disco-diffusion
CLIP

disco-diffusion | CLIP | |
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22 | 105 | |
7,474 | 27,248 | |
0.0% | 2.0% | |
0.0 | 2.4 | |
over 1 year ago | 7 months ago | |
Jupyter Notebook | Jupyter Notebook | |
GNU General Public License v3.0 or later | MIT License |
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disco-diffusion
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Halloween 2022
Disco-diffusion, a framework like Stable, which came out about 13 months ago: https://github.com/alembics/disco-diffusion
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Which is your favorite text to image model overall?
Runner-ups are Craiyon (for being more "creative" than SD), Disco Diffusion, minDALL-E, and CLIP Guided Diffusion.
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AI Generated Music Video using Disco Diffusion software
From the Disco Diffusion GitHub, "“A frankensteinian amalgamation of notebooks, models and techniques for the generation of AI Art and Animations.”
- List of open source machine learning AI image generation/text-to-image libraries that can be installed on an Amazon GPU instance? e.g. MinDall-E, Disco Diffusion, Pixray
- Colab notebook "Disco Diffusion v5.6, Inpainting_mode by cut_pow" by kostarion. From the developer: "Inpainting mode in #DiscoDiffusion! I've finally made the parametrised guided inpainting for disco, and applied it for more stable 2D and 3D animations. In the thread i show what's in there".
- I used an AI to create EVE Online themed Art!
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A good tutorial to get started?
Google Colab is probably the easiest way to run DD. To find the most recent version go to the GitHub page and then open the link to the Colab. Initially, you'll probably just want to experiment with the prompts. But there's also Zippy's Disco Diffusion Cheatsheet v0.3 which can be a useful place to learn more.
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Free/open-source AI Text-To-Image Models that can be run on AWS?
You can probably port Disco Diffusion pretty easily. It’s available on Google Colab, so should be straightforward. Their GitHub is: https://github.com/alembics/disco-diffusion
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Protests erupt outside of DALL-E offices after pricing implementation, press photograph
https://www.reddit.com/r/DiscoDiffusion/, https://github.com/alembics/disco-diffusion. As far as I'm aware the only way to use this is via Google Colab. Rather difficult to use because of this.
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First nice portrait on 5.6 running locally on 2070 (comparison untouched / GFPGAN)
https://github.com/alembics/disco-diffusion,
CLIP
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We used GPT-4o for image detection with 350 similar illustrations
Yes, you could implement image similarity search using embeddings: Create embeddings for the entire image set, save the embeddings in a database, and add embeddings incrementally as new images come in. To search for a similar image, create the embedding for the image that you are looking for and compute the cosine similarity between that embedding and the embeddings in your database. The closer the cosine similarity is to 1.0 the more similar the images.
For choosing a model, the article mentions the AWS Titan multimodal model, but you’d have to pay for API access to create the embeddings. Alternatively, self-hosting the CLIP model [0] to create embeddings would avoid API costs.
Follow-up question: Would the embeddings from the llama3.2-vision models be of higher quality (contain more information) than the original CLIP model?
The llama vision models use CLIP under the hood, but they add a projection head to align with the text model and the CLIP weights are mutated during alignment training, so I assume the llama vision embeddings would be of higher quality, but I don’t know for sure. Does anybody know?
(I would love to test this quality myself but Ollama does not yet support creating image embeddings from the llama vision models - a feature request with several upvotes has been opened [1].)
[0] https://github.com/openai/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
What are some alternatives?
latent-diffusion - High-Resolution Image Synthesis with Latent Diffusion Models
open_clip - An open source implementation of CLIP.
DALLE2-pytorch - Implementation of DALL-E 2, OpenAI's updated text-to-image synthesis neural network, in Pytorch
BLIP - PyTorch code for BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation
CLIP-Guided-Diffusion - Just playing with getting CLIP Guided Diffusion running locally, rather than having to use colab.
sentence-transformers - State-of-the-Art Text Embeddings
big-sleep - A simple command line tool for text to image generation, using OpenAI's CLIP and a BigGAN. Technique was originally created by https://twitter.com/advadnoun
segment-anything - The repository provides code for running inference with the SegmentAnything Model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.
discoart - 🪩 Create Disco Diffusion artworks in one line
txtai - 💡 All-in-one open-source embeddings database for semantic search, LLM orchestration and language model workflows
artroom-stable-diffusion
