Generative AI 新世界 | 扩散模型原理的代码实践之采样篇

from typing import Dict, Tuplefrom tqdm import tqdmimport torchimport torch.nn as nnimport torch.nn.functional as Ffrom torch.utils.data import DataLoaderfrom torchvision import models, transformsfrom torchvision.utils import save_image, make_gridimport matplotlib.pyplot as pltfrom matplotlib.animation import FuncAnimation, PillowWriterimport numpy as npfrom IPython.display import HTMLfrom diffusion_utilities import *

class ContextUnet(nn.Module):    def __init__(self, in_channels, n_feat=256, n_cfeat=10, height=28):  # cfeat - context features        super(ContextUnet, self).__init__()
        # number of input channels, number of intermediate feature maps and number of classes        self.in_channels = in_channels        self.n_feat = n_feat        self.n_cfeat = n_cfeat        self.h = height  #assume h == w. must be divisible by 4, so 28,24,20,16...
        # Initialize the initial convolutional layer        self.init_conv = ResidualConvBlock(in_channels, n_feat, is_res=True)# Initialize the down-sampling path of the U-Net with two levels        self.down1 = UnetDown(n_feat, n_feat)        # down1 #[10, 256, 8, 8]        self.down2 = UnetDown(n_feat, 2 * n_feat)    # down2 #[10, 256, 4,  4]                 # original: self.to_vec = nn.Sequential(nn.AvgPool2d(7), nn.GELU())        self.to_vec = nn.Sequential(nn.AvgPool2d((4)), nn.GELU())
        # Embed the timestep and context labels with a one-layer fully connected neural network        self.timeembed1 = EmbedFC(1, 2*n_feat)        self.timeembed2 = EmbedFC(1, 1*n_feat)        self.contextembed1 = EmbedFC(n_cfeat, 2*n_feat)        self.contextembed2 = EmbedFC(n_cfeat, 1*n_feat)
        # Initialize the up-sampling path of the U-Net with three levels        self.up0 = nn.Sequential(            nn.ConvTranspose2d(2 * n_feat, 2 * n_feat, self.h//4, self.h//4), # up-sample              nn.GroupNorm(8, 2 * n_feat), # normalize                                   nn.ReLU(),        )        self.up1 = UnetUp(4 * n_feat, n_feat)        self.up2 = UnetUp(2 * n_feat, n_feat)
        # Initialize the final convolutional layers to map to the same number of channels as the input image        self.out = nn.Sequential(            nn.Conv2d(2 * n_feat, n_feat, 3, 1, 1), # reduce number of feature maps   #in_channels, out_channels, kernel_size, stride=1, padding=0            nn.GroupNorm(8, n_feat), # normalize            nn.ReLU(),            nn.Conv2d(n_feat, self.in_channels, 3, 1, 1), # map to same number of channels as input        )
    def forward(self, x, t, c=None):        """        x : (batch, n_feat, h, w) : input image        t : (batch, n_cfeat)      : time step        c : (batch, n_classes)    : context label        """        # x is the input image, c is the context label, t is the timestep, context_mask says which samples to block the context on
        # pass the input image through the initial convolutional layer        x = self.init_conv(x)        # pass the result through the down-sampling path        down1 = self.down1(x)       #[10, 256, 8, 8]        down2 = self.down2(down1)   #[10, 256, 4, 4]                # convert the feature maps to a vector and apply an activation        hiddenvec = self.to_vec(down2)                # mask out context if context_mask == 1        if c is None:            c = torch.zeros(x.shape[0], self.n_cfeat).to(x)                    # embed context and timestep        cemb1 = self.contextembed1(c).view(-1, self.n_feat * 2, 1, 1)     # (batch, 2*n_feat, 1,1)        temb1 = self.timeembed1(t).view(-1, self.n_feat * 2, 1, 1)        cemb2 = self.contextembed2(c).view(-1, self.n_feat, 1, 1)        temb2 = self.timeembed2(t).view(-1, self.n_feat, 1, 1)        #print(f"uunet forward: cemb1 {cemb1.shape}. temb1 {temb1.shape}, cemb2 {cemb2.shape}. temb2 {temb2.shape}")
        up1 = self.up0(hiddenvec)        up2 = self.up1(cemb1*up1 + temb1, down2)  # add and multiply embeddings        up3 = self.up2(cemb2*up2 + temb2, down1)        out = self.out(torch.cat((up3, x), 1))        return out

# hyperparameters
# diffusion hyperparameterstimesteps = 500beta1 = 1e-4beta2 = 0.02
# network hyperparametersdevice = torch.device("cuda:0" if torch.cuda.is_available() else torch.device('cpu'))n_feat = 64 # 64 hidden dimension featuren_cfeat = 5 # context vector is of size 5height = 16 # 16x16 imagesave_dir = './weights/'

# construct DDPM noise scheduleb_t = (beta2 - beta1) * torch.linspace(0, 1, timesteps + 1, device=device) + beta1a_t = 1 - b_tab_t = torch.cumsum(a_t.log(), dim=0).exp()    ab_t[0] = 1

# construct modelnn_model = ContextUnet(in_channels=3, n_feat=n_feat, n_cfeat=n_cfeat, height=height).to(device)

# helper function; removes the predicted noise (but adds some noise back in to avoid collapse)def denoise_add_noise(x, t, pred_noise, z=None):    if z is None:        z = torch.randn_like(x)    noise = b_t.sqrt()[t] * z    mean = (x - pred_noise * ((1 - a_t[t]) / (1 - ab_t[t]).sqrt())) / a_t[t].sqrt()

# load in model weights and set to eval modenn_model.load_state_dict(torch.load(f"{save_dir}/model_trained.pth", map_location=device))nn_model.eval()print("Loaded in Model")

# sample using standard algorithm@torch.no_grad()def sample_ddpm(n_sample, save_rate=20):    # x_T ~ N(0, 1), sample initial noise    samples = torch.randn(n_sample, 3, height, height).to(device)  
    # array to keep track of generated steps for plotting    intermediate = []     for i in range(timesteps, 0, -1):        print(f'sampling timestep {i:3d}', end='\r')
        # reshape time tensor        t = torch.tensor([i / timesteps])[:, None, None, None].to(device)
        # sample some random noise to inject back in. For i = 1, don't add back in noise        z = torch.randn_like(samples) if i > 1 else 0
        eps = nn_model(samples, t)    # predict noise e_(x_t,t)        samples = denoise_add_noise(samples, i, eps, z)        if i % save_rate ==0 or i==timesteps or i<8:            intermediate.append(samples.detach().cpu().numpy())
    intermediate = np.stack(intermediate)    return samples, intermediate

eps = nn_model(samples, t)    # predict noise e_(x_t,t)

samples = denoise_add_noise(samples, i, eps, z)

# incorrectly sample without adding in noise@torch.no_grad()def sample_ddpm_incorrect(n_sample):    # x_T ~ N(0, 1), sample initial noise    samples = torch.randn(n_sample, 3, height, height).to(device)  
    # array to keep track of generated steps for plotting    intermediate = []     for i in range(timesteps, 0, -1):        print(f'sampling timestep {i:3d}', end='\r')
        # reshape time tensor        t = torch.tensor([i / timesteps])[:, None, None, None].to(device)
        # don't add back in noise        z = 0
        eps = nn_model(samples, t)    # predict noise e_(x_t,t)        samples = denoise_add_noise(samples, i, eps, z)        if i%20==0 or i==timesteps or i<8:            intermediate.append(samples.detach().cpu().numpy())
    intermediate = np.stack(intermediate)    return samples, intermediate