TimeSformer-pytorch
perceiver-pytorch
| TimeSformer-pytorch | perceiver-pytorch | |
|---|---|---|
| 1 | 11 | |
| 682 | 1,068 | |
| - | - | |
| 0.0 | 3.1 | |
| about 3 years ago | about 1 year ago | |
| Python | Python | |
| MIT License | MIT License |
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TimeSformer-pytorch
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?
DALLE-pytorch - Implementation / replication of DALL-E, OpenAI's Text to Image Transformer, in Pytorch
tab-transformer-pytorch - Implementation of TabTransformer, attention network for tabular data, in Pytorch
CoCa-pytorch - Implementation of CoCa, Contrastive Captioners are Image-Text Foundation Models, in Pytorch
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.
x-transformers - A concise but complete full-attention transformer with a set of promising experimental features from various papers
Perceiver - Implementation of Perceiver, General Perception with Iterative Attention in TensorFlow
generation-q - A cross-platform desktop app with a nice interface to Stable Diffusion and others
performer-pytorch - An implementation of Performer, a linear attention-based transformer, in Pytorch
slot-attention - Implementation of Slot Attention from GoogleAI
Fast-Transformer - An implementation of Fastformer: Additive Attention Can Be All You Need, a Transformer Variant in TensorFlow
UnstableFusion - A Stable Diffusion desktop frontend with inpainting, img2img and more!