Echo-Memory context_k1 โ€” complete Wan 2.1 1.3B DiT

DiffSynth-facing full DiT for the released Echo-Memory context_k1 row.

What this file is

diffusion_pytorch_model-*-of-00006.safetensors is a complete Wan 2.1 T2V 1.3B DiT (825 / 825 official keys), sharded for Hub upload. Load with origin_file_pattern="diffusion_pytorch_model*.safetensors".

It is official 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

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="SOTAowner/Echo-Memory-context-k1", 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, 640ร—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.
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