transformers
stable-diffusion
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transformers | stable-diffusion | |
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175 | 186 | |
125,021 | 3,146 | |
3.1% | - | |
10.0 | 0.0 | |
4 days ago | 7 months ago | |
Python | Jupyter Notebook | |
Apache License 2.0 | GNU General Public License v3.0 or later |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
transformers
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Maxtext: A simple, performant and scalable Jax LLM
Is t5x an encoder/decoder architecture?
Some more general options.
The Flax ecosystem
https://github.com/google/flax?tab=readme-ov-file
or dm-haiku
https://github.com/google-deepmind/dm-haiku
were some of the best developed communities in the Jax AI field
Perhaps the “trax” repo? https://github.com/google/trax
Some HF examples https://github.com/huggingface/transformers/tree/main/exampl...
Sadly it seems much of the work is proprietary these days, but one example could be Grok-1, if you customize the details. https://github.com/xai-org/grok-1/blob/main/run.py
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Lossless Acceleration of LLM via Adaptive N-Gram Parallel Decoding
The HuggingFace transformers library already has support for a similar method called prompt lookup decoding that uses the existing context to generate an ngram model: https://github.com/huggingface/transformers/issues/27722
I don't think it would be that hard to switch it out for a pretrained ngram model.
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AI enthusiasm #6 - Finetune any LLM you wantđź’ˇ
Most of this tutorial is based on Hugging Face course about Transformers and on Niels Rogge's Transformers tutorials: make sure to check their work and give them a star on GitHub, if you please ❤️
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Schedule-Free Learning – A New Way to Train
* Superconvergence + LR range finder + Fast AI's Ranger21 optimizer was the goto optimizer for CNNs, and worked fabulously well, but on transformers, the learning rate range finder sadi 1e-3 was the best, whilst 1e-5 was better. However, the 1 cycle learning rate stuck. https://github.com/huggingface/transformers/issues/16013
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Gemma doesn't suck anymore – 8 bug fixes
Thanks! :) I'm pushing them into transformers, pytorch-gemma and collabing with the Gemma team to resolve all the issues :)
The RoPE fix should already be in transformers 4.38.2: https://github.com/huggingface/transformers/pull/29285
My main PR for transformers which fixes most of the issues (some still left): https://github.com/huggingface/transformers/pull/29402
- HuggingFace Transformers: Qwen2
- HuggingFace Transformers Release v4.36: Mixtral, Llava/BakLlava, SeamlessM4T v2
- HuggingFace: Support for the Mixtral Moe
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Paris-Based Startup and OpenAI Competitor Mistral AI Valued at $2B
If you want to tinker with the architecture Hugging Face has a FOSS implementation in transformers: https://github.com/huggingface/transformers/blob/main/src/tr...
If you want to reproduce the training pipeline, you couldn't do that even if you wanted to because you don't have access to thousands of A100s.
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Fail to reproduce the same evaluation metrics score during inference.
I am aware that using mixed precision reduces the stability of weight and there will be little consistency but don't expect it to be this much. I have attached the graph of evaluation metrics. If someone can give me some insight into this issue, that would be great.
stable-diffusion
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Possible to load Civitai models in basujindal optimizedSD fork?
I am using this repo: https://github.com/basujindal/stable-diffusion and it works fine with e.g. this model: https://huggingface.co/CompVis/stable-diffusion-v-1-4-original
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40min to render 2x 256x256 pictures ..
That includes this optimized version : https://github.com/basujindal/stable-diffusion
- [Stable Diffusion] Coincé chez Unet: courir en mode EPS-Prédiction
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How to use safetensors locally (optimized-sd)?
Ah, I wasn't aware of that. I use this version, which was very easy to set up and use by CLI.
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[Stable Diffusion] stabile Diffusion 1.4 - CUDA-Speicherfehler
Used repo recommended in https://github.com/CompVis/stable-diffusion/issues/39 to use https://github.com/basujindal/stable-diffusion - same result.
- [Stable Diffusion] Aide avec Cuda hors de mémoire
- [Stable Diffusion] Comment créer notre propre modèle?
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Help installing optimisedSD please. Thank you so much!
As per the best solution I found, I have download this (https://github.com/basujindal/stable-diffusion) version and pasted the optimizedSD folder in the main (user>stable-diffusion-webui) folder as per site instruction.
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Stable Diffusion Web UI: Using Optimized SD Post-Installation
The git says you can simply grab the OptimizedSD folder and paste it into the installation path, which I did. However, I'm not sure how to call upon its functionality. Again, the reddit post says >Remember to call the optimized python script python optimizedSD/optimized_txt2img.py instead of standard scripts/txt2img. Though I'm not even sure where that script call is performed. Any ideas? Thanks in advance!
- [Stablediffusion] diffusion stable 1.4 - Erreur CUDA de mémoire insuffisante
What are some alternatives?
fairseq - Facebook AI Research Sequence-to-Sequence Toolkit written in Python.
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.
sentence-transformers - Multilingual Sentence & Image Embeddings with BERT
stable-diffusion
llama - Inference code for Llama models
diffusers-uncensored - Uncensored fork of diffusers
transformer-pytorch - Transformer: PyTorch Implementation of "Attention Is All You Need"
chaiNNer - A node-based image processing GUI aimed at making chaining image processing tasks easy and customizable. Born as an AI upscaling application, chaiNNer has grown into an extremely flexible and powerful programmatic image processing application.
text-generation-webui - A Gradio web UI for Large Language Models. Supports transformers, GPTQ, AWQ, EXL2, llama.cpp (GGUF), Llama models.
Dreambooth-Stable-Diffusion - Implementation of Dreambooth (https://arxiv.org/abs/2208.12242) with Stable Diffusion
huggingface_hub - The official Python client for the Huggingface Hub.
stable-diffusion-webui - Stable Diffusion web UI