Lightricks / LTX-2.5-22b-LoRA-Slow-Motion-Control

LTX-2.5 22B LoRA Slow Motion Control

This is a Slow Motion Control LoRA trained on top of LTX-2.5-22B , which turns physical motion speed into a controllable knob — real-time through extreme slow-motion — while the output video stays at original fps .

It is based on the LTX-2.5 foundation model.

Model Files

ltx-2.5-22b-lora-slow-motion-control-1.0.safetensors

Model Details

• Base Model: LTX-2.5-22B Video
• Training Type: standard LoRA
• Control Type: a speed conditioning value that decouples motion frame rate from playback frame rate
• Modality: image-to-video
• Pipeline details: no external models and no preprocessing at inference — one LoRA plus a pipeline that exposes the speed knob.

Intended Use & Out-of-Scope

Intended use: image-to-video generation where you want controllable slow-motion of physical action — splashes, punches, body shakes, spins, cloth and hair motion, falling liquids — at a fixed provided fps playback rate.

Out of scope: arbitrary video restyling, and any use without the speed / dual-fps wiring. With a generic loader that ignores the speed value you get a plain LoRA and none of the intended motion control.

How It Works

Unlike a normal LTX run, which uses a single frame rate for both the model and the output file, this LoRA needs two values:

Keeping frame_rate = 24 :

Without the LoRA, pushing motion fps high mostly produces smearing and ghosting. With it, you get genuine high-speed-camera-style slow-motion.

Prompting: write a normal image-to-video caption. There is no trigger word and no special phrasing — do not add "slow motion", "high fps", or any speed wording. The speed value alone drives the effect, and the same caption should be reused across every speed.

Usage

🔌 ComfyUI

1. Copy ltx-2.5-22b-lora-slow-motion-control-1.0.safetensors into models/loras .
2. Load LTX-2.5_I2V_Speed_Control_flow.json from this repo — it wires the LoRA and the speed knob for you, and lists the LTX-2.5 models it needs in its own note.
3. Set the speed node (ships at 0.2 ) to taste and keep the frame rate at 24 .

LTX-2 🐍 Python (CLI)

distilled_speed_demo.py in this repo is a minimal driver that adds the speed knob to the stock LTX-2.5 distilled two-stage pipeline.

Prerequisites

1. A checkout of LTX-2 with its environment installed ( uv sync --frozen from the repo root). Run the script with that environment's Python, so ltx_core and ltx_pipelines are importable.

2. This LoRA: ltx-2.5-22b-lora-slow-motion-control-1.0.safetensors .

3. The LTX-2.5 base components from Lightricks/LTX-2.5 :

   Slot	File
   Transformer	diffusion_models/ltx-2.5-22b-distilled-transformer-bf16.safetensors
   Text encoder	text_encoders/gemma4-12b-with-proj-ltx-2.5-bf16.safetensors
   Video VAE	vae/ltx-2.5-video-vae-bf16.safetensors
   Audio VAE	vae/ltx-2.5-audio-vae-bf16.safetensors
   Spatial upsampler	latent_upscale_models/ltx-2.5-latent-spatial-upscaler-x2-bf16-1.0.safetensors

Run

python distilled_speed_demo.py \
  --transformer-path       path/to/ltx-2.5-22b-distilled-transformer-bf16.safetensors \
  --text-encoder-path      path/to/gemma4-12b-with-proj-ltx-2.5-bf16.safetensors \
  --video-vae-path         path/to/ltx-2.5-video-vae-bf16.safetensors \
  --audio-vae-path         path/to/ltx-2.5-audio-vae-bf16.safetensors \
  --spatial-upsampler-path path/to/ltx-2.5-latent-spatial-upscaler-x2-bf16-1.0.safetensors \
  --lora path/to/ltx-2.5-22b-lora-slow-motion-control-1.0.safetensors 1.0 \
  --prompt "A wide shot of a busy city crosswalk, pedestrians crossing left to right, static camera." \
  --image reference_image.png 0 1.0 \
  --speed 0.2 \
  --num-frames 121 --frame-rate 24 \
  --height 1088 --width 1920 --seed 42 \
  --output-path output.mp4

Constraints

• --height / --width must both be divisible by 64 (this is a two-stage pipeline). 1088x1920 and 1920x1088 are the tested sizes.
• --num-frames must satisfy (frames - 1) % 8 == 0 — e.g. 121.
• --image PATH FRAME_IDX STRENGTH conditions the opening frame. The still is resized and center-cropped to the output aspect, so give it something close to the target ratio or expect to lose edges.

Notes

• The distilled pipeline uses fixed sigmas (8 steps, then 3 at full resolution), so there is no guidance scale and no negative prompt — LoRA strength and --speed are the controls.
• Audio is generated alongside the video, so describing the soundscape in the prompt has an effect.
• Add --duration-head-path path/to/ltx-2.5-duration-head-bf16.safetensors if you want to omit --num-frames and let the clip length be predicted from the prompt.
• Out of memory? Add --offload cpu , or generate at 544x960 and upscale afterwards.

Recommended Settings

• LoRA strength / weight: 1.0
• Resolution & frames: 1920×1088 , 121 frames , 24 fps playback
• Frame count: must satisfy (frames - 1) % 8 == 0 (e.g. 121)
• Playback frame rate: always 24
• Speeds to try: 1.0 , 0.5 , 0.2 , 0.1 , 0.05 , 0.025
• Seed: 42 for the published examples
• Prompting: regular captions only, identical across speeds, no trigger phrase

Dataset

Trained on real high-speed slow-motion footage from SloMo-44K (cut and speed-weighted), following the dual-fps idea from Seeing Fast and Slow .

The full cut is 85,389 clips . Each clip stores an effective training fps of src_fps / speed , which is the temporal-RoPE motion conditioner rather than the mp4 playback rate.

Source capture fps (container / camera fps of the originals):

Effective motion fps seen in training ( src_fps / speed ) — what the LoRA conditions on:

Median effective fps ≈ 195 ; p90 ≈ 480 . Dataset speed labels skew slow (median ≈ 0.15 ): ~49% very slow ( <0.15 ), ~40% mid ( 0.15–0.3 ), ~11% mid-fast ( 0.3–0.7 ), and under 1% near real-time.

Training

• Technique: LoRA (rank 64, alpha 64) on the DiT transformer
• Hyperparameters: bf16 mixed precision, learning rate 2e-4 (linear), batch size 8 (8× H100)
• Steps: full-80k recipe; shipped checkpoint is step 45,000
• Infrastructure: LTX-2 Community Trainer

Tips & Troubleshooting

• To adjust slow-motion change only speed and not frame_rate
• Do not rewrite the prompt per speed — use the same caption at every speed; only speed changes.
• Frame count must be 8k + 1 (e.g. 121); other values break the VAE temporal grid.
• No slow-motion effect at all? The speed value almost certainly is not reaching the model — a plain LoRA loader applies the weights but not the conditioning. Check the speed knob is wired.
• Motion looks smeared rather than slow? Lower speed in steps ( 0.5 → 0.2 → 0.1 ) rather than jumping straight to an extreme value, and confirm LoRA strength is 1.0 .

References

• Code: GitHub Repository
• ComfyUI: ComfyUI-LTXVideo

License

See the LTX-2-community-license for full terms.

Acknowledgments

• Base model by Lightricks
• Training infrastructure: LTX-2 Community Trainer
• Speed-conditioning concept inspired by Seeing Fast and Slow / SloMo-44K