genblaze is a production-grade Python monorepo SDK for orchestrating generative AI media pipelines — image, video, audio, text-to-speech, and chat — across 16+ provider integrations (OpenAI, Google Gemini/Imagen/Veo, NVIDIA NIM, GMICloud, Runway, Luma, Replicate, FAL, ElevenLabs, AssemblyAI, Hume, LMNT, Stability Audio, Decart, AtlasCloud). The public API is a chainable Pipeline/Step abstraction with batch, streaming, ingest, and agent-loop modes, plus a Click-based CLI (extract/verify/replay/index) for manifest inspection, a schema-first spec package generating TypeScript types from JSON Schemas, and ~30 runnable examples. Architecturally it is a monorepo: libs/core (genblaze-core) contains the framework (pipeline, providers, model registry, storage, media handlers, canonical hashing, signing, observability, webhooks), while each connector under libs/connectors/* is an independently versioned package registering itself via plugin entry points. Key entry points are Pipeline.run()/batch_run()/stream() for programmatic use and the genblaze command line; data flows through provider submit/poll/fetch_output lifecycles, optional caching, S3/B2 object-storage sinks with URL policies, and manifest generation that can be embedded directly into output media files. Two capabilities set this apart. First, provenance: every run produces a canonical, hash-verified Manifest (SHA-256 over deterministically serialized JSON), optionally Ed25519-signed, and byte-preservingly embedded into JPEG/PNG/WebP/MP4/audio files via XMP/atom splicing without re-encoding. Second, security and reliability engineering: SSRF protection with DNS pinning, credential redaction, ReDoS-safe pattern heuristics, ffmpeg injection escaping, configurable retry policies, model liveness probes, and discovery-aware validation (NATIVE/PARTIAL/NONE support levels) that reduces hardcoded slug rot. The project is actively maintained with unusually thorough tooling: golden hash-stability tests, a provider conformance test base class (~15 contract checks per connector), pin-parity and PyPI-metadata release gates, zizmor/dependabot CI, and live probe scripts that fail on API drift. Main risks are the inherent churn of 16+ third-party APIs (mitigated but not eliminated by mock-heavy tests) and a package/versioning layout complex enough that installs rely on documented make targets. Overall this is a strong, well-architected application SDK — suitable both for direct use and as a reference for multi-provider integration frameworks.
This repository is the official PyTorch library for Flow Matching generative models, developed by Meta FAIR. It provides a modular and extensible framework for implementing continuous, discrete, and Riemannian flow matching algorithms. The core library consists of four main components: probability paths (defining how data transforms from noise to samples), schedulers (noise/time schedules), solvers (ODE or CTMC integration for sampling), and loss functions (e.g., mixture path generalized KL divergence). It also includes utilities for common operations like model wrapping, manifold geometry (Euclidean, sphere, flat torus), and categorical sampling. The library is designed to be flexible, allowing researchers to easily combine different paths, schedulers, and solvers. Beyond the core library, the repository contains extensive examples for image generation (pixel-space continuous and discrete flow matching on CIFAR-10 and ImageNet) and text generation (discrete flow matching on FineWeb-Edu and Wikitext-103). These examples include full training pipelines, evaluation scripts, and model architectures (UNet, DiT-like transformer). The code is research-grade but well-structured, with clear separation of concerns and thorough unit tests for core components. While the examples are somewhat tied to specific datasets and hardware configurations, the library itself is a reusable foundation for any flow matching project. The repository is a hybrid of a research codebase and a production-ready library. The core modules are abstracted and documented, making them suitable for integration into other projects. However, the examples contain research artifacts like hardcoded paths and experimental features (torch.compile, mixed precision) that may require adaptation. Overall, this is a high-quality, actively maintained resource for anyone working on generative modeling with flow matching.
This repository is a curated awesome list and survey companion for diffusion models applied to time series, spatio-temporal, and tabular data. It contains no executable code; the sole content is a large README.md that organizes hundreds of papers into a taxonomy by task (prediction, generation, imputation, anomaly detection, classification/regression, causal inference, event prediction, foundation models) and by application domain (healthcare, sequential recommendation, weather, energy, finance, AIOps, environment, traffic, manufacturing, audio, biology, communication). Each entry links to the paper and, where available, official code repositories. It is the companion to an ACM Computing Surveys paper (DOI 10.1145/3783986) and includes citation information, a Google Sheets taxonomy, and an overview figure. The repository is actively maintained with recent commits and welcomes pull requests. As a research resource, it is highly reproducible: all content is in the markdown file, with no dependencies, datasets, or environment setup required. Although classified as data_research due to its research orientation, it is not a data pipeline or ML codebase. Its value is as a reference index and literature map for researchers; the README itself is the reusable asset.
This repository is a Jupyter Book titled 'Learning Machine', providing concise, intuitive explanations of machine learning and deep learning concepts. It is organized into sections covering basics, common tasks, layers, generative models, reinforcement learning, and more. The content is primarily textual with illustrative code snippets using numpy, scipy, and PyTorch. No real datasets are used; all examples are synthetic. The book is designed as a quick-reference handbook for learners, accompanying a university course. The repository is effectively archived by the author, with no planned updates.