Text-to-Video
Diffusers
Safetensors
LTX2Pipeline
image-to-video
video-to-video
image-text-to-video
audio-to-video
text-to-audio
video-to-audio
audio-to-audio
text-to-audio-video
image-to-audio-video
image-text-to-audio-video
ltx-2
ltx-video
ltxv
lightricks
ltx-2.5
Instructions to use Lightricks/LTX-2.5-Diffusers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Lightricks/LTX-2.5-Diffusers 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-2.5-Diffusers", 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
Add Prompt Enhancer and Processor for Standard Pipeline
#15
by dg845 - opened
No description provided.
This PR is a follow up to #13 that adds the prompt_enhancer and processor from google/gemma-4-E2B-it to the repo for the standard pipeline (which unlike the modular pipeline cannot be configured to directly load the weights from the Gemma 4 repo). Unlike #13, it doesn't accidentally duplicate the prompt_enhancer weights by including both model.safetensors and model shards.
You can test the changes by loading the pipeline with the refs/pr/15 revision corresponding to this PR:
import torch
from diffusers import LTX2Pipeline
pipe = LTX2Pipeline.from_pretrained("Lightricks/LTX-2.5-Diffusers", revision="refs/pr/15", torch_dtype=torch.bfloat16)
...
dg845 changed pull request status to open
art-alex changed pull request status to merged