Pro Version of Realism Pony V4-V5-V6 now Available on My Patreon

Onsite generations are permanently available on these models:
👉 Realism_By_Stable_Yogi V3: https://civitai.com/models/166609?modelVersionId=992946

Realism by Stable Yogi Pony V6.5

V6.5 is here — and you all helped build it.

Real thank-you to everyone who pushed V6 hard, sent feedback, and posted the broken hands. V6.5's fix list literally came from you. Anatomy, hand grips, expressions, twin-tails, full-body proportions, isolated objects, painterly style separation, hair color consistency — all worked on this round.

Trigger Word

99rbsy99 — add this to every prompt for the V6.5 realism style. Place it at the END of your tag list for soft activation, or earlier for stronger effect.

Compatible with my character LoRAs (which use 99bsy99) — they stack cleanly without conflict. Use both together for a character rendered in V6.5 realism.

All Variants in This Release

Seven variants ship today, covering everything from 4 GB CPU setups to 24 GB workstations.

FP32 (safetensors, around 13 GB)

Maximum precision. Research and production work. Best for 24 GB+ cards.

FP16 (safetensors, around 6.5 GB)

The default. Best quality and speed balance for most users.

BF16 (safetensors, around 6.5 GB)

Same size as FP16, slightly faster on RTX 3000+ with native BF16 support.

FP8 Scaled (safetensors, around 3.2 GB)

Near-FP16 quality at half the VRAM. Native in Forge and ComfyUI. Great for 8 GB cards.

DMD2 Merge (safetensors, around 6.5 GB)

FP16 with DMD2 distillation LoRA pre-merged. 4-step generation. LCM sampler, CFG 1.2. Fastest path for any card.

Q8_0 GGUF (around 3.9 GB)

8-bit quantized. Near-FP16 quality. For 12+ GB cards in GGUF workflows.

Q4_0 GGUF (around 2.7 GB)

4-bit quantized. Smallest file. Makes SDXL actually run on 6–8 GB entry-level cards.

Quick Pick by Your VRAM

24 GB+ (3090, 4090, 5090, A6000) — FP16 or BF16. No reason to compress.

12–16 GB (3060 12GB, 4070, 4080) — FP8 Scaled or Q8_0 GGUF. Near-FP16 quality with headroom for LoRAs.

8–12 GB (3060, 4060 Ti, 2080) — FP8 Scaled or Q8_0 GGUF. Solid quality, comfortable VRAM use.

6–8 GB (3050, 2060, 1660) — Q4_0 GGUF. Smallest file, makes SDXL actually work on entry-level cards.

CPU only or 4 GB cards — Q4_0 GGUF in ComfyUI-GGUF. Slow but functional.

DMD2_Fp16 variant. 4 steps instead of 25–30.

Recommended Settings

For FP32, FP16, BF16, FP8 Scaled, and GGUF variants:

Sampler — DPM++ 2M Karras, Euler a, or Restart
Steps — 25 to 30
CFG — 4 to 7
Resolution — Native SDXL (1024×1024 or aspect-ratio buckets)

For DMD2 specifically:

Sampler — LCM
Steps — 4 (not 25+)
CFG — 1.2 (not 7)
Result — Comparable quality to a 25-step generation in roughly 1/6 the time

Quants Explained — Which File Do I Pick?

If you've ever seen FP16, BF16, FP8, Q4, Q8 and just downloaded the biggest one, this section is for you.

What's a quant

? Same model, smaller file. Weights are compressed so they fit on less VRAM. Some quality loss vs FP16, but smart compression (Q8_0) is so close you won't see a difference in normal use.

Quality Ladder

FP16 ≈ BF16 ≈ Q8_0 > FP8 > Q4_0. Above Q4_0 the differences are basically invisible in normal generation.

About Speed

Smaller quants are NOT always faster. Generation speed is mostly compute-bound on most cards — quants help with VRAM fit, not raw iterations per second. Where they DO help speed: avoiding system-RAM offload, which is what kills speed on small cards when the model doesn't fit.

Three Reasons to Use a Quant

VRAM fit. A 6 GB card cannot load a 6.5 GB FP16 SDXL — your UI will try to offload to system RAM and generation crawls to under 0.1 iterations per second. A Q4_0 fits with room to spare.
Speed via avoiding offload. Once a model fits in VRAM, speed depends on your card's compute, not file size. But the second it doesn't fit, speed drops 10 to 100 times. Quants are insurance against that cliff.
More room for LoRAs, ControlNet, hires fix. Even if FP16 technically fits, loading a couple of LoRAs and a ControlNet on top can push you over. Q8_0 leaves you 2–3 GB of headroom for the rest of your stack.

How to Load GGUF Files

GGUFs need a loader, since most UIs don't natively support them yet.

For ComfyUI — install the ComfyUI-GGUF custom node:
https://github.com/city96/ComfyUI-GGUF

For Forge or Forge Neo — install my Forge SDXL GGUF extension:
https://github.com/brandulateai/sd-forge-sdxl-gguf-brandulateai

After installing, GGUFs load straight from the standard checkpoint dropdown. No external module picker, no extra setup.

All my GGUFs are bundled (UNet + CLIP-L + CLIP-G + VAE in one file) so they load without picking separate components.

Pro Version Available

This is the standard version of V6.5. The Pro version is trained on more data for longer, producing a more polished and refined output. Available on My Patreon

Found Anything Off?

Drop it in the comments or on Discord. V7's fix list starts now.

Want to contribute to checkpoint feedback, signup here Studio.Brandulate

Join me on Patreon for exclusive perks and early access to unique resources.

To discuss custom LoRa's or models, feel free to connect on Discord.

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Important Usage Tips

Add Stable_Yogis_PDXL_Positives at the beginning of your prompt section.
Add Stable_Yogis_PDXL_Negatives-neg at the beginning of your negative prompt section.

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