LyliaEngine · image

Pony_Diffusion_V6_XL

LyliaEngine/Pony_Diffusion_V6_XL

Pony_Diffusion_V6_XL at Q4_K is exactly 2,797,752,960 bytes (2.61 GiB / 2.80 GB) — an effective 8.718 bits per weight, not the nominal 4.

From the file· summed from 1 file(s)
Parameters
2.6B
Architecture
sdxl
Context
native (config.json)
License

Shipped quantizations

exact bytes, summed from published files
QuantSizeExact bytesEffective bpwTensorsPublisher
Q4_K2.61 GiB2,797,752,9608.718offgrid-ai
Q8_03.89 GiB4,180,186,81613.025offgrid-ai

Pipeline components

a diffusion model is a graph of parts, not one file
ComponentSizeShareCan live on the CPU?
denoiser6.77 GiB100%no, must be resident
Full pipeline6.77 GiBresident if nothing is offloaded

The parameter count published for a diffusion model describes the denoiser alone. Running it also requires its text encoder and VAE, and the text encoder is often nearly as large as the denoiser — which is why offloading it is the standard first move when you run out of memory.

We publish component sizes here, not throughput. Community-submitted image-generation rates do exist for many GPUs and we show them on the hardware pages, but they aggregate runs at different resolutions, step counts and settings, so they cannot be attributed to one model. Peak memory during sampling is unmeasured by any public source, and we do not estimate it.

Compare with

same modality, comparable size

Will it run on your card?

full quant x context sweep

Why other calculators give a different number

A parameters × bits ÷ 8 estimate puts Q4_K at roughly 1.35 GiB. The real file is 2.61 GiB, because a quantization is a mixture and some tensors are always kept at higher precision.

Architecture

Architecture unavailable — this repository is gated and no ungated mirror was found. Exact file sizes above are still authoritative; only the KV math needs the config.

Questions people ask

How much VRAM does Pony_Diffusion_V6_XL need?
Q4_K is exactly 2,797,752,960 bytes (2.61 GiB) in weights. Add the KV cache, which depends on your context length, plus roughly half a gigabyte of runtime overhead.
Which quantization of Pony_Diffusion_V6_XL should I use?
Q4_K_M is the usual default. Pick the largest quantization that fits your card at the context you actually need — the table above gives exact sizes for every one published.