Instructions to use SyFeee/LTX-2.3-SyFe-MSR-LoRA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LTX.io
How to use SyFeee/LTX-2.3-SyFe-MSR-LoRA with LTX.io:
# Install the LTX-2 pipelines git clone https://github.com/Lightricks/LTX-2.git cd LTX-2 uv sync --frozen
# Download the weights from this repo, plus the Gemma text encoder hf download SyFeee/LTX-2.3-SyFe-MSR-LoRA --local-dir models/LTX-2.3-SyFe-MSR-LoRA hf download google/gemma-3-12b-it-qat-q4_0-unquantized --local-dir models/gemma-3-12b
# Video-to-video with the IC-LoRA (runs on the distilled base model) uv run python -m ltx_pipelines.ic_lora \ --distilled-checkpoint-path path/to/distilled_checkpoint.safetensors \ --spatial-upsampler-path path/to/spatial_upsampler.safetensors \ --gemma-root models/gemma-3-12b \ --lora models/LTX-2.3-SyFe-MSR-LoRA/<weights>.safetensors 1.0 \ --video-conditioning reference.mp4 1.0 \ --prompt "your prompt here" \ --output-path output.mp4 - Inference
- Notebooks
- Google Colab
- Kaggle
SyFe LTX-2.3 MSR LoRA Checkpoints
Multiple Subject Reference LoRAs trained by SyFe on LTX-2.3 22B-dev. Multiple subject and scene images are encoded as reference-video latents so target tokens can retrieve them through native self-attention.
Checkpoints
| Run | Data / construction | Rank | Steps | Status |
|---|---|---|---|---|
msr_plain_01 |
Initial crop-fill references | 128 | 5,000 | Archived: crop-fill can crop full-body subjects |
msr_plain_02 |
Correct white-canvas, never-crop subjects | 128 | 5,000 | Validated in the combined talking stack |
msr_plain_rebuilt01 |
Rebuilt intermediate corpus | 128 | 5,000 | Superseded experiment |
msr_corpus36_run01 |
35 shows, 31,500 true-bilingual samples | 128 | 6,000 | Recommended; deployed final |
For msr_corpus36_run01, reference subjects must be contain-fit without cropping on a white canvas; scene references may be cover-fit. Prompts should begin with the ordered subject markers, for example [VISUAL]: char_1_person, char_2_person..., because marker order binds prompt subjects to reference slots.
The recommended checkpoint materially improves out-of-show costume, prop, hair, and scene adherence and removes memorized-cast substitution. Exact facial identity can still drift, especially when faces occupy few pixels. Two-subject reliability is not perfect.
The third-party Licon-MSR-V1 checkpoint is intentionally not included. Use is subject to the LTX-2 community license.
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Model tree for SyFeee/LTX-2.3-SyFe-MSR-LoRA
Base model
Lightricks/LTX-2.3