LHMPP-700M-PixelShuffle (mirror)
Unmodified mirror of Damo_XR_Lab/LHMPP-700M-PixelShuffle
from ModelScope, at revision 5f1c4274068e11b93721219618d36594b6087cb6.
All credit to the original authors (Damo XR Lab / LHM-plusplus). Redistributed under Apache-2.0, the licence declared on the source repository.
Why this mirror exists
ModelScope throughput from cloud datacenters in Europe/US measures around
0.7 MiB/s, so pulling the 4.87 GB model.safetensors takes upwards of three
hours — longer than a Hugging Face Space stage timeout allows, and the partial
download is discarded on failure. The same file over the HF CDN completes in
about a minute.
Contents
| File | Size | Note |
|---|---|---|
model.safetensors | 4984.3 MiB | byte-identical to source (5226465708 bytes, 1523 tensors) |
config.json | 3103 B | byte-identical to source |
configuration.json | 44 B | byte-identical to source |
README.md | — | rewritten for HF model-card metadata; source card was ModelScope-specific |
Only the model card differs from upstream. The weights and configs are unchanged.
This is the PixelShuffle variant
Not interchangeable with 3DAIGC/LHMPP-700M,
despite the similar name. This checkpoint carries 41 shape_head.* tensors and
predict_shape_dim: 10 in its config, giving it an image-predicted shape head;
3DAIGC/LHMPP-700M has no shape_head and instead defines a neural_renderer
block that this one lacks. Loading one where the other is expected fails on a
state-dict mismatch.
Verifying against the source
import json, struct
with open("model.safetensors", "rb") as f:
header = json.loads(f.read(struct.unpack("<Q", f.read(8))[0]))
print(len([k for k in header if k != "__metadata__"])) # 1523
print(len([k for k in header if k.startswith("shape_head.")])) # 41