Instructions to use FuzzPuppy/LTX-2.3-Foley-LoRA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use FuzzPuppy/LTX-2.3-Foley-LoRA with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Lightricks/LTX-2.3", torch_dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("FuzzPuppy/LTX-2.3-Foley-LoRA") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - LTX.io
How to use FuzzPuppy/LTX-2.3-Foley-LoRA with LTX.io:
# Install the LTX-2 pipelines git clone https://github.com/Lightricks/LTX-2.git cd LTX-2 uv sync --frozen
# Download the weights from this repo, plus the Gemma text encoder hf download FuzzPuppy/LTX-2.3-Foley-LoRA --local-dir models/LTX-2.3-Foley-LoRA hf download google/gemma-3-12b-it-qat-q4_0-unquantized --local-dir models/gemma-3-12b
# Text/image-to-video with the LoRA on the HQ two-stage base pipeline uv run python -m ltx_pipelines.ti2vid_two_stages_hq \ --checkpoint-path path/to/checkpoint.safetensors \ --distilled-lora path/to/distilled_lora.safetensors 0.8 \ --spatial-upsampler-path path/to/spatial_upsampler.safetensors \ --gemma-root models/gemma-3-12b \ --lora models/LTX-2.3-Foley-LoRA/<weights>.safetensors 1.0 \ --prompt "your prompt here" \ --output-path output.mp4 # For image-to-video, add: --image path/to/image.jpg 0 0.8 - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- Xet hash:
- 3dda03b2a4c727cd5d98cb6683413815e8a8079e26cbf07e755542b14dd54ec8
- Size of remote file:
- 1.34 MB
- SHA256:
- c92c520a019e5ebfd1edd34b1a067e97464d971d96784bf7be2a8c04fad6565f
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