Instructions to use Lightricks/LTX-Video with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Lightricks/LTX-Video 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-Video", 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") - Inference
- Notebooks
- Google Colab
- Kaggle
IC loras using diffusers pipeline.
#107
by julienflow - opened
i there and thanks for thos frequent updates!
i was cfreating a pipeline using diffusers and the IC loras.
every thing runs and loads fine, but the loras seems not effective. when i input depth conditioning image, i end up with those images in the final video.
details about the pipe :
latest diffusers version
using the base exemple on LTX video repo
loading loras like this :
pipe.load_lora_weights("Lightricks/LTX-Video-ICLoRA-depth-13b-0.9.7",adapter_name="depth",weight_name="ltxv-097-ic-lora-depth-control-diffusers.safetensors")
pipe.set_adapters(['depth'])
and inputing a depth image a condition
is there something i m missing ?
thanks in advance
any hints for us please?