Echo-Memory: A Controlled Study of Memory in Action World Models
Paper โข 2606.09803 โข Published โข 33
context_k1 โ complete Wan 2.1 1.3B DiT
DiffSynth-facing full DiT for the released Echo-Memory context_k1 row.
context_k1/epoch-0.safetensorsdiffusion_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.pthWan-AI/Wan2.1-T2V-1.3B Wan2.1_VAE.pthimport 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).
epoch-0 fine-tune, 640ร352, 81-frame chunks.Base model
Wan-AI/Wan2.1-T2V-1.3B