README.md

---
license: apache-2.0
library_name: diffusers
pipeline_tag: text-to-video
tags:
  - wan
  - echo-memory
  - world-model
  - community-pipeline
---

# Echo-Memory Diffusers pipeline

Community pipeline that overlays the Echo-Memory `context_k1` row onto official **Wan 2.1 1.3B** Diffusers weights.

- Paper: [arXiv:2606.09803](https://arxiv.org/abs/2606.09803)
- Code: [Echo-Team-Joy-Future-Academy-JD/Echo-Memory](https://github.com/Echo-Team-Joy-Future-Academy-JD/Echo-Memory)
- Original keys: [Echo-Team/Echo-Memory](https://huggingface.co/Echo-Team/Echo-Memory) `context_k1/epoch-0.safetensors`
- Converted transformer: this repo, `context_k1-diffusers/diffusion_pytorch_model.safetensors`

This is **not** the full multi-chunk camera-action / SSM research stack. It is the released DiT fine-tune remapped to Diffusers names (825 / 825 official Wan 1.3B transformer keys).

```python
import torch
from diffusers import DiffusionPipeline
from diffusers.utils import export_to_video

pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B-Diffusers",
    custom_pipeline="Wayne-King/echo-memory-diffusers",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
)
pipe.load_echo_memory_weights()  # remaps Echo-Team/Echo-Memory context_k1 on the fly
# or: pipe.load_converted_echo_memory_weights()  # already-remapped transformer in this repo
pipe.to("cuda")

frames = pipe(
    prompt="A golden retriever running across a sunny green field, cinematic camera follow.",
    negative_prompt="blurry, static, low quality, deformed",
    height=480,
    width=832,
    num_frames=33,
    num_inference_steps=30,
    guidance_scale=5.0,
).frames[0]
export_to_video(frames, "echo_memory_context_k1.mp4", fps=16)
```