lm-human-preferences
glide-text2im
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| lm-human-preferences | glide-text2im | |
|---|---|---|
| 8 | 32 | |
| 1,106 | 3,467 | |
| 5.3% | 1.2% | |
| 2.7 | 0.0 | |
| 9 months ago | about 2 months ago | |
| Python | Python | |
| MIT License | MIT License |
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lm-human-preferences
- Ask HN: Open-source GPT-3 alternatives
- El éxito continuo de OpenAI: Y como llegaron a crear la IA más avanzada del 2023. ChatGPT.
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Sam Altman on the best and worst case scenario for AI - "...the good case is just so unbelievably good that you sound like a really crazy person to start talking about it."
Lest you think that that sounds like a too galaxy-brained possibility, it has already happened at OpenAI (scroll down to "Bugs can optimize for bad behavior"), just with a model that was very far from being capable enough to be dangerous.
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Value head in GPT2
Found relevant code at https://github.com/openai/lm-human-preferences + all code implementations here
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Should we stick to the devil we know?
That's why, when they're serious, they use RL for finetuning from human preferences (would be hilarious if this attempt to solve the terrible bias you take to be evidence of AGI threat ends up creating a Woke Singleton itself, btw); it's a powerful general approach, and I see no sign of it being applied here.
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Dall-E 2
The kind of measures they are taking, like simply deleting wholesale anything problematic, don't really have a '-1'.
But amusingly, exactly that did happen in one of their GPT experiments! https://openai.com/blog/fine-tuning-gpt-2/
- Discussion Thread
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[D] Applications for using reinforcement learning to fine-tune GPT-2
Code for https://arxiv.org/abs/1909.08593 found: https://github.com/openai/lm-human-preferences
glide-text2im
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인공지능에 대한 이해 : https://youtu.be/g1ARrNTwBHg 1편 - 딥러닝의 원리 https://youtu.be/CA5Ggqg5x6o 2편 - 인공지능의 창의성과 테슬라 AI https://youtu.be/jHYYggG7qq8 3편 - 코딩, 과학, 수학 난제를 해결하려는 A.I. https://youtu.be/BWJWAdMZGNY ---------------------------------------------------- 영상에 등장하는 링크 : ADOP(2021) https://arxiv.org
GLIDE(2021) https://syncedreview.com/2021/12/24/deepmind-podracer-tpu-based-rl-frameworks-deliver-exceptional-performance-at-low-cost-173/ || 소스코드 : https://github.com/openai/glide-text2im
- [R][P] I made an app for Instant Image/Text to 3D using PointE from OpenAI
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"Teacher villainess, DreamWorks official character design sheet turnaround, studio, Best on Artstation, 4K HD, by Nate Wragg"
The bolded part is a reference to the publicly released version of OpenAI's GLIDE, which is the predecessor of DALL-E 2. OpenAI didn't release the GLIDE model(s) trained on human faces.
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Trying to remember the name of an upscaler. I thought it was Glide XL or something.
OpenAI's GLIDE text2im https://github.com/openai/glide-text2im
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It just struck me that text diffs do *not* require the image-generating prompt as a starting point, and my mind is blown to pieces.
If I can stop wasting my time playing video games for a while, I might work on getting the Dalle-2 open-source predecessor (GLIDE) to work. Also can't wait for this to be released, I have so many uses for it!
- [D] Making text-to-image even better - GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models, a 5-minute paper summary by Casual GAN Papers
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Dall-E 2
A few comments by someone who's spent way too much time in the AI-generated space:
* I recommend reading the System Card that came with it because it's very through: https://github.com/openai/dalle-2-preview/blob/main/system-c...
* Unlike GPT-3, my read of this announcement is that OpenAI does not intend to commercialize it, and that access to the waitlist is indeed more for testing its limits (and as noted, commercializing it would make it much more likely lead to interesting legal precedent). Per the docs, access is very explicitly limited: (https://github.com/openai/dalle-2-preview/blob/main/system-c... )
* A few months ago, OpenAI released GLIDE ( https://github.com/openai/glide-text2im ) which uses a similar approach to AI image generation, but suspiciously never received a fun blog post like this one. The reason for that in retrospect may be "because we made it obsolete."
* The images in the announcement are still cherry-picked, which is therefore a good reason why they tested DALL-E 1 vs. DALL-E 2 presumably on non-cherrypicked images.
* Cherry-picking is relevant because AI image generation is still slow unless you do real shenanigans that likely compromise image quality, although OpenAI has likely a better infra to handle large models as they have demonstrated with GPT-3.
- Glide-Text2Im
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AI-generated photos of European flags
The flags were generated using Glide. You can try it out yourself in Google Colab
- New AI technique that lets you generate images from text. Now better than ever!
What are some alternatives?
trl - Train transformer language models with reinforcement learning.
dalle-2-preview
GLM-130B - GLM-130B: An Open Bilingual Pre-Trained Model (ICLR 2023)
dalle-mini - DALL·E Mini - Generate images from a text prompt
glide-text2im-colab - Colab notebook for openai/glide-text2im.
tensorrtx - Implementation of popular deep learning networks with TensorRT network definition API
pixray
gpt-2 - Code for the paper "Language Models are Unsupervised Multitask Learners"
improved-diffusion - Release for Improved Denoising Diffusion Probabilistic Models
v-diffusion-pytorch - v objective diffusion inference code for PyTorch.