img2dataset
stable-diffusion
img2dataset | stable-diffusion | |
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13 | 382 | |
3,264 | 65,504 | |
- | 1.0% | |
7.1 | 0.0 | |
3 days ago | 19 days ago | |
Python | Jupyter Notebook | |
MIT License | GNU General Public License v3.0 or later |
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img2dataset
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OpenAI sued for web scraping from millions of internet users in order to train ChatGPT
Lmao, no it doesn't. As we can see, their downloader uses very obscure "no ai" headers (which can be disabled, so its useless). They only claim it respects "robots.txt" because the google crawler respects it, if a site changes their robots.txt rules they don't remove it from their dataset, that is not "respecting". https://github.com/rom1504/img2dataset
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Who kept the bots out? Stopping content being harvested by AI
The particular tool mentioned in the Vice article is Img2dataset, and right now, it doesn't pay attention to the robots.txt file, the normal mechanism you can use to dissuade well behaved bots from indexing your content. However, it does respect a new HTTP header directive, X-Robots-Tag: noai (and also noindex, though that's an existing and already well-known part of the robots.txt standard).
- AI used photographer’s photos for training, then slapped him with an invoice
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An AI Scraping Tool Is Overwhelming Websites with Traffic
The established norm is that scrapers have to download robots.txt and support the standard robots.txt features, notably including `Crawl-Delay` which sets a rate limit. This is the established standard by which websites tell scrapers what the rules are for scraping them.
This tool is scraping sites, it has webmasters reporting actual disruption, it doesn't have robots.txt support. When people complained (eg in https://github.com/rom1504/img2dataset/issues/48), the author's stance was basically "PRs welcome". It looks like a third party recently contributed a PR to make it respect robots.txt (https://github.com/rom1504/img2dataset/pull/302), albeit without `Crawl-Delay` support, which is not merged yet.
I have seen the same thing with other recent AI tools (eg https://github.com/m1guelpf/browser-agent/issues/2) and I think it's important to defend the robots.txt convention and nip this in the bud. If a bot doesn't make a reasonable effort to respect robots.txt and it causes disruption, it's a denial-of-service attack and should be treated as such. No excuses.
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Please make this tool “opt-in” by default
//First sentence unchanged
Websites can pass the http headers `X-Robots-Tag: noai`, `X-Robots-Tag: noindex` , `X-Robots-Tag: noimageai` and `X-Robots-Tag: noimageindex` By default img2dataset will ignore images with such headers.
//Then pull up the policy link first
To understand why image creators and artists may choose to specify such headers for their images, and choose to actively not consent to their images being collected, see [AI use impact](https://github.com/rom1504/img2dataset#ai-use-impact).
AI training would not be possible without the contribution of artists, and it is the recommendation of this tool's authors that you should respect their communicated wishes. However, if you have a legitimate reason to bypass these headers, to disable this behavior and download all images, you may pass --disallowed_header_directives '[]'
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- Img2dataset: Turns large sets of image URLs to an image dataset
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Stable Attribution
it's not remembering pixels. for it to do that, it would have to have the pixels stored somewhere. It does not.
The laion 5b dataset is in the neighborhood of 220TB. (1) That is how much storage space you need to remember the pixels.
The stable diffusion 1.5 checkpoint is 7gb. (2)
1 https://github.com/rom1504/img2dataset/blob/main/dataset_exa...
2 https://huggingface.co/runwayml/stable-diffusion-v1-5/tree/m...
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Stability AI plans to let artists opt out of Stable Diffusion 3 image training
AI Bots do not respect these flags anyways
- Rule
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A Stable Diffusion prompt changes its output for the style of 1500 artists
You can download it here:
https://github.com/rom1504/img2dataset/blob/main/dataset_exa...
You probably would want to stop after getting the metadata, unless you have 240TB available for the images :)
More details and links to dataset explorers here: https://laion.ai/blog/laion-5b/
stable-diffusion
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Go is bigger than crab!
Which is a 1-click install of Stable Diffusion with an alternative web interface. You can choose a different approach but this one is pretty simple and I am new to this stuff.
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Why & How to check Invisible Watermark
an invisible watermarking of the outputs, to help viewers identify the images as machine-generated.
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How to create an Image generating AI?
It sounds like you just want to set up Stable Diffusion to run locally. I don't think your computer's specs will be able to do it. You need a graphics card with a decent amount of VRAM. Stable diffusion is in Python as is almost every AI open source project I've seen. If you can get your hands on a system with an Nvidia RTX card with as much VRAM as possible, you're in business. I have an RTX 3060 with 12 gigs of VRAM and I can run stable diffusion and a whole variety of open source LLMs as well as other projects like face swap, Roop, tortoise TTS, sadtalker, etc...
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Two video cards...one dedicated to Stable Diffusion...the other for everything else on my PC?
Use specific GPU on multi GPU systems · Issue #87 · CompVis/stable-diffusion · GitHub
- Automatic1111 - Multiple GPUs
- Ist Google inzwischen einfach unbrauchbar?
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Why are people so against compensation for artists?
I dealt with this in one of my posts. At least SD 1.1 till 1.5 are all trained on a batch size of 2048. The version pretty much everyone uses (1.5) is first pretrained at a resolution of 256x256 for 237K steps on laion2B-en, at the end of those training steps it will have seen roughly 500M images in laion2B-en. After that it is pre-trained for 194K steps on laion-high-resolution at a resolution of 512x512, which is a subset of 170M images from laion5B. Finally it is trained for 1.110K steps on LAION aesthetic v2 5+. This is easily verified by taking a glance at the model card of SD 1.5. Though that one doesn't specify for part of the training exactly which aesthetic set was used for part of the training, for that you have to look at the CompVis github repo. Thus at the end of it all both the most recent images and the majority of images will have come from LAION aesthetic v2 5+ (seeing every image approx 4 times). Realistically a lot of the weights obtained from pretraining on 2B will have been lost, and only provided a good starting point for the weights.
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Is SDXL really open-source?
stable diffusion · CompVis/stable-diffusion@2ff270f · GitHub
- I want to ask the AI to draw me as a Pokemon anime character then draw six of Pokemon of my choice next to me. What are my best free, 15$ or under and 30$ or under choices?
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how can i create my own ai image model
Here for example --> https://github.com/CompVis/stable-diffusion
What are some alternatives?
chitra - A multi-functional library for full-stack Deep Learning. Simplifies Model Building, API development, and Model Deployment.
GFPGAN - GFPGAN aims at developing Practical Algorithms for Real-world Face Restoration.
google-images-download - Python Script to download hundreds of images from 'Google Images'. It is a ready-to-run code!
Real-ESRGAN - Real-ESRGAN aims at developing Practical Algorithms for General Image/Video Restoration.
dalle-mini - DALL·E Mini - Generate images from a text prompt
diffusers-uncensored - Uncensored fork of diffusers
docarray - Represent, send, store and search multimodal data
diffusers - 🤗 Diffusers: State-of-the-art diffusion models for image and audio generation in PyTorch and FLAX.
DeepViewAgg - [CVPR'22 Best Paper Finalist] Official PyTorch implementation of the method presented in "Learning Multi-View Aggregation In the Wild for Large-Scale 3D Semantic Segmentation"
VQGAN-CLIP - Just playing with getting VQGAN+CLIP running locally, rather than having to use colab.
browser-agent - A browser AI agent, using GPT-4
onnx - Open standard for machine learning interoperability