README.md

metadata

title: InstantRetouch
sdk: gradio
sdk_version: 4.44.1
python_version: '3.10'
app_file: app.py
pinned: false
license: other

InstantRetouch / IP2P-BiLA Demo

Public Hugging Face ZeroGPU demo for instruction-guided image retouching with the validation-selected IP2P/BiLA checkpoint.

• Model: IP2P/BiLA
• UI: image upload, optional instruction, seed, max side, strength, and selectable examples

This Space is isolated from the research repository. It does not import agent/, training scripts, or local experiment paths at runtime. Weights live in a separate Hugging Face model repo and are downloaded lazily through BILA_WEIGHTS_REPO.

Required Space Variables

Set one of these in the Space environment:

• BILA_WEIGHTS_REPO: Hugging Face model repo containing the IP2P weight layout below.
• BILA_MODEL_ROOT: local path with the same layout, useful only for staging/debugging.

Optional:

• HF_TOKEN: required if BILA_WEIGHTS_REPO is private.
• BILA_MODEL_CACHE: cache location. If unset, the app uses /data/bila-space-demo/hf-cache when persistent storage exists, otherwise /tmp/bila-space-demo/hf-cache.

Weight Repo Layout

Do not commit weights into this Space repo. Put them in a separate HF model repo:

ip2p/
  base/
    tokenizer/
    text_encoder/
    vae/
    unet/
  checkpoints/
    <bila-checkpoint>.pth
metrics/
  <metric-summary>.json

The app follows the validation-style direct flow: load the IP2P base model, load the BiLA checkpoint named in model_manifest.json, generate bila_output, then apply strength as input + strength * (bila_output - input).