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import os.path
from functools import wraps
import html
import time
import traceback
from modules_forge import main_thread
from modules import shared, progress, errors, devices, fifo_lock, profiling
queue_lock = fifo_lock.FIFOLock()
def wrap_queued_call(func):
def f(*args, **kwargs):
with queue_lock:
res = func(*args, **kwargs)
return res
return f
def wrap_gradio_gpu_call(func, extra_outputs=None):
@wraps(func)
def f(*args, **kwargs):
# if the first argument is a string that says "task(...)", it is treated as a job id
if args and type(args[0]) == str and args[0].startswith("task(") and args[0].endswith(")"):
id_task = args[0]
progress.add_task_to_queue(id_task)
else:
id_task = None
with queue_lock:
shared.state.begin(job=id_task)
progress.start_task(id_task)
try:
res = func(*args, **kwargs)
progress.record_results(id_task, res)
finally:
progress.finish_task(id_task)
shared.state.end()
return res
return wrap_gradio_call(f, extra_outputs=extra_outputs, add_stats=True)
def wrap_gradio_call(func, extra_outputs=None, add_stats=False):
@wraps(func)
def f(*args, **kwargs):
try:
res = func(*args, **kwargs)
finally:
shared.state.skipped = False
shared.state.interrupted = False
shared.state.stopping_generation = False
shared.state.job_count = 0
shared.state.job = ""
return res
return wrap_gradio_call_no_job(f, extra_outputs, add_stats)
def wrap_gradio_call_no_job(func, extra_outputs=None, add_stats=False):
@wraps(func)
def f(*args, extra_outputs_array=extra_outputs, **kwargs):
run_memmon = shared.opts.memmon_poll_rate > 0 and not shared.mem_mon.disabled and add_stats
if run_memmon:
shared.mem_mon.monitor()
t = time.perf_counter()
try:
res = list(func(*args, **kwargs))
except Exception as e:
if main_thread.last_exception is not None:
e = main_thread.last_exception
else:
traceback.print_exc()
print(e)
if extra_outputs_array is None:
extra_outputs_array = [None, '']
error_message = f'{type(e).__name__}: {e}'
res = extra_outputs_array + [f"<div class='error'>{html.escape(error_message)}</div>"]
devices.torch_gc()
if not add_stats:
return tuple(res)
elapsed = time.perf_counter() - t
elapsed_m = int(elapsed // 60)
elapsed_s = elapsed % 60
elapsed_text = f"{elapsed_s:.1f} sec."
if elapsed_m > 0:
elapsed_text = f"{elapsed_m} min. "+elapsed_text
if run_memmon:
mem_stats = {k: -(v//-(1024*1024)) for k, v in shared.mem_mon.stop().items()}
active_peak = mem_stats['active_peak']
reserved_peak = mem_stats['reserved_peak']
sys_peak = mem_stats['system_peak']
sys_total = mem_stats['total']
sys_pct = sys_peak/max(sys_total, 1) * 100
toltip_a = "Active: peak amount of video memory used during generation (excluding cached data)"
toltip_r = "Reserved: total amount of video memory allocated by the Torch library "
toltip_sys = "System: peak amount of video memory allocated by all running programs, out of total capacity"
text_a = f"<abbr title='{toltip_a}'>A</abbr>: <span class='measurement'>{active_peak/1024:.2f} GB</span>"
text_r = f"<abbr title='{toltip_r}'>R</abbr>: <span class='measurement'>{reserved_peak/1024:.2f} GB</span>"
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}%)"
vram_html = f"<p class='vram'>{text_a}, <wbr>{text_r}, <wbr>{text_sys}</p>"
else:
vram_html = ''
if shared.opts.profiling_enable and os.path.exists(shared.opts.profiling_filename):
profiling_html = f"<p class='profile'> [ <a href='{profiling.webpath()}' download>Profile</a> ] </p>"
else:
profiling_html = ''
# last item is always HTML
res[-1] += f"<div class='performance'><p class='time'>Time taken: <wbr><span class='measurement'>{elapsed_text}</span></p>{vram_html}{profiling_html}</div>"
return tuple(res)
return f
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