Neural networks for image generation are no longer unusual, but most of them only work on a subscription basis. Stable Diffusion is an exception: it is an open-source model that can be installed and run on your computer for free. It gives you complete control over the parameters and does not limit the number of generations. In three years, Stable Diffusion has become the standard for designers, artists, and game developers thanks to thousands of add-ons, open architecture, and an active community.
Stable Diffusion creates images based on text descriptions — just write a phrase like “cat in space,” and the neural network will turn it into a finished picture. The main difference from other generators is that the model is completely open and runs locally, without the cloud and without a usage fee. From a technical point of view, it is a latent diffusion model that processes the image not by pixels, but in a compressed numerical space, which speeds up the process and saves video memory.
The CreativeML Open RAIL-M open license allows commercial use of the model, except in cases involving deepfakes or fake logos. Thanks to its openness, many modifications and interfaces have appeared, such as AUTOMATIC1111 and ComfyUI, which allow you to control each generation parameter, connect your own models, and experiment with the results. For designers, this means independence: you can work with any images without restrictions or subscriptions.
Stable Diffusion works by gradually transforming noise into an image. The model starts with random noise and then removes it step by step until a meaningful image is produced. The text query is converted by the CLIP encoder into a numerical representation that guides the process. The main parameters that the user can change are: CFG scale (usually from 7 to 9) — affects the accuracy of following the description, the number of steps (20–30 for optimal results), sampler selection (DPM++ SDE Karras — the best balance of speed and quality), and Negative Prompt — the second line, where you specify unwanted elements, such as bad hands, watermark, or blurry.
Installing Stable Diffusion is easy. For Windows, the easiest way is to use the AUTOMATIC1111 interface: just download the archive from GitHub, unzip it, and run the run.bat file — the program will download all dependencies itself, and the interface will open in your browser. For macOS and Linux, InvokeAI Launcher is suitable, which automatically configures the environment and updates. The minimum requirements are a video card with 6 GB of memory and Python 3.10, although 8–10 GB is recommended for SDXL. If you don’t have enough power, you can use the —medvram and —lowvram flags or the xFormers library, which speeds up the process. SDXL-Turbo will help with weak GPUs — it creates an image in one step instead of thirty, which is convenient for quick sketches.
For those who do not have a suitable video card, cloud solutions are suitable. Google Colab allows you to run ready-made notebooks with SDXL directly in your browser, and the results are saved to Google Drive. The Replicate service allows you to use the API and pay only for the number of images generated, which is convenient for integration into applications.
After installation, you can start generating images right away. In the txt2img tab, enter a description, such as “mountain landscape, sunset, dramatic clouds,” select the DPM++ 2M Karras sampler, set CFG to 7–9 and 20–30 steps, then click Generate. The finished image will appear in a minute. If you are working with portraits, enable the Restore Faces option — it improves facial features and eliminates defects. To get a series of images, increase the Batch count to get several options with different seeds. All parameters are saved in the metadata of PNG files, so they will be automatically restored when you re-import them into the settings interface.
The key to high-quality results is a well-written prompt. It is best to use the following structure: main object, action, environment, style, and lighting. For example: “elderly wizard, casting spell, ancient library, oil painting, warm candlelight.” The order of the words is important — the last ones carry more weight, so they should be used for emphasis. You can enhance the effect with ::, for example, red car::1.5 makes the color more saturated. Add unwanted elements to the Negative Prompt: bad hands, low quality, distorted face — this helps to avoid typical defects.
Add-ons make Stable Diffusion particularly flexible. LoRA are lightweight modules that change the generation style: realism, anime, watercolor, etc. Simply place them in the models/Lora folder and connect them with the tag lora:style_name:0.8. ControlNet allows you to control the structure of the image — copy the pose from a photo, use a contour sketch or depth map. To do this, you need to install the plugin via the Extension Store and activate the desired ControlNet type (OpenPose, Canny, and others). If you need to correct a separate fragment of the image, the Inpaint mode will help — it will redraw the selected area, leaving the rest unchanged. For a smooth result, it is better to use a mask blur of 4–8 pixels. The SDXL Refiner model allows you to further improve the details and textures of a finished image, which is especially noticeable at a resolution of 1024×1024.
Stable Diffusion is an entire ecosystem that combines openness, flexibility, and professional quality. The model is suitable for designers, illustrators, marketers, and anyone who wants to create visual content without limitations. It can be run locally, in the cloud, or directly from a browser — quickly, free of charge, and without subscriptions. You can try Stable Diffusion on the FICHI.AI platform — generation is available without limits and without installation.