从零开始，用英伟达T4、A10训练小型文生视频模型，几小时搞定

# Operating System module for interacting with the operating system
import os

# Module for generating random numbers
import random

# Module for numerical operations
import numpy as np

# OpenCV library for image processing
import cv2

# Python Imaging Library for image processing
from PIL import Image, ImageDraw, ImageFont

# PyTorch library for deep learning
import torch

# Dataset class for creating custom datasets in PyTorch
from torch.utils.data import Dataset

# Module for image transformations
import torchvision.transforms as transforms

# Neural network module in PyTorch
import torch.nn as nn

# Optimization algorithms in PyTorch
import torch.optim as optim

# Function for padding sequences in PyTorch
from torch.nn.utils.rnn import pad_sequence

# Function for saving images in PyTorch
from torchvision.utils import save_image

# Module for plotting graphs and images
import matplotlib.pyplot as plt

# Module for displaying rich content in IPython environments
from IPython.display import clear_output, display, HTML

# Module for encoding and decoding binary data to text
import base64

# Create a directory named 'training_dataset'
os.makedirs('training_dataset', exist_ok=True)

# Define the number of videos to generate for the dataset
num_videos = 10000

# Define the number of frames per video (1 Second Video)
frames_per_video = 10

# Define the size of each image in the dataset
img_size = (64, 64)

# Define the size of the shapes (Circle)
shape_size = 10 

# Define text prompts and corresponding movements for circles
prompts_and_movements = [
 ("circle moving down", "circle", "down"), # Move circle downward
 ("circle moving left", "circle", "left"), # Move circle leftward
 ("circle moving right", "circle", "right"), # Move circle rightward
 ("circle moving diagonally up-right", "circle", "diagonal_up_right"), # Move circle diagonally up-right
 ("circle moving diagonally down-left", "circle", "diagonal_down_left"), # Move circle diagonally down-left
 ("circle moving diagonally up-left", "circle", "diagonal_up_left"), # Move circle diagonally up-left
 ("circle moving diagonally down-right", "circle", "diagonal_down_right"), # Move circle diagonally down-right
 ("circle rotating clockwise", "circle", "rotate_clockwise"), # Rotate circle clockwise
 ("circle rotating counter-clockwise", "circle", "rotate_counter_clockwise"), # Rotate circle counter-clockwise
 ("circle shrinking", "circle", "shrink"), # Shrink circle
 ("circle expanding", "circle", "expand"), # Expand circle
 ("circle bouncing vertically", "circle", "bounce_vertical"), # Bounce circle vertically
 ("circle bouncing horizontally", "circle", "bounce_horizontal"), # Bounce circle horizontally
 ("circle zigzagging vertically", "circle", "zigzag_vertical"), # Zigzag circle vertically
 ("circle zigzagging horizontally", "circle", "zigzag_horizontal"), # Zigzag circle horizontally
 ("circle moving up-left", "circle", "up_left"), # Move circle up-left
 ("circle moving down-right", "circle", "down_right"), # Move circle down-right
 ("circle moving down-left", "circle", "down_left"), # Move circle down-left
]

# Define function with parameters
def create_image_with_moving_shape(size, frame_num, shape, direction):  

# Create a new RGB image with specified size and white background
img = Image.new('RGB', size, color=(255, 255, 255)) 

 # Create a drawing context for the image
 draw = ImageDraw.Draw(img) 

 # Calculate the center coordinates of the image
 center_x, center_y = size[0] // 2, size[1] // 2 

 # Initialize position with center for all movements
 position = (center_x, center_y) 

 # Define a dictionary mapping directions to their respective position adjustments or image transformations
 direction_map = {  
# Adjust position downwards based on frame number 
"down": (0, frame_num * 5 % size[1]),  
# Adjust position to the left based on frame number 
"left": (-frame_num * 5 % size[0], 0),  
# Adjust position to the right based on frame number 
"right": (frame_num * 5 % size[0], 0),  
# Adjust position diagonally up and to the right 
"diagonal_up_right": (frame_num * 5 % size[0], -frame_num * 5 % size[1]),  
# Adjust position diagonally down and to the left 
"diagonal_down_left": (-frame_num * 5 % size[0], frame_num * 5 % size[1]),  
# Adjust position diagonally up and to the left 
"diagonal_up_left": (-frame_num * 5 % size[0], -frame_num * 5 % size[1]),  
# Adjust position diagonally down and to the right 
"diagonal_down_right": (frame_num * 5 % size[0], frame_num * 5 % size[1]),  
# Rotate the image clockwise based on frame number 
"rotate_clockwise": img.rotate(frame_num * 10 % 360, center=(center_x, center_y), fillcolor=(255, 255, 255)),  # Rotate the image counter-clockwise based on frame number 
"rotate_counter_clockwise": img.rotate(-frame_num * 10 % 360, center=(center_x, center_y), fillcolor=(255, 255, 255)),  
# Adjust position for a bouncing effect vertically 
"bounce_vertical": (0, center_y - abs(frame_num * 5 % size[1] - center_y)),  
# Adjust position for a bouncing effect horizontally 
"bounce_horizontal": (center_x - abs(frame_num * 5 % size[0] - center_x), 0),  
# Adjust position for a zigzag effect vertically 
"zigzag_vertical": (0, center_y - frame_num * 5 % size[1]) if frame_num % 2 == 0 else (0, center_y + frame_num * 5 % size[1]),  
# Adjust position for a zigzag effect horizontally 
"zigzag_horizontal": (center_x - frame_num * 5 % size[0], center_y) if frame_num % 2 == 0 else (center_x + frame_num * 5 % size[0], center_y),  
# Adjust position upwards and to the right based on frame number 
"up_right": (frame_num * 5 % size[0], -frame_num * 5 % size[1]),  
# Adjust position upwards and to the left based on frame number 
"up_left": (-frame_num * 5 % size[0], -frame_num * 5 % size[1]),  
# Adjust position downwards and to the right based on frame number 
"down_right": (frame_num * 5 % size[0], frame_num * 5 % size[1]),  
# Adjust position downwards and to the left based on frame number 
"down_left": (-frame_num * 5 % size[0], frame_num * 5 % size[1])  
}

# Check if direction is in the direction map 
if direction in direction_map:  
# Check if the direction maps to a position adjustment 
if isinstance(direction_map[direction], tuple):  
# Update position based on the adjustment 
position = tuple(np.add(position, direction_map[direction]))  
else: # If the direction maps to an image transformation 
# Update the image based on the transformation 
img = direction_map[direction] 

# Return the image as a numpy array 
return np.array(img)

# Iterate over the number of videos to generate
for i in range(num_videos):
    # Randomly choose a prompt and movement from the predefined list
    prompt, shape, direction = random.choice(prompts_and_movements)   
 
   # Create a directory for the current video 
   video_dir = f'training_dataset/video_{i}' 
   os.makedirs(video_dir, exist_ok=True)    

   # Write the chosen prompt to a text file in the video directory
   with open(f'{video_dir}/prompt.txt', 'w') as f:
        f.write(prompt)
   
   # Generate frames for the current video
   for frame_num in range(frames_per_video):
        # Create an image with a moving shape based on the current frame number, shape, and direction
        img = create_image_with_moving_shape(img_size, frame_num, shape, direction)        

        # Save the generated image as a PNG file in the video directory
        cv2.imwrite(f'{video_dir}/frame_{frame_num}.png', img)

# Define a dataset class inheriting from torch.utils.data.Dataset
class TextToVideoDataset(Dataset):
    def __init__(self, root_dir, transform=None):
        # Initialize the dataset with root directory and optional transform
        self.root_dir = root_dir
        self.transform = transform
        # List all subdirectories in the root directory
        self.video_dirs = [os.path.join(root_dir, d) for d in os.listdir(root_dir) if os.path.isdir(os.path.join(root_dir, d))]
        # Initialize lists to store frame paths and corresponding prompts
        self.frame_paths = []
        self.prompts = []

        # Loop through each video directory
        for video_dir in self.video_dirs:
            # List all PNG files in the video directory and store their paths
            frames = [os.path.join(video_dir, f) for f in os.listdir(video_dir) if f.endswith('.png')]
            self.frame_paths.extend(frames)
            # Read the prompt text file in the video directory and store its content
            with open(os.path.join(video_dir, 'prompt.txt'), 'r') as f:
                prompt = f.read().strip()
            # Repeat the prompt for each frame in the video and store in prompts list
            self.prompts.extend([prompt] * len(frames))

    # Return the total number of samples in the dataset
    def __len__(self):
        return len(self.frame_paths)

    # Retrieve a sample from the dataset given an index
    def __getitem__(self, idx):
        # Get the path of the frame corresponding to the given index
        frame_path = self.frame_paths[idx]
        # Open the image using PIL (Python Imaging Library)
        image = Image.open(frame_path)
        # Get the prompt corresponding to the given index
        prompt = self.prompts[idx]

        # Apply transformation if specified
        if self.transform:
            image = self.transform(image)

        # Return the transformed image and the prompt
        return image, prompt

# Define a class for text embedding
class TextEmbedding(nn.Module):
    # Constructor method with vocab_size and embed_size parameters
    def __init__(self, vocab_size, embed_size):
        # Call the superclass constructor
        super(TextEmbedding, self).__init__()
        # Initialize embedding layer
        self.embedding = nn.Embedding(vocab_size, embed_size)

    # Define the forward pass method
    def forward(self, x):
        # Return embedded representation of input
        return self.embedding(x) 

class Generator(nn.Module):
 def __init__(self, text_embed_size):
 super(Generator, self).__init__()

# Fully connected layer that takes noise and text embedding as input 
self.fc1 = nn.Linear(100 + text_embed_size, 256 * 8 * 8)  
# Transposed convolutional layers to upsample the input 
self.deconv1 = nn.ConvTranspose2d(256, 128, 4, 2, 1) 
self.deconv2 = nn.ConvTranspose2d(128, 64, 4, 2, 1) 
self.deconv3 = nn.ConvTranspose2d(64, 3, 4, 2, 1) # Output has 3 channels for RGB images  

# Activation functions 
self.relu = nn.ReLU(True) # ReLU activation function 
self.tanh = nn.Tanh() # Tanh activation function for final output

def forward(self, noise, text_embed): 
# Concatenate noise and text embedding along the channel dimension 
x = torch.cat((noise, text_embed), dim=1)  

# Fully connected layer followed by reshaping to 4D tensor 
x = self.fc1(x).view(-1, 256, 8, 8)  

# Upsampling through transposed convolution layers with ReLU activation 
x = self.relu(self.deconv1(x)) 
x = self.relu(self.deconv2(x))  

# Final layer with Tanh activation to ensure output values are between -1 and 1 (for images) 
x = self.tanh(self.deconv3(x))  

return x

class Discriminator(nn.Module):
    def __init__(self):
        super(Discriminator, self).__init__()       
      
        # Convolutional layers to process input images        
        self.conv1 = nn.Conv2d(3, 64, 4, 2, 1)   # 3 input channels (RGB), 64 output channels, kernel size 4x4, stride 2, padding 1
        self.conv2 = nn.Conv2d(64, 128, 4, 2, 1) # 64 input channels, 128 output channels, kernel size 4x4, stride 2, padding 1
        self.conv3 = nn.Conv2d(128, 256, 4, 2, 1) # 128 input channels, 256 output channels, kernel size 4x4, stride 2, padding 1
       
         # Fully connected layer for classification
        self.fc1 = nn.Linear(256 * 8 * 8, 1)  # Input size 256x8x8 (output size of last convolution), output size 1 (binary classification)       

         # Activation functions
        self.leaky_relu = nn.LeakyReLU(0.2, inplace=True)  # Leaky ReLU activation with negative slope 0.2
        self.sigmoid = nn.Sigmoid()  # Sigmoid activation for final output (probability)

    def forward(self, input):
        # Pass input through convolutional layers with LeakyReLU activation
        x = self.leaky_relu(self.conv1(input))
        x = self.leaky_relu(self.conv2(x))
        x = self.leaky_relu(self.conv3(x))        

        # Flatten the output of convolutional layers
        x = x.view(-1, 256 * 8 * 8)        

        # Pass through fully connected layer with Sigmoid activation for binary classification
        x = self.sigmoid(self.fc1(x))       

        return x

# Check for GPU
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# Create a simple vocabulary for text prompts
all_prompts = [prompt for prompt, _, _ in prompts_and_movements]  # Extract all prompts from prompts_and_movements list
vocab = {word: idx for idx, word in enumerate(set(" ".join(all_prompts).split()))}  # Create a vocabulary dictionary where each unique word is assigned an index
vocab_size = len(vocab)  # Size of the vocabulary
embed_size = 10  # Size of the text embedding vector

def encode_text(prompt):
    # Encode a given prompt into a tensor of indices using the vocabulary
    return torch.tensor([vocab[word] for word in prompt.split()])

# Initialize models, loss function, and optimizers
text_embedding = TextEmbedding(vocab_size, embed_size).to(device)  # Initialize TextEmbedding model with vocab_size and embed_size
netG = Generator(embed_size).to(device)  # Initialize Generator model with embed_size
netD = Discriminator().to(device)  # Initialize Discriminator model
criterion = nn.BCELoss().to(device)  # Binary Cross Entropy loss function
optimizerD = optim.Adam(netD.parameters(), lr=0.0002, betas=(0.5, 0.999))  # Adam optimizer for Discriminator
optimizerG = optim.Adam(netG.parameters(), lr=0.0002, betas=(0.5, 0.999))  # Adam optimizer for Generator

# Number of epochs
num_epochs = 13

# Iterate over each epoch
for epoch in range(num_epochs):
    # Iterate over each batch of data
    for i, (data, prompts) in enumerate(dataloader):
        # Move real data to device
        real_data = data.to(device)        

        # Convert prompts to list
        prompts = [prompt for prompt in prompts]

        # Update Discriminator
        netD.zero_grad()  # Zero the gradients of the Discriminator
        batch_size = real_data.size(0)  # Get the batch size
        labels = torch.ones(batch_size, 1).to(device)  # Create labels for real data (ones)
        output = netD(real_data)  # Forward pass real data through Discriminator
        lossD_real = criterion(output, labels)  # Calculate loss on real data
        lossD_real.backward()  # Backward pass to calculate gradients        
 
        # Generate fake data
        noise = torch.randn(batch_size, 100).to(device)  # Generate random noise
        text_embeds = torch.stack([text_embedding(encode_text(prompt).to(device)).mean(dim=0) for prompt in prompts])  # Encode prompts into text embeddings
        fake_data = netG(noise, text_embeds)  # Generate fake data from noise and text embeddings
        labels = torch.zeros(batch_size, 1).to(device)  # Create labels for fake data (zeros)
        output = netD(fake_data.detach())  # Forward pass fake data through Discriminator (detach to avoid gradients flowing back to Generator)
        lossD_fake = criterion(output, labels)  # Calculate loss on fake data
        lossD_fake.backward()  # Backward pass to calculate gradients
        optimizerD.step()  # Update Discriminator parameters

        # Update Generator
        netG.zero_grad()  # Zero the gradients of the Generator
        labels = torch.ones(batch_size, 1).to(device)  # Create labels for fake data (ones) to fool Discriminator
        output = netD(fake_data)  # Forward pass fake data (now updated) through Discriminator
        lossG = criterion(output, labels)  # Calculate loss for Generator based on Discriminator's response
        lossG.backward()  # Backward pass to calculate gradients
        optimizerG.step()  # Update Generator parameters        

# Print epoch information
print(f"Epoch [{epoch + 1}/{num_epochs}] Loss D: {lossD_real + lossD_fake}, Loss G: {lossG}")

## OUTPUT ##

Epoch [1/13] Loss D: 0.8798642754554749, Loss G: 1.300612449645996
Epoch [2/13] Loss D: 0.8235711455345154, Loss G: 1.3729925155639648
Epoch [3/13] Loss D: 0.6098687052726746, Loss G: 1.3266581296920776

...

# Save the Generator model's state dictionary to a file named 'generator.pth'
torch.save(netG.state_dict(), 'generator.pth')

# Save the Discriminator model's state dictionary to a file named 'discriminator.pth'
torch.save(netD.state_dict(), 'discriminator.pth')

# Inference function to generate a video based on a given text promptdef generate_video(text_prompt, num_frames=10):    # Create a directory for the generated video frames based on the text prompt    os.makedirs(f'generated_video_{text_prompt.replace(" ", "_")}', exist_ok=True)        # Encode the text prompt into a text embedding tensor    text_embed = text_embedding(encode_text(text_prompt).to(device)).mean(dim=0).unsqueeze(0)        # Generate frames for the video    for frame_num in range(num_frames):        # Generate random noise        noise = torch.randn(1, 100).to(device)                # Generate a fake frame using the Generator network        with torch.no_grad():            fake_frame = netG(noise, text_embed)                # Save the generated fake frame as an image file        save_image(fake_frame, f'generated_video_{text_prompt.replace(" ", "_")}/frame_{frame_num}.png')# usage of the generate_video function with a specific text promptgenerate_video('circle moving up-right')

# Define the path to your folder containing the PNG frames
folder_path = 'generated_video_circle_moving_up-right'

# Get the list of all PNG files in the folder
image_files = [f for f in os.listdir(folder_path) if f.endswith('.png')]

# Sort the images by name (assuming they are numbered sequentially)
image_files.sort()

# Create a list to store the frames
frames = []

# Read each image and append it to the frames list
for image_file in image_files:
 image_path = os.path.join(folder_path, image_file)
 frame = cv2.imread(image_path)
 frames.append(frame)

# Convert the frames list to a numpy array for easier processing
frames = np.array(frames)

# Define the frame rate (frames per second)
fps = 10

# Create a video writer object
fourcc = cv2.VideoWriter_fourcc(*'XVID')
out = cv2.VideoWriter('generated_video.avi', fourcc, fps, (frames[0].shape[1], frames[0].shape[0]))

# Write each frame to the video
for frame in frames:
out.write(frame)

# Release the video writer
out.release()