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End of preview. Expand in Data Studio

Add & Preserve

Can your editor add new objects for ten turns without changing the rest of the image?

100 images · 10 English editing instructions per image · object completion + background preservation.

Ten-turn editing comparison

One input, ten successive small-object additions. Each model feeds its own previous output into the next turn.

A simple test for cumulative drift

Add objects inside the red box. Keep everything outside it unchanged. Retain the objects added in earlier turns. This separates successful editing from preservation, so simply ignoring an instruction cannot count as doing well on both.

Images come from Open Images and MONET. We manually selected images and box positions; English addition prompts were annotated with GPT-6.1-sol.

Example benchmark inputs

COA — Cumulative Object Accuracy checks the requested objects inside the box using GPT-6.1-sol. PSNR, SSIM and LPIPS measure changes outside the box relative to the initial input. The evaluation reports these metrics after each turn.

Results from the TailSteer paper

Preservation curves across ten turns

Preservation across turns

Model COA (%) ↑ PSNR@10 ↑ SSIM@10 ↑ LPIPS@10 ↓
FLUX.1 Kontext dev 34.77 18.64 0.585 0.388
FLUX.1 Kontext dev after TailSteer 34.84 21.02 0.672 0.257
FLUX.2 [klein] Base 9B 31.02 13.85 0.289 0.524
FLUX.2 [klein] Base 9B after TailSteer 31.84 19.96 0.621 0.290
SenseNova-U1.5-8B-MoT 94.93 11.23 0.166 0.607
SenseNova-U1.5-8B-MoT after TailSteer 94.78 18.22 0.489 0.417
Qwen-Image-2.1 67.36 16.93 0.438 0.270
Qwen-Image-2.1 after TailSteer 66.94 18.15 0.513 0.239
GPT-Image-2.5-Sunburst (API) 98.29 18.55 0.540 0.378

TailSteer rows use the paper's reference steering onsets: Kontext 25, Klein 46, SenseNova 42 and Qwen 30. These are image-feedback results; GPT API scores use the paper's available-output protocol.

Qualitative results after ten editing turns

Turn-10 comparisons from the paper. The separately labeled “w/o VAE” columns illustrate the latent-feedback ablation.

Evaluate your editor

With TailSteer installed, prepare your backbone and configure the COA judge API, then run:

tailsteer prepare add-preserve
tailsteer infer --backbone flux2-klein-base-9b --policies base \
  --inference-suite add-preserve --gpus 8

For a trained run, use --run-dir YOUR_TRAINING_RUN instead of --backbone ... --policies base to compare the base model and TailSteer.

To download the inputs independently:

from huggingface_hub import snapshot_download
snapshot_download("Lyricccco/Add-and-Preserve", repo_type="dataset",
                  revision="v1.0.1", local_dir="data/add-preserve")

tasks.json contains the prompts and seeds; images/ contains the initial inputs; masks/ and manifest.json define the evaluation regions. Feed each generated image into the next turn and keep the supplied geometry unchanged.

Per-image attribution and licenses are in provenance.jsonl and LICENSE.md. Our training source collection is available as TailSteer-Source.

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