free_torch/filter.py b/Ovi/bigvgan/alias_free_torch/filter.py new file mode 100644 index 0000000..7ad6ea8 --- /dev/null +++ b/Ovi/bigvgan/alias_free_torch/filter.py @@ -0,0 +1,95 @@ +# Adapted from https://github.com/junjun3518/alias-free-torch under the Apache License 2.0 +# LICENSE is in incl_licenses directory. + +import torch +import torch.nn as nn +import torch.nn.functional as F +import math + +if 'sinc' in dir(torch): + sinc = torch.sinc +else: + # This code is adopted from adefossez's julius.core.sinc under the MIT License + # https://adefossez.github.io/julius/julius/core.html + # LICENSE is in incl_licenses directory. + def sinc(x: torch.Tensor): + """ + Implementation of sinc, i.e. sin(pi * x) / (pi * x) + __Warning__: Different to julius.sinc, the input is multiplied by `pi`! + """ + return torch.where(x == 0, + torch.tensor(1., device=x.device, dtype=x.dtype), + torch.sin(math.pi * x) / math.pi / x) + + +# This code is adopted from adefossez's julius.lowpass.LowPassFilters under the MIT License +# https://adefossez.github.io/julius/julius/lowpass.html +# LICENSE is in incl_licenses directory. +def kaiser_sinc_filter1d(cutoff, half_width, kernel_size): # return filter [1,1,kernel_size] + even = (kernel_size % 2 == 0) + half_size = kernel_size // 2 + + #For kaiser window + delta_f = 4 * half_width + A = 2.285 * (half_size - 1) * math.pi * delta_f + 7.95 + if A > 50.: + beta = 0.1102 * (A - 8.7) + elif A >= 21.: + beta = 0.5842 * (A - 21)**0.4 + 0.07886 * (A - 21.) + else: + beta = 0. + window = torch.kaiser_window(kernel_size, beta=beta, periodic=False) + + # ratio = 0.5/cutoff -> 2 * cutoff = 1 / ratio + if even: + time = (torch.arange(-half_size, half_size) + 0.5) + else: + time = torch.arange(kernel_size) - half_size + if cutoff == 0: + filter_ = torch.zeros_like(time) + else: + filter_ = 2 * cutoff * window * sinc(2 * cutoff * time) + # Normalize filter to have sum = 1, otherwise we will have a small leakage + # of the constant component in the input signal. + filter_ /= filter_.sum() + filter = filter_.view(1, 1, kernel_size) + + return filter + + +class LowPassFilter1d(nn.Module): + def __init__(self, + cutoff=0.5, + half_width=0.6, + stride: int = 1, + padding: bool = True, + padding_mode: str = 'replicate', + kernel_size: int = 12): + # kernel_size should be even number for stylegan3 setup, + # in this implementation, odd number is also possible. + super().__init__() + if cutoff < -0.: + raise ValueError("Minimum cutoff must be larger than zero.") + if cutoff > 0.5: + raise ValueError("A cutoff above 0.5 does not make sense.") + self.kernel_size = kernel_size + self.even = (kernel_size % 2 == 0) + self.pad_left = kernel_size // 2 - int(self.even) + self.pad_right = kernel_size // 2 + self.stride = stride + self.padding = padding + self.padding_mode = padding_mode + filter = kaiser_sinc_filter1d(cutoff, half_width, kernel_size) + self.register_buffer("filter", filter) + + #input [B, C, T] + def forward(self, x): + _, C, _ = x.shape + + if self.padding: + x = F.pad(x, (self.pad_left, self.pad_right), + mode=self.padding_mode) + out = F.conv1d(x, self.filter.expand(C, -1, -1), + stride=self.stride, groups=C) + + return out \ No newline at end of file diff --git a/Ovi/bigvgan/alias_free_torch/resample.py b/Ovi/bigvgan/alias_free_torch/resample.py new file mode 100644 index 0000000..750e6c3 --- /dev/null +++ b/Ovi/bigvgan/alias_free_torch/resample.py @@ -0,0 +1,49 @@ +# Adapted from https://github.com/junjun3518/alias-free-torch under the Apache License 2.0 +# LICENSE is in incl_licenses directory. + +import torch.nn as nn +from torch.nn import functional as F +from .filter import LowPassFilter1d +from .filter import kaiser_sinc_filter1d + + +class UpSample1d(nn.Module): + def __init__(self, ratio=2, kernel_size=None): + super().__init__() + self.ratio = ratio + self.kernel_size = int(6 * ratio // 2) * 2 if kernel_size is None else kernel_size + self.stride = ratio + self.pad = self.kernel_size // ratio - 1 + self.pad_left = self.pad * self.stride + (self.kernel_size - self.stride) // 2 + self.pad_right = self.pad * self.stride + (self.kernel_size - self.stride + 1) // 2 + filter = kaiser_sinc_filter1d(cutoff=0.5 / ratio, + half_width=0.6 / ratio, + kernel_size=self.kernel_size) + self.register_buffer("filter", filter) + + # x: [B, C, T] + def forward(self, x): + _, C, _ = x.shape + + x = F.pad(x, (self.pad, self.pad), mode='replicate') + x = self.ratio * F.conv_transpose1d( + x, self.filter.expand(C, -1, -1), stride=self.stride, groups=C) + x = x[..., self.pad_left:-self.pad_right] + + return x + + +class DownSample1d(nn.Module): + def __init__(self, ratio=2, kernel_size=None): + super().__init__() + self.ratio = ratio + self.kernel_size = int(6 * ratio // 2) * 2 if kernel_size is None else kernel_size + self.lowpass = LowPassFilter1d(cutoff=0.5 / ratio, + half_width=0.6 / ratio, + stride=ratio, + kernel_size=self.kernel_size) + + def forward(self, x): + xx = self.lowpass(x) + + return xx \ No newline at end of file diff --git a/Ovi/bigvgan/bigvgan.py b/Ovi/bigvgan/bigvgan.py new file mode 100644 index 0000000..a0be7a7 --- /dev/null +++ b/Ovi/bigvgan/bigvgan.py @@ -0,0 +1,62 @@ +import torch +import torch.nn as nn +from types import SimpleNamespace + +from .models import BigVGANVocoder + +from comfy.utils import load_torch_file + +# BigVGAN vocoder configuration +_bigvgan_vocoder_config = { + 'resblock': '1', + 'num_gpus': 0, + 'batch_size': 64, + 'num_mels': 80, + 'learning_rate': 0.0001, + 'adam_b1': 0.8, + 'adam_b2': 0.99, + 'lr_decay': 0.999, + 'seed': 1234, + 'upsample_rates': [4, 4, 2, 2, 2, 2], + 'upsample_kernel_sizes': [8, 8, 4, 4, 4, 4], + 'upsample_initial_channel': 1536, + 'resblock_kernel_sizes': [3, 7, 11], + 'resblock_dilation_sizes': [ + [1, 3, 5], + [1, 3, 5], + [1, 3, 5] + ], + 'activation': 'snakebeta', + 'snake_logscale': True, + 'resolutions': [ + [1024, 120, 600], + [2048, 240, 1200], + [512, 50, 240] + ], + 'mpd_reshapes': [2, 3, 5, 7, 11], + 'use_spectral_norm': False, + 'discriminator_channel_mult': 1, +} + +class BigVGAN(nn.Module): + + def __init__(self, ckpt_path): + super().__init__() + # Convert dictionary to namespace object for attribute access + vocoder_cfg = SimpleNamespace(**_bigvgan_vocoder_config) + self.vocoder = BigVGANVocoder(vocoder_cfg).eval() + vocoder_ckpt = load_torch_file(ckpt_path) + self.vocoder.load_state_dict(vocoder_ckpt) + + self.weight_norm_removed = False + self.remove_weight_norm() + + @torch.inference_mode() + def forward(self, x): + assert self.weight_norm_removed, 'call remove_weight_norm() before inference' + return self.vocoder(x) + + def remove_weight_norm(self): + self.vocoder.remove_weight_norm() + self.weight_norm_removed = True + return self diff --git a/Ovi/bigvgan/incl_licenses/LICENSE_1 b/Ovi/bigvgan/incl_licenses/LICENSE_1 new file mode 100644 index 0000000..5afae39 --- /dev/null +++ b/Ovi/bigvgan/incl_licenses/LICENSE_1 @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2020 Jungil Kong + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. \ No newline at end of file diff --git a/Ovi/bigvgan/incl_licenses/LICENSE_2 b/Ovi/bigvgan/incl_licenses/LICENSE_2 new file mode 100644 index 0000000..322b758 --- /dev/null +++ b/Ovi/bigvgan/incl_licenses/LICENSE_2 @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2020 Edward Dixon + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. 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IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, +DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. \ No newline at end of file diff --git a/Ovi/bigvgan/models.py b/Ovi/bigvgan/models.py new file mode 100644 index 0000000..b39e471 --- /dev/null +++ b/Ovi/bigvgan/models.py @@ -0,0 +1,255 @@ +# Copyright (c) 2022 NVIDIA CORPORATION. +# Licensed under the MIT license. + +# Adapted from https://github.com/jik876/hifi-gan under the MIT license. +# LICENSE is in incl_licenses directory. + +import torch +import torch.nn as nn +from torch.nn import Conv1d, ConvTranspose1d +from torch.nn.utils.parametrizations import weight_norm +from torch.nn.utils.parametrize import remove_parametrizations + +from . import activations +from .alias_free_torch import * +from .utils import get_padding, init_weights + +LRELU_SLOPE = 0.1 + + +class AMPBlock1(torch.nn.Module): + + def __init__(self, h, channels, kernel_size=3, dilation=(1, 3, 5), activation=None): + super(AMPBlock1, self).__init__() + self.h = h + + self.convs1 = nn.ModuleList([ + weight_norm( + Conv1d(channels, + channels, + kernel_size, + 1, + dilation=dilation[0], + padding=get_padding(kernel_size, dilation[0]))), + weight_norm( + Conv1d(channels, + channels, + kernel_size, + 1, + dilation=dilation[1], + padding=get_padding(kernel_size, dilation[1]))), + weight_norm( + Conv1d(channels, + channels, + kernel_size, + 1, + dilation=dilation[2], + padding=get_padding(kernel_size, dilation[2]))) + ]) + self.convs1.apply(init_weights) + + self.convs2 = nn.ModuleList([ + weight_norm( + Conv1d(channels, + channels, + kernel_size, + 1, + dilation=1, + padding=get_padding(kernel_size, 1))), + weight_norm( + Conv1d(channels, + channels, + kernel_size, + 1, + dilation=1, + padding=get_padding(kernel_size, 1))), + weight_norm( + Conv1d(channels, + channels, + kernel_size, + 1, + dilation=1, + padding=get_padding(kernel_size, 1))) + ]) + self.convs2.apply(init_weights) + + self.num_layers = len(self.convs1) + len(self.convs2) # total number of conv layers + + if activation == 'snake': # periodic nonlinearity with snake function and anti-aliasing + self.activations = nn.ModuleList([ + Activation1d( + activation=activations.Snake(channels, alpha_logscale=h.snake_logscale)) + for _ in range(self.num_layers) + ]) + elif activation == 'snakebeta': # periodic nonlinearity with snakebeta function and anti-aliasing + self.activations = nn.ModuleList([ + Activation1d( + activation=activations.SnakeBeta(channels, alpha_logscale=h.snake_logscale)) + for _ in range(self.num_layers) + ]) + else: + raise NotImplementedError( + "activation incorrectly specified. check the config file and look for 'activation'." + ) + + def forward(self, x): + acts1, acts2 = self.activations[::2], self.activations[1::2] + for c1, c2, a1, a2 in zip(self.convs1, self.convs2, acts1, acts2): + xt = a1(x) + xt = c1(xt) + xt = a2(xt) + xt = c2(xt) + x = xt + x + + return x + + def remove_weight_norm(self): + for l in self.convs1: + remove_parametrizations(l, 'weight') + for l in self.convs2: + remove_parametrizations(l, 'weight') + + +class AMPBlock2(torch.nn.Module): + + def __init__(self, h, channels, kernel_size=3, dilation=(1, 3), activation=None): + super(AMPBlock2, self).__init__() + self.h = h + + self.convs = nn.ModuleList([ + weight_norm( + Conv1d(channels, + channels, + kernel_size, + 1, + dilation=dilation[0], + padding=get_padding(kernel_size, dilation[0]))), + weight_norm( + Conv1d(channels, + channels, + kernel_size, + 1, + dilation=dilation[1], + padding=get_padding(kernel_size, dilation[1]))) + ]) + self.convs.apply(init_weights) + + self.num_layers = len(self.convs) # total number of conv layers + + if activation == 'snake': # periodic nonlinearity with snake function and anti-aliasing + self.activations = nn.ModuleList([ + Activation1d( + activation=activations.Snake(channels, alpha_logscale=h.snake_logscale)) + for _ in range(self.num_layers) + ]) + elif activation == 'snakebeta': # periodic nonlinearity with snakebeta function and anti-aliasing + self.activations = nn.ModuleList([ + Activation1d( + activation=activations.SnakeBeta(channels, alpha_logscale=h.snake_logscale)) + for _ in range(self.num_layers) + ]) + else: + raise NotImplementedError( + "activation incorrectly specified. check the config file and look for 'activation'." + ) + + def forward(self, x): + for c, a in zip(self.convs, self.activations): + xt = a(x) + xt = c(xt) + x = xt + x + + return x + + def remove_weight_norm(self): + for l in self.convs: + remove_parametrizations(l, 'weight') + + +class BigVGANVocoder(torch.nn.Module): + # this is our main BigVGAN model. Applies anti-aliased periodic activation for resblocks. + def __init__(self, h): + super().__init__() + self.h = h + + self.num_kernels = len(h.resblock_kernel_sizes) + self.num_upsamples = len(h.upsample_rates) + + # pre conv + self.conv_pre = weight_norm(Conv1d(h.num_mels, h.upsample_initial_channel, 7, 1, padding=3)) + + # define which AMPBlock to use. BigVGAN uses AMPBlock1 as default + resblock = AMPBlock1 if h.resblock == '1' else AMPBlock2 + + # transposed conv-based upsamplers. does not apply anti-aliasing + self.ups = nn.ModuleList() + for i, (u, k) in enumerate(zip(h.upsample_rates, h.upsample_kernel_sizes)): + self.ups.append( + nn.ModuleList([ + weight_norm( + ConvTranspose1d(h.upsample_initial_channel // (2**i), + h.upsample_initial_channel // (2**(i + 1)), + k, + u, + padding=(k - u) // 2)) + ])) + + # residual blocks using anti-aliased multi-periodicity composition modules (AMP) + self.resblocks = nn.ModuleList() + for i in range(len(self.ups)): + ch = h.upsample_initial_channel // (2**(i + 1)) + for j, (k, d) in enumerate(zip(h.resblock_kernel_sizes, h.resblock_dilation_sizes)): + self.resblocks.append(resblock(h, ch, k, d, activation=h.activation)) + + # post conv + if h.activation == "snake": # periodic nonlinearity with snake function and anti-aliasing + activation_post = activations.Snake(ch, alpha_logscale=h.snake_logscale) + self.activation_post = Activation1d(activation=activation_post) + elif h.activation == "snakebeta": # periodic nonlinearity with snakebeta function and anti-aliasing + activation_post = activations.SnakeBeta(ch, alpha_logscale=h.snake_logscale) + self.activation_post = Activation1d(activation=activation_post) + else: + raise NotImplementedError( + "activation incorrectly specified. check the config file and look for 'activation'." + ) + + self.conv_post = weight_norm(Conv1d(ch, 1, 7, 1, padding=3)) + + # weight initialization + for i in range(len(self.ups)): + self.ups[i].apply(init_weights) + self.conv_post.apply(init_weights) + + def forward(self, x): + # pre conv + x = self.conv_pre(x) + + for i in range(self.num_upsamples): + # upsampling + for i_up in range(len(self.ups[i])): + x = self.ups[i][i_up](x) + # AMP blocks + xs = None + for j in range(self.num_kernels): + if xs is None: + xs = self.resblocks[i * self.num_kernels + j](x) + else: + xs += self.resblocks[i * self.num_kernels + j](x) + x = xs / self.num_kernels + + # post conv + x = self.activation_post(x) + x = self.conv_post(x) + x = torch.tanh(x) + + return x + + def remove_weight_norm(self): + print('Removing weight norm...') + for l in self.ups: + for l_i in l: + remove_parametrizations(l_i, 'weight') + for l in self.resblocks: + l.remove_weight_norm() + remove_parametrizations(self.conv_pre, 'weight') + remove_parametrizations(self.conv_post, 'weight') diff --git a/Ovi/bigvgan/utils.py b/Ovi/bigvgan/utils.py new file mode 100644 index 0000000..d7e505a --- /dev/null +++ b/Ovi/bigvgan/utils.py @@ -0,0 +1,20 @@ +# Adapted from https://github.com/jik876/hifi-gan under the MIT license. +# LICENSE is in incl_licenses directory. + +from torch.nn.utils.parametrizations import weight_norm + + +def init_weights(m, mean=0.0, std=0.01): + classname = m.__class__.__name__ + if classname.find("Conv") != -1: + m.weight.data.normal_(mean, std) + + +def apply_weight_norm(m): + classname = m.__class__.__name__ + if classname.find("Conv") != -1: + weight_norm(m) + + +def get_padding(kernel_size, dilation=1): + return int((kernel_size * dilation - dilation) / 2) diff --git a/Ovi/mel_converter.py b/Ovi/mel_converter.py new file mode 100644 index 0000000..bb42d43 --- /dev/null +++ b/Ovi/mel_converter.py @@ -0,0 +1,212 @@ +# Reference: # https://github.com/bytedance/Make-An-Audio-2 +from typing import Literal + +import torch +import torch.nn as nn +import numpy as np + +# following is from librosa + +def hz_to_mel(frequencies, *, htk = False): + frequencies = np.asanyarray(frequencies) + + if htk: + mels: np.ndarray = 2595.0 * np.log10(1.0 + frequencies / 700.0) + return mels + + # Fill in the linear part + f_min = 0.0 + f_sp = 200.0 / 3 + + mels = (frequencies - f_min) / f_sp + + # Fill in the log-scale part + + min_log_hz = 1000.0 # beginning of log region (Hz) + min_log_mel = (min_log_hz - f_min) / f_sp # same (Mels) + logstep = np.log(6.4) / 27.0 # step size for log region + + if frequencies.ndim: + # If we have array data, vectorize + log_t = frequencies >= min_log_hz + mels[log_t] = min_log_mel + np.log(frequencies[log_t] / min_log_hz) / logstep + elif frequencies >= min_log_hz: + # If we have scalar data, heck directly + mels = min_log_mel + np.log(frequencies / min_log_hz) / logstep + + return mels + +def mel_to_hz(mels, *, htk = False): + mels = np.asanyarray(mels) + + if htk: + return 700.0 * (10.0 ** (mels / 2595.0) - 1.0) + + # Fill in the linear scale + f_min = 0.0 + f_sp = 200.0 / 3 + freqs = f_min + f_sp * mels + + # And now the nonlinear scale + min_log_hz = 1000.0 # beginning of log region (Hz) + min_log_mel = (min_log_hz - f_min) / f_sp # same (Mels) + logstep = np.log(6.4) / 27.0 # step size for log region + + if mels.ndim: + # If we have vector data, vectorize + log_t = mels >= min_log_mel + freqs[log_t] = min_log_hz * np.exp(logstep * (mels[log_t] - min_log_mel)) + elif mels >= min_log_mel: + # If we have scalar data, check directly + freqs = min_log_hz * np.exp(logstep * (mels - min_log_mel)) + + return freqs + +def mel_frequencies(n_mels = 128, *, fmin = 0.0, fmax = 11025.0, htk = False): + min_mel = hz_to_mel(fmin, htk=htk) + max_mel = hz_to_mel(fmax, htk=htk) + mels = np.linspace(min_mel, max_mel, n_mels) + hz: np.ndarray = mel_to_hz(mels, htk=htk) + return hz + +def librosa_mel_fn( + *, + sr: float, + n_fft: int, + n_mels: int = 128, + fmin: float = 0.0, + fmax = None, + htk = False, + norm = "slaney", + dtype = np.float32, +) -> np.ndarray: + + if fmax is None: + fmax = float(sr) / 2 + + # Initialize the weights + n_mels = int(n_mels) + weights = np.zeros((n_mels, int(1 + n_fft // 2)), dtype=dtype) + + # Center freqs of each FFT bin + fftfreqs = np.fft.rfftfreq(n=n_fft, d=1.0 / sr) + + # 'Center freqs' of mel bands - uniformly spaced between limits + mel_f = mel_frequencies(n_mels + 2, fmin=fmin, fmax=fmax, htk=htk) + + fdiff = np.diff(mel_f) + ramps = np.subtract.outer(mel_f, fftfreqs) + + for i in range(n_mels): + # lower and upper slopes for all bins + lower = -ramps[i] / fdiff[i] + upper = ramps[i + 2] / fdiff[i + 1] + + # .. then intersect them with each other and zero + weights[i] = np.maximum(0, np.minimum(lower, upper)) + + # Slaney-style mel is scaled to be approx constant energy per channel + enorm = 2.0 / (mel_f[2 : n_mels + 2] - mel_f[:n_mels]) + weights *= enorm[:, np.newaxis] + + return weights + + +def dynamic_range_compression_torch(x, C=1, clip_val=1e-5, *, norm_fn): + return norm_fn(torch.clamp(x, min=clip_val) * C) + + +def spectral_normalize_torch(magnitudes, norm_fn): + output = dynamic_range_compression_torch(magnitudes, norm_fn=norm_fn) + return output + + +class MelConverter(nn.Module): + + def __init__( + self, + *, + sampling_rate: float, + n_fft: int, + num_mels: int, + hop_size: int, + win_size: int, + fmin: float, + fmax: float, + norm_fn, + ): + super().__init__() + self.sampling_rate = sampling_rate + self.n_fft = n_fft + self.num_mels = num_mels + self.hop_size = hop_size + self.win_size = win_size + self.fmin = fmin + self.fmax = fmax + self.norm_fn = norm_fn + + mel = librosa_mel_fn(sr=self.sampling_rate, + n_fft=self.n_fft, + n_mels=self.num_mels, + fmin=self.fmin, + fmax=self.fmax) + mel_basis = torch.from_numpy(mel).float() + hann_window = torch.hann_window(self.win_size) + + self.register_buffer('mel_basis', mel_basis) + self.register_buffer('hann_window', hann_window) + + @property + def device(self): + return self.mel_basis.device + + def forward(self, waveform: torch.Tensor, center: bool = False) -> torch.Tensor: + waveform = waveform.clamp(min=-1., max=1.).to(self.device) + + waveform = torch.nn.functional.pad( + waveform.unsqueeze(1), + [int((self.n_fft - self.hop_size) / 2), + int((self.n_fft - self.hop_size) / 2)], + mode='reflect') + waveform = waveform.squeeze(1) + + spec = torch.stft(waveform, + self.n_fft, + hop_length=self.hop_size, + win_length=self.win_size, + window=self.hann_window, + center=center, + pad_mode='reflect', + normalized=False, + onesided=True, + return_complex=True) + + spec = torch.view_as_real(spec) + spec = torch.sqrt(spec.pow(2).sum(-1) + (1e-9)).float() + spec = torch.matmul(self.mel_basis, spec) + spec = spectral_normalize_torch(spec, self.norm_fn) + + return spec + + +def get_mel_converter(mode: Literal['16k', '44k']) -> MelConverter: + if mode == '16k': + return MelConverter(sampling_rate=16_000, + n_fft=1024, + num_mels=80, + hop_size=256, + win_size=1024, + fmin=0, + fmax=8_000, + norm_fn=torch.log10) + elif mode == '44k': + return MelConverter(sampling_rate=44_100, + n_fft=2048, + num_mels=128, + hop_size=512, + win_size=2048, + fmin=0, + fmax=44100 / 2, + norm_fn=torch.log) + else: + raise ValueError(f'Unknown mode: {mode}') diff --git a/Ovi/nodes_ovi.py b/Ovi/nodes_ovi.py new file mode 100644 index 0000000..5cc3dac --- /dev/null +++ b/Ovi/nodes_ovi.py @@ -0,0 +1,257 @@ +import torch +import torch.nn as nn +import folder_paths +import os + +from .mel_converter import get_mel_converter +from .vae.autoencoder import AutoEncoderModule +from .vae.distributions import DiagonalGaussianDistribution +import torchaudio + +from comfy import model_management as mm +device = mm.get_torch_device() +offload_device = mm.unet_offload_device() + +class FeaturesUtils(nn.Module): + + def __init__( + self, + *, + tod_vae_ckpt: str, + bigvgan_vocoder_ckpt = None, + mode=['16k', '44k'], + need_vae_encoder: bool = True, + ): + super().__init__() + + self.mel_converter = get_mel_converter(mode) + self.tod = AutoEncoderModule(vae_ckpt_path=tod_vae_ckpt, + vocoder_ckpt_path=bigvgan_vocoder_ckpt, + mode=mode, + need_vae_encoder=need_vae_encoder) + + def encode_audio(self, x) -> DiagonalGaussianDistribution: + assert self.tod is not None, 'VAE is not loaded' + # x: (B * L) + mel = self.mel_converter(x) + dist = self.tod.encode(mel) + + return dist + + def vocode(self, mel: torch.Tensor) -> torch.Tensor: + assert self.tod is not None, 'VAE is not loaded' + return self.tod.vocode(mel) + + def decode(self, z: torch.Tensor) -> torch.Tensor: + assert self.tod is not None, 'VAE is not loaded' + return self.tod.decode(z) + + @property + def device(self): + return next(self.parameters()).device + + @property + def dtype(self): + return next(self.parameters()).dtype + + def wrapped_decode(self, z): + with torch.amp.autocast('cuda', dtype=self.dtype): + mel_decoded = self.decode(z) + audio = self.vocode(mel_decoded) + + return audio + + def wrapped_encode(self, audio): + with torch.amp.autocast('cuda', dtype=self.dtype): + dist = self.encode_audio(audio) + + return dist.mean + +if not "mmaudio" in folder_paths.folder_names_and_paths: + folder_paths.add_model_folder_path("mmaudio", os.path.join(folder_paths.models_dir, "mmaudio")) + +class OviMMAudioVAELoader: + """Loads MMAudio VAE for audio encoding/decoding in Ovi""" + @classmethod + def INPUT_TYPES(s): + s.vae_files = folder_paths.get_filename_list("vae") + s.mmaudio_files = folder_paths.get_filename_list("mmaudio") + s.all_files = s.vae_files + s.mmaudio_files + + return { + "required": { + "vae": (s.all_files, {"tooltip": "MMAudio VAE 16k (v1-16.pth) model from models/vae or models/mmaudio"}), + "vocoder": (s.all_files, {"tooltip": "BigVGAN vocoder (best_netG.pt) from models/vae or models/mmaudio"}), + "precision": (["bf16", "fp16", "fp32"], {"default": "bf16"}), + } + } + + RETURN_TYPES = ("MMAUDIOVAE",) + RETURN_NAMES = ("mmaudio_vae",) + FUNCTION = "loadmodel" + CATEGORY = "WanVideoWrapper/Ovi" + DESCRIPTION = "Loads MMAudio VAE for Ovi audio generation" + + def loadmodel(self, vae, vocoder, precision): + dtype = {"bf16": torch.bfloat16, "fp16": torch.float16, "fp32": torch.float32}[precision] + + vae_path = folder_paths.get_full_path("vae", vae) if vae in self.vae_files else folder_paths.get_full_path("mmaudio", vae) + vocoder_path = folder_paths.get_full_path("vae", vocoder) if vocoder in self.vae_files else folder_paths.get_full_path("mmaudio", vocoder) + + vae = FeaturesUtils( + tod_vae_ckpt=vae_path, + bigvgan_vocoder_ckpt=vocoder_path, + mode='16k', + need_vae_encoder=True + ) + + vae.to(device=offload_device, dtype=dtype) + vae.eval() + + return (vae,) + +class WanVideoDecodeOviAudio: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "mmaudio_vae": ("MMAUDIOVAE",), + "samples": ("LATENT",), + } + } + + RETURN_TYPES = ("AUDIO",) + RETURN_NAMES = ("audio",) + FUNCTION = "decode" + CATEGORY = "WanVideoWrapper/Ovi" + + def decode(self, mmaudio_vae, samples): + mm.soft_empty_cache() + audio_latents = samples.get("latent_ovi_audio", None) + if audio_latents is None: + raise ValueError("No Ovi audio latents found in input samples") + + mmaudio_vae.to(device) + + waveform = mmaudio_vae.wrapped_decode(audio_latents.to(device=device, dtype=mmaudio_vae.dtype)) + audio = {"waveform": waveform.unsqueeze(0).cpu().float(), "sample_rate": 16000} + + mmaudio_vae.to(offload_device) + mm.soft_empty_cache() + + return (audio,) + +class WanVideoEncodeOviAudio: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "mmaudio_vae": ("MMAUDIOVAE",), + "audio": ("AUDIO",), + } + } + + RETURN_TYPES = ("LATENT",) + RETURN_NAMES = ("samples",) + FUNCTION = "decode" + CATEGORY = "WanVideoWrapper/Ovi" + + def decode(self, mmaudio_vae, audio): + + mmaudio_vae.to(device) + + waveform = audio.get("waveform", None) + sample_rate = audio.get("sample_rate", None) + if sample_rate != 16000: + waveform = torchaudio.functional.resample(waveform, sample_rate, 16000) + waveform = waveform.to(device=device, dtype=mmaudio_vae.dtype)[0][0].unsqueeze(0) + + samples = mmaudio_vae.wrapped_encode(waveform) + + mmaudio_vae.to(offload_device) + mm.soft_empty_cache() + + return ({"latent_ovi_audio": samples},) + + +class WanVideoAddOviAudioToLatents: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "original_samples": ("LATENT",), + "audio_samples": ("LATENT",), + } + } + + RETURN_TYPES = ("LATENT",) + RETURN_NAMES = ("samples",) + FUNCTION = "decode" + CATEGORY = "WanVideoWrapper/Ovi" + + def decode(self, original_samples, audio_samples): + samples = original_samples.copy() + samples.update(audio_samples) + + return (samples,) + +class WanVideoEmptyMMAudioLatents: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "length": ("INT", {"default": 157, "min": 1, "max": 10000, "step": 1, "tooltip": "Length of the audio latent sequence"}), + } + } + + RETURN_TYPES = ("LATENT",) + RETURN_NAMES = ("samples",) + FUNCTION = "decode" + CATEGORY = "WanVideoWrapper/Ovi" + + def decode(self, length): + audio_latents = torch.zeros((length, 20), device=torch.device("cpu"), dtype=torch.float32) # 1, l c -> l, c + + return ({"latent_ovi_audio": audio_latents},) + + +class WanVideoOviCFG: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "original_text_embeds": ("WANVIDEOTEXTEMBEDS",), + "ovi_negative_text_embeds": ("WANVIDEOTEXTEMBEDS",), + "ovi_audio_cfg": ("FLOAT", {"default": 3.0, "min": 0.0, "max": 100.0, "step": 0.01}), + }, + } + + RETURN_TYPES = ("WANVIDEOTEXTEMBEDS", ) + RETURN_NAMES = ("text_embeds",) + FUNCTION = "process" + CATEGORY = "WanVideoWrapper/Ovi" + DESCRIPTION = "Adds Ovi negative text embeddings and audio CFG scale to the text embeddings dictionary" + + def process(self, original_text_embeds, ovi_negative_text_embeds, ovi_audio_cfg): + negative_text_embeds = ovi_negative_text_embeds.get("negative_prompt_embeds", None) + if negative_text_embeds is None: + negative_text_embeds = original_text_embeds["prompt_embeds"] + + prompt_embeds_dict_copy = original_text_embeds.copy() + prompt_embeds_dict_copy.update({ + "ovi_negative_prompt_embeds": negative_text_embeds, + "ovi_audio_cfg": ovi_audio_cfg, + }) + return (prompt_embeds_dict_copy,) + +NODE_CLASS_MAPPINGS = { + "OviMMAudioVAELoader": OviMMAudioVAELoader, + "WanVideoDecodeOviAudio": WanVideoDecodeOviAudio, + "WanVideoEncodeOviAudio": WanVideoEncodeOviAudio, + "WanVideoOviCFG": WanVideoOviCFG, + "WanVideoAddOviAudioToLatents": WanVideoAddOviAudioToLatents, + "WanVideoEmptyMMAudioLatents": WanVideoEmptyMMAudioLatents, +} +NODE_DISPLAY_NAME_MAPPINGS = { + "OviMMAudioVAELoader": "Ovi MMAudio VAE Loader", + "WanVideoDecodeOviAudio": "WanVideo Decode Ovi Audio", + "WanVideoEncodeOviAudio": "WanVideo Encode Ovi Audio", + "WanVideoOviCFG": "WanVideo Ovi CFG", + "WanVideoAddOviAudioToLatents": "WanVideo Add MMAudio To Latents", + "WanVideoEmptyMMAudioLatents": "WanVideo Empty MMAudio Latents", +} \ No newline at end of file diff --git a/Ovi/vae/autoencoder.py b/Ovi/vae/autoencoder.py new file mode 100644 index 0000000..f48220a --- /dev/null +++ b/Ovi/vae/autoencoder.py @@ -0,0 +1,54 @@ +from typing import Literal, Optional + +import torch +import torch.nn as nn + +from .vae import VAE, get_my_vae +from .distributions import DiagonalGaussianDistribution +from ..bigvgan import BigVGAN + +from comfy.utils import load_torch_file + +class AutoEncoderModule(nn.Module): + + def __init__(self, + *, + vae_ckpt_path, + vocoder_ckpt_path: Optional[str] = None, + mode: Literal['16k', '44k'], + need_vae_encoder: bool = True): + super().__init__() + self.vae: VAE = get_my_vae(mode).eval() + #vae_state_dict = torch.load(vae_ckpt_path, weights_only=True, map_location='cpu')' + vae_state_dict = load_torch_file(vae_ckpt_path) + self.vae.load_state_dict(vae_state_dict) + self.vae.remove_weight_norm() + + if mode == '16k': + assert vocoder_ckpt_path is not None + self.vocoder = BigVGAN(vocoder_ckpt_path).eval() + elif mode == '44k': + raise NotImplementedError("44k mode requires BigVGANv2 which is not currently supported in this environment.") + self.vocoder = BigVGANv2.from_pretrained('nvidia/bigvgan_v2_44khz_128band_512x', + use_cuda_kernel=False) + self.vocoder.remove_weight_norm() + else: + raise ValueError(f'Unknown mode: {mode}') + + for param in self.parameters(): + param.requires_grad = False + + if not need_vae_encoder: + del self.vae.encoder + + @torch.inference_mode() + def encode(self, x: torch.Tensor) -> DiagonalGaussianDistribution: + return self.vae.encode(x) + + @torch.inference_mode() + def decode(self, z: torch.Tensor) -> torch.Tensor: + return self.vae.decode(z) + + @torch.inference_mode() + def vocode(self, spec: torch.Tensor) -> torch.Tensor: + return self.vocoder(spec) diff --git a/Ovi/vae/distributions.py b/Ovi/vae/distributions.py new file mode 100644 index 0000000..996f641 --- /dev/null +++ b/Ovi/vae/distributions.py @@ -0,0 +1,45 @@ +from typing import Optional +import torch +import numpy as np + + +class DiagonalGaussianDistribution: + + def __init__(self, parameters, deterministic=False): + self.parameters = parameters + self.mean, self.logvar = torch.chunk(parameters, 2, dim=1) + self.logvar = torch.clamp(self.logvar, -30.0, 20.0) + self.deterministic = deterministic + self.std = torch.exp(0.5 * self.logvar) + self.var = torch.exp(self.logvar) + if self.deterministic: + self.var = self.std = torch.zeros_like(self.mean).to(device=self.parameters.device) + + def sample(self, rng: Optional[torch.Generator] = None): + # x = self.mean + self.std * torch.randn(self.mean.shape).to(device=self.parameters.device) + + r = torch.empty_like(self.mean).normal_(generator=rng) + x = self.mean + self.std * r + + return x + + def kl(self, other=None): + if self.deterministic: + return torch.Tensor([0.]) + else: + if other is None: + + return 0.5 * torch.pow(self.mean, 2) + self.var - 1.0 - self.logvar + else: + return 0.5 * (torch.pow(self.mean - other.mean, 2) / other.var + + self.var / other.var - 1.0 - self.logvar + other.logvar) + + def nll(self, sample, dims=[1, 2, 3]): + if self.deterministic: + return torch.Tensor([0.]) + logtwopi = np.log(2.0 * np.pi) + return 0.5 * torch.sum(logtwopi + self.logvar + torch.pow(sample - self.mean, 2) / self.var, + dim=dims) + + def mode(self): + return self.mean \ No newline at end of file diff --git a/Ovi/vae/edm2_utils.py b/Ovi/vae/edm2_utils.py new file mode 100644 index 0000000..a18ffba --- /dev/null +++ b/Ovi/vae/edm2_utils.py @@ -0,0 +1,168 @@ +# Copyright (c) 2024, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# This work is licensed under a Creative Commons +# Attribution-NonCommercial-ShareAlike 4.0 International License. +# You should have received a copy of the license along with this +# work. If not, see http://creativecommons.org/licenses/by-nc-sa/4.0/ +"""Improved diffusion model architecture proposed in the paper +"Analyzing and Improving the Training Dynamics of Diffusion Models".""" + +import numpy as np +import torch + +#---------------------------------------------------------------------------- +# Variant of constant() that inherits dtype and device from the given +# reference tensor by default. + +_constant_cache = dict() + + +def constant(value, shape=None, dtype=None, device=None, memory_format=None): + value = np.asarray(value) + if shape is not None: + shape = tuple(shape) + if dtype is None: + dtype = torch.get_default_dtype() + if device is None: + device = torch.device('cpu') + if memory_format is None: + memory_format = torch.contiguous_format + + key = (value.shape, value.dtype, value.tobytes(), shape, dtype, device, memory_format) + tensor = _constant_cache.get(key, None) + if tensor is None: + tensor = torch.as_tensor(value.copy(), dtype=dtype, device=device) + if shape is not None: + tensor, _ = torch.broadcast_tensors(tensor, torch.empty(shape)) + tensor = tensor.contiguous(memory_format=memory_format) + _constant_cache[key] = tensor + return tensor + + +def const_like(ref, value, shape=None, dtype=None, device=None, memory_format=None): + if dtype is None: + dtype = ref.dtype + if device is None: + device = ref.device + return constant(value, shape=shape, dtype=dtype, device=device, memory_format=memory_format) + + +#---------------------------------------------------------------------------- +# Normalize given tensor to unit magnitude with respect to the given +# dimensions. Default = all dimensions except the first. + + +def normalize(x, dim=None, eps=1e-4): + if dim is None: + dim = list(range(1, x.ndim)) + norm = torch.linalg.vector_norm(x, dim=dim, keepdim=True, dtype=torch.float32) + norm = torch.add(eps, norm, alpha=np.sqrt(norm.numel() / x.numel())) + return x / norm.to(x.dtype) + + +class Normalize(torch.nn.Module): + + def __init__(self, dim=None, eps=1e-4): + super().__init__() + self.dim = dim + self.eps = eps + + def forward(self, x): + return normalize(x, dim=self.dim, eps=self.eps) + + +#---------------------------------------------------------------------------- +# Upsample or downsample the given tensor with the given filter, +# or keep it as is. + + +def resample(x, f=[1, 1], mode='keep'): + if mode == 'keep': + return x + f = np.float32(f) + assert f.ndim == 1 and len(f) % 2 == 0 + pad = (len(f) - 1) // 2 + f = f / f.sum() + f = np.outer(f, f)[np.newaxis, np.newaxis, :, :] + f = const_like(x, f) + c = x.shape[1] + if mode == 'down': + return torch.nn.functional.conv2d(x, + f.tile([c, 1, 1, 1]), + groups=c, + stride=2, + padding=(pad, )) + assert mode == 'up' + return torch.nn.functional.conv_transpose2d(x, (f * 4).tile([c, 1, 1, 1]), + groups=c, + stride=2, + padding=(pad, )) + + +#---------------------------------------------------------------------------- +# Magnitude-preserving SiLU (Equation 81). + + +def mp_silu(x): + return torch.nn.functional.silu(x) / 0.596 + + +class MPSiLU(torch.nn.Module): + + def forward(self, x): + return mp_silu(x) + + +#---------------------------------------------------------------------------- +# Magnitude-preserving sum (Equation 88). + + +def mp_sum(a, b, t=0.5): + return a.lerp(b, t) / np.sqrt((1 - t)**2 + t**2) + + +#---------------------------------------------------------------------------- +# Magnitude-preserving concatenation (Equation 103). + + +def mp_cat(a, b, dim=1, t=0.5): + Na = a.shape[dim] + Nb = b.shape[dim] + C = np.sqrt((Na + Nb) / ((1 - t)**2 + t**2)) + wa = C / np.sqrt(Na) * (1 - t) + wb = C / np.sqrt(Nb) * t + return torch.cat([wa * a, wb * b], dim=dim) + + +#---------------------------------------------------------------------------- +# Magnitude-preserving convolution or fully-connected layer (Equation 47) +# with force weight normalization (Equation 66). + + +class MPConv1D(torch.nn.Module): + + def __init__(self, in_channels, out_channels, kernel_size): + super().__init__() + self.out_channels = out_channels + self.weight = torch.nn.Parameter(torch.randn(out_channels, in_channels, kernel_size)) + + self.weight_norm_removed = False + + def forward(self, x, gain=1): + assert self.weight_norm_removed, 'call remove_weight_norm() before inference' + + w = self.weight * gain + if w.ndim == 2: + return x @ w.t() + assert w.ndim == 3 + return torch.nn.functional.conv1d(x, w, padding=(w.shape[-1] // 2, )) + + def remove_weight_norm(self): + w = self.weight.to(torch.float32) + w = normalize(w) # traditional weight normalization + w = w / np.sqrt(w[0].numel()) + w = w.to(self.weight.dtype) + self.weight.data.copy_(w) + + self.weight_norm_removed = True + return self diff --git a/Ovi/vae/vae.py b/Ovi/vae/vae.py new file mode 100644 index 0000000..d1b9254 --- /dev/null +++ b/Ovi/vae/vae.py @@ -0,0 +1,369 @@ +import logging +from typing import Optional + +import torch +import torch.nn as nn + +from .edm2_utils import MPConv1D +from .vae_modules import (AttnBlock1D, Downsample1D, ResnetBlock1D, + Upsample1D, nonlinearity) +from .distributions import DiagonalGaussianDistribution + +log = logging.getLogger() + +DATA_MEAN_80D = [ + -1.6058, -1.3676, -1.2520, -1.2453, -1.2078, -1.2224, -1.2419, -1.2439, -1.2922, -1.2927, + -1.3170, -1.3543, -1.3401, -1.3836, -1.3907, -1.3912, -1.4313, -1.4152, -1.4527, -1.4728, + -1.4568, -1.5101, -1.5051, -1.5172, -1.5623, -1.5373, -1.5746, -1.5687, -1.6032, -1.6131, + -1.6081, -1.6331, -1.6489, -1.6489, -1.6700, -1.6738, -1.6953, -1.6969, -1.7048, -1.7280, + -1.7361, -1.7495, -1.7658, -1.7814, -1.7889, -1.8064, -1.8221, -1.8377, -1.8417, -1.8643, + -1.8857, -1.8929, -1.9173, -1.9379, -1.9531, -1.9673, -1.9824, -2.0042, -2.0215, -2.0436, + -2.0766, -2.1064, -2.1418, -2.1855, -2.2319, -2.2767, -2.3161, -2.3572, -2.3954, -2.4282, + -2.4659, -2.5072, -2.5552, -2.6074, -2.6584, -2.7107, -2.7634, -2.8266, -2.8981, -2.9673 +] + +DATA_STD_80D = [ + 1.0291, 1.0411, 1.0043, 0.9820, 0.9677, 0.9543, 0.9450, 0.9392, 0.9343, 0.9297, 0.9276, 0.9263, + 0.9242, 0.9254, 0.9232, 0.9281, 0.9263, 0.9315, 0.9274, 0.9247, 0.9277, 0.9199, 0.9188, 0.9194, + 0.9160, 0.9161, 0.9146, 0.9161, 0.9100, 0.9095, 0.9145, 0.9076, 0.9066, 0.9095, 0.9032, 0.9043, + 0.9038, 0.9011, 0.9019, 0.9010, 0.8984, 0.8983, 0.8986, 0.8961, 0.8962, 0.8978, 0.8962, 0.8973, + 0.8993, 0.8976, 0.8995, 0.9016, 0.8982, 0.8972, 0.8974, 0.8949, 0.8940, 0.8947, 0.8936, 0.8939, + 0.8951, 0.8956, 0.9017, 0.9167, 0.9436, 0.9690, 1.0003, 1.0225, 1.0381, 1.0491, 1.0545, 1.0604, + 1.0761, 1.0929, 1.1089, 1.1196, 1.1176, 1.1156, 1.1117, 1.1070 +] + +DATA_MEAN_128D = [ + -3.3462, -2.6723, -2.4893, -2.3143, -2.2664, -2.3317, -2.1802, -2.4006, -2.2357, -2.4597, + -2.3717, -2.4690, -2.5142, -2.4919, -2.6610, -2.5047, -2.7483, -2.5926, -2.7462, -2.7033, + -2.7386, -2.8112, -2.7502, -2.9594, -2.7473, -3.0035, -2.8891, -2.9922, -2.9856, -3.0157, + -3.1191, -2.9893, -3.1718, -3.0745, -3.1879, -3.2310, -3.1424, -3.2296, -3.2791, -3.2782, + -3.2756, -3.3134, -3.3509, -3.3750, -3.3951, -3.3698, -3.4505, -3.4509, -3.5089, -3.4647, + -3.5536, -3.5788, -3.5867, -3.6036, -3.6400, -3.6747, -3.7072, -3.7279, -3.7283, -3.7795, + -3.8259, -3.8447, -3.8663, -3.9182, -3.9605, -3.9861, -4.0105, -4.0373, -4.0762, -4.1121, + -4.1488, -4.1874, -4.2461, -4.3170, -4.3639, -4.4452, -4.5282, -4.6297, -4.7019, -4.7960, + -4.8700, -4.9507, -5.0303, -5.0866, -5.1634, -5.2342, -5.3242, -5.4053, -5.4927, -5.5712, + -5.6464, -5.7052, -5.7619, -5.8410, -5.9188, -6.0103, -6.0955, -6.1673, -6.2362, -6.3120, + -6.3926, -6.4797, -6.5565, -6.6511, -6.8130, -6.9961, -7.1275, -7.2457, -7.3576, -7.4663, + -7.6136, -7.7469, -7.8815, -8.0132, -8.1515, -8.3071, -8.4722, -8.7418, -9.3975, -9.6628, + -9.7671, -9.8863, -9.9992, -10.0860, -10.1709, -10.5418, -11.2795, -11.3861 +] + +DATA_STD_128D = [ + 2.3804, 2.4368, 2.3772, 2.3145, 2.2803, 2.2510, 2.2316, 2.2083, 2.1996, 2.1835, 2.1769, 2.1659, + 2.1631, 2.1618, 2.1540, 2.1606, 2.1571, 2.1567, 2.1612, 2.1579, 2.1679, 2.1683, 2.1634, 2.1557, + 2.1668, 2.1518, 2.1415, 2.1449, 2.1406, 2.1350, 2.1313, 2.1415, 2.1281, 2.1352, 2.1219, 2.1182, + 2.1327, 2.1195, 2.1137, 2.1080, 2.1179, 2.1036, 2.1087, 2.1036, 2.1015, 2.1068, 2.0975, 2.0991, + 2.0902, 2.1015, 2.0857, 2.0920, 2.0893, 2.0897, 2.0910, 2.0881, 2.0925, 2.0873, 2.0960, 2.0900, + 2.0957, 2.0958, 2.0978, 2.0936, 2.0886, 2.0905, 2.0845, 2.0855, 2.0796, 2.0840, 2.0813, 2.0817, + 2.0838, 2.0840, 2.0917, 2.1061, 2.1431, 2.1976, 2.2482, 2.3055, 2.3700, 2.4088, 2.4372, 2.4609, + 2.4731, 2.4847, 2.5072, 2.5451, 2.5772, 2.6147, 2.6529, 2.6596, 2.6645, 2.6726, 2.6803, 2.6812, + 2.6899, 2.6916, 2.6931, 2.6998, 2.7062, 2.7262, 2.7222, 2.7158, 2.7041, 2.7485, 2.7491, 2.7451, + 2.7485, 2.7233, 2.7297, 2.7233, 2.7145, 2.6958, 2.6788, 2.6439, 2.6007, 2.4786, 2.2469, 2.1877, + 2.1392, 2.0717, 2.0107, 1.9676, 1.9140, 1.7102, 0.9101, 0.7164 +] + + +class VAE(nn.Module): + + def __init__( + self, + *, + data_dim: int, + embed_dim: int, + hidden_dim: int, + ): + super().__init__() + + if data_dim == 80: + self.data_mean = nn.Buffer(torch.tensor(DATA_MEAN_80D, dtype=torch.float32)) + self.data_std = nn.Buffer(torch.tensor(DATA_STD_80D, dtype=torch.float32)) + elif data_dim == 128: + self.data_mean = nn.Buffer(torch.tensor(DATA_MEAN_128D, dtype=torch.float32)) + self.data_std = nn.Buffer(torch.tensor(DATA_STD_128D, dtype=torch.float32)) + + self.data_mean = self.data_mean.view(1, -1, 1) + self.data_std = self.data_std.view(1, -1, 1) + + self.encoder = Encoder1D( + dim=hidden_dim, + ch_mult=(1, 2, 4), + num_res_blocks=2, + attn_layers=[3], + down_layers=[0], + in_dim=data_dim, + embed_dim=embed_dim, + ) + self.decoder = Decoder1D( + dim=hidden_dim, + ch_mult=(1, 2, 4), + num_res_blocks=2, + attn_layers=[3], + down_layers=[0], + in_dim=data_dim, + out_dim=data_dim, + embed_dim=embed_dim, + ) + + self.embed_dim = embed_dim + # self.quant_conv = nn.Conv1d(2 * embed_dim, 2 * embed_dim, 1) + # self.post_quant_conv = nn.Conv1d(embed_dim, embed_dim, 1) + + self.initialize_weights() + + def initialize_weights(self): + pass + + def encode(self, x: torch.Tensor, normalize: bool = True) -> DiagonalGaussianDistribution: + if normalize: + x = self.normalize(x) + moments = self.encoder(x) + posterior = DiagonalGaussianDistribution(moments) + return posterior + + def decode(self, z: torch.Tensor, unnormalize: bool = True) -> torch.Tensor: + dec = self.decoder(z) + if unnormalize: + dec = self.unnormalize(dec) + return dec + + def normalize(self, x: torch.Tensor) -> torch.Tensor: + return (x - self.data_mean) / self.data_std + + def unnormalize(self, x: torch.Tensor) -> torch.Tensor: + return x * self.data_std + self.data_mean + + def forward( + self, + x: torch.Tensor, + sample_posterior: bool = True, + rng: Optional[torch.Generator] = None, + normalize: bool = True, + unnormalize: bool = True, + ) -> tuple[torch.Tensor, DiagonalGaussianDistribution]: + + posterior = self.encode(x, normalize=normalize) + if sample_posterior: + z = posterior.sample(rng) + else: + z = posterior.mode() + dec = self.decode(z, unnormalize=unnormalize) + return dec, posterior + + def load_weights(self, src_dict) -> None: + self.load_state_dict(src_dict, strict=True) + + @property + def device(self) -> torch.device: + return next(self.parameters()).device + + def get_last_layer(self): + return self.decoder.conv_out.weight + + def remove_weight_norm(self): + for name, m in self.named_modules(): + if isinstance(m, MPConv1D): + m.remove_weight_norm() + log.debug(f"Removed weight norm from {name}") + return self + + +class Encoder1D(nn.Module): + + def __init__(self, + *, + dim: int, + ch_mult: tuple[int] = (1, 2, 4, 8), + num_res_blocks: int, + attn_layers: list[int] = [], + down_layers: list[int] = [], + resamp_with_conv: bool = True, + in_dim: int, + embed_dim: int, + double_z: bool = True, + kernel_size: int = 3, + clip_act: float = 256.0): + super().__init__() + self.dim = dim + self.num_layers = len(ch_mult) + self.num_res_blocks = num_res_blocks + self.in_channels = in_dim + self.clip_act = clip_act + self.down_layers = down_layers + self.attn_layers = attn_layers + self.conv_in = MPConv1D(in_dim, self.dim, kernel_size=kernel_size) + + in_ch_mult = (1, ) + tuple(ch_mult) + self.in_ch_mult = in_ch_mult + # downsampling + self.down = nn.ModuleList() + for i_level in range(self.num_layers): + block = nn.ModuleList() + attn = nn.ModuleList() + block_in = dim * in_ch_mult[i_level] + block_out = dim * ch_mult[i_level] + for i_block in range(self.num_res_blocks): + block.append( + ResnetBlock1D(in_dim=block_in, + out_dim=block_out, + kernel_size=kernel_size, + use_norm=True)) + block_in = block_out + if i_level in attn_layers: + attn.append(AttnBlock1D(block_in)) + down = nn.Module() + down.block = block + down.attn = attn + if i_level in down_layers: + down.downsample = Downsample1D(block_in, resamp_with_conv) + self.down.append(down) + + # middle + self.mid = nn.Module() + self.mid.block_1 = ResnetBlock1D(in_dim=block_in, + out_dim=block_in, + kernel_size=kernel_size, + use_norm=True) + self.mid.attn_1 = AttnBlock1D(block_in) + self.mid.block_2 = ResnetBlock1D(in_dim=block_in, + out_dim=block_in, + kernel_size=kernel_size, + use_norm=True) + + # end + self.conv_out = MPConv1D(block_in, + 2 * embed_dim if double_z else embed_dim, + kernel_size=kernel_size) + + self.learnable_gain = nn.Parameter(torch.zeros([])) + + def forward(self, x): + + # downsampling + hs = [self.conv_in(x)] + for i_level in range(self.num_layers): + for i_block in range(self.num_res_blocks): + h = self.down[i_level].block[i_block](hs[-1]) + if len(self.down[i_level].attn) > 0: + h = self.down[i_level].attn[i_block](h) + h = h.clamp(-self.clip_act, self.clip_act) + hs.append(h) + if i_level in self.down_layers: + hs.append(self.down[i_level].downsample(hs[-1])) + + # middle + h = hs[-1] + h = self.mid.block_1(h) + h = self.mid.attn_1(h) + h = self.mid.block_2(h) + h = h.clamp(-self.clip_act, self.clip_act) + + # end + h = nonlinearity(h) + h = self.conv_out(h, gain=(self.learnable_gain + 1)) + return h + + +class Decoder1D(nn.Module): + + def __init__(self, + *, + dim: int, + out_dim: int, + ch_mult: tuple[int] = (1, 2, 4, 8), + num_res_blocks: int, + attn_layers: list[int] = [], + down_layers: list[int] = [], + kernel_size: int = 3, + resamp_with_conv: bool = True, + in_dim: int, + embed_dim: int, + clip_act: float = 256.0): + super().__init__() + self.ch = dim + self.num_layers = len(ch_mult) + self.num_res_blocks = num_res_blocks + self.in_channels = in_dim + self.clip_act = clip_act + self.down_layers = [i + 1 for i in down_layers] # each downlayer add one + + # compute in_ch_mult, block_in and curr_res at lowest res + block_in = dim * ch_mult[self.num_layers - 1] + + # z to block_in + self.conv_in = MPConv1D(embed_dim, block_in, kernel_size=kernel_size) + + # middle + self.mid = nn.Module() + self.mid.block_1 = ResnetBlock1D(in_dim=block_in, out_dim=block_in, use_norm=True) + self.mid.attn_1 = AttnBlock1D(block_in) + self.mid.block_2 = ResnetBlock1D(in_dim=block_in, out_dim=block_in, use_norm=True) + + # upsampling + self.up = nn.ModuleList() + for i_level in reversed(range(self.num_layers)): + block = nn.ModuleList() + attn = nn.ModuleList() + block_out = dim * ch_mult[i_level] + for i_block in range(self.num_res_blocks + 1): + block.append(ResnetBlock1D(in_dim=block_in, out_dim=block_out, use_norm=True)) + block_in = block_out + if i_level in attn_layers: + attn.append(AttnBlock1D(block_in)) + up = nn.Module() + up.block = block + up.attn = attn + if i_level in self.down_layers: + up.upsample = Upsample1D(block_in, resamp_with_conv) + self.up.insert(0, up) # prepend to get consistent order + + # end + self.conv_out = MPConv1D(block_in, out_dim, kernel_size=kernel_size) + self.learnable_gain = nn.Parameter(torch.zeros([])) + + def forward(self, z): + # z to block_in + h = self.conv_in(z) + + # middle + h = self.mid.block_1(h) + h = self.mid.attn_1(h) + h = self.mid.block_2(h) + h = h.clamp(-self.clip_act, self.clip_act) + + # upsampling + for i_level in reversed(range(self.num_layers)): + for i_block in range(self.num_res_blocks + 1): + h = self.up[i_level].block[i_block](h) + if len(self.up[i_level].attn) > 0: + h = self.up[i_level].attn[i_block](h) + h = h.clamp(-self.clip_act, self.clip_act) + if i_level in self.down_layers: + h = self.up[i_level].upsample(h) + + h = nonlinearity(h) + h = self.conv_out(h, gain=(self.learnable_gain + 1)) + return h + + +def VAE_16k(**kwargs) -> VAE: + return VAE(data_dim=80, embed_dim=20, hidden_dim=384, **kwargs) + + +def VAE_44k(**kwargs) -> VAE: + return VAE(data_dim=128, embed_dim=40, hidden_dim=512, **kwargs) + + +def get_my_vae(name: str, **kwargs) -> VAE: + if name == '16k': + return VAE_16k(**kwargs) + if name == '44k': + return VAE_44k(**kwargs) + raise ValueError(f'Unknown model: {name}') + + +if __name__ == '__main__': + network = get_my_vae('standard') + + # print the number of parameters in terms of millions + num_params = sum(p.numel() for p in network.parameters()) / 1e6 + print(f'Number of parameters: {num_params:.2f}M') diff --git a/Ovi/vae/vae_modules.py b/Ovi/vae/vae_modules.py new file mode 100644 index 0000000..f083a0e --- /dev/null +++ b/Ovi/vae/vae_modules.py @@ -0,0 +1,117 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +from einops import rearrange + +from .edm2_utils import (MPConv1D, mp_silu, mp_sum, normalize) + + +def nonlinearity(x): + # swish + return mp_silu(x) + + +class ResnetBlock1D(nn.Module): + + def __init__(self, *, in_dim, out_dim=None, conv_shortcut=False, kernel_size=3, use_norm=True): + super().__init__() + self.in_dim = in_dim + out_dim = in_dim if out_dim is None else out_dim + self.out_dim = out_dim + self.use_conv_shortcut = conv_shortcut + self.use_norm = use_norm + + self.conv1 = MPConv1D(in_dim, out_dim, kernel_size=kernel_size) + self.conv2 = MPConv1D(out_dim, out_dim, kernel_size=kernel_size) + if self.in_dim != self.out_dim: + if self.use_conv_shortcut: + self.conv_shortcut = MPConv1D(in_dim, out_dim, kernel_size=kernel_size) + else: + self.nin_shortcut = MPConv1D(in_dim, out_dim, kernel_size=1) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + + # pixel norm + if self.use_norm: + x = normalize(x, dim=1) + + h = x + h = nonlinearity(h) + h = self.conv1(h) + + h = nonlinearity(h) + h = self.conv2(h) + + if self.in_dim != self.out_dim: + if self.use_conv_shortcut: + x = self.conv_shortcut(x) + else: + x = self.nin_shortcut(x) + + return mp_sum(x, h, t=0.3) + + +class AttnBlock1D(nn.Module): + + def __init__(self, in_channels, num_heads=1): + super().__init__() + self.in_channels = in_channels + + self.num_heads = num_heads + self.qkv = MPConv1D(in_channels, in_channels * 3, kernel_size=1) + self.proj_out = MPConv1D(in_channels, in_channels, kernel_size=1) + + def forward(self, x): + h = x + y = self.qkv(h) + y = y.reshape(y.shape[0], self.num_heads, -1, 3, y.shape[-1]) + q, k, v = normalize(y, dim=2).unbind(3) + + q = rearrange(q, 'b h c l -> b h l c') + k = rearrange(k, 'b h c l -> b h l c') + v = rearrange(v, 'b h c l -> b h l c') + + h = F.scaled_dot_product_attention(q, k, v) + h = rearrange(h, 'b h l c -> b (h c) l') + + h = self.proj_out(h) + + return mp_sum(x, h, t=0.3) + + +class Upsample1D(nn.Module): + + def __init__(self, in_channels, with_conv): + super().__init__() + self.with_conv = with_conv + if self.with_conv: + self.conv = MPConv1D(in_channels, in_channels, kernel_size=3) + + def forward(self, x): + x = F.interpolate(x, scale_factor=2.0, mode='nearest-exact') # support 3D tensor(B,C,T) + if self.with_conv: + x = self.conv(x) + return x + + +class Downsample1D(nn.Module): + + def __init__(self, in_channels, with_conv): + super().__init__() + self.with_conv = with_conv + if self.with_conv: + # no asymmetric padding in torch conv, must do it ourselves + self.conv1 = MPConv1D(in_channels, in_channels, kernel_size=1) + self.conv2 = MPConv1D(in_channels, in_channels, kernel_size=1) + + def forward(self, x): + + if self.with_conv: + x = self.conv1(x) + + x = F.avg_pool1d(x, kernel_size=2, stride=2) + + if self.with_conv: + x = self.conv2(x) + + return x diff --git a/Ovi/wanvideo_2_2_5B_ovi_testing.json b/Ovi/wanvideo_2_2_5B_ovi_testing.json new file mode 100644 index 0000000..134cdfa --- /dev/null +++ b/Ovi/wanvideo_2_2_5B_ovi_testing.json @@ -0,0 +1,1503 @@ +{ + "id": "7200c272-1c07-4834-9c36-c6ec5c140c21", + "revision": 0, + "last_node_id": 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Clear older male voices speaking dialogue, subtle outdoor ambience.", + "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走", + "disabled", + false, + "gpu" + ], + "color": "#432", + "bgcolor": "#653" + }, + { + "id": 126, + "type": "Note", + "pos": [ + 180.81106567382812, + 1191.43359375 + ], + "size": [ + 210, + 88 + ], + "flags": {}, + "order": 14, + "mode": 0, + "inputs": [], + "outputs": [], + "title": "Audio latent", + "properties": {}, + "widgets_values": [ + "Default audio latent length is 157 for 121 frames, adjusting this is experimental!!" + ], + "color": "#432", + "bgcolor": "#653" + } + ], + "links": [ + [ + 152, + 12, + 0, + 84, + 0, + "WANVIDEOMODEL" + ], + [ + 153, + 84, + 0, + 80, + 0, + "WANVIDEOMODEL" + ], + [ + 154, + 83, + 0, + 84, + 1, + "BLOCKSWAPARGS" + ], + [ + 157, + 78, + 0, + 12, + 4, + "VACEPATH" + ], + [ + 158, + 80, + 0, + 86, + 1, + "LATENT" + ], + [ + 159, + 87, + 0, + 86, + 0, + "WANVAE" + ], + [ + 160, + 86, + 0, + 88, + 0, + "IMAGE" + ], + [ + 161, + 89, + 0, + 90, + 0, + "MMAUDIOVAE" + ], + [ + 162, + 80, + 0, + 90, + 1, + "LATENT" + ], + [ + 163, + 90, + 0, + 88, + 1, + "AUDIO" + ], + [ + 164, + 91, + 0, + 12, + 0, + "WANCOMPILEARGS" + ], + [ + 167, + 85, + 0, + 94, + 0, + "WANVIDEOTEXTEMBEDS" + ], + [ + 169, + 96, + 1, + 94, + 1, + "WANVIDEOTEXTEMBEDS" + ], + [ + 170, + 94, + 0, + 80, + 2, + "WANVIDEOTEXTEMBEDS" + ], + [ + 178, + 81, + 0, + 80, + 1, + "WANVIDIMAGE_EMBEDS" + ], + [ + 190, + 90, + 0, + 108, + 0, + "AUDIO" + ], + [ + 191, + 93, + 0, + 80, + 8, + "SLGARGS" + ], + [ + 192, + 109, + 0, + 110, + 0, + "IMAGE" + ], + [ + 193, + 87, + 0, + 111, + 0, + "WANVAE" + ], + [ + 194, + 110, + 0, + 111, + 1, + "IMAGE" + ], + [ + 195, + 111, + 0, + 81, + 1, + "LATENT" + ], + [ + 197, + 110, + 2, + 81, + 3, + "INT" + ], + [ + 198, + 110, + 1, + 81, + 2, + "INT" + ], + [ + 210, + 118, + 0, + 80, + 6, + "CACHEARGS" + ], + [ + 211, + 125, + 0, + 80, + 3, + "LATENT" + ] + ], + "groups": [], + "config": {}, + "extra": { + "ds": { + "scale": 0.5644739300537776, + "offset": [ + 785.7769735686938, + -2.4187927272334946 + ] + }, + "frontendVersion": "1.28.3", + "VHS_latentpreview": true, + "VHS_latentpreviewrate": 0, + "VHS_MetadataImage": true, + "VHS_KeepIntermediate": true, + "node_versions": { + "ComfyUI-WanVideoWrapper": "586d9148a0306ef5d30e9a971a9c3be4cd3ecc97", + "ComfyUI-KJNodes": "3fcd22f2fe2be69c3229f192362b91888277cbcb", + "comfy-core": "0.3.64", + "comfyui-videohelpersuite": "8e4d79471bf1952154768e8435a9300077b534fa" + } + }, + "version": 0.4 +} \ No newline at end of file diff --git a/__init__.py b/__init__.py index f018608..94012c1 100644 --- a/__init__.py +++ b/__init__.py @@ -68,6 +68,13 @@ except Exception as e: LYNX_NODE_CLASS_MAPPINGS = {} LYNX_NODE_DISPLAY_NAME_MAPPINGS = {} +try: + from .Ovi.nodes_ovi import NODE_CLASS_MAPPINGS as OVI_NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS as OVI_NODE_DISPLAY_NAME_MAPPINGS +except Exception as e: + log.warning(f"WanVideoWrapper WARNING: Ovi nodes not available due to error in importing them: {e}") + OVI_NODE_CLASS_MAPPINGS = {} + OVI_NODE_DISPLAY_NAME_MAPPINGS = {} + NODE_CLASS_MAPPINGS.update(RECAM_MASTER_NODE_CLASS_MAPPINGS) NODE_CLASS_MAPPINGS.update(UNIANIMATE_NODE_CLASS_MAPPINGS) NODE_CLASS_MAPPINGS.update(SKYREELS_NODE_CLASS_MAPPINGS) @@ -88,6 +95,7 @@ NODE_CLASS_MAPPINGS.update(S2V_NODE_CLASS_MAPPINGS) NODE_CLASS_MAPPINGS.update(HUMO_NODE_CLASS_MAPPINGS) NODE_CLASS_MAPPINGS.update(SAMPLER_NODE_CLASS_MAPPINGS) NODE_CLASS_MAPPINGS.update(LYNX_NODE_CLASS_MAPPINGS) +NODE_CLASS_MAPPINGS.update(OVI_NODE_CLASS_MAPPINGS) NODE_DISPLAY_NAME_MAPPINGS.update(RECAM_MASTER_NODE_DISPLAY_NAME_MAPPINGS) NODE_DISPLAY_NAME_MAPPINGS.update(UNIANIMATE_NODE_DISPLAY_NAME_MAPPINGS) @@ -109,5 +117,6 @@ NODE_DISPLAY_NAME_MAPPINGS.update(S2V_NODE_DISPLAY_NAME_MAPPINGS) NODE_DISPLAY_NAME_MAPPINGS.update(HUMO_NODE_DISPLAY_NAME_MAPPINGS) NODE_DISPLAY_NAME_MAPPINGS.update(SAMPLER_NODE_DISPLAY_NAME_MAPPINGS) NODE_DISPLAY_NAME_MAPPINGS.update(LYNX_NODE_DISPLAY_NAME_MAPPINGS) +NODE_DISPLAY_NAME_MAPPINGS.update(OVI_NODE_DISPLAY_NAME_MAPPINGS) __all__ = ["NODE_CLASS_MAPPINGS", "NODE_DISPLAY_NAME_MAPPINGS"] \ No newline at end of file diff --git a/cache_methods/cache_methods.py b/cache_methods/cache_methods.py index 1b7bb12..1c5d1e6 100644 --- a/cache_methods/cache_methods.py +++ b/cache_methods/cache_methods.py @@ -113,6 +113,7 @@ class EasyCacheState: 'cache': None, 'accumulated_error': 0.0, 'skipped_steps': [], + 'cache_ovi': None, } return pred_id diff --git a/nodes_model_loading.py b/nodes_model_loading.py index 30e4efa..1b0e4de 100644 --- a/nodes_model_loading.py +++ b/nodes_model_loading.py @@ -892,8 +892,8 @@ def load_weights(transformer, sd=None, weight_dtype=None, base_dtype=None, cnt += 1 if cnt % 100 == 0: pbar.update(100) - #for name, param in transformer.named_parameters(): - # print(name, param.device, param.dtype) + + #[print(name, param.device, param.dtype) for name, param in transformer.named_parameters()] pbar.update_absolute(0) @@ -1112,7 +1112,8 @@ class WanVideoModelLoader: if "vace_blocks.0.after_proj.weight" in sd and not "patch_embedding.weight" in sd: raise ValueError("You are attempting to load a VACE module as a WanVideo model, instead you should use the vace_model input and matching T2V base model") - # currently this can be VAE or MTV-Crafter weights + # currently this can be VACE, MTV-Crafter, Lynx or Ovi-audio weights + extra_audio_model = False if extra_model is not None: for _model in extra_model: print("Loading extra model: ", _model["path"]) @@ -1126,32 +1127,38 @@ class WanVideoModelLoader: if _model["path"].endswith(".gguf"): raise ValueError("With GGUF extra model the main model must also be GGUF quantized model") extra_sd = load_torch_file(_model["path"], device=transformer_load_device, safe_load=True) + if "audio_model.patch_embedding.0.weight" in extra_sd: + extra_audio_model = True sd.update(extra_sd) del extra_sd first_key = next(iter(sd)) + if first_key.startswith("audio_model.") and not extra_audio_model: + sd = {key.replace("audio_model.", "", 1): value for key, value in sd.items()} if first_key.startswith("model.diffusion_model."): - new_sd = {} - for key, value in sd.items(): - new_key = key.replace("model.diffusion_model.", "", 1) - new_sd[new_key] = value - sd = new_sd + sd = {key.replace("model.diffusion_model.", "", 1): value for key, value in sd.items()} elif first_key.startswith("model."): - new_sd = {} - for key, value in sd.items(): - new_key = key.replace("model.", "", 1) - new_sd[new_key] = value - sd = new_sd - if not "patch_embedding.weight" in sd: - raise ValueError("Invalid WanVideo model selected") - dim = sd["patch_embedding.weight"].shape[0] + sd = {key.replace("model.", "", 1): value for key, value in sd.items()} + + if "patch_embedding.weight" in sd: + dim = sd["patch_embedding.weight"].shape[0] + in_channels = sd["patch_embedding.weight"].shape[1] + elif "patch_embedding.0.weight" in sd: + dim = sd["patch_embedding.0.weight"].shape[0] + in_channels = sd["patch_embedding.0.weight"].shape[1] + else: + raise ValueError("No patch_embedding weight found, is the selected model a full WanVideo model?") + in_features = sd["blocks.0.self_attn.k.weight"].shape[1] out_features = sd["blocks.0.self_attn.k.weight"].shape[0] - in_channels = sd["patch_embedding.weight"].shape[1] log.info(f"Detected model in_channels: {in_channels}") ffn_dim = sd["blocks.0.ffn.0.bias"].shape[0] ffn2_dim = sd["blocks.0.ffn.2.weight"].shape[1] + patch_size=(1, 2, 2) + if "patch_embedding.0.weight" in sd: + patch_size = [1] + is_humo = "audio_proj.audio_proj_glob_1.layer.weight" in sd is_wananimate = "pose_patch_embedding.weight" in sd @@ -1273,6 +1280,7 @@ class WanVideoModelLoader: "dim": dim, "in_features": in_features, "out_features": out_features, + "patch_size": patch_size, "ffn_dim": ffn_dim, "ffn2_dim": ffn2_dim, "eps": 1e-06, @@ -1309,8 +1317,32 @@ class WanVideoModelLoader: } with init_empty_weights(): - transformer = WanModel(**TRANSFORMER_CONFIG) - transformer.eval() + transformer = WanModel(**TRANSFORMER_CONFIG).eval() + + if extra_audio_model: + log.info("Ovi extra audio model detected, initializing...") + TRANSFORMER_CONFIG.update({ + "patch_size": [1], + "in_dim": 20, + "out_dim": 20, + }) + + with init_empty_weights(): + transformer.audio_model = WanModel(**TRANSFORMER_CONFIG).eval() + + from .wanvideo.modules.model import WanLayerNorm, WanRMSNorm + + for block in transformer.blocks: + block.cross_attn.k_fusion = nn.Linear(block.dim, block.dim) + block.cross_attn.v_fusion = nn.Linear(block.dim, block.dim) + block.cross_attn.pre_attn_norm_fusion = WanLayerNorm(block.dim, elementwise_affine=True) + block.cross_attn.norm_k_fusion = WanRMSNorm(block.dim, eps=1e-6) if block.qk_norm else nn.Identity() + + for block in transformer.audio_model.blocks: + block.cross_attn.k_fusion = nn.Linear(block.dim, block.dim) + block.cross_attn.v_fusion = nn.Linear(block.dim, block.dim) + block.cross_attn.pre_attn_norm_fusion = WanLayerNorm(block.dim, elementwise_affine=True) + block.cross_attn.norm_k_fusion = WanRMSNorm(block.dim, eps=1e-6) if block.qk_norm else nn.Identity() #ReCamMaster if "blocks.0.cam_encoder.weight" in sd: @@ -1422,10 +1454,10 @@ class WanVideoModelLoader: for k, v in sd.items(): if k.endswith(".scale_weight"): scale_weights[k] = v.to(device, base_dtype) - - if "fp8_e4m3fn" in quantization: + + if quantization == "fp8_e4m3fn": weight_dtype = torch.float8_e4m3fn - elif "fp8_e5m2" in quantization: + elif quantization == "fp8_e5m2": weight_dtype = torch.float8_e5m2 else: weight_dtype = base_dtype diff --git a/nodes_sampler.py b/nodes_sampler.py index bb6f317..c0651aa 100644 --- a/nodes_sampler.py +++ b/nodes_sampler.py @@ -46,9 +46,7 @@ class MetaParameter(torch.nn.Parameter): self.quant_type = quant_type return self -def offload_transformer(transformer): - for block in transformer.blocks: - block.kv_cache = None +def offload_transformer(transformer): transformer.teacache_state.clear_all() transformer.magcache_state.clear_all() transformer.easycache_state.clear_all() @@ -73,6 +71,11 @@ def offload_transformer(transformer): else: transformer.to(offload_device) + for block in transformer.blocks: + block.kv_cache = None + if transformer.audio_model is not None and hasattr(block, 'audio_block'): + block.audio_block = None + mm.soft_empty_cache() gc.collect() @@ -257,6 +260,18 @@ class WanVideoSampler: if arg not in step_sig.parameters: scheduler_step_args.pop(arg) + # Ovi + if transformer.audio_model is not None: # temporary workaround (...nothing more permanent) + for i, block in enumerate(transformer.blocks): + block.audio_block = transformer.audio_model.blocks[i] + sample_scheduler_ovi = copy.deepcopy(sample_scheduler) + rope_function = "default" # comfy rope not implemented for ovi model yet + ovi_negative_text_embeds = text_embeds.get("ovi_negative_prompt_embeds", None) + ovi_audio_cfg = text_embeds.get("ovi_audio_cfg", None) + if ovi_audio_cfg is not None: + if not isinstance(ovi_audio_cfg, list): + ovi_audio_cfg = [ovi_audio_cfg] * (steps + 1) + if isinstance(cfg, list): if steps < len(cfg): log.info(f"Received {len(cfg)} cfg values, but only {steps} steps. Slicing cfg list to match steps.") @@ -486,6 +501,21 @@ class WanVideoSampler: pos_latent = neg_latent = None + # Ovi + noise_audio = latent_ovi = seq_len_ovi = None + if transformer.audio_model is not None: + noise_audio = samples.get("latent_ovi_audio", None) if samples is not None else None + if noise_audio is not None: + if not torch.any(noise_audio): + noise_audio = torch.randn(noise_audio.shape, device=torch.device("cpu"), dtype=torch.float32, generator=seed_g) + else: + noise_audio = noise_audio.squeeze().movedim(0, 1).to(device, dtype) + else: + noise_audio = torch.randn((157, 20), device=torch.device("cpu"), dtype=torch.float32, generator=seed_g) # T C + log.info(f"Ovi audio latent shape: {noise_audio.shape}") + latent_ovi = noise_audio + seq_len_ovi = noise_audio.shape[0] + if transformer.dim == 1536 and humo_image_cond is not None: #small humo model #noise = torch.cat([noise[:, :-humo_reference_count], humo_image_cond[4:, -humo_reference_count:]], dim=1) pos_latent = humo_image_cond[4:, -humo_reference_count:].to(device, dtype) @@ -734,33 +764,35 @@ class WanVideoSampler: saved_generator_state = samples.get("generator_state", None) if saved_generator_state is not None: seed_g.set_state(saved_generator_state) - input_samples = samples["samples"].squeeze(0).to(noise) - if input_samples.shape[1] != noise.shape[1]: - input_samples = torch.cat([input_samples[:, :1].repeat(1, noise.shape[1] - input_samples.shape[1], 1, 1), input_samples], dim=1) + input_samples = samples.get("samples", None) + if input_samples is not None: + input_samples = input_samples.squeeze(0).to(noise) + if input_samples.shape[1] != noise.shape[1]: + input_samples = torch.cat([input_samples[:, :1].repeat(1, noise.shape[1] - input_samples.shape[1], 1, 1), input_samples], dim=1) - if add_noise_to_samples: - latent_timestep = timesteps[:1].to(noise) - noise = noise * latent_timestep / 1000 + (1 - latent_timestep / 1000) * input_samples - else: - noise = input_samples + if add_noise_to_samples: + latent_timestep = timesteps[:1].to(noise) + noise = noise * latent_timestep / 1000 + (1 - latent_timestep / 1000) * input_samples + else: + noise = input_samples - noise_mask = samples.get("noise_mask", None) - if noise_mask is not None: - log.info(f"Latent noise_mask shape: {noise_mask.shape}") - original_image = samples.get("original_image", None) - if original_image is None: - original_image = input_samples - if len(noise_mask.shape) == 4: - noise_mask = noise_mask.squeeze(1) - if noise_mask.shape[0] < noise.shape[1]: - noise_mask = noise_mask.repeat(noise.shape[1] // noise_mask.shape[0], 1, 1) + noise_mask = samples.get("noise_mask", None) + if noise_mask is not None: + log.info(f"Latent noise_mask shape: {noise_mask.shape}") + original_image = samples.get("original_image", None) + if original_image is None: + original_image = input_samples + if len(noise_mask.shape) == 4: + noise_mask = noise_mask.squeeze(1) + if noise_mask.shape[0] < noise.shape[1]: + noise_mask = noise_mask.repeat(noise.shape[1] // noise_mask.shape[0], 1, 1) - noise_mask = torch.nn.functional.interpolate( - noise_mask.unsqueeze(0).unsqueeze(0), # Add batch and channel dims [1,1,T,H,W] - size=(noise.shape[1], noise.shape[2], noise.shape[3]), - mode='trilinear', - align_corners=False - ).repeat(1, noise.shape[0], 1, 1, 1) + noise_mask = torch.nn.functional.interpolate( + noise_mask.unsqueeze(0).unsqueeze(0), # Add batch and channel dims [1,1,T,H,W] + size=(noise.shape[1], noise.shape[2], noise.shape[3]), + mode='trilinear', + align_corners=False + ).repeat(1, noise.shape[0], 1, 1, 1) # extra latents (Pusa) and 5b latents_to_insert = add_index = noise_multipliers = None @@ -797,7 +829,8 @@ class WanVideoSampler: if extra_channel_latents is not None: extra_channel_latents = extra_channel_latents[0].to(noise) - latent = noise.to(device) + latent = noise + print("Latent shape:", latent.shape, "Latent dtype:", latent.dtype, "Latent device:", latent.device) #controlnet controlnet_latents = controlnet = None @@ -869,10 +902,8 @@ class WanVideoSampler: # Initialize cache state if samples is not None: previous_cache_states = samples.get("cache_states", None) - print("Using previous cache states", previous_cache_states) if previous_cache_states is not None: log.info("Using cache states from previous sampler") - self.cache_state = previous_cache_states["cache_state"] transformer.easycache_state = previous_cache_states["easycache_state"] transformer.magcache_state = previous_cache_states["magcache_state"] @@ -1069,9 +1100,9 @@ class WanVideoSampler: add_cond=None, cache_state=None, context_window=None, multitalk_audio_embeds=None, fantasy_portrait_input=None, reverse_time=False, mtv_motion_tokens=None, s2v_audio_input=None, s2v_ref_motion=None, s2v_motion_frames=[1, 0], s2v_pose=None, humo_image_cond=None, humo_image_cond_neg=None, humo_audio=None, humo_audio_neg=None, wananim_pose_latents=None, - wananim_face_pixels=None, uni3c_data=None,): + wananim_face_pixels=None, uni3c_data=None, latent_model_input_ovi=None): nonlocal transformer - #z = z.to(dtype) + autocast_enabled = ("fp8" in model["quantization"] and not transformer.patched_linear) with torch.autocast(device_type=mm.get_autocast_device(device), dtype=dtype) if autocast_enabled else nullcontext(): @@ -1301,6 +1332,9 @@ class WanVideoSampler: "wananim_pose_strength": wananim_pose_strength, "wananim_face_strength": wananim_face_strength, "lynx_embeds": lynx_embeds, # Lynx face and reference embeddings + "x_ovi": [latent_model_input_ovi.to(z)] if latent_model_input_ovi is not None else None, # Audio latent model input for Ovi + "seq_len_ovi": seq_len_ovi, # Audio latent model sequence length for Ovi + "ovi_negative_text_embeds": ovi_negative_text_embeds, # Audio latent model negative text embeds for Ovi } batch_size = 1 @@ -1316,17 +1350,18 @@ class WanVideoSampler: #conditional (positive) pass if pos_latent is not None: # for humo base_params['x'] = [torch.cat([z[:, :-humo_reference_count], pos_latent], dim=1)] - noise_pred_cond, cache_state_cond = transformer( + noise_pred_cond, noise_pred_ovi, cache_state_cond = transformer( context=positive_embeds, pred_id=cache_state[0] if cache_state else None, vace_data=vace_data, attn_cond=attn_cond, **base_params ) noise_pred_cond = noise_pred_cond[0] + noise_pred_ovi = noise_pred_ovi[0] if noise_pred_ovi is not None else None if math.isclose(cfg_scale, 1.0): if use_fresca: noise_pred_cond = fourier_filter(noise_pred_cond, fresca_scale_low, fresca_scale_high, fresca_freq_cutoff) - return noise_pred_cond, [cache_state_cond] + return noise_pred_cond, noise_pred_ovi, [cache_state_cond] #unconditional (negative) pass base_params['is_uncond'] = True @@ -1338,12 +1373,13 @@ class WanVideoSampler: if neg_latent is not None: base_params['x'] = [torch.cat([z[:, :-humo_reference_count], neg_latent], dim=1)] - noise_pred_uncond, cache_state_uncond = transformer( + noise_pred_uncond, noise_pred_ovi_uncond, cache_state_uncond = transformer( context=negative_embeds if humo_audio_input_neg is None else positive_embeds, #ti #t pred_id=cache_state[1] if cache_state else None, vace_data=vace_data, attn_cond=attn_cond_neg, **base_params) noise_pred_uncond = noise_pred_uncond[0] + noise_pred_ovi_uncond = noise_pred_ovi_uncond[0] if noise_pred_ovi_uncond is not None else None # HuMo if not math.isclose(humo_audio_cfg_scale[idx], 1.0): @@ -1353,7 +1389,7 @@ class WanVideoSampler: if t > 980 and humo_image_cond_neg_input is not None: # use image cond for first timesteps base_params['y'] = [humo_image_cond_neg_input] - noise_pred_humo_audio_uncond, cache_state_humo = transformer( + noise_pred_humo_audio_uncond, _, cache_state_humo = transformer( context=negative_embeds, pred_id=cache_state[2] if cache_state else None, vace_data=None, **base_params) @@ -1364,13 +1400,13 @@ class WanVideoSampler: if cache_state is not None and len(cache_state) != 4: cache_state.append(None) # audio - noise_pred_humo_null, cache_state_humo = transformer( + noise_pred_humo_null, _, cache_state_humo = transformer( context=negative_embeds, pred_id=cache_state[2] if cache_state else None, vace_data=None, **base_params) # negative if humo_audio_input is not None: base_params['humo_audio'] = humo_audio_input - noise_pred_humo_audio, cache_state_humo2 = transformer( + noise_pred_humo_audio, _, cache_state_humo2 = transformer( context=positive_embeds, pred_id=cache_state[3] if cache_state else None, vace_data=None, **base_params) noise_pred = (humo_audio_cfg_scale[idx] * (noise_pred_cond - noise_pred_humo_audio[0]) @@ -1383,7 +1419,7 @@ class WanVideoSampler: if use_phantom and not math.isclose(phantom_cfg_scale[idx], 1.0): if cache_state is not None and len(cache_state) != 3: cache_state.append(None) - noise_pred_phantom, cache_state_phantom = transformer( + noise_pred_phantom, _, cache_state_phantom = transformer( context=negative_embeds, pred_id=cache_state[2] if cache_state else None, vace_data=None, **base_params) @@ -1404,7 +1440,7 @@ class WanVideoSampler: base_params['multitalk_audio'] = torch.zeros_like(multitalk_audio_input)[-1:] audio_context = negative_embeds base_params['is_uncond'] = False - noise_pred_no_audio, cache_state_audio = transformer( + noise_pred_no_audio, _, cache_state_audio = transformer( context=audio_context, pred_id=cache_state[2] if cache_state else None, vace_data=vace_data, @@ -1419,7 +1455,7 @@ class WanVideoSampler: base_params['is_uncond'] = False if cache_state is not None and len(cache_state) != 3: cache_state.append(None) - noise_pred_lynx, cache_state_lynx = transformer( + noise_pred_lynx, _, cache_state_lynx = transformer( context=negative_embeds, pred_id=cache_state[2] if cache_state else None, vace_data=None, **base_params) @@ -1433,7 +1469,7 @@ class WanVideoSampler: base_params['y'] = [image_cond_input] * 2 if image_cond_input is not None else None base_params['clip_fea'] = torch.cat([clip_fea, clip_fea], dim=0) cache_state_uncond = None - [noise_pred_cond, noise_pred_uncond], cache_state_cond = transformer( + [noise_pred_cond, noise_pred_uncond], _, cache_state_cond = transformer( context=positive_embeds + negative_embeds, is_uncond=False, pred_id=cache_state[0] if cache_state else None, **base_params @@ -1471,7 +1507,14 @@ class WanVideoSampler: noise_pred = noise_pred_uncond_scaled + cfg_scale * (noise_pred_cond - noise_pred_uncond_scaled) del noise_pred_uncond_scaled, noise_pred_cond, noise_pred_uncond - return noise_pred, [cache_state_cond, cache_state_uncond] + if latent_model_input_ovi is not None: + if ovi_audio_cfg is None: + audio_cfg_scale = cfg_scale - 1.0 if cfg_scale > 4.0 else cfg_scale + else: + audio_cfg_scale = ovi_audio_cfg[idx] + noise_pred_ovi = noise_pred_ovi_uncond + audio_cfg_scale * (noise_pred_ovi - noise_pred_ovi_uncond) + + return noise_pred, noise_pred_ovi, [cache_state_cond, cache_state_uncond] if args.preview_method in [LatentPreviewMethod.Auto, LatentPreviewMethod.Latent2RGB]: #default for latent2rgb from latent_preview import prepare_callback @@ -1546,7 +1589,7 @@ class WanVideoSampler: # Store initial noise for first iteration if freeinit_args is not None and iter_idx == 0: initial_noise_saved = current_latent.detach().clone() - if samples is not None: + if input_samples is not None: current_latent = input_samples.to(device) continue @@ -1586,6 +1629,7 @@ class WanVideoSampler: latent[:, add_index:add_index+num_extra_frames] = entry["samples"].to(latent) latent_model_input = latent.to(device) + latent_model_input_ovi = latent_ovi.to(device) if latent_ovi is not None else None current_step_percentage = idx / len(timesteps) @@ -1665,7 +1709,7 @@ class WanVideoSampler: partial_img_emb[:, 0, :, :] = source_image_cond[:, 0, :, :].to(intermediate_device) partial_zt_src = zt_src[:, c, :, :] - vt_src_context, new_teacache = predict_with_cfg( + vt_src_context, _, new_teacache = predict_with_cfg( partial_zt_src, cfg[idx], positive, source_embeds["negative_prompt_embeds"], timestep, idx, partial_img_emb, control_latents, @@ -1679,7 +1723,7 @@ class WanVideoSampler: counter[:, c, :, :] += window_mask vt_src /= counter else: - vt_src, self.cache_state_source = predict_with_cfg( + vt_src, _, self.cache_state_source = predict_with_cfg( zt_src, cfg[idx], source_embeds["prompt_embeds"], source_embeds["negative_prompt_embeds"], @@ -1722,7 +1766,7 @@ class WanVideoSampler: partial_control_latents = control_latents[:, c, :, :] partial_zt_tgt = zt_tgt[:, c, :, :] - vt_tgt_context, new_teacache = predict_with_cfg( + vt_tgt_context, _, new_teacache = predict_with_cfg( partial_zt_tgt, cfg[idx], positive, text_embeds["negative_prompt_embeds"], timestep, idx, partial_img_emb, partial_control_latents, @@ -1736,7 +1780,7 @@ class WanVideoSampler: counter[:, c, :, :] += window_mask vt_tgt /= counter else: - vt_tgt, self.cache_state = predict_with_cfg( + vt_tgt, _,self.cache_state = predict_with_cfg( zt_tgt, cfg[idx], text_embeds["prompt_embeds"], text_embeds["negative_prompt_embeds"], @@ -1896,7 +1940,7 @@ class WanVideoSampler: partial_timestep = timestep #print("Partial timestep:", partial_timestep) - noise_pred_context, new_teacache = predict_with_cfg( + noise_pred_context, _, new_teacache = predict_with_cfg( partial_latent_model_input, cfg[idx], positive, text_embeds["negative_prompt_embeds"], @@ -2034,24 +2078,26 @@ class WanVideoSampler: if samples is not None: noise_mask = samples.get("noise_mask", None) - input_samples = samples["samples"].squeeze(0).to(noise) - # Check if we have enough frames in input_samples - if latent_end_idx > input_samples.shape[1]: - # We need more frames than available - pad the input_samples at the end - pad_length = latent_end_idx - input_samples.shape[1] - last_frame = input_samples[:, -1:].repeat(1, pad_length, 1, 1) - input_samples = torch.cat([input_samples, last_frame], dim=1) - input_samples = input_samples[:, latent_start_idx:latent_end_idx] - if noise_mask is not None: - original_image = input_samples.to(device) + input_samples = samples["samples"] + if input_samples is not None: + input_samples = input_samples.squeeze(0).to(noise) + # Check if we have enough frames in input_samples + if latent_end_idx > input_samples.shape[1]: + # We need more frames than available - pad the input_samples at the end + pad_length = latent_end_idx - input_samples.shape[1] + last_frame = input_samples[:, -1:].repeat(1, pad_length, 1, 1) + input_samples = torch.cat([input_samples, last_frame], dim=1) + input_samples = input_samples[:, latent_start_idx:latent_end_idx] + if noise_mask is not None: + original_image = input_samples.to(device) - assert input_samples.shape[1] == noise.shape[1], f"Slice mismatch: {input_samples.shape[1]} vs {noise.shape[1]}" + assert input_samples.shape[1] == noise.shape[1], f"Slice mismatch: {input_samples.shape[1]} vs {noise.shape[1]}" - if add_noise_to_samples: - latent_timestep = timesteps[0] - noise = noise * latent_timestep / 1000 + (1 - latent_timestep / 1000) * input_samples - else: - noise = input_samples + if add_noise_to_samples: + latent_timestep = timesteps[0] + noise = noise * latent_timestep / 1000 + (1 - latent_timestep / 1000) * input_samples + else: + noise = input_samples # diff diff prep if noise_mask is not None: @@ -2154,22 +2200,6 @@ class WanVideoSampler: else: positive = text_embeds["prompt_embeds"] - window_vace_data = None - # if vace_data is not None: - # window_vace_data = [] - # for vace_entry in vace_data: - # partial_context = vace_entry["context"][0][:, latent_start_idx:latent_end_idx] - # if has_ref: - # partial_context[:, 0] = vace_entry["context"][0][:, 0] - - # window_vace_data.append({ - # "context": [partial_context], - # "scale": vace_entry["scale"], - # "start": vace_entry["start"], - # "end": vace_entry["end"], - # "seq_len": vace_entry["seq_len"] - # }) - # uni3c slices if uni3c_embeds is not None: vae.to(device) @@ -2226,9 +2256,9 @@ class WanVideoSampler: if humo_image_cond is None or not is_first_clip: latent_model_input[:, :cur_motion_frames_latent_num] = latent_motion_frames - noise_pred, self.cache_state = predict_with_cfg( + noise_pred, _, self.cache_state = predict_with_cfg( latent_model_input, cfg[min(i, len(timesteps)-1)], positive, text_embeds["negative_prompt_embeds"], - timestep, i, y, clip_embeds, control_latents, window_vace_data, partial_unianim_data, audio_proj, control_camera_latents, add_cond, + timestep, i, y, clip_embeds, control_latents, None, partial_unianim_data, audio_proj, control_camera_latents, add_cond, cache_state=self.cache_state, multitalk_audio_embeds=audio_embs, fantasy_portrait_input=partial_fantasy_portrait_input, humo_image_cond=partial_humo_cond_input, humo_image_cond_neg=partial_humo_cond_neg_input, humo_audio=partial_humo_audio, humo_audio_neg=partial_humo_audio_neg, uni3c_data = uni3c_data) @@ -2457,7 +2487,7 @@ class WanVideoSampler: for i, t in enumerate(tqdm(timesteps, desc=f"Sampling audio indices {left_idx}-{right_idx}", position=0)): latent_model_input = latent.to(device) timestep = torch.tensor([t]).to(device) - noise_pred, self.cache_state = predict_with_cfg( + noise_pred, _, self.cache_state = predict_with_cfg( latent_model_input, cfg[idx], text_embeds["prompt_embeds"], @@ -2621,24 +2651,26 @@ class WanVideoSampler: face_images_in = face_images[:, :, start:end].to(device, torch.float32) if face_images is not None else None if samples is not None: - input_samples = samples["samples"].squeeze(0).to(noise) - # Check if we have enough frames in input_samples - if end_latent > input_samples.shape[1]: - # We need more frames than available - pad the input_samples at the end - pad_length = end_latent - input_samples.shape[1] - last_frame = input_samples[:, -1:].repeat(1, pad_length, 1, 1) - input_samples = torch.cat([input_samples, last_frame], dim=1) - input_samples = input_samples[:, start_latent:end_latent] - if noise_mask is not None: - original_image = input_samples.to(device) + input_samples = samples["samples"] + if input_samples is not None: + input_samples = input_samples.squeeze(0).to(noise) + # Check if we have enough frames in input_samples + if latent_end_idx > input_samples.shape[1]: + # We need more frames than available - pad the input_samples at the end + pad_length = latent_end_idx - input_samples.shape[1] + last_frame = input_samples[:, -1:].repeat(1, pad_length, 1, 1) + input_samples = torch.cat([input_samples, last_frame], dim=1) + input_samples = input_samples[:, latent_start_idx:latent_end_idx] + if noise_mask is not None: + original_image = input_samples.to(device) - assert input_samples.shape[1] == noise.shape[1], f"Slice mismatch: {input_samples.shape[1]} vs {noise.shape[1]}" + assert input_samples.shape[1] == noise.shape[1], f"Slice mismatch: {input_samples.shape[1]} vs {noise.shape[1]}" - if add_noise_to_samples: - latent_timestep = timesteps[0] - noise = noise * latent_timestep / 1000 + (1 - latent_timestep / 1000) * input_samples - else: - noise = input_samples + if add_noise_to_samples: + latent_timestep = timesteps[0] + noise = noise * latent_timestep / 1000 + (1 - latent_timestep / 1000) * input_samples + else: + noise = input_samples # diff diff prep noise_mask = samples.get("noise_mask", None) @@ -2701,7 +2733,7 @@ class WanVideoSampler: timestep = timesteps[i] latent_model_input = latent.to(device) - noise_pred, self.cache_state = predict_with_cfg( + noise_pred, _, self.cache_state = predict_with_cfg( latent_model_input, cfg[min(i, len(timesteps)-1)], positive, text_embeds["negative_prompt_embeds"], timestep, i, cache_state=self.cache_state, image_cond=image_cond_in, clip_fea=clip_fea, wananim_face_pixels=face_images_in, wananim_pose_latents=pose_input_slice, uni3c_data=uni3c_data_input, @@ -2794,16 +2826,16 @@ class WanVideoSampler: #region normal inference else: - noise_pred, self.cache_state = predict_with_cfg( - latent_model_input, + noise_pred, noise_pred_ovi, self.cache_state = predict_with_cfg( + latent_model_input, cfg[idx], text_embeds["prompt_embeds"], text_embeds["negative_prompt_embeds"], timestep, idx, image_cond, clip_fea, control_latents, vace_data, unianim_data, audio_proj, control_camera_latents, add_cond, cache_state=self.cache_state, fantasy_portrait_input=fantasy_portrait_input, multitalk_audio_embeds=multitalk_audio_embeds, mtv_motion_tokens=mtv_motion_tokens, s2v_audio_input=s2v_audio_input, humo_image_cond=humo_image_cond, humo_image_cond_neg=humo_image_cond_neg, humo_audio=humo_audio, humo_audio_neg=humo_audio_neg, - wananim_face_pixels=wananim_face_pixels, wananim_pose_latents=wananim_pose_latents, uni3c_data = uni3c_data, + wananim_face_pixels=wananim_face_pixels, wananim_pose_latents=wananim_pose_latents, uni3c_data = uni3c_data, latent_model_input_ovi=latent_model_input_ovi ) if bidirectional_sampling: - noise_pred_flipped, self.cache_state = predict_with_cfg( + noise_pred_flipped, _,self.cache_state = predict_with_cfg( latent_model_input_flipped, cfg[idx], text_embeds["prompt_embeds"], text_embeds["negative_prompt_embeds"], timestep, idx, image_cond, clip_fea, control_latents, vace_data, unianim_data, audio_proj, control_camera_latents, add_cond, @@ -2858,6 +2890,9 @@ class WanVideoSampler: **scheduler_step_args)[0].squeeze(0) latent_backwards = torch.flip(latent_backwards, dims=[1]) latent = latent * 0.5 + latent_backwards * 0.5 + + if latent_ovi is not None: + latent_ovi = sample_scheduler_ovi.step(noise_pred_ovi.unsqueeze(0), t, latent_ovi.to(device).unsqueeze(0), **scheduler_step_args)[0].squeeze(0) #InfiniteTalk first frame handling if (extra_latents is not None @@ -2941,7 +2976,8 @@ class WanVideoSampler: "drop_last": drop_last, "generator_state": seed_g.get_state(), "original_image": original_image.cpu() if original_image is not None else None, - "cache_states": cache_states + "cache_states": cache_states, + "latent_ovi_audio": latent_ovi.unsqueeze(0).transpose(1, 2).cpu() if latent_ovi is not None else None, },{ "samples": callback_latent.unsqueeze(0).cpu() if callback is not None else None, }) diff --git a/utils.py b/utils.py index 1e97fd4..99055b1 100644 --- a/utils.py +++ b/utils.py @@ -160,7 +160,8 @@ def patch_weight_to_device(self, key, device_to=None, inplace_update=False, back else: set_func(out_weight, inplace_update=inplace_update, seed=string_to_seed(key)) -def apply_lora(model, device_to, transformer_load_device, params_to_keep=None, dtype=None, base_dtype=None, state_dict=None, low_mem_load=False, control_lora=False, scale_weights={}): +def apply_lora(model, device_to, transformer_load_device, params_to_keep=None, dtype=None, + base_dtype=None, state_dict=None, low_mem_load=False, control_lora=False, scale_weights={}): model.patch_weight_to_device = types.MethodType(patch_weight_to_device, model) to_load = [] for n, m in model.model.named_modules(): diff --git a/wanvideo/modules/model.py b/wanvideo/modules/model.py index a26bb4b..8b8a95f 100644 --- a/wanvideo/modules/model.py +++ b/wanvideo/modules/model.py @@ -238,7 +238,7 @@ def sinusoidal_embedding_1d(dim, position): x = torch.cat([torch.cos(sinusoid), torch.sin(sinusoid)], dim=1) return x -def rope_params(max_seq_len, dim, theta=10000, L_test=25, k=0): +def rope_params(max_seq_len, dim, theta=10000, L_test=25, k=0, freqs_scaling=1.0): assert dim % 2 == 0 exponents = torch.arange(0, dim, 2, dtype=torch.float64).div(dim) inv_theta_pow = 1.0 / torch.pow(theta, exponents) @@ -246,6 +246,8 @@ def rope_params(max_seq_len, dim, theta=10000, L_test=25, k=0): if k > 0: print(f"RifleX: Using {k}th freq") inv_theta_pow[k-1] = 0.9 * 2 * torch.pi / L_test + + inv_theta_pow *= freqs_scaling freqs = torch.outer(torch.arange(max_seq_len), inv_theta_pow) freqs = torch.polar(torch.ones_like(freqs), freqs) @@ -254,6 +256,13 @@ def rope_params(max_seq_len, dim, theta=10000, L_test=25, k=0): @torch.autocast(device_type=mm.get_autocast_device(mm.get_torch_device()), enabled=False) @torch.compiler.disable() def rope_apply(x, grid_sizes, freqs, reverse_time=False): + x_ndim = grid_sizes.shape[-1] + if x_ndim == 3: + return rope_apply_3d(x, grid_sizes, freqs, reverse_time=reverse_time) + else: + return rope_apply_1d(x, grid_sizes, freqs) + +def rope_apply_3d(x, grid_sizes, freqs, reverse_time=False): n, c = x.size(2), x.size(3) // 2 # split freqs @@ -295,6 +304,30 @@ def rope_apply(x, grid_sizes, freqs, reverse_time=False): return torch.stack(output).to(x.dtype) +def rope_apply_1d(x, grid_sizes, freqs): + n, c = x.size(2), x.size(3) // 2 ## b l h d + c_rope = freqs.shape[1] # number of complex dims to rotate + assert c_rope <= c, "RoPE dimensions cannot exceed half of hidden size" + + # loop over samples + output = [] + for i, (l, ) in enumerate(grid_sizes.tolist()): + seq_len = l + # precompute multipliers + x_i = torch.view_as_complex(x[i, :seq_len].to(torch.float64).reshape( + seq_len, n, -1, 2)) # [l n d//2] + x_i_rope = x_i[:, :, :c_rope] * freqs[:seq_len, None, :] # [L, N, c_rope] + x_i_passthrough = x_i[:, :, c_rope:] # untouched dims + x_i = torch.cat([x_i_rope, x_i_passthrough], dim=2) + + # apply rotary embedding + x_i = torch.view_as_real(x_i).flatten(2) + x_i = torch.cat([x_i, x[i, seq_len:]]) + + # append to collection + output.append(x_i) + return torch.stack(output).to(x.dtype) + class WanRMSNorm(nn.Module): def __init__(self, dim, eps=1e-5): @@ -630,6 +663,7 @@ class WanT2VCrossAttention(WanSelfAttention): super().__init__(in_features, out_features, num_heads, qk_norm, eps, kv_dim=kv_dim, rms_norm_function=rms_norm_function) self.attention_mode = attention_mode self.ip_adapter = None + self.k_fusion = None def forward(self, x, context, grid_sizes=None, clip_embed=None, audio_proj=None, audio_scale=1.0, num_latent_frames=21, nag_params={}, nag_context=None, is_uncond=False, rope_func="comfy", @@ -688,8 +722,19 @@ class WanT2VCrossAttention(WanSelfAttention): adapter_x = adapter_x.flatten(2) x[:, :orig_seq_len] = x[:, :orig_seq_len] + adapter_x * ip_scale - return self.o(x) + if self.k_fusion is not None: + # compute target attention + target_seq = self.pre_attn_norm_fusion(kwargs["target_seq"]) + k_target = self.norm_k_fusion(self.k_fusion(target_seq)).view(b, -1, n, d) + v_target = self.v_fusion(target_seq).view(b, -1, n, d) + q = rope_apply(q, grid_sizes, kwargs["src_freqs"]) + k_target = rope_apply(k_target, kwargs["target_grid_sizes"], kwargs["target_freqs"]) + target_x = attention(q, k_target, v_target, k_lens=kwargs["target_seq_lens"]).flatten(2) + + x = x.add(target_x) + + return self.o(x) class WanI2VCrossAttention(WanSelfAttention): @@ -918,18 +963,18 @@ class WanAttentionBlock(nn.Module): self.cross_attn.ip_adapter = WanLynxIPCrossAttention(cross_attention_dim=2048, dim=self.dim, n_registers=0, bias=False) #@torch.compiler.disable() - def get_mod(self, e): + def get_mod(self, e, modulation): if e.dim() == 3: - return (self.modulation + e).chunk(6, dim=1) # 1, 6, dim + return (modulation + e).chunk(6, dim=1) # 1, 6, dim elif e.dim() == 4: - e_mod = self.modulation.unsqueeze(2) + e + e_mod = modulation.unsqueeze(2) + e return [ei.squeeze(1) for ei in e_mod.unbind(dim=1)] - def modulate(self, x, shift_msa, scale_msa, seg_idx=None): + + def modulate(self, norm_x, shift_msa, scale_msa, seg_idx=None): """ Modulate x with shift and scale. If seg_idx is provided, apply segmented modulation. """ - norm_x = self.norm1(x) if seg_idx is not None: parts = [] for i in range(2): @@ -983,6 +1028,7 @@ class WanAttentionBlock(nn.Module): mtv_motion_tokens=None, mtv_motion_rotary_emb=None, mtv_strength=1.0, mtv_freqs=None, #mtv crafter humo_audio_input=None, humo_audio_scale=1.0, #humo audio lynx_x_ip=None, lynx_ref_feature=None, lynx_ip_scale=1.0, lynx_ref_scale=1.0, #lynx + x_ovi=None, e_ovi=None, freqs_ovi=None, context_ovi=None, seq_lens_ovi=None, grid_sizes_ovi=None #ovi ): r""" Args: @@ -1000,18 +1046,22 @@ class WanAttentionBlock(nn.Module): self.seg_idx = [0, self.seg_idx, x.size(1)] e = e[0] - shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.get_mod(e.to(x.device)) + shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.get_mod(e.to(x.device), self.modulation) del e - input_x = self.modulate(x, shift_msa, scale_msa, seg_idx=self.seg_idx) + input_x = self.modulate(self.norm1(x), shift_msa, scale_msa, seg_idx=self.seg_idx) del shift_msa, scale_msa if x_ip is not None: - shift_msa_ip, scale_msa_ip, gate_msa_ip, shift_mlp_ip, scale_mlp_ip, gate_mlp_ip = self.get_mod(e_ip.to(x.device)) - input_x_ip = self.modulate(x_ip, shift_msa_ip, scale_msa_ip) + shift_msa_ip, scale_msa_ip, gate_msa_ip, shift_mlp_ip, scale_mlp_ip, gate_mlp_ip = self.get_mod(e_ip.to(x.device), self.modulation) + input_x_ip = self.modulate(self.norm1(x_ip), shift_msa_ip, scale_msa_ip) self.cond_size = input_x_ip.shape[1] input_x = torch.concat([input_x, input_x_ip], dim=1) self.kv_cache = None + if x_ovi is not None: + shift_msa_ovi, scale_msa_ovi, gate_msa_ovi, shift_mlp_ovi, scale_mlp_ovi, gate_mlp_ovi = self.get_mod(e_ovi.to(x.device), self.audio_block.modulation) + input_x_ovi = self.modulate(self.audio_block.norm1(x_ovi), shift_msa_ovi, scale_msa_ovi) + if camera_embed is not None: # encode ReCamMaster camera camera_embed = self.cam_encoder(camera_embed.to(x)) @@ -1054,8 +1104,16 @@ class WanAttentionBlock(nn.Module): elif self.rope_func == "comfy_chunked": q, k = apply_rope_comfy_chunked(q, k, freqs) else: - q=rope_apply(q, grid_sizes, freqs, reverse_time=reverse_time) - k=rope_apply(k, grid_sizes, freqs, reverse_time=reverse_time) + q = rope_apply(q, grid_sizes, freqs, reverse_time=reverse_time) + k = rope_apply(k, grid_sizes, freqs, reverse_time=reverse_time) + + if x_ovi is not None: + q_ovi, k_ovi, v_ovi = self.audio_block.self_attn.qkv_fn(input_x_ovi) + q_ovi = rope_apply(q_ovi, grid_sizes_ovi, freqs_ovi) + k_ovi = rope_apply(k_ovi, grid_sizes_ovi, freqs_ovi) + y_ovi = self.audio_block.self_attn.forward(q_ovi, k_ovi, v_ovi, seq_lens_ovi) + x_ovi = x_ovi.addcmul(y_ovi, gate_msa_ovi) + # FETA if enhance_enabled: @@ -1076,7 +1134,7 @@ class WanAttentionBlock(nn.Module): current_step=current_step, video_attention_split_steps=video_attention_split_steps ) - elif ref_target_masks is not None: + elif ref_target_masks is not None: #multi/infinite talk y, x_ref_attn_map = self.self_attn.forward_multitalk(q, k, v, seq_lens, grid_sizes, ref_target_masks) elif self.attention_mode == "radial_sage_attention": if self.dense_block or self.dense_timesteps is not None and current_step < self.dense_timesteps: @@ -1091,7 +1149,7 @@ class WanAttentionBlock(nn.Module): y = self.self_attn.forward(q, k, v, seq_lens, attention_mode_override="sageattn_3") else: y = self.self_attn.forward(q, k, v, seq_lens, attention_mode_override="sageattn") - elif x_ip is not None and self.kv_cache is None: + elif x_ip is not None and self.kv_cache is None: #stand-in # First pass: cache IP keys/values and compute attention self.kv_cache = {"k_ip": k_ip.detach(), "v_ip": v_ip.detach()} y = self.self_attn.forward_ip(q, k, v, q_ip, k_ip, v_ip, seq_lens) @@ -1112,17 +1170,19 @@ class WanAttentionBlock(nn.Module): if enhance_enabled: y.mul_(feta_scores) - #ReCamMaster + # ReCamMaster if camera_embed is not None: y = self.projector(y) + # Stand-in if x_ip is not None: y, y_ip = ( y[:, : -self.cond_size], y[:, -self.cond_size :], ) - if self.zero_timestep: + # S2V + if self.zero_timestep: z = [] for i in range(2): z.append(y[:, self.seg_idx[i]:self.seg_idx[i + 1]] * gate_msa[:, i:i + 1]) @@ -1134,7 +1194,30 @@ class WanAttentionBlock(nn.Module): # cross-attention & ffn function if context is not None: - if split_attn: + if x_ovi is not None: + #audio + og_ovi_x = x_ovi + x_ovi = x_ovi + self.audio_block.cross_attn(self.audio_block.norm3(x_ovi), context_ovi, grid_sizes_ovi, + src_freqs=freqs_ovi, + target_seq=x, + target_seq_lens=seq_lens, + target_grid_sizes=grid_sizes, + target_freqs=freqs) + y = self.audio_block.ffn(torch.addcmul(shift_mlp_ovi, self.audio_block.norm2(x_ovi), 1 + scale_mlp_ovi)) + x_ovi = x_ovi.addcmul(y, gate_mlp_ovi) + + assert not torch.equal(og_ovi_x, x_ovi), "Audio should be changed after cross-attention!" + + # video + x = x + self.cross_attn(self.norm3(x), context, grid_sizes, + src_freqs=freqs, + target_seq=og_ovi_x, + target_seq_lens=seq_lens_ovi, + target_grid_sizes=grid_sizes_ovi, + target_freqs=freqs_ovi) + y = self.ffn(torch.addcmul(shift_mlp, self.norm2(x), 1 + scale_mlp)) + x = x.addcmul(y, gate_mlp) + elif split_attn: if nag_context is not None: raise NotImplementedError("nag_context is not supported in split_cross_attn_ffn") x = self.split_cross_attn_ffn(x, context, shift_mlp, scale_mlp, gate_mlp, clip_embed, grid_sizes) @@ -1154,12 +1237,12 @@ class WanAttentionBlock(nn.Module): x = x.addcmul(y, gate_mlp) del gate_mlp - if x_ip is not None: + if x_ip is not None: #stand-in x_ip = x_ip.addcmul(y_ip, gate_msa_ip) y_ip = self.ffn(torch.addcmul(shift_mlp_ip, self.norm2(x_ip), 1 + scale_mlp_ip)) x_ip = x_ip.addcmul(y_ip, gate_mlp_ip) - return x, x_ip, lynx_ref_feature + return x, x_ip, lynx_ref_feature, x_ovi def cross_attn_ffn(self, x, context, grid_sizes, shift_mlp, scale_mlp, gate_mlp, clip_embed, @@ -1172,7 +1255,7 @@ class WanAttentionBlock(nn.Module): audio_proj=audio_proj, audio_scale=audio_scale, num_latent_frames=num_latent_frames, nag_params=nag_params, nag_context=nag_context, is_uncond=is_uncond, rope_func=self.rope_func, inner_t=inner_t, inner_c=inner_c, cross_freqs=cross_freqs, - adapter_proj=adapter_proj, ip_scale=ip_scale, orig_seq_len=self.original_seq_len, lynx_x_ip=lynx_x_ip, lynx_ip_scale=lynx_ip_scale) + adapter_proj=adapter_proj, ip_scale=ip_scale, orig_seq_len=self.original_seq_len, lynx_x_ip=lynx_x_ip, lynx_ip_scale=lynx_ip_scale, ) # MultiTalk if multitalk_audio_embedding is not None and not isinstance(self, VaceWanAttentionBlock): x_audio = self.audio_cross_attn(self.norm_x(x), encoder_hidden_states=multitalk_audio_embedding, @@ -1318,14 +1401,14 @@ class BaseWanAttentionBlock(WanAttentionBlock): self.block_id = block_id def forward(self, x, vace_hints=None, vace_context_scale=[1.0], **kwargs): - x, x_ip, lynx_ref_feature = super().forward(x, **kwargs) + x, x_ip, lynx_ref_feature, x_ovi = super().forward(x, **kwargs) if vace_hints is None: - return x, x_ip, lynx_ref_feature + return x, x_ip, lynx_ref_feature, x_ovi if self.block_id is not None: for i in range(len(vace_hints)): x.add_(vace_hints[i][self.block_id].to(x.device), alpha=vace_context_scale[i]) - return x, x_ip, lynx_ref_feature + return x, x_ip, lynx_ref_feature, x_ovi class Head(nn.Module): @@ -1528,6 +1611,8 @@ class WanModel(torch.nn.Module): # lynx lynx_ip_layers=None, lynx_ref_layers=None, + # ovi + is_ovi_audio_model=False, ): r""" Initialize the diffusion model backbone. @@ -1646,9 +1731,20 @@ class WanModel(torch.nn.Module): self.base_dtype = dtype + self.is_ovi_audio_model = patch_size == [1] + + self.audio_model = None + # embeddings - self.patch_embedding = nn.Conv3d( - in_dim, dim, kernel_size=patch_size, stride=patch_size) + if not self.is_ovi_audio_model: + self.patch_embedding = nn.Conv3d(in_dim, dim, kernel_size=patch_size, stride=patch_size) + else: + from ...Ovi.audio_model_layers import ChannelLastConv1d, ConvMLP + self.patch_embedding = nn.Sequential( + ChannelLastConv1d(in_dim, dim, kernel_size=7, padding=3), + nn.SiLU(), + ConvMLP(dim, dim * 4, kernel_size=7, padding=3), + ) self.original_patch_embedding = self.patch_embedding self.expanded_patch_embedding = self.patch_embedding @@ -2056,6 +2152,7 @@ class WanModel(torch.nn.Module): wananim_pose_latents=None, wananim_face_pixel_values=None, wananim_pose_strength=1.0, wananim_face_strength=1.0, lynx_embeds=None, + x_ovi=None, seq_len_ovi=None, ovi_negative_text_embeds=None, ): r""" Forward pass through the diffusion model @@ -2136,7 +2233,7 @@ class WanModel(torch.nn.Module): merged_audio_emb = audio_emb[:, s2v_motion_frames[1]:, :] # params - device = self.patch_embedding.weight.device + device = self.main_device if freqs is not None and freqs.device != device: freqs = freqs.to(device) @@ -2161,17 +2258,21 @@ class WanModel(torch.nn.Module): # patch embed if control_lora_enabled: - self.expanded_patch_embedding.to(device) - x = [ - self.expanded_patch_embedding(u.unsqueeze(0).to(torch.float32)).to(x[0].dtype) - for u in x - ] + self.expanded_patch_embedding.to(self.main_device) + x = [self.expanded_patch_embedding(u.unsqueeze(0).to(torch.float32)).to(x[0].dtype) for u in x] else: self.original_patch_embedding.to(self.main_device) - x = [ - self.original_patch_embedding(u.unsqueeze(0).to(torch.float32)).to(x[0].dtype) - for u in x - ] + x = [self.original_patch_embedding(u.unsqueeze(0).to(torch.float32)).to(x[0].dtype) for u in x] + + # ovi audio model + if self.audio_model is not None: + x_ovi = [self.audio_model.original_patch_embedding(u.unsqueeze(0).to(torch.float32)).to(x_ovi[0].dtype) for u in x_ovi] + grid_sizes_ovi = torch.stack([torch.tensor(u.shape[1:2], dtype=torch.long) for u in x_ovi]) + seq_lens_ovi = torch.tensor([u.size(1) for u in x_ovi], dtype=torch.int32) + x_ovi = torch.cat([torch.cat([u, u.new_zeros(1, seq_len_ovi - u.size(1), u.size(2))], dim=1) for u in x_ovi]) + d = self.dim // self.num_heads + freqs_ovi = rope_params(1024, d - 4 * (d // 6), freqs_scaling=0.19676).to(self.main_device) + x_ovi = x_ovi.to(self.main_device, self.base_dtype) # WanAnimate motion_vec = None @@ -2190,10 +2291,11 @@ class WanModel(torch.nn.Module): fun_camera = self.control_adapter(fun_camera) x = [u + v for u, v in zip(x, fun_camera)] + # grid sizes and seq len grid_sizes = torch.stack([torch.tensor(u.shape[2:], device=device, dtype=torch.long) for u in x]) original_grid_sizes = grid_sizes.clone() x = [u.flatten(2).transpose(1, 2) for u in x] - + self.original_seq_len = x[0].shape[1] seq_lens = torch.tensor([u.size(1) for u in x], dtype=torch.int32) assert seq_lens.max() <= seq_len @@ -2201,8 +2303,6 @@ class WanModel(torch.nn.Module): if self.trainable_cond_mask is not None: cond_mask_weight = self.trainable_cond_mask.weight.to(x[0]).unsqueeze(1).unsqueeze(1) - self.original_seq_len = x[0].shape[1] - if add_cond is not None: add_cond = self.add_conv_in(add_cond.to(self.add_conv_in.weight.dtype)).to(x[0].dtype) add_cond = add_cond.flatten(2).transpose(1, 2) @@ -2243,10 +2343,7 @@ class WanModel(torch.nn.Module): x = [torch.cat([u, end_ref_latent.unsqueeze(0)], dim=1) for end_ref_latent, u in zip(end_ref_latent, x)] - x = torch.cat([ - torch.cat([u, u.new_zeros(1, seq_len - u.size(1), u.size(2))], - dim=1) for u in x - ]) + x = torch.cat([torch.cat([u, u.new_zeros(1, seq_len - u.size(1), u.size(2))], dim=1) for u in x]) if self.trainable_cond_mask is not None: x = x + cond_mask_weight[0] @@ -2334,6 +2431,21 @@ class WanModel(torch.nn.Module): e = self.time_embedding(sinusoidal_embedding_1d(self.freq_dim, t.flatten()).to(time_embed_dtype)) # b, dim e0 = self.time_projection(e).unflatten(1, (6, self.dim)) # b, 6, dim + if self.audio_model is not None: + #if t.dim() == 1: + # t_ovi = t.unsqueeze(1).expand(t.size(0), seq_len_ovi) + if t.dim() == 2: + last_timestep = t[:, -1:] + padding = last_timestep.expand(t.size(0), seq_len_ovi - t.size(1)) + t_ovi = torch.cat([t, padding], dim=1) + + e_ovi = self.audio_model.time_embedding(sinusoidal_embedding_1d(self.audio_model.freq_dim, t_ovi.flatten()).to(time_embed_dtype)).unsqueeze(0) # b, dim + e0_ovi = self.audio_model.time_projection(e_ovi).unflatten(2, (6, self.dim)).movedim(1, 2) # B, seq_len, 6, dim + else: + e_ovi = self.audio_model.time_embedding(sinusoidal_embedding_1d(self.audio_model.freq_dim, t.flatten()).to(time_embed_dtype)) # b, dim + e0_ovi = self.audio_model.time_projection(e_ovi).unflatten(1, (6, self.dim)) # b, 6, dim + + #S2V zero timestep if self.zero_timestep: e = e[:-1] @@ -2386,13 +2498,17 @@ class WanModel(torch.nn.Module): raise NotImplementedError("nag_context is not supported with EchoShot") inner_c = [[u.shape[0] for u in context]] + if self.audio_model is not None: + if is_uncond and ovi_negative_text_embeds is not None: + context_ovi = ovi_negative_text_embeds + else: + context_ovi = context + context_ovi = self.audio_model.text_embedding( + torch.stack([torch.cat([u, u.new_zeros(self.text_len - u.size(0), u.size(1))]) for u in context_ovi]).to(text_embed_dtype)) + context = self.text_embedding( - torch.stack([ - torch.cat( - [u, u.new_zeros(self.text_len - u.size(0), u.size(1))]) - for u in context - ]).to(text_embed_dtype)) - + torch.stack([torch.cat([u, u.new_zeros(self.text_len - u.size(0), u.size(1))]) for u in context]).to(text_embed_dtype)) + # NAG if nag_context is not None: nag_context = self.text_embedding( @@ -2547,6 +2663,7 @@ class WanModel(torch.nn.Module): previous_raw_input = state.get('previous_raw_input') previous_raw_output = state.get('previous_raw_output') cache = state.get('cache') + cache_ovi = state.get('cache_ovi') if self.audio_model is not None else None accumulated_error = state.get('accumulated_error') k = state.get('k', 1) @@ -2565,6 +2682,8 @@ class WanModel(torch.nn.Module): if accumulated_error < self.easycache_thresh: should_calc = False x = raw_input + cache.to(x.device) + if cache_ovi is not None: + x_ovi = x_ovi + cache_ovi.to(x_ovi.device) state['skipped_steps'].append(current_step) else: should_calc = True @@ -2579,6 +2698,8 @@ class WanModel(torch.nn.Module): if self.enable_easycache: original_x = x.clone().to(self.cache_device) + if x_ovi is not None: + original_x_ovi = x_ovi.clone().to(self.cache_device) if should_calc: if self.enable_teacache or self.enable_magcache: original_x = x.clone().to(self.cache_device) @@ -2623,6 +2744,13 @@ class WanModel(torch.nn.Module): lynx_ip_scale=lynx_ip_scale, lynx_ref_scale=lynx_ref_scale, ) + if self.audio_model is not None: + kwargs['e_ovi'] = e0_ovi.to(self.base_dtype) + kwargs['context_ovi'] = context_ovi + kwargs['grid_sizes_ovi'] = grid_sizes_ovi + kwargs['seq_lens_ovi'] = seq_lens_ovi + kwargs['freqs_ovi'] = freqs_ovi + if vace_data is not None: vace_hint_list = [] @@ -2706,7 +2834,7 @@ class WanModel(torch.nn.Module): if b in self.slg_blocks and is_uncond: if self.slg_start_percent <= current_step_percentage <= self.slg_end_percent: continue - x, x_ip, lynx_ref_feature = block(x, x_ip=x_ip, lynx_ref_feature=lynx_ref_feature, **kwargs) #run block + x, x_ip, lynx_ref_feature, x_ovi = block(x, x_ip=x_ip, lynx_ref_feature=lynx_ref_feature, x_ovi=x_ovi, **kwargs) #run block if self.audio_injector is not None and s2v_audio_input is not None: x = self.audio_injector_forward(b, x, merged_audio_emb, scale=s2v_audio_scale) #s2v if block.has_face_fuser_block and motion_vec is not None: @@ -2758,8 +2886,11 @@ class WanModel(torch.nn.Module): previous_raw_output=x_out, cache=x.to(original_x.device) - original_x, k = output_change / input_change, - accumulated_error = 0.0 + accumulated_error = 0.0, + cache_ovi = x_ovi.clone().to(original_x.device) - original_x_ovi if x_ovi is not None else None ) + + if self.enable_easycache and (self.easycache_start_step <= current_step <= self.easycache_end_step) and pred_id is not None: self.easycache_state.update( @@ -2785,9 +2916,17 @@ class WanModel(torch.nn.Module): x = x[:, :self.original_seq_len] x = self.head(x, e.to(x.device)) + + if x_ovi is not None: + x_ovi = self.audio_model.head(x_ovi, e_ovi.to(x_ovi.device)) + grid_sizes_ovi = [gs[0] for gs in grid_sizes_ovi] + assert len(x) == len(grid_sizes_ovi) + x_ovi = [u[:gs] for u, gs in zip(x_ovi, grid_sizes_ovi)] + x_ovi = [u.float() for u in x_ovi] + x = self.unpatchify(x, original_grid_sizes) # type: ignore[arg-type] x = [u.float() for u in x] - return (x, pred_id) if pred_id is not None else (x, None) + return (x, x_ovi, pred_id) if pred_id is not None else (x, x_ovi, None) def unpatchify(self, x, grid_sizes): r""" diff --git a/wanvideo/modules/s2v/audio_encoder.py b/wanvideo/modules/s2v/audio_encoder.py index 05fea4e..480d76e 100644 --- a/wanvideo/modules/s2v/audio_encoder.py +++ b/wanvideo/modules/s2v/audio_encoder.py @@ -1,7 +1,5 @@ # Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved. import math - -import librosa import numpy as np import torch import torch.nn.functional as F @@ -63,31 +61,6 @@ class AudioEncoder(): self.video_rate = 30 - def extract_audio_feat(self, - audio_path, - return_all_layers=False, - dtype=torch.float32): - audio_input, sample_rate = librosa.load(audio_path, sr=16000) - - input_values = self.processor( - audio_input, sampling_rate=sample_rate, - return_tensors="pt").input_values - - # INFERENCE - - # retrieve logits & take argmax - res = self.model( - input_values.to(self.model.device), output_hidden_states=True) - if return_all_layers: - feat = torch.cat(res.hidden_states) - else: - feat = res.hidden_states[-1] - feat = linear_interpolation( - feat, input_fps=50, output_fps=self.video_rate) - - z = feat.to(dtype) # Encoding for the motion - return z - def get_audio_embed_bucket(self, audio_embed, stride=2, diff --git a/wanvideo/modules/tokenizers.py b/wanvideo/modules/tokenizers.py index 121e591..e67a026 100644 --- a/wanvideo/modules/tokenizers.py +++ b/wanvideo/modules/tokenizers.py @@ -21,17 +21,17 @@ def whitespace_clean(text): return text -def canonicalize(text, keep_punctuation_exact_string=None): - text = text.replace('_', ' ') - if keep_punctuation_exact_string: - text = keep_punctuation_exact_string.join( - part.translate(str.maketrans('', '', string.punctuation)) - for part in text.split(keep_punctuation_exact_string)) - else: - text = text.translate(str.maketrans('', '', string.punctuation)) - text = text.lower() - text = re.sub(r'\s+', ' ', text) - return text.strip() +# def canonicalize(text, keep_punctuation_exact_string=None): +# text = text.replace('_', ' ') +# if keep_punctuation_exact_string: +# text = keep_punctuation_exact_string.join( +# part.translate(str.maketrans('', '', string.punctuation)) +# for part in text.split(keep_punctuation_exact_string)) +# else: +# text = text.translate(str.maketrans('', '', string.punctuation)) +# text = text.lower() +# text = re.sub(r'\s+', ' ', text) +# return text.strip() class HuggingfaceTokenizer: