diff --git a/docs/snippets/6_supported-models.snippet b/docs/snippets/6_supported-models.snippet
index 7d3bdf75d..640d880e6 100644
--- a/docs/snippets/6_supported-models.snippet
+++ b/docs/snippets/6_supported-models.snippet
@@ -28,6 +28,7 @@
 1. **Depth Pro** (from Apple) released with the paper [Depth Pro: Sharp Monocular Metric Depth in Less Than a Second](https://arxiv.org/abs/2410.02073) by Aleksei Bochkovskii, Amaël Delaunoy, Hugo Germain, Marcel Santos, Yichao Zhou, Stephan R. Richter, Vladlen Koltun.
 1. **[DETR](https://huggingface.co/docs/transformers/model_doc/detr)** (from Facebook) released with the paper [End-to-End Object Detection with Transformers](https://arxiv.org/abs/2005.12872) by Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, Sergey Zagoruyko.
 1. **[DINOv2](https://huggingface.co/docs/transformers/model_doc/dinov2)** (from Meta AI) released with the paper [DINOv2: Learning Robust Visual Features without Supervision](https://arxiv.org/abs/2304.07193) by Maxime Oquab, Timothée Darcet, Théo Moutakanni, Huy Vo, Marc Szafraniec, Vasil Khalidov, Pierre Fernandez, Daniel Haziza, Francisco Massa, Alaaeldin El-Nouby, Mahmoud Assran, Nicolas Ballas, Wojciech Galuba, Russell Howes, Po-Yao Huang, Shang-Wen Li, Ishan Misra, Michael Rabbat, Vasu Sharma, Gabriel Synnaeve, Hu Xu, Hervé Jegou, Julien Mairal, Patrick Labatut, Armand Joulin, Piotr Bojanowski.
+1. **[DINOv2 with Registers](https://huggingface.co/docs/transformers/model_doc/dinov2_with_registers)** (from Meta AI) released with the paper [DINOv2 with Registers](https://arxiv.org/abs/2309.16588) by Timothée Darcet, Maxime Oquab, Julien Mairal, Piotr Bojanowski.
 1. **[DistilBERT](https://huggingface.co/docs/transformers/model_doc/distilbert)** (from HuggingFace), released together with the paper [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://arxiv.org/abs/1910.01108) by Victor Sanh, Lysandre Debut and Thomas Wolf. The same method has been applied to compress GPT2 into [DistilGPT2](https://github.com/huggingface/transformers/tree/main/examples/research_projects/distillation), RoBERTa into [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/research_projects/distillation), Multilingual BERT into [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/research_projects/distillation) and a German version of DistilBERT.
 1. **[DiT](https://huggingface.co/docs/transformers/model_doc/dit)** (from Microsoft Research) released with the paper [DiT: Self-supervised Pre-training for Document Image Transformer](https://arxiv.org/abs/2203.02378) by Junlong Li, Yiheng Xu, Tengchao Lv, Lei Cui, Cha Zhang, Furu Wei.
 1. **[Donut](https://huggingface.co/docs/transformers/model_doc/donut)** (from NAVER), released together with the paper [OCR-free Document Understanding Transformer](https://arxiv.org/abs/2111.15664) by Geewook Kim, Teakgyu Hong, Moonbin Yim, Jeongyeon Nam, Jinyoung Park, Jinyeong Yim, Wonseok Hwang, Sangdoo Yun, Dongyoon Han, Seunghyun Park.
diff --git a/src/models.js b/src/models.js
index f6828391b..f5156a8af 100644
--- a/src/models.js
+++ b/src/models.js
@@ -5389,6 +5389,26 @@ export class Dinov2ForImageClassification extends Dinov2PreTrainedModel {
 }
 //////////////////////////////////////////////////
 
+//////////////////////////////////////////////////
+export class Dinov2WithRegistersPreTrainedModel extends PreTrainedModel { }
+
+/**
+ * The bare Dinov2WithRegisters Model transformer outputting raw hidden-states without any specific head on top.
+ */
+export class Dinov2WithRegistersModel extends Dinov2WithRegistersPreTrainedModel { }
+
+/**
+ * Dinov2WithRegisters Model transformer with an image classification head on top (a linear layer on top of the final hidden state of the [CLS] token) e.g. for ImageNet.
+ */
+export class Dinov2WithRegistersForImageClassification extends Dinov2WithRegistersPreTrainedModel {
+    /**
+     * @param {any} model_inputs
+     */
+    async _call(model_inputs) {
+        return new SequenceClassifierOutput(await super._call(model_inputs));
+    }
+}
+//////////////////////////////////////////////////
 
 //////////////////////////////////////////////////
 export class YolosPreTrainedModel extends PreTrainedModel { }
@@ -7018,6 +7038,7 @@ const MODEL_MAPPING_NAMES_ENCODER_ONLY = new Map([
     ['convnext', ['ConvNextModel', ConvNextModel]],
     ['convnextv2', ['ConvNextV2Model', ConvNextV2Model]],
     ['dinov2', ['Dinov2Model', Dinov2Model]],
+    ['dinov2_with_registers', ['Dinov2WithRegistersModel', Dinov2WithRegistersModel]],
     ['resnet', ['ResNetModel', ResNetModel]],
     ['swin', ['SwinModel', SwinModel]],
     ['swin2sr', ['Swin2SRModel', Swin2SRModel]],
@@ -7263,6 +7284,7 @@ const MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES = new Map([
     ['convnext', ['ConvNextForImageClassification', ConvNextForImageClassification]],
     ['convnextv2', ['ConvNextV2ForImageClassification', ConvNextV2ForImageClassification]],
     ['dinov2', ['Dinov2ForImageClassification', Dinov2ForImageClassification]],
+    ['dinov2_with_registers', ['Dinov2WithRegistersForImageClassification', Dinov2WithRegistersForImageClassification]],
     ['resnet', ['ResNetForImageClassification', ResNetForImageClassification]],
     ['swin', ['SwinForImageClassification', SwinForImageClassification]],
     ['segformer', ['SegformerForImageClassification', SegformerForImageClassification]],