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Pierre Colle

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posted an update about 22 hours 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
posted an update 7 days ago
We have trained a LBM-Eraser with RORD-Dataset in the open 🔥 🚀 1-step only inference, no distillation 🪶 Light backbone :SD1.5 🧠 Light training : converge in 6k steps Now let's improve this, especially the inpainting capabilities. Stay tuned for more :-) LBM paper : https://huggingface.co/papers/2503.07535 Our LBM fork : https://github.com/finegrain-ai/LBM
posted an update 9 days ago
In LBM paper, the noise and the conditioning image are merged into a single composite image. Unlike other inpainting methods (which typically grey-mask the missing area), LBM replaces the masked region with uniformly sampled random pixels. Intuitively, since LBM is trained from a text-to-image (T2I) model, those random pixels act as a strong signal to the pretrained model — essentially saying: “This is where you can do your generative magic.” LBM Paper: https://huggingface.co/papers/2503.07535 Our fork (work in progress): https://github.com/finegrain-ai/LBM
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