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LTX-2.5

The LTX-2.5 backend supports Stage0.5 native rectified-flow LoRA training, inference, and preencoding for 153-frame 512×768 video.

Availability and data

Route Data Status
Stage0.5 preencoded ltx-153f-h512-w768 latent Code and weights available; latent upload coming soon
Stage0.5 online raw-WDS Available
Inference ltx-153f-h512-w768 latent Code and weights available; latent upload coming soon
Preencoding raw-WDS input Available

Preencoded training is the recommended route because it avoids running the VAE and text encoder during every training step. Track the payload in the latent-WDS release list, or use Download and access for raw-WDS.

Setup

Activate the LTX environment from Runtime environments, then set:

export SOLAR_REPO=/path/to/SolarWM
export SOLAR_MODEL_ROOT=/path/to/SolarWM-models
export SOLAR_DATA_ROOT=/path/to/SolarWM-Data/releases-v1
export SOLAR_OUTPUT_ROOT=/path/to/outputs
cd "$SOLAR_REPO"

Download the LTX base model and released adapter:

python -m pip install --upgrade huggingface_hub
hf download junchaoh-cs/SolarWM \
  --include "SolarWM-ltx-22B-*/**" \
  --local-dir "$SOLAR_MODEL_ROOT"

Set the model paths used below:

export LTX_BASE="$SOLAR_MODEL_ROOT/SolarWM-ltx-22B-base"
export LTX_TRANSFORMER="$LTX_BASE/diffusion_models/ltx-2.5-22b-dev-transformer-bf16.safetensors"
export LTX_VAE="$LTX_BASE/vae/ltx-2.5-video-vae-bf16.safetensors"
export LTX_GEMMA="$LTX_BASE/text_encoders/gemma4-12b-with-proj-ltx-2.5-bf16.safetensors"
export LTX_NEGATIVE="$LTX_BASE/conditioning/ltx25-negative-caption.safetensors"

Preencoded training

The released training profile uses two eight-GPU nodes. Set NODE_RANK=0 on the first node and NODE_RANK=1 on the second.

export NODE_RANK=0
export MASTER_ADDR=hostname-or-ip-of-node-0
export MASTER_PORT=29500

torchrun --nnodes=2 --node-rank="$NODE_RANK" --nproc-per-node=8 \
  --rdzv-backend=c10d --rdzv-endpoint="$MASTER_ADDR:$MASTER_PORT" \
  -m solarwm train \
  --config configs/examples/ltx25/stage0p5-train-153f-lora384-sp2.yaml \
  --set distributed.world_size=16 \
  --set distributed.local_world_size=8 \
  --set train.global_batch_size=16 \
  --set validation.sample_count=16 \
  --set model.checkpoint_path="$LTX_TRANSFORMER" \
  --set model.codec.video_vae_path="$LTX_VAE" \
  --set data.index_root="$SOLAR_DATA_ROOT" \
  --set data.transport.root="$SOLAR_DATA_ROOT" \
  --set validation.inference.negative_caption_cache="$LTX_NEGATIVE" \
  --set runtime.output_dir="$SOLAR_OUTPUT_ROOT/ltx-stage0p5-153f"

Run solarwm config resolve with the same config and --set arguments before training if you want to inspect the resolved configuration.

Inference

Inference uses two GPUs and the released adapter:

torchrun --standalone --nproc-per-node=2 -m solarwm infer \
  --config configs/examples/ltx25/stage0p5-infer-153f.yaml \
  --set model.checkpoint_path="$LTX_TRANSFORMER" \
  --set model.codec.video_vae_path="$LTX_VAE" \
  --set model.adapter_checkpoint_path="$SOLAR_MODEL_ROOT/SolarWM-ltx-22B-bid-stage0p5-153f" \
  --set data.index_root="$SOLAR_DATA_ROOT" \
  --set data.transport.root="$SOLAR_DATA_ROOT" \
  --set inference.negative_caption_cache="$LTX_NEGATIVE" \
  --set runtime.output_dir="$SOLAR_OUTPUT_ROOT/ltx-stage0p5-153f-infer"

Optional raw-WDS workflows

Online training uses configs/examples/ltx25/stage0p5-train-online-153f-unpaired.yaml with the same training command. Add --set model.codec.gemma4_path="$LTX_GEMMA" and choose a new output directory.

To create a new LTX latent generation from raw-WDS:

torchrun --standalone --nproc-per-node=8 -m solarwm preencode \
  --config configs/examples/ltx25/preencode-153f.yaml \
  --set model.checkpoint_path="$LTX_TRANSFORMER" \
  --set model.codec.video_vae_path="$LTX_VAE" \
  --set model.codec.gemma4_path="$LTX_GEMMA" \
  --set data.index_root="$SOLAR_DATA_ROOT" \
  --set data.transport.root="$SOLAR_DATA_ROOT" \
  --set preencode.output_root="$SOLAR_OUTPUT_ROOT/preencoded/ltx-153f-h512-w768" \
  --set runtime.output_dir="$SOLAR_OUTPUT_ROOT/ltx-preencode-153f"

Model and camera conventions

Raw records store camera poses as C2W. LTX preencoding converts them to first-frame-relative W2C with normalized intrinsics, which is also the camera format stored in the released LTX latent generation. Released LTX configs use the linear camera-translation transform.

License

The LTX base model and released adapter remain subject to the LTX Community License included with the model packages. Review that license before use or redistribution; the SolarWM code license does not replace it.