Model Quality: Hugging Face Is All You Need
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Building the Agentic Camera: an on-device system that produces the intended photo at capture time, eliminating image editing in most cases.
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#2 opened almost 2 years ago
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bingbangboom
Support for dpm++ solvers
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#10 opened over 1 year ago
by 
baffaabba
Enhance this image
3
#15 opened 6 months ago
by 
hamdy74
catwell
updated 5
Spaces 2 days ago
Finegrain Image Enhancer
🖼
2.15k
Clarity AI Upscaler Reproduction
Finegrain Object Cutter
✂
518
Create HD cutouts from any image with just a prompt
Finegrain Object Eraser (Lite Version)
🧽
434
Erase any object from an image with just a prompt
Finegrain Light Switcher (Lite Version)
💡
51
Instantly turn lamps on in your images
Finegrain Product Placement LoRA
📚
34
Flux Kontext extended with product placement capabilities
deltheil
updated 4
Spaces 13 days ago
Finegrain Light Switcher (Lite Version)
💡
51
Instantly turn lamps on in your images
Finegrain Object Cutter
✂
518
Create HD cutouts from any image with just a prompt
Finegrain Object Eraser (Lite Version)
🧽
434
Erase any object from an image with just a prompt
Finegrain Image Enhancer
🖼
2.15k
Clarity AI Upscaler Reproduction
Post
372
The Loss is a field with holes 🕳️⛳️
🛞 Batch Size is your wheel size.
🏎️ Learning Rate is your speed.
🛞 Batch Size is your wheel size.
🏎️ Learning Rate is your speed.
- 3 replies
Post
4085
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] LBM: Latent Bridge Matching for Fast Image-to-Image Translation (2503.07535)
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] LBM: Latent Bridge Matching for Fast Image-to-Image Translation (2503.07535)
- 1 reply
Post
490
🚧 Reproducing LBM-Eraser… in the open [1] !
A major recent paper on erasing is OmniEraser [2].
They open-sourced an evaluation dataset [3] (and I'm using it for the evaluation of our LBM-Eraser 😉).
It's not a big dataset (70 samples), but it's good quality pairs, and that's what matters !
cc @BaiLing
[1] Finegrain LBM Fork : https://github.com/finegrain-ai/LBM
[2] OmniEraser: VDOR: A Video-based Dataset for Object Removal via Sequence Consistency (2501.07397)
[3] BaiLing/RemovalBench
[4] LBM paper: LBM: Latent Bridge Matching for Fast Image-to-Image Translation (2503.07535)
A major recent paper on erasing is OmniEraser [2].
They open-sourced an evaluation dataset [3] (and I'm using it for the evaluation of our LBM-Eraser 😉).
It's not a big dataset (70 samples), but it's good quality pairs, and that's what matters !
cc @BaiLing
[1] Finegrain LBM Fork : https://github.com/finegrain-ai/LBM
[2] OmniEraser: VDOR: A Video-based Dataset for Object Removal via Sequence Consistency (2501.07397)
[3] BaiLing/RemovalBench
[4] LBM paper: LBM: Latent Bridge Matching for Fast Image-to-Image Translation (2503.07535)
Post
874
🚧 Reproducing LBM-Eraser… in the open [1] !
Today we have trained a LBM [2] promptless inpainter using
We use a subset of 1.25M images with
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] LBM: Latent Bridge Matching for Fast Image-to-Image Translation (2503.07535)
[3] supermodelresearch/Re-LAION-Caption19M
[4] Resolution-robust Large Mask Inpainting with Fourier Convolutions (2109.07161)
[5] https://huggingface.co/posts/piercus/778833977889788
cc @supermodelresearch @presencesw
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] LBM: Latent Bridge Matching for Fast Image-to-Image Translation (2503.07535)
[3] supermodelresearch/Re-LAION-Caption19M
[4] Resolution-robust Large Mask Inpainting with Fourier Convolutions (2109.07161)
[5] https://huggingface.co/posts/piercus/778833977889788
cc @supermodelresearch @presencesw
Post
1884
🚧 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: OmniPaint: Mastering Object-Oriented Editing via Disentangled Insertion-Removal Inpainting (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
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: OmniPaint: Mastering Object-Oriented Editing via Disentangled Insertion-Removal Inpainting (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
- 2 replies
Post
3170
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 : LBM: Latent Bridge Matching for Fast Image-to-Image Translation (2503.07535)
Our LBM fork : https://github.com/finegrain-ai/LBM
🚀 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 : LBM: Latent Bridge Matching for Fast Image-to-Image Translation (2503.07535)
Our LBM fork : https://github.com/finegrain-ai/LBM
Post
233
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: LBM: Latent Bridge Matching for Fast Image-to-Image Translation (2503.07535)
Our fork (work in progress): https://github.com/finegrain-ai/LBM
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: LBM: Latent Bridge Matching for Fast Image-to-Image Translation (2503.07535)
Our fork (work in progress): https://github.com/finegrain-ai/LBM
Post
178
LBM-Eraser reproduction on going ...
We are starting with RORD Dataset-only !
Our RORD -> webdataset extraction script : https://github.com/finegrain-ai/LBM/pull/4
RORD paper : https://bmvc2022.mpi-inf.mpg.de/542/
Thanks @presencesw for presencesw/RORD
We are starting with RORD Dataset-only !
Our RORD -> webdataset extraction script : https://github.com/finegrain-ai/LBM/pull/4
RORD paper : https://bmvc2022.mpi-inf.mpg.de/542/
Thanks @presencesw for presencesw/RORD
- 1 reply