This repository contains custom nodes for InvokeAI to create cross-view or parallel-view stereograms. Stereograms are 2D images that, when viewed properly, reveal a 3D scene. Check out r/crossview for tutorials.
Following is an example stereo image with an embedded workflow. To load the workflow, drag and drop it into your InvokeAI (copy and Ctrl-V seems to drop metdata, including the workflow). Then, in InvokeAI, right-click the image and select "Load Workflow".
Note
The main branch is for InvokeAI 5.x and 4.x. Please use the invoke3
branch if you're on an older version of InvokeAI.
Clone this repository into your <invokeai>/nodes/ directory. Then restart
InvokeAI. This will make new nodes available in the workflow editor (see below
for details). For example:
cd $INVOKE_ROOT/nodes
git clone https://github.com/simonfuhrmann/invokeai-stereo.gitAlternatively, create a directory under your <invokeai>/nodes/ directory, and
copy stereogram.py and __init__.py into this directory.
Alternatively (NOT recommended), the stereogram.py can also be placed into the
application directory under
<invokeai>/.venv/lib/python3.10/site-packages/invokeai/app/invocations/.
The file stereogram.py contains three nodes for InvokeAI's workflow system. An
example cross-view workflow for the nodes is here. To turn this into a
parallel-view image, simply swap the left/right images on the "Make Stereo
Pair" node.
Note
If you get depth artifacts around the silhouettes of foreground objects, it is likely related to the depth processor. See GitHub bug.
In particular, these nodes are provided:
-
Dilate Depth Map: This node is optional. It takes an input depth map and performs a dialtion operation on the depth map. This causes close depth values (white) to expand spatially. This is useful for depth maps that are auto-generated by AI models (such as DepthAnything), which are usually quite tight around the foreground objects, leading to foreground colors bleeding into the background during
-
Warp Image: Takes an input depth map and color image, and warps the color image according to geometric information from the depth map, and fills occlusions. The node can be configured using a
divergenceand aseparationvalue (both in percept). Larger divergence causes a stronger 3D effect (and more artifacts). Separation simply shifts the image. A positive divergence generates right-eye images, a negative divergence generates left-eye images. (But note for cross-view, the images have to be swapped.) A positive separation value causes the image to get shifted left, a negative separation value causes the image to get shifted right. -
Make Stereo Pair: Horizontally stacks a left and right input image into a stereogram. Optionally, a border is drawn around the left and right image with a configurable color.
- Cross-view stereograms are viewed by crossing your eyes such that the left image pane becomes the right-eye image, and the right image pane becomes the left eye image.
- Parallel-view stereograms are viewed by diverging your eyes such that left and right images are viewed by the left and right eye, respectively. Note, for larger disparities (e.g., on a computer screen), cross-view images are much easier to view.
Image warping converts per-pixel depth values into disparities. Pixels in the input image are then shifted to their new location in the output image with respect to these disparities. This reveals occluded (unknown) areas in the output image that need to be filled.
Filling occlusions is a hard problem, and this implementation is by no means perfect. Most of the warping implementation has been taken from the A1111 plugin here.