ComfyUI vs SDXL 1.0 in ComfyUI
Flux.1 Dev wins on natural-language prompt adherence, hands, and rendered text; SDXL remains faster on 8 GB cards and has a massive LoRA library.
Updated Oct 17, 2026 · 1 min read
- Compared Against
- SDXL 1.0 in ComfyUI
- ComfyUI Edge
- Lower VRAM, Flux/Video support, node graph
- Shared Models
- Yes via extra_model_paths.yaml
- Windows Build
- Free download at /download
How ComfyUI compares to SDXL 1.0 in ComfyUI
Flux.1 Dev wins on natural-language prompt adherence, hands, and rendered text; SDXL remains faster on 8 GB cards and has a massive LoRA library.
**Where ComfyUI pulls ahead:** ComfyUI runs both side-by-side in the same installation and switches checkpoints in seconds.
**Where SDXL 1.0 in ComfyUI fits:** No need to pick one — keep SDXL in models/checkpoints and Flux in models/unet.
Running both tools on the same Windows PC without duplicating checkpoints
You do not need to delete SDXL 1.0 in ComfyUI or duplicate 100 GB of `.safetensors` files to switch to ComfyUI. Configure extra_model_paths.yaml so ComfyUI reads your existing checkpoints, LoRAs, VAEs, and ControlNets directly from disk.
Install the Windows desktop or portable build from /download and point it at your existing model library in under two minutes.
ComfyUI vs SDXL 1.0 in ComfyUI Feature Matrix
| Capability | ComfyUI (Windows) | SDXL 1.0 in ComfyUI |
|---|---|---|
| Text rendering in images | Flux.1 Dev spells words accurately | SDXL struggles with long text |
| VRAM footprint | Flux: 8–16 GB (FP8/GGUF) | SDXL: 6–8 GB (FP16) |
| ControlNet maturity | Flux Union & Depth available | SDXL has dozens of specialized ControlNets |
| Generation speed (RTX 4070) | Flux: ~12–18 sec (20 steps) | SDXL: ~4–6 sec (25 steps) |
FAQs
Can ComfyUI and SDXL 1.0 in ComfyUI share the same models folder on Windows?
Yes. Edit `extra_model_paths.yaml` in your ComfyUI root folder to point `base_path` at your existing models directory so both apps load the exact same files.
Does ComfyUI use less VRAM than SDXL 1.0 in ComfyUI?
ComfyUI only keeps the active node's sub-model (CLIP, UNet, or VAE) in VRAM at each step and supports native FP8 and GGUF quantization, making it significantly lighter on 8 GB and 12 GB GPUs.
Run on your hardware. Free forever.
The full ComfyUI engine: open source, fast, extensible, and yours to run however you want. Download the desktop app and open this workflow on your own machine.
