AnimateDiff Lightning - Local Install Guide - Stable Video
TLDRThe video script introduces viewers to the use of the Anime Diff model within the Automatic 1111 and Comy UI platforms. It outlines the process of selecting and downloading models, configuring settings for optimal results, and highlights the versatility of the model for various inputs. The tutorial progresses with detailed steps on integrating the model into both platforms, emphasizing the importance of the Animated Diff extension and the configuration of specific parameters for best outcomes. The video concludes with suggestions for experimentation with different prompts and settings to achieve high-quality animations, inviting viewers to share their thoughts in the comments.
Takeaways
- ๐ The video introduces how to use the 'lightning for anime diff' tool within Automatic 1111 and Comy UI.
- ๐ There are two models available in the dropdown menu, but additional models can be tested for free online.
- ๐ Users can download models with varying steps (1, 2, 4, 8), with the narrator finding the 4-step Comy models to work better.
- ๐ A PDF is recommended for its interesting information on control nets for DV pose and head, and the ability to use video to video input.
- ๐ง To use the tool in Automatic 1111, the 'animated diff' extension is required, which can be updated and installed through the platform.
- ๐จ The settings recommended by the narrator include DPM Plus+ SD with four sampling steps and a noise scale of 0.65 for upscaling.
- ๐ The CFG scale is set to 1, contrary to the PDF examples which show no CFG, as the narrator found it more effective.
- ๐น The video demonstrates the process of using the tool with a short video, noting that longer videos did not work as well.
- ๐ฅ The narrator also explains how to use the tool in Comy UI, including setting up the workflow and managing extensions.
- ๐ For looping videos longer than 16 frames, the tool can split and merge them into multiple 16-frame videos.
- ๐ The video concludes with suggestions to experiment with different prompts and settings for optimal results and smoother animations.
Q & A
What is the main topic of the video transcript?
-The main topic of the video transcript is how to use the Lightning for Anime Diff model within Automatic 1111 and Comy UI.
What are the two models available in the dropdown menu for testing?
-The two models available for testing are the one-step and two-step models, with additional models like four-step, eight-step, etc., available for download.
What is the recommended model for use within Automatic 1111?
-For Automatic 1111, the Comy models are recommended as they work better according to the speaker's experience.
What is the purpose of the PDF mentioned in the transcript?
-The PDF contains interesting information about control nets for DV pose and head, as well as the capability to use video to video input, highlighting the versatility of the model.
How does one install and update the Animated Diff extension?
-To install or update the Animated Diff extension, one should go to the 'Available' tab, search for 'Animate Diff', and follow the prompts to apply and restart the application.
What are the recommended settings for using the four-step model in Automatic 1111?
-The recommended settings include DPM Plus+ SD with four sampling steps, upscale latent with D noise of 0.65, and upscaling of 1.5.
What is the optimal frame size for the Lightning for Anime Diff model?
-The optimal frame size that works best at the moment is 16 frames.
How does the video combiners work in Comy UI?
-The video combiners take a loop longer than 16 frames, split it into multiple 16-frames videos, and then merge them together.
What is the role of the CFG scale in the settings?
-The CFG scale is used to adjust the control over the generated content, with the speaker setting it to one for better results.
What is the significance of the motion scale in the Comy UI workflow?
-The motion scale allows users to adjust the amount of motion in the generated animation, with the option to lower it if there's too much motion.
What advice does the speaker give for using the four-step model?
-The speaker advises starting with a short prompt, then gradually experimenting with longer prompts, negative prompts, and different settings to achieve the desired quality and speed of rendering.
Outlines
๐จ Introduction to Anime Diff in Automatic 1111 and Comy UI
This paragraph introduces the Anime Diff tool and its integration with Automatic 1111 and Comy UI. It explains the availability of two models from a dropdown menu and the option to test them for free. The speaker shares their preference for the Comu models and mentions the availability of different step models. A PDF with additional information, such as control nets for DV pose and head, and the versatility of the model for video to video input is also highlighted. The paragraph continues with instructions on how to use Anime Diff within Automatic 1111, emphasizing the need for the Animated Diff extension and the process of updating it. The speaker provides specific settings they found effective, including the use of the four-step model, sampling steps, and CFG scale. The process of loading the model and the recommended frame count for optimal results is also discussed.
๐ Advanced Usage and Results with Anime Diff in Comy UI
This paragraph delves into the advanced usage of Anime Diff within Comy UI, particularly for Patreon supporters. It covers the workflow for using the tool, including the setup of the extensions and the process of handling loops longer than 16 frames. The speaker explains how to manage individual notes and the importance of using the correct folders for model loading. The paragraph also discusses the use of the Legacy model for simplicity and the adjustment of motion scale to manage motion in the output. The speaker suggests experimenting with short prompts and varying settings for optimal results. The paragraph concludes with a comparison of the output quality and frame rate between Automatic 1111 and Comy UI, noting the smoother and more detailed results achieved with the latter. The speaker encourages viewers to experiment with different settings and provides a call to action for feedback in the comments section.
Mindmap
Keywords
๐กLightning for Anime
๐กAutomatic 1111
๐กComy UI
๐กModels
๐กControl Nets
๐กVideo to Video Input
๐กDP PM Plus+ SD
๐กUpscale Latent with D Noise
๐กCFG Scale
๐กAnimate Diff
๐กFrame Rate
Highlights
Lightning for anime diff is out, and this tutorial will show you how to use it within Automatic 1111 and Comy UI.
There are only two models available in the dropdown menu, but they can be tested for free to see if you like the results.
The models available are one step, two step, four step, and eight step models.
Inside Automatic 1111, the Comy models work better for the user.
There is a PDF available with interesting information, including control nets for DV pose and head, and the capability to use video to video input.
To use the extension in Automatic 1111, you need to have the Animated Diff extension installed and updated.
The user found that the DPM Plus+ SD works best with four sampling steps when using the four-step model.
The upscaling options with a D noise of 0.65 and a scale of 1.5 are mentioned as part of the settings.
CFG scale is set to one for the user's preference, contrary to the examples in the PDF which show no CFG.
The user shares a workflow for Patreon supporters, including how to manage and use extensions effectively.
The user mentions that longer videos did not work as well as shorter ones, with 16 frames being the optimal size.
The output of the process is described as looking like a painting with higher fix and upscaling.
The user also demonstrates how to use the model in Comy, including setting up the environment and loading the checkpoint.
The user suggests playing around with motion scale and different VAEs to find the best settings for the user's needs.
The user recommends starting with a short prompt and experimenting with longer prompts and negative prompts for faster rendering and decent quality.
The user invites feedback in the comments and encourages viewers to try out the process.
The user mentions that the End screen has other content to watch, encouraging viewers to explore and engage further.