Flux.1 Schnell in ComfyUI
Apache-2.0 distilled 4-step transformer built for rapid local prototyping and sub-second iterations on consumer RTX cards. Requires 8 GB–12 GB VRAM and loads from models/unet/ or models/checkpoints/.
Updated Oct 17, 2026 · 1 min read
- Model Family
- Black Forest Labs
- VRAM Target
- 8 GB–12 GB
- ComfyUI Folder
- models/unet/ or models/checkpoints/
- Precision
- FP8 or GGUF Q4/Q8
- Default Sampler
- euler + simple, CFG 1.0, 4 steps
Where to place Flux.1 Schnell weights on Windows
Apache-2.0 distilled 4-step transformer built for rapid local prototyping and sub-second iterations on consumer RTX cards.
Place the primary `.safetensors` or `.gguf` weight file inside `models/unet/ or models/checkpoints/` inside your ComfyUI directory, or point extra_model_paths.yaml to your existing shared models drive.
After moving the file, press **R** on the ComfyUI Studio canvas (or click Refresh) so the loader dropdown lists the new checkpoint immediately without restarting the server.
Recommended loader nodes and sampler settings
- **Precision & Memory:** FP8 or GGUF Q4/Q8. Cards at the lower end of 8 GB–12 GB should pair this model with FP8 or GGUF quantization and VAE Decode (Tiled).
- **Sampler & Scheduler:** euler + simple, CFG 1.0, 4 steps. Wire the model output into KSampler and connect the matching CLIP and VAE streams.
- **Installer:** If you have not set up the desktop environment yet, get the Windows build at /download.
Flux.1 Schnell Quick Reference for ComfyUI Windows
| Parameter | Recommended Setting | Notes |
|---|---|---|
| Target Directory | models/unet/ or models/checkpoints/ | Refresh canvas with R key after copying |
| VRAM Tier | 8 GB–12 GB | Use --lowvram or GGUF Q4 on 8 GB cards |
| Weight Format | FP8 or GGUF Q4/Q8 | Never load untrusted .ckpt pickle files |
| Sampler Recipe | euler + simple, CFG 1.0, 4 steps | Wire through core KSampler node |
FAQs
Which folder does Flux.1 Schnell go into in ComfyUI?
Put Flux.1 Schnell inside `models/unet/ or models/checkpoints/` in your ComfyUI installation, then press R on the canvas to refresh node dropdowns.
Can I run Flux.1 Schnell on an 8 GB NVIDIA GPU?
Yes — use FP8 or GGUF Q4/Q5 weights via ComfyUI-GGUF and keep tiled VAE decoding enabled so peak VRAM stays under 8 GB.
Run on your hardware. Free forever.
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