yasd-discord-bot
CLIP
yasd-discord-bot | CLIP | |
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14 | 104 | |
112 | 22,472 | |
- | 3.6% | |
10.0 | 1.2 | |
over 1 year ago | 12 days ago | |
Python | Jupyter Notebook | |
MIT License | MIT License |
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yasd-discord-bot
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Discord bot?
There's always my bot: https://github.com/AmericanPresidentJimmyCarter/yasd-discord-bot
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Outpainting and inpainting (via clipseg) with the latest RunwayML 1.5 weights and VAE is out of beta and live on the LAION Discord Server
YASD-Discord-Bot
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Who needs prompt2prompt anyway? SD 1.5 inpainting model with clipseg prompt for "hair" and various prompts for different hair colors
clipseg is an image segmentation method used to find a mask for an image from a prompt. I implemented it as an executor for dalle-flow and added it to my bot yasd-discord-bot.
- People who use Unstable diffusion on discord
- API?
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Sequential token weighting invented by Birch-san@Github allows you to bypass the 77 token limit and use any amount of tokens you want, also allows you to sequentially alter an image
Merged into [dalle-flow](https://github.com/jina-ai/dalle-flow/pull/112) this morning and works on my Discord bot [yasd-discord-bot](https://github.com/AmericanPresidentJimmyCarter/yasd-discord-bot).
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Can I remotely run stable diffusion on my computer but access it from my phone?
personally i run a discord bot for myself. its much more convenient to be able to look at your run history and use a good UI for generation. https://github.com/AmericanPresidentJimmyCarter/yasd-discord-bot/
- Multi-subprompt positive/negative weights and SD concepts library live on YASD Discord Bot at the LAION Discord Server
- YASD Discord Bot updated with experimental "outriffing" that allows you to img2img to different sizes, docker image instructions
- Free and Open Source Stable Diffusion Bot featuring Array Prompts, Interpolation, and more!
CLIP
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Anomaly Detection with FiftyOne and Anomalib
pip install -U huggingface_hub umap-learn git+https://github.com/openai/CLIP.git
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How to Cluster Images
We will also need two more libraries: OpenAI’s CLIP GitHub repo, enabling us to generate image features with the CLIP model, and the umap-learn library, which will let us apply a dimensionality reduction technique called Uniform Manifold Approximation and Projection (UMAP) to those features to visualize them in 2D:
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Show HN: Memories, FOSS Google Photos alternative built for high performance
Biggest missing feature for all these self hosted photo hosting is the lack of a real search. Being able to search for things like "beach at night" is a time saver instead of browsing through hundreds or thousands of photos. There are trained neural networks out there like https://github.com/openai/CLIP which are quite good.
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Zero-Shot Prediction Plugin for FiftyOne
In computer vision, this is known as zero-shot learning, or zero-shot prediction, because the goal is to generate predictions without explicitly being given any example predictions to learn from. With the advent of high quality multimodal models like CLIP and foundation models like Segment Anything, it is now possible to generate remarkably good zero-shot predictions for a variety of computer vision tasks, including:
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A History of CLIP Model Training Data Advances
(Github Repo | Most Popular Model | Paper | Project Page)
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NLP Algorithms for Clustering AI Content Search Keywords
the first thing that comes to mind is CLIP: https://github.com/openai/CLIP
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How to Build a Semantic Search Engine for Emojis
Whenever I’m working on semantic search applications that connect images and text, I start with a family of models known as contrastive language image pre-training (CLIP). These models are trained on image-text pairs to generate similar vector representations or embeddings for images and their captions, and dissimilar vectors when images are paired with other text strings. There are multiple CLIP-style models, including OpenCLIP and MetaCLIP, but for simplicity we’ll focus on the original CLIP model from OpenAI. No model is perfect, and at a fundamental level there is no right way to compare images and text, but CLIP certainly provides a good starting point.
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COMFYUI SDXL WORKFLOW INBOUND! Q&A NOW OPEN! (WIP EARLY ACCESS WORKFLOW INCLUDED!)
in the modal card it says: pretrained text encoders (OpenCLIP-ViT/G and CLIP-ViT/L).
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Stability Matrix v1.1.0 - Portable mode, Automatic updates, Revamped console, and more
Command: "C:\StabilityMatrix\Packages\stable-diffusion-webui\venv\Scripts\python.exe" -m pip install https://github.com/openai/CLIP/archive/d50d76daa670286dd6cacf3bcd80b5e4823fc8e1.zip --prefer-binary
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[D] LLM or model that does image -> prompt?
CLIP might work for your needs.
What are some alternatives?
discord-stable-diffusion - A neat Discord bot to run Stable Diffusion locally
open_clip - An open source implementation of CLIP.
rocm-build - build scripts for ROCm
sentence-transformers - Multilingual Sentence & Image Embeddings with BERT
stable-diffusion - Latent Text-to-Image Diffusion
latent-diffusion - High-Resolution Image Synthesis with Latent Diffusion Models
stable-diffusion - Go to lstein/stable-diffusion for all the best stuff and a stable release. This repository is my testing ground and it's very likely that I've done something that will break it.
disco-diffusion
taming-transformers - Taming Transformers for High-Resolution Image Synthesis
DALLE2-pytorch - Implementation of DALL-E 2, OpenAI's updated text-to-image synthesis neural network, in Pytorch
stable-diffusion - A latent text-to-image diffusion model
BLIP - PyTorch code for BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation