362cacc448
Adapters are baked at load time: they attach to the dense transformer, then quantize_ converts only the frozen base linears (the lora_ side path is excluded by name), then the loader compiles. Post-quant PEFT injection is not possible on a manually quantized module, so the prequant shortcut is skipped for a baked load and the memory plan is sized for the dense build (force_dense on the quant candidate). At generation time the baked topology is frozen: weight tweaks and disabling (scale 0 reproduces the quantized base exactly) go through set_adapters, while adding or removing adapters returns a clean 400 telling the client to reload with the new selection. supports_lora now returns True for int8/fp8 diffusers loads (checked before the gguf-kind early return, since the quant fast path keeps the picker kind); nvfp4/mxfp8 and GGUF-via-diffusers stay blocked. The load request model takes an optional loras list, threaded through begin_load on both engines (native ignores it and keeps applying LoRA at generation). Verified end to end on GPU: Z-Image GGUF picker + int8 + trained adapter loads through the API, bake marker logged, weight 1.0 vs 0 renders differ visibly, weight 0.5 accepted live, unknown adapter rejected as 400. Affected suites: 296 passed. |
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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 | ||