db72016c39
Two live-test findings on the images load path: - transformer_quant with baked LoRAs, when the dense quantized build is declined for memory or fails: the load completed as a plain GGUF with the adapters silently dropped (HTTP success, supports_lora=false after the fact) -- wrong output with no signal. The load now fails with the recovery options (drop the adapters, free VRAM, or pick a smaller model). Weight-0 adapters still count as no bake request, and the plain no-LoRA decline keeps its silent GGUF fallback. - A fresh GGUF load on a small GPU prefetched the base repo's full bf16 transformer shards (~47 GB on Qwen-Image) because the dense-quant prefetch widening only checked scheme viability, not whether the device could ever hold the candidate resident. Gate the widening on total device capacity (reserve + 0.85 margin, the plan_fits_total_capacity bar) so a card that is certain to decline the dense build never pays the download; capable devices keep the prefetch. |
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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 | ||