Downloads · 30 days
78
59% of all-time downloads
rootonchair/LTX-2.3-spatial-upscaler-x2-v1.1-Diffusers
LTX-2.3-spatial-upscaler-x2-v1.1-Diffusers is a machine learning model from rootonchair. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for diffusers. The card lists the license as other.
Diffusers-format weights for the v1.1 x2 spatial latent upsampler of Lightricks/LTX-2.3, packaged as an LTX2LatentUpsamplePipeline (VAE + latent upsampler).
Downloads · 30 days
78
59% of all-time downloads
All-time downloads
133
Public
Repo size
2.4 GB
Likes
0
Public
Click a slice to open those files.
.safetensors2.4 GB · 100%
From the Hugging Face model README
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.
ltx-2.3-spatial-upscaler-x2-1.1.safetensors from
Lightricks/LTX-2.3scripts/convert_ltx2_to_diffusers.py --version 2.3 --latent_upsampler from a
local huggingface/diffusers checkout (0.40.0.dev0).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.
These weights are released under the LTX Video 2 Open Source License.