8b385c44fb
Codex review on the native-engine routing: - The /images/load route took the GPU arbiter (acquire_for(DIFFUSION) -> evict chat) unconditionally after engine selection. A native sd.cpp load on a pure-CPU host never touches the GPU, so that needlessly tore down the resident chat model. The handoff is now gated: diffusers always takes it, a force-native sd.cpp load on a CUDA/XPU/MPS box still takes it, but a native sd.cpp load on a CPU host skips it. - sd_cpp status() hardcoded offload_policy 'none' / cpu_offload False even when _run_load computed real offload flags (balanced/low_vram/cpu_offload off-CPU), so the setting was unverifiable. status now derives them from state.offload_flags (still 'none' on CPU, where the flags are empty). - _run_load committed the new state without cancelling/waiting on a generation that started during the (slow) asset download, so a stale sd-cli run against the OLD model could finish afterward and persist an image from the previous model once the new load reported ready. The commit now signals the in-flight cancel and waits on _generate_lock before swapping _state (taken only at commit, so the download never serialises against generation), mirroring the diffusers load path. Tests: CPU native load skips the arbiter while a GPU native load takes it; status reports offload active when flags are set; _run_load cancels and waits for an in-flight generation before committing. |
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| .. | ||
| data_recipe | ||
| .gitkeep | ||
| __init__.py | ||
| auth.py | ||
| chat_history.py | ||
| datasets.py | ||
| export.py | ||
| inference.py | ||
| llama.py | ||
| mcp_servers.py | ||
| models.py | ||
| preview.py | ||
| prompts.py | ||
| providers.py | ||
| rag.py | ||
| settings.py | ||
| training.py | ||
| training_history.py | ||
| training_vram.py | ||