ADOP
glide-text2im
| ADOP | glide-text2im | |
|---|---|---|
| 13 | 32 | |
| 2,014 | 3,475 | |
| - | 0.7% | |
| 5.3 | 0.0 | |
| 4 months ago | 2 months ago | |
| C++ | Python | |
| MIT License | MIT License |
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ADOP
- 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
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[D] Would the 8gb VRAM of the 3060ti mean that some models in computer vision cannot be trained with it at all?
To train something like https://github.com/darglein/ADOP , can 8gb VRAM in a 3060ti prove to be a hard limit? If it can still train the model by using cpu RAM, how much of a performance hit will it suffer? Should I go with 3060 12gb instead?
- ADOP: New AI Rendering Pipeline that generates incredible results
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[D] Does this even exist?
I recommend taking a look at this repository: https://github.com/darglein/ADOP There's cool stuff happening in this area!
- ADOP: Approximate Differentiable One-Pixel Point Rendering
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New AI: Photos Go In, Reality Comes Out! | Two Minute Papers. More latent space synthesis from photos!
code here: https://github.com/darglein/ADOP
- AI pre-processing image sets
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Create your own 3D scenes from a set of photos with this cool neural network-based algorithm.
Any hope for a compiled file executable under Windows? They only released the bare code https://github.com/darglein/ADOP/releases/tag/v1.0
- AI Synthesizes Smooth Videos from a Couple of Images! Rückert, D. et al., (2021), ADOP
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?
omnimatte
dalle-2-preview
koila - Prevent PyTorch's `CUDA error: out of memory` in just 1 line of code.
dalle-mini - DALL·E Mini - Generate images from a text prompt
glide-text2im-colab - Colab notebook for openai/glide-text2im.
pixray
improved-diffusion - Release for Improved Denoising Diffusion Probabilistic Models
v-diffusion-pytorch - v objective diffusion inference code for PyTorch.
diffusion - Denoising Diffusion Probabilistic Models
lm-human-preferences - Code for the paper Fine-Tuning Language Models from Human Preferences
gpt-2 - Code for the paper "Language Models are Unsupervised Multitask Learners"
jukebox - Code for the paper "Jukebox: A Generative Model for Music"