
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.
Deploy Dedicated GPU server to run AI models
Deploy ModelStable 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

Dedicated enterprise hosting is useful for Stable Diffusion when the workload includes sensitive prompts, proprietary assets, internal product context, or runtime customization that does not belong on a shared public endpoint.
Deploy Stable Diffusion with dedicated GPUs, private data flow, code access, and S3-backed storage so your team can run production workloads without shared infrastructure tradeoffs.
Pricing
$1999/month
Starting price for enterprise dedicated GPU plans. Move to higher GPU tiers when you need more VRAM, throughput, or concurrency.
Use these related pages to compare adjacent models in the same deployment category.

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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