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import datetime
import logging
import threading
import time
import traceback
import torch
from modules import errors, shared, devices
from typing import Optional
log = logging.getLogger(__name__)
class State:
skipped = False
interrupted = False
stopping_generation = False
job = ""
job_no = 0
job_count = 0
processing_has_refined_job_count = False
job_timestamp = '0'
sampling_step = 0
sampling_steps = 0
current_latent = None
current_image = None
current_image_sampling_step = 0
id_live_preview = 0
textinfo = None
time_start = None
server_start = None
_server_command_signal = threading.Event()
_server_command: Optional[str] = None
def __init__(self):
self.server_start = time.time()
@property
def need_restart(self) -> bool:
# Compatibility getter for need_restart.
return self.server_command == "restart"
@need_restart.setter
def need_restart(self, value: bool) -> None:
# Compatibility setter for need_restart.
if value:
self.server_command = "restart"
@property
def server_command(self):
return self._server_command
@server_command.setter
def server_command(self, value: Optional[str]) -> None:
"""
Set the server command to `value` and signal that it's been set.
"""
self._server_command = value
self._server_command_signal.set()
def wait_for_server_command(self, timeout: Optional[float] = None) -> Optional[str]:
"""
Wait for server command to get set; return and clear the value and signal.
"""
if self._server_command_signal.wait(timeout):
self._server_command_signal.clear()
req = self._server_command
self._server_command = None
return req
return None
def request_restart(self) -> None:
self.interrupt()
self.server_command = "restart"
log.info("Received restart request")
def skip(self):
self.skipped = True
log.info("Received skip request")
def interrupt(self):
self.interrupted = True
log.info("Received interrupt request")
def stop_generating(self):
self.stopping_generation = True
log.info("Received stop generating request")
def nextjob(self):
if shared.opts.live_previews_enable and shared.opts.show_progress_every_n_steps == -1:
self.do_set_current_image()
self.job_no += 1
self.sampling_step = 0
self.current_image_sampling_step = 0
def dict(self):
obj = {
"skipped": self.skipped,
"interrupted": self.interrupted,
"stopping_generation": self.stopping_generation,
"job": self.job,
"job_count": self.job_count,
"job_timestamp": self.job_timestamp,
"job_no": self.job_no,
"sampling_step": self.sampling_step,
"sampling_steps": self.sampling_steps,
}
return obj
def begin(self, job: str = "(unknown)"):
self.sampling_step = 0
self.time_start = time.time()
self.job_count = -1
self.processing_has_refined_job_count = False
self.job_no = 0
self.job_timestamp = datetime.datetime.now().strftime("%Y%m%d%H%M%S")
self.current_latent = None
self.current_image = None
self.current_image_sampling_step = 0
self.id_live_preview = 0
self.skipped = False
self.interrupted = False
self.stopping_generation = False
self.textinfo = None
self.job = job
devices.torch_gc()
log.info("Starting job %s", job)
def end(self):
duration = time.time() - self.time_start
log.info("Ending job %s (%.2f seconds)", self.job, duration)
self.job = ""
self.job_count = 0
devices.torch_gc()
@torch.inference_mode()
def set_current_image(self):
"""if enough sampling steps have been made after the last call to this, sets self.current_image from self.current_latent, and modifies self.id_live_preview accordingly"""
if not shared.parallel_processing_allowed:
return
if self.sampling_step - self.current_image_sampling_step >= shared.opts.show_progress_every_n_steps and shared.opts.live_previews_enable and shared.opts.show_progress_every_n_steps != -1:
self.do_set_current_image()
@torch.inference_mode()
def do_set_current_image(self):
if self.current_latent is None:
return
import modules.sd_samplers
try:
if shared.opts.show_progress_grid:
self.assign_current_image(modules.sd_samplers.samples_to_image_grid(self.current_latent))
else:
self.assign_current_image(modules.sd_samplers.sample_to_image(self.current_latent))
self.current_image_sampling_step = self.sampling_step
except Exception as e:
# traceback.print_exc()
# print(e)
# when switching models during genration, VAE would be on CPU, so creating an image will fail.
# we silently ignore this error
errors.record_exception()
@torch.inference_mode()
def assign_current_image(self, image):
if shared.opts.live_previews_image_format == 'jpeg' and image.mode in ('RGBA', 'P'):
image = image.convert('RGB')
self.current_image = image
self.id_live_preview += 1
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