| # Elastic Forcing — 14B |
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| [Overview](../README.md) · [1.3B guide](../1.3B/README.md) · [Checkpoints](https://huggingface.co/Elastic-forcing/Elastic-forcing-checkpoint/tree/main/checkpoints/14B) |
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| The 14B model uses Wan2.1-T2V-14B with DINOv3, V-JEPA 2, and VideoMAEv2 for distribution matching. It generates 81-frame videos at 832 × 480 resolution and 16 FPS. Inference uses four causal denoising steps followed by terminal cleanup. |
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| ## Environment |
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| Use Linux, Python 3.10, and NVIDIA GPUs with BF16 support. Run these commands from `14B/`: |
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| ```bash |
| conda create -n elastic-forcing-14b python=3.10 -y |
| conda activate elastic-forcing-14b |
| python -m pip install torch==2.5.1 torchvision==0.20.1 \ |
| --index-url https://download.pytorch.org/whl/cu121 |
| python -m pip install -r requirements.txt |
| python -m pip install flash-attn==2.7.4.post1 --no-build-isolation |
| export PYTHON_BIN="$(command -v python)" |
| ``` |
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| ## Models and assets |
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| Inference requires the Wan2.1-T2V-14B base model, T5 text encoder, tokenizer, VAE, and the Elastic Forcing generator checkpoint. Place the base-model bundle under `$ASSET_ROOT/models/Wan2.1-T2V-14B/`. |
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| The generator checkpoint is stored as eight FP32 safetensors shards. Download them and create the `.pt` file expected by the inference launcher: |
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| ```python |
| import json |
| from pathlib import Path |
| import torch |
| from huggingface_hub import snapshot_download |
| from safetensors.torch import load_file |
| |
| folder = Path(snapshot_download( |
| "Elastic-forcing/Elastic-forcing-checkpoint", |
| allow_patterns=["checkpoints/14B/*"], |
| )) / "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") |
| ``` |
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| Conversion preserves tensor values and precision. Allow enough host memory and disk space for the approximately 57 GB FP32 generator and the output file. |
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| For inference, prepare the base-model assets and generate the local configuration: |
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| ```bash |
| export ASSET_ROOT=/path/to/assets |
| python scripts/prepare_config.py --asset-root "$ASSET_ROOT" --inference-only |
| ``` |
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| ## Inference |
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| ```bash |
| CHECKPOINT=/path/to/local/model.pt \ |
| GPUS=0 \ |
| PROMPTS=code/prompts/test_8.txt \ |
| OUTPUT_DIR=outputs/demo \ |
| bash scripts/infer.sh |
| ``` |
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| The defaults are seed 0 and five samples per prompt with non-EMA generator weights. Set `NUM_SAMPLES=1` for a single sample. The generator executes one video at a time while retaining the sampling order across prompts. Model loading uses local base-model assets. |
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| ## Training |
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| Training requires the causal ODE initialization, frozen encoders, matched prompt/video manifest, reference feature bank, and Nyström summaries. Prepare the model and reference assets at the locations specified in [configs/train.yaml](configs/train.yaml), then run: |
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| ```bash |
| python scripts/prepare_config.py --asset-root "$ASSET_ROOT" |
| GPUS=0,1,2,3,4,5,6,7 LOGDIR=runs/train bash scripts/train.sh |
| ``` |
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| | Setting | Value | |
| |---|---| |
| | Training updates | 80 | |
| | Distribution / differentiation / reference batch | 256 / 128 / 128 | |
| | Per-GPU generated / differentiated videos | 32 / 16 | |
| | Reference videos | 8,000 | |
| | Encoder weights | DINOv3 : V-JEPA 2 : VideoMAEv2 = 10 : 1 : 1 | |
| | Loss divisor | 3 | |
| | Nyström / Monte Carlo weights | 2 : 1 | |
| | Optimizer | AdamW, betas (0, 0.999), weight decay 0 | |
| | Learning rate | Cosine from 2.5e-6 to 5e-7 over 200 updates | |
| | Gradient clipping | Maximum norm 10 | |
| | Checkpoint interval | Every ten updates | |
| | Memory management | FSDP, activation checkpointing, optimizer and encoder CPU offload | |
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| The learning-rate schedule spans 200 updates while training stops at update 80. Use a separate `LOGDIR` for each run. Keep checkpoint companion files for resuming training with the same distributed topology and asset configuration. |
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| To resume: |
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| ```bash |
| python scripts/prepare_config.py --asset-root "$ASSET_ROOT" \ |
| --resume-checkpoint /path/to/checkpoint/model.pt |
| GPUS=0,1,2,3,4,5,6,7 LOGDIR=runs/resume bash scripts/train.sh |
| ``` |
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| ## Evaluation and tests |
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| Generate benchmark videos with consistent prompt ordering and sampling settings, then evaluate them with the official [VBench](https://github.com/Vchitect/VBench) implementation. |
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| Run the CPU numerical tests from `14B/`: |
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| ```bash |
| python -m pip install pytest |
| python scripts/test_cpu.py |
| ``` |
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