SDXL UNet only, in four self-describing GGUF variants. Text encoders and VAE are not in these files — they come from any SDXL checkpoint. All four carry the same self-description KVs and load through the same patch/loader. CPU and CPU-offload deployments should pick Q40 (fastest download, same runtime speed as the others once decoded). The file carries its own integration metadata as GGUF string KVs: Loaders read only the header to classify the file — no weight scan. Requires the SDXL-GGUF patch (config + loader + CUDA dequant dispatch): https://github.com/aeuphoraex/invokeai-sdxl-gguf The loader fetches text encoders + VAE from the embedded source-checkpoint path, falling back to any…
Open weights
apache-2.0
diffusers
UNet only. The two text encoders (CLIP-L + OpenCLIP-G) and the VAE are not in these files — they come from any Illustrious XL / SDXL checkpoint you already worth quantizing. You keep the encoders at full precision and still drop the UNet to 1.79 GB. Quantized from illustriousXLv01.safetensors (Illustrious XL v0.1 line, modelspec.architecture: stable-diffusion-xl-v1-base). All three are 1680 tensors / 2,567,463,684 parameters. The F16 tensors that remain even in the quantized builds are the 1-D biases and the 51 4-D convolution weights — Q40/Q80 block layouts require a row length divisible by 32, and a (320, 4, 3, 3) conv does not satisfy that. Those stay exact. Pick Q80 for normal use…
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other
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