{"record":{"id":"db0e8e7e1ac47fe0","repo":"unslothai/unsloth","slug":"the-stable-diffusion-cpp-server-binary-was-replace","errorCode":null,"errorMessage":"The stable-diffusion.cpp server binary was replaced by an install for a different accelerator while this model was loading. Try the load again.","messagePattern":"The stable-diffusion\\.cpp server binary was replaced by an install for a different accelerator while this model was loading\\. Try the load again\\.","errorType":"exception","errorClass":"RuntimeError","httpStatus":null,"severity":"error","filePath":"studio/backend/core/inference/sd_cpp_backend.py","lineNumber":1486,"sourceCode":"                            mode, server_binary, engine = \"oneshot\", None, self._resolve_engine()\n                            # And pin off THIS engine. The one-shot pin above was taken while the\n                            # mode was still \"server\", i.e. off an engine of None, so leaving it\n                            # would compare the sd-cli just resolved against None and refuse the\n                            # documented fallback on every load that reaches it. Resolved here,\n                            # inside the claim, so it is vetted at the moment it is pinned.\n                            engine_accelerator = _installed_accelerator_of(\n                                getattr(engine, \"binary\", None)\n                            )\n                        elif _installed_accelerator_of(server_binary) != server_accelerator:\n                            # Runnable, and at the same path -- and still not the build this load\n                            # resolved. An install that landed during the download (an H3 load\n                            # putting the CPU fallback in, say) leaves a server that starts\n                            # perfectly well on a device this load has already committed elsewhere,\n                            # so it would generate on the CPU while the GPU offload policy and the\n                            # arbiter's accounting both describe a GPU run. Asked here, inside the\n                            # claim, where no further install can start: refusing costs a retry\n                            # that re-resolves device, accelerator and install from scratch.\n                            raise RuntimeError(\n                                \"The stable-diffusion.cpp server binary was replaced by an install \"\n                                \"for a different accelerator while this model was loading. Try the \"\n                                \"load again.\"\n                            )\n                        else:\n                            server = SdCppServer(server_binary)\n                            # Published INSIDE the claim: _tree_in_use reads _pending_server, so\n                            # this is the handover from \"a reader holds the tree\" to \"a starting\n                            # server does\", with no gap between them.\n                            #\n                            # Cancellation is re-read in the SAME block. The revalidation above\n                            # can sit for 20s in _server_binary_runnable, and an unload arriving\n                            # in that window finds no _pending_server to stop, so without this the\n                            # load would go on to spawn the process anyway and hold the device for\n                            # the whole start() timeout before the commit below noticed. Asked\n                            # under the lock that publishes, so an unload either stops this server\n                            # or is seen here; it cannot fall between the two.\n                            with self._lock:","sourceCodeStart":1468,"sourceCodeEnd":1504,"githubUrl":"https://github.com/unslothai/unsloth/blob/203007d19051dcd2ae33876786d117c99f6b0368/studio/backend/core/inference/sd_cpp_backend.py#L1468-L1504","documentation":"RuntimeError raised inside the tree claim after the asset download: the sd-server binary at the same path was replaced mid-load by an install for a DIFFERENT accelerator (e.g. an H3 load pulled in the CPU fallback). A runnable-but-different build would silently generate on the wrong device while offload policy and the arbiter still describe the committed device, so the load refuses and asks for a retry that re-resolves everything.","triggerScenarios":"Two loads with different accelerator targets (one GPU, one CPU/H3) racing; an auto-install landing during a multi-minute asset download replaces sd-server in place; the check _installed_accelerator_of(server_binary) != server_accelerator fires under the reader claim.","commonSituations":"Multi-user or automated studios issuing concurrent model loads; a fallback-to-CPU load triggered while a GPU load is mid-download.","solutions":["Retry the load — the retry re-resolves device, accelerator and install from scratch.","Serialize loads (one begin_load at a time) so installs cannot interleave.","Pin the accelerator explicitly per load so concurrent loads do not flip the install target."],"exampleFix":null,"handlingStrategy":"retry","validationCode":null,"typeGuard":null,"tryCatchPattern":"try:\n    backend.begin_load(repo_id=r, gguf_filename=f)\nexcept RuntimeError as e:\n    if 'replaced by an install for a different accelerator' in str(e):\n        backend.begin_load(repo_id=r, gguf_filename=f)  # retry re-resolves device+install\n    else:\n        raise","preventionTips":["Serialize model loads so installs cannot interleave with downloads.","Pin the accelerator per load to stop concurrent loads flipping the installed build."],"tags":["concurrency","race-condition","retryable","accelerator","sd-cpp"],"backgroundTag":null,"analyzedSha":"203007d19051dcd2ae33876786d117c99f6b0368","analyzedAt":"2026-08-15T02:48:39.846Z","schemaVersion":2},"datasetVersion":"2026-08-15T22:17:37.221Z"}