An "opinionated" Wave Function Collapse implementation with a set of nodes for comfyui.
The workflows folder contains workflow examples and also the sample image used as input (sample_img.png). The tileset used in the sample image is cc0, and is available at opengameart/mage-city.
Usage and Implementation clarifications
This implementation is not a pure-to-form implementation of the wave collapse algorithm.
In the spirit of being used as a visual tool, there is no way to specify global constraints, and all local constraints are inferred from the given samples and can't be further refined. Although this makes some sets of rules hard to specify, the envisioned application is not to arrive at a complete solution necessarily, but rather a partial one, which can be completed using diffusion.
In this implementation the “wave” of possibilities is not kept and updated on the entire grid; instead, only the boundary of the collapsed nodes is evaluated, expanding the boundary at each iteration, and validating only the states of cells adjacent to the expanded ones.
In light of this, it would be fair to name it something else, since instead of a wave of possibilities the algorithm only satisfies local constraints until reaching an impossible state, at which point it backtracks. Nevertheless, the wave-function-collapse captures and helps clarify the potential applications of this algorithm.
This implementation searches for possible states using a best-first search which also considers the node’s depth to make the search greedy towards already deep paths, speeding up the generation towards a partially acceptable state, i.e. a state that hasn’t collapsed all the cells but should be somewhat complete, provided the constraints are not very intricate.
The search nodes store a hashcode of the world state and the number of collapsed tiles (depth).
This information is not only used to prune the search but also for backtracking.
Instead of storing the complete state in each node to enable backtracking, stored actions are undone until a common ancestor node is found, i.e. nodes that share the same depth and hashcode.
Altought expected to be rare, it is possible when backtracking that two different states share the same depth and hashcode pair. In such cases, the set of open tiles will mismatch the actual underlying state and the search may stop early due to a key error. There may also exist edge cases where no error is raised and a potentially invalid state is returned.
Troubleshooting
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The custom nodes in this module require py_search, listed in the
requirement.txtfile.When using a portable release of comfyui, navigate to the python_embeded folder and using the cmd/terminal run:
.\python -s -m pip install -r ..\ComfyUI\custom_nodes\comfyui_wfc_like\requirements.txt -
wfc.pyrequires the use of Python version 3.5 or higher due to the use of PEP 448 - Additional Unpacking Generalizations.
Custom Nodes Documentation
This node analyses an image made out of tiles and extracts: each tile type, their count, and all existing 3x3 tile arrangments (constraints).
This data can be sent via the sample output slot to generator nodes, to create "similar" images.
Required Inputs
img_batch : an image, or batch of images of the same tileset.
If given a batch as input, the node will only return a single output, where the tile counts and adjacencies in all the images in the batch are considered. If given a list, it will analyze each image, or batch, in the list, and the output will be a list.
tile_width & tile_height : the width and height in pixels of a single tile.
output_tiles : if set to true, all the different tile types will be sent as an image batch via the unique_tiles output slot.
Outputs
sample : the data obtained from the input samples, used as input to generator nodes.
unique_tiles : image batch with the tile types.
Generates a state using the provided sample data and starting_state using wave-function-collapse.
Required Inputs
sample : the sample data with the possible tile neighborhoods (constraints), and frequencies.
starting_state : the starting state to generate, or complete.
seed : controls randomness, to ensure reproduceable outputs.
max_freq_adjust : the maximum possible weight for frequency adjustments.
If set to Zero ( 0 ), the tile frequency is the sample input is completely ignored. Depending on the constraints of a given sample, this might result in a seldom biased generation that tends to overplace a subset of tile types which better minimizes the entropy.
If set to One ( 1 ), the search will consider the difference in the tile frequencies of the generation versus the provided sample, and try to skew the generation to compensate for the differences. This can make the generation slower since selected tiles might be worse at minimizing entropy and skew the generation towards contradictions.
The frequency adjustment is not weighted equally at every step in the generation, and it can be negated if the generation has frequent and high-depth backtracks that suggest hard-to-satisfy constraints. There is no way to adjust this behavior besides editing the source code.
use_8_cardinals : if set to true, all the 3x3 sections in the generation must correspond to an existing 3x3 tile neighborhood in the sample input. Otherwise, the diagonals are ignored.
relax_validation : if set to true, does not check if tiles adjacent to tiles considered for open positions retain a valid neighbor configuration. This allows for a potentially faster generation with fewer incomplete sections; however, it will likely generate some invalid 3x3 tile patches, that do not satisfy the given constraints.
plateau_check_interval : defines how many nodes to process, when exploring the possibilities tree, before checking the highest depth found so far. If two consecutive checks share the same highest depth then the search is stopped.
As an alternative to specifying a concrete interval, the following options can be used instead:
Zero ( 0 ): The search continues until either: a fully complete state is found; or all possible combinations have been explored.
Minus One ( -1 ): The interval is automatically set based on the size of the initial_state. The goal is to strike a balance: not too long (to avoid excessive runtime). not too short (to prevent stopping prematurely). Keep in mind that the effectiveness of this auto-setup may vary depending on your specific use case.
Optional Inputs
custom_temperature_config : temperature is a custom mechanic implemented to weight the random component, and frequency adjustment, of a node's cost. The temperature increases as backtracks increase in frequency and depth, lowering the influence of these components and favoring the most probable states to skew the generation away from contradictions.
Use the Custom Temperature custom node to constrain the temperature bounds and define the initial temperature. ( additional clarifications in the Custom Temperature node documentation )
custom_node_value_config : change the weight of the different components used to weight a node's value. Nodes with lower values are visited first.
By default, if not set by the user, the used weights are 1, 1, and 0.
Use the Custom Node Value custom node to create an alternative configuration. ( additional clarifications in the Custom Node Value node documentation )
Outputs
state : The generated state.
To convert it to an image, use the Decode (WFC) node.
It can also be used as input to other nodes for additional processing.
The Generate node uses a numeric representation of a tile, it does not use the tiles directly. These representations are kept in a Sample node's output sample.
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The Decode (WFC) node converts a state, i.e. a 2D matrix with these representations, into an image using the tiles stored in a given sample.
Incomplete states can also be converted into images. Cells without any tile assigned are outputted with the color black; the mask output will be similar, but in reverse, having non-empty cells black and empty cells white.
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The Encode (WFC) converts an image to a state, which can be passed to a Generate node.
Tiles not present in the samples are set as empty cells; the Encode node can encode partially complete states for a Generate node to fill the missing tiles.
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The EmptyState (WFC) generates a state with the specified number of tiles specified by the
widthandheightinputs.
Given a set of tiles, as an image batch - tiles_batch, sets all other tile types as empty cells in the provided state. Alternatively, if reverse is set to True, the same tile type cells are set to empty.
Some potential applications:
- Obtain a mask using a Decode (WFC) for inpainting.
- Use different WFC samples at different semantic levels; e.g. generate a forest and some rivers; then, generate the forest details.
- Attempt to patch problematic tiles; this is a workaround since there is no way to remove a specific tile from a sample in the current implementation.
Similar to Generate node, however, different generations can be executed in parallel when using lists as input.
max_parallel_tasks defines the maximum number of generations that can run simultaneously.