Search Results for "generative ai"
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Showing 12 open source projects for "generative ai"
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Clarity AI Upscaler
AI Image Upscaler & Enhancer
Clarity AI Upscaler is an open-source AI image enhancement tool designed to increase the resolution and visual quality of images using modern generative techniques. The system uses deep learning models based on diffusion and other image generation methods to reconstruct high-resolution versions of low-resolution images while preserving important visual details. -
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Pixal3D
Pixel-Aligned 3D Generation from Images
...The system is designed to produce detailed geometry and physically based rendering textures rather than only coarse 3D approximations. It is useful for researchers, 3D artists, game asset workflows, and generative AI experiments focused on image-conditioned reconstruction. Its main value is combining generative flexibility with reconstruction-like visual fidelity. -
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Perfect Pixel
Refine and quantize messy AI pixel art into clean, perfect pixels
perfectPixel is a workflow tool for turning messy “pixel-style” images, especially those produced by generative models, into truly grid-aligned pixel art that reads cleanly at any scale. It tackles a common problem with AI pixel art: edges that look pixelated at first glance but are not actually aligned to a coherent pixel grid, which causes shimmer, blur, and uneven block sizes when you zoom in. The tool analyzes an image to infer the intended grid size, then refines and quantizes the artwork so pixels snap into consistent cells and the final result looks crisp and intentional. ... -
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Dream Textures
Stable Diffusion built-in to Blender
Create textures, concept art, background assets, and more with a simple text prompt. Use the 'Seamless' option to create textures that tile perfectly with no visible seam. Texture entire scenes with 'Project Dream Texture' and depth to image. Re-style animations with the Cycles render pass. Run the models on your machine to iterate without slowdowns from a service. Create textures, concept art, and more with text prompts. Learn how to use the various configuration options to get exactly what... -
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Seadanse for Blender
Clay pass to Seedance 2.5 video, keeping the camera you keyframed
A Blender extension and an Agent Skill that turn a grey clay pass into a Seedance 2.5 video. Home: https://seadanse.com/tool/blender-to-seedance-2-5 A text prompt cannot say where the camera starts, where it ends, or what blocks what. A blockout says all three. Render a Workbench clay pass from your scene camera, hand it over as the reference, and the take comes back following the path, framing and timing you keyframed. Two ways in, one workflow. The extension installs from disk into... -
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RetroShine
Restore your old photos with one click
...Whether you’re reviving family memories or preserving your artistic legacy for future generations, RetroShine uses intelligent color enhancement and curve corrections to restore natural tones and give your photos a fresh, contemporary look. Authenticity is our mission. Unlike generative AI tools, RetroShine doesn’t manipulate your images or invent fake details that never existed. It analyzes each photo and adjusts color curves dynamically for a crisp, natural-looking, beautifully balanced result, just the way people expect in the digital age. No subscriptions. No ads. No data collection. Privacy guaranteed. -
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DALL-E 2 - Pytorch
Implementation of DALL-E 2, OpenAI's updated text-to-image synthesis
Implementation of DALL-E 2, OpenAI's updated text-to-image synthesis neural network, in Pytorch. The main novelty seems to be an extra layer of indirection with the prior network (whether it is an autoregressive transformer or a diffusion network), which predicts an image embedding based on the text embedding from CLIP. Specifically, this repository will only build out the diffusion prior network, as it is the best performing variant (but which incidentally involves a causal transformer as... -
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Stable Diffusion in Docker
Run the Stable Diffusion releases in a Docker container
Run the Stable Diffusion releases in a Docker container with txt2img, img2img, depth2img, pix2pix, upscale4x, and inpaint. Run the Stable Diffusion releases on Huggingface in a GPU-accelerated Docker container. By default, the pipeline uses the full model and weights which requires a CUDA capable GPU with 8GB+ of VRAM. It should take a few seconds to create one image. On less powerful GPUs you may need to modify some of the options; see the Examples section for more details. If you lack a... -
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audio-diffusion-pytorch
Audio generation using diffusion models, in PyTorch
A fully featured audio diffusion library, for PyTorch. Includes models for unconditional audio generation, text-conditional audio generation, diffusion autoencoding, upsampling, and vocoding. The provided models are waveform-based, however, the U-Net (built using a-unet), DiffusionModel, diffusion method, and diffusion samplers are both generic to any dimension and highly customizable to work on other formats. Note: no pre-trained models are provided here, this library is meant for research... -
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Stable Diffusion v 2.1 web UI
Lightweight Stable Diffusion v 2.1 web UI: txt2img, img2img, depth2img
Lightweight Stable Diffusion v 2.1 web UI: txt2img, img2img, depth2img, in paint and upscale4x. Gradio app for Stable Diffusion 2 by Stability AI. It uses Hugging Face Diffusers implementation. Currently supported pipelines are text-to-image, image-to-image, inpainting, upscaling and depth-to-image. -
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GIMP ML
AI for GNU Image Manipulation Program
This repository introduces GIMP3-ML, a set of Python plugins for the widely popular GNU Image Manipulation Program (GIMP). It enables the use of recent advances in computer vision to the conventional image editing pipeline. Applications from deep learning such as monocular depth estimation, semantic segmentation, mask generative adversarial networks, image super-resolution, de-noising and coloring have been incorporated with GIMP through Python-based plugins. Additionally, operations on... -
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Image Super-Resolution (ISR)
Super-scale your images and run experiments with Residual Dense
The goal of this project is to upscale and improve the quality of low-resolution images. This project contains Keras implementations of different Residual Dense Networks for Single Image Super-Resolution (ISR) as well as scripts to train these networks using content and adversarial loss components. Docker scripts and Google Colab notebooks are available to carry training and prediction. Also, we provide scripts to facilitate training on the cloud with AWS and Nvidia-docker with only a few...
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