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Big-sleep Alternatives
Similar projects and alternatives to big-sleep
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deep-daze
Simple command line tool for text to image generation using OpenAI's CLIP and Siren (Implicit neural representation network). Technique was originally created by https://twitter.com/advadnoun
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DALLE-pytorch
Implementation / replication of DALL-E, OpenAI's Text to Image Transformer, in Pytorch
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latent-diffusion
High-Resolution Image Synthesis with Latent Diffusion Models
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DALLE2-pytorch
Implementation of DALL-E 2, OpenAI's updated text-to-image synthesis neural network, in Pytorch
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WorkOS
The modern identity platform for B2B SaaS. The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.
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big-sleep
Discontinued A simple command line tool for text to image generation, using OpenAI's CLIP and a BigGAN (by afiaka87)
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feed_forward_vqgan_clip
Feed forward VQGAN-CLIP model, where the goal is to eliminate the need for optimizing the latent space of VQGAN for each input prompt
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StyleCLIP
Official Implementation for "StyleCLIP: Text-Driven Manipulation of StyleGAN Imagery" (ICCV 2021 Oral)
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stylized-neural-painting
Official Pytorch implementation of the preprint paper "Stylized Neural Painting", in CVPR 2021.
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StyleCLIP
Using CLIP and StyleGAN to generate faces from prompts. (by vipermu)
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dl-colab-notebooks
Discontinued Try out deep learning models online on Google Colab
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SaaSHub
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NOTE:
The number of mentions on this list indicates mentions on common posts plus user suggested alternatives.
Hence, a higher number means a better big-sleep alternative or higher similarity.
big-sleep reviews and mentions
Posts with mentions or reviews of big-sleep.
We have used some of these posts to build our list of alternatives
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- Besides Gaming - for what can be a 4080 useful?
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DALL-E 2 open source implementation
and after a few hours got this: https://i.imgur.com/FxdfdmV.png
Not nearly as cool as the real DALL-e, but maybe I'm missing something.
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List of sites/programs/projects that use OpenAI's CLIP neural network for steering image/video creation to match a text description
(Added Mar. 23, 2021) Big Sleep - Colaboratory by LtqxWYEG. Uses BigGAN to generate images. Reference.
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test
(Added Feb. 5, 2021) Big Sleep - Colaboratory by lucidrains. Uses BigGAN to generate images. The GitHub repo has a local machine version. GitHub. How to use the latest features in Colab.
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I have read Neuromancer to an AI and this is how she imagines it!
What algorithm are you using? big sleep?
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Steven and his Family [Aphantasia]
You have to install it on your PC and it's pretty tricky if you're not familiar with command lines or python. https://github.com/lucidrains/big-sleep
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Text to Image Generation
True. For the article, I experimented with a number of 'in the style of' prompts. Where there's a distinctive visual iconography with strong key features, BigSleep [1]does an amazing job of abstracting and reproducing that style. Besides artists, it also does very well with iconic movies like Blade Runner and The Matrix.
[1]The Deep-Daze author's most popular T2I mashup: https://github.com/lucidrains/big-sleep
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New item added to my list: Colab notebook "Big-Sleep w/ EMA and Video Creation" by afiaka87. Example: text="a tree made of stained glass". Details in a comment.
which I guess gets code from https://github.com/afiaka87/big-sleep. That project is a fork of https://github.com/lucidrains/big-sleep. I don't know if there is any reason to use this notebook instead of lucidrains' https://colab.research.google.com/drive/1MEWKbm-driRNF8PrU7ogS5o3se-ePyPb except that this notebook makes a video of the output images.
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"The Lost Boys" (2 images) using Colab notebook ClipBigGAN by eyaler with a modification to one line (for testing purposes) and 2 changed defaults. This notebook, unlike the original Big Sleep notebook, uses unmodified BigGAN code. Test results are in a comment.
A class vector is necessary for BigGAN; BigGAN is a class-conditioned generator. For the original Big Sleep code, see the variable "params_other". Unmodified BigGAN (well, actually BigGAN-deep) uses 1128 parameters: 1000 for the class vector, and 128 for the noise vector. The original BigSleep modified the BigGAN code to have each of 32 BigGAN layers (well, actually not all of them) use its own set of 1128 parameters. I have concerns that doing this might make it statistically less likely to get the type of near-photorealistic images that BigGAN can produce.
I see that you changed the 32 to 16. Was that to try to address an issue noted at https://github.com/lucidrains/big-sleep/issues/34 ? I'm not sure offhand if this is a correct thing to do without also changing code in other places that I did not do. I haven't looked at this issue in depth.
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Stats
Basic big-sleep repo stats
62
2,548
0.0
about 2 years ago
lucidrains/big-sleep is an open source project licensed under MIT License which is an OSI approved license.
The primary programming language of big-sleep is Python.
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