7f59cd6c1e
Re-reviewed each merged phase PR against this branch's tip and fixed what is still real: - A superseded background load no longer cancels the current model's in-flight generation: the load-token check now runs BEFORE the cancel signal, with a re-check under the generate lock (Phase 1 review). - enable_model_cpu_offload / enable_sequential_cpu_offload now forward the resolved target device; diffusers defaults to CUDA, which broke offloaded loads on non-CUDA accelerators such as Intel XPU (Phase 2 review). - build_sd_cpp_command rejects a None prompt (str(None) previously slipped into argv as the literal "None") and a mask without an init image, which is an invalid sd-cli inpaint invocation (Phase 4/6 review). - The dense-quant OOM fallback drops the caught exception before clear_gpu_cache(): the traceback pinned the partially built dense transformer, so the VRAM this cleanup exists to reclaim stayed allocated through the GGUF rebuild (Phase 8 review). - Pre-quantized transformers (built via from_config) are eval()'d to match the from_pretrained paths, so train-mode layers cannot make prequant inference nondeterministic (Phase 9 review). - FBCache state is reset before each generation when a step cache is engaged: diffusers never clears the stateful first-block residuals on the resident transformer, so a resolution or batch change on the next request hit a shape mismatch, and an unchanged request could reuse stale residuals (Phase 12 review). Each fix carries a regression test; the full diffusion battery passes. |
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| .. | ||
| backend | ||
| frontend | ||
| src-tauri | ||
| __init__.py | ||
| install_llama_prebuilt.py | ||
| install_node_prebuilt.py | ||
| install_python_stack.py | ||
| install_sd_cpp_prebuilt.py | ||
| LICENSE.AGPL-3.0 | ||
| node_prebuilt_pins.json | ||
| package-lock.json | ||
| package.json | ||
| setup.bat | ||
| setup.ps1 | ||
| setup.sh | ||
| Unsloth_Studio_Colab.ipynb | ||