indigo/LTX-2
1
0
Fork 0
Code Issues Pull Requests 1 Actions Packages Projects Releases Wiki Activity

Automated PR - 2026-06-17

Browse Source
This commit is contained in:
github-actions[bot]
2026-06-17 14:06:32 +00:00
parent d6053703e0
commit 0d3d3a3855
90 changed files with 8265 additions and 4511 deletions
Download Patch File Download Diff File Expand all files Collapse all files
packages/ltx-trainer/README.md
+12 -3
View File
@@ -1,8 +1,10 @@
# LTX-2 Trainer
This package provides tools and scripts for training and fine-tuning
Lightricks' **LTX-2** audio-video generation model. It enables LoRA training, full
fine-tuning, and training of video-to-video transformations (IC-LoRA) on custom datasets.
Lightricks' **LTX-2** audio-video generation model. It supports LoRA training, full
fine-tuning, and a flexible conditioning framework covering text-to-video, text-to-audio, image-to-video,
video extension, audio extension, video inpainting, audio inpainting, video outpainting, IC-LoRA for video, audio, and joint
audio-video references, audio-to-video, and video-to-audio.
---
@@ -17,9 +19,16 @@ All detailed guides and technical documentation are in the [docs](./docs/) direc
- [🚀 Training Guide](docs/training-guide.md)
- [🧪 Inference Guide](../ltx-pipelines/README.md)
- [🔧 Utility Scripts](docs/utility-scripts.md)
- [🧩 Custom Training Strategies](docs/custom-training-strategies.md)
- [📚 LTX-Core Documentation](../ltx-core/README.md)
- [🛡️ Troubleshooting Guide](docs/troubleshooting.md)
### 🤖 Agent-Assisted Training
Use the [`train-model`](../../.claude/skills/train-model/SKILL.md) repository skill for an end-to-end guided run:
it probes your data and hardware, chooses the matching training mode, prepares/preprocesses the dataset, launches
training, and monitors the job while using the docs above as the source of truth.
---
## 🔧 Requirements
@@ -28,7 +37,7 @@ All detailed guides and technical documentation are in the [docs](./docs/) direc
- **Gemma Text Encoder** - Local Gemma model directory (required for LTX-2)
- **Linux with CUDA** - CUDA 13+ recommended for optimal performance
- **Nvidia GPU with 80GB+ VRAM** - Recommended for the standard config. For GPUs with 32GB VRAM (e.g., RTX 5090),
use the [low VRAM config](configs/ltx2_av_lora_low_vram.yaml) which enables INT8 quantization and other
use the [low VRAM config](configs/t2v_lora_low_vram.yaml) which enables INT8 quantization and other
memory optimizations
---