Text-to-Video
Diffusers
Safetensors
LTX2Pipeline
video-generation
audio-generation
audio-visual
ltx-2
marimo
molab
Instructions to use dragonlimited/LTX2-marimo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use dragonlimited/LTX2-marimo with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("dragonlimited/LTX2-marimo", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
Commit History
5 days ago
-
Legend of Valor: Act I rendered, notebook viewer + LTX-2 load fixes
Verified signature7e7101e -
Upload notebook.py with huggingface_hub
Verified signature0dd75a9
6 days ago
-
LTX-2 marimo notebook: drop a stale comment my autopilot left in the film cell
Verified signature7ea2f65 -
LTX-2 marimo notebook: product rewrite — inputs first, quality/length presets, self-loading model, generate button + inline download, optional film section, OOM-safe residency rule
Verified signature90ebe61 -
Upload notebook.py with huggingface_hub
Verified signaturee0ca740 -
Fix stale model name in the compute card
Verified signaturec5ec884 -
Product pass: reproducible takes, graceful asset errors, stale copy fixed
Verified signature15b6320 -
OOM handling, expandable segments, prompt default
Verified signature5132e36 -
Upload notebook.py with huggingface_hub
Verified signatured05c5d7 -
Upload notebook.py with huggingface_hub
Verified signature6f1319d -
Upload notebook.py with huggingface_hub
Verified signature63f023b