Full SD Models
Run any version of Stable DiffusionโSD 1.5, 2.1, SDXL, or SD 3.5โon your terms. Choose your UI (ComfyUI or AUTOMATIC1111), customize pipelines, switch checkpoints, and fine-tune models with LoRA or ControlNet integration.
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Stable Diffusion Hosting Service: Self-Host SDXL/SD-3.5/SD-2/SD-1.5 Efficiently
Stable Diffusion Hosting allows you to run powerful generative AI modelsโlike SDXL, SD 3.5, SD 2, and SD 1.5โon your own GPU servers or cloud infrastructure. By self-hosting, you gain full control over model selection, performance tuning, custom workflows, and data privacy. Whether you prefer the modular precision of ComfyUI or the streamlined UI of AUTOMATIC1111, you can deploy models optimized for your hardwareโfrom lightweight 1.5-based pipelines to high-memory SDXL and SD 3.5 setups. Ideal for developers, artists, and enterprises, Stable Diffusion Hosting delivers scalable, cost-effective AI image generation without relying on third-party APIs.
Choose The Best GPUs for Stable Diffusion Service Hosting
Black Friday Sale
Advanced GPU Dedicated Server - RTX 3060 Ti
$ย 117.11/mo
51% OFF Recurring (Was $239.00)
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Order Now- 128GB RAM
- GPU: GeForce RTX 3060 Ti
- Dual 12-Core E5-2697v2๎
- 240GB SSD + 2TB SSD
- 100Mbps-1Gbps๎
- OS: Windows / Linux
- Single GPU Specifications:
- Microarchitecture: Ampere
- CUDA Cores: 4864
- Tensor Cores: 152
- GPU Memory: 8GB GDDR6
- FP32 Performance: 16.2 TFLOPS๎
Basic GPU Dedicated Server - RTX 5060
$ย 159.00/mo
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Order Now- 64GB RAM
- GPU: Nvidia GeForce RTX 5060
- 24-Core Platinum 8160๎
- 120GB SSD + 960GB SSD
- 100Mbps-1Gbps๎
- OS: Windows / Linux
- Single GPU Specifications:
- Microarchitecture: Blackwell 2.0
- CUDA Cores: 4608
- Tensor Cores: 144
- GPU Memory: 8GB GDDR7
- FP32 Performance: 23.22 TFLOPS๎
Black Friday Sale
Advanced GPU Dedicated Server - A4000
$ย 99.99/mo
64% OFF Recurring (Was $279.00)
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Order Now- 128GB RAM
- GPU: Nvidia Quadro RTX A4000
- Dual 12-Core E5-2697v2๎
- 240GB SSD + 2TB SSD
- 100Mbps-1Gbps๎
- OS: Windows / Linux
- Single GPU Specifications:
- Microarchitecture: Ampere
- CUDA Cores: 6144
- Tensor Cores: 192
- GPU Memory: 16GB GDDR6
- FP32 Performance: 19.2 TFLOPS๎
Advanced GPU Dedicated Server - A5000
$ย 269.00/mo
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Order Now- 128GB RAM
- GPU: Nvidia Quadro RTX A5000
- Dual 12-Core E5-2697v2๎
- 240GB SSD + 2TB SSD
- 100Mbps-1Gbps๎
- OS: Windows / Linux
- Single GPU Specifications:
- Microarchitecture: Ampere
- CUDA Cores: 8192
- Tensor Cores: 256
- GPU Memory: 24GB GDDR6
- FP32 Performance: 27.8 TFLOPS๎
Black Friday Sale
Enterprise GPU Dedicated Server - RTX A6000
$ย 274.50/mo
50% OFF Recurring (Was $549.00)
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Order Now- 256GB RAM
- GPU: Nvidia Quadro RTX A6000
- Dual 18-Core E5-2697v4๎
- 240GB SSD + 2TB NVMe + 8TB SATA
- 100Mbps-1Gbps๎
- OS: Windows / Linux
- Single GPU Specifications:
- Microarchitecture: Ampere
- CUDA Cores: 10,752
- Tensor Cores: 336
- GPU Memory: 48GB GDDR6
- FP32 Performance: 38.71 TFLOPS๎
Enterprise GPU Dedicated Server - A40
$ย 439.00/mo
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Order Now- 256GB RAM
- GPU: Nvidia A40
- Dual 18-Core E5-2697v4๎
- 240GB SSD + 2TB NVMe + 8TB SATA
- 100Mbps-1Gbps๎
- OS: Windows / Linux
- Single GPU Specifications:
- Microarchitecture: Ampere
- CUDA Cores: 10,752
- Tensor Cores: 336
- GPU Memory: 48GB GDDR6
- FP32 Performance: 37.48 TFLOPS๎
Enterprise GPU Dedicated Server - RTX 4090
$ย 409.00/mo
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Order Now- 256GB RAM
- GPU: GeForce RTX 4090
- Dual 18-Core E5-2697v4๎
- 240GB SSD + 2TB NVMe + 8TB SATA
- 100Mbps-1Gbps๎
- OS: Windows / Linux
- Single GPU Specifications:
- Microarchitecture: Ada Lovelace
- CUDA Cores: 16,384
- Tensor Cores: 512
- GPU Memory: 24 GB GDDR6X
- FP32 Performance: 82.6 TFLOPS๎
New Arrival
Enterprise GPU Dedicated Server - RTX 5090
$ย 479.00/mo
1mo3mo12mo24mo
Order Now- 256GB RAM
- GPU: GeForce RTX 5090
- Dual 18-Core E5-2697v4๎
- 240GB SSD + 2TB NVMe + 8TB SATA
- 100Mbps-1Gbps๎
- OS: Windows / Linux
- Single GPU Specifications:
- Microarchitecture: Blackwell 2.0
- CUDA Cores: 21,760
- Tensor Cores: 680
- GPU Memory: 32 GB GDDR7
- FP32 Performance: 109.7 TFLOPS๎
Stable Diffusion Model Hosting Compatibility Matrix
This table provides a detailed overview of the most widely used Stable Diffusion models, evaluating their compatibility with GPU types, web interfaces (like ComfyUI or AUTOMATIC1111), and advanced features such as LoRA, ControlNet, and SDXL Refiner support. It also highlights whether additional components like FFmpeg are needed for audio/video models, and clarifies each model's licensing termsโcritical for commercial or research deployments.
| Model Name | Size (fp16) | Recommended GPU | Figure/sec | LoRA Support | ControlNet Support | Recommended UI | Suit for Refiner? | Additional components required | License Agreement |
|---|---|---|---|---|---|---|---|---|---|
| stabilityai/stable-diffusion-v1-4 | ~4.27GB | RTX3060/5060 | 1.5-2 | โ | โ (needs expansion) | AUTOMATIC1111 | โ | none | CreativeML OpenRAIL-M |
| stabilityai/stable-diffusion-v1-5 | ~4.27GB | RTX3060/5060 | 1.8-2.2 | โ | โ | AUTOMATIC1111 | โ | none | CreativeML OpenRAIL-M |
| stabilityai/stable-diffusion-xl-base-1.0 | ~6.76GB | A4000/A5000 | 1.2-1.5 | โ | โ (SDXL version required) | ComfyUI | โ | none | CreativeML OpenRAIL++-M |
| stabilityai/stable-diffusion-xl-refiner-1.0 | ~6.74GB | A4000/A5000 | 0.8-1.1 | โ | โ | ComfyUI | โ (As a Refiner) | none | CreativeML OpenRAIL++-M |
| stabilityai/stable-audio-open-1.0 | ~7.6GB | A4000/A5000 | - | โ | โ | Web UI | โ | FFmpeg, TTS preprocessing | Non-commercial RAIL |
| stabilityai/stable-video-diffusion-img2vid-xt | ~8GB | A4000/A5000 | Depends on the frame rate | โ | โ | Web UI | โ | FFmpeg | Non-commercial RAIL |
| stabilityai/stable-diffusion-2 | ~5.2GB | RTX 3060 / 5060 | 1.6-2.0 | โ | โ | AUTOMATIC1111 | โ | none | CreativeML OpenRAIL-M |
| stabilityai/stable-diffusion-3-medium | ~10GB | RTX4090 / 5090 | 1.0-1.5 | โ | Partial support | ComfyUI | โ | none | Not open source, requires API license |
| stabilityai/stable-diffusion-3.5-large | ~20GB | A100-40GB / RTX5090 | 0.5-0.9 | unknown | unknown | Web UI / API | โ (Need to combine with Refiner) | unknown | API-only license |
| stabilityai/stable-diffusion-3.5-large-turbo | ~20GB | A100-40GB / RTX5090 | >2.0 | unknown | unknown | Web UI / API | โ (Need to combine with Refiner) | unknown | API-only license |
What is Stable Diffusion Hosting Service?
Stable Diffusion Hosting Service is to running Stable Diffusion models on dedicated servers or cloud-based GPU infrastructure to generate AI-generated content such as images, audio, or video. Instead of relying on third-party APIs, users can self-host these models using tools like ComfyUI or AUTOMATIC1111, allowing greater control, customization, and privacy. Hosting solutions are tailored to meet the performance needs of various modelsโranging from lightweight versions like SD 1.5 to advanced ones like SDXL and SD 3.5โensuring compatibility with features such as LoRA fine-tuning, ControlNet, and multi-stage rendering with Refiner models.
Stable Diffusion Hosting is ideal for artists, developers, businesses, and researchers who require high-performance, cost-effective, and scalable local or remote generation workflows.
Features of Stable Diffusion Service
High Performance & Scalability
Deploy on powerful GPUs (e.g. RTX 4090, A100) for fast, multi-user inference. Handle image, audio, or even video generation at scale, with support for batching, concurrency, and memory-efficient backends like vLLM.
Data Privacy & Offline Capability
Self-hosted means no third-party API calls. Keep your prompts, generations, and models completely privateโideal for secure environments or enterprise use cases. Run everything fully offline once models are downloaded.
Modular UI Support (ComfyUI / A1111)
Use AUTOMATIC1111 for quick generation and ease of use, or ComfyUI for advanced, node-based workflows supporting Refiner stages, multi-model chaining, and fine-grained controlโall with visual, drag-and-drop interfaces.
Why SD Hosting Needs a Specialized Hardware + Software Stack
High GPU Requirements for Real-Time Image Generation
Stable Diffusion relies on large deep learning models that require powerful GPUs with high VRAM (typically 8GBโ24GB or more) to perform fast, real-time image generation. CPUs alone cannot handle the parallel processing demands of these models.
Complex Software Dependencies
SD hosting requires a tightly integrated software stack including PyTorch, CUDA/cuDNN, and libraries like diffusers, xformers, and transformers. These tools must be correctly matched in version and configured for GPU acceleration, which adds complexity.
Interactive Interfaces with GPU-Driven Backends
Web UIs like AUTOMATIC1111 or ComfyUI enable users to generate images through a browser, but the actual image processing is handled by a GPU backend. This requires hosting to support both frontend servers and GPU runtimes simultaneously.
Heavy Storage and Bandwidth Demands
Each model (e.g., SDXL, ControlNet) can be several GBs in size. Generated images, embeddings, and custom LoRA/DreamBooth models also require fast SSD storage and stable network bandwidth, making regular hosting infrastructure insufficient.
How to Start SD Hosting with GPU Server
How to Install Stable Diffusion AUTOMATIC1111 on Ubuntu Linux
How to Install Stable Diffusion AUTOMATIC1111 on Windows
Stable Diffusion Prompt: a Definitive Guide
Self-hosted Stable Diffusion vs. Stable Diffusion as a Service
| Feature | ๐ฅ๏ธ Self-hosted Stable Diffusion | โ๏ธ Stable Diffusion as a Service (SDaaS) |
|---|---|---|
| Setup & Maintenance | Requires manual setup (GPU, drivers, PyTorch, Web UI, models) and ongoing updates | No setup needed โ instantly usable via web/app/API |
| Hardware Cost | High upfront cost (GPU server or local RTX 30/40 series) | Pay-as-you-go or subscription-based |
| Customization | Full control: install any model, plugin (e.g., LoRA, ControlNet, A1111 mods) | Limited to features provided by the service |
| Performance | Best performance if running on high-end hardware | May be limited by shared resources or pricing tier |
| Privacy & Security | 100% local โ no image/text data leaves your machine or server | Data passes through third-party servers (risk of leakage) |
| Scaling | Requires your own GPU cluster or cloud setup | Easy to scale โ no need to manage infrastructure |
| Internet Requirement | Can run offline once set up | Requires internet connection |
| Technical Skill Required | Medium to High โ need Linux/GPU/Python experience | None โ beginner-friendly via browser or API |
FAQs: Stable Diffusion Hosting with ComfyUI or Automatic1111
Whatโs the difference between ComfyUI and AUTOMATIC1111 for Stable Diffusion?
AUTOMATIC1111 offers a feature-rich, web-based interface ideal for quick image generation, prompt crafting, and model switching.
ComfyUI is a node-based workflow engine, better suited for advanced pipelines, fine-grained control, multi-model setups, and automation.
ComfyUI is a node-based workflow engine, better suited for advanced pipelines, fine-grained control, multi-model setups, and automation.
What GPU do I need to self-host Stable Diffusion efficiently?
Is it possible to run multiple users or batch jobs?
Yes, especially with a high-memory GPU and optimized backend like vLLM or TorchServe. For production, containerization and GPU scheduling are recommended.
Is internet access required to use the tools?
Not necessarily. Once models and weights are downloaded, both UIs can run fully offline, ideal for secure or air-gapped environments.
Which one should I choose for self-hosting?
If youโre a beginner or want fast experimentation, start with AUTOMATIC1111. If you need precision control, complex pipelines, or SDXL/Refiner integration, ComfyUI is recommended.
Can I use LoRA and ControlNet with both UIs?
AUTOMATIC1111 supports both natively. ComfyUI supports them via custom nodes, often offering deeper customization and flexibility.
Do I need a license to use these models?
SD 1.5 / SDXL are available under CreativeML OpenRAIL-M licenses. SD 3.x / audio/video models may have non-commercial or API-only licensesโalways review usage terms before deployment.
Can I integrate Stable Diffusion Service into my own app or API?
Yes. Both ComfyUI and AUTOMATIC1111 have API support or can be wrapped via Python, FastAPI, or Flask for full automation.