AutoForge

AutoForge is a Python tool for generating 3D printed layered models from an input image. Using a learned optimization strategy with a Gumbel softmax formulation, AutoForge assigns materials per layer and produces both a discretized composite image and a 3D-printable STL file. It also generates swap instructions to guide the printer through material changes during a multi-material print.

TLDR: It uses a picture to generate a 3D layer image that you can print with a 3d printer. Similar to Hueforge, but without the manual work (and without the artistic control).

You can now run Autoforge for free in your browser thanks to Huggingface space support.

This includes the option to run it locally if you have a powerful pc and don't want to limit yourself to the Huggingface computing limits.
For this simply go to the Huggingface space and pull the docker container for this project (upper right corner -> three dots -> "run locally")

Example

All examples use only the 27 BambuLab Basic PLA filaments, currently available in Hueforge 0.9.0, the background color is set to black. The pruning is set to a maximum of 8 color and 20 swaps, so each image uses at most 8 different colors and swaps the filament at most 20 times.

Input Image

Autoforge Output

Features

• Image-to-Model Conversion: Converts an input image into a layered model suitable for 3D printing.
• Learned Optimization: Optimizes per-pixel height and per-layer material assignments using PyTorch.
• Learned Heightmap: Optimizes the height of the layered model to create more detailed prints.
• Gumbel Softmax Sampling: Leverages the Gumbel softmax method to decide material assignments for each layer.
• STL File Generation: Exports an ASCII STL file based on the optimized height map.
• Swap Instructions: Generates clear swap instructions for changing materials during printing.
• Live Visualization: (Optional) Displays live composite images during the optimization process.
• Hueforge export: Outputs a project file that can be opened with hueforge.

Manual Installation

To install AutoForge, simply install the current version from PyPI:

   pip install -U autoforge

If you have problems running the code on your gpu, please refer to the Pytorch Homepage for help.
CUDA, ROCm, and MPS (Apple Metal) are supported, but you need to install the correct version of pytorch for your system.

Usage

The script is run from the command line and accepts several arguments. Below is an example command:

Note: You will need Hueforge installed to export your filament CSV.
To get your CSV file, simply go to the "Filaments" menu in Hueforge, click the export button, select your filaments, and export them as a CSV file.

autoforge --input_image path/to/input_image.jpg --csv_file path/to/materials.csv 

We also support json files. If you want to use your personal Hueforge library (found in %APPDATA%\HueForge\Filaments\personal_library.json) you can run the command with:

autoforge --input_image path/to/input_image.jpg --json_file %APPDATA%\HueForge\Filaments\personal_library.json

If you want to limit the amount of colors the program can use, you can set these as command line arguments.
For Example: 8 colors and a maximum of 20 swaps:

autoforge --input_image path/to/input_image.jpg --csv_file path/to/materials.csv --pruning_max_colors 8 --pruning_max_swaps 20

Command Line Arguments

• --config (Optional) Path to a configuration file with the settings.

• --input_image (Required) Path to the input image.

• --csv_file Path to the CSV file containing material data. The CSV should include columns for the brand, name, color (hex code), and TD values.

• --json_file Path to the json file containing material data.
  Note: Either a csv or json file has to be given.

• --output_folder Folder where output files will be saved (default: output).

• --iterations Number of optimization iterations (default: 2000).

• --warmup_fraction Fraction of iterations for keeping the tau at the initial value (default: 0.25).

• --learning_rate_warmup_fraction Fraction of iterations that the learning rate is increasing (warmup) (default: 0.25).

• --init_tau Initial tau value for Gumbel-Softmax (default: 1.0).

• --final_tau Final tau value for the Gumbel-Softmax formulation (default: 0.01).

• --learning_rate Learning rate for optimization (default: 0.015).

• --layer_height Layer thickness in millimeters (default: 0.04).

• --max_layers Maximum number of layers (default: 75).
  Note: This is about 3mm + the background height

• --min_layers Minimum number of layers (default: 0). Used to limit height of pruning.

• --background_height Height of the background in millimeters (default: 0.24).
  Note: The background height must be divisible by the layer height.

• --background_color Background color in hexadecimal format (default: #000000 aka Black).
  Note: The solver currently assumes that you have a solid color in the background, which means a color with a TD value of 4 or less (if you have a background height of 0.4).

• --visualize enable live visualization of the composite image during optimization (default: True).

• --stl_output_size Size of the longest dimension of the output STL file in millimeters (default: 200).

• --processing_reduction_factor Reduction factor for the processing size compared to the output size (default: 2 - half resolution).

• --nozzle_diameter Diameter of the printer nozzle in millimeters (default: 0.4).
  Note: Details smaller than half this value will be ignored.

• --early_stopping Number of steps without improvement before stopping (default: 10000).

• --perform_pruning Perform pruning after optimization (default: True).
  Note: This is highly recommended even if you don't have a color/color swap limit, as it actually increases the quality of the output.

• --fast_pruning Perform pruning in chunks. 10-15x speedup compared to accurate method (default: False).

• --fast_pruning_percent Size of fast pruning chunks in percent (default: 0.5) (50%).

• --pruning_max_colors Max number of colors allowed after pruning (default: 100).

• --pruning_max_swaps Max number of swaps allowed after pruning (default: 100).

• --pruning_max_layer Max number of layers allowed after pruning (default: 75).

• --random_seed Random seed for reproducibility (default: 0 (disabled)).

• --mps Flag to use the Metal Performance Shaders (MPS) backend if available.

• --tensorboard Flag to enable TensorBoard logging.

• --run_name (Optional) Name of the run used for TensorBoard logging.

• --num_init_rounds Number of rounds to choose the starting height map from (default: 128).

• --num_init_cluster_layers Number of layers to cluster the image into (default: -1).

• --disable_visualization_for_gradio Simple switch to disable the matplotlib render window for gradio rendering (default: 0).

• --best_of Run the entire program multiple times and output the best result (default: 1)

Outputs

After running, the following files will be created in your specified output folder:

• Discrete Composite Image: final_model.png
• STL File: final_model.stl
• Hueforge Project File: project_file.hfp
• Swap Instructions: swap_instructions.txt

Development

To have a "nightly" version of the repository or have live updating changes during development please do the following:

git clone https://github.com/hvoss-techfak/AutoForge.git
cd AutoForge
conda create -n forge python=3.11
conda activate forge
pip install -e .

If the installed pytorch version has no cuda support execute the following:

conda activate forge
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118

Known Bugs

• The optimizer can sometimes get stuck in a local minimum. If this happens, try running the optimization again with different settings.

License

AutoForge © 2025 by Hendric Voss is licensed under CC BY-NC-SA 4.0. The software is provided as-is and comes with no warranty or guarantee of support.

Acknowledgements

First and foremost:

• Hueforge for providing the inspiration for this project. Without it, this project would not have been possible.

AutoForge makes use of several open source libraries:

• PyTorch
• Optax
• OpenCV
• Matplotlib
• Pandas
• TQDM
• ConfigArgParse

Example Images:
Nature Stock photos by Vecteezy
Ai Generated Stock photos by Vecteezy
Animal Stock photos by Vecteezy
People Stock photos by Vecteezy
Psychedelic Stock photos by Vecteezy
Ocean Stock photos by Vecteezy
Psychic Stock photos by Vecteezy
Psychedelic Stock photos by Vecteezy
Pattern Stock photos by Vecteezy
Forest Stock photos by Vecteezy
Stick Figure Kids Stock photos by Vecteezy
Forest Stock photos by Vecteezy
Nature Stock photos by Vecteezy
Ai Generated Stock photos by Vecteezy
Animal Stock photos by Vecteezy

Happy printing!