sd-webui-lobe-theme
RWKV-LM
sd-webui-lobe-theme | RWKV-LM | |
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77 | 84 | |
2,198 | 11,704 | |
6.5% | - | |
9.3 | 8.8 | |
4 days ago | 15 days ago | |
TypeScript | Python | |
GNU Affero General Public License v3.0 | Apache License 2.0 |
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sd-webui-lobe-theme
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Upscayl – Free and Open Source AI Image Upscaler
upscayl is very approachable, but lacked many features i needed. i ended up using https://github.com/AUTOMATIC1111/stable-diffusion-webui after upscaling became part of my regular workflow, but for someone who just needs a few images enhanced, it's an ideal tool.
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The Basics of AI Image Generation: How to create your own AI-generated image using Stable Diffusion on your local machine.
For the Git alternative, simply right-click on the location you want to put the Stable Diffusion and select “Git Bash Here”, then paste this on the CLI: git clone https://github.com/AUTOMATIC1111/stable-diffusion-webui
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Stable Cascade
ComfyUI is similar to Houdini in complexity, but immensely powerful. It's a joy to use.
There are also a large amount of resources available for it on YouTube, GitHub (https://github.com/comfyanonymous/ComfyUI_examples), reddit (https://old.reddit.com/r/comfyui), CivitAI, Comfy Workflows (https://comfyworkflows.com/), and OpenArt Flow (https://openart.ai/workflows/).
I still use AUTO1111 (https://github.com/AUTOMATIC1111/stable-diffusion-webui) and the recently released and heavily modified fork of AUTO1111 called Forge (https://github.com/lllyasviel/stable-diffusion-webui-forge).
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Show HN: I made a local wrapper for Automatic 1111
Seems like an interesting project. Regarding the name, is there permission to use something so similar to AUTOMATIC1111 [1]?
> Diffusers will Cuda out of memory/perform very slowly for huge generations, like 2048x2048 images, while Auto 1111 SDK won't.
Do we have some numbers on this? I have seen AUTOMATIC1111 fall-over whilst using only half the available of GPU VRAM - there seems to be some weirdness where it tries to allocate before de-allocating the last batch or something.
> You can use any of the 6 compatible RealEsrgran models/weights with our RealEsrgran pipeline for upscaling images. Here are the model ids:
I've previously had trouble trying to use AUTOMATIC1111 upscalers, it seems like it needs more GPU VRAM than just generating the image already upscaled.
[1] https://github.com/AUTOMATIC1111/stable-diffusion-webui
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Stable Code 3B: Coding on the Edge
You might be thinking of Fooocus: https://github.com/lllyasviel/Fooocus
The Stable Diffusion web interface that got a lot of people's attention originally was Automatic1111: https://github.com/AUTOMATIC1111/stable-diffusion-webui
Fooocus is definitely more beginner friendly. It does a lot of the prompt engineering for you. Automatic1111 has a ton of plugins, most notably ControlNet which gives you fine grained control over the images, but there is a learning curve.
- Google Imagen 2
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Free or "practically-free" Ai picture generator?
Stable Diffusion https://github.com/AUTOMATIC1111/stable-diffusion-webui
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Things to do, to put my old PC to use?
Make it into a stable diffusion server!
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GTA 6 trailer screencaps, photorealistic style
There's no link version, you have to run it locally. You install it from here
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Automatic1111 v1.7.0-RC published
Repository: AUTOMATIC1111/stable-diffusion-webui · Tag: v1.7.0-RC · Commit: 48fae7c · Released by: AUTOMATIC1111
RWKV-LM
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Do LLMs need a context window?
https://github.com/BlinkDL/RWKV-LM#rwkv-discord-httpsdiscord... lists a number of implementations of various versions of RWKV.
https://github.com/BlinkDL/RWKV-LM#rwkv-parallelizable-rnn-w... :
> RWKV: Parallelizable RNN with Transformer-level LLM Performance (pronounced as "RwaKuv", from 4 major params: R W K V)
> RWKV is an RNN with Transformer-level LLM performance, which can also be directly trained like a GPT transformer (parallelizable). And it's 100% attention-free. You only need the hidden state at position t to compute the state at position t+1. You can use the "GPT" mode to quickly compute the hidden state for the "RNN" mode.
> So it's combining the best of RNN and transformer - great performance, fast inference, saves VRAM, fast training, "infinite" ctx_len, and free sentence embedding (using the final hidden state).
> "Our latest version is RWKV-6,*
- People who've used RWKV, whats your wishlist for it?
- Paving the way to efficient architectures: StripedHyena-7B
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Understanding Deep Learning
That is not true. There are RNNs with transformer/LLM-like performance. See https://github.com/BlinkDL/RWKV-LM.
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Q-Transformer: Scalable Reinforcement Learning via Autoregressive Q-Functions
This is what RWKV (https://github.com/BlinkDL/RWKV-LM) was made for, and what it will be good at.
Wow. Pretty darn cool! <3 :'))))
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Personal GPT: A tiny AI Chatbot that runs fully offline on your iPhone
Thanks for the support! Two weeks ago, I'd have said longer contexts on small on-device LLMs are at least a year away, but developments from last week seem to indicate that it's well within reach. Once the low hanging product features are done, I think it's a worthy problem to spend a couple of weeks or perhaps even months on. Speaking of context lengths, recurrent models like RWKV technically have infinite context lengths, but in practice the context slowly fades away after a few thousands of tokens.
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"If you see a startup claiming to possess top-secret results leading to human level AI, they're lying or delusional. Don't believe them!" - Yann LeCun, on the conspiracy theories of "X company has reached AGI in secret"
This is the reason there are only a few AI labs, and they show little of the theoretical and scientific understanding you believe is required. Go check their code, there's nothing there. Even the transformer with it's heads and other architectural elements turns out to not do anything and it is less efficient than RNNs. (see https://github.com/BlinkDL/RWKV-LM)
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The Secret Sauce behind 100K context window in LLMs: all tricks in one place
I've been pondering the same thing, as simply extending the context window in a straightforward manner would lead to a significant increase in computational resources. I've had the opportunity to experiment with Anthropics' 100k model, and it's evident that they're employing some clever techniques to make it work, albeit with some imperfections. One interesting observation is that their prompt guide recommends placing instructions after the reference text when inputting lengthy text bodies. I noticed that the model often disregarded the instructions if placed beforehand. It's clear that the model doesn't allocate the same level of "attention" to all parts of the input across the entire context window.
Moreover, the inability to cache transformers makes the use of large context windows quite costly, as all previous messages must be sent with each call. In this context, the RWKV-LM project on GitHub (https://github.com/BlinkDL/RWKV-LM) might offer a solution. They claim to achieve performance comparable to transformers using an RNN, which could potentially handle a 100-page document and cache it, thereby eliminating the need to process the entire document with each subsequent query. However, I suspect RWKV might fall short in handling complex tasks that require maintaining multiple variables in memory, such as mathematical computations, but it should suffice for many scenarios.
On a related note, I believe Anthropics' Claude is somewhat underappreciated. In some instances, it outperforms GPT4, and I'd rank it somewhere between GPT4 and Bard overall.
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Meta's plan to offer free commercial AI models puts pressure on Google, OpenAI
> The only reason open-source LLMs have a heartbeat is they’re standing on Meta’s weights.
Not necessarily.
RWKV, for example, is a different architecture that wasn't based on Facebook's weights whatsoever. I don't know where BlinkDL (the author) got the training data, but they seem to have done everything mostly independently otherwise.
https://github.com/BlinkDL/RWKV-LM
disclaimer: I've been doing a lot of work lately on an implementation of CPU inference for this model, so I'm obviously somewhat biased since this is the model I have the most experience in.
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Eliezer Yudkowsky - open letter on AI
I think the main concern is that, due to the resources put into LLM research for finding new ways to refine and improve them, that work can then be used by projects that do go the extra mile and create things that are more than just LLMs. For example, RWKV is similar to an LLM but will actually change its own model after every processed token, thus letting it remember things longer-term without the use of 'context tokens'.
What are some alternatives?
stable-diffusion-webui - Stable Diffusion web UI
llama - Inference code for Llama models
ComfyUI - The most powerful and modular stable diffusion GUI, api and backend with a graph/nodes interface.
alpaca-lora - Instruct-tune LLaMA on consumer hardware
automatic - SD.Next: Advanced Implementation of Stable Diffusion and other Diffusion-based generative image models
flash-attention - Fast and memory-efficient exact attention
stable-diffusion-webui-directml - Stable Diffusion web UI
koboldcpp - A simple one-file way to run various GGML and GGUF models with KoboldAI's UI
stable-diffusion-webui-ux - Stable Diffusion web UI UX
gpt4all - gpt4all: run open-source LLMs anywhere
stable-diffusion-webui-colab - stable diffusion webui colab
RWKV-CUDA - The CUDA version of the RWKV language model ( https://github.com/BlinkDL/RWKV-LM )