laion.ai
stable-diffusion-webui
laion.ai | stable-diffusion-webui | |
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25 | 2,808 | |
106 | 131,658 | |
6.6% | - | |
8.5 | 9.9 | |
26 days ago | 2 days ago | |
HTML | Python | |
MIT License | MIT |
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laion.ai
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How Open is Generative AI? Part 2
LAION (Large-scale Artificial Intelligence Open Network), a German non-profit established in 2020, is dedicated to advancing open-source models and datasets (primarily under Apache 2 and MIT licenses) to foster open research and the evolution of benevolent AI. Their datasets, encompassing both images and text, have been pivotal in the training of renowned text-to-image models like Stable Diffusion.
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How artists are sabotaging AI to take revenge on image generators
> there is going to be a "pre-GPT" internet training set from 2022
Well, yeah, there are several here, and I think all the major image generators are using some combination of them as their starting points: https://laion.ai/
> As AI increases as an overall % of all online posts and activity it will death spiral on model quality.
Nope, it will just mean that it will be more expensive to source additional training data on top of the massive trove of existing "clean" (from intentional poisoning) data (much of which isn't perfectly captioned and human work on improving captioning can improve its utility in model training, as can more advanced models with more advanced text encoders, etc.)
If poisoning was widespread, it wouldn't impact "big model" quality much -- they aren't grabbing new random data on the internet for continuous training. It might drive up the expense of community fine tuning, which often does depend on sourcing representative imagery for target styles or concepts from, among other places, the internet.
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[D] Why is most Open Source AI happening outside the USA?
Also don't forget https://laion.ai/ from Germany. They focus more on datasets, but still.
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OpenAI is too cheap to beat
I think the weird thing about this is that it's completely true right now but in X months it may be totally outdated advice.
For example, efforts like OpenMOE https://github.com/XueFuzhao/OpenMoE or similar will probably eventually lead to very competitive performance and cost-effectiveness for open source models. At least in terms of competing with GPT-3.5 for many applications.
Also see https://laion.ai/
I also believe that within say 1-3 years there will be a different type of training approach that does not require such large datasets or manual human feedback.
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MJ images sources?
Billions. MJ's initial training dataset was from LAION: https://laion.ai/ . Not sure which version, and I am pretty sure additional data has been added since MJ v1, but MJ doesn't release anything more exact. However my guess is: more billions, lol.
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AI tools apps in one place sorted by category
Missing LAION and OpenAssistant: https://laion.ai/
- GPT detectors are biased against non-native English writers
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Model Suggestions
As far as I am concerned weights of llama are not allowed for commercial use, but if you are willing to do full training and change it's all weights it would probably be fine. There was a discussion on this topic on forums and no one was sure, you can research it. Also you can take a look at laion.ai and dolly from databricks, they are open source and are allowed for commercial use, if they meet your needs.
- HuggingChat, the first open source alternative to ChatGPT
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Hugging Face releases its own version of ChatGPT
that's OpenAssistant's / LAION AI model, HuggingFace provided the infrastructure.
stable-diffusion-webui
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Show HN: I made an app to use local AI as daily driver
* LLaVA model: I'll add more documentation. You are right Llava could not generate images. For image generation I don't have immediate plans, but checkout these projects for local image generation.
- https://diffusionbee.com/
- https://github.com/comfyanonymous/ComfyUI
- https://github.com/AUTOMATIC1111/stable-diffusion-webui
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AMD Funded a Drop-In CUDA Implementation Built on ROCm: It's Open-Source
I would love to be able to have a native stable diffusion experience, my rx 580 takes 30s to generate a single image. But it does work after following https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki...
I got this up and running on my windows machine in short order and I don't even know what stable diffusion is.
But again, it would be nice to have first class support to locally participate in the fun.
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Ask HN: What is the state of the art in AI photo enhancement?
In Auto1111, that just uses Image.blend. :)
https://github.com/AUTOMATIC1111/stable-diffusion-webui/blob...
- How To Increase Performance Time on MacOS
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Can anyone suggest an AI model that can help me enhance a poorly drawn logo?
I used SDXL in automatic1111 webui for both images. Now that I think about it, the procedure I described was how I made this one, but the one that looks like an illustration was done in two steps. I used the canny ControlNet as I said for the outer part of the logo to preserve the shape of the fonts, but I had to turn it off for the boot to give SDXL leeway to add detail and make it look more like a boot.
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Seeking out an experienced and empathetic coding buddy.
That said, please do learn coding and don't get discouraged when somebody says to learn PyTorch or recommends using a Jupiter notebook with no further information on how to translate the skill into images. I would highly recommend some short term goals. Get your feet wet by taking apart the UIs. The comfy API documentation is here and the A1111 API documentation is here. There is a difference in completeness, welcome to programming. Writing nodes or plugins is also a good way to jump into this world. Custom wildcard logic might be very attractive to you if you aren't the type that want to deal with a nested file structure to simulate logic.
- can't get it working with an AMD gpu
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SD extension that allows for setting override
Possibly Unprompted? https://github.com/AUTOMATIC1111/stable-diffusion-webui/discussions/8094
- Need to write an application to use Stable Diffusion on my desktop PC - which resource should I learn to use?
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4090 Speed Decrease on each Generation/Iteration
version: v1.6.1 • python: 3.10.13 • torch: 2.0.1+cu118 • xformers: 0.0.20 • gradio: 3.41.2 • checkpoint: 6e8d4871f8
What are some alternatives?
llm-foundry - LLM training code for Databricks foundation models
stable-diffusion-ui - Easiest 1-click way to install and use Stable Diffusion on your computer. Provides a browser UI for generating images from text prompts and images. Just enter your text prompt, and see the generated image. [Moved to: https://github.com/easydiffusion/easydiffusion]
llama - Inference code for Llama models
ComfyUI - The most powerful and modular stable diffusion GUI, api and backend with a graph/nodes interface.
qlora - QLoRA: Efficient Finetuning of Quantized LLMs
SHARK - SHARK - High Performance Machine Learning Distribution
gpt-neox - An implementation of model parallel autoregressive transformers on GPUs, based on the Megatron and DeepSpeed libraries
lora - Using Low-rank adaptation to quickly fine-tune diffusion models.
Open-Assistant - OpenAssistant is a chat-based assistant that understands tasks, can interact with third-party systems, and retrieve information dynamically to do so.
InvokeAI - InvokeAI is a leading creative engine for Stable Diffusion models, empowering professionals, artists, and enthusiasts to generate and create visual media using the latest AI-driven technologies. The solution offers an industry leading WebUI, supports terminal use through a CLI, and serves as the foundation for multiple commercial products.
alpaca.cpp - Locally run an Instruction-Tuned Chat-Style LLM
safetensors - Simple, safe way to store and distribute tensors