Vito Sansevero
b98c716b48
Both jobs processed one file at a time on a single core. Pillow decodes the whole image to reach trailing PNG text chunks and releases the GIL while it does, so a bounded thread pool scales almost linearly: on a 1 GB sample of 400 PNGs the metadata pass dropped from 18 s to 2.5 s at 8 threads, and 120 thumbnails from 4.6 s to 0.6 s. - utils/parallel.py: map_parallel(func, items, workers) keeps order, runs on the caller's thread when workers is 1, and caps the pool at the item count - PromptManagerConfig.WORKER_THREADS defaults to half the detected cores, is clamped to max_worker_threads() when loaded from config.json, and is persisted under "performance" - settings API returns worker_threads and cpu_count and validates saves; the settings modal gets a "Worker threads" slider capped at the core count - the scan reads each metadata batch on the configured threads; thumbnail generation processes batches of files per executor job, still emitting one progress event per file Threads rather than processes: this runs inside ComfyUI's server, where forking a CUDA-initialised process is unsafe and the spawn start method would re-import ComfyUI's main module in every child.
27 lines
1.1 KiB
Python
27 lines
1.1 KiB
Python
"""Bounded thread-pool map for the long jobs (output scan, thumbnails).
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Threads, not processes: this code runs inside ComfyUI's server, where forking
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a CUDA-initialised, multi-threaded process is unsafe and the spawn start
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method would re-import ComfyUI's main module in every child. Pillow releases
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the GIL while decoding, resizing and encoding, so threads still scale close
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to linearly for both jobs.
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"""
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from concurrent.futures import ThreadPoolExecutor
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def map_parallel(func, items, workers):
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"""Apply ``func`` to every item on up to ``workers`` threads, keeping order.
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``workers`` of 1 (or fewer) runs on the calling thread with no pool. An
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exception in ``func`` propagates to the caller, so callers that must
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survive bad input wrap ``func`` themselves.
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"""
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items = list(items)
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workers = max(1, int(workers or 1))
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if workers == 1 or len(items) <= 1:
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return [func(item) for item in items]
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max_workers = min(workers, len(items))
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with ThreadPoolExecutor(max_workers, thread_name_prefix="pm-worker") as pool:
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return list(pool.map(func, items))
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