Stable Video Diffusion:手搓视频,3步生成电影级画面

Stable Video Diffusion简介

Stable Video Diffusion是Stability AI发布的视频生成大模型,基于Stability AI原有的Stable Diffusion文生图模型,Stable Video Diffusion可实现文生视频。‌

应用场景

内容创作:快速生成短视频广告、教学素材或电影分镜。

3D 建模辅助:通过多视角生成简化 3D 模型构建流程。

创意探索:实验动态艺术效果或虚拟角色动画。

Stable Video Diffusion的使用方法

Stable Video Diffusion现已上线东方超算AI应用商店(访问网址appmall.com),使用价格低至2.3元/小时,平台按分钟数实际计费,比如使用10分钟,那么只会收费约0.4元。

记住用户名“admin”,点击【复制】按钮复制密码,点击【进入应用】就可以登录使用啦。

操作步骤

点击video选项-输入提示词(每个画面分行)-点击执行队列即可

提示词:

(((masterpiece, best quality))), cyberpunk cityscape at night, holographic billboards, rain-soaked streets, neon reflections, flying drones, motion blur effect, camera pans upward –neg (blurry, low contrast)
(((highly detailed))), enchanted forest, bioluminescent plants, misty atmosphere, fairies dancing, sunlight rays through trees, camera moves in a circular path –neg (monotone, unrealistic colors)
(((intricate gears))), steam-powered robot, brass pipes, smoke particles, clockwork mechanisms, gears rotating, camera zooms into moving parts –neg (plastic textures, messy composition)

让我们看看效果吧

### Stable Video Diffusion Implementation and Techniques In the context of video processing within computer vision, stable video diffusion refers to methods that ensure consistency across frames while performing tasks such as denoising or pose estimation. For instance, VIBE (Video Inference for Human Body Pose and Shape Estimation) employs a robust approach combining temporal smoothing with spatial refinement to achieve stability over time[^1]. #### Temporal Smoothing Temporal smoothing is crucial for maintaining consistent estimations throughout consecutive frames. This technique leverages information from neighboring frames to refine current frame predictions. Specifically, algorithms like Kalman filters can be utilized where prediction updates are made based on previous states. ```python import numpy as np def kalman_filter(x, P, measurement): """ Simple example of applying Kalman filter. :param x: State estimate vector :param P: Estimate covariance matrix :param measurement: Current measurement value """ H = np.eye(len(x)) # Measurement function R = np.eye(len(measurement)) * 0.1 # Measurement noise y = measurement - np.dot(H, x) S = np.dot(np.dot(H, P), H.T) + R K = np.dot(np.dot(P, H.T), np.linalg.inv(S)) x = x + np.dot(K, y) I = np.eye(len(x)) P = np.dot((I - np.dot(K, H)), P) return x, P ``` #### Spatial Refinement Spatial refinement focuses on enhancing local details by considering pixel-level relationships within individual frames. Convolutional neural networks (CNNs) play an essential role here due to their ability to capture hierarchical patterns effectively. By integrating CNN-based models into pipelines, more accurate feature extraction becomes possible leading to improved overall performance. For both aspects mentioned above, it's important to note how they contribute towards achieving stable results when dealing with dynamic scenes involving human body movements captured through videos.
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