naoto先生のAI講座 #4 stable diffusionを使った動画作成

The speaker begins by greeting the audience and expressing gratitude for the fourth session of an AI lecture series. They mention that they would appreciate comments and interactions from the audience. The session is a follow-up to previous discussions about the cutting-edge applications of AI, particularly focusing on content created using AI Stable Diffusion. The speaker suggests starting with the first session for context and plans to continue the discussion based on that foundation. The main topic for today involves using Stable Diffusion, specifically with control nets, to create videos. The speaker chooses a theme related to Mahalo-chan's roller debut and aims to create a video using it. They discuss the process of video creation, starting from static images to animated sequences, and the use of control nets to process individual frames into a video format. The speaker also touches on the limitations and possibilities of AI in video creation, suggesting that while there are many methods, they will focus on a simple approach for this session.

The speaker dives into the process of creating dance videos using AI, starting with a dance video that will be used as the base for learning. They mention the challenges of creating videos with complex movements and the limitations of current AI capabilities in handling such details. The focus is on using a video of a dance that has a clear front-facing view, as the AI model is primarily trained on front-facing images. The speaker then introduces the use of FFmpeg, a tool for converting videos into image frames, and provides a brief tutorial on how to use it. They also discuss the importance of image size and resolution in the process and decide to test a smaller image size to save time. The speaker demonstrates the process of converting a video into frames and explains the next steps, which involve replacing characters in the images using control nets.

The speaker explains the concept of control nets, which are tools that use the features of a character's shape to apply an image onto it. They mention that there are many types of control nets, but for this session, they will use a simple and effective one. The speaker demonstrates the process of applying an image to a character using control nets, highlighting the importance of matching the character's pose and shape to achieve a realistic result. They also discuss the use of depth maps and head position in image processing, but ultimately decide to use an open pose for the dance video, as it captures the character's stance and movement information. The speaker then shows how to extract the character's pose information using a control net and mentions the use of batch processing to apply the control net to multiple images efficiently.

The speaker discusses the process of batch processing to apply control nets to multiple images at once, which is necessary for creating a video. They mention the use of a tool called 'Autosaving' to extract pose information from the images and prepare them for video creation. The speaker also talks about the challenges of using batch processing, such as the potential loss of original image information due to denoising. They demonstrate how to set up the batch processing, including selecting the correct pose information and adjusting settings like sampling steps and denoising strength. The speaker runs a test to show the progress of the batch processing and notes that while it takes some time, it is manageable for creating a video. They also mention the possibility of using the processed images to create a video with only the open pose information, which can be useful for various video editing tasks.

The speaker concludes the video creation process by discussing the final steps, which involve converting the processed images back into a video format using FFmpeg. They show the before and after results of the video, highlighting the improvements made through the use of control nets and image processing. The speaker also touches on the potential for using AI in video creation, suggesting that it can be applied to various types of videos, such as dance or personal videos. They encourage the audience to experiment with creating videos using AI and to stay updated with the latest developments in the field. The speaker ends the session by inviting questions and hints at future topics, such as using AI to create videos with multiple characters.

In the final paragraph, the speaker wraps up the session by summarizing the video creation process and inviting the audience to engage in creating their own videos using AI. They mention the importance of staying informed about the latest advancements in AI and video technology and suggest various platforms, such as Twitter and Discord, where one can learn and share knowledge. The speaker expresses gratitude for the audience's participation and looks forward to future sessions, hinting at potential topics like creating videos with multiple characters using AI. They conclude with a farewell, signaling the end of the current session.