Stable Diffusion v2-1-unCLIP model released

Information taken from the GitHub page: https://github.com/Stability-AI/stablediffusion/blob/main/doc/UNCLIP.MD HuggingFace checkpoints and diffusers integration: https://huggingface.co/stabilityai/stable-diffusion-2-1-unclip Public web-demo: https://clipdrop.co/stable-diffusion-reimagine unCLIP is the approach behind OpenAI’s DALL·E 2, trained to invert CLIP image embeddings. We finetuned SD 2.1 to accept a CLIP ViT-L/14 image embedding in addition to the text encodings. This means that the model can be used …

I’m the creator of LoRA. How can I make it better?

I wrote this paper two years ago: https://arxiv.org/abs/2106.09685 Super happy that people find it useful for diffusion models. I had text in mind when I wrote the paper, so there are probably things we can tweak to make LoRA more suited for image generation. I want to better understand how exactly LoRA is used in …

Embrace a Cookieless Future With Generative AI and First-Party Data

Cookies had a good run. The third-party data trackers beloved by marketers have been a part of the consumer web experience since the launch of Netscape in 1994. But, in an effort to position themselves as guardians of consumer privacy—and to comply with increasing regulations—tech giants Apple, Mozilla, and Google have rendered cookies virtually obsolete. …

Continuous Pseudo-Labeling from the Start

Self-training (ST), or pseudo-labeling has sparked significant interest in the automatic speech recognition (ASR) community recently because of its success in harnessing unlabeled data. Unlike prior semi-supervised learning approaches that relied on iteratively regenerating pseudo-labels (PLs) from a trained model and using them to train a new model, recent state-of-the-art methods perform ‘continuous training’ where …