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---
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> |