Image-to-Video
Diffusers
text-to-video
video-to-video
image-text-to-video
audio-to-video
text-to-audio
video-to-audio
audio-to-audio
text-to-audio-video
image-to-audio-video
image-text-to-audio-video
ltx-2
ltx-2-3
ltx-video
ltxv
lightricks
Instructions to use Lightricks/LTX-2.3-fp8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Lightricks/LTX-2.3-fp8 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline from diffusers.utils import load_image, export_to_video # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Lightricks/LTX-2.3-fp8", dtype=torch.bfloat16, device_map="cuda") pipe.to("cuda") prompt = "A man with short gray hair plays a red electric guitar." image = load_image( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/guitar-man.png" ) output = pipe(image=image, prompt=prompt).frames[0] export_to_video(output, "output.mp4") - Notebooks
- Google Colab
- Kaggle
ltx-2.3-22b-distilled-fp8.safetensors get very bad results
#5
by qwerdf4 - opened

when i use ltx-2.3-22b-distilled-fp8.safetensors ,i get very bad results;
python -m ltx_pipelines.distilled --quantization fp8-cast --distilled-checkpoint-path "E:/AI_model/LTX-2.3-fp8/ltx-2.3-22b-distilled-fp8.safetensors" --gemma-root "E:/AI_model/gemma-3-12b-it-qat-q4_0-unquantized" --prompt "A cinematic shot of a golden retriever playing in a field of flowers, golden hour lighting, 4k, highly detailed." --output-path "./outputs/my_video.mp4" --height 512 --width 768 --num-frames 121 --frame-rate 24 --spatial-upsampler-path "E:/AI_model/LTX-2.3/ltx-2.3-spatial-upscaler-x2-1.0.safetensors"