Run Stable Diffusion Online

Stable Diffusion is an open-source text-to-image diffusion model that turns natural-language prompts into photorealistic, stylized, or illustrative images. This playground runs it in the browser, so you don't have to install local checkpoints, set up a WebUI, or manage GPU dependencies.

Yes. Every visitor gets a free starting balance for Stable Diffusion text-to-image and image-to-image runs. Credit packs and monthly or yearly plans unlock higher-volume generation when you need it — credits on one-time packs never expire.

No. The playground runs Stable Diffusion server-side and streams the result back to your browser. You can generate, refine, and inpaint without installing it locally — no AUTOMATIC1111, ComfyUI, or GPU drivers.

Text-to-image generates a Stable Diffusion image from a prompt alone. Image-to-image uses an existing image as a visual anchor while the prompt pushes the output toward a new style, composition, or subject — useful when prompt-only is too loose for the look you want.

Inpainting lets you mask a region of an image and have Stable Diffusion fill that region from a new prompt while leaving the rest of the image untouched. It's the standard workflow for replacing objects, removing artifacts, or refining specific parts of a generation.

Stable Diffusion 1.5 runs at a 512x512 native resolution and is fast and lightweight. SDXL uses a much larger UNet and dual text encoders for sharper 1024x1024 output and better composition. Pick by use case: speed and stylization -> SD 1.5, balanced quality -> SDXL.

Structure your Stable Diffusion prompt by subject, medium, lighting, camera, style, and negative prompt. Specificity beats keyword-spam — naming the lens, time of day, or art reference typically produces a stronger result than a long flat tag list. The prompt guide page walks through reusable templates.

A negative prompt tells Stable Diffusion what you do not want in the image — common entries are 'blurry, deformed, extra fingers, low quality, watermark, text'. Negative prompts are one of the highest-leverage fixes when generations look off: instead of rewording the positive prompt, exclude the failure mode directly.

CFG scale (Classifier-Free Guidance) controls how strictly Stable Diffusion follows your prompt. Low values (3-5) leave the model more creative freedom; higher values (8-12) force tighter prompt adherence at the cost of artifacts. A value around 7 is the safe default — start there and adjust if the result drifts from your intent or looks overcooked.

Denoising strength (0-1) controls how much Stable Diffusion is allowed to alter the input image during image-to-image. At 0.2-0.4 the output stays very close to the source — good for style nudges; at 0.6-0.8 the prompt takes over and the source mostly anchors composition; at 1.0 it behaves like text-to-image. Match the value to how much of the original you want to keep.

Stable Diffusion's open-source license permits commercial use of the images you generate. You're responsible for following our terms (no impersonation, no IP infringement, no disallowed content) and the upstream model license for the specific checkpoint you used. When in doubt, treat the output like any other asset you commission — keep your prompts and seeds so you can show provenance.

Yes. Your generations are scoped to your account by default — they're not posted to a public feed, and other users can't browse them. Prompts and outputs are stored so you can revisit your history; you can delete them at any time from the playground.

Each generation deducts credits based on the model and resolution. Credit packs are one-time purchases that never expire. Monthly and yearly plans grant a credits allowance that refreshes on each renewal — unused allowance does not roll over. Failed generations don't consume credits.

Three quick fixes cover most cases: (1) add a negative prompt — 'blurry, deformed, low quality, extra limbs' clears the common failure modes; (2) raise the CFG scale toward 7-10 if the result is ignoring your prompt; (3) lower the denoising strength in image-to-image if the source identity is being lost. If the subject itself is rendered poorly, switch from SD 1.5 to SDXL.