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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)
```
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