Open Source AI Image Generators - Page 2
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pwa-asset-generator
Automates PWA asset generation and image declaration
Automates PWA asset generation and image declaration. Automatically generates icon and splash screen images, favicons and mstile images. Updates manifest.json and index.html files with the generated images according to Web App Manifest specs and Apple Human Interface guidelines. When you build a PWA with a goal of providing native-like experiences on multiple platforms and stores, you need to meet with the criteria of those platforms and stores with your PWA assets; icon sizes and splash screens. Google's Android platform respects Web App Manifest API specs, and it expects you to provide at least 2 icon sizes in your manifest file. Apple's iOS currently doesn't support Web App Manifest API specs. You need to introduce custom HTML tags to set icons and splash screens to your PWA. You need to introduce a special html link tag with rel apple-touch-icon to provide icons for your PWA when it's added to home screen. -
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video-subtitle-remover
AI-based tool for removing hardsubs and text-like watermarks
Video-subtitle-remover (VSR) is an AI-based software that removes hardcoded subtitles from videos or Pictures. -
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AI Logo Generator
A free + OSS logo generator powered by Flux on Together AI
AI Logo Generator is an open-source AI logo generator that lets you create professional-looking logos in seconds from a simple text prompt. It uses the Flux Pro 1.1 model hosted on Together AI to generate logos, so the heavy lifting is done by a state-of-the-art image model while the app focuses on UX and workflow. The project is built with Next.js and TypeScript, and it uses shadcn/ui plus Tailwind CSS for a modern, responsive interface that feels like a polished SaaS product rather than a demo. It integrates Clerk for authentication so users can sign in, save their logo history (planned via a dashboard), and potentially manage usage tied to their own API key. Upstash Redis is used for rate limiting, which is important for controlling API usage and preventing abuse when generating many images. Analytics and observability are baked in with Plausible and Helicone, so developers can monitor usage patterns and model behavior over time. -
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Hunyuan3D-1
A Unified Framework for Text-to-3D and Image-to-3D Generation
Hunyuan3D-1 is an earlier version in the same 3D generation line (the unified framework for text-to-3D and image-to-3D tasks) by Tencent Hunyuan. It provides a framework combining shape generation and texture synthesis, enabling users to create 3D assets from images or text conditions. While less advanced than version 2.1, it laid the foundations for the later PBR, higher resolution, and open-source enhancements. (Note: less detailed public documentation was found for Hunyuan3D-1 compared to 2.1.). Community and ecosystem support (e.g. usage via Blender addon for geometry/texture). Integration into user-friendly tools/platforms. -
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OpenAI Web Application
A web application that allows users to interact with OpenAI's models
A web application that allows users to interact with OpenAI's modles through a simple and user-friendly interface. This app is for demo purpose to test OpenAI API and may contain issues/bugs. User-friendly interface for making requests to the OpenAI API. Responses are displayed in a chat-like format. Select Models (Davinci, Codex, DALL·E, Whisper) based on your needs. Create AI Images (DALL·E). Audio-Text Transcribe (Whisper). Highlight code syntax. Type in the input field and press enter or click on the send button to make a request to the OpenAI API. Use control+enter to add line breaks in the input field. Responses are displayed in the chat-like format on top of the page. Generate code, including translating natural language to code. Take advantage of DALL·E models to generate AI images. Utilize Whisper Model to transcribe audio into text. -
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Free AI Watermark Remover - FreeRepair
AI-powered tool to quickly remove watermarks from images flawlessly
AI Watermark Remover (Free And Open-Source) & Make Blurry Images Clearer Or Larger Tool - FreeRepair, Simulation IOPaint Based On The Django Of Python With No Sign-Up. As a free, open-source, AI-powered tool, FreeRepair makes it easy to remove watermarks, logos, text or clutter from images, and blurry images can be made clearer or larger. No installation, no internet connection, it works out of the box, safe and secure, unlimited. -
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DALL-E in Pytorch
Implementation / replication of DALL-E, OpenAI's Text to Image
Implementation / replication of DALL-E (paper), OpenAI's Text to Image Transformer, in Pytorch. It will also contain CLIP for ranking the generations. Kobiso, a research engineer from Naver, has trained on the CUB200 dataset here, using full and deepspeed sparse attention. You can also skip the training of the VAE altogether, using the pretrained model released by OpenAI! The wrapper class should take care of downloading and caching the model for you auto-magically. You can also use the pretrained VAE offered by the authors of Taming Transformers! Currently only the VAE with a codebook size of 1024 is offered, with the hope that it may train a little faster than OpenAI's, which has a size of 8192. In contrast to OpenAI's VAE, it also has an extra layer of downsampling, so the image sequence length is 256 instead of 1024 (this will lead to a 16 reduction in training costs, when you do the math). -
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Deep Exemplar-based Video Colorization
The source code of CVPR 2019 paper "Deep Exemplar-based Colorization"
The source code of CVPR 2019 paper "Deep Exemplar-based Video Colorization". End-to-end network for exemplar-based video colorization. The main challenge is to achieve temporal consistency while remaining faithful to the reference style. To address this issue, we introduce a recurrent framework that unifies the semantic correspondence and color propagation steps. Both steps allow a provided reference image to guide the colorization of every frame, thus reducing accumulated propagation errors. Video frames are colorized in sequence based on the colorization history, and its coherency is further enforced by the temporal consistency loss. All of these components, learned end-to-end, help produce realistic videos with good temporal stability. Experiments show our result is superior to the state-of-the-art methods both quantitatively and qualitatively. In order to colorize your own video, it requires to extract the video frames, and provide a reference image as an example. -
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PaddleGAN
PaddlePaddle GAN library, including lots of interesting applications
PaddlePaddle GAN library, including lots of interesting applications like First-Order motion transfer, Wav2Lip, picture repair, image editing, photo2cartoon, image style transfer, GPEN, and so on. PaddleGAN provides developers with high-performance implementation of classic and SOTA Generative Adversarial Networks, and supports developers to quickly build, train and deploy GANs for academic, entertainment, and industrial usage. GAN-Generative Adversarial Network, was praised by "the Father of Convolutional Networks" Yann LeCun (Yang Likun) as [One of the most interesting ideas in the field of computer science in the past decade]. It's the one research area in deep learning that AI researchers are most concerned about. -
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Stable-Dreamfusion
Text-to-3D & Image-to-3D & Mesh Exportation with NeRF + Diffusion
A pytorch implementation of the text-to-3D model Dreamfusion, powered by the Stable Diffusion text-to-2D model. This project is a work-in-progress and contains lots of differences from the paper. The current generation quality cannot match the results from the original paper, and many prompts still fail badly! Since the Imagen model is not publicly available, we use Stable Diffusion to replace it (implementation from diffusers). Different from Imagen, Stable-Diffusion is a latent diffusion model, which diffuses in a latent space instead of the original image space. Therefore, we need the loss to propagate back from the VAE's encoder part too, which introduces extra time costs in training. We use the multi-resolution grid encoder to implement the NeRF backbone (implementation from torch-ngp), which enables much faster rendering. -
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ruDALL-E
Generate images from texts. In Russian
We present a family of generative models from SberDevices and Sber AI! Models allow you to create images that did not exist before. All you need is a text description in Russian or another language. Try to create unique images together with generative artists using your own formulations. Ask generative artists to depict something special for you as well. The Kandinsky 2.0 model uses the reverse diffusion method and creates colorful images on various topics in a matter of seconds by text query in Russian and other languages. You can even combine different languages within a single query. This neural network has been developed and trained by Sber AI researchers in close collaboration with scientists from Artificial Intelligence Research Institute using joined datasets by Sber AI and SberDevices. Russian text-to-image model that generates images from text. The architecture is the same as ruDALL-E XL. Even more parameters in the new version. -
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website-to-gif
Turn your website into a GIF
This Github Action automatically creates an animated GIF or WebP from a given web page to display on your project README (or anywhere else). In your GitHub repo, create a workflow file or extend an existing one. You have to also include a step to checkout and commit to the repo. You can use the following example gif.yml. Make sure to modify the url value and add any other input you want to use. WebP rendering will take a lot of time to benefit from lossless quality and file size optimization. -
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Deface GUI - Face Anonymization Tool
Graphical User Interface Face Anonymization Tool
This application is a professional tool with a graphical user interface that enables anonymization of faces using the Deface Engine. Cross-Platform Compatible (Linux-Windows) NOTE: To use on Windows, first install Python. Then, if necessary, install “pip install deface” (only if necessary). -
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NodeTool
Visual AI Workflow Builder
NodeTool is an open‑source, visual AI workflow builder that lets you connect nodes for text, images, audio, video, data, and automation—then run them locally or on the cloud. Build multi‑step agents, RAG systems, and creative media pipelines without coding, inspect execution in real time, and deploy anywhere: home server, private VPC, RunPod, or Cloud Run. With a local‑first design, NodeTool keeps models and data under your control while still supporting providers like OpenAI, Anthropic, Replicate, and HuggingFace. Use templates to get started fast, customize every step, and share workflows as simple apps across desktop and mobile via secure connections. -
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FLUX.1 Krea
Powerful open source image generation model
FLUX.1 Krea [dev] is an open-source 12-billion parameter image generation model developed collaboratively by Krea and Black Forest Labs, designed to deliver superior aesthetic control and high image quality. It is a rectified-flow model distilled from the original Krea 1, providing enhanced sampling efficiency through classifier-free guidance distillation. The model supports generation at resolutions between 1024 and 1280 pixels with recommended inference steps between 28 and 32 for optimal balance of speed and quality. FLUX.1 Krea is fully compatible with the FLUX.1 architecture, making it easy to integrate into existing workflows and pipelines. The repository offers easy-to-use inference scripts and a Jupyter Notebook example to facilitate quick experimentation and adoption. Users can run the model locally after downloading weights from Hugging Face and benefit from a live demo available on krea.ai. -
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A Netflix film cover generator Nuxt.js
A tool for generating Netflix show image
We love Netflix, but we love memes even more. We thought that helping Netflix on their UI/UX testing with a tool that can create show images easily with an export function to png. A tool for generating Netflix shows an image. You can visit the demo website hosted on Netlify. This is an open-source tool and it is available on Github. On this tool you have a full editable canvas where you can edit content, text position, text dimension, gradient position and change the background image. In order to change the element position you can just click and drag anywhere. Meanwhile, if yuo want to change the content inside an element you need to double-click on it. By double clicking on an element it will show a textarea where you can edit and confirm the changes by clicking elsewhere or by clicking Enter. In order to change the background image you can drag-n-drop any image onto the canvas and it will change the background image. -
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AI Atelier
Based on the Disco Diffusion, version of the AI art creation software
Based on the Disco Diffusion, we have developed a Chinese & English version of the AI art creation software "AI Atelier". We offer both Text-To-Image models (Disco Diffusion and VQGAN+CLIP) and Text-To-Text (GPT-J-6B and GPT-NEOX-20B) as options. Making available complete source code of licensed works and modifications, which include larger works using a licensed work, under the same license. Copyright and license notices must be preserved. When a modified version is used to provide a service over a network, the complete source code of the modified version must be made available. Create 2D and 3D animations and not only still frames (from Disco Diffusion v5 and VQGAN Animations). Input audio and images for generation instead of just text. Simplify tool setup process on colab, and enable ‘one-click’ sharing of the generated link to other users. Experiment with the possibilities for multi-user access to the same link. -
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BCI
BCI: Breast Cancer Immunohistochemical Image Generation
Breast Cancer Immunohistochemical Image Generation through Pyramid Pix2pix. We have released the trained model on BCI and LLVIP datasets. We host a competition for breast cancer immunohistochemistry image generation on Grand Challenge. Project pix2pix provides a python script to generate pix2pix training data in the form of pairs of images {A,B}, where A and B are two different depictions of the same underlying scene, these can be pairs {HE, IHC}. Then we can learn to translate A(HE images) to B(IHC images). The evaluation of human epidermal growth factor receptor 2 (HER2) expression is essential to formulate a precise treatment for breast cancer. The routine evaluation of HER2 is conducted with immunohistochemical techniques (IHC), which is very expensive. Therefore, for the first time, we propose a breast cancer immunohistochemical (BCI) benchmark attempting to synthesize IHC data directly with the paired hematoxylin and eosin (HE) stained images. -
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Big Sleep
A simple command line tool for text to image generation
A simple command line tool for text to image generation, using OpenAI's CLIP and a BigGAN. Ryan Murdock has done it again, combining OpenAI's CLIP and the generator from a BigGAN! This repository wraps up his work so it is easily accessible to anyone who owns a GPU. You will be able to have the GAN dream-up images using natural language with a one-line command in the terminal. User-made notebook with bug fixes and added features, like google drive integration. Images will be saved to wherever the command is invoked. If you have enough memory, you can also try using a bigger vision model released by OpenAI for improved generations. You can set the number of classes that you wish to restrict Big Sleep to use for the Big GAN with the --max-classes flag as follows (ex. 15 classes). This may lead to extra stability during training, at the cost of lost expressivity. -
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CLIP Guided Diffusion
A CLI tool/python module for generating images from text
A CLI tool/python module for generating images from text using guided diffusion and CLIP from OpenAI. Text to image generation (multiple prompts with weights). Non-square Generations (experimental) Generate portrait or landscape images by specifying a number to offset the width and/or height. Uses fewer timesteps over the same diffusion schedule. Sacrifices accuracy/alignment for quicker runtime. options: - 25, 50, 150, 250, 500, 1000, ddim25,ddim50,ddim150, ddim250,ddim500,ddim1000 (default: 1000) Prepending a number with ddim will use the ddim scheduler. e.g. ddim25 will use the 25 timstep ddim scheduler. This method may be better at shorter timestep_respacing values. Multiple prompts can be specified with the | character. You may optionally specify a weight for each prompt. -
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ChatFred
Alfred workflow using ChatGPT, DALL·E 2 and other models for chatting
Alfred workflow using ChatGPT, DALL·E 2 and other models for chatting, image generation and more. Access ChatGPT, DALL·E 2, and other OpenAI models. Language models often give wrong information. Verify answers if they are important. Talk with ChatGPT via the cf keyword. Answers will show as Large Type. Alternatively, use the Universal Action, Fallback Search, or Hotkey. To generate text with InstructGPT models and see results in-line, use the cft keyword. ⤓ Install on the Alfred Gallery or download it over GitHub and add your OpenAI API key. If you have used ChatGPT or DALL·E 2, you already have an OpenAI account. Otherwise, you can sign up here - You will receive $5 in free credit, no payment data is required. Afterward you can create your API key. To start a conversation with ChatGPT either use the keyword cf, setup the workflow as a fallback search in Alfred or create your custom hotkey to directly send the clipboard content to ChatGPT. -
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CogView
Text-to-Image generation. The repo for NeurIPS 2021 paper
CogView is a large-scale pretrained text-to-image transformer model, introduced in the NeurIPS 2021 paper CogView: Mastering Text-to-Image Generation via Transformers. With 4 billion parameters, it was one of the earliest transformer-based models to successfully generate high-quality images from natural language descriptions in Chinese, with partial support for English via translation. The model incorporates innovations such as PB-relax and Sandwich-LN to enable stable training of very deep transformers without NaN loss issues. CogView supports multiple tasks beyond text-to-image, including image captioning, post-selection (ranking candidate images by relevance to a prompt), and super-resolution (upscaling model-generated images). The repository provides pretrained models, inference scripts, and training examples, along with a Docker environment for reproducibility. -
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Core ML Stable Diffusion
Stable Diffusion with Core ML on Apple Silicon
Run Stable Diffusion on Apple Silicon with Core ML. python_coreml_stable_diffusion, a Python package for converting PyTorch models to Core ML format and performing image generation with Hugging Face diffusers in Python. StableDiffusion, a Swift package that developers can add to their Xcode projects as a dependency to deploy image generation capabilities in their apps. The Swift package relies on the Core ML model files generated by python_coreml_stable_diffusion. Hugging Face ran the conversion procedure on the following models and made the Core ML weights publicly available on the Hub. If you would like to convert a version of Stable Diffusion that is not already available on the Hub, please refer to the Converting Models to Core ML. Log in to or register for your Hugging Face account, generate a User Access Token and use this token to set up Hugging Face API access by running huggingface-cli login in a Terminal window. -
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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 the denoising network) To train DALLE-2 is a 3 step process, with the training of CLIP being the most important. To train CLIP, you can either use x-clip package, or join the LAION discord, where a lot of replication efforts are already underway. Then, you will need to train the decoder, which learns to generate images based on the image embedding coming from the trained CLIP. -
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Deep Daze
Simple command line tool for text to image generation
Simple command-line tool for text to image generation using OpenAI's CLIP and Siren (Implicit neural representation network). In true deep learning fashion, more layers will yield better results. Default is at 16, but can be increased to 32 depending on your resources. Technique first devised and shared by Mario Klingemann, it allows you to prime the generator network with a starting image, before being steered towards the text. Simply specify the path to the image you wish to use, and optionally the number of initial training steps. We can also feed in an image as an optimization goal, instead of only priming the generator network. Deepdaze will then render its own interpretation of that image. The regular mode for texts only allows 77 tokens. If you want to visualize a full story/paragraph/song/poem, set create_story to True.
