A new technology that unlocks a huge part of Stable Diffusion's dataset. Learn how to train and use SLIDER LORAs for unlimited control and more!
You can make progress sliders, train simple concepts that should be understood by AI but aren't (like right and left or full and empty), and even enhance or erase concepts from your models.
I encourage anyone to train their crazy ideas and fully develop the potencial of this incredible tool.
Join our Discrod server: https://discord.gg/Js8pnNwq8T to learn about this and more!
------------- Links used in the VIDEO ----------
-Installation and project: https://github.com/rohitgandikota/sliders
-Sliders website: https://sliders.baulab.info
-Google Colab: https://colab.research.google.com/github/rohitgandikota/sliders/blob/main/demo_concept_sliders.ipynb
-HuggingFace page: https://huggingface.co/spaces/baulab/ConceptSliders
-WEB-UI: https://ko-fi.com/s/2fe3a2d863
(in beta, let me know if it gives issues)
-Rohit's x/twitter: https://twitter.com/RohitGandikota
Extensions used in the video:
Composable LORA: https://github.com/ashen-sensored/stable-diffusion-webui-two-shot
Dynamic LORA weights: https://github.com/cheald/sd-webui-loractl
Controlnet: https://github.com/Mikubill/sd-webui-controlnet
------------- Project by ----------
Rohit Gandikota, Joanna Materzyńska, Tingrui Zhou, Antonio Torralba, David Bau. "Concept Sliders: LoRA Adaptors for Precise Control in Diffusion Models" arXiv preprint arXiv:2311.12092 (2023).
------------- Social Media ----------
-Instagram: https://www.instagram.com/not4talent_ai/
-Twitter: https://twitter.com/not4talent
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0:00 intro
0:20 Index + demo
0:56 Installing
1:44 UI
2:31 Training Textsliders
8:07 Online Training options
8:17 Google colab
8:58 HuggingFace
9:08 Testing the LORA
11:00 Finding good prompts
12:33 Training Imagesliders
15:06 More than two pairs
15:56 Using SLIDER LORA
16:06 Transition point
16:20 Avoiding bleeding
16:40 Prompt Editing
18:17 Img2imge and inpainting
19:05 Thanks to Rohit and the team
19:34 Enhance and Erase concepts