Instructions to use rootonchair/LTX-2.3-Distilled-v1.1-Diffusers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use rootonchair/LTX-2.3-Distilled-v1.1-Diffusers with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline from diffusers.utils import load_image, export_to_video # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("rootonchair/LTX-2.3-Distilled-v1.1-Diffusers", torch_dtype=torch.bfloat16, device_map="cuda") pipe.to("cuda") prompt = "A man with short gray hair plays a red electric guitar." image = load_image( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/guitar-man.png" ) output = pipe(image=image, prompt=prompt).frames[0] export_to_video(output, "output.mp4") - Notebooks
- Google Colab
- Kaggle
LTX-2.3 Distilled v1.1 (Diffusers)
Diffusers-format weights for the v1.1 distilled checkpoint of Lightricks/LTX-2.3 โ a DiT-based foundation model that jointly generates synchronized video and audio. Runs in 8 steps with CFG = 1.
This is an unofficial staging conversion. The existing
diffusers/LTX-2.3-Distilled-Diffusers
repo was converted before the upstream v1.1 distilled checkpoint was published
(v1.0 dates to Mar 5, this repo's source dg845/LTX-2.3-Distilled-Diffusers conversion was
done Mar 14, and the v1.1 checkpoint was added upstream on Apr 13) โ so that repo still ships
v1.0 weights. This repo swaps in the v1.1 transformer/ weights on top of the same base
pipeline; every other component (VAE, audio VAE, text encoder, tokenizer, connectors, vocoder,
scheduler config) is unchanged from diffusers/LTX-2.3-Distilled-Diffusers and was not
re-converted, since only the DiT was updated upstream.
Source checkpoint
- File:
ltx-2.3-22b-distilled-1.1.safetensorsfromLightricks/LTX-2.3 - Converted with
scripts/convert_ltx2_to_diffusers.py --version 2.3 --ditfrom a localhuggingface/diffuserscheckout (0.40.0.dev0). - Architecture (
transformer/config.json) is byte-for-byte identical to the v1.0 conversion indiffusers/LTX-2.3-Distilled-Diffusersaside from the_diffusers_versionmetadata field โ only the weights changed upstream.
Usage
Requires a recent build of diffusers with LTX-2 support:
pip install -U git+https://github.com/huggingface/diffusers
import torch
from diffusers import LTX2Pipeline
from diffusers.pipelines.ltx2.export_utils import encode_video
from diffusers.pipelines.ltx2.utils import DEFAULT_NEGATIVE_PROMPT, DISTILLED_SIGMA_VALUES
pipe = LTX2Pipeline.from_pretrained(
"rootonchair/LTX-2.3-Distilled-v1.1-Diffusers", torch_dtype=torch.bfloat16
)
pipe.enable_model_cpu_offload()
prompt = "A flowing river in a forest at golden hour, gentle wind in the leaves."
frame_rate = 24.0
video, audio = pipe(
prompt=prompt,
negative_prompt=DEFAULT_NEGATIVE_PROMPT,
width=768,
height=512,
num_frames=121,
frame_rate=frame_rate,
num_inference_steps=8,
sigmas=DISTILLED_SIGMA_VALUES,
guidance_scale=1.0,
output_type="np",
return_dict=False,
)
encode_video(
video[0],
fps=frame_rate,
audio=audio[0].float().cpu(),
audio_sample_rate=pipe.vocoder.config.output_sampling_rate,
output_path="ltx2_distilled_v1_1_t2v.mp4",
)
Notes
width/heightmust be divisible by 32;num_framesmust equal8k + 1.- Always pass
sigmas=DISTILLED_SIGMA_VALUES,num_inference_steps=8,guidance_scale=1.0for this distilled checkpoint. - See the Diffusers LTX-2 docs for multimodal guidance, prompt enhancement, and the upscaling/refinement pipeline.
License
These weights are released under the LTX Video 2 Open Source License.
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Model tree for rootonchair/LTX-2.3-Distilled-v1.1-Diffusers
Base model
Lightricks/LTX-2.3