AI Image Generation Cloud | Stable Diffusion & FLUX GPU Hosting

TL;DR

→RTX 4090 at $0.49/hr — generates 1024×1024 images in ~2-4 seconds
→Full stack support — A1111, ComfyUI, InvokeAI, or custom pipelines
→Batch processing — scale to dozens of GPUs for production image pipelines
→60% cheaper than RunPod and Replicate for sustained workloads

Why VectorLay for AI Image Generation

AI image generation has exploded. Stable Diffusion, FLUX, and their successors have made it possible to generate photorealistic images, concept art, product photography, and illustrations in seconds. But running these models at scale — for a product, a platform, or a creative business — means paying for GPU compute. And most cloud providers charge far more than necessary.

VectorLay gives you the same GPUs that power the best image generation results — RTX 4090s with 24GB VRAM — at consumer hardware prices. No markup for "AI cloud" branding. No egress fees when you download your generated images. No cold start delays that ruin user experience.

Supported Models & Frameworks

VectorLay runs any image generation model that fits in a Docker container. Here are the most popular models our users deploy:

Stable Diffusion XL (SDXL)

The industry standard for high-quality image generation. Produces stunning 1024×1024 images with excellent prompt adherence. Runs beautifully on a single RTX 4090 — about 2-3 seconds per image at 30 steps.

FLUX (by Black Forest Labs)

The next generation of open-source image models. FLUX.1 Dev and FLUX.1 Schnell deliver exceptional quality with superior text rendering and composition. Requires more VRAM — RTX 4090 recommended for optimal performance.

Stable Diffusion 1.5 & Fine-tunes

Still widely used for custom fine-tuned models, LoRAs, and specialized styles. Extremely fast on RTX 4090 (~1-2 seconds per image). RTX 3090 handles it easily at $0.29/hour — the cheapest way to run SD 1.5 in the cloud.

Midjourney-Style Open Alternatives

Models like Playground v2.5, Juggernaut XL, and RealVisXL deliver Midjourney-tier quality as open-weight SDXL fine-tunes. Run them on your own infrastructure with full control over prompts, negative prompts, and generation parameters.

UI & Pipeline Support

You're not limited to API-only access. Deploy your preferred creative workflow:

Automatic1111 WebUI — The most popular Stable Diffusion interface. Full extension ecosystem, ControlNet, inpainting, img2img, and more.

ComfyUI — Node-based workflow editor for complex generation pipelines. Chain models, apply ControlNet, upscale, and post-process in a single graph.

InvokeAI — Professional-grade interface with a canvas editor, unified pipeline, and built-in model management.

Custom API Pipelines — Use diffusers, PyTorch, or any framework directly. Build custom endpoints for your product's image generation features.

GPU Recommendations for Image Generation

IDEAL

RTX 4090 — All Image Models

24GB VRAM with Ada Lovelace architecture. The gold standard for image generation. Handles SDXL, FLUX, and even multi-model pipelines (generation + upscaling + face fix). Fastest consumer GPU for diffusion models.

SDXL: ~2-3 sec/image | FLUX Dev: ~4-6 sec/image | SD 1.5: ~1-2 sec/image | $0.49/hr

GREAT VALUE

RTX 3090 — SD 1.5 & Budget SDXL

24GB VRAM, Ampere architecture. Runs all Stable Diffusion models including SDXL. Slightly slower than the 4090 but at 40% lower cost. Perfect for batch processing where time-per-image is less critical than cost-per-image.

SDXL: ~3-5 sec/image | SD 1.5: ~2-3 sec/image | $0.29/hr

ENTERPRISE

H100 / A100 — Multi-Model Serving

80GB VRAM for loading multiple models simultaneously. Useful for platforms serving dozens of custom models or running very large FLUX variants. Generally overkill for single-model image generation.

Multi-model serving | Ultra-high-res generation | Contact for pricing

Batch Image Generation at Scale

Need to generate thousands or millions of images? VectorLay's auto-scaling makes batch processing straightforward. Spin up 10, 50, or 100 GPUs in parallel, process your queue, and scale back down. You only pay for active compute time.

Common batch use cases on VectorLay:

→E-commerce product images — Generate variations, backgrounds, and lifestyle shots for product catalogs. Process 10,000+ images overnight.

→Training data augmentation — Generate synthetic training data for computer vision models. Create diverse datasets with controlled variations.

→Creative asset pipelines — Produce social media content, ad creatives, and marketing materials at scale. A/B test hundreds of visual variations.

→Game & app asset generation — Create textures, sprites, icons, and concept art. Generate entire asset libraries from text descriptions.

With an RTX 4090 generating an SDXL image every ~3 seconds, a 10-GPU cluster can produce over 12,000 images per hour. At $4.90/hour total, that's less than $0.0004 per image — orders of magnitude cheaper than API-based services like Midjourney ($0.01+/image) or DALL-E ($0.04+/image).

Image Generation Pricing Comparison

How does VectorLay compare to other ways of generating AI images? Here's the real cost breakdown for sustained image generation workloads:

Platform	Cost Model	Cost per 1K images	Control
VectorLay (4090)	$0.49/hr GPU	~$0.41	Full
VectorLay (3090)	$0.29/hr GPU	~$0.32	Full
RunPod (4090)	$0.74/hr GPU	~$0.62	Full
Replicate (SDXL)	Per-image API	~$6.50	Limited
Midjourney	Subscription	~$10-30	Minimal
DALL-E 3 (OpenAI)	Per-image API	~$40-80	Minimal

Cost per 1K images assumes SDXL at 1024×1024, 30 steps. VectorLay and RunPod costs assume sustained generation (~3 sec/image on 4090, ~4 sec on 3090). API costs based on published pricing as of 2025.

The difference is stark: for any sustained workload, self-hosted image generation on VectorLay is 15-100× cheaper than API-based services. And you get full control over models, parameters, LoRAs, ControlNet, and post-processing — no content filters, no rate limits, no vendor lock-in.

Self-Hosted vs. API-Based Image Generation

When should you self-host your image generation instead of using an API? Here's the decision framework:

Choose Self-Hosted (VectorLay) When:

You generate more than ~1,000 images per day

You need custom models, LoRAs, or ControlNet

You want full control over content filtering and safety

Latency matters — no cold starts, no queue waits

You need complex pipelines (generation → upscale → face fix → inpaint)

Choose API When:

You generate fewer than ~100 images per day

You need a specific closed model (DALL-E 3, Midjourney)

You don't want to manage infrastructure at all

For most production use cases — SaaS products, creative platforms, e-commerce tools, game studios — self-hosted wins on cost, control, and latency. VectorLay makes self-hosting as simple as deploying a container.

Getting Started with Image Generation on VectorLay

Deploy your image generation stack in minutes:

Pick Your GPU

RTX 4090 for maximum speed, RTX 3090 for best value. Check current pricing.

Choose Your Interface

Use a pre-built template for A1111, ComfyUI, or InvokeAI — or bring your own Docker image with a custom diffusers pipeline.

Load Your Models

Models are cached on persistent storage — no re-downloading on restart. Load checkpoints, LoRAs, VAEs, and embeddings from HuggingFace or your own storage.

Generate at Scale

Your endpoint is live. Send prompts, receive images. Scale up with auto-scaling for batch workloads, scale down when idle. Failover keeps your pipeline running 24/7.

Start generating images for less

Deploy Stable Diffusion, FLUX, or any image model in minutes. No credit card required. No egress fees. Just fast, affordable GPU compute with built-in fault tolerance.

Deploy your image pipeline View GPU pricing

AI Image Generation on Affordable GPUs