FLUX.1-schnell on 8GB VRAM (AMD, no CUDA): the GGUF format mismatch that wastes hours

A developer discovered that FLUX.1-schnell GGUF models from city96's repository fail to load in stable-diffusion.cpp on AMD RX 580 8GB via Vulkan, while leejet's builds work. The error message 'new_sd_ctx_t failed' misleads users into thinking it's a VRAM issue rather than a quantization-source mismatch. Switching to leejet's GGUF files and using specific offloading flags (CLIP on VRAM, T5XXL and VAE on CPU, VAE tiling enabled) enables inference on low-VRAM AMD cards.

Sharing this because it cost me real time and I haven’t seen it documented clearly anywhere: if you’re trying to run FLUX.1-schnell through stable-diffusion.cpp the C++ inference engine, not ComfyUI on a low-VRAM AMD card via Vulkan, GGUF source matters and the error message doesn’t tell you why. The setup: AMD RX 580 8GB Polaris/GCN4, 2017 card — no ROCm support, no CUDA obviously , running stable-diffusion.cpp compiled with -DGGML VULKAN=ON, no DirectML, no cloud. The gotcha: city96’s FLUX GGUF builds on HF the ones most tutorials link, since they’re the most popular — only work inside ComfyUI with the ComfyUI-GGUF custom node. They will NOT load in sd-server / the stable-diffusion.cpp CLI. leejet’s FLUX GGUF builds — these are the ones built for stable-diffusion.cpp specifically and actually load. Using a city96 file in sd-server just gives: ERROR main.cpp:92 - new sd ctx t failed No further explanation, no hint that it’s a packaging/quantization-method mismatch rather than a VRAM or flag problem. I spent a while assuming it was a memory issue before realizing the file itself was the wrong build. Once I switched to leejet’s FLUX.1-schnell-gguf repo, it loaded fine. For an 8GB card, the practical split that works: Diffusion model on VRAM ~6.5GB for q4 k CLIP L on VRAM ~235MB T5XXL and VAE offloaded to system RAM –clip-on-cpu --vae-on-cpu –vae-tiling is NOT optional — without it, VAE decode OOMs even with everything else offloaded correctly. Full command and timing breakdown T5XXL conditioning ~11s, sampling ~14min at 4 steps/1024x1024, VAE decode ~40s here if useful: GitHub - aivisionslab-studios/rx580-local-ai-guide: Complete guide to running local AI on AMD RX 580 8GB via Vulkan — llama.cpp, Ollama, OpenWebUI, Stable Diffusion. No CUDA. No cloud. Free. · GitHub https://github.com/aivisionslab-studios/rx580-local-ai-guide Posting mainly because “GGUF doesn’t load” is a generic enough error that it’s easy to misdiagnose as a VRAM or driver problem instead of a quantization-source mismatch. Curious if anyone’s hit the same wall with other GGUF-quantized diffusion models from different sources.