Improve the WorldDiT model card layout (#2)

- Improve the WorldDiT model card layout (4b7fb954f1c92b19a043b1eaf4de8bf9b642ca09)

README.md

@@ -38,91 +38,81 @@ widget:
   <img src="https://pub-2c09ae97630f4932a23e622b450076e0.r2.dev/paris2/model-card/v1/bagel_labs_logo.png" alt="Bagel Labs">
 </p>
 
-# WorldDiT
+<h1 align="center">WorldDiT</h1>
 
-## One diffusion backbone learns what to do and what comes next.
+<h2 align="center">One diffusion backbone learns what to do and what comes next.</h2>
 
 <p align="center">
-  <a href="https://huggingface.co/bageldotcom/worlddit" target="_blank">
-    <img src="https://img.shields.io/badge/🤗_DOWNLOAD_WORLDDIT_WEIGHTS-FFD21E?style=for-the-badge&logoColor=000000" alt="Download WorldDiT Weights">
-  </a>
-  <a href="https://github.com/Lifelong-Robot-Learning/LIBERO" target="_blank">
-    <img src="https://img.shields.io/badge/🤖_LIBERO_BENCHMARK-FF6B6B?style=for-the-badge&logoColor=white" alt="LIBERO Benchmark">
-  </a>
-</p>
-
 WorldDiT learns continuous robot action chunks and a future visual target
 through one shared diffusion transformer. Deployment keeps only the action
 path.
+</p>
 
+<p align="center">
 This release includes four LIBERO checkpoints, a self contained inference
 runtime, and an evaluator for reproducing the reported suite results.
+</p>
 
 ## See WorldDiT act
 
 The four clips below show successful rollouts from the released checkpoints.
 Each clip covers a different LIBERO suite and camera view.
 
-<Gallery />
+<table>
+  <tr>
+    <td width="50%" valign="top">
+      <video width="100%" controls muted loop playsinline preload="metadata"
+        src="https://pub-2c09ae97630f4932a23e622b450076e0.r2.dev/worlddit/model-card/v1/worlddit_libero_spatial_frontview_task05_episode01.mp4"></video>
+      <strong>LIBERO Spatial</strong><br>
+      Task 5, front view.
+    </td>
+    <td width="50%" valign="top">
+      <video width="100%" controls muted loop playsinline preload="metadata"
+        src="https://pub-2c09ae97630f4932a23e622b450076e0.r2.dev/worlddit/model-card/v1/worlddit_libero_object_agentview_task08_episode01.mp4"></video>
+      <strong>LIBERO Object</strong><br>
+      Task 8, agent view.
+    </td>
+  </tr>
+  <tr>
+    <td width="50%" valign="top">
+      <video width="100%" controls muted loop playsinline preload="metadata"
+        src="https://pub-2c09ae97630f4932a23e622b450076e0.r2.dev/worlddit/model-card/v1/worlddit_libero_goal_sideview_task10_episode01.mp4"></video>
+      <strong>LIBERO Goal</strong><br>
+      Task 10, side view.
+    </td>
+    <td width="50%" valign="top">
+      <video width="100%" controls muted loop playsinline preload="metadata"
+        src="https://pub-2c09ae97630f4932a23e622b450076e0.r2.dev/worlddit/model-card/v1/worlddit_libero_10_frontview_task06_episode01.mp4"></video>
+      <strong>LIBERO Long</strong><br>
+      Task 6, front view.
+    </td>
+  </tr>
+</table>
+
+[Spatial MP4](https://pub-2c09ae97630f4932a23e622b450076e0.r2.dev/worlddit/model-card/v1/worlddit_libero_spatial_frontview_task05_episode01.mp4)
+· [Object MP4](https://pub-2c09ae97630f4932a23e622b450076e0.r2.dev/worlddit/model-card/v1/worlddit_libero_object_agentview_task08_episode01.mp4)
+· [Goal MP4](https://pub-2c09ae97630f4932a23e622b450076e0.r2.dev/worlddit/model-card/v1/worlddit_libero_goal_sideview_task10_episode01.mp4)
+· [Long MP4](https://pub-2c09ae97630f4932a23e622b450076e0.r2.dev/worlddit/model-card/v1/worlddit_libero_10_frontview_task06_episode01.mp4)
+
+## Release snapshot
+
+| Reported LIBERO result | Released model |
+|---|---|
+| **94.9 percent** selection aware mean<br>**1,898 of 2,000** successful episodes | **399.084 million** total parameters<br>**135.107 million** trainable parameters |
+| **98.0 percent** Spatial<br>**97.0 percent** Object | **Three** observation frames<br>**Seven** predicted actions |
+| **92.8 percent** Goal<br>**91.8 percent** Long | **Three** actions executed before replanning<br>**Seven** action dimensions |
 
-| Suite | View | Video |
+| Checkpoints | Runtime | Encoders and environment |
 |---|---|---|
-| LIBERO Spatial | Front view | [Open MP4](https://pub-2c09ae97630f4932a23e622b450076e0.r2.dev/worlddit/model-card/v1/worlddit_libero_spatial_frontview_task05_episode01.mp4) |
-| LIBERO Object | Agent view | [Open MP4](https://pub-2c09ae97630f4932a23e622b450076e0.r2.dev/worlddit/model-card/v1/worlddit_libero_object_agentview_task08_episode01.mp4) |
-| LIBERO Goal | Side view | [Open MP4](https://pub-2c09ae97630f4932a23e622b450076e0.r2.dev/worlddit/model-card/v1/worlddit_libero_goal_sideview_task10_episode01.mp4) |
-| LIBERO Long | Front view | [Open MP4](https://pub-2c09ae97630f4932a23e622b450076e0.r2.dev/worlddit/model-card/v1/worlddit_libero_10_frontview_task06_episode01.mp4) |
+| Spatial<br>Object<br>Goal<br>Long | `inference.py`<br>`eval.py`<br>`config.json` | MAE ViT B<br>OpenAI CLIP ViT B 32<br>SafeTensors and pinned requirements |
 
-## What is in this release
-
-| Release component | Included artifact |
-|---|---|
-| LIBERO Spatial policy | SafeTensors checkpoint |
-| LIBERO Object policy | SafeTensors checkpoint |
-| LIBERO Goal policy | SafeTensors checkpoint |
-| LIBERO Long policy | SafeTensors checkpoint |
-| Model runtime | `inference.py` |
-| Evaluation runtime | `eval.py` |
-| Frozen encoders | CLIP ViT B 32 and MAE ViT B |
-| Configuration | `config.json` |
-| Environment | Pinned Python requirements |
-
-The repository is self contained for WorldDiT inference. LIBERO still provides
-the benchmark environments, assets, task definitions, and initial states.
-
-## Reported LIBERO results
-
-Across the four released suite checkpoints, WorldDiT records 1,898 successful
-episodes out of 2,000 under the selection aware evaluation protocol.
-
-| Suite | Successful episodes | Success rate |
-|---|---:|---:|
-| LIBERO Spatial | 490 of 500 | 98.0 percent |
-| LIBERO Object | 485 of 500 | 97.0 percent |
-| LIBERO Goal | 464 of 500 | 92.8 percent |
-| LIBERO Long | 459 of 500 | 91.8 percent |
-| Selection aware mean | 1,898 of 2,000 | 94.9 percent |
+The repository is self contained for WorldDiT inference. LIBERO provides the
+benchmark environments, assets, task definitions, and initial states.
 
 The released runtime and checkpoints were revalidated from a clean installation
-on eight RTX Pro 6000 Blackwell GPUs.
-
-The result is selection aware because three hundred episodes per suite informed
-staged checkpoint selection before the final five hundred episode score was
-assembled.
-
-## Model at a glance
-
-| Property | Released configuration |
-|---|---|
-| Total parameters | 399.084 million |
-| Trainable parameters | 135.107 million |
-| Observation context | Three frames |
-| Predicted action horizon | Seven actions |
-| Executed before replanning | Three actions |
-| Action dimension | Seven |
-| Visual encoder | MAE ViT B |
-| Language encoder | OpenAI CLIP ViT B 32 |
-| Checkpoint format | SafeTensors |
-| Evaluation environment | Headless LIBERO with EGL |
+on eight RTX Pro 6000 Blackwell GPUs. The result is selection aware because
+three hundred episodes per suite informed staged checkpoint selection before
+the final five hundred episode score was assembled.
 
 ## Run a smoke test
 
@@ -179,7 +169,10 @@ and replans.
 | Future visual supervision is present | No future visual output is requested |
 | The complete training objective is active | Three actions execute before replanning |
 
-## Evaluation
+## Reference
+
+<details>
+<summary><strong>Full evaluation commands</strong></summary>
 
 ### One GPU
 
@@ -213,7 +206,10 @@ libero_goal
 libero_10
 ```
 
-## What this repository contains
+</details>
+
+<details>
+<summary><strong>Repository contents</strong></summary>
 
 ```text
 .
@@ -234,7 +230,10 @@ libero_10
 `dependencies/` contains the frozen visual and language encoder weights needed
 by the released policy. No additional model downloads are required.
 
-## Inference API
+</details>
+
+<details>
+<summary><strong>Inference API and tensor shapes</strong></summary>
 
 ```python
 from inference import load_model
@@ -253,7 +252,10 @@ actions = model(primary_images, wrist_images, robot_state, text_tokens)
 
 Evaluation uses the final temporal slot of the predicted action tensor.
 
-## Architecture details
+</details>
+
+<details>
+<summary><strong>Architecture details</strong></summary>
 
 | Component | Specification |
 |---|---|
@@ -267,21 +269,14 @@ Evaluation uses the final temporal slot of the predicted action tensor.
 | Evaluation | Headless LIBERO with EGL |
 | Checkpoint format | SafeTensors |
 
-## Intended use
-
-WorldDiT is intended for research on language conditioned robot manipulation in
-the LIBERO simulator. The released checkpoints support reproduction,
-evaluation, and architecture research across the four released suites.
+</details>
 
-## Scope of the release
+## Use and scope
 
-The reported results describe LIBERO simulation under the released evaluation
-protocol. They do not establish real robot reliability, safety, or transfer
-across embodiments.
-
-The present release does not isolate the causal contribution of the future
-visual target. Total parameter count also does not measure training cost,
-deployment latency, or runtime efficiency.
+| Intended use | Scope of the release |
+|---|---|
+| Research on language conditioned robot manipulation in the LIBERO simulator. The released checkpoints support reproduction, evaluation, and architecture research across the four released suites. | The results describe LIBERO simulation under the released evaluation protocol. They do not establish real robot reliability, safety, or transfer across embodiments. |
+| The released checkpoints cover all four LIBERO suites. | The release does not isolate the causal contribution of the future visual target. Total parameter count does not measure training cost, deployment latency, or runtime efficiency. |
 
 ## Authors and contact