ComfyUI vs Automatic1111: Stable Diffusion UI Comparison

UI Overview: ComfyUI vs A1111

ComfyUI and Automatic1111 (A1111) are the two most popular interfaces for running Stable Diffusion on a dedicated GPU server. Both provide web-based UIs for text-to-image, image-to-image, and inpainting workflows, but their architectures differ fundamentally. GigaGPU offers ComfyUI hosting and Stable Diffusion hosting with both UIs pre-configured.

Image Generation Speed Benchmarks

We benchmarked both UIs generating identical images with the same model, seed, and settings. ComfyUI uses its optimised execution graph; A1111 uses default settings with xformers enabled. Tested on three GPUs from GigaGPU’s image generation hosting lineup.

SD 1.5 (512×512, 30 steps, Euler a)

SDXL (1024×1024, 30 steps, Euler a)

ComfyUI is consistently 33-41% faster than A1111 across all GPUs. The advantage comes from ComfyUI’s node-based execution graph, which avoids redundant computation and manages VRAM more efficiently. For full GPU image generation benchmarks, see our best GPU for Stable Diffusion guide and Stable Diffusion images/sec benchmark.

Feature Comparison

ComfyUI has better support for newer models like Flux and advanced workflows like multi-ControlNet setups. A1111 has a more mature extension ecosystem for traditional SD 1.5 workflows. For Flux deployment, see our Flux.1 hosting guide.

GPU Requirements and VRAM Usage

ComfyUI uses VRAM more efficiently than A1111 due to its lazy execution model and automatic memory management.

ComfyUI’s lower VRAM usage means you can run SDXL on 16 GB GPUs like the RTX 4060 Ti and RTX 5080, while A1111 struggles without a 24 GB card. For GPU comparisons, see our GPU comparisons tool.

Workflow and Extensibility

ComfyUI workflows are visual node graphs that can be exported as JSON and shared. Complex pipelines like img2img-with-ControlNet-and-upscaling become reusable templates. The node architecture makes it easy to add custom processing steps without writing code. ComfyUI also excels at video generation with AnimateDiff nodes. See our AI video generation GPU guide for related benchmarks.

A1111 workflows are configured through the traditional form UI. Extensions add features via tabs and settings. This approach is more accessible for beginners but less flexible for complex multi-step pipelines.

For production API integration, ComfyUI’s WebSocket API supports real-time progress updates and queue management, making it better suited for building applications on top. A1111’s REST API is simpler but less capable for high-throughput production use.

Cost per Image by GPU

Using ComfyUI (faster) for cost calculations:

Self-hosted Stable Diffusion costs well under $0.01 per image regardless of GPU choice. See our cheapest GPU for AI inference and cost analysis for broader comparisons.

Which UI Should You Choose?

Choose ComfyUI if: You want maximum speed, lower VRAM usage, support for the latest models (Flux, SVD), and reproducible node-based workflows. ComfyUI is the better choice for production deployments and power users. Deploy on GigaGPU ComfyUI hosting.

Choose Automatic1111 if: You prefer a traditional UI with a gentler learning curve, have existing A1111 extensions you depend on, or primarily work with SD 1.5 and established ControlNet workflows. A1111 remains excellent for its mature ecosystem.

For new deployments in 2025, we recommend ComfyUI. The performance advantage, better VRAM management, and superior support for new models make it the forward-looking choice. The initial learning curve pays off quickly.

Related guides: best GPU for Stable Diffusion, best GPU for AI video generation, best GPU for deep learning training, and our LLM inference GPU guide.