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
Upload README.md with huggingface_hub
Browse files
README.md
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---
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tags:
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- quantized
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- safetensors
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- diffusers
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- mecha
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- weight-only
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library_name: diffusers
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---
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# LTX-2.5-Diffusers
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Quantised media packages of `Lightricks/LTX-2.5-Diffusers`.
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## MECHA Forge
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MECHA — Model Engineering & Contextual Hearth Assembly
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These media packages have been forged and optimized in the MECHA Forge.
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Each one holds the Denoiser, Text Encoder, and VAE components packaged together for easier deployment.
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## Available quantizations
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| Quant | Denoiser | Text encoder | VAE |
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|---|---|---|---|
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| FP4 | FP4 | FP4 | FP8 |
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| FP8 | FP8 | FP8 | FP8 |
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| INT4 | INT4 | INT4 | INT8 |
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| INT8 | INT8 | INT8 | INT8 |
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The VAE is shared between formats: FP4 reuses the FP8 file, INT4 reuses the INT8 file.
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