GGUF file(s) available. Select which one to use:

ltxv-2b-0.9.8-distilled-iq4_nl.gguf

ltxv-2b-0.9.8-distilled-q6_k.gguf

ltxv-2b-0.9.8-distilled-q8_0.gguf

Enter your choice (1 to 3): _

opt a gguf file in your current directory to interact with; nothing else

note: for the latest update, should be able to adjust number of steps, i.e., for distilled model, use 15 instead of 30; save a lot of loading time

run it with gguf-node via comfyui

drag ltx-video-2b-v0.9.1-r2-q4_0.gguf (1.09GB) to > ./ComfyUI/models/diffusion_models
drag t5xxl_fp16-q4_0.gguf (2.9GB) to > ./ComfyUI/models/text_encoders
drag ltxv_vae_fp32-f16.gguf (838MB) to > ./ComfyUI/models/vae

run it straight (no installation needed way)

run the .bat file in the main directory (assuming you are using the gguf-node pack below)
drag the workflow json file (below) to > your browser

workflow

example workflow for gguf (see demo above)
example workflow for the original safetensors

review

q2_k gguf is super fast but not usable; keep it for testing only
surprisingly 0.9_fp8_e4m3fn and 0.9-vae_fp8_e4m3fn are working pretty good
mix-and-match possible; you could mix up using the vae(s) available with different model file(s) here; test which combination works best
you could opt to use the t5xxl scaled safetensors or t5xxl gguf (more quantized versions of t5xxl can be found here) as text encoder
new set of enhanced vae (from fp8 to fp32) added in this pack; the low ram version gguf vae is also available right away; upgrade your node for the new feature: gguf vae loader
gguf-node is available (see details here) for running the new features (the point below might not be directly related to the model)
you are able to make your own fp8_e4m3fn scaled safetensors and/or convert it to gguf with the new node via comfyui

run it with diffusers🧨 (alternative 1)

import torch
from transformers import T5EncoderModel
from diffusers import LTXPipeline, GGUFQuantizationConfig, LTXVideoTransformer3DModel
from diffusers.utils import export_to_video

model_path = (
    "https://huggingface.co/calcuis/ltxv-gguf/blob/main/ltx-video-2b-v0.9-q8_0.gguf"
    )
transformer = LTXVideoTransformer3DModel.from_single_file(
    model_path,
    quantization_config=GGUFQuantizationConfig(compute_dtype=torch.bfloat16),
    torch_dtype=torch.bfloat16,
    )

text_encoder = T5EncoderModel.from_pretrained(
    "calcuis/ltxv-gguf",
    gguf_file="t5xxl_fp16-q4_0.gguf",
    torch_dtype=torch.bfloat16,
    )

pipe = LTXPipeline.from_pretrained(
    "callgg/ltxv-decoder",
    text_encoder=text_encoder,
    transformer=transformer,
    torch_dtype=torch.bfloat16
    ).to("cuda")

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=25,
    num_inference_steps=50,
    ).frames[0]
export_to_video(video, "output.mp4", fps=24)

run it with gguf-connector (alternative 2: auto-mode)

simply execute the command below in console/terminal
note: during the first time launch, it will pull the model file(s) to local cache automatically; then opt to run it entirely offline; i.e., from local URL: http://127.0.0.1:7860 with lazy webui
upgraded the base model from 0.9 to 0.9.6 distilled for better results

ggc vg

the above command is for text to video (t2v) panel
for image-text to video (i2v) panel, please execute:

ggc v1

reference

gguf-node (pypi|repo|pack)
gguf-connector (pypi)