disco-diffusion
disco-diffusion | guided-diffusion | |
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22 | 14 | |
7,457 | 5,439 | |
0.1% | 0.0% | |
0.0 | 0.0 | |
10 months ago | about 1 year ago | |
Jupyter Notebook | Python | |
GNU General Public License v3.0 or later | MIT License |
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disco-diffusion
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Halloween 2022
Disco-diffusion, a framework like Stable, which came out about 13 months ago: https://github.com/alembics/disco-diffusion
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Which is your favorite text to image model overall?
Runner-ups are Craiyon (for being more "creative" than SD), Disco Diffusion, minDALL-E, and CLIP Guided Diffusion.
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AI Generated Music Video using Disco Diffusion software
From the Disco Diffusion GitHub, "“A frankensteinian amalgamation of notebooks, models and techniques for the generation of AI Art and Animations.”
- 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
- Colab notebook "Disco Diffusion v5.6, Inpainting_mode by cut_pow" by kostarion. From the developer: "Inpainting mode in #DiscoDiffusion! I've finally made the parametrised guided inpainting for disco, and applied it for more stable 2D and 3D animations. In the thread i show what's in there".
- I used an AI to create EVE Online themed Art!
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A good tutorial to get started?
Google Colab is probably the easiest way to run DD. To find the most recent version go to the GitHub page and then open the link to the Colab. Initially, you'll probably just want to experiment with the prompts. But there's also Zippy's Disco Diffusion Cheatsheet v0.3 which can be a useful place to learn more.
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Free/open-source AI Text-To-Image Models that can be run on AWS?
You can probably port Disco Diffusion pretty easily. It’s available on Google Colab, so should be straightforward. Their GitHub is: https://github.com/alembics/disco-diffusion
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Protests erupt outside of DALL-E offices after pricing implementation, press photograph
https://www.reddit.com/r/DiscoDiffusion/, https://github.com/alembics/disco-diffusion. As far as I'm aware the only way to use this is via Google Colab. Rather difficult to use because of this.
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First nice portrait on 5.6 running locally on 2070 (comparison untouched / GFPGAN)
https://github.com/alembics/disco-diffusion,
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.
What are some alternatives?
latent-diffusion - High-Resolution Image Synthesis with Latent Diffusion Models
score_sde - Official code for Score-Based Generative Modeling through Stochastic Differential Equations (ICLR 2021, Oral)
DALLE2-pytorch - Implementation of DALL-E 2, OpenAI's updated text-to-image synthesis neural network, in Pytorch
CLIP - CLIP (Contrastive Language-Image Pretraining), Predict the most relevant text snippet given an image
dalle-2-preview
pytorch-lightning - Pretrain, finetune and deploy AI models on multiple GPUs, TPUs with zero code changes.
ColossalAI - Making large AI models cheaper, faster and more accessible
big-sleep - A simple command line tool for text to image generation, using OpenAI's CLIP and a BigGAN. Technique was originally created by https://twitter.com/advadnoun
denoising-diffusion-pytorch - Implementation of Denoising Diffusion Probabilistic Model in Pytorch
VQGAN-CLIP - Just playing with getting VQGAN+CLIP running locally, rather than having to use colab.
glid-3-xl-stable - stable diffusion training