| import torch |
| import torch.nn as nn |
| from torch.utils.checkpoint import checkpoint |
| import math |
|
|
| from .misc import create_diffusion |
|
|
|
|
| class DiffLoss(nn.Module): |
| """Diffusion Loss""" |
| def __init__(self, target_channels, z_channels, depth, width, num_sampling_steps, grad_checkpointing=False): |
| super(DiffLoss, self).__init__() |
| self.in_channels = target_channels |
| self.net = SimpleMLPAdaLN( |
| in_channels=target_channels, |
| model_channels=width, |
| out_channels=target_channels * 2, |
| z_channels=z_channels, |
| num_res_blocks=depth, |
| grad_checkpointing=grad_checkpointing |
| ) |
|
|
| self.train_diffusion = create_diffusion(timestep_respacing="", noise_schedule="cosine") |
| self.gen_diffusion = create_diffusion(timestep_respacing=num_sampling_steps, noise_schedule="cosine") |
|
|
| def forward(self, target, z, mask=None): |
| t = torch.randint(0, self.train_diffusion.num_timesteps, (target.shape[0],), device=target.device) |
| model_kwargs = dict(c=z) |
| loss_dict = self.train_diffusion.training_losses(self.net, target, t, model_kwargs) |
| loss = loss_dict["loss"] |
| if mask is not None: |
| loss = (loss * mask).sum() / mask.sum() |
| return loss.mean() |
|
|
| def sample(self, z, temperature=1.0, cfg=1.0): |
| |
| if not cfg == 1.0: |
| noise = torch.randn(z.shape[0] // 2, self.in_channels, device=z.device) |
| noise = torch.cat([noise, noise], dim=0) |
| model_kwargs = dict(c=z, cfg_scale=cfg) |
| sample_fn = self.net.forward_with_cfg |
| else: |
| noise = torch.randn(z.shape[0], self.in_channels, device=z.device) |
| model_kwargs = dict(c=z) |
| sample_fn = self.net.forward |
|
|
| sampled_token_latent = self.gen_diffusion.p_sample_loop( |
| sample_fn, noise.shape, noise, clip_denoised=False, model_kwargs=model_kwargs, progress=False, |
| temperature=temperature |
| ) |
|
|
| return sampled_token_latent |
|
|
|
|
| def modulate(x, shift, scale): |
| return x * (1 + scale) + shift |
|
|
|
|
| class TimestepEmbedder(nn.Module): |
| """ |
| Embeds scalar timesteps into vector representations. |
| """ |
| def __init__(self, hidden_size, frequency_embedding_size=256): |
| super().__init__() |
| self.mlp = nn.Sequential( |
| nn.Linear(frequency_embedding_size, hidden_size, bias=True), |
| nn.SiLU(), |
| nn.Linear(hidden_size, hidden_size, bias=True), |
| ) |
| self.frequency_embedding_size = frequency_embedding_size |
|
|
| @staticmethod |
| def timestep_embedding(t, dim, max_period=10000): |
| """ |
| Create sinusoidal timestep embeddings. |
| :param t: a 1-D Tensor of N indices, one per batch element. |
| These may be fractional. |
| :param dim: the dimension of the output. |
| :param max_period: controls the minimum frequency of the embeddings. |
| :return: an (N, D) Tensor of positional embeddings. |
| """ |
| |
| half = dim // 2 |
| freqs = torch.exp( |
| -math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half |
| ).to(device=t.device) |
| args = t[:, None].float() * freqs[None] |
| embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1) |
| if dim % 2: |
| embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1) |
| return embedding |
|
|
| def forward(self, t): |
| t_freq = self.timestep_embedding(t, self.frequency_embedding_size) |
| t_emb = self.mlp(t_freq.to(self.mlp[0].weight.data.dtype)) |
| return t_emb |
|
|
|
|
| class ResBlock(nn.Module): |
| """ |
| A residual block that can optionally change the number of channels. |
| :param channels: the number of input channels. |
| """ |
|
|
| def __init__( |
| self, |
| channels |
| ): |
| super().__init__() |
| self.channels = channels |
|
|
| self.in_ln = nn.LayerNorm(channels, eps=1e-6) |
| self.mlp = nn.Sequential( |
| nn.Linear(channels, channels, bias=True), |
| nn.SiLU(), |
| nn.Linear(channels, channels, bias=True), |
| ) |
|
|
| self.adaLN_modulation = nn.Sequential( |
| nn.SiLU(), |
| nn.Linear(channels, 3 * channels, bias=True) |
| ) |
|
|
| def forward(self, x, y): |
| shift_mlp, scale_mlp, gate_mlp = self.adaLN_modulation(y).chunk(3, dim=-1) |
| h = modulate(self.in_ln(x), shift_mlp, scale_mlp) |
| h = self.mlp(h) |
| return x + gate_mlp * h |
|
|
|
|
| class FinalLayer(nn.Module): |
| """ |
| The final layer adopted from DiT. |
| """ |
| def __init__(self, model_channels, out_channels): |
| super().__init__() |
| self.norm_final = nn.LayerNorm(model_channels, elementwise_affine=False, eps=1e-6) |
| self.linear = nn.Linear(model_channels, out_channels, bias=True) |
| self.adaLN_modulation = nn.Sequential( |
| nn.SiLU(), |
| nn.Linear(model_channels, 2 * model_channels, bias=True) |
| ) |
|
|
| def forward(self, x, c): |
| shift, scale = self.adaLN_modulation(c).chunk(2, dim=-1) |
| x = modulate(self.norm_final(x), shift, scale) |
| x = self.linear(x) |
| return x |
|
|
|
|
| class SimpleMLPAdaLN(nn.Module): |
| """ |
| The MLP for Diffusion Loss. |
| :param in_channels: channels in the input Tensor. |
| :param model_channels: base channel count for the model. |
| :param out_channels: channels in the output Tensor. |
| :param z_channels: channels in the condition. |
| :param num_res_blocks: number of residual blocks per downsample. |
| """ |
|
|
| def __init__( |
| self, |
| in_channels, |
| model_channels, |
| out_channels, |
| z_channels, |
| num_res_blocks, |
| grad_checkpointing=False |
| ): |
| super().__init__() |
|
|
| self.in_channels = in_channels |
| self.model_channels = model_channels |
| self.out_channels = out_channels |
| self.num_res_blocks = num_res_blocks |
| self.grad_checkpointing = grad_checkpointing |
|
|
| self.time_embed = TimestepEmbedder(model_channels) |
| self.cond_embed = nn.Linear(z_channels, model_channels) |
|
|
| self.input_proj = nn.Linear(in_channels, model_channels) |
|
|
| res_blocks = [] |
| for i in range(num_res_blocks): |
| res_blocks.append(ResBlock( |
| model_channels, |
| )) |
|
|
| self.res_blocks = nn.ModuleList(res_blocks) |
| self.final_layer = FinalLayer(model_channels, out_channels) |
|
|
| self.initialize_weights() |
|
|
| def initialize_weights(self): |
| def _basic_init(module): |
| if isinstance(module, nn.Linear): |
| torch.nn.init.xavier_uniform_(module.weight) |
| if module.bias is not None: |
| nn.init.constant_(module.bias, 0) |
| self.apply(_basic_init) |
|
|
| |
| nn.init.normal_(self.time_embed.mlp[0].weight, std=0.02) |
| nn.init.normal_(self.time_embed.mlp[2].weight, std=0.02) |
|
|
| |
| for block in self.res_blocks: |
| nn.init.constant_(block.adaLN_modulation[-1].weight, 0) |
| nn.init.constant_(block.adaLN_modulation[-1].bias, 0) |
|
|
| |
| nn.init.constant_(self.final_layer.adaLN_modulation[-1].weight, 0) |
| nn.init.constant_(self.final_layer.adaLN_modulation[-1].bias, 0) |
| nn.init.constant_(self.final_layer.linear.weight, 0) |
| nn.init.constant_(self.final_layer.linear.bias, 0) |
|
|
| def forward(self, x, t, c): |
| """ |
| Apply the model to an input batch. |
| :param x: an [N x C] Tensor of inputs. |
| :param t: a 1-D batch of timesteps. |
| :param c: conditioning from AR transformer. |
| :return: an [N x C] Tensor of outputs. |
| """ |
| |
| x = self.input_proj(x.to(self.input_proj.weight.data.dtype)) |
| t = self.time_embed(t) |
| c = self.cond_embed(c.to(self.cond_embed.weight.data.dtype)) |
|
|
| y = t + c |
|
|
| if self.grad_checkpointing and not torch.jit.is_scripting(): |
| for block in self.res_blocks: |
| x = checkpoint(block, x, y) |
| else: |
| for block in self.res_blocks: |
| x = block(x, y) |
|
|
| return self.final_layer(x, y) |
|
|
| def forward_with_cfg(self, x, t, c, cfg_scale): |
| half = x[: len(x) // 2] |
| combined = torch.cat([half, half], dim=0) |
| model_out = self.forward(combined, t, c) |
| eps, rest = model_out[:, :self.in_channels], model_out[:, self.in_channels:] |
| cond_eps, uncond_eps = torch.split(eps, len(eps) // 2, dim=0) |
| half_eps = uncond_eps + cfg_scale * (cond_eps - uncond_eps) |
| eps = torch.cat([half_eps, half_eps], dim=0) |
| return torch.cat([eps, rest], dim=1) |
|
|
