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 14:21:07 +00:00
parent d6053703e0
commit f4b06fb977
103 changed files with 12887 additions and 3665 deletions
Download Patch File Download Diff File Expand all files Collapse all files
packages/ltx-trainer/docs/training-guide.md
+24 -12
View File
@@ -8,7 +8,7 @@ model uploads.
After preprocessing your dataset and preparing a configuration file, you can start training using the trainer script:
```bash
uv run python scripts/train.py configs/ltx2_av_lora.yaml
uv run python scripts/train.py configs/t2v_lora.yaml
```
The trainer will:
@@ -19,19 +19,31 @@ The trainer will:
4. **Generate validation videos** (if configured)
5. **Save the trained weights** in your output directory
### Agent-Assisted Training
If your environment supports repository skills, the
[`train-model`](../../../.claude/skills/train-model/SKILL.md) skill provides an end-to-end
orchestrator for this package. It asks what you want the model to learn, maps that intent to
one of the documented [training modes](training-modes.md), probes your filesystem and GPU,
prepares/preprocesses the dataset, writes a run-specific config, launches training, and
monitors the job. It uses the trainer docs as its source of truth and stops for approval before
captioning, preprocessing, or starting expensive training work.
### Output Files
**For LoRA training:**
- `lora_weights.safetensors` - Main LoRA weights file
- `checkpoints/lora_weights_step_00000.safetensors` - LoRA checkpoint weights, with the current step in the filename
- `training_config.yaml` - Copy of training configuration
- `validation_samples/` - Generated validation videos (if enabled)
- `samples/` - Generated validation samples (if enabled)
- `checkpoints/training_state_step_00000.pt` - Optional resume state, depending on `checkpoints.save_training_state`
**For full model fine-tuning:**
- `model_weights.safetensors` - Full model weights
- `checkpoints/model_weights_step_00000.safetensors` - Full model checkpoint weights, with the current step in the filename
- `training_config.yaml` - Copy of training configuration
- `validation_samples/` - Generated validation videos (if enabled)
- `samples/` - Generated validation samples (if enabled)
- `checkpoints/training_state_step_00000.pt` - Optional resume state, depending on `checkpoints.save_training_state`
## 🖥️ Distributed / Multi-GPU Training
@@ -62,22 +74,22 @@ Launch with a specific config using `--config_file`:
# DDP (2 GPUs shown as example)
CUDA_VISIBLE_DEVICES=0,1 \
uv run accelerate launch --config_file configs/accelerate/ddp.yaml \
scripts/train.py configs/ltx2_av_lora.yaml
scripts/train.py configs/t2v_lora.yaml
# DDP + torch.compile
CUDA_VISIBLE_DEVICES=0,1 \
uv run accelerate launch --config_file configs/accelerate/ddp_compile.yaml \
scripts/train.py configs/ltx2_av_lora.yaml
scripts/train.py configs/t2v_lora.yaml
# FSDP (4 GPUs shown as example)
CUDA_VISIBLE_DEVICES=0,1,2,3 \
uv run accelerate launch --config_file configs/accelerate/fsdp.yaml \
scripts/train.py configs/ltx2_av_lora.yaml
scripts/train.py configs/t2v_lora.yaml
# FSDP + torch.compile
CUDA_VISIBLE_DEVICES=0,1,2,3 \
uv run accelerate launch --config_file configs/accelerate/fsdp_compile.yaml \
scripts/train.py configs/ltx2_av_lora.yaml
scripts/train.py configs/t2v_lora.yaml
```
**Notes:**
@@ -93,13 +105,13 @@ If you prefer to use your default Accelerate profile:
```bash
# Use settings from your default accelerate config
uv run accelerate launch scripts/train.py configs/ltx2_av_lora.yaml
uv run accelerate launch scripts/train.py configs/t2v_lora.yaml
# Override number of processes on the fly (e.g., 2 GPUs)
uv run accelerate launch --num_processes 2 scripts/train.py configs/ltx2_av_lora.yaml
uv run accelerate launch --num_processes 2 scripts/train.py configs/t2v_lora.yaml
# Select specific GPUs
CUDA_VISIBLE_DEVICES=0,1 uv run accelerate launch scripts/train.py configs/ltx2_av_lora.yaml
CUDA_VISIBLE_DEVICES=0,1 uv run accelerate launch scripts/train.py configs/t2v_lora.yaml
```
> [!TIP]