A1111 vs ComfyUI Performance on GPU Servers

Choosing the Right Stable Diffusion Frontend for Your GPU

You have a dedicated GPU server ready for image generation and two mature options: Automatic1111’s Web UI (A1111) and ComfyUI. Both generate identical quality images from the same models, but they differ substantially in how efficiently they use your GPU. The performance gap widens with complex workflows, batch generation, and production serving.

Raw Generation Speed Comparison

For a single image at standard settings, the difference is modest:

# Benchmark setup: RTX 5090 32 GB, SD 1.5, 512x512, 30 steps, Euler a
# A1111 (latest stable):   ~2.8 seconds per image
# ComfyUI (latest):        ~2.4 seconds per image

# SDXL, 1024x1024, 25 steps, DPM++ 2M Karras
# A1111:                   ~8.5 seconds per image
# ComfyUI:                 ~7.1 seconds per image

# The gap comes from ComfyUI's lighter Python overhead
# and more efficient graph execution

ComfyUI’s node-based architecture avoids the overhead of A1111’s extensive extension system and WebSocket-based preview pipeline.

VRAM Usage Differences

This is where the gap becomes significant:

# SDXL loaded and idle (no generation)
# A1111:  ~7.2 GB VRAM (base model + VAE + text encoders + extensions)
# ComfyUI: ~6.1 GB VRAM (base model + VAE + text encoders)

# During generation at 1024x1024
# A1111:  ~11.5 GB peak VRAM
# ComfyUI: ~9.8 GB peak VRAM

# With ControlNet + LoRA loaded
# A1111:  ~14.2 GB peak VRAM
# ComfyUI: ~11.5 GB peak VRAM

A1111 keeps extensions loaded in VRAM even when they are not actively used. ComfyUI only allocates memory for nodes present in the current workflow, then releases it when the workflow completes.

Batch and Queue Throughput

For production workloads generating hundreds of images, throughput matters more than single-image speed:

# A1111 batch processing
# - Queue-based: one image at a time through the full pipeline
# - Extensions run for every image (even if unused in prompt)
# - WebSocket preview adds overhead per image

# ComfyUI batch processing
# - Graph-compiled: only executes needed nodes
# - Can batch at the node level (generate 4 images, then upscale all 4)
# - API mode skips preview overhead entirely

# Throughput test: 100 images, SD 1.5, 512x512, 20 steps
# A1111 via API:   ~95 seconds total
# ComfyUI via API: ~72 seconds total

ComfyUI’s advantage grows with complex workflows because it caches intermediate results and skips redundant computation.

Extension and Plugin Ecosystem

A1111’s strength is its massive extension library, but this comes at a cost:

# A1111 with 15 common extensions installed
# Startup time: ~45 seconds
# Idle VRAM: +1.5 GB over base
# Per-image overhead: +200-400ms

# ComfyUI with equivalent custom nodes
# Startup time: ~15 seconds
# Idle VRAM: +0.2 GB (nodes loaded on demand)
# Per-image overhead: negligible for unused nodes

# To reduce A1111 overhead, disable unneeded extensions:
# Settings > Extensions > uncheck unused ones
# Or use --no-half-vae and --xformers flags

Production Deployment Recommendation

For API-driven production workloads on your GPU server, ComfyUI generally wins on efficiency. Its API accepts full workflow JSON, making it suitable for programmatic image generation pipelines. A1111 remains better for interactive exploration and quick experiments with its familiar form-based interface.

# ComfyUI API usage for production
curl -X POST http://localhost:8188/prompt \
  -H "Content-Type: application/json" \
  -d '{"prompt": {"3": {"class_type": "KSampler", ...}}}'

# A1111 API usage
curl -X POST http://localhost:7860/sdapi/v1/txt2img \
  -H "Content-Type: application/json" \
  -d '{"prompt": "...", "steps": 25}'

Both frontends benefit from the same GPU optimisations: xformers, FP16, torch.compile. For Stable Diffusion hosting at scale, consider running ComfyUI behind an API gateway. Check the benchmarks for GPU-specific numbers, our PyTorch guide for PyTorch optimisation, the Docker GPU guide for containerised deployments, and the tutorials section for detailed setup instructions. The CUDA guide ensures your driver supports both tools.