add AIBOM

Dear model owner(s),
We are a group of researchers investigating the usefulness of sharing AIBOMs (Artificial Intelligence Bill of Materials) to document AI models – AIBOMs are machine-readable structured lists of components (e.g., datasets and models) used to enhance transparency in AI-model supply chains.

To pursue the above-mentioned objective, we identified popular models on HuggingFace and, based on your model card (and some configuration information available in HuggingFace), we generated your AIBOM according to the CyclonDX (v1.6) standard (see https://cyclonedx.org/docs/1.6/json/). AIBOMs are generated as JSON files by using the following open-source supporting tool: https://github.com/MSR4SBOM/ALOHA (technical details are available in the research paper: https://github.com/MSR4SBOM/ALOHA/blob/main/ALOHA.pdf).

The JSON file in this pull request is your AIBOM (see https://github.com/MSR4SBOM/ALOHA/blob/main/documentation.json for details on its structure).

Clearly, the submitted AIBOM matches the current model information, yet it can be easily regenerated when the model evolves, using the aforementioned AIBOM generator tool.

We open this pull request containing an AIBOM of your AI model, and hope it will be considered. We would also like to hear your opinion on the usefulness (or not) of AIBOM by answering a 3-minute anonymous survey: https://forms.gle/WGffSQD5dLoWttEe7.

Thanks in advance, and regards,
Riccardo D’Avino, Fatima Ahmed, Sabato Nocera, Simone Romano, Giuseppe Scanniello (University of Salerno, Italy),
Massimiliano Di Penta (University of Sannio, Italy),
The MSR4SBOM team

prs-eth_marigold-depth-v1-0.json

@@ -0,0 +1,61 @@
+{
+    "bomFormat": "CycloneDX",
+    "specVersion": "1.6",
+    "serialNumber": "urn:uuid:3a11ce96-81a7-4bc0-a085-2820bc9fc036",
+    "version": 1,
+    "metadata": {
+        "timestamp": "2025-06-05T09:36:35.172916+00:00",
+        "component": {
+            "type": "machine-learning-model",
+            "bom-ref": "prs-eth/marigold-depth-v1-0-6b1a73d3-6054-5433-8afb-7093fb56c7c8",
+            "name": "prs-eth/marigold-depth-v1-0",
+            "externalReferences": [
+                {
+                    "url": "https://huggingface.co/prs-eth/marigold-depth-v1-0",
+                    "type": "documentation"
+                }
+            ],
+            "modelCard": {
+                "modelParameters": {
+                    "task": "depth-estimation"
+                },
+                "properties": [
+                    {
+                        "name": "library_name",
+                        "value": "diffusers"
+                    }
+                ]
+            },
+            "authors": [
+                {
+                    "name": "prs-eth"
+                }
+            ],
+            "licenses": [
+                {
+                    "license": {
+                        "id": "Apache-2.0",
+                        "url": "https://spdx.org/licenses/Apache-2.0.html"
+                    }
+                }
+            ],
+            "description": "- **Developed by:** [Bingxin Ke](http://www.kebingxin.com/), [Anton Obukhov](https://www.obukhov.ai/), [Shengyu Huang](https://shengyuh.github.io/), [Nando Metzger](https://nandometzger.github.io/), [Rodrigo Caye Daudt](https://rcdaudt.github.io/), [Konrad Schindler](https://scholar.google.com/citations?user=FZuNgqIAAAAJ).- **Model type:** Generative latent diffusion-based affine-invariant monocular depth estimation from a single image.- **Language:** English.- **License:** [Apache License License Version 2.0](https://www.apache.org/licenses/LICENSE-2.0).- **Model Description:** This model can be used to generate an estimated depth map of an input image.- **Resolution**: Even though any resolution can be processed, the model inherits the base diffusion model's effective resolution of roughly **768** pixels.This means that for optimal predictions, any larger input image should be resized to make the longer side 768 pixels before feeding it into the model.- **Steps and scheduler**: This model was designed for usage with the **DDIM** scheduler and between **10 and 50** denoising steps.It is possible to obtain good predictions with just **one** step by overriding the `\"timestep_spacing\": \"trailing\"` settingin the [scheduler configuration file](scheduler/scheduler_config.json) or by adding `pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config, timestep_spacing=\"trailing\")`after the pipeline is loaded in the code before the first usage. For compatibility reasons we kept this `v1-0` model identical to the paper setting and provided a[newer v1-1 model](https://huggingface.co/prs-eth/marigold-depth-v1-1) with optimal settings for all possible step configurations.- **Outputs**:- **Affine-invariant depth map**: The predicted values are between 0 and 1, interpolating between the near and far planes of the model's choice.- **Uncertainty map**: Produced only when multiple predictions are ensembled with ensemble size larger than 2.- **Resources for more information:** [Project Website](https://marigoldmonodepth.github.io/), [Paper](https://arxiv.org/abs/2312.02145), [Code](https://github.com/prs-eth/marigold).- **Cite as:**```bibtex@InProceedings{ke2023repurposing,title={Repurposing Diffusion-Based Image Generators for Monocular Depth Estimation},author={Bingxin Ke and Anton Obukhov and Shengyu Huang and Nando Metzger and Rodrigo Caye Daudt and Konrad Schindler},booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},year={2024}}@misc{ke2025marigold,title={Marigold: Affordable Adaptation of Diffusion-Based Image Generators for Image Analysis},author={Bingxin Ke and Kevin Qu and Tianfu Wang and Nando Metzger and Shengyu Huang and Bo Li and Anton Obukhov and Konrad Schindler},year={2025},eprint={2505.09358},archivePrefix={arXiv},primaryClass={cs.CV}}",
+            "tags": [
+                "diffusers",
+                "safetensors",
+                "depth estimation",
+                "image analysis",
+                "computer vision",
+                "in-the-wild",
+                "zero-shot",
+                "depth-estimation",
+                "en",
+                "arxiv:2312.02145",
+                "arxiv:2505.09358",
+                "license:apache-2.0",
+                "diffusers:MarigoldDepthPipeline",
+                "region:us"
+            ]
+        }
+    }
+}