Instructions to use wanshanmu/LTX-2.3-spatial-upscaler-x2-v1.1-Diffusers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use wanshanmu/LTX-2.3-spatial-upscaler-x2-v1.1-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("wanshanmu/LTX-2.3-spatial-upscaler-x2-v1.1-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
LTX-2.3 Spatial Upscaler x2 v1.1 (Diffusers)
Diffusers-format weights for the v1.1 x2 spatial latent upsampler of
Lightricks/LTX-2.3, packaged as an
LTX2LatentUpsamplePipeline (VAE + latent upsampler).
This is an unofficial staging conversion — there is no existing Diffusers-format repo for
any version of this component on the Hub. The VAE submodule is reused as-is from
diffusers/LTX-2.3-Diffusers (unchanged
between v1.0 and v1.1); only the latent-upsampler weights were converted from the v1.1
checkpoint.
Source checkpoint
- File:
ltx-2.3-spatial-upscaler-x2-1.1.safetensorsfromLightricks/LTX-2.3 - Converted with
scripts/convert_ltx2_to_diffusers.py --version 2.3 --latent_upsamplerfrom a localhuggingface/diffuserscheckout (0.40.0.dev0).
Usage
import torch
from diffusers import LTX2LatentUpsamplePipeline
pipe = LTX2LatentUpsamplePipeline.from_pretrained(
"rootonchair/LTX-2.3-spatial-upscaler-x2-v1.1-Diffusers", torch_dtype=torch.bfloat16
).to("cuda")
# `latents` are the video latents produced by an LTX2Pipeline generation
upsampled_latents = pipe(latents=latents, output_type="latent", return_dict=False)[0]
See the Diffusers LTX-2 docs
for the full upscaling/refinement workflow alongside LTX2Pipeline.
License
These weights are released under the LTX Video 2 Open Source License.
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Base model
Lightricks/LTX-2.3