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mlp-moe
Code: https://github.com/massyzs/Video-Editor-MoE (scripts/moe/mlp-moe/)
Optimizer state: removed (weights only).
Package contents
The weights are shipped as a tar.gz stream split into ~20 GB chunks (Hugging Face limits single files to 50 GB).
| chunk | size | sha256 |
|---|---|---|
mlp-moe.tar.gz.part-00 |
21.47 GB | ab1e1388feb391f19ac711030c4175597feb2e79f58a82508fc96cb80e7155f2 |
mlp-moe.tar.gz.part-01 |
8.30 GB | f613d6eeb910071d8998d3bcccb7e1c908ab19f752c7c2a35ce384c00e11b797 |
Total: 2 chunks, 29.77 GB compressed / 37.14 GB extracted.
Reassemble and verify:
huggingface-cli download Massyzs/mlp-moe --local-dir mlp-moe-package # after your access request is approved
cd mlp-moe-package
cat mlp-moe.tar.gz.part-* | tar -xzf - -C <BASE_DIR>/ckpt/ # creates <BASE_DIR>/ckpt/mlp-moe/
cp SHA256SUMS.contents <BASE_DIR>/ckpt/ && (cd <BASE_DIR>/ckpt && sha256sum -c SHA256SUMS.contents) # optional check
MANIFEST.json lists every payload file with its size and sha256, and every chunk with its sha256 (SHA256SUMS.parts).
Extracted files:
| file | size | sha256 |
|---|---|---|
mlp-moe/meta.json |
373 B | d0a7b3e850616b946d2ebb23b50514136502c74feb30e1c75b99265a74b07542 |
mlp-moe/moe_v3_trainable.safetensors |
37.14 GB | 82bfbe6509636b5027385ecb92c1168623bdd4fe33532b02bfe7dbe5439d8fe3 |
Base weights you also need
The package contains the full DiT weights (37.1 GB bf16); only the MLLM encoder (qwen/), the VAE and moe_expert_init.safetensors are needed in addition.
All frozen base components are published, already converted to the layout this code expects, in
Massyzs/kiwi-edit-5b-instruct-only-videoxfun:
huggingface-cli download Massyzs/kiwi-edit-5b-instruct-only-videoxfun --local-dir <BASE_DIR>/ckpt/kiwi-edit-instruct-only-videoxfun
See BASE_WEIGHTS.md for where each component comes from (Kiwi-Edit, Wan2.2) and its checksum.
Inference
python scripts/moe/mlp-moe/moe_v3_infer.py --base_dir <BASE_DIR> --ckpt <BASE_DIR>/ckpt/mlp-moe \
--src_video input.mp4 --prompt "Replace the red car with a blue truck" --name edited --out_dir <OUT_DIR>
# multi-step instruction: --prompts "Remove the dog ||| Convert to oil painting style"
Single 80 GB GPU. Output: <OUT_DIR>/<name>.mp4 plus lossless PNG frames in <OUT_DIR>/frames/<name>/.
Training
The training launcher is scripts/moe/mlp-moe/train_multinode.sh in the code repository: edit the configuration block at the top
(BASE_DIR, data paths, node addresses) and run bash scripts/moe/mlp-moe/train_multinode.sh <node rank> on every node.
scripts/moe/mlp-moe/train_smoke.sh is a single-node smoke test. The data formats are described in docs/DATA.md of the repository.
License
The released weights and code are under Apache-2.0. The frozen base components (Kiwi-Edit MLLM encoder / DiT base, Wan2.2 VAE) are not redistributed here and remain subject to their own licenses.
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linyq/kiwi-edit-5b-instruct-only-diffusers