README.md

metadata

title: LTX-2.3 Multi-Effect Workflow
emoji: 🎬
colorFrom: indigo
colorTo: green
sdk: gradio
sdk_version: 6.13.0
python_version: '3.12'
app_file: app.py
pinned: false
hardware: zero-a10g
short_description: LTX-2.3 video restoration LoRA workflow
models:
  - diffusers/LTX-2.3-Distilled-Diffusers
  - Lightricks/LTX-2.3-22b-IC-LoRA-Decompression
  - Lightricks/LTX-2.3-22b-IC-LoRA-Deblur
  - Lightricks/LTX-2.3-22b-IC-LoRA-Colorization

LTX-2.3 Multi-Effect Video Workflow

Upload a video once, then run Decompress, Deblur, or Colorize from the same page. Each completed render becomes the current input video for the next iteration.

Render history is stored under the mounted /data/ltxvideo-renders bucket path. The page shows a sequential list of available renderings and rebuilds a download-all zip after every successful run.

The approximately 50 GB text encoder, tokenizer, and downloaded LoRAs are cached persistently under /data/ltx-model-cache. Startup copies those components into /tmp/ltx-model and loads the encoder into RAM first. It then removes those local staging files before downloading and loading the remaining model components. This sequential disk use avoids both the 100 GB workload limit and slow safetensors memory mapping through the bucket mount. Xet's duplicate chunk cache is disabled. Set LTX_MODEL_CACHE_ROOT, LTX_RUNTIME_MODEL_ROOT, or LTX_RUNTIME_HF_CACHE_ROOT only when different cache paths are required.

Each effect processes the uploaded video's complete duration at 24 fps. There is no manual frame limit; the app only rounds to the nearest frame count accepted by LTX (8k+1). Longer videos take proportionally more time and memory.

The restoration LoRAs are gated. Add a Space secret named HF_TOKEN with a read token from an account that has accepted the relevant Lightricks model terms. The app also recognizes HUGGINGFACE_HUB_TOKEN, HUGGING_FACE_HUB_TOKEN, and HUGGINGFACE_TOKEN.