Search Results for "generate image" - Page 4
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Showing 103 open source projects for "generate image"
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VQGAN-CLIP web app
Local image generation using VQGAN-CLIP or CLIP guided diffusion
VQGAN-CLIP has been in vogue for generating art using deep learning. Searching the r/deepdream subreddit for VQGAN-CLIP yields quite a number of results. Basically, VQGAN can generate pretty high-fidelity images, while CLIP can produce relevant captions for images. Combined, VQGAN-CLIP can take prompts from human input, and iterate to generate images that fit the prompts. Thanks to the generosity of creators sharing notebooks on Google Colab, the VQGAN-CLIP technique has seen widespread... -
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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. ... -
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gpt-2-simple
Python package to easily retrain OpenAI's GPT-2 text-generating model
...Additionally, this package allows easier generation of text, generating to a file for easy curation, allowing for prefixes to force the text to start with a given phrase. For finetuning, it is strongly recommended to use a GPU, although you can generate using a CPU (albeit much more slowly). If you are training in the cloud, using a Colaboratory notebook or a Google Compute Engine VM w/ the TensorFlow Deep Learning image is strongly recommended. (as the GPT-2 model is hosted on GCP) You can use gpt-2-simple to retrain a model using a GPU for free in this Colaboratory notebook, which also demos additional features of the package. ... -
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qiji-font
Typeface from Ming Dynasty woodblock printed books
...Intended to be used with Kenyan-lang, the Classical Chinese programming language. Download high-resolution PDFs and split pages into images. Manually lay a grid on top of each page to generate bounding boxes for characters (potentially replaceable by an automatic corner-detection algorithm). Generate a low-poly mask for each character on the grid, and save the thumbnails (using OpenCV). First, red channel is subtracted from the grayscale, in order to clean the annotations printed in red ink. Next, the image is thresholded and fed into the contour-tracing algorithm. ... -
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earthengine-py-notebooks
A collection of 360+ Jupyter Python notebook examples
earthengine-py-notebooks is a comprehensive collection of hundreds of Jupyter Python notebooks that serve as examples and tutorials for using the Google Earth Engine Python API. These notebooks are organized into thematic areas such as image processing, machine learning, visualization, filtering, and asset management, exposing users to real geospatial analysis tasks. The repository makes it easier to explore Earth Engine’s large geospatial data catalog, interactively display map layers, and generate visual insights without the need for external GIS software by leveraging interactive widgets and mapping libraries. ... -
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ALAE
Adversarial Latent Autoencoders
...This design allows the model to learn interpretable latent representations that can be manipulated to control generated image attributes. -
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Bulk LUTs Previewer 2.8.4
Generate a preview gallery for your LUTs.
Quickly generate a gallery to preview your (Look Up Tables) luts on selected still frame, or video. 1) Import a still image. (jpg, jpeg, png, bmp) *) (Or select video. (mpg, mpeg, mp4, mov, mts, avi, webm, mkv)) 2) Choose path to your lutsfolder. (cube, 3dl, dat, m3d) 3) Generate. (If video selected, use scroll wheel to preview luts) All preview images that is generated is stored in '. -
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MMdnn
Tools to help users inter-operate among deep learning frameworks
MMdnn is a set of tools to help users inter-operate among different deep learning frameworks. E.g. model conversion and visualization. Convert models between Caffe, Keras, MXNet, Tensorflow, CNTK, PyTorch Onnx and CoreML. MMdnn is a comprehensive and cross-framework tool to convert, visualize and diagnose deep learning (DL) models. The "MM" stands for model management, and "dnn" is the acronym of deep neural network. We implement a universal converter to convert DL models between frameworks,... -
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Super-résolution via CNN
Super resolution using a CNN, based on the work of the DGtal team
...First of all, an Nvidia graphics card (neither AMD nor Intel integrated) is highly recommended to parallelize the CNN. You will then need to install CUDA. No CUDA = dozens of times slower. This program will generate "model_epoch_ .pth" files corresponding to the model at epoch n, in a folder saved_model_u t_bs bs_tbs tbs_lr lr, where corresponds to the scale factor, bsthe size of the training batch, tbsthe size of the test batch and lrto the learning rate. Low res images should be located in a "dataset/input" folder, and high res targets in a "dataset/target" folder, where each different quality image has the same name in both folders. -
Error to trace to log to deploy. One click. No SSH.AppSignal links every error to the trace, the trace to the log, the log to the deploy that shipped it.
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gditools
A Python program/library aimed at GD-ROM image files.
This Python program/library is designed to handle GD-ROM image (GDI) files. It can be used to list files, extract data, generate sorttxt file, extract bootstrap (IP.BIN) file and more. This project can be used in standalone mode, in interactive mode or as a library in another Python program (check the 'addons' folder to learn how). For your convenience, you can use the gditools.py GUI program supplied in the Files section (optional). -
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DCVGAN
DCVGAN: Depth Conditional Video Generation, ICIP 2019.
...The proposed model explicitly uses the information of depth in a video sequence as additional information for a GAN-based video generation scheme to make the model understands scene dynamics more accurately. The model uses pairs of color video and depth video for training and generates a video using the two steps. Generate the depth video to model the scene dynamics based on the geometrical information. To add appropriate color to the geometrical information of the scene, the domain translation from depth to color is performed for each image. This model has three networks in the generator. In addition, the model has two discriminators. -
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captcha_break
Identification codes
...If you want to write a python script, you can run it normally with a little modification. Of course, you can also remove these visualization codes. captcha is a library written in python to generate verification codes. It supports image verification codes and voice verification codes. We use its function of generating image verification codes. First, we set our verification code format to numbers and capital letters, and generate a string of verification codes. It is well known that tensorflow occupies all video memory by default, which is not conducive to us conducting multiple experiments at the same time, so we can use the following code when tensorflow uses the video memory it needs instead of directly occupying all video memory. -
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PYWAL
Generate and change color-schemes on the fly
...It also includes a set of predefined themes if you don’t want to generate from an image. Many users integrate PYWAL into their login or wallpaper-change workflows so the desktop “breathes” with whatever image they set. It’s lightweight, fast, and fits perfectly into the r/unixporn workflow of building cohesive, themed desktops. -
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howmanypeoplearearound
Count the number of people around you by monitoring wifi signals
howmanypeoplearearound calculates the number of people in the vicinity using the approximate number of smartphones as a proxy (since ~70% of people have smartphones nowadays). A cellphone is determined to be in proximity to the computer based on sniffing WiFi probe requests. Possible uses of howmanypeoplearearound include, monitoring foot traffic in your house with Raspberry Pis, seeing if your roommates are home, etc. There are a number of possible USB WiFi adapters that support monitor... -
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mzitu
Python crawler that downloads image galleries and analyzes titles
mzitu is a Python-based web crawling project designed to automatically download and organize image galleries from a specific photography site. It demonstrates how to build a scraper that navigates gallery pages, retrieves image links, and saves the images locally in a structured directory layout. It focuses on automating the collection of large sets of images by programmatically parsing page content and iterating through gallery entries. mzitu also includes a simple analysis script that processes downloaded folder names to generate statistics and visualizations. ... -
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Siamese and triplet learning
Siamese and triplet networks with online triplet mining in PyTorch
...The project also implements online pair and triplet mining techniques to efficiently generate training examples during model training. -
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i-Map - Plot Geolocation from Images
Automatically plots latitude, longitude from images on Google maps.
i-Map is a Photo metadata forensic tool for Geo-location analysis of images that are clicked from GPS enabled devices. In this tool, you can load 100s of images from a suspect's device and analyze them to know various locations where photos were clicked on mobile phone/tablet. After loading images, with a single click, iMap plots all the images on World Map to visually check where they have been captured, generate timeline and activity of suspect and match them with CDR (Call Detail... -
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httpbin
HTTP request and response service, written in Python + Flask
...The images feature returns different image formats, and you can return different redirect responses as well. Finally, you can return anything that is passed to request. -
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Fulcon-VirtualPlatform
Fulcon can generate full OS environment to the container like the VM.
- It is container generable in the image of Docker (CentOS or Ubuntu image). - The generated container can be operated while maintaining it at a long term. - The resource of CPU, the memory, and IO of the container can be dynamically updated. - Arbitrary IPl address can be added and be deleted dynamically the container. - The package of two or more containers can be collectively updated from the host. -
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GimpPy uses img maps & an img as the input, output is a report.py file used to generate PDFs, the out files may run solo or chained together to make more complex multi page reports. Input required is a dict with vals for flds you have mapped on your img.
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OpenNumismat
Coin collecting software for organize and manage your own catalogue
This project has been migrated to github - please visit http://opennumismat.github.io OpenNumismat is a handy and reliable application aimed at coin lovers, numismatists or amateurs looking to create a numismatics collection. With OpenNumismat, you will be able to create, organize and manage your own coin catalogue with detailed description and photos for each of the items. -
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Importer library to import assets from different common 3D file formats such as Collada, Blend, Obj, X, 3DS, LWO, MD5, MD2, MD3, MDL, MS3D and a lot of other formats. The data is stored in an own in-memory data-format, which can be easily processed. www.open3mod.com/ is a 3D model viewer and exporter based on Assimp that is also Open Source.
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Dia Diagram Editor
Free drawing software for Windows, Mac OS X, and Linux
...Dia supports more than 30 different diagram types like flowcharts, network diagrams, database models. More than a thousand readymade objects help to draw professional diagrams. Dia can read and write a number of different raster and vector image formats. Software developers and database specialists can use Dia as a CASE tool to generate code skeletons from their drawings. Dia can be scripted and extended using Python. -
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awb combines simple but powerful AsciiDoc markup with templates, blog and image gallery generation, and sitemap.xml generation to allow you to easily maintain and update a website.
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A script to generate shapes representing colors, using the ColorAdd code developped by Miguel Neiva: http://coloradd.net The ColorAdd code is a monochromatic Graphical Code allowing colorblind to recognize colors.
