| # LoRA fine-tuning for Cosmos Predict 2.5 |
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| This example shows how to fine-tune [Cosmos Predict 2.5](https://huggingface.co/nvidia/Cosmos-Predict2.5-2B) using LoRA on a custom video dataset. |
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| ## Requirements |
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| Install the library from source and the example-specific dependencies: |
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| ```bash |
| git clone https://github.com/huggingface/diffusers |
| cd diffusers |
| pip install -e ".[dev]" |
| cd examples/cosmos |
| pip install -r requirements.txt |
| ``` |
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| ## Data preparation |
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| The training script expects a dataset directory with the following layout: |
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| ``` |
| <dataset_dir>/ |
| ├── videos/ # .mp4 files |
| └── metas/ # one .txt prompt file per video (same stem) |
| ├── 0.txt |
| ├── 1.txt |
| └── ... |
| ``` |
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| ### GR1 dataset (quick start) |
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| The `download_and_preprocess_datasets.sh` script downloads the GR1-100 training set and the EVAL-175 test set, then runs the preprocessing script to create the per-video prompt files. |
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| ```bash |
| bash download_and_preprocess_datasets.sh |
| ``` |
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| This produces: |
| - `gr1_dataset/train/` — training videos + prompts |
| - `gr1_dataset/test/` — evaluation images + prompts |
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| ## Training |
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| Launch LoRA training with `accelerate`: |
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| ```bash |
| export MODEL_NAME="nvidia/Cosmos-Predict2.5-2B" |
| export DATA_DIR="gr1_dataset/train" |
| export OUT_DIR="lora-output" |
| |
| accelerate launch --mixed_precision="bf16" train_cosmos_predict25_lora.py \ |
| --pretrained_model_name_or_path=$MODEL_NAME \ |
| --revision diffusers/base/post-trained \ |
| --train_data_dir=$DATA_DIR \ |
| --output_dir=$OUT_DIR \ |
| --train_batch_size=1 \ |
| --num_train_epochs=500 \ |
| --checkpointing_epochs=100 \ |
| --seed=0 \ |
| --height 432 --width 768 \ |
| --allow_tf32 \ |
| --gradient_checkpointing \ |
| --lora_rank 32 --lora_alpha 32 \ |
| --report_to=wandb |
| ``` |
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| Or use the provided shell script: |
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| ```bash |
| bash train_lora.sh |
| ``` |
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| ## Evaluation |
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| Run inference with the trained LoRA adapter: |
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| ```bash |
| export DATA_DIR="gr1_dataset/test" |
| export LORA_DIR="lora-output" |
| export OUT_DIR="eval-output" |
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| python eval_cosmos_predict25_lora.py \ |
| --data_dir $DATA_DIR \ |
| --output_dir $OUT_DIR \ |
| --lora_dir $LORA_DIR \ |
| --revision diffusers/base/post-trained \ |
| --height 432 --width 768 \ |
| --num_output_frames 93 \ |
| --num_steps 36 \ |
| --seed 0 |
| ``` |
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| Or use the provided shell script: |
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| ```bash |
| bash eval_lora.sh |
| ``` |
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