---
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| `msr_plain_rebuilt01` | Rebuilt intermediate corpus | 128 | 5,000 | Superseded experiment |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.