Marigold

• Original code: https://github.com/prs-eth/Marigold at 2bfbdea
• Code licence: Apache-2.0 (permissive); files: LICENSE, LICENSE-MODEL, LICENSE-huggingface
• Weights licence: LicenseRef-CreativeML-OpenRAIL-PlusPlus-M
• Licence evidence: https://huggingface.co/prs-eth/marigold-depth-v1-1/blob/9571e7123e258cf052b4e54241f17971c290e9a8/README.md (License: CreativeML Open RAIL++-M)
• Training data: Stable Diffusion 2 (LAION-5B subsets) fine-tuned on the synthetic Hypersim and Virtual KITTI 2 datasets per the Marigold papers (arXiv 2312.02145, 2505.09358)
• Licence note: The weights use the OpenRAIL++-M licence: commercial use is allowed but with use restrictions that must be passed on; the training data terms are not checked.
• Options: steps = 4 (1 to 1000), long_side = 768 (>= 0; also processing_res), seed = 2024
• Kernel libraries: cudnn_hacc
• Variants: marigold-depth-v1-1
• Outputs: depth; kind: affine_invariant
• Input size: any H x W >= 8, resized or padded inside the model
• Tested up to (portable, fp16, RTX 5060 Ti 16 GB): marigold-depth-v1-1 3000x4000 (3.0 GiB peak allocated, 3.8 reserved)
• marigold-depth-v1-1 994x1482, first call: portable + torch.compile 127 s (cold Inductor cache), TensorRT fp16 285 s (ONNX + engine).
• marigold-depth-v1-1 original 994x1482: the authors' float16 model option; fp16 = upstream's own torch_dtype=float16 pipeline
• marigold-depth-v1-1 load / first call: original 1.32 s / 0.599 s, portable 8.2 s / 0.556 s, kernels 1.1† s / 0.523 s
• † measured in the speed sweep after the portable load (or an earlier size) in the same process: file cache and kernels are warm, so the time is lower than a fresh start.
• Protocol, median of 5 after 3 warm-up, uint8 CUDA input (synthetic for bench_inputs sizes), card defaults; gc before each load; peak = max over the first call and the timed calls

Measured

AbsRel / delta1 after a least-squares scale + shift fit (in disparity space for inverse-depth models), mean over images. Original = the original code named under Details, same checkpoint and protocol.

Measured on NVIDIA GeForce RTX 5060 Ti, torch 2.14.1+cu130, fastforks 939adfe8, 2026-11-06.

Parity

CPU, float32: max |diff| <= 0.0001 vs the original. GPU, float16 (the default), portable and kernels: mean |diff| <= 1 % and p99 <= 3 % of the reference's median |value| (absolute error of the output depth).

Limitations

• Diffusion model: the seeded noise comes from a CPU generator and differs from the original code's CUDA generator for the same seed.
• A traced or exported module reuses the noise of its traced shape.
• Kernels time is dominated by unfused elementwise kernels (GroupNorm / SiLU / adds).