---
base_model: Lightricks/LTX-Video
library_name: gguf
quantized_by: city96
tags:
- ltx-video
- text-to-video
- image-to-video
language:
  - en
license: other
license_link: LICENSE.md
---

This is a direct GGUF conversion of [Lightricks/LTX-Video](https://huggingface.co/Lightricks/LTX-Video)

As this is a quantized model not a finetune, all the same restrictions/original license terms still apply.

The model files can be used with the [ComfyUI-GGUF](https://github.com/city96/ComfyUI-GGUF) custom node.

Place model files in `ComfyUI/models/unet` - see the GitHub readme for further install instructions.

Please refer to [this chart](https://github.com/ggerganov/llama.cpp/blob/master/examples/perplexity/README.md#llama-3-8b-scoreboard) for a basic overview of quantization types.

## Diffusers support

You can also use the checkpoints with the `diffusers` library.

Make sure to install `diffusers` from source:

```bash
pip install git+https://github.com/huggingface/diffusers
```

And then install `gguf`:

```bash
pip install -U gguf
```

And then we're ready to perform inference:

<details>
<summary>Inference code</summary>

```py
import torch
from diffusers.utils import export_to_video
from diffusers import LTXPipeline, LTXVideoTransformer3DModel, GGUFQuantizationConfig

ckpt_path = (
    "https://huggingface.co/city96/LTX-Video-gguf/blob/main/ltx-video-2b-v0.9-Q3_K_S.gguf"
)
transformer = LTXVideoTransformer3DModel.from_single_file(
    ckpt_path,
    quantization_config=GGUFQuantizationConfig(compute_dtype=torch.bfloat16),
    torch_dtype=torch.bfloat16,
)
pipe = LTXPipeline.from_pretrained(
    "Lightricks/LTX-Video",
    transformer=transformer,
    generator=torch.manual_seed(0),
    torch_dtype=torch.bfloat16,
)
pipe.enable_model_cpu_offload()

prompt = "A woman with long brown hair and light skin smiles at another woman with long blonde hair. The woman with brown hair wears a black jacket and has a small, barely noticeable mole on her right cheek. The camera angle is a close-up, focused on the woman with brown hair's face. The lighting is warm and natural, likely from the setting sun, casting a soft glow on the scene. The scene appears to be real-life footage"
negative_prompt = "worst quality, inconsistent motion, blurry, jittery, distorted"

video = pipe(
    prompt=prompt,
    negative_prompt=negative_prompt,
    width=704,
    height=480,
    num_frames=161,
    num_inference_steps=50,
).frames[0]
export_to_video(video, "output_gguf_ltx.mp4", fps=24)
```
  
</details>