AI Image2025
Edits and generates images from natural-language prompts: blend photos, swap backgrounds, change a pose, or remove an object, keeping a person's or pet's face consistent across edits. Multi-turn refinement; outputs carry a SynthID watermark.
AI Image2022
Generates polished images and image-to-video animations, with web and Discord workflows for creating, editing, remixing, and exploring visual outputs. Best suited to aesthetic ideation rather than fully controllable production pipelines.
AI Train2023
Trains and fine-tunes diffusion models on consumer GPUs: LoRA and LoKr for image families like FLUX.1/2, SDXL and Qwen-Image, plus video models such as Wan 2.x and LTX. Layer-specific targeting, configurable VRAM, and a browser dashboard for runs.
AI Video2024
Generates videos and images from text or reference images, with model updates aimed at higher motion realism and creator-friendly controls. Best for fast concept clips, ads, and social assets rather than fully predictable production footage.
AI Image2012
Provides a comprehensive set of computer-vision algorithms and image/video processing utilities with multi-language bindings (C++, Python, Java), contrib modules, and community docs/forums — suitable for prototyping, production pipelines, and real-time applications.
AI Image2017
Swaps faces in images and videos using deep learning, offering tools to extract faces, train generative models, and convert media via CLI or GUI for research, VFX, and ethical experimentation.
AI Image2018
Brings classic computer vision into PyTorch as differentiable, GPU-accelerated tensor operators — filters, geometric transforms, feature matching, camera calibration — so each step lives inside autograd and trains end-to-end with neural networks.
AI Image2019
Bundles hundreds of pretrained image backbones — ResNet, EfficientNet, ViT, ConvNeXt, Swin and more — behind one consistent API for classification and feature extraction, with training and inference scripts that reproduce published ImageNet results.
AI Image2019
Modular implementations of object detection, instance/semantic/panoptic segmentation and related vision models for research and deployment. Offers a large model zoo, export to TorchScript/Caffe2, and PyTorch-native optimizations for faster training and extensibility.