Elastic Forcing Checkpoints
Generator checkpoints for autoregressive text-to-video generation. Use them with the Elastic Forcing code.
| Model | Training updates | Format | Precision |
|---|---|---|---|
| 1.3B | 150 | checkpoints/1.3B/model.pt | FP32 |
| 14B | 80 | Eight safetensors shards and an index under checkpoints/14B/ | FP32 |
Inference requires the matching Wan2.1 base-model assets, T5 text encoder, tokenizer, and VAE. The frozen feature encoders are required for training. Use the generator weights without EMA.
Download
from huggingface_hub import snapshot_download
folder = snapshot_download(
"Elastic-forcing/Elastic-forcing-checkpoint",
allow_patterns=["checkpoints/1.3B/*"], # Select checkpoints/14B/* for the 14B model.
)
print(folder)
Inference
Follow the 1.3B guide or 14B guide to prepare the environment and local base-model assets.
For 1.3B, set CHECKPOINT to the downloaded model.pt. For 14B, combine the safetensors shards into the generator checkpoint expected by the launcher:
import json
from pathlib import Path
import torch
from safetensors.torch import load_file
folder = Path("/path/to/checkpoints/14B")
index = json.loads((folder / "model.safetensors.index.json").read_text())
state = {}
for shard in sorted(set(index["weight_map"].values())):
state.update(load_file(str(folder / shard), device="cpu"))
torch.save({"generator": state}, "/path/to/local/model.pt")
The conversion preserves FP32 tensor values and needs sufficient host memory and disk space for the approximately 57 GB model and its output file. Set CHECKPOINT to the resulting file and run the corresponding inference launcher.
Source-code license notices are provided in the code repository. Base-model assets and datasets follow their respective upstream terms.