Beginner Explanation
Imagine you have a magic coloring book that can bring drawings to life. Instead of just coloring in the lines, you can whisper a story to it, and it will create a short movie based on your words, slowly adding details and movement to the scenes. This is similar to how a Video Diffusion Model works. It starts with a blank slate (like a white page) and gradually fills in the details of a video, using hints from text or images you provide, making the final result look realistic and engaging.Technical Explanation
A Video Diffusion Model is a type of generative model that generates videos by simulating a diffusion process over time. It starts with a random noise tensor and iteratively refines it through a series of denoising steps, conditioned on input data such as text or images. The model employs a neural network, typically a U-Net architecture, to predict the noise at each step. Here’s a simplified code snippet using PyTorch: “`python import torch from torchvision import transforms class VideoDiffusionModel(torch.nn.Module): def __init__(self): super(VideoDiffusionModel, self).__init__() self.unet = UNet() # Define your U-Net architecture here def forward(self, noise, condition): for t in range(num_timesteps): noise = self.unet(noise, condition) return noise “` In this model, ‘noise’ is the initial random input, and ‘condition’ is the guiding information (text/image). The model refines the noise over several iterations to generate a coherent video.Academic Context
Video Diffusion Models build on the principles of diffusion processes and generative modeling, notably leveraging concepts from stochastic differential equations (SDEs) and variational inference. Key papers include ‘Denoising Diffusion Probabilistic Models’ by Ho et al., which introduced diffusion models for image generation, and subsequent works extending these ideas to video. The mathematical foundation involves defining a forward noise process and a reverse denoising process, allowing for the generation of high-dimensional data (videos) from simple distributions. The model’s effectiveness is often evaluated using metrics like Fréchet Video Distance (FVD) and Inception Score (IS).Code Examples
Example 1:
import torch
from torchvision import transforms
class VideoDiffusionModel(torch.nn.Module):
def __init__(self):
super(VideoDiffusionModel, self).__init__()
self.unet = UNet() # Define your U-Net architecture here
def forward(self, noise, condition):
for t in range(num_timesteps):
noise = self.unet(noise, condition)
return noiseExample 2:
def __init__(self):
super(VideoDiffusionModel, self).__init__()
self.unet = UNet() # Define your U-Net architecture hereExample 3:
def forward(self, noise, condition):
for t in range(num_timesteps):
noise = self.unet(noise, condition)
return noiseExample 4:
import torch
from torchvision import transforms
class VideoDiffusionModel(torch.nn.Module):
def __init__(self):Example 5:
from torchvision import transforms
class VideoDiffusionModel(torch.nn.Module):
def __init__(self):
super(VideoDiffusionModel, self).__init__()Example 6:
class VideoDiffusionModel(torch.nn.Module):
def __init__(self):
super(VideoDiffusionModel, self).__init__()
self.unet = UNet() # Define your U-Net architecture here
Example 7:
def __init__(self):
super(VideoDiffusionModel, self).__init__()
self.unet = UNet() # Define your U-Net architecture here
def forward(self, noise, condition):Example 8:
def forward(self, noise, condition):
for t in range(num_timesteps):
noise = self.unet(noise, condition)
return noise
```View Source: https://arxiv.org/abs/2511.16669v1