Instructions to use CalamitousFelicitousness/LTX-2.3-Spatial-Upsampler-x2-1.1-Diffusers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use CalamitousFelicitousness/LTX-2.3-Spatial-Upsampler-x2-1.1-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("CalamitousFelicitousness/LTX-2.3-Spatial-Upsampler-x2-1.1-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
metadata
license: apache-2.0
library_name: diffusers
tags:
- video
- ltx-2
- ltx-2.3
- latent-upsampler
base_model: Lightricks/LTX-2.3
LTX-2.3 Spatial Upscaler x2 1.1 (Diffusers)
Diffusers-format conversion of ltx-2.3-spatial-upscaler-x2-1.1.safetensors from the official Lightricks/LTX-2.3 release.
Weights are byte-identical to the upstream release; only the layout has been rearranged so LTX2LatentUpsamplerModel.from_pretrained and LTX2LatentUpsamplePipeline can consume it directly.
Usage
import torch
from diffusers.pipelines.ltx2 import LTX2LatentUpsamplerModel
from diffusers.pipelines.ltx2.pipeline_ltx2_latent_upsample import LTX2LatentUpsamplePipeline
latent_upsampler = LTX2LatentUpsamplerModel.from_pretrained(
'<your-username>/<your-repo>',
subfolder='latent_upsampler',
torch_dtype=torch.bfloat16,
)
upsample_pipe = LTX2LatentUpsamplePipeline(vae=your_pipe.vae, latent_upsampler=latent_upsampler)
License
Apache 2.0, inherited from the upstream Lightricks/LTX-2.3 release.