Pony Diffusion V6 XL in ComfyUI
Tag-driven SDXL checkpoint widely used for stylized illustration and character LoRA training. Requires 8 GB+ VRAM and loads from models/checkpoints/.
Updated Oct 17, 2026 · 1 min read
- Model Family
- Community SDXL Derivative
- VRAM Target
- 8 GB+
- ComfyUI Folder
- models/checkpoints/
- Precision
- FP16 safetensors
- Default Sampler
- euler_a + karras, clip_skip -2, CFG 6.0
Where to place Pony Diffusion V6 XL weights on Windows
Tag-driven SDXL checkpoint widely used for stylized illustration and character LoRA training.
Place the primary `.safetensors` or `.gguf` weight file inside `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:** FP16 safetensors. Cards at the lower end of 8 GB+ should pair this model with FP8 or GGUF quantization and VAE Decode (Tiled).
- **Sampler & Scheduler:** euler_a + karras, clip_skip -2, CFG 6.0. 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.
Pony Diffusion V6 XL Quick Reference for ComfyUI Windows
| Parameter | Recommended Setting | Notes |
|---|---|---|
| Target Directory | models/checkpoints/ | Refresh canvas with R key after copying |
| VRAM Tier | 8 GB+ | Use --lowvram or GGUF Q4 on 8 GB cards |
| Weight Format | FP16 safetensors | Never load untrusted .ckpt pickle files |
| Sampler Recipe | euler_a + karras, clip_skip -2, CFG 6.0 | Wire through core KSampler node |
FAQs
Which folder does Pony Diffusion V6 XL go into in ComfyUI?
Put Pony Diffusion V6 XL inside `models/checkpoints/` in your ComfyUI installation, then press R on the canvas to refresh node dropdowns.
Can I run Pony Diffusion V6 XL 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.
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