min-dalle
metaseq
min-dalle | metaseq | |
---|---|---|
31 | 53 | |
3,474 | 6,389 | |
- | 0.4% | |
0.0 | 6.2 | |
over 1 year ago | 7 days ago | |
Python | Python | |
MIT License | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
min-dalle
- Open source Python libraries for AI image generation that you can install on an Amazon GPU instance, like min(DALL-E) and Pixray?
- List of open source machine learning AI image generation/text-to-image libraries that can be installed on an Amazon GPU instance? e.g. MinDall-E, Disco Diffusion, Pixray
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Free/open-source AI Text-To-Image Models that can be run on AWS?
min(DALL·E).
- I'm building a timeline for generative image ML models. What's missing?
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DALL·E Now Available in Beta
Additionally, it's also open-sourced on GitHub and can be self-hosted, with easy instructions to do so: https://github.com/kuprel/min-dalle
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dalle update
For CPU, even highly-optimized models like mindalle are prohibitively slow.
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Hii everyone ,Can I build the dalle mini from scratch or not?? Please help!!
Maybe you would be interested in this GitHub repo.
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World of Warcraft Character Beanie Babies
These were generated with DALL-E Mega via min-dalle, which is a more advanced version of DALL-E Mini with better visual fidelity (less blurry) but otherwise similar results.
- Show HN: Generate webpage summary images with DALL-E mini
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"min(DALL·E)" is "a minimal implementation of Boris Dayma's DALL·E Mini in PyTorch. It has been stripped to the bare essentials necessary for doing inference." This uses the DALL-E Mega model. The Google Colab notebook using a Tesla T4 GPU takes 35 seconds to generate 4 images, and 17 seconds for 1.
GitHub repo (contains links to Colab notebook and web app at site Replicate[dot]com). The times mentioned in the title don't included setup time.
metaseq
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Training great LLMs from ground zero in the wilderness as a startup
This is a super important issue that affects the pace and breadth of iteration of AI almost as much as the raw hardware improvements do. The blog is fun but somewhat shallow and not technical or very surprising if you’ve worked with clusters of GPUs in any capacity over the years. (I liked the perspective of a former googler, but I’m not sure why past colleagues would recommend Jax over pytorch for LLMs outside of Google.) I hope this newco eventually releases a more technical report about their training adventures, like the PDF file here: https://github.com/facebookresearch/metaseq/tree/main/projec...
- Chronicles of Opt Development
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See the pitch memo that raised €105M for four-week-old startup Mistral
The number of people who can actually pre-train a true LLM is very small.
It remains a major feat with many tweaks and tricks. Case in point: the 114 pages of OPT175B logbook [1]
[1] https://github.com/facebookresearch/metaseq/blob/main/projec...
- Technologie: „Austro-ChatGPT“ – aber kein Geld zum Testen
- OPT (Open Pre-trained Transformers) is a family of NLP models trained on billions of tokens of text obtained from the internet
- Current state-of-the-art open source LLM
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Elon Musk Buys Ten Thousand GPUs for Secretive AI Project
Reliability at scale: take a look at the OPT training log book for their 175B model run. It needed a lot of babysitting. In my experience, that scale of TPU training run requires a restart about once every 1-2 weeks—and they provide the middleware to monitor the health of the cluster and pick up on hardware failures.
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Is AI Development more fun than Software Development?
I really appreciated this log of Facebook training a large language model of how troublesome AI development can be: https://github.com/facebookresearch/metaseq/tree/main/projects/OPT/chronicles
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Visual ChatGPT
Stable Diffusion will run on any decent gaming GPU or a modern MacBook, meanwhile LLMs comparable to GPT-3/ChatGPT have had pretty insane memory requirements - e.g., <https://github.com/facebookresearch/metaseq/issues/146>
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Ask HN: Is There On-Call in ML?
It seems so, check this log book from Meta: https://github.com/facebookresearch/metaseq/blob/main/projec...
What are some alternatives?
dalle-mini - DALL·E Mini - Generate images from a text prompt
stable-diffusion - A latent text-to-image diffusion model
dalle-playground - A playground to generate images from any text prompt using Stable Diffusion (past: using DALL-E Mini)
nlp-resume-parser - NLP-powered, GPT-3 enabled Resume Parser from PDF to JSON.
CogVideo - Text-to-video generation. The repo for ICLR2023 paper "CogVideo: Large-scale Pretraining for Text-to-Video Generation via Transformers"
GLM-130B - GLM-130B: An Open Bilingual Pre-Trained Model (ICLR 2023)
imagen-pytorch - Implementation of Imagen, Google's Text-to-Image Neural Network, in Pytorch
gpt-2 - Code for the paper "Language Models are Unsupervised Multitask Learners"
KoboldAI-Client
manim - Animation engine for explanatory math videos
DALLE2-pytorch - Implementation of DALL-E 2, OpenAI's updated text-to-image synthesis neural network, in Pytorch
cupscale - Image Upscaling GUI based on ESRGAN