从零学AI动画 ComfyUI AnimateDiff 工作流详细教程 AnimateDiff V3 AI 无闪烁动画转绘 丝滑动画制作

The video begins with an introduction to AnimateDiff, a detailed workflow guide suitable for novices. The presenter suggests saving the video for future reference and highlights the recent update to version 3, available on GitHub. The workflow construction is demonstrated step by step, starting with building a processor and using image prompts. The presenter guides through connecting nodes, downloading a decoder, and setting up a basic workflow for micro-maps. The video also covers adding the AnimDev model, adjusting parameters, and compiling a video with a specified frame rate. Issues with frame support are addressed, and a solution involving a uniform upper and lower text option is presented. The GPU performance is briefly discussed, and the video concludes with a demonstration of the animation effect and a reminder to check the video description for resources.

The second paragraph delves into advanced animation techniques using AnimDev. It covers creating an animation effect with 48 frames, adjusting text options, and rendering a video in MP4 format to avoid color gaps. The presenter introduces dynamic movement controls, including slots for various directions and transformations. The video demonstrates the effect of a shrink node and discusses the optimization of the workflow. It also touches on using LCM and LoRa models, adjusting their intensity, and the importance of the simulator for LCM models. The paragraph concludes with a workflow for grouping and renaming, and a brief mention of disinfection, possibly referring to a cleaning or optimization process in the context of the software.

The third paragraph introduces the time travel function in AnimateDiff, which allows for frame-specific commands to create dynamic effects. The presenter explains how to use this feature by setting different prompts at various frame intervals, such as opening and closing eyes or head movements. The video demonstrates the effect of these commands using the latest v3 model and adjusting the frame rate for better results. The GPU usage is noted to be efficient, and the paragraph concludes with a mention of the rich action of AnimateDiff and the option for viewers to experiment with the time travel function on their own.

The fourth paragraph focuses on video upscaling and kernel control within the AnimateDiff workflow. It details the process of selecting an upscale model and connecting it with a kernel model to enhance image quality and resolution. The presenter guides through the workflow of connecting nodes, including the image font and video output, and setting the output format to MP4. The paragraph also covers adjusting the kernel model for different effects and the use of full weight control to soften lines and achieve desired outcomes. The presenter encourages viewers to experiment with different models and parameters to optimize their results.

The final paragraph introduces the Adept workflow, combining Adept with IMDef for a video reset-like effect. The presenter outlines the steps to build this workflow, including connecting output nodes, loading RP Adept model nodes, and selecting full body or face models. The video demonstrates the similarity in style between the enemy def video and the load image, despite using different models. The paragraph concludes with a reminder that viewers can adjust the Adept model and checkpoint model to achieve different results. The presenter also mentions that the models and workflow used in the video will be available for download and encourages viewers to leave a comment if the video was helpful.