Improve model card for Optimize Any Topology (OAT)

README.md

@@ -1,198 +1,102 @@
 ---
 library_name: diffusers
+pipeline_tag: text-to-image
+license: apache-2.0
+datasets:
+  - OpenTO/OpenTO
 ---
 
-# Model Card for Model ID
+# Optimize Any Topology (OAT)
 
-<!-- Provide a quick summary of what the model is/does. -->
+This repository contains the official implementation of the **Optimize Any Topology (OAT)** model, a foundation model framework for shape- and resolution-free structural topology optimization.
 
+**Paper**: [Optimize Any Topology: A Foundation Model for Shape- and Resolution-Free Structural Topology Optimization](https://huggingface.co/papers/2510.23667)
+**Code**: https://github.com/ahnobari/OptimizeAnyTopology
 
+<p align="center">
+  <img src="https://github.com/user-attachments/assets/6200fa2c-0cd5-49af-897c-67688f28c446" alt="Optimize Any Topology Image">
+</p>
 
 ## Model Details
 
 ### Model Description
 
-<!-- Provide a longer summary of what this model is. -->
+Structural topology optimization (TO) is central to engineering design but remains computationally intensive due to complex physics and hard constraints. We introduce Optimize Any Topology (OAT), a foundation-model framework that directly predicts minimum-compliance layouts for arbitrary aspect ratios, resolutions, volume fractions, loads, and fixtures.
 
-This is the model card of a 🧨 diffusers model that has been pushed on the Hub. This model card has been automatically generated.
+OAT combines a resolution- and shape-agnostic autoencoder with an implicit neural-field decoder and a conditional latent-diffusion model. It is trained on OpenTO, a new corpus of 2.2 million optimized structures covering 2 million unique boundary-condition configurations. OAT lowers mean compliance up to 90% relative to the best prior models and delivers sub-1 second inference on a single GPU across resolutions from 64x64 to 256x256 and aspect ratios as high as 10:1. These results establish OAT as a general, fast, and resolution-free framework for physics-aware topology optimization.
 
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
+**NEWS: Accepted to Neurips 2025!**
 
-### Model Sources [optional]
+-   **Developed by:** The authors of the OAT paper.
+-   **Model type:** Conditional Latent Diffusion Model for structural topology optimization.
+-   **Language(s):** Not applicable (generates structural layouts).
+-   **Finetuned from model [optional]:** The model is trained from scratch using a two-stage process (NFAE then LDM).
 
-<!-- Provide the basic links for the model. -->
+### Model Sources
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+-   **Repository:** https://github.com/ahnobari/OptimizeAnyTopology
+-   **Paper:** [Optimize Any Topology: A Foundation Model for Shape- and Resolution-Free Structural Topology Optimization](https://huggingface.co/papers/2510.23667)
 
 ## Uses
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
 ### Direct Use
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
-
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
+OAT is intended for direct use in structural topology optimization. It can generate minimum-compliance layouts for a wide range of engineering design problems with arbitrary aspect ratios, resolutions, volume fractions, loads, and fixtures. Its fast inference capabilities make it suitable for rapid prototyping and design exploration.
 
 ### Out-of-Scope Use
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
+This model is specifically designed for structural topology optimization. Its use for general-purpose image generation, tasks unrelated to engineering design, or without understanding the physical constraints and domain limitations is considered out-of-scope.
 
 ## Bias, Risks, and Limitations
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
+The model's performance is tied to the distribution of the OpenTO training data. While comprehensive, potential biases or limitations may arise when applied to highly novel or out-of-distribution boundary conditions or material properties not well-represented in the dataset. Users should be aware that generated designs may require further validation (e.g., via Finite Element Analysis) to ensure real-world structural integrity and performance.
 
 ### Recommendations
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+Users should refer to the paper and the GitHub repository for a complete understanding of the model's capabilities and limitations. Validation of generated designs against established engineering principles is recommended, especially for critical applications.
 
 ## How to Get Started with the Model
 
-Use the code below to get started with the model.
-
-[More Information Needed]
+For detailed instructions on installation, environment setup (including MKL optimized packages), training, and inference (sample generation and evaluation), please refer to the official [GitHub repository](https://github.com/ahnobari/OptimizeAnyTopology). The repository provides scripts and guidelines to replicate results and use the pre-trained checkpoints.
 
 ## Training Details
 
-### Training Data
-
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-[More Information Needed]
-
-### Training Procedure
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
+The model is trained in two stages:
 
-[More Information Needed]
+1.  **Neural Field Auto-Encoder (NFAE)**: Maps variable resolution and shapes into a common latent space.
+2.  **Latent Diffusion Model (LDM)**: Trained to generate samples using a conditional diffusion process on the pre-computed latents from the NFAE.
 
+### Training Data
 
-#### Training Hyperparameters
+The model is trained on **OpenTO**, a new corpus of 2.2 million optimized structures covering 2 million unique boundary-condition configurations. The dataset is publicly available on Hugging Face 🤗 at [OpenTO/OpenTO](https://huggingface.co/datasets/OpenTO/OpenTO).
 
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+### Pre-Trained Checkpoints
 
-#### Speeds, Sizes, Times [optional]
+Pre-trained checkpoints for both the Auto Encoder (NFAE) and Latent Diffusion Model (LDM) are available on Hugging Face:
 
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+*   **Auto Encoder**: [OpenTO/NFAE](https://huggingface.co/OpenTO/NFAE)
+*   **Latent Diffusion Model**: [OpenTO/LDM](https://huggingface.co/OpenTO/LDM)
+*   **Auto Encoder Large Latent**: [OpenTO/NFAE_L](https://huggingface.co/OpenTO/NFAE_L)
+*   **Latent Diffusion Large Latent**: [OpenTO/LDM_L](https://huggingface.co/OpenTO/LDM_L)
 
-[More Information Needed]
+These checkpoints can be loaded using the `.from_pretrained` function from the `OAT.Models` module.
 
 ## Evaluation
 
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
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-[More Information Needed]
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-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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-[More Information Needed]
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-#### Metrics
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-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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-[More Information Needed]
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-### Results
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-[More Information Needed]
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-#### Summary
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-
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-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
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-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
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-[More Information Needed]
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-### Compute Infrastructure
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-[More Information Needed]
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-#### Hardware
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-[More Information Needed]
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-#### Software
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-[More Information Needed]
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-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
-
-[More Information Needed]
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-**APA:**
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-[More Information Needed]
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-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
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-## More Information [optional]
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-[More Information Needed]
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-## Model Card Authors [optional]
+OAT has been rigorously evaluated on four public benchmarks and two challenging unseen tests. The results demonstrate that OAT significantly lowers mean compliance (up to 90%) compared to previous state-of-the-art models. It also achieves impressive inference speeds, delivering sub-1 second results on a single GPU across various resolutions and aspect ratios.
 
-[More Information Needed]
+## Citation
 
-## Model Card Contact
+If you find this work useful or inspiring for your research, please consider citing our paper:
 
-[More Information Needed]
+```bibtex
+@misc{optimizeanytopology2025,
+      title={Optimize Any Topology: A Foundation Model for Shape- and Resolution-Free Structural Topology Optimization},
+      author={Ahnobari, [Authors Not Provided In Prompt]},
+      year={2025},
+      eprint={2510.23667},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={https://arxiv.org/abs/2510.23667},
+}
+```