---
pipeline_tag: text-to-video
tags:
  - omnimem
  - wan
  - video-generation
  - long-video
---

# long_rolling_plain_from_ode_init

OmniMem long rolling video generation with a fixed prompt, using an ODE-derived self-forcing initialization.

This release contains the **EMA generator checkpoint at training step 1500**.
The plain rolling-training run initialized from step-800 self-forcing EMA weights in the ODE lineage.

## Contents

- `config.json`
- Two `safetensors` weight shards and their index
- `SHA256SUMS` for payload integrity checking

The weights retain their original FP32 storage. The model configuration uses NSA top-12 block selection.

## Usage

Use compatible OmniMem code with `CausalWanNSAModel` and `CausalWanT2VPipeline`.
The Wan2.1 VAE, UMT5 text encoder, and tokenizer are separate required components.

```python
from omnimem.models.transformers.causal_wan_nsa_model import CausalWanNSAModel

model = CausalWanNSAModel.from_pretrained(
    "omnimem/long_rolling_plain_from_ode_init"
)
```

The original checkpoint configuration is preserved unchanged. Its saved class name
records the training wrapper, and `_name_or_path` records the initialization source.
The released weights are the **step-1500 EMA** weights.

## Training evaluation settings

| Setting | Value |
| --- | --- |
| Prompt mode | Fixed prompt throughout each video |
| Resolution | 832 × 480 |
| Video duration | 60 seconds |
| Frame rate | 16 FPS |
| Exported frames | 960 (969 generated, tail cropped) |
| Sampling steps | 4 |
| Guidance scale | 1 |
| Timestep shift | 5 |
| Seed | 42 |
| Frame block size | 3 |
| KV history window | 3 |
| KV cache LRU maximum | 7 |
| VAE decode chunk size | 3 |

Training evaluation uses five full-length static prompts.