扩散模型（Diffusion model）代码详细解读

扩散模型代码详细解读

代码地址：denoising-diffusion-pytorch/denoising_diffusion_pytorch.py at main · lucidrains/denoising-diffusion-pytorch (github.com)

前向过程和后向过程的代码都在

GaussianDiffusion

​这个类中。​

有问题可以一起讨论！

常见问题解决

Why self-conditioning? · Issue #94 · lucidrains/denoising-diffusion-pytorch (github.com)

"pred_x0" preforms better than "pred_noise" · Issue #58 · lucidrains/denoising-diffusion-pytorch (github.com)

What is objective=pred_x0 and how do you use it? · Issue #34 · lucidrains/denoising-diffusion-pytorch (github.com)

Conditional generation · Issue #7 · lucidrains/denoising-diffusion-pytorch (github.com)

Questions About DDPM · Issue #10 · lucidrains/denoising-diffusion-pytorch (github.com)
The difference between pred_x0, pred_v, pred_noise three objectives · Issue #153 · lucidrains/denoising-diffusion-pytorch (github.com)

前向训练过程

p_losses

首先是p_losses函数，这个是训练过程的主体部分。

defp_losses(self, x_start, t, noise =None):
        b, c, h, w = x_start.shape
    # 首先随机生成噪声
        noise = default(noise,lambda: torch.randn_like(x_start))# noise sample# 噪声采样,注意这个是一次性完成的
        x = self.q_sample(x_start = x_start, t = t, noise = noise)# if doing self-conditioning, 50% of the time, predict x_start from current set of times# and condition with unet with that# this technique will slow down training by 25%, but seems to lower FID significantly# 判断是否进行self-condition,就是利用前面步骤预测出的x0来辅助当前的预测
        x_self_cond =Noneif self.self_condition and random()<0.5:with torch.no_grad():
                x_self_cond = self.model_predictions(x, t).pred_x_start
                x_self_cond.detach_()# predict and take gradient step# 将采样的x和self condition的x一起输入到model当中,这个model是UNet结构
        model_out = self.model(x, t, x_self_cond)# 模型预测的目标，分为三种if self.objective =='pred_noise':
            target = noise
        elif self.objective =='pred_x0':
            target = x_start
        elif self.objective =='pred_v':
            v = self.predict_v(x_start, t, noise)
            target = v
        else:raise ValueError(f'unknown objective {self.objective}')# 计算损失
        loss = self.loss_fn(model_out, target, reduction ='none')
        loss =reduce(loss,'b ... -> b (...)','mean')

        loss = loss * extract(self.p2_loss_weight, t, loss.shape)return loss.mean()

对其中的extract函数进行分析，extract函数实现如下：

defextract(a, t, x_shape):# Extract some coefficients at specified timesteps,# then reshape to [batch_size, 1, 1, 1, 1, ...] for broadcasting purposes.
    b,*_ = t.shape
    # 使用了gather函数
    out = a.gather(-1, t)return out.reshape(b,*((1,)*(len(x_shape)-1)))

q_sample

然后介绍p_losses函数中使用的其他函数，第一个是q_sample函数，它的作用是加上噪声，对应论文的公式：

其中

self.sqrt_alphas_cumprod

​和

self.sqrt_one_minus_alphas_cumprod

​分别是alpha的累乘值和1-alpha的累乘值，x_start相当于x0，noise相当于z。

defq_sample(self, x_start, t, noise=None):
        noise = default(noise,lambda: torch.randn_like(x_start))return(
            extract(self.sqrt_alphas_cumprod, t, x_start.shape)* x_start +
            extract(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape)* noise
        )

model_predictions

然后是model_predictions函数，它的实现如下：

defmodel_predictions(self, x, t, x_self_cond =None, clip_x_start =False):# 输入到UNet结构中获得输出
        model_output = self.model(x, t, x_self_cond)
        maybe_clip = partial(torch.clamp,min=-1.,max=1.)if clip_x_start else identity
    # 暂不明确它的作用if self.objective =='pred_noise':
            pred_noise = model_output
            x_start = self.predict_start_from_noise(x, t, pred_noise)
            x_start = maybe_clip(x_start)elif self.objective =='pred_x0':
            x_start = model_output
            x_start = maybe_clip(x_start)
            pred_noise = self.predict_noise_from_start(x, t, x_start)elif self.objective =='pred_v':
            v = model_output
            x_start = self.predict_start_from_v(x, t, v)
            x_start = maybe_clip(x_start)
            pred_noise = self.predict_noise_from_start(x, t, x_start)# 返回得到的噪声和return ModelPrediction(pred_noise, x_start)

几种objective

model_predictions函数中有一个难点，就是其中的self.objective，它有三种形式：

• pred_noise：这个相当于是预测噪声，此时UNet模型的输出是噪声
• pred_x0：这个相当于是预测最开始的x，此时UNet模型的输出是去噪的图像
• pred_v：这个相当于是预测速度v，它在这篇文章中提出。然后根据速度求出最开始的x，最后预测出噪声。

如图所示：​

在上面的三种objective中，还涉及到了几种预测方法的实现，具体如下：

（1）predict_start_from_noise：这个函数的作用是根据噪声noise预测最开始的x，也就是去噪的图像。

其中

self.sqrt_recip_alphas_cumprod

​和

self.sqrt_recipm1_alphas_cumprod

​来自
公式，它们分别为：
和
。

公式来源文章：DDPM

defpredict_start_from_noise(self, x_t, t, noise):return(
        extract(self.sqrt_recip_alphas_cumprod, t, x_t.shape)* x_t -
        extract(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape)* noise
    )

它对应论文中的公式如下：

（2）predict_noise_from_start：这个函数的作用是根据图像预测噪声，也就是加噪声。

defpredict_noise_from_start(self, x_t, t, x0):return((extract(self.sqrt_recip_alphas_cumprod, t, x_t.shape)* x_t - x0)/ \
        extract(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape))

它对应论文中的公式如下：

需要注意它是反推过来的，过程如下：

（3）predict_v：预测速度v

defpredict_v(self, x_start, t, noise):return(
         extract(self.sqrt_alphas_cumprod, t, x_start.shape)* noise -
         extract(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape)* x_start
     )

它对应论文中的公式：

（4）predict_start_from_v：根据速度v预测最初的x，也就是图像

defpredict_start_from_v(self, x_t, t, v):return(
        extract(self.sqrt_alphas_cumprod, t, x_t.shape)* x_t -
        extract(self.sqrt_one_minus_alphas_cumprod, t, x_t.shape)* v
    )

它对应论文中的公式如下：其中zt相当于xt。

后向采样过程

sample函数

@torch.no_grad()defsample(self, batch_size =16, return_all_timesteps =False):
    image_size, channels = self.image_size, self.channels
    # 采样的函数
    sample_fn = self.p_sample_loop ifnot self.is_ddim_sampling else self.ddim_sample
    # 调用该函数return sample_fn((batch_size, channels, image_size, image_size), return_all_timesteps = return_all_timesteps)

该函数的作用是获取采样的函数然后进行调用，采样函数分成两种：p_sample_loop和ddim_sample。

p_sample_loop函数

@torch.no_grad()defp_sample_loop(self, shape, return_all_timesteps =False):
     batch, device = shape[0], self.betas.device
     # 随机生成噪声图像
     img = torch.randn(shape, device = device)
     imgs =[img]

     x_start =None# 遍历所有的tfor t in tqdm(reversed(range(0, self.num_timesteps)), desc ='sampling loop time step', total = self.num_timesteps):# 判断是否使用self-condition
     self_cond = x_start if self.self_condition elseNone# 进行采样,得到去噪的图像
         img, x_start = self.p_sample(img, t, self_cond)
         imgs.append(img)# 判断是否返回每个步骤的img还是最后一步的img
     ret = img ifnot return_all_timesteps else torch.stack(imgs, dim =1)# 归一化
     ret = self.unnormalize(ret)return ret

其中涉及到归一化函数

self.unnormalize

​，含有两种

# normalization functionsdefnormalize_to_neg_one_to_one(img):return img *2-1defunnormalize_to_zero_to_one(t):return(t +1)*0.5

p_sample函数

@torch.no_grad()defp_sample(self, x, t:int, x_self_cond =None):
    b,*_, device =*x.shape, x.device
    batched_times = torch.full((b,), t, device = x.device, dtype = torch.long)# 获得平均值,方差和x0
    model_mean, _, model_log_variance, x_start = self.p_mean_variance(x = x, t = batched_times, x_self_cond = x_self_cond, clip_denoised =True)# 随机生成一个噪声      
    noise = torch.randn_like(x)if t >0else0.# no noise if t == 0# 得到预测的图像,img = 平均值 + exp(0.5 * 方差) * noise
    pred_img = model_mean +(0.5* model_log_variance).exp()* noise
    return pred_img, x_start

p_mean_variance函数

其中含有

p_mean_variance

​函数，代码实现如下：

defp_mean_variance(self, x, t, x_self_cond =None, clip_denoised =True):# 输入到UNet网络进行预测
    preds = self.model_predictions(x, t, x_self_cond)# 得到预测的x0
    x_start = preds.pred_x_start
    # 压缩x0中值的范围至[-1,1]if clip_denoised:
        x_start.clamp_(-1.,1.)# 得到x0后根据xt和t得到分布的平均值和方差
    model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start = x_start, x_t = x, t = t)return model_mean, posterior_variance, posterior_log_variance, x_start

q_posterior函数

其中

q_posterior

​函数的实现如下：

defq_posterior(self, x_start, x_t, t):# 计算平均值
    posterior_mean =(
        extract(self.posterior_mean_coef1, t, x_t.shape)* x_start +
        extract(self.posterior_mean_coef2, t, x_t.shape)* x_t
    )# 计算方差
    posterior_variance = extract(self.posterior_variance, t, x_t.shape)# 获得一个压缩范围的方差,且取对数
    posterior_log_variance_clipped = extract(self.posterior_log_variance_clipped, t, x_t.shape)return posterior_mean, posterior_variance, posterior_log_variance_clipped

平均值和方差对应的公式如下：

其中

self.posterior_mean_coef1

​对应的是x0前面的系数，

self.posterior_mean_coef2

​对应的是xt前面的系数。

​

self.posterior_variance

​对应的beta那部分的系数。

ddim_sample函数

@torch.no_grad()defddim_sample(self, shape, return_all_timesteps =False):
    batch, device, total_timesteps, sampling_timesteps, eta, objective = shape[0], self.betas.device, self.num_timesteps, self.sampling_timesteps, self.ddim_sampling_eta, self.objective
    times = torch.linspace(-1, total_timesteps -1, steps = sampling_timesteps +1)# [-1, 0, 1, 2, ..., T-1] when sampling_timesteps == total_timesteps
    times =list(reversed(times.int().tolist()))
    time_pairs =list(zip(times[:-1], times[1:]))# [(T-1, T-2), (T-2, T-3), ..., (1, 0), (0, -1)]
    img = torch.randn(shape, device = device)
    imgs =[img]
    x_start =Nonefor time, time_next in tqdm(time_pairs, desc ='sampling loop time step'):
        time_cond = torch.full((batch,), time, device = device, dtype = torch.long)
        self_cond = x_start if self.self_condition elseNone
        pred_noise, x_start,*_ = self.model_predictions(img, time_cond, self_cond, clip_x_start =True)
        imgs.append(img)if time_next <0:
            img = x_start
            continue

        alpha = self.alphas_cumprod[time]
        alpha_next = self.alphas_cumprod[time_next]
        sigma = eta *((1- alpha / alpha_next)*(1- alpha_next)/(1- alpha)).sqrt()
        c =(1- alpha_next - sigma **2).sqrt()
        noise = torch.randn_like(img)
        img = x_start * alpha_next.sqrt()+ \
              c * pred_noise + \
              sigma * noise
    ret = img ifnot return_all_timesteps else torch.stack(imgs, dim =1)
    ret = self.unnormalize(ret)return ret

上面部分依据的公式为：（文章）

训练的模型(UNet)

后续会继续更新！
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