dalle-2-preview VS community-events

We believe that many decisions about our defaults and hard bounds should be made collectively, and while practical implementation is a challenge, we aim to include as many perspectives as possible. As a starting point, we’ve sought external input on our technology in the form of red teaming. We also recently began soliciting public input on AI in education (one particularly important context in which our technology is being deployed).

From this OpenAI document:

It's also of note that you can't sell a game using this method, as Dalle-2's terms of service prevent use in commercial projects. It's hard to justify rate of return considering you can only ever give it away for free, and even in that case there are some uncertain legal elements regarding copyright and the images that are used to train the dataset.

On their GitHub https://github.com/openai/dalle-2-preview/blob/main/system-card.md I can only see references to v1.

"b) Exploration of the bolded part of OpenAI's comment "Each generated image includes a signature in the lower right corner, with the goal of indicating when DALL·E 2 helped generate a certain image." (source)." (source link: https://github.com/openai/dalle-2-preview/blob/main/system-c...)

I feel the DALL-E 2 watermark signature could be a seed or something.

DALL-E's docs for example mention it can output whole copyrighted logos and characters[1] and understands it's possible to generate human faces that are bear the likeness of those in the training data. We've also seen people recently critique Stable Diffusion's output for attempting to recreate artists' signatures that came from the commercial trained data.

That said by a certain point the kinks will be ironed out and likely skirt around such issues by only incorporating/manipulating just enough to be considered fair use and creative transformation.

[1] "The model can generate known entities including trademarked logos and copyrighted characters." https://github.com/openai/dalle-2-preview/blob/main/system-c...

Some types of content were purposely excluded from the training dataset(s) (source).

Best applications that will come out of this sprint will receive prizes. You can find more information here. If you want to get started, simply join huggingface.co/discord, take the role 🧨 Diffusers and head to #jax-diffusers-ideas to share your idea or join one of the teams, and fill this form: https://forms.gle/t3M7aNPuLL9V1sfa9

It will start on 17th of April. To join us, you can join huggingface.co/join/discord and take the role Diffusers from #role-assignment. After this, simply fill the form provided in this guide to later get access to TPUs. https://github.com/huggingface/community-events/tree/main/jax-controlnet-sprint

Details and sign-up: https://github.com/huggingface/community-events/tree/main/jax-controlnet-sprint

For the training, I extremely recommend checking out the Whisper Fine-Tuning Event. It has a python script to train in one command, tons of tips, even a walkthrough video.

We are happy to invite you to the Gradio Blocks Party - a community event in which we will create interactive demos for state-of-the-art machine learning models. Demos are powerful because they allow anyone — not just ML engineers — to try out models in the browser, give feedback on predictions, identify trustworthy models. The event will take place from May 17th to 31st. We will be organizing this event on Github and the Hugging Face discord channel. Prizes will be given at the end of the event, see the Prizes section

If you're interested in generative models, Hugging Face is putting on an event around generative models right now called the HugGAN sprint, where they're giving away free access to compute to train models like this.

You can join it by following the steps in the guide here: https://github.com/huggingface/community-events/tree/main/hu...

There will also be talks from awesome folks at EleutherAI, Google, and Deepmind