Depth Anything: update conversion script for V2 (#31522)

* Depth Anything: update conversion script for V2

* Update docs

* Style

* Revert "Update docs"

This reverts commit be0ca47ea1be4f3cd9aa2113bdd8efcc9959119e.

* Add docs for depth anything v2

* Add depth_anything_v2 to MODEL_NAMES_MAPPING

Done similarly to Flan-T5: https://github.com/huggingface/transformers/pull/19892/files

* Add tip in original docs

docs/source/en/model_doc/depth_anything.md

@@ -20,6 +20,12 @@ rendered properly in your Markdown viewer.
 
 The Depth Anything model was proposed in [Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data](https://arxiv.org/abs/2401.10891) by Lihe Yang, Bingyi Kang, Zilong Huang, Xiaogang Xu, Jiashi Feng, Hengshuang Zhao. Depth Anything is based on the [DPT](dpt) architecture, trained on ~62 million images, obtaining state-of-the-art results for both relative and absolute depth estimation.
 
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+[Depth Anything V2](depth_anything_v2) was released in June 2024. It uses the same architecture as Depth Anything and therefore it is compatible with all code examples and existing workflows. However, it leverages synthetic data and a larger capacity teacher model to achieve much finer and robust depth predictions.
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+</Tip>
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 The abstract from the paper is the following:
 
 *This work presents Depth Anything, a highly practical solution for robust monocular depth estimation. Without pursuing novel technical modules, we aim to build a simple yet powerful foundation model dealing with any images under any circumstances. To this end, we scale up the dataset by designing a data engine to collect and automatically annotate large-scale unlabeled data (~62M), which significantly enlarges the data coverage and thus is able to reduce the generalization error. We investigate two simple yet effective strategies that make data scaling-up promising. First, a more challenging optimization target is created by leveraging data augmentation tools. It compels the model to actively seek extra visual knowledge and acquire robust representations. Second, an auxiliary supervision is developed to enforce the model to inherit rich semantic priors from pre-trained encoders. We evaluate its zero-shot capabilities extensively, including six public datasets and randomly captured photos. It demonstrates impressive generalization ability. Further, through fine-tuning it with metric depth information from NYUv2 and KITTI, new SOTAs are set. Our better depth model also results in a better depth-conditioned ControlNet.*