GGUF-quantized versions of huchukato/pimp-my-pony
— an SDXL/Pony-based merge — for use with
ComfyUI-GGUF.
Each model variant lives in its own folder, with multiple quantization levels.
Contents
| Variant |
Quants |
Notes |
SemiRealAnime/ (pimpmypony-SRA) |
Q8_0, Q6_K, Q5_K_M, Q4_K_M |
semi-realistic anime style |
More variants will be added over time.
Usage (ComfyUI)
These files contain only the UNet — CLIP and VAE are not included.
You need three separate loaders:
| Component |
Node |
What to load |
| UNet |
Unet Loader (GGUF) |
the .gguf file from this repo |
| CLIP |
DualCLIPLoader |
SDXL text encoders (clip_l + clip_g), e.g. from the base repo or any SDXL CLIP pack |
| VAE |
Load VAE |
any SDXL VAE (a special VAE is included in the base model — recommended) |
Requires the ComfyUI-GGUF custom node
(pip install --upgrade gguf).
Which quant should I pick?
SDXL is conv2d-heavy, so gains are smaller than on transformer models
(Flux/Wan) — but still meaningful on limited VRAM / unified memory.
| Quant |
Quality |
Use case |
Q8_0 |
~lossless |
plenty of VRAM, max quality |
Q6_K |
very good |
balanced |
Q5_K_M |
good |
balanced, slightly smaller |
Q4_K_M |
decent, some loss |
tight VRAM (≤8GB) |
Recommended generation settings are the same as the
base model:
clip skip 2, included VAE, avoid excessive negative prompts.
Conversion
Converted with the official
ComfyUI-GGUF tools:
UNet extracted to safetensors → convert.py (BF16 GGUF) → llama-quantize
(llama.cpp b3962 + lcpp.patch).
Limitations
- Conv2d layers cannot be quantized; low quants may degrade fine details
- Quantized CLIP is not provided here — text encoders stay FP16/BF16
- GGUF files are not usable by llama.cpp itself (image-model format for ComfyUI-GGUF only)
License
Apache-2.0, same as the base model. Free for personal use and personal merging.