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reacted to piercus's post with 👍 about 1 month ago
Starts erasing! 🎉 🎉 🎉 This is made with a one-step SD1.5 LBM [1] eraser ! Data is open. Data pipeline is open. Training code is open. On our LBM fork : https://github.com/finegrain-ai/LBM [1] https://huggingface.co/papers/2503.07535
reacted to piercus's post with 👍 about 1 month ago
🚧 Reproducing LBM-Eraser… in the open [1] ! Today we have trained a LBM [2] promptless inpainter using `Re-LAION-Caption19M`[3]. We use a subset of 1.25M images with `aesthetic_score > 5.6` and `pwatermark < 0.2` and LaMa [2] mask generation. 2 takeaways : 🖼 Inpainting is better compared to our RORD experiments [5] 🦶 "4 steps" outperforms single-step [1] Finegrain LBM Fork : https://github.com/finegrain-ai/LBM [2] https://huggingface.co/papers/2503.07535 [3] https://huggingface.co/datasets/supermodelresearch/Re-LAION-Caption19M [4] https://huggingface.co/papers/2109.07161 [5] https://huggingface.co/posts/piercus/778833977889788 cc @supermodelresearch @presencesw
reacted to piercus's post with 👀 about 2 months ago
🚧 Reproducing LBM-Eraser… in progress! [1] When repurposing a T2I model into a pure I2I model, there’s always that orphaned text path — what do we do with it? 🤔 You can reuse it as learnable embeddings in multi-task setups [2], freeze an empty text prompt, distillate or prune the corresponding part. In LBM, they take a clever route — zeroing [3] and reshaping [4] the text-related cross-attentions into self-attentions. This gives you fresh weights for I2I computation, nicely integrated into your SD architecture. 📎 References [1] Our LBM Fork: https://github.com/finegrain-ai/LBM [2] OmniPaint: https://huggingface.co/papers/2503.08677 [3] LBM Zeroing: https://github.com/gojasper/LBM/blob/cafebc46a9ac16dcc61691d289cc4676b5c75380/examples/training/train_lbm_surface.py#L147-L148 [4] LBM Reshaping: https://github.com/gojasper/LBM/blob/cafebc46a9ac16dcc61691d289cc4676b5c75380/examples/training/train_lbm_surface.py#L100
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