Stable Diffusion web UI: Gradio-based interface for image generation
Web UI for Stable Diffusion enabling AI image generation and editing in browser.
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]1.10.1
- –fix image upscale on cpu (#16275)
1.10.0
- –A lot of performance improvements (see below in Performance section)
- –Stable Diffusion 3 support (#16030, #16164, #16212)
- –Recommended Euler sampler; DDIM and other timestamp samplers currently not supported
- –T5 text model is disabled by default, enable it in settings
- –New schedulers:
1.10.0-RC
- –A lot of performance improvements (see below in Performance section)
- –Stable Diffusion 3 support (#16030)
- –Recommended Euler sampler; DDIM and other timestamp samplers currently not supported
- –T5 text model is disabled by default, enable it in settings
- –New schedulers:
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