base_model: Lightricks/LTX-2.3
library_name: ltx
license: other
license_name: ltx-2-community-license
license_link: https://github.com/Lightricks/LTX-2/blob/main/LICENSE
pipeline_tag: image-to-video
tags:
- ltx-video
- ltx-2.3
- lora
- ic-lora
- multi-reference
- msr
- chinese-drama
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Rebuilt intermediate corpus | 128 | 5,000 | Superseded experiment |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.