mirror of
https://github.com/AUTOMATIC1111/stable-diffusion-webui.git
synced 2025-10-30 17:38:51 +00:00
120 lines
4.3 KiB
Python
120 lines
4.3 KiB
Python
from functools import wraps
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import html
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import threading
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import time
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from modules import shared, progress, errors, devices
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queue_lock = threading.Lock()
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def wrap_queued_call(func):
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def f(*args, **kwargs):
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with queue_lock:
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res = func(*args, **kwargs)
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return res
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return f
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def wrap_gradio_gpu_call(func, extra_outputs=None):
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@wraps(func)
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def f(*args, **kwargs):
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# if the first argument is a string that says "task(...)", it is treated as a job id
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if args and type(args[0]) == str and args[0].startswith("task(") and args[0].endswith(")"):
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id_task = args[0]
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progress.add_task_to_queue(id_task)
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else:
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id_task = None
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with queue_lock:
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shared.state.begin(job=id_task)
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progress.start_task(id_task)
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try:
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res = func(*args, **kwargs)
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progress.record_results(id_task, res)
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finally:
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progress.finish_task(id_task)
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shared.state.end()
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return res
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return wrap_gradio_call(f, extra_outputs=extra_outputs, add_stats=True)
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def wrap_gradio_call(func, extra_outputs=None, add_stats=False):
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@wraps(func)
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def f(*args, extra_outputs_array=extra_outputs, **kwargs):
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run_memmon = shared.opts.memmon_poll_rate > 0 and not shared.mem_mon.disabled and add_stats
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if run_memmon:
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shared.mem_mon.monitor()
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t = time.perf_counter()
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try:
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res = list(func(*args, **kwargs))
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except Exception as e:
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# When printing out our debug argument list,
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# do not print out more than a 100 KB of text
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max_debug_str_len = 131072
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message = "Error completing request"
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arg_str = f"Arguments: {args} {kwargs}"[:max_debug_str_len]
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if len(arg_str) > max_debug_str_len:
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arg_str += f" (Argument list truncated at {max_debug_str_len}/{len(arg_str)} characters)"
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errors.report(f"{message}\n{arg_str}", exc_info=True)
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shared.state.job = ""
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shared.state.job_count = 0
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if extra_outputs_array is None:
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extra_outputs_array = [None, '']
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error_message = f'{type(e).__name__}: {e}'
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res = extra_outputs_array + [f"<div class='error'>{html.escape(error_message)}</div>"]
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devices.torch_gc()
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shared.state.skipped = False
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shared.state.interrupted = False
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shared.state.job_count = 0
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if not add_stats:
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return tuple(res)
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elapsed = time.perf_counter() - t
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elapsed_m = int(elapsed // 60)
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elapsed_s = elapsed % 60
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elapsed_text = f"{elapsed_s:.1f} sec."
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if elapsed_m > 0:
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elapsed_text = f"{elapsed_m} min. "+elapsed_text
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if run_memmon:
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mem_stats = {k: -(v//-(1024*1024)) for k, v in shared.mem_mon.stop().items()}
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active_peak = mem_stats['active_peak']
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reserved_peak = mem_stats['reserved_peak']
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sys_peak = mem_stats['system_peak']
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sys_total = mem_stats['total']
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sys_pct = sys_peak/max(sys_total, 1) * 100
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toltip_a = "Active: peak amount of video memory used during generation (excluding cached data)"
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toltip_r = "Reserved: total amout of video memory allocated by the Torch library "
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toltip_sys = "System: peak amout of video memory allocated by all running programs, out of total capacity"
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text_a = f"<abbr title='{toltip_a}'>A</abbr>: <span class='measurement'>{active_peak/1024:.2f} GB</span>"
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text_r = f"<abbr title='{toltip_r}'>R</abbr>: <span class='measurement'>{reserved_peak/1024:.2f} GB</span>"
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text_sys = f"<abbr title='{toltip_sys}'>Sys</abbr>: <span class='measurement'>{sys_peak/1024:.1f}/{sys_total/1024:g} GB</span> ({sys_pct:.1f}%)"
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vram_html = f"<p class='vram'>{text_a}, <wbr>{text_r}, <wbr>{text_sys}</p>"
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else:
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vram_html = ''
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# last item is always HTML
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res[-1] += f"<div class='performance'><p class='time'>Time taken: <wbr><span class='measurement'>{elapsed_text}</span></p>{vram_html}</div>"
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return tuple(res)
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return f
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