metadata
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
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 custom node.
Place model files in ComfyUI/models/unet - see the GitHub readme for further install instructions.
Please refer to this chart 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:
pip install git+https://github.com/huggingface/diffusers
And then install gguf:
pip install -U gguf
And then we're ready to perform inference:
Inference code
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)