Instructions to use Rin247/LTX-2.5-Diffusers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Rin247/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("Rin247/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
File size: 971 Bytes
be0dcf5 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 | {
"_class_name": "LTX2Pipeline",
"_diffusers_version": "0.40.0.dev0",
"_name_or_path": "Lightricks/LTX-2.5-Diffusers",
"audio_vae": [
"diffusers",
"AutoencoderKLLTX2Audio"
],
"connectors": [
"ltx2",
"LTX2TextConnectors"
],
"diffusion_decoder": [
"diffusers",
"LTX2VideoDiffusionDecoderModel"
],
"duration_head": [
"ltx2",
"LTX2DurationHead"
],
"processor": [
"transformers",
"Gemma4Processor"
],
"prompt_enhancer": [
"transformers",
"Gemma4ForConditionalGeneration"
],
"scheduler": [
"diffusers",
"FlowMatchEulerDiscreteScheduler"
],
"text_encoder": [
"transformers",
"Gemma4UnifiedForConditionalGeneration"
],
"tokenizer": [
"transformers",
"GemmaTokenizer"
],
"transformer": [
"diffusers",
"LTX2VideoTransformer3DModel"
],
"vae": [
"diffusers",
"AutoencoderKLLTX2Video"
],
"vocoder": [
"ltx2",
"LTX2VocoderWithBWE"
]
}
|