Spark local media generation

Hero still first, frames second, clips last, interpolation after. Each step is anchored to the one before it, so product identity is decided where a retry costs seconds, not minutes. The default film is a 30-second master: six storyboard shots, so six frames and six 5-second clips, generated at native 1080p and interpolated to 60 fps.

Before every GPU phase

1. Check memory with spark-model-scheduler. One diffusion family resident at a time; cinefoundry mem free when switching between them. The harness does this at every phase boundary (enterPhase) and puts the language models to sleep for frames and clips.

2. Evict the page cache of the model files. On DGX Spark CUDA can only allocate memory that is truly free (MemFree), not what Linux counts as available: MemAvailable includes the page cache, and every model file just read from NVMe - tens of GB of safetensors - sits there looking reclaimable. With a warm cache a video model load can fail, or sample with far less room than cinefoundry mem status suggests. Compare the two, and drop the cache when the gap is large:

   grep -E '^(MemFree|MemAvailable|Cached):' /proc/meminfo
   # The engine does this itself at every phase boundary (enterPhase -> releaseFileCache, no root needed).
   # By hand, for a process outside the engine:
   python3 -c "import os,sys; [os.posix_fadvise(os.open(os.path.join(d,f),os.O_RDONLY),0,0,os.POSIX_FADV_DONTNEED) for d,_,fs in os.walk(sys.argv[1]) for f in fs]" ~/models
   

   Do it after cinefoundry mem free and before the video phase above all: LTX-2.5 at native 1080p is the largest allocation of the film.

Workflow