Stable Diffusion web UI: Gradio-based interface for image generation
Feature-rich web UI for Stable Diffusion that enables AI image generation, editing, and enhancement through an intuitive browser interface.
Learn more about stable-diffusion-webui
Stable Diffusion WebUI is a comprehensive Gradio-based web interface for Stable Diffusion models, enabling text-to-image and image-to-image generation without coding. The platform supports multiple Stable Diffusion checkpoints, LoRA models, embeddings, and VAEs. Key features include img2img transformation, inpainting, outpainting, prompt weighting, highres fix, attention mechanisms, and batch processing. It integrates extensions for custom scripts, supports multiple samplers (Euler, DPM, DDIM), includes CLIP interrogator for reverse prompt engineering, and offers training capabilities for embeddings and hypernetworks. The architecture leverages PyTorch for model inference with optimizations for CUDA, DirectML, and CPU execution.
Extensible Plugin Architecture
Built-in extension system allows community-developed plugins to add functionality like ControlNet, dynamic prompts, and custom preprocessors. Extensions integrate seamlessly into the UI, enabling users to customize their workflow without modifying core code. The marketplace includes hundreds of community extensions for specialized generation techniques, model management, and workflow automation.
Advanced Prompt Engineering
Supports sophisticated prompt syntax including attention/emphasis mechanisms with weighted tokens, prompt scheduling across generation steps, and alternating words for variation. Features CLIP interrogator to reverse-engineer prompts from existing images, prompt templates, and styles library for consistent aesthetic generation. Enables fine-grained control over composition through regional prompting and negative prompt capabilities.
Comprehensive Model Management
Seamlessly handles multiple model formats including Safetensors, CKPT, and Diffusers. Built-in support for LoRA, LyCORIS, hypernetworks, textual inversion embeddings, and VAE models with hot-swapping capabilities. Includes model merging, pruning, and conversion tools. Automatic model downloading from CivitAI and HuggingFace with hash verification for reproducible results.
import requests
payload = {
"prompt": "a serene mountain landscape at sunset",
"steps": 20,
"width": 512,
"height": 512
}
response = requests.post('http://localhost:7860/sdapi/v1/txt2img', json=payload)
image_data = response.json()['images'][0]Patch release fixing a crash when upscaling images on CPU-only systems.
- –Apply this update if you run image upscaling without a GPU to restore functionality.
- –Release notes do not specify breaking changes or new requirements.
Adds Stable Diffusion 3 support (Euler sampler only; T5 text model disabled by default) and ships major performance optimizations including --precision half flag.
- –Enable T5 text model in settings if needed for SD3; DDIM and timestamp samplers are not yet supported.
- –Use --precision half flag to skip casting during inference for faster generation; check model hash validation now prevents corrupt downloads.
Adds Stable Diffusion 3 support (Euler sampler recommended, T5 disabled by default) and multiple performance optimizations including --precision half flag.
- –Enable T5 text model in settings if needed for SD3; DDIM and timestamp samplers are not yet supported.
- –Use --precision half flag to skip casting during inference for faster generation; new schedulers include Align Your Steps and KL Optimal.
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