sd-webui-controlnet
llama
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sd-webui-controlnet | llama | |
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247 | 184 | |
15,859 | 53,053 | |
- | 5.5% | |
9.7 | 8.1 | |
6 days ago | 19 days ago | |
Python | Python | |
GNU General Public License v3.0 only | GNU General Public License v3.0 or later |
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sd-webui-controlnet
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OpenPose ControlNet: A Beginner's Guide
A crucial step for achieving stable diffusion controlnet settings is the installation of the controlnet extension in Google Colab. Whether on a Windows PC or Mac, installing controlnet is vital for stable diffusion of human pose details. Additionally, updating the controlnet extension is necessary to maintain stability and achieve the desired results in OpenPose model. To install the v1.1 controlnet extension, go to the “extensions” tab and install it from this URL: https://github.com/Mikubill/sd-webui-controlnet. If you already have v1 controlnets installed, delete the folder from stable-diffusion-webui/extensions/. Install the v1.
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StyleAligned node for ComfyUI
1.1.420 Image-wise ControlNet and StyleAlign
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PATCHFUSION is really impressive. High resolution depth maps in 16bit. I've been waiting for this. https://github.com/zhyever/PatchFusion
I opened a request thread on ControlNet GitHub you can give a support : https://github.com/Mikubill/sd-webui-controlnet/issues/2319
- Going to lose my mind at this point with this problem
- Samples of style-aligned
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Is it possible to outpaint with SD or SDXL as easy as with photoshop? (no prompts)
It has been possible for 7 months now
- Reference Only Broken (Can someone with a working Reference Only CN upload there extension folder)
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Web app prototype to create controlnet segmentation maps for Stable Diffusion
I sometimes use a very similar technique in Cinema4d (here is a link to a c4d file with preset materials referencing proper colors for Semantic Segmentation if any other c4d user wants to try it), but yours is a much more accessible solution as it's free and it's accessible online.
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Dalle-3 Examples
There are models available that give you more control - in some senses, at least.
For example, you can use Stable Diffusion with 'ControlNet' [1] where for example, you can input an 'openpose' to choose the pose of people in the scene.
There's also a 'Regional Prompter' [2] which lets you use different prompts for different areas of the image, giving you some control over the composition.
You can also use 'inpainting' to regenerate select parts of your image if, for example, you don't like the shape of the clouds.
Of course this stuff isn't perfect - for example, you'll get hands with the wrong number of fingers sometimes, no matter what you specify :)
[1] https://github.com/Mikubill/sd-webui-controlnet
- ControlNet SDXL for Automatic1111-WebUI official release: sd-webui-controlnet 1.1.400
llama
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Mark Zuckerberg: Llama 3, $10B Models, Caesar Augustus, Bioweapons [video]
derivative works thereof).”
https://github.com/meta-llama/llama/blob/b8348da38fde8644ef0...
Also even if you did use Llama for something, they could unilaterally pull the rug on you when you got 700 million years, AND anyone who thinks Meta broke their copyright loses their license. (Checking if you are still getting screwed is against the rules)
Therefore, Zuckerberg is accountable for explicitly anticompetitive conduct, I assumed an MMA fighter would appreciate the value of competition, go figure.
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Hello OLMo: A Open LLM
One thing I wanted to add and call attention to is the importance of licensing in open models. This is often overlooked when we blindly accept the vague branding of models as “open”, but I am noticing that many open weight models are actually using encumbered proprietary licenses rather than standard open source licenses that are OSI approved (https://opensource.org/licenses). As an example, Databricks’s DBRX model has a proprietary license that forces adherence to their highly restrictive Acceptable Use Policy by referencing a live website hosting their AUP (https://github.com/databricks/dbrx/blob/main/LICENSE), which means as they change their AUP, you may be further restricted in the future. Meta’s Llama is similar (https://github.com/meta-llama/llama/blob/main/LICENSE ). I’m not sure who can depend on these models given this flaw.
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Reaching LLaMA2 Performance with 0.1M Dollars
It looks like Llama 2 7B took 184,320 A100-80GB GPU-hours to train[1]. This one says it used a 96×H100 GPU cluster for 2 weeks, for 32,256 hours. That's 17.5% of the number of hours, but H100s are faster than A100s [2] and FP16/bfloat16 performance is ~3x better.
If they had tried to replicate Llama 2 identically with their hardware setup, it'd cost a little bit less than twice their MoE model.
[1] https://github.com/meta-llama/llama/blob/main/MODEL_CARD.md#...
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DBRX: A New Open LLM
Ironically, the LLaMA license text [1] this is lifted verbatim from is itself copyrighted [2] and doesn't grant you the permission to copy it or make changes like s/meta/dbrx/g lol.
[1] https://github.com/meta-llama/llama/blob/main/LICENSE#L65
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How Chain-of-Thought Reasoning Helps Neural Networks Compute
This is kind of an epistemological debate at this level, and I make an effort to link to some source code [1] any time it seems contentious.
LLMs (of the decoder-only, generative-pretrained family everyone means) are next token predictors in a literal implementation sense (there are some caveats around batching and what not, but none that really matter to the philosophy of the thing).
But, they have some emergent behaviors that are a trickier beast. Probably the best way to think about a typical Instruct-inspired “chat bot” session is of them sampling from a distribution with a KL-style adjacency to the training corpus (sidebar: this is why shops that do and don’t train/tune on MMLU get ranked so differently than e.g. the arena rankings) at a response granularity, the same way a diffuser/U-net/de-noising model samples at the image batch (NCHW/NHWC) level.
The corpus is stocked with everything from sci-fi novels with computers arguing their own sentience to tutorials on how to do a tricky anti-derivative step-by-step.
This mental model has adequate explanatory power for anything a public LLM has ever been shown to do, but that only heavily implies it’s what they’re doing.
There is active research into whether there is more going on that is thus far not conclusive to the satisfaction of an unbiased consensus. I personally think that research will eventually show it’s just sampling, but that’s a prediction not consensus science.
They might be doing more, there is some research that represents circumstantial evidence they are doing more.
[1] https://github.com/meta-llama/llama/blob/54c22c0d63a3f3c9e77...
- Asking Meta to stop using the term "open source" for Llama
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Markov Chains Are the Original Language Models
Predicting subsequent text is pretty much exactly what they do. Lots of very cool engineering that’s a real feat, but at its core it’s argmax(P(token|token,corpus)):
https://github.com/facebookresearch/llama/blob/main/llama/ge...
The engineering feats are up there with anything, but it’s a next token predictor.
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Meta AI releases Code Llama 70B
https://github.com/facebookresearch/llama/pull/947/
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Stuff we figured out about AI in 2023
> Instead, it turns out a few hundred lines of Python is genuinely enough to train a basic version!
actually its not just a basic version. Llama 1/2's model.py is 500 lines: https://github.com/facebookresearch/llama/blob/main/llama/mo...
Mistral (is rumored to have) forked llama and is 369 lines: https://github.com/mistralai/mistral-src/blob/main/mistral/m...
and both of these are SOTA open source models.
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[D] What is a good way to maintain code readability and code quality while scaling up complexity in libraries like Hugging Face?
In transformers, they tried really hard to have a single function or method to deal with both self and cross attention mechanisms, masking, positional and relative encodings, interpolation etc. While it allows a user to use the same function/method for any model, it has led to severe parameter bloat. Just compare the original implementation of llama by FAIR with the implementation by HF to get an idea.
What are some alternatives?
ComfyUI - The most powerful and modular stable diffusion GUI, api and backend with a graph/nodes interface.
langchain - ⚡ Building applications with LLMs through composability ⚡ [Moved to: https://github.com/langchain-ai/langchain]
openpose-editor - Openpose Editor for AUTOMATIC1111's stable-diffusion-webui
text-generation-webui - A Gradio web UI for Large Language Models. Supports transformers, GPTQ, AWQ, EXL2, llama.cpp (GGUF), Llama models.
T2I-Adapter - T2I-Adapter
chatgpt-vscode - A VSCode extension that allows you to use ChatGPT
ControlNet - Let us control diffusion models!
DeepSpeed - DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.
stable-diffusion-webui-colab - stable diffusion webui colab
ollama - Get up and running with Llama 3, Mistral, Gemma, and other large language models.
stable-diffusion-webui - Stable Diffusion web UI
transformers - 🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.