Instructions to use SimpleTuner/ltxvideo-2.3-convrot-int8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use SimpleTuner/ltxvideo-2.3-convrot-int8 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("SimpleTuner/ltxvideo-2.3-convrot-int8", torch_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-Video 2.3 distilled โ INT8 ConvRot
This repository contains a transformer-only INT8 ConvRot quantization of the official
Lightricks/LTX-2.3 distilled checkpoint. It is derived directly from
ltx-2.3-22b-distilled.safetensors; no fine-tune, task adapter, or third-party model
weights were merged.
Artifact
- File:
ltx-2.3-convrot-int8.safetensors - Size: 19,485,049,192 bytes
- SHA-256:
722e2498f59306aa007f25560d6069f621ae57c90705cdaa0e2de4a1db996d05 - Source SHA-256:
14409a4d1337a8ded02fa87fb895b17a91ab2c6588f7cc3352e624ff18a689bf
The file contains the main audiovisual transformer only. Load the text encoder,
connectors, VAE, audio VAE, and vocoder from the official LTX 2.3 Diffusers pipeline.
Weights use Comfy-style per-layer int8_tensorwise sidecars and are consumed by an
SDNQ Hadamard/ConvRot loader.
Quantization
The checkpoint was quantized on an NVIDIA H200 with
Comfy-Org/comfy-model-tools commit
1fe341bb8a4e46f161a978b5faa2412d8c39c768:
python quant_int8_convrot.py ltx-2.3-22b-distilled.safetensors ltx-2.3-22b-distilled-int8-convrot.safetensors --mseclip --verify-report quantization-report.tsv
The packaging step retained model.diffusion_model.* tensors and omitted the bundled
connector copies, since the target Diffusers pipeline supplies its connector components
separately.
- ConvRot group size: 256
- Quantized transformer linears: 1,344
- Mean relative weight reconstruction error: 0.859%
- Maximum relative weight reconstruction error: 1.446%
- Worst per-layer cosine similarity: above 0.99990
The full per-layer measurements are in quantization-report.tsv.
Validation
The artifact was loaded with SDNQ 0.2.3 into
LTX2VideoTransformer3DModel with all 1,344 quantized parameters wrapped and no
remaining meta tensors. A real audiovisual transformer forward at 768ร512, 121 frames,
and 1,024 text tokens produced finite video and audio outputs. On the calibration H200,
three warmed steps averaged 0.5493 seconds with 19.139 GiB peak allocated memory.
These are transformer-only measurements, not end-to-end generation timings.
License
This is a derivative of LTX-2.3 and remains subject to the LTX-2 Community License Agreement. Review that agreement, including its commercial-use and acceptable-use restrictions, before using or redistributing the checkpoint.
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Lightricks/LTX-2.3