cf1910c92d
A cold FLUX.2-dev int8 load on an idle 183 GB B200 planned offload=model (companions exceed budget) and silently served the GGUF as-is; the identical retry went resident and engaged the hosted prequant. The plan arithmetic was byte-identical across both loads (required 90,228 MiB, resident needs free of about 124 GB); the only divergent input was torch.cuda.mem_get_info, which is device-wide and instantaneous: a transient foreign CUDA context briefly held about 100 GB at the first snapshot, and the planner trusted that single read. Three changes: - settled_snapshot_device_memory: on cuda, synchronize + empty_cache (best-effort) and take the MAX free over up to 3 spaced reads. A transient can only shrink free, so the max rejects transient undercounts while a persistent tenant still caps every read. _plan_memory now uses it. - plan_fits_total_capacity + one replan retry: when the dense/prequant candidate fits TOTAL device capacity under the standard reserve and the 0.85 resident margin, an offload verdict can only stem from the free reading, so the loader re-snapshots and replans once before declining the fast path. Explicit balanced/low_vram modes skip the retry (they offload by mode). - diffusion.transformer_quant_declined log line with required/budget/free and the plan reasons, so the next decline is diagnosable from the server log (previously silent). Verified: cold FLUX.2-dev int8 first load in a fresh server now engages the hosted prequant resident (offload=none). |
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| .. | ||
| assets | ||
| auth | ||
| core | ||
| hub | ||
| loggers | ||
| models | ||
| plugins | ||
| requirements | ||
| routes | ||
| state | ||
| storage | ||
| tests | ||
| utils | ||
| __init__.py | ||
| _platform_compat.py | ||
| cloudflare_tunnel.py | ||
| colab.py | ||
| main.py | ||
| run.py | ||
| startup_banner.py | ||