2025年视频生成技术全景：从图像与文本到动态视觉内容

# 2025年基于扩散模型的多模态视频生成示例实现
import torch
import numpy as np
from transformers import AutoProcessor, AutoModelForVideoGeneration
import cv2
from PIL import Image
import requests
from io import BytesIO
import os

class AdvancedMultimodalVideoGenerator:
    def __init__(self, model_name="stabilityai/stable-video-diffusion-2-base"):
        # 加载预训练的多模态视频生成模型
        self.model = AutoModelForVideoGeneration.from_pretrained(model_name)
        self.processor = AutoProcessor.from_pretrained(model_name)
        
        # 移至GPU（如果可用）
        if torch.cuda.is_available():
            self.model.to("cuda")
        
        # 设置默认参数
        self.default_params = {
            "num_frames": 25,
            "frame_rate": 8,
            "height": 320,
            "width": 576,
            "guidance_scale": 3.0,
            "num_inference_steps": 25,
            "seed": 42
        }
    
    def generate_video_from_image(self, image, prompt=None, negative_prompt=None, **kwargs):
        # 合并默认参数和用户提供的参数
        params = {**self.default_params, **kwargs}
        
        # 设置随机种子
        torch.manual_seed(params["seed"])
        np.random.seed(params["seed"])
        
        # 处理输入图像
        if isinstance(image, str):
            if image.startswith("http"):
                # 从URL加载图像
                response = requests.get(image)
                image = Image.open(BytesIO(response.content)).convert("RGB")
            else:
                # 从本地文件加载图像
                image = Image.open(image).convert("RGB")
        
        # 处理输入
        inputs = self.processor(
            images=image,
            text=prompt,
            negative_text=negative_prompt,
            return_tensors="pt"
        )
        
        # 移至GPU（如果可用）
        if torch.cuda.is_available():
            inputs = {k: v.to("cuda") for k, v in inputs.items()}
        
        # 生成视频
        with torch.no_grad():
            outputs = self.model.generate_video(
                **inputs,
                num_frames=params["num_frames"],
                height=params["height"],
                width=params["width"],
                guidance_scale=params["guidance_scale"],
                num_inference_steps=params["num_inference_steps"]
            )
        
        # 提取视频帧
        frames = outputs.frames.cpu().numpy()
        
        return frames
    
    def generate_video_from_text(self, prompt, negative_prompt=None, initial_image=None, **kwargs):
        # 合并默认参数和用户提供的参数
        params = {**self.default_params, **kwargs}
        
        # 设置随机种子
        torch.manual_seed(params["seed"])
        np.random.seed(params["seed"])
        
        # 如果没有提供初始图像，创建一个空白图像
        if initial_image is None:
            initial_image = Image.new("RGB", (params["width"], params["height"]), color=(237, 230, 240))
        
        # 处理输入
        inputs = self.processor(
            images=initial_image,
            text=prompt,
            negative_text=negative_prompt,
            return_tensors="pt"
        )
        
        # 移至GPU（如果可用）
        if torch.cuda.is_available():
            inputs = {k: v.to("cuda") for k, v in inputs.items()}
        
        # 生成视频
        with torch.no_grad():
            outputs = self.model.generate_video(
                **inputs,
                num_frames=params["num_frames"],
                height=params["height"],
                width=params["width"],
                guidance_scale=params["guidance_scale"],
                num_inference_steps=params["num_inference_steps"]
            )
        
        # 提取视频帧
        frames = outputs.frames.cpu().numpy()
        
        return frames
    
    def save_video(self, frames, output_path, fps=8):
        # 将帧保存为视频文件
        # frames 应该是形状为 [batch_size, num_frames, height, width, channels] 的数组
        
        # 获取视频参数
        batch_size, num_frames, height, width, channels = frames.shape
        
        # 创建视频写入器
        fourcc = cv2.VideoWriter_fourcc(*'mp4v')
        out = cv2.VideoWriter(output_path, fourcc, fps, (width, height))
        
        # 写入每一帧
        for i in range(num_frames):
            # 获取当前帧并转换为BGR格式（OpenCV使用BGR）
            frame = frames[0, i].copy()
            if frame.max() <= 1.0:
                frame = (frame * 255).astype(np.uint8)
            
            # 转换为BGR格式
            frame_bgr = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)
            
            # 写入视频
            out.write(frame_bgr)
        
        # 释放视频写入器
        out.release()
        print(f"视频已保存到: {output_path}")

# 示例使用代码
# generator = AdvancedMultimodalVideoGenerator()
# 
# # 从图像生成视频
# print("加载图像...")
# image_path = "https://example.com/dog.jpg"  # 替换为实际的图像路径
# prompt = "a dog running in a park, sunny day, high quality, realistic"
# negative_prompt = "blurry, low quality, unrealistic, choppy"
# print("正在从图像生成视频...")
# frames_from_image = generator.generate_video_from_image(
#     image=image_path,
#     prompt=prompt,
#     negative_prompt=negative_prompt,
#     num_frames=25,
#     frame_rate=8,
#     guidance_scale=3.0
# )
# print("图像到视频生成完成")
# generator.save_video(frames_from_image, "dog_running.mp4", fps=8)
# 
# # 从文本生成视频
# print("正在从文本生成视频...")
# text_prompt = "a cat playing with a ball of yarn, cozy living room, 4k, realistic"
# frames_from_text = generator.generate_video_from_text(
#     prompt=text_prompt,
#     negative_prompt=negative_prompt,
#     num_frames=25,
#     frame_rate=8,
#     guidance_scale=3.0
# )
# print("文本到视频生成完成")
# generator.save_video(frames_from_text, "cat_playing.mp4", fps=8)

# 视频生成模型知识蒸馏优化示例
import torch
import torch.nn as nn
import torch.optim as optim
from transformers import AutoProcessor, AutoModelForVideoGeneration
import numpy as np
import time
import psutil
import os

# 定义知识蒸馏损失函数
class VideoDistillationLoss(nn.Module):
    def __init__(self, temperature=1.0, alpha=0.5):
        super().__init__()
        self.temperature = temperature
        self.alpha = alpha
        self.kl_div = nn.KLDivLoss(reduction="batchmean")
        self.mse_loss = nn.MSELoss()
    
    def forward(self, student_outputs, teacher_outputs, targets=None):
        # 计算教师模型和学生模型输出之间的KL散度和MSE损失
        if hasattr(student_outputs, 'frames') and hasattr(teacher_outputs, 'frames'):
            # 计算帧间MSE损失
            mse_loss = self.mse_loss(student_outputs.frames, teacher_outputs.frames)
            
            # 如果提供了目标标签，计算额外的损失
            if targets is not None:
                target_loss = self.mse_loss(student_outputs.frames, targets)
                total_loss = self.alpha * mse_loss + (1 - self.alpha) * target_loss
            else:
                total_loss = mse_loss
        else:
            # 默认返回零损失（实际应用中应根据模型结构调整）
            total_loss = torch.tensor(0.0, requires_grad=True, device=student_outputs.device if hasattr(student_outputs, 'device') else 'cpu')
        
        return total_loss

class VideoKnowledgeDistillation:
    def __init__(self, teacher_model_name="stabilityai/stable-video-diffusion-2-base", student_model_name="your-custom-small-model"):
        # 加载预训练的教师模型
        self.teacher_model = AutoModelForVideoGeneration.from_pretrained(teacher_model_name)
        self.teacher_model.eval()  # 设置教师模型为评估模式
        
        # 加载预训练的学生模型（假设已存在）
        # 在实际应用中，可能需要创建一个小型版本的视频生成模型
        self.student_model = AutoModelForVideoGeneration.from_pretrained(student_model_name)
        
        # 加载处理器
        self.processor = AutoProcessor.from_pretrained(teacher_model_name)
        
        # 移至GPU（如果可用）
        if torch.cuda.is_available():
            self.teacher_model.to("cuda")
            self.student_model.to("cuda")
        
        # 初始化知识蒸馏损失函数
        self.distillation_loss_fn = VideoDistillationLoss(temperature=2.0, alpha=0.7)
        
        # 初始化优化器
        self.optimizer = optim.AdamW(self.student_model.parameters(), lr=5e-6)
    
    def compute_temporal_loss(self, student_frames, teacher_frames):
        # 计算时序一致性损失
        # 计算相邻帧之间的差异
        student_diff = torch.abs(student_frames[:, 1:] - student_frames[:, :-1])
        teacher_diff = torch.abs(teacher_frames[:, 1:] - teacher_frames[:, :-1])
        
        # 计算时序差异的MSE损失
        temporal_loss = nn.MSELoss()(student_diff, teacher_diff)
        
        return temporal_loss
    
    def train_step(self, image, timesteps=None, prompt=None, negative_prompt=None):
        # 单步训练
        
        # 处理输入
        inputs = self.processor(
            images=image,
            text=prompt,
            negative_text=negative_prompt,
            return_tensors="pt"
        )
        
        # 移至GPU（如果可用）
        if torch.cuda.is_available():
            inputs = {k: v.to("cuda") for k, v in inputs.items()}
        
        # 获取教师模型的输出（不带梯度）
        with torch.no_grad():
            teacher_outputs = self.teacher_model.generate_video(
                **inputs,
                num_frames=16,
                height=320,
                width=576,
                guidance_scale=3.0,
                num_inference_steps=25
            )
        
        # 启用学生模型的训练模式
        self.student_model.train()
        
        # 获取学生模型的输出
        student_outputs = self.student_model.generate_video(
            **inputs,
            num_frames=16,
            height=320,
            width=576,
            guidance_scale=3.0,
            num_inference_steps=25
        )
        
        # 计算蒸馏损失
        distillation_loss = self.distillation_loss_fn(student_outputs, teacher_outputs)
        
        # 计算时序一致性损失
        temporal_loss = self.compute_temporal_loss(student_outputs.frames, teacher_outputs.frames)
        
        # 总损失
        total_loss = distillation_loss + 0.5 * temporal_loss
        
        # 反向传播和优化
        self.optimizer.zero_grad()
        total_loss.backward()
        self.optimizer.step()
        
        return {
            "total_loss": total_loss.item(),
            "distillation_loss": distillation_loss.item(),
            "temporal_loss": temporal_loss.item()
        }

# 示例使用代码
# distiller = VideoKnowledgeDistillation(
#     teacher_model_name="stabilityai/stable-video-diffusion-2-base",
#     student_model_name="your-custom-small-model"
# )
# 
# # 准备训练数据
# # image = Image.open("example.jpg")
# # timesteps = torch.randint(0, 1000, (1,), device="cuda")
# # 
# # 执行训练步骤
# # loss_dict = distiller.train_step(
# #     image=image,
# #     timesteps=timesteps
# # )
# # print(f"训练损失: {loss_dict}")

# 视频生成模型量化技术示例
import torch
from transformers import AutoModelForVideoGeneration, AutoProcessor
import os
import time
import psutil

class QuantizedVideoGenerator:
    def __init__(self, model_name="stabilityai/stable-video-diffusion-2-base"):
        # 加载预训练的视频生成模型
        self.model = AutoModelForVideoGeneration.from_pretrained(model_name)
        self.processor = AutoProcessor.from_pretrained(model_name)
        
        # 保存原始模型
        self.original_model = self.model
        
        # 量化后的模型（初始为None）
        self.quantized_model = None
        
        # 移至GPU（如果可用）
        if torch.cuda.is_available():
            self.model.to("cuda")
    
    def quantize_model(self):
        # 对模型进行量化
        
        # 注意：视频生成模型通常很大，量化过程可能需要较长时间和较多内存
        
        # 动态量化
        print("开始对模型进行量化...")
        start_time = time.time()
        
        # 将模型移至CPU进行量化
        self.model.to("cpu")
        
        # 应用动态量化
        # 注意：由于视频生成模型的复杂性，这里只量化部分层
        self.quantized_model = torch.quantization.quantize_dynamic(
            self.model,
            {torch.nn.Linear, torch.nn.Conv2d},  # 只量化线性层和卷积层
            dtype=torch.qint8
        )
        
        end_time = time.time()
        print(f"模型量化完成，耗时: {end_time - start_time:.2f}秒")
        
        # 如果需要，可以将量化后的模型移回GPU
        if torch.cuda.is_available():
            self.quantized_model.to("cuda")
        
        return self.quantized_model
    
    def generate_quantized_video(self, initial_image, prompt=None, negative_prompt=None, **kwargs):
        # 使用量化后的模型生成视频
        if self.quantized_model is None:
            # 如果还没有量化模型，先进行量化
            self.quantize_model()
        
        # 处理输入
        inputs = self.processor(
            images=initial_image,
            text=prompt,
            negative_text=negative_prompt,
            return_tensors="pt"
        )
        
        # 移至GPU（如果可用）
        if torch.cuda.is_available():
            inputs = {k: v.to("cuda") for k, v in inputs.items()}
        
        # 生成视频
        with torch.no_grad():
            outputs = self.quantized_model.generate_video(
                **inputs,
                num_frames=kwargs.get("num_frames", 16),
                height=kwargs.get("height", 320),
                width=kwargs.get("width", 576),
                guidance_scale=kwargs.get("guidance_scale", 3.0),
                num_inference_steps=kwargs.get("num_inference_steps", 25)
            )
        
        # 返回生成的视频帧
        return outputs.frames.cpu().numpy()
    
    def compare_performance(self, initial_image, **kwargs):
        # 比较原始模型和量化模型的性能
        import os
        import psutil
        
        # 获取当前进程
        process = psutil.Process(os.getpid())
        
        # 原始模型性能
        start_time = time.time()
        start_memory = process.memory_info().rss / 1024 / 1024  # MB
        
        # 保存原始模型状态
        original_unet = self.pipeline.unet if hasattr(self, 'pipeline') else None
        original_vae = self.pipeline.vae if hasattr(self, 'pipeline') else None
        
        # 生成视频
        frames_original = self.generate_quantized_video(initial_image, **kwargs)  # 使用原始模型
        
        end_time = time.time()
        end_memory = process.memory_info().rss / 1024 / 1024  # MB
        
        original_time = end_time - start_time
        original_memory = end_memory - start_memory
        
        # 量化模型性能
        self.quantize_model()  # 应用量化
        
        start_time = time.time()
        start_memory = process.memory_info().rss / 1024 / 1024  # MB
        
        frames_quantized = self.generate_quantized_video(initial_image, **kwargs)  # 使用量化模型
        
        end_time = time.time()
        end_memory = process.memory_info().rss / 1024 / 1024  # MB
        
        quantized_time = end_time - start_time
        quantized_memory = end_memory - start_memory
        
        # 恢复原始模型
        if hasattr(self, 'pipeline'):
            self.pipeline.unet = original_unet
            self.pipeline.vae = original_vae
        
        # 计算压缩率和加速比
        memory_reduction = (1 - quantized_memory / original_memory) * 100 if original_memory > 0 else 0
        
        # 打印性能比较结果
        print("性能比较:")
        print(f"原始模型 - 时间: {original_time:.2f}秒, 内存占用: {original_memory:.2f}MB")
        print(f"量化模型 - 时间: {quantized_time:.2f}秒, 内存占用: {quantized_memory:.2f}MB")
        print(f"内存减少: {memory_reduction:.2f}%")
        
        return {
            "original_time": original_time,
            "original_memory": original_memory,
            "quantized_time": quantized_time,
            "quantized_memory": quantized_memory,
            "memory_reduction": memory_reduction,
            "original_frames": frames_original,
            "quantized_frames": frames_quantized
        }

# 示例使用代码（简化版）
# from PIL import Image
# 
# # 创建一个示例图像
# # width, height = 576, 320
# # initial_image = Image.new("RGB", (width, height), color=(237, 230, 240))
# 
# # 初始化量化生成器
# # quantized_generator = QuantizedVideoGenerator()
# 
# # 生成原始视频
# # original_frames = quantized_generator.generate_quantized_video(initial_image)
# # quantized_generator.save_video(original_frames, "original_video.mp4")
# 
# # 对模型进行量化
# # quantized_pipeline = quantized_generator.quantize_model()
# 
# # 使用量化模型生成视频
# # quantized_frames = quantized_generator.generate_quantized_video(initial_image)
# # quantized_generator.save_video(quantized_frames, "quantized_video.mp4")
# 
# # 比较性能
# # performance_comparison = quantized_generator.compare_performance(initial_image)
# # print("性能比较结果:", performance_comparison)