提示词如下:
The look of a cabin in the middle of lush woods during the day, a serene setting for a professional movie shooting style --ar 16:9
A tunnel made of trees and vines leading to an open road, in the style of anime, in the style of cartoon, soft lighting, video clip, screenshot from the movie "Fully Detailed". --ar 16:9
A tunnel with a road going through it with trees at the end
A tunnel with a road going through it with trees at the end
接下来我们做一个对比
Walk through the forest and see a wooden house, then keep walking forward.
Runway的图加文生成视频模式和运动模式为创作者提供了极大的创作灵活性。通过结合静态图片和文本提示,用户能够快速生成高质量的动态视频。同时,运动模式(Motion)允许根据不同的需求调整视频的动态效果,从轻微的细节移动到剧烈的场景变化都有所覆盖。这种工具不仅简化了复杂视频的制作流程,还大幅提升了创作效率,极大地拓宽了视频内容创作的可能性,为创作者提供了前所未有的便利和创意空间。
import torch,torch.nn as nn,torch.optim as optim,cv2,numpy as np;class Generator(nn.Module):def __init__(self,z_dim,img_dim):super(Generator,self).__init__();self.gen=nn.Sequential(nn.Linear(z_dim,256),nn.LeakyReLU(0.2),nn.Linear(256,512),nn.LeakyReLU(0.2),nn.Linear(512,1024),nn.LeakyReLU(0.2),nn.Linear(1024,img_dim),nn.Tanh());def forward(self,x):return self.gen(x);class Discriminator(nn.Module):def __init__(self,img_dim):super(Discriminator,self).__init__();self.disc=nn.Sequential(nn.Linear(img_dim,1024),nn.LeakyReLU(0.2),nn.Linear(1024,512),nn.LeakyReLU(0.2),nn.Linear(512,256),nn.LeakyReLU(0.2),nn.Linear(256,1),nn.Sigmoid());def forward(self,x):return self.disc(x);z_dim,img_dim,lr,batch_size,epochs=100,64*64*3,0.0002,32,50000;generator=Generator(z_dim,img_dim);discriminator=Discriminator(img_dim);opt_gen,opt_disc=optim.Adam(generator.parameters(),lr=lr),optim.Adam(discriminator.parameters(),lr=lr);criterion=nn.BCELoss();def generate_noise(batch_size,z_dim):return torch.randn(batch_size,z_dim);def generate_video_frames(generator,z_dim,num_frames=30):frames=[];for _ in range(num_frames):noise=generate_noise(1,z_dim);frame=generator(noise).detach().numpy().reshape(64,64,3);frames.append((frame*255).astype(np.uint8));return frames;def save_video(frames,filename="output_video.mp4",fps=10):height,width,_=frames[0].shape;video=cv2.VideoWriter(filename,cv2.VideoWriter_fourcc(*'mp4v'),fps,(width,height));for frame in frames:video.write(cv2.cvtColor(frame,cv2.COLOR_RGB2BGR));video.release();for epoch in range(epochs):real_labels,fake_labels=torch.ones(batch_size,1),torch.zeros(batch_size,1);real_data=torch.randn(batch_size,img_dim);noise=generate_noise(batch_size,z_dim);fake_data=generator(noise);disc_real,disc_fake=discriminator(real_data).reshape(-1),discriminator(fake_data).reshape(-1);loss_disc_real,loss_disc_fake=criterion(disc_real,real_labels),criterion(disc_fake,fake_labels);loss_disc=(loss_disc_real+loss_disc_fake)/2;opt_disc.zero_grad();loss_disc.backward();opt_disc.step();output=discriminator(fake_data).reshape(-1);loss_gen=criterion(output,real_labels);opt_gen.zero_grad();loss_gen.backward();opt_gen.step();if epoch%100==0:print(f"Epoch [{epoch}/{epochs}] | Loss D: {loss_disc.item():.4f}, Loss G: {loss_gen.item():.4f}");if epoch%1000==0:frames=generate_video_frames(generator,z_dim);save_video(frames,f"generated_video_epoch_{epoch}.mp4");print("Training complete. Video generation finished.")