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| import math | |
| import torch | |
| from einops import rearrange | |
| from model.base import BaseModule | |
| class Mish(BaseModule): | |
| def forward(self, x): | |
| return x * torch.tanh(torch.nn.functional.softplus(x)) | |
| class Upsample(BaseModule): | |
| def __init__(self, dim): | |
| super(Upsample, self).__init__() | |
| self.conv = torch.nn.ConvTranspose2d(dim, dim, 4, 2, 1) | |
| def forward(self, x): | |
| return self.conv(x) | |
| class Downsample(BaseModule): | |
| def __init__(self, dim): | |
| super(Downsample, self).__init__() | |
| self.conv = torch.nn.Conv2d(dim, dim, 3, 2, 1) # kernel=3, stride=2, padding=1. | |
| def forward(self, x): | |
| return self.conv(x) | |
| class Rezero(BaseModule): | |
| def __init__(self, fn): | |
| super(Rezero, self).__init__() | |
| self.fn = fn | |
| self.g = torch.nn.Parameter(torch.zeros(1)) | |
| def forward(self, x): | |
| return self.fn(x) * self.g | |
| class Block(BaseModule): | |
| def __init__(self, dim, dim_out, groups=8): | |
| super(Block, self).__init__() | |
| self.block = torch.nn.Sequential(torch.nn.Conv2d(dim, dim_out, 3, | |
| padding=1), torch.nn.GroupNorm( | |
| groups, dim_out), Mish()) | |
| def forward(self, x, mask): | |
| output = self.block(x * mask) | |
| return output * mask | |
| class ResnetBlock(BaseModule): | |
| def __init__(self, dim, dim_out, time_emb_dim, groups=8): | |
| super(ResnetBlock, self).__init__() | |
| self.mlp = torch.nn.Sequential(Mish(), torch.nn.Linear(time_emb_dim, | |
| dim_out)) | |
| self.block1 = Block(dim, dim_out, groups=groups) | |
| self.block2 = Block(dim_out, dim_out, groups=groups) | |
| if dim != dim_out: | |
| self.res_conv = torch.nn.Conv2d(dim, dim_out, 1) | |
| else: | |
| self.res_conv = torch.nn.Identity() | |
| def forward(self, x, mask, time_emb): | |
| h = self.block1(x, mask) | |
| h += self.mlp(time_emb).unsqueeze(-1).unsqueeze(-1) | |
| h = self.block2(h, mask) | |
| output = h + self.res_conv(x * mask) | |
| return output | |
| class LinearAttention(BaseModule): | |
| def __init__(self, dim, heads=4, dim_head=32): | |
| super(LinearAttention, self).__init__() | |
| self.heads = heads | |
| hidden_dim = dim_head * heads | |
| self.to_qkv = torch.nn.Conv2d(dim, hidden_dim * 3, 1, bias=False) # NOTE: 1x1 conv | |
| self.to_out = torch.nn.Conv2d(hidden_dim, dim, 1) | |
| def forward(self, x): | |
| b, c, h, w = x.shape | |
| qkv = self.to_qkv(x) | |
| q, k, v = rearrange(qkv, 'b (qkv heads c) h w -> qkv b heads c (h w)', heads=self.heads, qkv=3) | |
| k = k.softmax(dim=-1) | |
| context = torch.einsum('bhdn,bhen->bhde', k, v) | |
| out = torch.einsum('bhde,bhdn->bhen', context, q) | |
| out = rearrange(out, 'b heads c (h w) -> b (heads c) h w', | |
| heads=self.heads, h=h, w=w) | |
| return self.to_out(out) | |
| class Residual(BaseModule): | |
| def __init__(self, fn): | |
| super(Residual, self).__init__() | |
| self.fn = fn | |
| def forward(self, x, *args, **kwargs): | |
| output = self.fn(x, *args, **kwargs) + x | |
| return output | |
| class SinusoidalPosEmb(BaseModule): | |
| def __init__(self, dim): | |
| super(SinusoidalPosEmb, self).__init__() | |
| self.dim = dim | |
| def forward(self, x, scale=1000): | |
| device = x.device | |
| half_dim = self.dim // 2 | |
| emb = math.log(10000) / (half_dim - 1) | |
| emb = torch.exp(torch.arange(half_dim, device=device).float() * -emb) | |
| emb = scale * x.unsqueeze(1) * emb.unsqueeze(0) | |
| emb = torch.cat((emb.sin(), emb.cos()), dim=-1) | |
| return emb | |
| class GradLogPEstimator2d(BaseModule): | |
| def __init__(self, dim, dim_mults=(1, 2, 4), groups=8, spk_emb_dim=64, n_feats=80, pe_scale=1000): | |
| super(GradLogPEstimator2d, self).__init__() | |
| self.dim = dim | |
| self.dim_mults = dim_mults | |
| self.groups = groups | |
| self.spk_emb_dim = spk_emb_dim | |
| self.pe_scale = pe_scale | |
| self.spk_mlp = torch.nn.Sequential(torch.nn.Linear(spk_emb_dim, spk_emb_dim * 4), Mish(), | |
| torch.nn.Linear(spk_emb_dim * 4, n_feats)) | |
| self.time_pos_emb = SinusoidalPosEmb(dim) | |
| self.mlp = torch.nn.Sequential(torch.nn.Linear(dim, dim * 4), Mish(), | |
| torch.nn.Linear(dim * 4, dim)) | |
| dims = [3, *map(lambda m: dim * m, dim_mults)] | |
| in_out = list(zip(dims[:-1], dims[1:])) | |
| self.downs = torch.nn.ModuleList([]) | |
| self.ups = torch.nn.ModuleList([]) | |
| num_resolutions = len(in_out) | |
| for ind, (dim_in, dim_out) in enumerate(in_out): | |
| is_last = ind >= (num_resolutions - 1) | |
| self.downs.append(torch.nn.ModuleList([ | |
| ResnetBlock(dim_in, dim_out, time_emb_dim=dim), | |
| ResnetBlock(dim_out, dim_out, time_emb_dim=dim), | |
| Residual(Rezero(LinearAttention(dim_out))), | |
| Downsample(dim_out) if not is_last else torch.nn.Identity()])) | |
| mid_dim = dims[-1] | |
| self.mid_block1 = ResnetBlock(mid_dim, mid_dim, time_emb_dim=dim) | |
| self.mid_attn = Residual(Rezero(LinearAttention(mid_dim))) | |
| self.mid_block2 = ResnetBlock(mid_dim, mid_dim, time_emb_dim=dim) | |
| for ind, (dim_in, dim_out) in enumerate(reversed(in_out[1:])): | |
| self.ups.append(torch.nn.ModuleList([ | |
| ResnetBlock(dim_out * 2, dim_in, time_emb_dim=dim), | |
| ResnetBlock(dim_in, dim_in, time_emb_dim=dim), | |
| Residual(Rezero(LinearAttention(dim_in))), | |
| Upsample(dim_in)])) | |
| self.final_block = Block(dim, dim) | |
| self.final_conv = torch.nn.Conv2d(dim, 1, 1) | |
| def forward(self, x, mask, mu, t, spk=None): | |
| # x, mu: [B, 80, L], t: [B, ], mask: [B, 1, L] | |
| if not isinstance(spk, type(None)): | |
| s = self.spk_mlp(spk) | |
| t = self.time_pos_emb(t, scale=self.pe_scale) | |
| t = self.mlp(t) # [B, 64] | |
| s = s.unsqueeze(-1).repeat(1, 1, x.shape[-1]) | |
| x = torch.stack([mu, x, s], 1) # [B, 3, 80, L] | |
| mask = mask.unsqueeze(1) # [B, 1, 1, L] | |
| hiddens = [] | |
| masks = [mask] | |
| for resnet1, resnet2, attn, downsample in self.downs: | |
| mask_down = masks[-1] | |
| x = resnet1(x, mask_down, t) # [B, 64, 80, L] | |
| x = resnet2(x, mask_down, t) | |
| x = attn(x) | |
| hiddens.append(x) | |
| x = downsample(x * mask_down) | |
| masks.append(mask_down[:, :, :, ::2]) | |
| masks = masks[:-1] | |
| mask_mid = masks[-1] | |
| x = self.mid_block1(x, mask_mid, t) | |
| x = self.mid_attn(x) | |
| x = self.mid_block2(x, mask_mid, t) | |
| for resnet1, resnet2, attn, upsample in self.ups: | |
| mask_up = masks.pop() | |
| x = torch.cat((x, hiddens.pop()), dim=1) | |
| x = resnet1(x, mask_up, t) | |
| x = resnet2(x, mask_up, t) | |
| x = attn(x) | |
| x = upsample(x * mask_up) | |
| x = self.final_block(x, mask) | |
| output = self.final_conv(x * mask) | |
| return (output * mask).squeeze(1) | |
| def get_noise(t, beta_init, beta_term, cumulative=False): | |
| if cumulative: | |
| noise = beta_init*t + 0.5*(beta_term - beta_init)*(t**2) | |
| else: | |
| noise = beta_init + (beta_term - beta_init)*t | |
| return noise | |
| class Diffusion(BaseModule): | |
| def __init__(self, n_feats, dim, spk_emb_dim=64, | |
| beta_min=0.05, beta_max=20, pe_scale=1000): | |
| super(Diffusion, self).__init__() | |
| self.n_feats = n_feats | |
| self.dim = dim | |
| # self.n_spks = n_spks | |
| self.spk_emb_dim = spk_emb_dim | |
| self.beta_min = beta_min | |
| self.beta_max = beta_max | |
| self.pe_scale = pe_scale | |
| self.estimator = GradLogPEstimator2d(dim, | |
| spk_emb_dim=spk_emb_dim, | |
| pe_scale=pe_scale, | |
| n_feats=n_feats) | |
| def forward_diffusion(self, x0, mask, mu, t): | |
| time = t.unsqueeze(-1).unsqueeze(-1) | |
| cum_noise = get_noise(time, self.beta_min, self.beta_max, cumulative=True) # it is actually the integral of beta | |
| mean = x0*torch.exp(-0.5*cum_noise) + mu*(1.0 - torch.exp(-0.5*cum_noise)) | |
| variance = 1.0 - torch.exp(-cum_noise) | |
| z = torch.randn(x0.shape, dtype=x0.dtype, device=x0.device, | |
| requires_grad=False) | |
| xt = mean + z * torch.sqrt(variance) | |
| return xt * mask, z * mask | |
| def reverse_diffusion(self, z, mask, mu, n_timesteps, stoc=False, spk=None, | |
| use_classifier_free=False, | |
| classifier_free_guidance=3.0, | |
| dummy_spk=None): # emo need to be merged by spk | |
| # looks like a plain Euler-Maruyama method | |
| h = 1.0 / n_timesteps | |
| xt = z * mask | |
| for i in range(n_timesteps): | |
| t = (1.0 - (i + 0.5)*h) * torch.ones(z.shape[0], dtype=z.dtype, | |
| device=z.device) | |
| time = t.unsqueeze(-1).unsqueeze(-1) | |
| noise_t = get_noise(time, self.beta_min, self.beta_max, | |
| cumulative=False) | |
| if not use_classifier_free: | |
| if stoc: # adds stochastic term | |
| dxt_det = 0.5 * (mu - xt) - self.estimator(xt, mask, mu, t, spk) | |
| dxt_det = dxt_det * noise_t * h | |
| dxt_stoc = torch.randn(z.shape, dtype=z.dtype, device=z.device, | |
| requires_grad=False) | |
| dxt_stoc = dxt_stoc * torch.sqrt(noise_t * h) | |
| dxt = dxt_det + dxt_stoc | |
| else: | |
| dxt = 0.5 * (mu - xt - self.estimator(xt, mask, mu, t, spk)) | |
| dxt = dxt * noise_t * h | |
| xt = (xt - dxt) * mask | |
| else: | |
| if stoc: # adds stochastic term | |
| score_estimate = (1 + classifier_free_guidance) * self.estimator(xt, mask, mu, t, spk) \ | |
| - classifier_free_guidance * self.estimator(xt, mask, mu, t, dummy_spk) | |
| dxt_det = 0.5 * (mu - xt) - score_estimate | |
| dxt_det = dxt_det * noise_t * h | |
| dxt_stoc = torch.randn(z.shape, dtype=z.dtype, device=z.device, | |
| requires_grad=False) | |
| dxt_stoc = dxt_stoc * torch.sqrt(noise_t * h) | |
| dxt = dxt_det + dxt_stoc | |
| else: | |
| score_estimate = (1 + classifier_free_guidance) * self.estimator(xt, mask, mu, t, spk) \ | |
| - classifier_free_guidance * self.estimator(xt, mask, mu, t, dummy_spk) | |
| dxt = 0.5 * (mu - xt - score_estimate) | |
| dxt = dxt * noise_t * h | |
| xt = (xt - dxt) * mask | |
| return xt | |
| def forward(self, z, mask, mu, n_timesteps, stoc=False, spk=None, | |
| use_classifier_free=False, | |
| classifier_free_guidance=3.0, | |
| dummy_spk=None | |
| ): | |
| return self.reverse_diffusion(z, mask, mu, n_timesteps, stoc, spk, use_classifier_free, classifier_free_guidance, dummy_spk) | |
| def loss_t(self, x0, mask, mu, t, spk=None): | |
| xt, z = self.forward_diffusion(x0, mask, mu, t) # z is sampled from N(0, I) | |
| time = t.unsqueeze(-1).unsqueeze(-1) | |
| cum_noise = get_noise(time, self.beta_min, self.beta_max, cumulative=True) | |
| noise_estimation = self.estimator(xt, mask, mu, t, spk) | |
| noise_estimation *= torch.sqrt(1.0 - torch.exp(-cum_noise)) # multiply by lambda which is set to be variance | |
| # actually multiplied by sqrt(lambda), but not lambda | |
| # NOTE: here use a trick to put lambda into L2 norm so that don't divide z with std. | |
| loss = torch.sum((noise_estimation + z)**2) / (torch.sum(mask)*self.n_feats) | |
| return loss, xt | |
| def compute_loss(self, x0, mask, mu, spk=None, offset=1e-5): | |
| t = torch.rand(x0.shape[0], dtype=x0.dtype, device=x0.device, | |
| requires_grad=False) | |
| t = torch.clamp(t, offset, 1.0 - offset) | |
| return self.loss_t(x0, mask, mu, t, spk) | |
| def classifier_decode(self, z, mask, mu, n_timesteps, stoc=False, spk=None, classifier_func=None, guidance=1.0, control_emo=None, classifier_type="conformer"): | |
| # control_emo should be [B, ] tensor | |
| h = 1.0 / n_timesteps | |
| xt = z * mask | |
| for i in range(n_timesteps): | |
| t = (1.0 - (i + 0.5) * h) * torch.ones(z.shape[0], dtype=z.dtype, | |
| device=z.device) | |
| time = t.unsqueeze(-1).unsqueeze(-1) | |
| noise_t = get_noise(time, self.beta_min, self.beta_max, | |
| cumulative=False) | |
| # =========== classifier part ============== | |
| xt = xt.detach() | |
| xt.requires_grad_(True) | |
| if classifier_type == 'CNN-with-time': | |
| logits = classifier_func(xt.transpose(1, 2), mu.transpose(1, 2), (mask == 1.0).squeeze(1), t=t) | |
| else: | |
| logits = classifier_func(xt.transpose(1, 2), mu.transpose(1, 2), (mask == 1.0).squeeze(1)) | |
| if classifier_type == 'conformer': # [B, C] | |
| probs = torch.log_softmax(logits, dim=-1) # [B, C] | |
| elif classifier_type == 'CNN' or classifier_type == 'CNN-with-time' : | |
| probs_every_place = torch.softmax(logits, dim=-1) # [B, T', C] | |
| probs_mean = torch.mean(probs_every_place, dim=1) # [B, C] | |
| probs = torch.log(probs_mean) | |
| else: | |
| raise NotImplementedError | |
| control_emo_probs = probs[torch.arange(len(control_emo)).to(control_emo.device), control_emo] | |
| control_emo_probs.sum().backward(retain_graph=True) | |
| # NOTE: sum is to treat all the components as the same weight. | |
| xt_grad = xt.grad | |
| # ========================================== | |
| if stoc: # adds stochastic term | |
| dxt_det = 0.5 * (mu - xt) - self.estimator(xt, mask, mu, t, spk) - guidance * xt_grad | |
| dxt_det = dxt_det * noise_t * h | |
| dxt_stoc = torch.randn(z.shape, dtype=z.dtype, device=z.device, | |
| requires_grad=False) | |
| dxt_stoc = dxt_stoc * torch.sqrt(noise_t * h) | |
| dxt = dxt_det + dxt_stoc | |
| else: | |
| dxt = 0.5 * (mu - xt - self.estimator(xt, mask, mu, t, spk) - guidance * xt_grad) | |
| dxt = dxt * noise_t * h | |
| xt = (xt - dxt) * mask | |
| return xt | |
| def classifier_decode_DPS(self, z, mask, mu, n_timesteps, stoc=False, spk=None, classifier_func=None, guidance=1.0, control_emo=None, classifier_type="conformer"): | |
| # control_emo should be [B, ] tensor | |
| h = 1.0 / n_timesteps | |
| xt = z * mask | |
| for i in range(n_timesteps): | |
| t = (1.0 - (i + 0.5) * h) * torch.ones(z.shape[0], dtype=z.dtype, device=z.device) | |
| time = t.unsqueeze(-1).unsqueeze(-1) | |
| noise_t = get_noise(time, self.beta_min, self.beta_max, cumulative=False) | |
| beta_integral_t = get_noise(time, self.beta_min, self.beta_max, cumulative=True) | |
| bar_alpha_t = math.exp(-beta_integral_t) | |
| # =========== classifier part ============== | |
| xt = xt.detach() | |
| xt.requires_grad_(True) | |
| score_estimate = self.estimator(xt, mask, mu, t, spk) | |
| x0_hat = (xt + (1-bar_alpha_t) * score_estimate) / math.sqrt(bar_alpha_t) | |
| if classifier_type == 'CNN-with-time': | |
| raise NotImplementedError | |
| else: | |
| logits = classifier_func(x0_hat.transpose(1, 2), mu.transpose(1, 2), (mask == 1.0).squeeze(1)) | |
| if classifier_type == 'conformer': # [B, C] | |
| probs = torch.log_softmax(logits, dim=-1) # [B, C] | |
| elif classifier_type == 'CNN': | |
| probs_every_place = torch.softmax(logits, dim=-1) # [B, T', C] | |
| probs_mean = torch.mean(probs_every_place, dim=1) # [B, C] | |
| probs_mean = probs_mean + 10E-10 | |
| # NOTE: at the first few steps, x0 may be very large. Then the classifier output logits will also have extreme value range. | |
| # | |
| probs = torch.log(probs_mean) | |
| else: | |
| raise NotImplementedError | |
| control_emo_probs = probs[torch.arange(len(control_emo)).to(control_emo.device), control_emo] | |
| control_emo_probs.sum().backward(retain_graph=True) | |
| # NOTE: sum is to treat all the components as the same weight. | |
| xt_grad = xt.grad | |
| # ========================================== | |
| if stoc: # adds stochastic term | |
| dxt_det = 0.5 * (mu - xt) - score_estimate - guidance * xt_grad | |
| dxt_det = dxt_det * noise_t * h | |
| dxt_stoc = torch.randn(z.shape, dtype=z.dtype, device=z.device, requires_grad=False) | |
| dxt_stoc = dxt_stoc * torch.sqrt(noise_t * h) | |
| dxt = dxt_det + dxt_stoc | |
| else: | |
| dxt = 0.5 * (mu - xt - score_estimate - guidance * xt_grad) | |
| dxt = dxt * noise_t * h | |
| xt = (xt - dxt) * mask | |
| return xt | |
| def classifier_decode_mixture(self, z, mask, mu, n_timesteps, stoc=False, spk=None, classifier_func=None, guidance=1.0, control_emo1=None,control_emo2=None, emo1_weight=None, classifier_type="conformer"): | |
| # control_emo should be [B, ] tensor | |
| h = 1.0 / n_timesteps | |
| xt = z * mask | |
| for i in range(n_timesteps): | |
| t = (1.0 - (i + 0.5) * h) * torch.ones(z.shape[0], dtype=z.dtype, | |
| device=z.device) | |
| time = t.unsqueeze(-1).unsqueeze(-1) | |
| noise_t = get_noise(time, self.beta_min, self.beta_max, | |
| cumulative=False) | |
| # =========== classifier part ============== | |
| xt = xt.detach() | |
| xt.requires_grad_(True) | |
| if classifier_type == 'CNN-with-time': | |
| logits = classifier_func(xt.transpose(1, 2), mu.transpose(1, 2), (mask == 1.0).squeeze(1), t=t) | |
| else: | |
| logits = classifier_func(xt.transpose(1, 2), mu.transpose(1, 2), (mask == 1.0).squeeze(1)) | |
| if classifier_type == 'conformer': # [B, C] | |
| probs = torch.log_softmax(logits, dim=-1) # [B, C] | |
| elif classifier_type == 'CNN' or classifier_type == 'CNN-with-time' : | |
| probs_every_place = torch.softmax(logits, dim=-1) # [B, T', C] | |
| probs_mean = torch.mean(probs_every_place, dim=1) # [B, C] | |
| probs = torch.log(probs_mean) | |
| else: | |
| raise NotImplementedError | |
| control_emo_probs1 = probs[torch.arange(len(control_emo1)).to(control_emo1.device), control_emo1] | |
| control_emo_probs2 = probs[torch.arange(len(control_emo2)).to(control_emo2.device), control_emo2] | |
| control_emo_probs = control_emo_probs1 * emo1_weight + control_emo_probs2 * (1-emo1_weight) # interpolate | |
| control_emo_probs.sum().backward(retain_graph=True) | |
| # NOTE: sum is to treat all the components as the same weight. | |
| xt_grad = xt.grad | |
| # ========================================== | |
| if stoc: # adds stochastic term | |
| dxt_det = 0.5 * (mu - xt) - self.estimator(xt, mask, mu, t, spk) - guidance * xt_grad | |
| dxt_det = dxt_det * noise_t * h | |
| dxt_stoc = torch.randn(z.shape, dtype=z.dtype, device=z.device, | |
| requires_grad=False) | |
| dxt_stoc = dxt_stoc * torch.sqrt(noise_t * h) | |
| dxt = dxt_det + dxt_stoc | |
| else: | |
| dxt = 0.5 * (mu - xt - self.estimator(xt, mask, mu, t, spk) - guidance * xt_grad) | |
| dxt = dxt * noise_t * h | |
| xt = (xt - dxt) * mask | |
| return xt | |
| def classifier_decode_mixture_DPS(self, z, mask, mu, n_timesteps, stoc=False, spk=None, classifier_func=None, guidance=1.0, control_emo1=None,control_emo2=None, emo1_weight=None, classifier_type="conformer"): | |
| # control_emo should be [B, ] tensor | |
| h = 1.0 / n_timesteps | |
| xt = z * mask | |
| for i in range(n_timesteps): | |
| t = (1.0 - (i + 0.5) * h) * torch.ones(z.shape[0], dtype=z.dtype, | |
| device=z.device) | |
| time = t.unsqueeze(-1).unsqueeze(-1) | |
| noise_t = get_noise(time, self.beta_min, self.beta_max, | |
| cumulative=False) | |
| beta_integral_t = get_noise(time, self.beta_min, self.beta_max, cumulative=True) | |
| bar_alpha_t = math.exp(-beta_integral_t) | |
| # =========== classifier part ============== | |
| xt = xt.detach() | |
| xt.requires_grad_(True) | |
| score_estimate = self.estimator(xt, mask, mu, t, spk) | |
| x0_hat = (xt + (1 - bar_alpha_t) * score_estimate) / math.sqrt(bar_alpha_t) | |
| if classifier_type == 'CNN-with-time': | |
| raise NotImplementedError | |
| else: | |
| logits = classifier_func(x0_hat.transpose(1, 2), mu.transpose(1, 2), (mask == 1.0).squeeze(1)) | |
| if classifier_type == 'conformer': # [B, C] | |
| probs = torch.log_softmax(logits, dim=-1) # [B, C] | |
| elif classifier_type == 'CNN' or classifier_type == 'CNN-with-time' : | |
| probs_every_place = torch.softmax(logits, dim=-1) # [B, T', C] | |
| probs_mean = torch.mean(probs_every_place, dim=1) # [B, C] | |
| probs_mean = probs_mean + 10E-10 | |
| probs = torch.log(probs_mean) | |
| else: | |
| raise NotImplementedError | |
| control_emo_probs1 = probs[torch.arange(len(control_emo1)).to(control_emo1.device), control_emo1] | |
| control_emo_probs2 = probs[torch.arange(len(control_emo2)).to(control_emo2.device), control_emo2] | |
| control_emo_probs = control_emo_probs1 * emo1_weight + control_emo_probs2 * (1-emo1_weight) # interpolate | |
| control_emo_probs.sum().backward(retain_graph=True) | |
| # NOTE: sum is to treat all the components as the same weight. | |
| xt_grad = xt.grad | |
| # ========================================== | |
| if stoc: # adds stochastic term | |
| dxt_det = 0.5 * (mu - xt) - score_estimate - guidance * xt_grad | |
| dxt_det = dxt_det * noise_t * h | |
| dxt_stoc = torch.randn(z.shape, dtype=z.dtype, device=z.device, | |
| requires_grad=False) | |
| dxt_stoc = dxt_stoc * torch.sqrt(noise_t * h) | |
| dxt = dxt_det + dxt_stoc | |
| else: | |
| dxt = 0.5 * (mu - xt - score_estimate - guidance * xt_grad) | |
| dxt = dxt * noise_t * h | |
| xt = (xt - dxt) * mask | |
| return xt | |