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README.md

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+---
+license: apache-2.0
+library_name: diffsynth
+pipeline_tag: text-to-video
+tags:
+  - wan
+  - wan2.1
+  - echo-memory
+  - text-to-video
+base_model: Wan-AI/Wan2.1-T2V-1.3B
+---
+
+# Echo-Memory `context_k1` — complete Wan 2.1 1.3B DiT
+
+DiffSynth-facing **full DiT** for the released Echo-Memory `context_k1` row.
+
+- 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)
+- Research overlay (HF): [Echo-Team/Echo-Memory](https://huggingface.co/Echo-Team/Echo-Memory) `context_k1/epoch-0.safetensors`
+
+## What this file is
+
+`diffusion_pytorch_model.safetensors` is a **complete Wan 2.1 T2V 1.3B DiT** (825 / 825 official keys).
+
+It is official [Wan-AI/Wan2.1-T2V-1.3B](https://huggingface.co/Wan-AI/Wan2.1-T2V-1.3B) DiT keys with the Echo-Memory `context_k1` overlay already merged. Research extras (`action_mlp`, `self_attn_with_action`, SSM / spatial slots) are **not** included, so the file loads like a normal Wan transformer (`strict=True` on `pipe.dit`).
+
+T5 and VAE stay the official Wan files:
+
+- `Wan-AI/Wan2.1-T2V-1.3B` `models_t5_umt5-xxl-enc-bf16.pth`
+- `Wan-AI/Wan2.1-T2V-1.3B` `Wan2.1_VAE.pth`
+
+## DiffSynth
+
+```python
+import torch
+from diffsynth.pipelines.wan_video import WanVideoPipeline, ModelConfig
+from diffsynth.utils.data import save_video
+
+pipe = WanVideoPipeline.from_pretrained(
+    torch_dtype=torch.bfloat16,
+    device="cuda",
+    model_configs=[
+        ModelConfig(model_id="Wayne-King/echo-memory-context-k1-full", origin_file_pattern="diffusion_pytorch_model.safetensors"),
+        ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth"),
+        ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="Wan2.1_VAE.pth"),
+    ],
+)
+video = pipe(prompt="A toy bear on a table, the camera rotates around it", seed=42)
+save_video(video, "echo_memory_context_k1.mp4")
+```
+
+After this repo is mirrored on ModelScope, replace the DiT `model_id` with that ModelScope id (DiffSynth downloads from ModelScope by default).
+
+## Notes
+
+- 30,000-step `epoch-0` fine-tune, 640×352, 81-frame chunks.
+- Camera-action / multi-chunk revisit protocol stays in the Echo-Memory repo; this file is the Wan DiT overlay only.
+- Apache-2.0. Please cite the Echo-Memory paper if you use it.