63 lines
3.9 KiB
Markdown
63 lines
3.9 KiB
Markdown
# Steerable Motion, ComfyUI custom nodes & workflows node for steering videos with batches of images
|
|
|
|
Steerable Motion is a set of ComfyUI nodes and workflows for travelling between images.
|
|
|
|
## Installation in Comfy
|
|
|
|
1. If you haven't already, install [ComfyUI](https://github.com/comfyanonymous/ComfyUI) and [Comfy Manager](https://github.com/ltdrdata/ComfyUI-Manager) - you can find instructions on their pages.
|
|
2. When the workflow opens, download the dependent nodes by pressing "Install Missing Custom Nodes" in Comfy Manager. Search and download the required models from Comfy Manager also.
|
|
|
|
## Wan
|
|
|
|
The Wan approach uses VACE to create anchor images and continuations from previous images, which are chained together at the end:
|
|
|
|

|
|
|
|
|
|
### Sample workflow for Wan
|
|
|
|
You can find a workflow [here](demo/Vace_Travel.json) to get started.
|
|
|
|
## Animatediff
|
|
|
|
The Animatediff approach uses a combination of IP-Adapter and SparseCtrl to travel between images:
|
|
|
|

|
|
|
|
|
|
### 5 basic workflows for Animatediff
|
|
|
|
Below are 5 basic workflows - each with their own weird and unique characteristics - all with differing levels of adherence and different types of motion - most of the changes come from tweaking the IPA configuration and switching out base models:
|
|
|
|
- [Smooth n' Steady](https://raw.githubusercontent.com/banodoco/steerable-motion/main/demo/steerable-motion_smooth-n-steady.json): tends to have nice smooth motion - good starting point
|
|
- [Rad Attack](https://raw.githubusercontent.com/banodoco/steerable-motion/main/demo/steerable-motion_rad-attack.json): probably the best for realistic motion
|
|
- [Slurshy Realistiche](https://raw.githubusercontent.com/banodoco/steerable-motion/main/demo/steerable-motion_slurshy-realistiche.json): moves in a slightly realistic manner but is a little bit slurshy
|
|
- [Chocky Realistiche](https://raw.githubusercontent.com/banodoco/steerable-motion/main/demo/steerable-motion_chocky-realistiche.json): realistic-ish but very blocky
|
|
- [Liquidy Loop](https://raw.githubusercontent.com/banodoco/steerable-motion/main/demo/steerable-motion_liquidy-loop.json): smooth and liquidy
|
|
|
|
You can see each in acton below:
|
|
|
|

|
|
|
|
## Philosophy for getting the most from these
|
|
|
|
This isn't an approach like text to video that will perform well out of the box, it's more like a paint brush - an artistic tool that you need to figure out how to get the best from.
|
|
|
|
Through trial and error, you'll need to build an understanding of how the motion and settings work, what its limitations are, which inputs images work best with it, etc.
|
|
|
|
It won't work for everything but if you can figure out how to wield it, this approach can provide enough control for you to make beautiful things that match your imagination precisely.
|
|
|
|
In both cases, tweaking the settings can greatly influence the motion - for example, below you can see two examples of the same images animated - but with the one setting tweaked, the length of each frame's influence:
|
|
|
|

|
|
|
|
## Want to give feedback, or join a community who are pushing open source models to their artistic and technical limits?
|
|
|
|
You're very welcome to drop into our Discord [here](https://discord.com/invite/8Wx9dFu5tP).
|
|
|
|
## Credits
|
|
|
|
For Animatediff, the code draws heavily from Cubiq's [IPAdapter_plus](https://github.com/cubiq/ComfyUI_IPAdapter_plus), while the workflow uses Kosinkadink's [Animatediff Evolved](https://github.com/Kosinkadink/ComfyUI-AnimateDiff-Evolved) and [ComfyUI-Advanced-ControlNet](https://github.com/Kosinkadink/ComfyUI-Advanced-ControlNet), Fizzledorf's [Fizznodes](https://github.com/FizzleDorf/ComfyUI_FizzNodes), Fannovel16's [Frame Interpolation](https://github.com/Fannovel16/ComfyUI-Frame-Interpolation) and more.
|
|
|
|
For Wan, it's built on top of the work of Kijai's wonderful [ComfyUI-WanVideoWrapper](https://github.com/kijai/ComfyUI-WanVideoWrapper) and of course the VACE and Wan teams.
|