---
license: apache-2.0
library_name: cosmos-predict2
tags:
  - video-generation
  - world-model
  - cosmos-predict2
  - lora
  - robotics
base_model: nvidia/Cosmos-Predict2-2B-Video2World
---

# cosmos-task1-task2

LoRA fine-tunes of [Cosmos-Predict2-2B-Video2World](https://huggingface.co/nvidia/Cosmos-Predict2-2B-Video2World) on the `push-that-thing` task1 and task2 datasets.

The checkpoints in this repo are **unfused** — the LoRA adapters (`lora_A`, `lora_B`) are still stored separately from the base layer weights. You must run the one-shot fusion step below before using them with the `run_video2world.py` inference pipeline.

## Repository layout

Each iteration of training writes four parallel files. They are uploaded as-is:

```
model/iter_<NNNNNNNNN>.pt       # net.* + net_ema.* weights with LoRA adapters
optim/iter_<NNNNNNNNN>.pt       # optimizer state (resume only)
scheduler/iter_<NNNNNNNNN>.pt   # LR scheduler state (resume only)
trainer/iter_<NNNNNNNNN>.pt     # grad scaler + iteration counter (resume only)
```

For **inference** you only need `model/iter_<NNNNNNNNN>.pt`. The other three folders are only required to resume training from this iteration.

## Inference: fuse, then run

The inference pipeline does not apply LoRA adapters at load time, so an unfused checkpoint will load but produce garbage outputs. Fuse it first.

### 1. Download

```bash
ITER=000002500  # set to the iteration you want
hf download push-that-thing/cosmos-task1-task2 \
    model/iter_${ITER}.pt --local-dir ./ckpts
```

### 2. Fuse

`fuse_lora_ckpt.py` lives in [push-that-thing/pdt-mimic](https://github.com/push-that-thing/pdt-mimic) under the `mimic-video` submodule. Clone with submodules first:

```bash
git clone --recurse-submodules https://github.com/push-that-thing/pdt-mimic.git
# or, if already cloned:
git submodule update --init --recursive

python pdt-mimic/mimic-video/model/scripts/fuse_lora_ckpt.py \
    ./ckpts/model/iter_${ITER}.pt
# writes ./ckpts/model/iter_${ITER}_fused.pt
```

Fusion is deterministic: it walks every key, finds matching `lora_A` / `lora_B` pairs, computes `base + (alpha / rank) * B @ A`, and replaces the `base_layer` entry with the merged tensor. Both `net.*` (regular) and `net_ema.*` (EMA) weights are fused in the same pass.

### 3. Run video2world

```bash
python pdt-mimic/mimic-video/model/scripts/run_video2world.py \
    --dit_path ./ckpts/model/iter_${ITER}_fused.pt \
    --input_path /path/to/conditioning.mp4 \
    --num_conditional_frames 5 \
    --prompt "Push the white object to the right into the goal white circle." \
    --save_path ./out.mp4
```

## Important: ALPHA must match training

`fuse_lora_ckpt.py` hardcodes `ALPHA = 32`. These checkpoints were trained with the same value, so the default works as-is. If you ever re-train with a different LoRA alpha you must update that constant before fusing or the merged weights will be scaled incorrectly.

## Resuming training

To resume training from a given iteration, download all four folders for that iteration and place them under `<job_dir>/checkpoints/{model,optim,scheduler,trainer}/iter_<NNNNNNNNN>.pt`, then write `iter_<NNNNNNNNN>.pt` into `<job_dir>/checkpoints/latest_checkpoint.txt`. The Cosmos `Checkpointer` will pick it up automatically.

Do **not** resume from a fused checkpoint — fusion deletes the `lora_A`/`lora_B` keys that the optimizer state references.