Instructions to use Muapi/ltx-2.3-bodybuilding-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Muapi/ltx-2.3-bodybuilding-lora with Diffusers:

pip install -U diffusers transformers accelerate

import torch
from diffusers import DiffusionPipeline
from diffusers.utils import export_to_video

# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("Lightricks/LTX-Video", dtype=torch.bfloat16, device_map="cuda")
pipe.load_lora_weights("Muapi/ltx-2.3-bodybuilding-lora")

prompt = "A man with short gray hair plays a red electric guitar."

output = pipe(prompt=prompt).frames[0]
export_to_video(output, "output.mp4")

LTX-2

How to use Muapi/ltx-2.3-bodybuilding-lora with LTX-2:

# 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 Muapi/ltx-2.3-bodybuilding-lora --local-dir models/ltx-2.3-bodybuilding-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-bodybuilding-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

ltx-2.3-bodybuilding-lora

Commit History

May 23

19:52
Add LTX-2.3 Bodybuilding LoRA

Muapi

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e3664a3
19:50
initial commit

Muapi

Verified signature
960aba7

Commit History

May 23

Add LTX-2.3 Bodybuilding LoRA

initial commit