| # LTX-Video |
|
|
| ## Training |
|
|
| For LoRA training, specify `--training_type lora`. For full finetuning, specify `--training_type full-finetune`. |
|
|
| Examples available: |
| - [PIKA crush effect](../../examples/training/sft/ltx_video/crush_smol_lora/) |
|
|
| To run an example, run the following from the root directory of the repository (assuming you have installed the requirements and are using Linux/WSL): |
|
|
| ```bash |
| chmod +x ./examples/training/sft/ltx_video/crush_smol_lora/train.sh |
| ./examples/training/sft/ltx_video/crush_smol_lora/train.sh |
| ``` |
|
|
| On Windows, you will have to modify the script to a compatible format to run it. [TODO(aryan): improve instructions for Windows] |
|
|
| ## Inference |
|
|
| Assuming your LoRA is saved and pushed to the HF Hub, and named `my-awesome-name/my-awesome-lora`, we can now use the finetuned model for inference: |
|
|
| ```diff |
| import torch |
| from diffusers import LTXPipeline |
| from diffusers.utils import export_to_video |
| |
| pipe = LTXPipeline.from_pretrained( |
| "Lightricks/LTX-Video", torch_dtype=torch.bfloat16 |
| ).to("cuda") |
| + pipe.load_lora_weights("my-awesome-name/my-awesome-lora", adapter_name="ltxv-lora") |
| + pipe.set_adapters(["ltxv-lora"], [0.75]) |
| |
| video = pipe("<my-awesome-prompt>").frames[0] |
| export_to_video(video, "output.mp4", fps=8) |
| ``` |
|
|
| You can refer to the following guides to know more about the model pipeline and performing LoRA inference in `diffusers`: |
|
|
| * [LTX-Video in Diffusers](https://huggingface.co/docs/diffusers/main/en/api/pipelines/ltx_video) |
| * [Load LoRAs for inference](https://huggingface.co/docs/diffusers/main/en/tutorials/using_peft_for_inference) |
| * [Merge LoRAs](https://huggingface.co/docs/diffusers/main/en/using-diffusers/merge_loras) |
