Instructions to use TheBurgstall/ltx-2.3-googlyeyes-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use TheBurgstall/ltx-2.3-googlyeyes-lora 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", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("TheBurgstall/ltx-2.3-googlyeyes-lora") prompt = "A man with short gray hair plays a red electric guitar." input_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/guitar-man.png") image = pipe(image=input_image, prompt=prompt).frames[0] export_to_video(output, "output.mp4") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
Googly Eyes LoRA for LTX-2.3
Slaps oversized googly-eye stickers onto whatever subject is in your LTX-2.3 generation. Pure, dumb fun.
Trigger word: googlyeyes โ that's all you need. The LoRA does its thing on the trigger word alone, you don't have to write any other prompt. You can add a regular prompt on top if you want to try and direct the scene somehow, no promises you'll succeed though; anyway the trigger just needs to be in there somewhere.
Sample outputs
Usage in ComfyUI
Standard LTX-2.3 IC-LoRA workflow. Drop the .safetensors into your ComfyUI models/loras/ folder, add a Load LoRA node, set strength to 1.0, and put googlyeyes somewhere in your prompt.
Reference workflows are in the official LTX-2 repository.
Dataset
I built the entire dataset myself โ fully synthetic, and curated and filtered by hand. No real people in the training data.
License
CC BY-NC 4.0
Acknowledgements
- Lightricks for releasing LTX-2.3
oumad/LTX-2for the trainer fork
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Model tree for TheBurgstall/ltx-2.3-googlyeyes-lora
Base model
Lightricks/LTX-2.3