# vladmandic/sdnext

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7,139 stars · 563 forks · Python · Apache-2.0

## Links

- GitHub: https://github.com/vladmandic/sdnext
- Homepage: https://vladmandic.github.io/sdnext/
- awesome-repositories: https://awesome-repositories.com/repository/vladmandic-sdnext.md

## Topics

`ai-art` `caption` `diffusers` `generative-art` `python` `pytorch` `sdnext` `stable-diffusion` `transformers` `webui`

## Description

SD.Next is an all-in-one web interface and multi-backend inference engine for generating, editing, and processing images and videos using diffusion models. It functions as a comprehensive tool for diffusion model management and an automated image processing pipeline for bulk operations.

The project is distinguished by its hardware-backend abstraction layer, which provides automatic detection and acceleration for NVIDIA CUDA, AMD ROCm, Intel OpenVINO, and DirectML. It features a headless generative API and a programmatic command interface, allowing users to trigger tasks via REST API or CLI without launching the graphical user interface.

The system covers a wide range of capabilities, including multimodal visual generation, model weight quantization, and batch processing pipelines for automated captioning and upscaling. It also includes a plugin-based extension system and a modular UI theme engine for visual customization.

The software supports deployment across Linux, Windows, macOS, and WSL, with a containerized model for reproducible execution via Docker.

## Tags

### Artificial Intelligence & ML

- [AI-Powered Image and Video Generation](https://awesome-repositories.com/f/artificial-intelligence-ml/ai-powered-image-selection/ai-background-removal-and-inpainting/ai-powered-image-and-video-generation.md) — Ships a comprehensive web interface for generating and editing images and videos using diffusion models. ([source](https://cdn.jsdelivr.net/gh/vladmandic/sdnext@master/README.md))
- [Hardware Abstraction Layers](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-optimization-and-inference/hardware-and-acceleration/hardware-abstraction-layers.md) — Provides a hardware abstraction layer that auto-detects and configures CUDA, ROCm, DirectML, and OpenVINO.
- [Adaptive Network Weights](https://awesome-repositories.com/f/artificial-intelligence-ml/adaptive-network-weights.md) — Applies network modules like LoRA to steer the generation toward specific styles or subjects. ([source](https://vladmandic.github.io/sdnext-docs/))
- [Generative API Exposures](https://awesome-repositories.com/f/artificial-intelligence-ml/ai-agent-integrations/ai-agent-tool-integrations/generative-api-exposures.md) — Exposes server capabilities to external tools and scripts through a programmable API. ([source](https://vladmandic.github.io/sdnext-docs/))
- [AMD Hardware Acceleration](https://awesome-repositories.com/f/artificial-intelligence-ml/amd-hardware-acceleration.md) — Enables AMD GPU acceleration by leveraging ZLUDA and the ROCm stack. ([source](https://vladmandic.github.io/sdnext-docs/ZLUDA/))
- [ControlNet Guidance](https://awesome-repositories.com/f/artificial-intelligence-ml/controlnet-guidance.md) — Uses ControlNet to steer image generation by constraining pose, depth, and style. ([source](https://vladmandic.github.io/sdnext-docs/))
- [CUDA Accelerated Neural Networks](https://awesome-repositories.com/f/artificial-intelligence-ml/cuda-accelerated-neural-networks.md) — Accelerates image generation through optimized neural network implementations on NVIDIA GPUs. ([source](https://vladmandic.github.io/sdnext-docs/Platforms/))
- [Diffusion Model Managers](https://awesome-repositories.com/f/artificial-intelligence-ml/generative-ai-resources/diffusion-visual-models/generative-ai-models/diffusion-model-managers.md) — Provides tools for downloading, quantizing, and optimizing diffusion model weights to reduce VRAM usage.
- [Diffusion Models](https://awesome-repositories.com/f/artificial-intelligence-ml/generative-ai-resources/diffusion-visual-models/generative-ai-models/diffusion-models.md) — Provides a specialized web user interface for initializing and running diffusion-based image and video synthesis.
- [Hardware Acceleration Backends](https://awesome-repositories.com/f/artificial-intelligence-ml/hardware-acceleration-backends.md) — Provides a configuration layer to select specific GPU or accelerator backends for optimized model inference. ([source](https://vladmandic.github.io/sdnext-docs/CLI-Arguments/))
- [Hardware Device Selection](https://awesome-repositories.com/f/artificial-intelligence-ml/hardware-device-selection.md) — Provides utilities to specify and target particular hardware accelerators for model execution and inference pipelines. ([source](https://vladmandic.github.io/sdnext-docs/OpenVINO/))
- [Automated Processing Pipelines](https://awesome-repositories.com/f/artificial-intelligence-ml/image-classification/image-level-tagging/automated-image-tagging/automated-processing-pipelines.md) — Runs automated image pipelines for bulk operations such as captioning, tagging, and filtering. ([source](https://vladmandic.github.io/sdnext/))
- [Image-Conditioned Generation](https://awesome-repositories.com/f/artificial-intelligence-ml/image-generation/image-conditioned-generation.md) — Generates or edits images using other images as starting points or visual references. ([source](https://vladmandic.github.io/sdnext-docs/Model-Support/))
- [Multi-Backend GPU Inference Engines](https://awesome-repositories.com/f/artificial-intelligence-ml/inference-backends/multi-backend-gpu-inference-engines.md) — Provides a multi-backend inference engine that automatically detects and accelerates across CUDA, ROCm, OpenVINO, and DirectML.
- [Multi-Backend Inference Support](https://awesome-repositories.com/f/artificial-intelligence-ml/inference-backends/multi-backend-inference-support.md) — Implements a multi-backend execution layer that runs AI models across diverse GPU and CPU accelerators. ([source](https://vladmandic.github.io/sdnext-docs/Platforms/))
- [Model Lifecycle Managers](https://awesome-repositories.com/f/artificial-intelligence-ml/model-lifecycle-managers.md) — Provides a system for validating, converting, and managing the local storage of diffusion model weights. ([source](https://vladmandic.github.io/sdnext-docs/))
- [VRAM Offloading](https://awesome-repositories.com/f/artificial-intelligence-ml/vram-offloading.md) — Implements techniques to reduce GPU memory usage by offloading model components to system RAM during inference. ([source](https://vladmandic.github.io/sdnext-docs/))
- [Language Model Prompt Rewriters](https://awesome-repositories.com/f/artificial-intelligence-ml/ai-prompt-configurations/prompt-evaluation-tools/prompt-refinement-utilities/language-model-prompt-rewriters.md) — Employs language models to rewrite and enhance simple text prompts for better visual output. ([source](https://vladmandic.github.io/sdnext-docs/))
- [Attention Kernel Optimizers](https://awesome-repositories.com/f/artificial-intelligence-ml/attention-mechanisms/attention-kernel-configurations/attention-kernel-optimizers.md) — Utilizes optimized attention kernels to increase processing speed for diffusion model operations. ([source](https://vladmandic.github.io/sdnext-docs/AMD-ROCm/))
- [Attention Slicing Tuners](https://awesome-repositories.com/f/artificial-intelligence-ml/attention-mechanisms/attention-kernel-configurations/attention-kernel-optimizers/attention-slicing-tuners.md) — Adjusts dynamic attention slicing thresholds to optimize model execution within specific GPU memory limits. ([source](https://vladmandic.github.io/sdnext-docs/Intel-ARC/))
- [Attention Memory Optimizations](https://awesome-repositories.com/f/artificial-intelligence-ml/attention-mechanisms/attention-memory-optimizations.md) — Manages memory allocation for attention mechanisms using slicing to prevent VRAM-related crashes. ([source](https://vladmandic.github.io/sdnext-docs/Intel-ARC/))
- [FlashAttention-2](https://awesome-repositories.com/f/artificial-intelligence-ml/attention-mechanisms/flashattention-2.md) — Integrates FlashAttention-2 via Triton to significantly reduce memory usage and accelerate computation. ([source](https://vladmandic.github.io/sdnext-docs/ZLUDA/))
- [Compute Device Aggregators](https://awesome-repositories.com/f/artificial-intelligence-ml/compute-device-aggregators.md) — Merges multiple compatible compute devices into a single execution unit to enable parallel processing. ([source](https://vladmandic.github.io/sdnext-docs/OpenVINO/))
- [Image Inpainting](https://awesome-repositories.com/f/artificial-intelligence-ml/generative-ai-resources/diffusion-visual-models/generative-ai-pipelines/text-to-image-generators/image-inpainting.md) — Supports image inpainting and outpainting to modify or extend specific image regions. ([source](https://vladmandic.github.io/sdnext/))
- [Iterative Image Reprocessing](https://awesome-repositories.com/f/artificial-intelligence-ml/generative-image-models/iterative-image-reprocessing.md) — Allows users to reload previously generated images and apply new parameters for iterative editing. ([source](https://vladmandic.github.io/sdnext-docs/))
- [Triton Kernels](https://awesome-repositories.com/f/artificial-intelligence-ml/gpu-kernel-implementations/triton-kernels.md) — Integrates Triton wheels to compile custom GPU kernels for advanced performance optimizations. ([source](https://vladmandic.github.io/sdnext-docs/ZLUDA/))
- [Hardware-Accelerated Inference](https://awesome-repositories.com/f/artificial-intelligence-ml/hardware-accelerated-inference.md) — Leverages DirectML to provide hardware-accelerated model inference on Windows devices. ([source](https://vladmandic.github.io/sdnext-docs/DirectML/))
- [Image Generation APIs](https://awesome-repositories.com/f/artificial-intelligence-ml/image-generation-apis.md) — Exposes a programmable API for triggering generative image and video synthesis from text prompts headlessly.
- [Post-Generation Refinements](https://awesome-repositories.com/f/artificial-intelligence-ml/image-generation/post-generation-refinements.md) — Implements post-generation passes to refine facial features and fine visual details. ([source](https://vladmandic.github.io/sdnext-docs/))
- [Intel ARC GPU Acceleration](https://awesome-repositories.com/f/artificial-intelligence-ml/intel-arc-gpu-acceleration.md) — Launches the image generation server using Intel extensions to utilize ARC graphics hardware. ([source](https://vladmandic.github.io/sdnext-docs/Platforms/))
- [ONNX Runtime Inference](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-inference-serving/inference-engines/onnx-runtime-inference.md) — Executes image generation models through the ONNX Runtime for cross-platform hardware acceleration. ([source](https://vladmandic.github.io/sdnext-docs/ONNX-Runtime/))
- [Inference Hardware Tuning](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-optimization-and-inference/training-algorithms/deep-learning-optimization/hardware-performance-tuning/model-hardware-tuning-recommenders/inference-hardware-tuning.md) — Tunes models using quantization and memory offloading to improve inference speed and reduce VRAM usage. ([source](https://vladmandic.github.io/sdnext-docs/))
- [Model Loading](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-training-and-tuning/data-and-checkpointing/model-loading.md) — Implements efficient model loading and binary caching to accelerate the startup of diffusion models.
- [Hardware Kernel Selectors](https://awesome-repositories.com/f/artificial-intelligence-ml/model-architecture-selection/heuristic-selection-logic/gpu-kernel-selection-heuristics/cpu-kernel-selection/hardware-kernel-selectors.md) — Benchmarks hardware during runtime to automatically select the most efficient execution kernels for the system. ([source](https://vladmandic.github.io/sdnext-docs/AMD-ROCm/))
- [Model Compilation Optimizers](https://awesome-repositories.com/f/artificial-intelligence-ml/model-compilation-optimizers.md) — Uses Triton to enable model compilation for faster execution during inference. ([source](https://vladmandic.github.io/sdnext-docs/ZLUDA/))
- [Graph Compilation Caching](https://awesome-repositories.com/f/artificial-intelligence-ml/model-compilation-optimizers/graph-compilation-caching.md) — Caches compiled model graphs locally to eliminate the overhead of repeated compilation during startup. ([source](https://vladmandic.github.io/sdnext-docs/OpenVINO/))
- [Olive Model Compression](https://awesome-repositories.com/f/artificial-intelligence-ml/model-optimization/profiling-and-benchmarking/model-performance-optimization/olive-model-compression.md) — Utilizes the Olive toolkit to compress and compile models into optimized formats for faster inference. ([source](https://vladmandic.github.io/sdnext-docs/ONNX-Runtime/))
- [Container Weight Persistence](https://awesome-repositories.com/f/artificial-intelligence-ml/model-weight-management/container-weight-persistence.md) — Mounts host directories to the container to ensure model weights and configurations persist across restarts. ([source](https://vladmandic.github.io/sdnext-docs/Docker/))
- [Multi-Architecture Model Compilation](https://awesome-repositories.com/f/artificial-intelligence-ml/multi-architecture-model-compilation.md) — Compiles model graphs using torch.compile and Triton kernels to target specific hardware accelerators.
- [OpenVINO Inference Acceleration](https://awesome-repositories.com/f/artificial-intelligence-ml/openvino-inference-acceleration.md) — Accelerates image generation using OpenVINO-optimized hardware. ([source](https://vladmandic.github.io/sdnext-docs/Platforms/))
- [Precision Configuration Tools](https://awesome-repositories.com/f/artificial-intelligence-ml/precision-configuration-tools.md) — Provides utilities for adjusting the numerical precision of models to balance performance and compatibility. ([source](https://vladmandic.github.io/sdnext-docs/Model-Support/))
- [Programmatic Generation Triggers](https://awesome-repositories.com/f/artificial-intelligence-ml/programmatic-generation-triggers.md) — Allows users to trigger image generation and processing tasks programmatically through an HTTP API or CLI. ([source](https://vladmandic.github.io/sdnext-docs/))
- [Weight Quantization](https://awesome-repositories.com/f/artificial-intelligence-ml/quantized-inference-runtimes/weight-quantization.md) — Implements weight quantization to reduce the memory footprint and accelerate the inference of diffusion models. ([source](https://vladmandic.github.io/sdnext-docs/))
- [Text-Driven Image Editing](https://awesome-repositories.com/f/artificial-intelligence-ml/text-driven-image-editing.md) — Modifies existing images using natural language instructions and specialized editing models. ([source](https://vladmandic.github.io/sdnext-docs/Model-Support/))
- [Video Generation](https://awesome-repositories.com/f/artificial-intelligence-ml/video-generation.md) — Synthesizes short video sequences using generative models from text or image prompts. ([source](https://vladmandic.github.io/sdnext/CHANGELOG.md))

### Development Tools & Productivity

- [Batch Image Processors](https://awesome-repositories.com/f/development-tools-productivity/batch-image-processors.md) — Provides batch image processing to apply generation or editing operations to multiple files simultaneously. ([source](https://vladmandic.github.io/sdnext/CHANGELOG.md))
- [Batch Processing Pipelines](https://awesome-repositories.com/f/development-tools-productivity/batch-processing-pipelines.md) — Implements batch processing pipelines to automate sequential image captioning, upscaling, and filtering.
- [Behavioral Extension Scripts](https://awesome-repositories.com/f/development-tools-productivity/extensible-configuration-interfaces/behavioral-extension-scripts.md) — Supports custom extension development via behavioral scripts that add functionality to the generation pipeline. ([source](https://vladmandic.github.io/sdnext-docs/Dev-Home/))
- [Programmatic Application Control APIs](https://awesome-repositories.com/f/development-tools-productivity/programmatic-application-control-apis.md) — Provides programmatic application control APIs to manage generative tasks via external automation. ([source](https://vladmandic.github.io/sdnext-docs/))

### DevOps & Infrastructure

- [Cross-Platform Execution](https://awesome-repositories.com/f/devops-infrastructure/cross-platform-deployment-targets/cross-platform-execution.md) — Executes generation and processing tasks across diverse GPU and CPU architectures with optimized runtimes. ([source](https://cdn.jsdelivr.net/gh/vladmandic/sdnext@master/README.md))
- [Headless Execution Modes](https://awesome-repositories.com/f/devops-infrastructure/headless-execution-modes.md) — Provides a headless execution mode for remote access and scripted generation tasks. ([source](https://vladmandic.github.io/sdnext-docs/CLI-Arguments/))
- [Multi-Backend Inference Executions](https://awesome-repositories.com/f/devops-infrastructure/model-conversion/tensorflow-lite/webassembly-inference-executions/multi-backend-inference-executions.md) — Utilizes the OpenVINO execution provider to accelerate model inference on Intel hardware. ([source](https://vladmandic.github.io/sdnext-docs/OpenVINO/))
- [AI Deployment Containers](https://awesome-repositories.com/f/devops-infrastructure/ai-deployment-containers.md) — Provides pre-configured Docker containers optimized for deploying GPU-accelerated generative AI across different operating systems.
- [Cloud Container Deployments](https://awesome-repositories.com/f/devops-infrastructure/cloud-container-deployments.md) — Supports running the application in containers on managed cloud orchestration services with public hostname exposure. ([source](https://vladmandic.github.io/sdnext-docs/Docker/))
- [Container Execution](https://awesome-repositories.com/f/devops-infrastructure/container-images/container-execution.md) — Provides the ability to launch the application in an isolated container environment with GPU access. ([source](https://vladmandic.github.io/sdnext-docs/Docker/))
- [AI Server Containerization](https://awesome-repositories.com/f/devops-infrastructure/container-orchestration/container-runtimes/runtime-configuration-interfaces/docker-socket-orchestrators/docker-target-configurators/docker-container-deployments/ai-server-containerization.md) — Packages the generative AI server and API into Docker containers for reproducible deployment.
- [Container Image Registry Uploads](https://awesome-repositories.com/f/devops-infrastructure/container-orchestration/container-runtimes/runtime-configuration-interfaces/docker-socket-orchestrators/docker-target-configurators/docker-container-deployments/docker-image-building/container-image-registry-uploads.md) — Tags and pushes built container images to remote registries for distribution and cloud deployment. ([source](https://vladmandic.github.io/sdnext-docs/Docker/))
- [Containerized Deployments](https://awesome-repositories.com/f/devops-infrastructure/containerized-deployments.md) — Ships containerized deployments via Docker to ensure reproducible execution across different hardware backends.
- [Hardware-Specific Container Images](https://awesome-repositories.com/f/devops-infrastructure/hardware-specific-container-images.md) — Builds portable container images tailored for specific hardware targets like CUDA or ROCm. ([source](https://vladmandic.github.io/sdnext-docs/Docker/))
- [Headless Server Execution](https://awesome-repositories.com/f/devops-infrastructure/headless-server-execution.md) — Provides a headless server mode allowing the application to run as an API backend without a graphical interface. ([source](https://vladmandic.github.io/sdnext-docs/CLI-Arguments/))

### Graphics & Multimedia

- [Headless APIs](https://awesome-repositories.com/f/graphics-multimedia/image-editing-processing/image-processing/processing-apis/headless-apis.md) — Offers a headless REST API to trigger image and video generation without a graphical interface. ([source](https://vladmandic.github.io/sdnext-docs/CLI-Arguments/))
- [Image Processing Pipelines](https://awesome-repositories.com/f/graphics-multimedia/image-processing-pipelines.md) — Implements sequential workflows that chain captioning, upscaling, and filtering for automated image processing.
- [Translation Layers](https://awesome-repositories.com/f/graphics-multimedia/gpu-accelerated-shaders/translation-layers.md) — Employs a translation layer to enable hardware acceleration on AMD GPUs by converting API calls. ([source](https://vladmandic.github.io/sdnext-docs/ZLUDA/))
- [AI Upscaling](https://awesome-repositories.com/f/graphics-multimedia/image-editing-processing/image-enhancement-tools/ai-upscaling.md) — Provides AI-driven resolution upscaling to increase image clarity and detail. ([source](https://cdn.jsdelivr.net/gh/vladmandic/sdnext@master/README.md))

### User Interface & Experience

- [Inference Performance Optimizers](https://awesome-repositories.com/f/user-interface-experience/landing-page-templates/seo-and-metadata-optimization/performance-optimization/inference-performance-optimizers.md) — Optimizes inference performance by employing weight quantization and model memory offloading. ([source](https://vladmandic.github.io/sdnext-docs/))
- [Theme Application & Switching](https://awesome-repositories.com/f/user-interface-experience/color-themes/theme-application-switching.md) — Provides a system for selecting and applying pre-configured color schemes and visual themes. ([source](https://vladmandic.github.io/sdnext-docs/Themes/))
- [Interface Appearance Customizations](https://awesome-repositories.com/f/user-interface-experience/interface-appearance-customizations.md) — Allows users to modify the visual themes, layouts, colors, and fonts of the user interface. ([source](https://vladmandic.github.io/sdnext-docs/))
- [UI Theming Engines](https://awesome-repositories.com/f/user-interface-experience/ui-theming-engines.md) — Implements a modular engine that switches themes at runtime using a layered CSS override system.

### Part of an Awesome List

- [Image Captioning](https://awesome-repositories.com/f/awesome-lists/ai/image-captioning.md) — Uses vision-language models to automatically generate descriptive text captions for images. ([source](https://vladmandic.github.io/sdnext-docs/))
- [CLI Applications](https://awesome-repositories.com/f/awesome-lists/devtools/cli-applications.md) — Provides a command-line interface for triggering generation tasks and managing the server. ([source](https://vladmandic.github.io/sdnext-docs/))

### Data & Databases

- [Stateless Application Images](https://awesome-repositories.com/f/data-databases/horizontal-scaling/stateless-web-scaling/stateless-application-images.md) — Packages the application into stateless images for different hardware backends to simplify scaling. ([source](https://vladmandic.github.io/sdnext-docs/Docker/))

### Networking & Communication

- [Local Web Interfaces](https://awesome-repositories.com/f/networking-communication/local-web-interfaces.md) — Hosts the user interface over HTTP for access via local networks or public URLs. ([source](https://vladmandic.github.io/sdnext-docs/CLI-Arguments/))
- [Programmatic API Interfaces](https://awesome-repositories.com/f/networking-communication/programmatic-api-interfaces.md) — Exposes a programmatic API interface for automating image generation and system control without the UI. ([source](https://vladmandic.github.io/sdnext-docs/))

### Operating Systems & Systems Programming

- [Cross-Platform Compatibility](https://awesome-repositories.com/f/operating-systems-systems-programming/cross-platform-compatibility.md) — Supports full feature operation across Linux, Windows, and WSL environments. ([source](https://vladmandic.github.io/sdnext-docs/Platforms/))
- [CUDA Driver Wrappers](https://awesome-repositories.com/f/operating-systems-systems-programming/cuda-driver-wrappers.md) — Implements a wrapper that translates CUDA calls, allowing CUDA-based software to run on AMD hardware. ([source](https://vladmandic.github.io/sdnext-docs/ZLUDA/))

### Software Engineering & Architecture

- [Hook-Based Plugin Systems](https://awesome-repositories.com/f/software-engineering-architecture/software-architecture/architectural-patterns/plugin-module-systems/modular-plugin-architectures/plugin-based-architectures/hook-based-plugin-systems.md) — Utilizes a hook-based plugin system to allow isolated extensions to modify generation and UI pipelines.

### System Administration & Monitoring

- [GPU Memory Monitors](https://awesome-repositories.com/f/system-administration-monitoring/monitoring-and-observability/observability-platforms/metric-performance-monitors/system-usage-monitoring/system-usage-monitors/gpu-memory-monitors.md) — Provides real-time tracking of CPU and GPU memory usage to monitor resource consumption during image generation. ([source](https://vladmandic.github.io/sdnext-docs/Debug/))