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.
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.
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.
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.
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.
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.
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.
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.