import torch import torch.nn as nn from torch.utils.checkpoint import checkpoint import math from .diffusion 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, # for vlb loss 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): # diffusion loss sampling if not cfg == 1.0: noise = torch.randn(z.shape[0] // 2, self.in_channels).cuda() 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).cuda() 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. """ # https://github.com/openai/glide-text2im/blob/main/glide_text2im/nn.py 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) 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) # Initialize timestep embedding MLP nn.init.normal_(self.time_embed.mlp[0].weight, std=0.02) nn.init.normal_(self.time_embed.mlp[2].weight, std=0.02) # Zero-out adaLN modulation layers for block in self.res_blocks: nn.init.constant_(block.adaLN_modulation[-1].weight, 0) nn.init.constant_(block.adaLN_modulation[-1].bias, 0) # Zero-out output layers 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) t = self.time_embed(t) c = self.cond_embed(c) 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)