
Stable Diffusion XL
SDXL is the default open model choice for teams that want strong prompt adherence and broad ecosystem support without giving up deployment control.
Seedance 2.0 is here - create consistent, multimodal AI videos faster with images, videos, and audio in one prompt.
Try NowStable Diffusion is still the broadest open image generation family for teams that want checkpoint flexibility, custom fine-tunes, adapters, and private asset pipelines.
Inputs
Text prompts, init images, masks, LoRA and ControlNet style adapters
Outputs
Generated images, edited images, inpainted assets, internal creative workflows

Teams choose a dedicated GPU for Stable Diffusion when they need full control over sensitive prompts, proprietary assets, or custom runtime configurations that shared endpoints can't provide.
Get Stable Diffusion running on a GPU dedicated to your team — with private data flow, full code access, and S3-backed storage for production workloads.
Starting at
$1999/month
Scale to higher GPU tiers when you need more VRAM, throughput, or concurrency.
Explore similar models in the same category for your deployment needs.

SDXL is the default open model choice for teams that want strong prompt adherence and broad ecosystem support without giving up deployment control.

Stable Diffusion 3.5 Large is positioned for higher-quality image generation teams that want a modern Stable Diffusion stack on infrastructure they fully control.

Stable Diffusion 3.5 Medium is a lighter entry point for teams that want newer Stable Diffusion quality with more practical dedicated GPU cost envelopes.

SD 1.5 still matters for legacy fine-tunes, mature community checkpoints, and teams that have existing prompt libraries they do not want to migrate yet.

SDXL Turbo is useful when speed matters more than maximal quality and teams want a fast, private image generation runtime on their own dedicated GPU envelope.

FLUX.1 Dev is a strong open image generation baseline for teams that want modern prompt performance and private inference without shared platform bottlenecks.
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