add upload script for the record

scripts/upload_to_huggingface.py

@@ -0,0 +1,649 @@
+#!/usr/bin/env python3
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+#
+"""
+Script to upload JEPA-WMs pretrained model checkpoints to Hugging Face Hub.
+
+This script downloads checkpoints from dl.fbaipublicfiles.com and uploads them
+to the Hugging Face Hub repository.
+
+Usage:
+    # Upload all models to a new HF repository
+    python scripts/upload_to_huggingface.py --repo-id facebook/jepa-wms
+
+    # Upload only JEPA-WM models
+    python scripts/upload_to_huggingface.py --repo-id facebook/jepa-wms --category jepa_wm
+
+    # Upload a specific model
+    python scripts/upload_to_huggingface.py --repo-id facebook/jepa-wms --model jepa_wm_droid
+
+    # Dry run (show what would be uploaded)
+    python scripts/upload_to_huggingface.py --repo-id facebook/jepa-wms --dry-run
+
+    # Update only the README (without re-uploading checkpoints)
+    python scripts/upload_to_huggingface.py --repo-id facebook/jepa-wms --readme-only
+
+    # Upload from local files (instead of downloading from CDN)
+    python scripts/upload_to_huggingface.py --repo-id facebook/jepa-wms --local
+
+Requirements:
+    pip install huggingface_hub
+"""
+
+import argparse
+import os
+import tempfile
+from pathlib import Path
+
+# Model weight URLs from https://dl.fbaipublicfiles.com/jepa-wms/
+MODEL_URLS = {
+    # JEPA-WM models
+    "jepa_wm_droid": "https://dl.fbaipublicfiles.com/jepa-wms/droid_jepa-wm_noprop.pth.tar",
+    "jepa_wm_metaworld": "https://dl.fbaipublicfiles.com/jepa-wms/mw_jepa-wm.pth.tar",
+    "jepa_wm_pointmaze": "https://dl.fbaipublicfiles.com/jepa-wms/mz_jepa-wm.pth.tar",
+    "jepa_wm_pusht": "https://dl.fbaipublicfiles.com/jepa-wms/pt_jepa-wm.pth.tar",
+    "jepa_wm_wall": "https://dl.fbaipublicfiles.com/jepa-wms/wall_jepa-wm.pth.tar",
+    # DINO-WM baseline models
+    "dino_wm_droid": "https://dl.fbaipublicfiles.com/jepa-wms/droid_dino-wm_noprop.pth.tar",
+    "dino_wm_metaworld": "https://dl.fbaipublicfiles.com/jepa-wms/mw_dino-wm.pth.tar",
+    "dino_wm_pointmaze": "https://dl.fbaipublicfiles.com/jepa-wms/mz_dino-wm.pth.tar",
+    "dino_wm_pusht": "https://dl.fbaipublicfiles.com/jepa-wms/pt_dino-wm.pth.tar",
+    "dino_wm_wall": "https://dl.fbaipublicfiles.com/jepa-wms/wall_dino-wm.pth.tar",
+    # V-JEPA-2-AC baseline models
+    "vjepa2_ac_droid": "https://dl.fbaipublicfiles.com/jepa-wms/droid_vj2ac_noprop.pth.tar",
+    "vjepa2_ac_oss": "https://dl.fbaipublicfiles.com/jepa-wms/droid_vj2ac_oss-prop.pth.tar",
+}
+
+# Image decoder URLs
+IMAGE_DECODER_URLS = {
+    "dinov2_vits_224": "https://dl.fbaipublicfiles.com/jepa-wms/vm2m_lpips_dv2vits_vitldec_224_05norm.pth.tar",
+    "dinov2_vits_224_INet": "https://dl.fbaipublicfiles.com/jepa-wms/vm2m_lpips_dv2vits_vitldec_224_INet.pth.tar",
+    "dinov3_vitl_256_INet": "https://dl.fbaipublicfiles.com/jepa-wms/vm2m_lpips_dv3vitl_256_INet.pth.tar",
+    "vjepa2_vitg_256_INet": "https://dl.fbaipublicfiles.com/jepa-wms/vm2m_lpips_vj2vitgnorm_vitldec_dup_256_INet.pth.tar",
+}
+
+# Model metadata for creating model cards
+MODEL_METADATA = {
+    "jepa_wm_droid": {
+        "environment": "DROID & RoboCasa",
+        "resolution": "256×256",
+        "encoder": "DINOv3 ViT-L/16",
+        "pred_depth": 12,
+        "description": "JEPA-WM trained on DROID real-robot manipulation dataset",
+    },
+    "jepa_wm_metaworld": {
+        "environment": "Metaworld",
+        "resolution": "224×224",
+        "encoder": "DINOv2 ViT-S/14",
+        "pred_depth": 6,
+        "description": "JEPA-WM trained on Metaworld simulation environments",
+    },
+    "jepa_wm_pointmaze": {
+        "environment": "PointMaze",
+        "resolution": "224×224",
+        "encoder": "DINOv2 ViT-S/14",
+        "pred_depth": 6,
+        "description": "JEPA-WM trained on PointMaze navigation tasks",
+    },
+    "jepa_wm_pusht": {
+        "environment": "Push-T",
+        "resolution": "224×224",
+        "encoder": "DINOv2 ViT-S/14",
+        "pred_depth": 6,
+        "description": "JEPA-WM trained on Push-T manipulation tasks",
+    },
+    "jepa_wm_wall": {
+        "environment": "Wall",
+        "resolution": "224×224",
+        "encoder": "DINOv2 ViT-S/14",
+        "pred_depth": 6,
+        "description": "JEPA-WM trained on Wall environment",
+    },
+    "dino_wm_droid": {
+        "environment": "DROID & RoboCasa",
+        "resolution": "224×224",
+        "encoder": "DINOv2 ViT-S/14",
+        "pred_depth": 6,
+        "description": "DINO-WM baseline trained on DROID dataset",
+    },
+    "dino_wm_metaworld": {
+        "environment": "Metaworld",
+        "resolution": "224×224",
+        "encoder": "DINOv2 ViT-S/14",
+        "pred_depth": 6,
+        "description": "DINO-WM baseline trained on Metaworld",
+    },
+    "dino_wm_pointmaze": {
+        "environment": "PointMaze",
+        "resolution": "224×224",
+        "encoder": "DINOv2 ViT-S/14",
+        "pred_depth": 6,
+        "description": "DINO-WM baseline trained on PointMaze",
+    },
+    "dino_wm_pusht": {
+        "environment": "Push-T",
+        "resolution": "224×224",
+        "encoder": "DINOv2 ViT-S/14",
+        "pred_depth": 6,
+        "description": "DINO-WM baseline trained on Push-T",
+    },
+    "dino_wm_wall": {
+        "environment": "Wall",
+        "resolution": "224×224",
+        "encoder": "DINOv2 ViT-S/14",
+        "pred_depth": 6,
+        "description": "DINO-WM baseline trained on Wall environment",
+    },
+    "vjepa2_ac_droid": {
+        "environment": "DROID & RoboCasa",
+        "resolution": "256×256",
+        "encoder": "V-JEPA-2 ViT-G/16",
+        "pred_depth": 24,
+        "description": "V-JEPA-2-AC (fixed) baseline trained on DROID dataset",
+    },
+    "vjepa2_ac_oss": {
+        "environment": "DROID & RoboCasa",
+        "resolution": "256×256",
+        "encoder": "V-JEPA-2 ViT-G/16",
+        "pred_depth": 24,
+        "description": "V-JEPA-2-AC OSS baseline (with loss bug from original repo)",
+    },
+}
+
+
+def download_file(url: str, dest_path: str, verbose: bool = True) -> None:
+    """Download a file from URL to destination path."""
+    import urllib.request
+
+    if verbose:
+        print(f"  Downloading from {url}...")
+
+    urllib.request.urlretrieve(url, dest_path)
+
+    if verbose:
+        size_mb = os.path.getsize(dest_path) / (1024 * 1024)
+        print(f"  Downloaded {size_mb:.1f} MB")
+
+
+def create_model_card(model_name: str, repo_id: str) -> str:
+    """Create a model card (README.md) for a model."""
+    meta = MODEL_METADATA.get(model_name, {})
+
+    model_type = (
+        "JEPA-WM"
+        if model_name.startswith("jepa_wm")
+        else ("DINO-WM" if model_name.startswith("dino_wm") else "V-JEPA-2-AC")
+    )
+
+    card = f"""---
+license: cc-by-nc-4.0
+tags:
+- robotics
+- world-model
+- jepa
+- planning
+- pytorch
+library_name: pytorch
+pipeline_tag: robotics
+datasets:
+- facebook/jepa-wms
+---
+
+# {model_name}
+
+{meta.get('description', f'{model_type} pretrained world model')}
+
+## Model Details
+
+- **Model Type:** {model_type}
+- **Environment:** {meta.get('environment', 'N/A')}
+- **Resolution:** {meta.get('resolution', 'N/A')}
+- **Encoder:** {meta.get('encoder', 'N/A')}
+- **Predictor Depth:** {meta.get('pred_depth', 'N/A')}
+
+## Usage
+
+### Via PyTorch Hub
+
+```python
+import torch
+
+model, preprocessor = torch.hub.load('facebookresearch/jepa-wms', '{model_name}')
+```
+
+### Via Hugging Face Hub
+
+```python
+from huggingface_hub import hf_hub_download
+import torch
+
+# Download the checkpoint
+checkpoint_path = hf_hub_download(
+    repo_id="{repo_id}",
+    filename="{model_name}.pth.tar"
+)
+
+# Load checkpoint (contains 'encoder', 'predictor', and 'heads' state dicts)
+checkpoint = torch.load(checkpoint_path, map_location="cpu")
+print(checkpoint.keys())  # dict_keys(['encoder', 'predictor', 'heads', 'opt', 'scaler', 'epoch', 'batch_size', 'lr', 'amp'])
+```
+
+> **Note**: This only downloads the weights. To instantiate the full `EncPredWM` model with the correct
+> architecture and load the weights, we recommend using PyTorch Hub (see above) or cloning the
+> [jepa-wms repository](https://github.com/facebookresearch/jepa-wms) and using the training/eval scripts.
+
+## Paper
+
+This model is from the paper ["What Drives Success in Physical Planning with Joint-Embedding Predictive World Models?"](https://arxiv.org/abs/2512.24497)
+
+```bibtex
+@misc{{terver2025drivessuccessphysicalplanning,
+      title={{What Drives Success in Physical Planning with Joint-Embedding Predictive World Models?}},
+      author={{Basile Terver and Tsung-Yen Yang and Jean Ponce and Adrien Bardes and Yann LeCun}},
+      year={{2025}},
+      eprint={{2512.24497}},
+      archivePrefix={{arXiv}},
+      primaryClass={{cs.AI}},
+      url={{https://arxiv.org/abs/2512.24497}},
+}}
+```
+
+## License
+
+This model is licensed under [CC-BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/).
+"""
+    return card
+
+
+def create_repo_readme(repo_id: str) -> str:
+    """Create main README for the model repository."""
+    return f"""---
+license: cc-by-nc-4.0
+tags:
+- robotics
+- world-model
+- jepa
+- planning
+- pytorch
+library_name: pytorch
+pipeline_tag: robotics
+datasets:
+- facebook/jepa-wms
+---
+
+<h1 align="center">
+    <p>🤖 <b>JEPA-WMs Pretrained Models</b></p>
+</h1>
+
+<div align="center" style="line-height: 1;">
+  <a href="https://github.com/facebookresearch/jepa-wms" target="_blank" style="margin: 2px;"><img alt="Github" src="https://img.shields.io/badge/Github-facebookresearch/jepa--wms-black?logo=github" style="display: inline-block; vertical-align: middle;"/></a>
+  <a href="https://huggingface.co/{repo_id}" target="_blank" style="margin: 2px;"><img alt="HuggingFace" src="https://img.shields.io/badge/🤗%20HuggingFace-{repo_id.replace('/', '/')}-ffc107" style="display: inline-block; vertical-align: middle;"/></a>
+  <a href="https://arxiv.org/abs/2512.24497" target="_blank" style="margin: 2px;"><img alt="ArXiv" src="https://img.shields.io/badge/arXiv-2512.24497-b5212f?logo=arxiv" style="display: inline-block; vertical-align: middle;"/></a>
+</div>
+
+<br>
+
+<p align="center">
+  <b><a href="https://ai.facebook.com/research/">Meta AI Research, FAIR</a></b>
+</p>
+
+<p align="center">
+  This 🤗 HuggingFace repository hosts pretrained <b>JEPA-WM</b> world models.<br>
+  👉 See the <a href="https://github.com/facebookresearch/jepa-wms">main repository</a> for training code and datasets.
+</p>
+
+This repository contains pretrained world model checkpoints from the paper
+["What Drives Success in Physical Planning with Joint-Embedding Predictive World Models?"](https://arxiv.org/abs/2512.24497)
+
+## Available Models
+
+### JEPA-WM Models
+
+| Model | Environment | Resolution | Encoder | Pred. Depth |
+|-------|-------------|------------|---------|-------------|
+| `jepa_wm_droid` | DROID & RoboCasa | 256×256 | DINOv3 ViT-L/16 | 12 |
+| `jepa_wm_metaworld` | Metaworld | 224×224 | DINOv2 ViT-S/14 | 6 |
+| `jepa_wm_pusht` | Push-T | 224×224 | DINOv2 ViT-S/14 | 6 |
+| `jepa_wm_pointmaze` | PointMaze | 224×224 | DINOv2 ViT-S/14 | 6 |
+| `jepa_wm_wall` | Wall | 224×224 | DINOv2 ViT-S/14 | 6 |
+
+### DINO-WM Baseline Models
+
+| Model | Environment | Resolution | Encoder | Pred. Depth |
+|-------|-------------|------------|---------|-------------|
+| `dino_wm_droid` | DROID & RoboCasa | 224×224 | DINOv2 ViT-S/14 | 6 |
+| `dino_wm_metaworld` | Metaworld | 224×224 | DINOv2 ViT-S/14 | 6 |
+| `dino_wm_pusht` | Push-T | 224×224 | DINOv2 ViT-S/14 | 6 |
+| `dino_wm_pointmaze` | PointMaze | 224×224 | DINOv2 ViT-S/14 | 6 |
+| `dino_wm_wall` | Wall | 224×224 | DINOv2 ViT-S/14 | 6 |
+
+### V-JEPA-2-AC Baseline Models
+
+| Model | Environment | Resolution | Encoder | Pred. Depth |
+|-------|-------------|------------|---------|-------------|
+| `vjepa2_ac_droid` | DROID & RoboCasa | 256×256 | V-JEPA-2 ViT-G/16 | 24 |
+| `vjepa2_ac_oss` | DROID & RoboCasa | 256×256 | V-JEPA-2 ViT-G/16 | 24 |
+
+### VM2M Decoder Heads
+
+| Model | Encoder | Resolution |
+|-------|---------|------------|
+| `dinov2_vits_224` | DINOv2 ViT-S/14 | 224×224 |
+| `dinov2_vits_224_INet` | DINOv2 ViT-S/14 | 224×224 |
+| `dinov3_vitl_256_INet` | DINOv3 ViT-L/16 | 256×256 |
+| `vjepa2_vitg_256_INet` | V-JEPA-2 ViT-G/16 | 256×256 |
+
+## Usage
+
+### Via PyTorch Hub (Recommended)
+
+```python
+import torch
+
+# Load JEPA-WM models
+model, preprocessor = torch.hub.load('facebookresearch/jepa-wms', 'jepa_wm_droid')
+model, preprocessor = torch.hub.load('facebookresearch/jepa-wms', 'jepa_wm_metaworld')
+
+# Load DINO-WM baselines
+model, preprocessor = torch.hub.load('facebookresearch/jepa-wms', 'dino_wm_metaworld')
+
+# Load V-JEPA-2-AC baseline
+model, preprocessor = torch.hub.load('facebookresearch/jepa-wms', 'vjepa2_ac_droid')
+```
+
+### Via Hugging Face Hub
+
+```python
+from huggingface_hub import hf_hub_download
+import torch
+
+# Download a specific checkpoint
+checkpoint_path = hf_hub_download(
+    repo_id="{repo_id}",
+    filename="jepa_wm_droid.pth.tar"
+)
+
+# Load checkpoint (contains 'encoder', 'predictor', and 'heads' state dicts)
+checkpoint = torch.load(checkpoint_path, map_location="cpu")
+print(checkpoint.keys())  # dict_keys(['encoder', 'predictor', 'heads', 'opt', 'scaler', 'epoch', 'batch_size', 'lr', 'amp'])
+```
+
+> **Note**: This only downloads the weights. To instantiate the full model with the correct
+> architecture and load the weights, we recommend using PyTorch Hub (see above) or cloning the
+> [jepa-wms repository](https://github.com/facebookresearch/jepa-wms) and using the training/eval scripts.
+
+## Citation
+
+```bibtex
+@misc{{terver2025drivessuccessphysicalplanning,
+      title={{What Drives Success in Physical Planning with Joint-Embedding Predictive World Models?}},
+      author={{Basile Terver and Tsung-Yen Yang and Jean Ponce and Adrien Bardes and Yann LeCun}},
+      year={{2025}},
+      eprint={{2512.24497}},
+      archivePrefix={{arXiv}},
+      primaryClass={{cs.AI}},
+      url={{https://arxiv.org/abs/2512.24497}},
+}}
+```
+
+## License
+
+These models are licensed under [CC-BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/).
+
+## Links
+
+- 📄 [Paper](https://arxiv.org/abs/2512.24497)
+- 💻 [GitHub Repository](https://github.com/facebookresearch/jepa-wms)
+- 🤗 [Datasets](https://huggingface.co/datasets/facebook/jepa-wms)
+- 🤗 [Models](https://huggingface.co/facebook/jepa-wms)
+"""
+
+
+def upload_readme_only(
+    repo_id: str,
+    dry_run: bool = False,
+    verbose: bool = True,
+) -> None:
+    """Upload only the README to Hugging Face Hub."""
+    from huggingface_hub import HfApi
+
+    api = HfApi()
+
+    with tempfile.TemporaryDirectory() as tmpdir:
+        readme_path = os.path.join(tmpdir, "README.md")
+        with open(readme_path, "w") as f:
+            f.write(create_repo_readme(repo_id))
+
+        if dry_run:
+            print(f"\n[DRY RUN] Would upload README.md to {repo_id}")
+        else:
+            api.upload_file(
+                path_or_fileobj=readme_path,
+                path_in_repo="README.md",
+                repo_id=repo_id,
+                repo_type="model",
+            )
+            if verbose:
+                print("✓ Uploaded README.md")
+
+
+def upload_models(
+    repo_id: str,
+    models: dict,
+    category: str,
+    dry_run: bool = False,
+    verbose: bool = True,
+    use_local: bool = False,
+    local_dir: str = ".",
+) -> None:
+    """Upload models to Hugging Face Hub."""
+    from huggingface_hub import create_repo, HfApi
+
+    api = HfApi()
+    local_dir_path = Path(local_dir).resolve()
+
+    if not dry_run:
+        # Create repository if it doesn't exist
+        try:
+            create_repo(repo_id, repo_type="model", exist_ok=True)
+            if verbose:
+                print(f"Repository {repo_id} is ready")
+        except Exception as e:
+            print(f"Note: {e}")
+
+    with tempfile.TemporaryDirectory() as tmpdir:
+        # Create main README
+        readme_path = os.path.join(tmpdir, "README.md")
+        with open(readme_path, "w") as f:
+            f.write(create_repo_readme(repo_id))
+
+        if dry_run:
+            print(f"\n[DRY RUN] Would upload README.md to {repo_id}")
+        else:
+            api.upload_file(
+                path_or_fileobj=readme_path,
+                path_in_repo="README.md",
+                repo_id=repo_id,
+                repo_type="model",
+            )
+            if verbose:
+                print("Uploaded README.md")
+
+        # Upload each model
+        for model_name, url in models.items():
+            if verbose:
+                print(f"\nProcessing {model_name}...")
+
+            hf_filename = f"{model_name}.pth.tar"
+
+            if use_local:
+                # Use local file
+                local_path = local_dir_path / hf_filename
+                if not local_path.exists():
+                    print(f"  ⚠ Local file not found: {local_path}, skipping...")
+                    continue
+
+                if dry_run:
+                    size_mb = local_path.stat().st_size / (1024 * 1024)
+                    print(
+                        f"  [DRY RUN] Would upload local file {local_path} ({size_mb:.1f} MB)"
+                    )
+                    print(f"  [DRY RUN] Would upload as {hf_filename}")
+                    continue
+
+                if verbose:
+                    size_mb = local_path.stat().st_size / (1024 * 1024)
+                    print(f"  Using local file: {local_path} ({size_mb:.1f} MB)")
+                    print(f"  Uploading as {hf_filename}...")
+
+                api.upload_file(
+                    path_or_fileobj=str(local_path),
+                    path_in_repo=hf_filename,
+                    repo_id=repo_id,
+                    repo_type="model",
+                )
+            else:
+                # Download from URL
+                original_filename = url.split("/")[-1]
+
+                if dry_run:
+                    print(f"  [DRY RUN] Would download from {url}")
+                    print(f"  [DRY RUN] Would upload as {hf_filename}")
+                    continue
+
+                # Download checkpoint
+                local_path = os.path.join(tmpdir, original_filename)
+                download_file(url, local_path, verbose=verbose)
+
+                # Upload to HF Hub
+                if verbose:
+                    print(f"  Uploading as {hf_filename}...")
+
+                api.upload_file(
+                    path_or_fileobj=local_path,
+                    path_in_repo=hf_filename,
+                    repo_id=repo_id,
+                    repo_type="model",
+                )
+
+                # Clean up to save space
+                os.remove(local_path)
+
+            if verbose:
+                print(f"  ✓ Uploaded {hf_filename}")
+
+
+def main():
+    parser = argparse.ArgumentParser(
+        description="Upload JEPA-WMs checkpoints to Hugging Face Hub"
+    )
+    parser.add_argument(
+        "--repo-id",
+        type=str,
+        required=True,
+        help="Hugging Face repository ID (e.g., 'facebook/jepa-wms')",
+    )
+    parser.add_argument(
+        "--category",
+        type=str,
+        choices=["all", "jepa_wm", "dino_wm", "vjepa2_ac", "decoders"],
+        default="all",
+        help="Category of models to upload",
+    )
+    parser.add_argument(
+        "--model",
+        type=str,
+        help="Upload a specific model by name (e.g., 'jepa_wm_droid')",
+    )
+    parser.add_argument(
+        "--dry-run",
+        action="store_true",
+        help="Show what would be uploaded without actually uploading",
+    )
+    parser.add_argument(
+        "--readme-only",
+        action="store_true",
+        help="Only upload the README.md (skip checkpoint uploads)",
+    )
+    parser.add_argument(
+        "--quiet",
+        action="store_true",
+        help="Reduce output verbosity",
+    )
+    parser.add_argument(
+        "--local",
+        action="store_true",
+        help="Upload from local files instead of downloading from CDN",
+    )
+    parser.add_argument(
+        "--local-dir",
+        type=str,
+        default=".",
+        help="Directory containing local checkpoint files (default: current directory)",
+    )
+
+    args = parser.parse_args()
+    verbose = not args.quiet
+
+    # Handle README-only upload
+    if args.readme_only:
+        if verbose:
+            print(
+                f"{'[DRY RUN] ' if args.dry_run else ''}Uploading README.md to {args.repo_id}"
+            )
+        upload_readme_only(
+            repo_id=args.repo_id,
+            dry_run=args.dry_run,
+            verbose=verbose,
+        )
+        if verbose and not args.dry_run:
+            print(f"\n✓ Done! README updated at: https://huggingface.co/{args.repo_id}")
+        return
+
+    # Select models to upload
+    if args.model:
+        # Upload specific model
+        all_models = {**MODEL_URLS, **IMAGE_DECODER_URLS}
+        if args.model not in all_models:
+            print(f"Error: Unknown model '{args.model}'")
+            print(f"Available models: {list(all_models.keys())}")
+            return
+        models = {args.model: all_models[args.model]}
+    elif args.category == "all":
+        models = {**MODEL_URLS, **IMAGE_DECODER_URLS}
+    elif args.category == "jepa_wm":
+        models = {k: v for k, v in MODEL_URLS.items() if k.startswith("jepa_wm")}
+    elif args.category == "dino_wm":
+        models = {k: v for k, v in MODEL_URLS.items() if k.startswith("dino_wm")}
+    elif args.category == "vjepa2_ac":
+        models = {k: v for k, v in MODEL_URLS.items() if k.startswith("vjepa2_ac")}
+    elif args.category == "decoders":
+        models = IMAGE_DECODER_URLS
+
+    if verbose:
+        mode_str = "local files" if args.local else "dl.fbaipublicfiles.com"
+        print(
+            f"{'[DRY RUN] ' if args.dry_run else ''}Uploading {len(models)} models to {args.repo_id} (from {mode_str})"
+        )
+        print(f"Models: {list(models.keys())}")
+
+    upload_models(
+        repo_id=args.repo_id,
+        models=models,
+        category=args.category,
+        dry_run=args.dry_run,
+        verbose=verbose,
+        use_local=args.local,
+        local_dir=args.local_dir,
+    )
+
+    if verbose and not args.dry_run:
+        print(f"\n✓ Done! Models available at: https://huggingface.co/{args.repo_id}")
+
+
+if __name__ == "__main__":
+    main()