| --- |
| license: cc-by-4.0 |
| base_model: Wan-AI/Wan2.1-T2V-1.3B |
| pipeline_tag: text-to-video |
| tags: |
| - world-model |
| - video-generation |
| - action-conditioned |
| - memory |
| - ablation |
| - research-artifact |
| --- |
| |
| # echo — Echo-Memory ablation arm (`ucpe` + two-chunk memory) |
|
|
| > **This is an unofficial derivative artifact, not the official Echo-Memory release.** |
| > It contains one ablation arm re-trained from the Echo-Memory codebase, produced for |
| > internal study. For the official code, paper and checkpoints, use the links below. |
|
|
| ## Upstream / attribution |
|
|
| | | | |
| |---|---| |
| | Original work | **Echo-Memory: A Controlled Study of Memory in Action World Models** | |
| | Authors | Echo Team @ Joy Future Academy, JD | |
| | Paper | [arXiv:2606.09803](https://arxiv.org/abs/2606.09803) | |
| | Official code | https://github.com/Echo-Team-Joy-Future-Academy-JD/Echo-Memory | |
| | Official checkpoints | https://huggingface.co/Echo-Team/Echo-Memory | |
| | Code license | CC BY 4.0 | |
| | Base model | [Wan2.1-T2V-1.3B](https://huggingface.co/Wan-AI/Wan2.1-T2V-1.3B) (Apache 2.0) | |
|
|
| The Echo-Memory code included here is redistributed under CC BY 4.0 with attribution to |
| the Echo Team. The weights are derived from Wan2.1-T2V-1.3B, which is Apache 2.0; that |
| license and its notices continue to apply to the derived weights. |
|
|
| ## What this repository contains |
|
|
| ``` |
| code/ Echo-Memory codebase as used for this run |
| checkpoints/ |
| Step-10000.safetensors 3.5 GB |
| Step-20000.safetensors 3.5 GB |
| Step-30000.safetensors 3.5 GB (final, max_train_steps = 30000) |
| ``` |
|
|
| ## Training configuration |
|
|
| Single arm of a memory-mechanism ablation, run name |
| `memory_baselines_basic_abl_ucpe_memory_two_chunk`. |
|
|
| - Base weights: `Wan2.1-T2V-1.3B` (DiT + `umt5-xxl` text encoder + `Wan2.1_VAE`) |
| - Trainable: `dit` only, `--remove_prefix_in_ckpt pipe.dit.` |
| - Memory: `--enable_context_memory --context_source replay` |
| `--prev_chunk_frames 81 --context_memory_frames 5` |
| - Mechanism under test: `--use_cgla_memory --cgla_mechanism ucpe` |
| `--cgla_every_n_blocks 4 --cgla_aux_loss_weight 0.01` |
| - Also enabled: `--use_moc --moc_temperature 1.0`, `--train_cam_pose`, |
| `--add_action_attn`, `--use_rt_relative`, `--use_anchor_frame`, `--cfg_target_only` |
| - Resolution / length: 352 x 640, 81 frames |
| - Optimisation: lr 2e-5, 1 epoch, batch 1/device, grad-accum 1, |
| `--timestep_shift 15`, `--context_drop_prob 0.1`, 30,000 steps |
| - Hardware: 1x NVIDIA H20 |
|
|
| ## Data — not redistributed here |
|
|
| Training used the **Context-as-Memory** dataset (Unreal-Engine rendered environments, |
| ~331 GB of frames). It is **not** included in this repository: it is third-party data |
| and the copy used here carried no redistribution licence. Obtain it from its original |
| source. |
|
|
| ## Status and caveats |
|
|
| - This is **one arm of an ablation sweep**, uploaded as a research artifact. |
| Its results had not been analysed at the time of upload — no quality claim is made, |
| and it should not be read as the best or recommended configuration. |
| - It is **not** a reproduction of, or a replacement for, the official Echo-Memory |
| checkpoints. Comparisons against the paper's numbers are not valid without matching |
| the official evaluation protocol. |
|
|
| ## Citation |
|
|
| Please cite the original work: |
|
|
| ```bibtex |
| @article{echomemory2026, |
| title = {Echo-Memory: A Controlled Study of Memory in Action World Models}, |
| author = {Echo Team, Joy Future Academy, JD}, |
| journal= {arXiv preprint arXiv:2606.09803}, |
| year = {2026} |
| } |
| ``` |
|
|
