用PyTorch實現雜訊去除擴散模型

class ConvNextBlock(nn.Module):def __init__(self,in_channels,out_channels,mult=2,time_embedding_dim=None,norm=True,group=8,):super().__init__()self.mlp = (nn.Sequential(nn.GELU(), nn.Linear(time_embedding_dim, in_channels))if time_embedding_dimelse None) self.in_conv = nn.Conv2d(in_channels, in_channels, 7, padding=3, groups=in_channels) self.block = nn.Sequential(nn.GroupNorm(1, in_channels) if norm else nn.Identity(),nn.Conv2d(in_channels, out_channels * mult, 3, padding=1),nn.GELU(),nn.GroupNorm(1, out_channels * mult),nn.Conv2d(out_channels * mult, out_channels, 3, padding=1),) self.residual_conv = (nn.Conv2d(in_channels, out_channels, 1)if in_channels != out_channelselse nn.Identity()) def forward(self, x, time_embedding=None):h = self.in_conv(x)if self.mlp is not None and time_embedding is not None:assert self.mlp is not None, "MLP is None"h = h + rearrange(self.mlp(time_embedding), "b c -> b c 1 1")h = self.block(h)return h + self.residual_conv(x)

class SinusoidalPosEmb(nn.Module):def __init__(self, dim, theta=10000):super().__init__()self.dim = dimself.theta = theta def forward(self, x):device = x.devicehalf_dim = self.dim // 2emb = math.log(self.theta) / (half_dim - 1)emb = torch.exp(torch.arange(half_dim, device=device) * -emb)emb = x[:, None] * emb[None, :]emb = torch.cat((emb.sin(), emb.cos()), dim=-1)return emb

class DownSample(nn.Module):def __init__(self, dim, dim_out=None):super().__init__()self.net = nn.Sequential(Rearrange("b c (h p1) (w p2) -> b (c p1 p2) h w", p1=2, p2=2),nn.Conv2d(dim * 4, default(dim_out, dim), 1),) def forward(self, x):return self.net(x)   class Upsample(nn.Module):def __init__(self, dim, dim_out=None):super().__init__()self.net = nn.Sequential(nn.Upsample(scale_factor=2, mode="nearest"),nn.Conv2d(dim, dim_out or dim, kernel_size=3, padding=1),) def forward(self, x):return self.net(x)

sinu_pos_emb = SinusoidalPosEmb(dim, theta=10000) time_dim = dim * 4 time_mlp = nn.Sequential(sinu_pos_emb,nn.Linear(dim, time_dim),nn.GELU(),nn.Linear(time_dim, time_dim),)

class BlockAttention(nn.Module):def __init__(self, gate_in_channel, residual_in_channel, scale_factor):super().__init__()self.gate_conv = nn.Conv2d(gate_in_channel, gate_in_channel, kernel_size=1, stride=1)self.residual_conv = nn.Conv2d(residual_in_channel, gate_in_channel, kernel_size=1, stride=1)self.in_conv = nn.Conv2d(gate_in_channel, 1, kernel_size=1, stride=1)self.relu = nn.ReLU()self.sigmoid = nn.Sigmoid() def forward(self, x: torch.Tensor, g: torch.Tensor) -> torch.Tensor:in_attention = self.relu(self.gate_conv(g) + self.residual_conv(x))in_attention = self.in_conv(in_attention)in_attention = self.sigmoid(in_attention)return in_attention * x

class TwoResUNet(nn.Module):def __init__(self,dim,init_dim=None,out_dim=None,dim_mults=(1, 2, 4, 8),channels=3,sinusoidal_pos_emb_theta=10000,convnext_block_groups=8,):super().__init__()self.channels = channelsinput_channels = channelsself.init_dim = default(init_dim, dim)self.init_conv = nn.Conv2d(input_channels, self.init_dim, 7, padding=3) dims = [self.init_dim, *map(lambda m: dim * m, dim_mults)]in_out = list(zip(dims[:-1], dims[1:])) sinu_pos_emb = SinusoidalPosEmb(dim, theta=sinusoidal_pos_emb_theta) time_dim = dim * 4 self.time_mlp = nn.Sequential(sinu_pos_emb,nn.Linear(dim, time_dim),nn.GELU(),nn.Linear(time_dim, time_dim),) self.downs = nn.ModuleList([])self.ups = 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(nn.ModuleList([ConvNextBlock(in_channels=dim_in,out_channels=dim_in,time_embedding_dim=time_dim,group=convnext_block_groups,),ConvNextBlock(in_channels=dim_in,out_channels=dim_in,time_embedding_dim=time_dim,group=convnext_block_groups,),DownSample(dim_in, dim_out)if not is_lastelse nn.Conv2d(dim_in, dim_out, 3, padding=1),])) mid_dim = dims[-1]self.mid_block1 = ConvNextBlock(mid_dim, mid_dim, time_embedding_dim=time_dim)self.mid_block2 = ConvNextBlock(mid_dim, mid_dim, time_embedding_dim=time_dim) for ind, (dim_in, dim_out) in enumerate(reversed(in_out)):is_last = ind == (len(in_out) - 1)is_first = ind == 0 self.ups.append(nn.ModuleList([ConvNextBlock(in_channels=dim_out + dim_in,out_channels=dim_out,time_embedding_dim=time_dim,group=convnext_block_groups,),ConvNextBlock(in_channels=dim_out + dim_in,out_channels=dim_out,time_embedding_dim=time_dim,group=convnext_block_groups,),Upsample(dim_out, dim_in)if not is_lastelse nn.Conv2d(dim_out, dim_in, 3, padding=1)])) default_out_dim = channelsself.out_dim = default(out_dim, default_out_dim) self.final_res_block = ConvNextBlock(dim * 2, dim, time_embedding_dim=time_dim)self.final_conv = nn.Conv2d(dim, self.out_dim, 1) def forward(self, x, time):b, _, h, w = x.shapex = self.init_conv(x)r = x.clone() t = self.time_mlp(time) unet_stack = []for down1, down2, downsample in self.downs:x = down1(x, t)unet_stack.append(x)x = down2(x, t)unet_stack.append(x)x = downsample(x) x = self.mid_block1(x, t)x = self.mid_block2(x, t) for up1, up2, upsample in self.ups:x = torch.cat((x, unet_stack.pop()), dim=1)x = up1(x, t)x = torch.cat((x, unet_stack.pop()), dim=1)x = up2(x, t)x = upsample(x) x = torch.cat((x, r), dim=1)x = self.final_res_block(x, t) return self.final_conv(x)

class DiffusionModel(nn.Module):SCHEDULER_MAPPING = {"linear": linear_beta_schedule,"cosine": cosine_beta_schedule,"sigmoid": sigmoid_beta_schedule,} def __init__(self,model: nn.Module,image_size: int,*,beta_scheduler: str = "linear",timesteps: int = 1000,schedule_fn_kwargs: dict | None = None,auto_normalize: bool = True,) -> None:super().__init__()self.model = model self.channels = self.model.channelsself.image_size = image_size self.beta_scheduler_fn = self.SCHEDULER_MAPPING.get(beta_scheduler)if self.beta_scheduler_fn is None:raise ValueError(f"unknown beta schedule {beta_scheduler}") if schedule_fn_kwargs is None:schedule_fn_kwargs = {} betas = self.beta_scheduler_fn(timesteps, **schedule_fn_kwargs)alphas = 1.0 - betasalphas_cumprod = torch.cumprod(alphas, dim=0)alphas_cumprod_prev = F.pad(alphas_cumprod[:-1], (1, 0), value=1.0)posterior_variance = (betas * (1.0 - alphas_cumprod_prev) / (1.0 - alphas_cumprod)) register_buffer = lambda name, val: self.register_buffer(name, val.to(torch.float32)) register_buffer("betas", betas)register_buffer("alphas_cumprod", alphas_cumprod)register_buffer("alphas_cumprod_prev", alphas_cumprod_prev)register_buffer("sqrt_recip_alphas", torch.sqrt(1.0 / alphas))register_buffer("sqrt_alphas_cumprod", torch.sqrt(alphas_cumprod))register_buffer("sqrt_one_minus_alphas_cumprod", torch.sqrt(1.0 - alphas_cumprod))register_buffer("posterior_variance", posterior_variance) timesteps, *_ = betas.shapeself.num_timesteps = int(timesteps) self.sampling_timesteps = timesteps self.normalize = normalize_to_neg_one_to_one if auto_normalize else identityself.unnormalize = unnormalize_to_zero_to_one if auto_normalize else identity @torch.inference_mode()def p_sample(self, x: torch.Tensor, timestamp: int) -> torch.Tensor:b, *_, device = *x.shape, x.devicebatched_timestamps = torch.full((b,), timestamp, device=device, dtype=torch.long) preds = self.model(x, batched_timestamps) betas_t = extract(self.betas, batched_timestamps, x.shape)sqrt_recip_alphas_t = extract(self.sqrt_recip_alphas, batched_timestamps, x.shape)sqrt_one_minus_alphas_cumprod_t = extract(self.sqrt_one_minus_alphas_cumprod, batched_timestamps, x.shape) predicted_mean = sqrt_recip_alphas_t * (x - betas_t * preds / sqrt_one_minus_alphas_cumprod_t) if timestamp == 0:return predicted_meanelse:posterior_variance = extract(self.posterior_variance, batched_timestamps, x.shape)noise = torch.randn_like(x)return predicted_mean + torch.sqrt(posterior_variance) * noise @torch.inference_mode()def p_sample_loop(self, shape: tuple, return_all_timesteps: bool = False) -> torch.Tensor:batch, device = shape[0], "mps" img = torch.randn(shape, device=device)# This cause me a RunTimeError on MPS device due to MPS back out of memory# No ideas how to resolve it at this point # imgs = [img] for t in tqdm(reversed(range(0, self.num_timesteps)), total=self.num_timesteps):img = self.p_sample(img, t)# imgs.append(img) ret = img # if not return_all_timesteps else torch.stack(imgs, dim=1) ret = self.unnormalize(ret)return ret def sample(self, batch_size: int = 16, return_all_timesteps: bool = False) -> torch.Tensor:shape = (batch_size, self.channels, self.image_size, self.image_size)return self.p_sample_loop(shape, return_all_timesteps=return_all_timesteps) def q_sample(self, x_start: torch.Tensor, t: int, noise: torch.Tensor = None) -> torch.Tensor:if noise is None:noise = torch.randn_like(x_start) sqrt_alphas_cumprod_t = extract(self.sqrt_alphas_cumprod, t, x_start.shape)sqrt_one_minus_alphas_cumprod_t = extract(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) return sqrt_alphas_cumprod_t * x_start + sqrt_one_minus_alphas_cumprod_t * noise def p_loss(self,x_start: torch.Tensor,t: int,noise: torch.Tensor = None,loss_type: str = "l2",) -> torch.Tensor:if noise is None:noise = torch.randn_like(x_start)x_noised = self.q_sample(x_start, t, noise=noise)predicted_noise = self.model(x_noised, t) if loss_type == "l2":loss = F.mse_loss(noise, predicted_noise)elif loss_type == "l1":loss = F.l1_loss(noise, predicted_noise)else:raise ValueError(f"unknown loss type {loss_type}")return loss def forward(self, x: torch.Tensor) -> torch.Tensor:b, c, h, w, device, img_size = *x.shape, x.device, self.image_sizeassert h == w == img_size, f"image size must be {img_size}" timestamp = torch.randint(0, self.num_timesteps, (1,)).long().to(device)x = self.normalize(x)return self.p_loss(x, timestamp)