- 18 Sep, 2026 2 commits
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Stefy Lanza (nextime / spora ) authoredvLLM on the pod died with 'Engine core initialization failed. See root cause above' — and the root cause was above the six-line tail the 503 carried. Neither core has a C compiler; Triton JIT-compiles vLLM's (and SGLang's) kernels at run time and needs one. A <profile>.apt marker keeps runtime apt packages in the image; vllm and engines-kt get gcc. The worker's error now surfaces the lines that name an error first. Co-Authored-By:
Claude Opus 5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01FKm7Z56iKjHgNkHc6VhWKz
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Stefy Lanza (nextime / spora ) authored
A pod terminated behind the pool's back — the harness reaping by API, a reclaimed spot machine — stayed 'healthy' until the next timed probe, and the next request failed on connection refused for something the pool could have known. The backend now discards it immediately and retries the request once on a fresh acquire; a second refusal is a real outage and raises. Co-Authored-By:
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- 17 Sep, 2026 7 commits
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Stefy Lanza (nextime / spora ) authored
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Stefy Lanza (nextime / spora ) authored
coderai-llama came out at 11.6 GB — the CUDA wheel for five GPU generations on top of the full core — over RunPod's size line. The only thing the CUDA GGUF backend needed torch for was three questions about the card (is there one, total memory, free memory); gpu_probe answers them from torch when present and NVML otherwise. The image drops to the light core plus NVIDIA's runtime, cuBLAS and NCCL wheels. Co-Authored-By:
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Stefy Lanza (nextime / spora ) authored
coderai-text was transformers only, so a GGUF or a vLLM-served repo still went to an upstream image: no TLS on the direct path, no /boot, no local LoRAs, nothing on the Tasks page. coderai-llama compiles llama-cpp-python with CUDA for every pod GPU on the full core; coderai-vllm is vLLM in its own uv-resolved venv on the light core, found by the worker through CODERAI_VLLM_VENV, with the local vLLM settings forwarded. The upstream engines stay selectable as vllm / llamacpp. Co-Authored-By:
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Stefy Lanza (nextime / spora ) authored
So callers and tests that patch requests.get/post keep working; only https-to-a-bare-IP takes the pinned path. Co-Authored-By:
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Stefy Lanza (nextime / spora ) authored
direct_tcp traded the proxy's 100 s cutoff for plain HTTP. Now a coderai pod on that path brings its own TLS: a per-install CA (openssl, kept beside the pod registry) signs a short-lived leaf per pod, sent as env like the token; boot.sh and the venv-on-volume bootstrap start uvicorn on it. This side verifies https-to-a-bare-IP against that CA only — pod_http, a requests shim every pod-facing call now goes through. No hostname check: the IP is unknown at issue time, and a foreign cert fails the chain before a name is compared. Proven end to end in a test with a real https server. vLLM/llama.cpp images stay plain on this path. Co-Authored-By:
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Stefy Lanza (nextime / spora ) authored
Every pod request went through <pod>-<port>.proxy.runpod.net, which sits behind Cloudflare: a 100 s idle limit per request and a body-size cap. A slow non-streaming render can be cut off with a 524 while the pod is still working. direct_tcp (off by default) exposes the port as tcp and talks to the public ip:port the runtime reports — plain HTTP, no limits. Co-Authored-By:
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Stefy Lanza (nextime / spora ) authored
pip spent an hour and gigabytes of RAM on sglang-kt's forty exact pins without installing a byte; uv resolved the same set in seconds. A <profile>.venv-<name>.uv marker opts a venv in. Also drop __pycache__ from baked venvs. Co-Authored-By:
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- 16 Sep, 2026 20 commits
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Stefy Lanza (nextime / spora ) authored
kt was refused because it looked unshippable: a Python stack pinning its own torch, thought to be AMX-only. kt-kernel 0.7 ships prebuilt manylinux wheels that dispatch at runtime (AMX, AVX-512, AVX2 llamafile, AMD BLIS), and sglang-kt pins the matching torch — so it is a venv on the light core like the other split images. The worker now launches SGLang from that venv (CODERAI_KT_VENV / ktransformers.venv) instead of assuming coderai's interpreter has it, and accepts a HF repo id, which SGLang downloads into the volume's cache. Co-Authored-By:
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Stefy Lanza (nextime / spora ) authored
The engines are C binaries and coderai only drives them; the full core's torch was dead weight. All they link is the CUDA runtime and cuBLAS, which NVIDIA ships as wheels at the 12.8 they were compiled against. Co-Authored-By:
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Stefy Lanza (nextime / spora ) authored
Twice the build disk filled with 500+ GB of layers no image referenced, pinned by mount records of containers the daemon no longer knew about, while docker system df reported 90 GB. Nothing in docker can see them. This walks imagedb/layerdb/mounts the way a fresh daemon would, reports the stale set, and with --fix (daemon stopped) removes it. Co-Authored-By:
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Stefy Lanza (nextime / spora ) authored
The local ds4-server is an sm_86 build against CUDA 13 and the local colibri has no GPU backend: neither could be copied into a pod. The new engines image compiles both, plus kimi-k3-in-c, in a CUDA 12.8 stage for sm_80/86/89/90/120a (ds4's Blackwell kernels need the arch-specific target; plain sm_120 fails in ptxas) on a portable x86-64-v3 baseline, and fails the build if a binary cannot find its libraries. A RunPod-only model names the engine on its runpod block (backend: runpod takes the pin slot); a local model pinned to the engine and bursting carries the pin. Either way the pod arrives with the engine enabled and this install's settings forwarded (CODERAI_<ENG>_CONFIG), and volume_path points it at weights already on the network volume — the only sane way to run 150 GB+ models. The model page warns loudly the moment one of these engines is picked. ktransformers is refused with a reason: it has no pod image. Also: the batch build/publish scripts no longer list core-light and venv profiles as images. Co-Authored-By:
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Stefy Lanza (nextime / spora ) authored
RunPod-only was an entry in the compute-backend dropdown, between OpenCL and wav2vec2 — not where anyone deciding WHERE a model runs would look. The Placement section now asks 'Runs on' (this machine / RunPod only / a machine of yours) and the RunPod block sits under it; the radios and the backend select are one stored value, kept in sync. The host backend existed only as JSON. It now has its fields on the form, a save handler, and — like a pod — no local weight or VRAM bookkeeping. Co-Authored-By:
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Stefy Lanza (nextime / spora ) authored
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Stefy Lanza (nextime / spora ) authored
gpu_count was passed to RunPod's gpuCount and then ignored: the price ceiling checked one card, min_vram_gb one card's memory, and the engine loaded on GPU 0 while the rest sat idle. Now the pod is ranked as a whole (price x N, VRAM x N, '2x A100'), vLLM gets --tensor-parallel-size N and llama.cpp --split-mode layer. Exposed on the model page, clamped 1-8. Requests forwarded to a pod or host never appeared on the Tasks page, so a running remote job was invisible from the admin. _forward now registers a 'remote' task whose message names the pod it landed on. Co-Authored-By:
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Stefy Lanza (nextime / spora ) authored
weights_gb, on the model entry or its runpod block, is taken as-is and the HuggingFace estimate is skipped. It is the only honest answer for a URL download, an upload or a local file — none of which the estimator can see — and for an HF repo it beats a heuristic that deliberately errs high (SDXL: estimated 35 GB, actually ~7 for one precision). A nonsense value is ignored rather than trusted, and the field is never seeded to a pod: it describes the pod's disk, not the model. /v1/models/test/state now reports the disk a pod would be rented with, the weight size used, and whether that size was stated or estimated — decided before any money is spent. Co-Authored-By:
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Stefy Lanza (nextime / spora ) authored
remote-execution.md: the capability images are two kinds now — ten on the full core and five on a torch-free light core for stacks that cannot share a venv — and the doc says which, why each split was measured rather than assumed, what the .venv-*.check scripts catch that a bare import cannot, and why the light core exists (RunPod refuses images above ~10 GB; a specialised image on the full core carried 7 GB of torch it never used). The 'speaker rides stt' line was wrong — that image shipped nothing for speaker work — and is fixed. A new section covers what travels to a pod as environment: the HF token, licence acceptances, baked-venv paths, OCR engine gates. Surya on a pod is documented as served by docTR, with the reason. The host backend gets its own section: always-on and on-demand shapes, what it deliberately lacks, and service_token. runpod.md: the size line with the numbers behind it, EXITED as a dead pod rather than a slow pull, container-disk sizing from HF metadata, and the field table corrected — boot_timeout_s is 900, container_disk_gb grows to fit. README: the fifteen images and the host backend, one line each. Co-Authored-By:
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Stefy Lanza (nextime / spora ) authored
The pod was launched with docTR as its default and still answered 'OCR engine surya is not enabled' — because the request carried engine=surya, and the OCR route honours the request's own field over the configured default. Setting the pod's env changed the default; it did not change what the request asked for. A pod target for OCR now rewrites the request's engine field on the way out, to the engine the pod will actually serve (pod_ocr_engine mirrors the launch env). Multipart is edited on the bytes with every other part untouched and Content-Length corrected; verified through FastAPI's own parser with the real probe body, file intact. Co-Authored-By:
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Stefy Lanza (nextime / spora ) authored
XTTS reached its own loader on a pod and died on 'unparseable worker reply'. coqui prints an interactive CPML licence prompt to stdout and then blocks on input() — on a pipe, that corrupts the JSON protocol and then waits forever. Two fixes. The service now keeps the real stdout for the protocol and sends everything else the process prints to stderr, so no library chatter can reach the caller's parser again. And the CPML acceptance travels as COQUI_TOS_AGREED, locally and to a pod: configuring an XTTS model at all IS the decision, exactly as surya's licence is handled, and the pod inherits it. Verified in the image: the load returns clean JSON with the full speaker list. Surya: 0.22 is a VLM (surya-ocr-2), not a self-contained OCR library. It needs a server — vLLM, which it spawns IN DOCKER, or llama-server on a GGUF — and the pod image has neither; locally it rides coderai's own vLLM engine. A pod asked for surya now serves the request with docTR, which is in the image and has passed, and logs why — instead of loading surya, reaching inference, and dying on 'docker binary not found'. Surya on a pod belongs with the engine images, where a llama-server and the GGUF are the same work as colibri and ds4. Co-Authored-By:
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Stefy Lanza (nextime / spora ) authored
The pod reported 'No module named surya.ocr' — an API that has not existed for a year. The real error was two tiers up: the loader wrapped each version branch in a bare except, so a failure INSIDE the right branch was swallowed and it fell through to the oldest one. The reported error was from code that never should have run. The real error was surya 0.17.1's config class choking on transformers 5.17. The pin came from a stale requirements-surya.txt. The local install — read from its venv's dist-info — runs surya 0.22.1 on transformers 5.15, and works. Both now pin 0.22.1. 0.22 dropped surya.foundation and takes RecognitionPredictor() alone, which the loader already handled — but only as a fallback reached by swallowed exception. It now selects the API by what IMPORTS: foundation present is 0.17, absent is 0.22 (or pre-0.17). Verified loading 0.22.1 through the engine class on the pod stack beside docTR. tts-xtts: coqui's TTS(name) splits on '/' and wants its own four-part catalogue name; a HuggingFace id has two parts and fails 'expected 4, got 2'. The service translates (coqui/XTTS-v2 -> tts_models/multilingual/multi-dataset/xtts_v2), verified against coqui's catalogue. This means XTTS through _CoquiBackend had never worked with an HF id anywhere, not only on a pod. Co-Authored-By:
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Stefy Lanza (nextime / spora ) authored
The capability images are ordinary containers; nothing about them needs RunPod. The same image can run on a box you own, a server rented by the month, a second GPU in the next room — and coderai should use it the way it uses a pod: per model, with an endpoint and a token, and nothing else. always on: {"backend": "host", "host": {"url": ..., "api_key": ...}} on demand: add start_cmd (+ optional stop_cmd, boot_timeout_s, idle_timeout_s) start_cmd is whatever starts the thing — docker run, ssh box docker start, a systemd unit. coderai runs it, waits for /healthz, uses the host, and runs stop_cmd after idle_timeout_s with nothing in flight. It only ever stops a host it started itself; one that was already up is never touched. Deliberately absent: GPU search, price ranking, budgets, spot, capacity fallback, the orphan reaper. Those exist because a cloud rents an anonymous card by the second and can lose it. A host is a named machine you are responsible for — the machinery would be wrong, not just unnecessary. Verified for real: a capability container started by command from a cold state, healthy in 6s, used, and stopped by the idle timer. Also fixed on the way: a plain service_url carried no token, so a hand-run capability image behind one answered 401 to everything. service_token travels now. Neither host nor service_* is seeded to a pod: they say where a model runs from HERE, and would make a pod try to forward to itself. Co-Authored-By: -
Stefy Lanza (nextime / spora ) authored
'function object has no attribute tqdm', on a rented pod, after the model had loaded. Current f5-tts defaults its progress argument to the tqdm MODULE and calls progress.tqdm(iterable); older releases called progress(iterable). Our call site passed a bare lambda, written for the old shape, and the new shape went looking for a .tqdm attribute on it. _NoProgress answers both — callable, and with a .tqdm — and shows nothing, which is what a server wants. Confirmed against the f5-tts actually in the voice image: its infer_batch_process calls progress.tqdm(...). Co-Authored-By:
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Stefy Lanza (nextime / spora ) authored
The HF token fix worked: pyannote got past the gate and failed one layer deeper, instantiating the pipeline. Reproduced by running the real service with a GPU: ModuleNotFoundError: No module named 'matplotlib'. pyannote's segmentation tasks import matplotlib.pyplot at module load; it is not declared, and 'import pyannote.audio' never reaches it, so the build's import check passed and the venv shipped broken. This is the fourth venv whose bare import check passed while the real load failed. A <profile>.venv-<name>.check script now runs at build time in the venv's own python and imports what the REAL load reaches — pipeline modules, model classes — without a GPU or a token, which a build has neither of. The pyannote check fails on the shipped image and passes with the fix; the XTTS check passes as-is, so that venv is genuinely complete. Also: every isolated-venv worker reported failures as the LAST FIVE LINES of the subprocess tail. A Python traceback's last five lines are frame headers — the exception is the final line, and cutting to five dropped it whenever the trace was deep. Ten workers said where a process died and never why. Fixed in all of them. Co-Authored-By:
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Stefy Lanza (nextime / spora ) authored
coqui-tts imports a transformers 4.x symbol that 5.x removed. Unlike parler and the OCR engines it had never been isolated locally: _CoquiBackend did a plain 'from TTS.api import TTS' and gave up when it was missing. On a pod that meant an image with a verified, working TTS venv answered 'pip install coqui-tts' — the venv was there and nothing knew to use it. xtts_worker finds the venv the way the others do (explicit env var, the baked /opt/coderai/venvs/TTS, then per-user) and drives tools/xtts_service.py in it over stdin/stdout. XttsSubprocess wears the TTS object's shape — speakers, tts(**kwargs), synthesizer.output_sample_rate, and the TypeError the backend relies on to retry without speed — so synthesize() runs unchanged whether the library is in this process or next door. Verified inside the pushed image: the venv is found and the service answers from it. The fall-through is unit-tested with a fake worker, and the error when no venv exists names the places it looked. Co-Authored-By:
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Stefy Lanza (nextime / spora ) authored
pyannote is gated. On a pod it loaded from its baked venv, reached from_pretrained, and stopped: 'the model is gated/unavailable'. The local install has HF_TOKEN in its environment; a pod was never sent one — every gated model was unservable remotely, not just this one. It is resolved the way pyannote_worker already resolves it (request, env, token file) and travels as pod ENVIRONMENT only. A test proves it reaches the pod and appears nowhere else in the plan — not the seed list the pod parses, not the catalogue, not the args. Fixing this exposed a precedence bug: at provision time the plan's env was applied OVER the model's own env, so a token a model was explicitly configured with was silently replaced by whatever this process happened to have. The model's own env wins now. Co-Authored-By:
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Stefy Lanza (nextime / spora ) authored
The failure this prevents was observable, not hypothetical: a pod boots, pulls the image, starts downloading, fills its 40 GB disk and dies at load_timeout_s having paid for every minute. The size of a HuggingFace repo is one metadata call, so it is asked before the pod exists, and the disk is grown to fit — weights plus ~25 GB for the unpacked image and the download cache. It counts what a load will actually FETCH, not the whole repo. SDXL's repo is 77 GB, of which a diffusers load pulls 28: the rest is the same weights again as Flax .msgpack, legacy .bin and ONNX exports. Sizing to the repo would have rented 100 GB for a 28 GB model. An explicit container_disk_gb that is already large enough is respected. And with a network volume attached the weights land there, not on the container disk, so the disk stays at its configured size — which is the point of the volume. Co-Authored-By:
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Stefy Lanza (nextime / spora ) authored
RunPod refuses images above a size line — measured: 10.1 GB boots, 14.5 GB is 'Exited by Runpod' two seconds after rent on every machine, no other reason given. Container disk (tried 80 GB), layer format and the full image config were each ruled out; size was the only difference. All five specialised images were above the line. They were above it for one reason: each was built on the full core, so it carried 7 GB of CUDA, torch and triton the main venv never used on that pod, beside the venv that did the work. The speaker image was 14.8 GB, of which pyannote's own venv was 5.6. The light core is coderai's Python dependencies with no GPU stack: 1.2 GB. codai.api.app imports with no torch present (verified first), so the main interpreter still boots the API, serves /healthz and /boot, and drives the subprocess worker. A profile marked <name>.light builds on it, its main venv carries only what the API layer needs, and the build FAILS if a torch reaches that venv — because that is exactly the leak that would put the image back over the line. speaker: 14,796 MB -> 7,077 MB, complete, nothing on a volume. Co-Authored-By:
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Stefy Lanza (nextime / spora ) authored
A dead pod produced three hundred seconds of 'still no port' and no reason at all, because the one Pod field that carries the reason was never queried. It now travels into the progress line and the failure message, so an EXITED pod says what RunPod says about it instead of leaving the cause to be guessed. The strict-mode removal in boot.sh was one such guess: the image boots cleanly here with a minimal environment and with a GPU exposed, so it does not reproduce the exit. This is how the real cause gets found. Co-Authored-By:
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- 15 Sep, 2026 11 commits
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Stefy Lanza (nextime / spora ) authored
A speaker pod reported EXITED with uptime 0s from its very first poll. The loop waited out the full budget on it — three machines in a row — printing 'image pull in progress', which it never was: wait_ready treated only TERMINATED and FAILED as terminal. EXITED past a short restart grace now fails fast and says what it means: the image pulled and the process died on boot. The budget it waited out was 300s, not the 900s set on the pool class: the dataclass default was never updated. Two numbers for one thing, and the smaller one silently won. There is one now. Why the process died: boot.sh ran under set -u, and every line before the exec is a diagnostic — it exists to describe the pod, and a diagnostic must never be able to stop the boot it describes. The image boots fine locally; on RunPod an environment difference tripped a strict-mode abort before uvicorn was reached. That is the best-supported explanation, not a proven one — RunPod publishes no log API to confirm it — so the script is now unable to abort before its exec under any environment, verified against a bare sh with nothing set. Co-Authored-By:
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Stefy Lanza (nextime / spora ) authored
The 150 GB warning's call site in _provision_one used a name that did not exist in that scope. The helper was unit-tested and passed; every pod test then failed in two seconds with "name 'entry' is not defined" before renting anything. A helper can be correct while the line that calls it is not. The entry is looked up the way the rest of the pool does it. And since pyflakes sees exactly this class of bug, a test now runs it over the modules a pod boot goes through, so an undefined name in that path fails the suite instead of the first real request. Co-Authored-By:
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Stefy Lanza (nextime / spora ) authored
The venv built and then failed its import check with ModuleNotFoundError. Verified the whole spec in a throwaway container first this time: with click, 'from TTS.api import TTS' works on torch 2.5.1+cu124 with transformers 4.46.3 — the stack this image exists to provide. Co-Authored-By:
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Stefy Lanza (nextime / spora ) authored
Everything around pods is tuned for images of ~10 GB and models of a few. The native MoE engines are a different shape: DeepSeek-V4 is ~154 GB and they go well past that, so a pod spends one to two hours filling its disk before it answers anything — on EVERY cold start, since a pod is disposable storage. The warning names the model, the size, and the fix: attach a network volume so the weights are fetched once and every later pod attaches to them instead. It appears at provision time, boxed so it cannot be missed in a log, and in the test run — which is where someone looks BEFORE committing to a configuration, while at provision time the money is already being spent. It says nothing when a volume is attached, and nothing for ordinary models. Co-Authored-By:
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Stefy Lanza (nextime / spora ) authored
Diarization had no probe, so a speaker pod could only ever report reachability — and reachability is what reported ok:true for an STT image that shipped nothing for speaker work at all. espeak provides the audio; the check is that the model returns at least one labelled turn, which a pod with no diarization stack cannot do. One speaker saying one sentence is a thin test musically and an honest one here. Co-Authored-By:
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Stefy Lanza (nextime / spora ) authored
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Stefy Lanza (nextime / spora ) authored
The pod images were built to satisfy imports, not to serve models. The audit found coqui/XTTS-v2, every whisper.cpp model, diarization, speaker ID, SALAD place recognition and Surya all unservable on a pod, and the OCR image shipping no OCR engine at all. Three of the eight 'isolated' stacks are not isolated any more. Surya, speechbrain and pytorch_lightning all import cleanly beside torch 2.11 — their venvs were decided against an older torch and never re-checked, exactly like audiocraft's torch==2.1.0. They go straight into the ocr, stt and embeddings profiles: place recognition, speaker ID and Surya now work for a few hundred MB instead of three new images. Surya's subprocess engine is pointed at the pod's own interpreter, since building a venv to hold what is already installed would only cost rental time. Five stacks genuinely cannot share, each verified rather than assumed: pyannote (torchaudio.AudioMetaData is gone in 2.11), coqui-tts (a transformers 4.x symbol), CrisperWhisper (transformers 4.46 AND torch 2.5), NeMo (large, own torch) and paddle (its own CUDA runtime). Each gets an image carrying a baked venv, rather than 6 GB added to an image everyone pulls. The `speaker` capability no longer points at the STT image: that was the assumption that it was 'the STT deps plus a bit', and it shipped nothing for diarization or voiceprints at all. Co-Authored-By:
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Stefy Lanza (nextime / spora ) authored
The OCR pod image installed NO OCR engine — only transformers and pypdfium2. A pod could boot, pass its health check, accept a request and have nothing to do it with. Three gates in front of that each reported their own refusal first — 'OCR subsystem is disabled', then 'OCR engine surya is not enabled' — so it took three rounds to see that the engine was never there. docTR goes in, because it is the only one of the three that runs in-process. Surya caps pillow<11 and lives in an isolated venv by design; paddle wants paddlepaddle in one of its own. Either would put a second torch in the image — 6 GB to avoid a Pillow conflict — and the venv mechanism is there if a pod ever needs Surya's layout quality specifically. Each engine also has its own enable flag on top of the subsystem's, and two of the three default to off. A pod now enables the engine it was asked for, falls back to docTR when the request names something the image cannot serve, and still inherits the Surya licence decision from here rather than making it itself. Co-Authored-By:
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Stefy Lanza (nextime / spora ) authored
The isolated venv was solving a problem that was not real. audiocraft is pure Python: its torch==2.1.0 is a declared pin, not an ABI requirement, so --no-deps sidesteps it. The only genuine constraint was xformers, which it imports at module load — and 0.0.35 ships a py39-none wheel requiring torch>=2.10, which this image satisfies. I had ruled that out by grepping wheel filenames for cp312 and missing that abi3/py39 wheels cover 3.12. Verified in the built core image: torch 2.11.0+cu128 with xformers 0.0.35, MusicGen and AudioGen import, ops.memory_efficient_attention present. Cost in the main venv: 950 MB. The second venv cost 6.2 GB, because it carried a duplicate torch and a duplicate CUDA stack. The audio image drops from 15.8 GB to about 10.2 GB, which also removes the reason to split it in two. audio_backend (per model, or CODERAI_AUDIO_BACKEND for a pod) selects audiocraft or transformers explicitly, because they are not equivalent — melody conditioning and AudioGen exist only in the first. Asking for audiocraft when it is absent raises rather than quietly substituting something less capable. A model's own setting beats the environment it lands in, and the choice travels to a pod instead of being decided by what happens to be installed there. The isolated-venv worker stays in the tree: if audiocraft 1.3.0 turns out to misbehave against torch 2.11 at generation time, restoring one profile file brings it back. Co-Authored-By:
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Stefy Lanza (nextime / spora ) authored
Three build cycles, three different missing modules — xformers, then torch at install time, then transformers. The list had been trimmed against what the MAIN venv provides, which is exactly backwards: an isolated venv starts with nothing, so it needs the whole declared set, minus only the torch pins we choose and the dev-only tools. Verified in a throwaway container against the real core image before building anything: audiocraft 1.3.0 on torch 2.5.1+cu121 with xformers 0.0.28.post3, and MusicGen and AudioGen both import. That check takes two minutes; the image build it replaces takes fifteen, which is why the last three rounds were so expensive. Co-Authored-By:
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Stefy Lanza (nextime / spora ) authored
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