latent-diffusion
perceiver-pytorch
latent-diffusion | perceiver-pytorch | |
---|---|---|
70 | 11 | |
10,622 | 1,049 | |
2.8% | - | |
0.0 | 3.1 | |
2 months ago | 9 months ago | |
Jupyter Notebook | Python | |
MIT License | MIT License |
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Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
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latent-diffusion
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SDXL: The next generation of Stable Diffusion models for text-to-image synthesis
Stable Diffusion XL (SDXL) is the latest text-to-image generation model developed by Stability AI, based on the latent diffusion techniques. SDXL has the potential to create highly realistic images for media, entertainment, education, and industry domains, opening new ways in practical uses of AI imagery.
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Is it possible to create a checkpoint from scratch?
Here's a link to the early latent-diffusion git, that might be able to create a blank model (I haven't tested it): https://github.com/CompVis/latent-diffusion
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Anything better than pix2pixHD?
Latent diffusion could work for you: https://github.com/CompVis/latent-diffusion (https://arxiv.org/abs/2112.10752)
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Image Upscaler AI
There are a lot but the one implemented as LDSR in most stable guis is this one. https://github.com/CompVis/latent-diffusion
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I've been collecting millions of images of only public domain /cc0 licensing. I'd like to train a stable diffusion model on the collection. Could some one share their knowledge of what this would take? Otherwise, simply enjoy my library.
CompVis/latent-diffusion: High-Resolution Image Synthesis with Latent Diffusion Models (github.com)
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Run Clip on iPhone to Search Photos
The "retrieval based model" refers to https://github.com/CompVis/latent-diffusion#retrieval-augmen..., which uses ScaNN to train a knn embedding searcher.
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Class Action Lawsuit filed against Stable Diffusion and Midjourney.
Stability is basically https://github.com/CompVis/latent-diffusion + training data.
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[D] Influential papers round-up 2022. What are your favorites?
Found relevant code at https://github.com/CompVis/latent-diffusion + all code implementations here
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Can anyone explain differences between sampling methods and their uses to me in simple terms, because all the info I've found so far is either very contradicting or complex and goes over my head
DDIM and PLMS were the original samplers. They were part of Latent Diffusion's repository. They stand for the papers that introduced them, Denoising Diffusion Implicit Models and Pseudo Numerical Methods for Diffusion Models on Manifolds.
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AI art is very dystopian.
yes, https://github.com/CompVis/latent-diffusion
perceiver-pytorch
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/r/StableDiffusion – Mod here – My side of the story
I think that's confusing between:
1. Accusations that AUTOMATIC1111 (the web frontend developer) copied code from the NovelAI leak relating to the loading of hypernetworks
2. Anlatan (company behind NovelAI) copying code from AUTOMATIC1111's repo, which does not have a permissive license, relating to the weighting of words
The third party MIT-licensed code is relevant to #1. Some code AUTOMATIC1111 was accused of copying from the leak (https://i.imgur.com/r1AkvBG.png) actually already appears in multiple older permissively-licensed public repos (https://github.com/lucidrains/perceiver-pytorch/blame/main/p..., https://github.com/CompVis/stable-diffusion/blob/main/ldm/mo...), one of which was credited in the readme by AUTOMATICC1111.
For #2, the Anlatan CEO blamed it on an intern (https://i.imgur.com/BFjKG1V.png). The leak shows that the offending code was committed by the CEO (https://i.imgur.com/aLiA2tr.png), which doesn't necessarily rule it out originating from an intern (e.g: "send me the code over teams to review and I'll add it") but doesn't look great.
From other examples I'd say AUTOMATIC1111 did get a bit sloppy in terms of not following clean-room design regarding the leak, but I'm inclined to give some leeway to a solo developer making a hugely popular public tool for free.
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[Hobby Scuffles] Week of October 10, 2022
Auto refuses to comply, and explains that the code he wrote is based upon research and development that was done quite some time ago already and is open-source. The function in question was published on December 21, 2021 here: https://github.com/CompVis/latent-diffusion/commit/e66308c7f2e64cb581c6d27ab6fbeb846828253b. But that is in fact still not the original source. The original source code was published on On August 3, 2021 here: https://github.com/lucidrains/perceiver-pytorch. The original code's license allows commercial use, so nobody is wrong for using it. The license can be read here: https://github.com/lucidrains/perceiver-pytorch/blob/main/LICENSE.
- Creators of AI Art generator exclude Automatic1111 when his work eclipses their own in popularity. Appearing to be moving to /r/sdforall (+2000 users in 8 hours). Automatic1111's windows install supports features unpopular with the original authors that are ultimately open source (sources below).
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Show HN: InvokeAI, an open source Stable Diffusion toolkit and WebUI
This is the file in that other repo that the code actually seems to originate from https://github.com/lucidrains/perceiver-pytorch/blame/main/p...
As you can see, that repo from 2 years ago even originates the "# attention, what we cannot get enough of" comment and is an exact 1:1 match to Automatics commit, while the one from NovelAI even has a small change in the if clause that Automatic doesn't have.
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AUTOMATIC111 Code reference
from the original repo, as posted by OP https://github.com/lucidrains/perceiver-pytorch/blob/main/LICENSE
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Recent announcement from Emad
From a quick search, a big part of the other code also seems to be basic boilerplate? For example half the lines match exactly to https://github.com/lucidrains/perceiver-pytorch/blob/main/perceiver_pytorch/perceiver_pytorch.py
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[D] Handling variable number of outputs in an NN
You want Perceiver IO (GitHub) (Paper). It's specifically intended to generate arbitrary shape outputs (and to accept arbitrary shape inputs).
What are some alternatives?
disco-diffusion
tab-transformer-pytorch - Implementation of TabTransformer, attention network for tabular data, in Pytorch
dalle-mini - DALL·E Mini - Generate images from a text prompt
ai-notes - notes for software engineers getting up to speed on new AI developments. Serves as datastore for https://latent.space writing, and product brainstorming, but has cleaned up canonical references under the /Resources folder.
hent-AI - Automation of censor bar detection
TimeSformer-pytorch - Implementation of TimeSformer from Facebook AI, a pure attention-based solution for video classification
dalle-2-preview
generation-q - A cross-platform desktop app with a nice interface to Stable Diffusion and others
stable-diffusion
slot-attention - Implementation of Slot Attention from GoogleAI
DALLE2-pytorch - Implementation of DALL-E 2, OpenAI's updated text-to-image synthesis neural network, in Pytorch
x-transformers - A simple but complete full-attention transformer with a set of promising experimental features from various papers