CLIP
dream-textures
CLIP | dream-textures | |
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103 | 72 | |
22,209 | 7,599 | |
6.3% | - | |
1.2 | 5.8 | |
18 days ago | 9 days ago | |
Jupyter Notebook | Python | |
MIT License | GNU General Public License v3.0 only |
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CLIP
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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.
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Where can this be used? I have seen some tutorials to run deepfloyd on Google colab. Any way it can be done on local?
pip install deepfloyd_if==1.0.2rc0 pip install xformers==0.0.16 pip install git+https://github.com/openai/CLIP.git --no-deps pip install huggingface_hub --upgrade
dream-textures
- Donut done with Artificial Intelligence and Blender
- Tell HN: The next generation of videogames will be great with midjourney
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After Diffusion, an After Effects Extension Integrating the SD web UI seamlessly.
I'm a long time advanced AE user and would gladly give feedback according to how I envision a nice workflow to be if you want. I recently got into dream textures for blender, which I think is a great reference for the direction things could be heading. It's still not viable for consistent video, but I love how they expose multiple control nets and their weights to be animatable for example. I also suggested them exposed (animatable) prompt weights, which the author now also plans for future release. I see you have such things planned as well for this plugin so big thumbs up!
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Resources for artists interesting in using StableDiffusion as a tool?
Dream Textures (SD for Blender) - https://github.com/carson-katri/dream-textures
- Using AI for 3d Game art
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ControlNet fully integrated with Blender using nodes!
Yes, and it can also automatically bake the texture onto the original UV map instead of the projected UVs. The guide is here: https://github.com/carson-katri/dream-textures/wiki/Texture-Projection
- Using DALL-E 2 to create brick and water textures in Unity.
- 3D animation attempt using Sketchup screenshots and ControlNet
- Blender 3.5
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Master AI Texture Projection for Blender 3
Dream AI latest release: https://github.com/carson-katri/dream-textures/releases
What are some alternatives?
open_clip - An open source implementation of CLIP.
stable-diffusion-webui - Stable Diffusion web UI
sentence-transformers - Multilingual Sentence & Image Embeddings with BERT
stable-diffusion - This version of CompVis/stable-diffusion features an interactive command-line script that combines text2img and img2img functionality in a "dream bot" style interface, a WebGUI, and multiple features and other enhancements. [Moved to: https://github.com/invoke-ai/InvokeAI]
latent-diffusion - High-Resolution Image Synthesis with Latent Diffusion Models
stable-diffusion - Optimized Stable Diffusion modified to run on lower GPU VRAM
disco-diffusion
stable-diffusion-nvidia-docker - GPU-ready Dockerfile to run Stability.AI stable-diffusion model v2 with a simple web interface. Includes multi-GPUs support.
DALLE2-pytorch - Implementation of DALL-E 2, OpenAI's updated text-to-image synthesis neural network, in Pytorch
DeepBump - Normal & height maps generation from single pictures
BLIP - PyTorch code for BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation
stable-diffusion-webui - Stable Diffusion web UI [Moved to: https://github.com/Sygil-Dev/sygil-webui]