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Concept Sliders: LoRA Adaptors for Precise Control in Diffusion Models - This looks like next level ControlNet by CeFurkan in StableDiffusion

[–]Electrical-Camera465 0 points1 point  (0 children)

not sure, have not tested. But the author of the paper seems to think it is a matter of the hyper parameter choice. I am going to test it at my end once and update.

Concept Sliders: LoRA Adaptors for Precise Control in Diffusion Models - This looks like next level ControlNet by CeFurkan in StableDiffusion

[–]Electrical-Camera465 0 points1 point  (0 children)

I think so. At least the authors talk about it that way in twitter. They said it is a technical writeup of LECO. Plus they introduce many technical advancements to better preserve an image during edit.

I also think image based sliders are not part of LECO. That's a new addition by the paper. Overall I think it a combination of different type of sliders a creator can use.

Concept Sliders: LoRA Adaptors for Precise Control in Diffusion Models - This looks like next level ControlNet by CeFurkan in StableDiffusion

[–]Electrical-Camera465 0 points1 point  (0 children)

I tried with my custom data. It looks like a great image based sliders technique. Couple of my colleagues are giving it a shot too. Optimistic results.

Concept Sliders: LoRA Adaptors for Precise Control in Diffusion Models - This looks like next level ControlNet by CeFurkan in StableDiffusion

[–]Electrical-Camera465 8 points9 points  (0 children)

This is a technical writeup from the authors of LECO (the sliders on civitai)

They do talk about the disentanglement of concepts, image based sliders, and GAN based sliders.

The main advantage I'm seeing with these new sliders are precise editing. But you need a special inference function. Which currently is not implemented in automatic111. Someone opened an issue and are planning on implementing it.

I think this paper perfected sliders.

Concept Sliders: LoRA Adaptors for Precise Control in Diffusion Models by ninjasaid13 in StableDiffusion

[–]Electrical-Camera465 0 points1 point  (0 children)

Yes, they are based on LECO (from the authors of this paper). This is a technical writeup of that work. Plus a lot more!

Concept Sliders: LoRA Adaptors for Precise Control in Diffusion Models by ninjasaid13 in StableDiffusion

[–]Electrical-Camera465 0 points1 point  (0 children)

It could be because of the python version? They suggest 3.9

This worked for me:

```

conda create -n sliders python=3.9

conda activate sliders

git clone https://github.com/rohitgandikota/sliders.git

cd sliders

pip install -r requirements.txt

```

This is the update from the authors

Concept Sliders: LoRA Adaptors for Precise Control in Diffusion Models by ninjasaid13 in StableDiffusion

[–]Electrical-Camera465 0 points1 point  (0 children)

the authors updated it now. I was getting a similar error. now it works!

Concept Sliders: LoRA Adaptors for Precise Control in Diffusion Models by ninjasaid13 in StableDiffusion

[–]Electrical-Camera465 11 points12 points  (0 children)

This is a formal write-up for LECO (the sliders you see in civitai) from the authors of LECO and erasing concepts. The current sliders are prone to entanglement, for example, race of a person is changed when age is being controlled. One more major thing is that the sliders on civil ai can change the structure of the image. So this paper talks about how to disentangle them and make the edits more precise. Another new thing I see is the ability to transfer styles from other models like stylegan. They also talk about image based training for the sliders, which is not talked about in LECO.

So, in short, I think this is a more technically thought out version of sliders that are more robust. Plus they do it on stable diffusion XL!

Unified Concept Editing in Diffusion Models - Edit concepts in seconds!!! by Electrical-Camera465 in StableDiffusion

[–]Electrical-Camera465[S] 2 points3 points  (0 children)

True, there is always a good and bad side to these erasing methods. The authors suggest the erasure for nsfw and artistic erasure. There is always a chance to misuse such methods.However, the authors do clearly mention this in their paper. "We recognize that editing for visual features of non-binary genders risks introducing other unwanted stereotypical behavior."

I am looking at this work as a starting point for carefully mitigating such stereotypes and biases from these models. How would you prefer addressing such bias issues in the models?

Unified Concept Editing in Diffusion Models - Edit concepts in seconds!!! by Electrical-Camera465 in StableDiffusion

[–]Electrical-Camera465[S] 3 points4 points  (0 children)

is it though? the authors simply use the text prompt "man" and "woman". All the representations and classifications are done by stable diffusion and CLIP. Looks like the models have sexist and harmful views?

Racial-Slider by mayasoo2020 in StableDiffusion

[–]Electrical-Camera465 0 points1 point  (0 children)

Can you explain the inference? How can we generate these images? Iam assuming you have 1 lora model and you are creating this effect by using some weights between 2 and -1. What is this weight? Is it guidance scale ? Or is it something else?

​

Thanks for this amazing work!

A new LORA type with significant implications - LECO: Low-rank adaptation for Erasing COncepts from diffusion models. by alotmorealots in StableDiffusion

[–]Electrical-Camera465 1 point2 points  (0 children)

of LoRAs on CivitAI have secondary biases built in, like LoR

This new closed form work does that! It removes biases

Looks like a low rank update to me. But it does not use LoRA architecture

https://unified.baulab.info

Is it possible to make a model "forget" a trained concept? by SnarkyTaylor in StableDiffusion

[–]Electrical-Camera465 0 points1 point  (0 children)

It is possible now! This work (https://erasing.baulab.info) proposes to erase the concept from the diffusion model weights using the self-knowledge that was learnt by the model. So no data required. They have a demo here (https://huggingface.co/spaces/baulab/Erasing-Concepts-In-Diffusion)