akhaliq
Abstract
We introduce Imagen 3, a latent diffusion model that generates high quality images from text prompts. We describe our quality and responsibility evaluations. Imagen 3 is preferred over other state-of-the-art (SOTA) models at the time of evaluation. In addition, we discuss issues around safety and representation, as well as methods we used to minimize the potential harm of our models.
Community
I cannot find any implementation details. I think Imagen 3 is harmful without control. However, I’m sad not to release the details as an engineer.
Google is as always lying and showing off
Unless they publish weights and we can try I say SOTA is FLUX
Here evidence
no one has seen imagen1 and imagen2. They are just pictures in pdf
so true. 0 reason to believe google at this point. hack i dont believe any of their AI claims. so far Gemini was only a joke for me
Total nothingburger of a paper. Bunch of unreplicatable user studies claiming everyone likes Imagen 3 better than other models, and a lot of waffling about safety and fairness. No implementation details, no info about the dataset or training, nothing. Basically just a PR stunt.
This is an automated message from the Librarian Bot. I found the following papers similar to this paper.
The following papers were recommended by the Semantic Scholar API
- Analyzing Quality, Bias, and Performance in Text-to-Image Generative Models (2024)
- An Improved Method for Personalizing Diffusion Models (2024)
- REVISION: Rendering Tools Enable Spatial Fidelity in Vision-Language Models (2024)
- ARTIST: Improving the Generation of Text-rich Images by Disentanglement (2024)
- SynthVLM: High-Efficiency and High-Quality Synthetic Data for Vision Language Models (2024)
Please give a thumbs up to this comment if you found it helpful!
If you want recommendations for any Paper on Hugging Face checkout this Space
You can directly ask Librarian Bot for paper recommendations by tagging it in a comment:
@librarian-bot
recommend
😍😍
Models citing this paper 0
No model linking this paper
Datasets citing this paper 2
Spaces citing this paper 0
No Space linking this paper