ColossalAI
guided-diffusion | ColossalAI | |
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14 | 42 | |
5,439 | 37,911 | |
0.0% | 1.2% | |
0.0 | 9.7 | |
about 1 year ago | 6 days ago | |
Python | Python | |
MIT License | Apache License 2.0 |
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guided-diffusion
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Why is there speculation that midjourney is based on stable diffusion if MJ is released earlier than SD?
People who made these colabs better and better also the same people who are at Midjourney now. But the "mother" of it all, was Katherine Crowson. She made a fine tuned model that uses a 512x512 unconditional ImageNet diffusion model fine-tuned from OpenAI's 512x512 class-conditional ImageNet diffusion model (https://github.com/openai/guided-diffusion) together with CLIP (https://github.com/openai/CLIP) to connect text prompts with images. It uses a smaller secondary diffusion model trained by Katherine Crowson to remove noise from intermediate timesteps to prepare them for CLIP.
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Any Tips on OpenAI's Guided Diffusion?
I am trying to use OpenAI's Guided Diffusion Github to train my own diffusion model. I thought to ask here to see if anyone had any experience with it as I've been having trouble training my own models on it. If anyone has any resources to point me towards it would be greatly appreciated!
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We just release a complete open-source solution for accelerating Stable Diffusion pretraining and fine-tuning!
Our codebase for the diffusion models builds heavily on OpenAI's ADM codebase , lucidrains, Stable Diffusion, Lightning and Hugging Face. Thanks for open-sourcing!
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guided diffusion super resolution network training is diverging
I am working with guided diffusion. I would like to reproduce the results of the repository for the 64->256 super resolution network. https://github.com/openai/guided-diffusion
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New custom inpainting model
this code is (mostly) just the original openai guided diffusion code: https://github.com/openai/guided-diffusion
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Tips for Training Diffusion Model (DD) With Images and Resource Links
Starting resource, as it is all done through this code (information on how to do it on Colab is out there) https://github.com/openai/guided-diffusion
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What was Disco trained with?
Original notebook by Katherine Crowson (https://github.com/crowsonkb, https://twitter.com/RiversHaveWings). It uses either OpenAI's 256x256 unconditional ImageNet or Katherine Crowson's fine-tuned 512x512 diffusion model (https://github.com/openai/guided-diffusion), together with CLIP (https://github.com/openai/CLIP) to connect text prompts with images.
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[D] Diffusion Models Beat GANs on Image Synthesis Explained: 5-minute paper summary (by Casual GAN Papers)
Code for https://arxiv.org/abs/2105.05233 found: https://github.com/openai/guided-diffusion
- "Everything the AI can create" using diffusion model
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Since this sub has a fair portion of AI-generated images, have you guys seen OpenAI's guided diffusion models yet?
Paper, repo, Colab. It's really good.
ColossalAI
- FLaNK AI-April 22, 2024
- Making large AI models cheaper, faster and more accessible
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ColossalChat: An Open-Source Solution for Cloning ChatGPT with a RLHF Pipeline
> open-source a complete RLHF pipeline ... based on the LLaMA pre-trained model
I've gotten to where when I see "open source AI" I now know it's "well, except for $some_other_dependencies"
Anyway: https://scribe.rip/@yangyou_berkeley/colossalchat-an-open-so... and https://github.com/hpcaitech/ColossalAI#readme (Apache 2) can save you some medium.com heartache at least
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Meet ColossalChat: An Open-Source AI Solution For Cloning ChatGPT With A Complete RLHF Pipeline
Quick Read: https://www.marktechpost.com/2023/04/01/meet-colossalchat-an-open-source-ai-solution-for-cloning-chatgpt-with-a-complete-rlhf-pipeline/ Github: https://github.com/hpcaitech/ColossalAI Examples: https://chat.colossalai.org/
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A top AI researcher reportedly left Google for OpenAI after sharing concerns the company was training Bard on ChatGPT data
One of the current methods for training competing models is to have ChatGPT literally create prompt -> completion data sets. That's what was used for https://github.com/hpcaitech/ColossalAI. A model based off of the Llama weights released by facebook, then fine tuned on ChatGPT3.5 prompt + completions. So yes, there is a good chance that google is literally using ChatGPT in the training loop.
- Colossal-AI: open-source RLHF pipeline based on LLaMA pre-trained model
- ColossalChat
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ColossalChat: An Open-Source Solution for Cloning ChatGPT with RLHF Pipeline
Here's the github from the article:
https://github.com/hpcaitech/ColossalAI
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Open source solution replicates ChatGPT training process
The article talks about their RLHF implementation briefly. There’s details on their RLHF implementation here: https://github.com/hpcaitech/ColossalAI/blob/a619a190df71ea3...
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how can I make my own chatGPT?
Here’s the project on GitHub: https://github.com/hpcaitech/ColossalAI
What are some alternatives?
disco-diffusion
DeepSpeed - DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.
score_sde - Official code for Score-Based Generative Modeling through Stochastic Differential Equations (ICLR 2021, Oral)
Megatron-LM - Ongoing research training transformer models at scale
CLIP - CLIP (Contrastive Language-Image Pretraining), Predict the most relevant text snippet given an image
determined - Determined is an open-source machine learning platform that simplifies distributed training, hyperparameter tuning, experiment tracking, and resource management. Works with PyTorch and TensorFlow.
pytorch-lightning - Pretrain, finetune and deploy AI models on multiple GPUs, TPUs with zero code changes.
fairscale - PyTorch extensions for high performance and large scale training.
denoising-diffusion-pytorch - Implementation of Denoising Diffusion Probabilistic Model in Pytorch
DeepFaceLive - Real-time face swap for PC streaming or video calls
glid-3-xl-stable - stable diffusion training
PaddlePaddle - PArallel Distributed Deep LEarning: Machine Learning Framework from Industrial Practice (『飞桨』核心框架,深度学习&机器学习高性能单机、分布式训练和跨平台部署)