InvokeAI/server/services.py
2022-10-14 00:03:06 -04:00

393 lines
14 KiB
Python

# Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
from argparse import ArgumentParser
import base64
from datetime import datetime, timezone
import glob
import json
import os
from pathlib import Path
from queue import Empty, Queue
import shlex
from threading import Thread
import time
from flask_socketio import SocketIO, join_room, leave_room
from ldm.invoke.args import Args
from ldm.invoke.generator import embiggen
from PIL import Image
from ldm.invoke.pngwriter import PngWriter
from ldm.invoke.server import CanceledException
from ldm.generate import Generate
from server.models import DreamResult, JobRequest, PaginatedItems, ProgressType, Signal
class JobQueueService:
__queue: Queue = Queue()
def push(self, dreamRequest: DreamResult):
self.__queue.put(dreamRequest)
def get(self, timeout: float = None) -> DreamResult:
return self.__queue.get(timeout= timeout)
class SignalQueueService:
__queue: Queue = Queue()
def push(self, signal: Signal):
self.__queue.put(signal)
def get(self) -> Signal:
return self.__queue.get(block=False)
class SignalService:
__socketio: SocketIO
__queue: SignalQueueService
def __init__(self, socketio: SocketIO, queue: SignalQueueService):
self.__socketio = socketio
self.__queue = queue
def on_join(data):
room = data['room']
join_room(room)
self.__socketio.emit("test", "something", room=room)
def on_leave(data):
room = data['room']
leave_room(room)
self.__socketio.on_event('join_room', on_join)
self.__socketio.on_event('leave_room', on_leave)
self.__socketio.start_background_task(self.__process)
def __process(self):
# preload the model
print('Started signal queue processor')
try:
while True:
try:
signal = self.__queue.get()
self.__socketio.emit(signal.event, signal.data, room=signal.room, broadcast=signal.broadcast)
except Empty:
pass
finally:
self.__socketio.sleep(0.001)
except KeyboardInterrupt:
print('Signal queue processor stopped')
def emit(self, signal: Signal):
self.__queue.push(signal)
# TODO: Name this better?
# TODO: Logging and signals should probably be event based (multiple listeners for an event)
class LogService:
__location: str
__logFile: str
def __init__(self, location:str, file:str):
self.__location = location
self.__logFile = file
def log(self, dreamResult: DreamResult, seed = None, upscaled = False):
with open(os.path.join(self.__location, self.__logFile), "a") as log:
log.write(f"{dreamResult.id}: {dreamResult.to_json()}\n")
class ImageStorageService:
__location: str
__pngWriter: PngWriter
__legacyParser: ArgumentParser
def __init__(self, location):
self.__location = location
self.__pngWriter = PngWriter(self.__location)
self.__legacyParser = Args() # TODO: inject this?
def __getName(self, dreamId: str, postfix: str = '') -> str:
return f'{dreamId}{postfix}.png'
def save(self, image, dreamResult: DreamResult, postfix: str = '') -> str:
name = self.__getName(dreamResult.id, postfix)
meta = dreamResult.to_json() # TODO: make all methods consistent with writing metadata. Standardize metadata.
path = self.__pngWriter.save_image_and_prompt_to_png(image, dream_prompt=meta, metadata=None, name=name)
return path
def path(self, dreamId: str, postfix: str = '') -> str:
name = self.__getName(dreamId, postfix)
path = os.path.join(self.__location, name)
return path
# Returns true if found, false if not found or error
def delete(self, dreamId: str, postfix: str = '') -> bool:
path = self.path(dreamId, postfix)
if (os.path.exists(path)):
os.remove(path)
return True
else:
return False
def getMetadata(self, dreamId: str, postfix: str = '') -> DreamResult:
path = self.path(dreamId, postfix)
image = Image.open(path)
text = image.text
if text.__contains__('Dream'):
dreamMeta = text.get('Dream')
try:
j = json.loads(dreamMeta)
return DreamResult.from_json(j)
except ValueError:
# Try to parse command-line format (legacy metadata format)
try:
opt = self.__parseLegacyMetadata(dreamMeta)
optd = opt.__dict__
if (not 'width' in optd) or (optd.get('width') is None):
optd['width'] = image.width
if (not 'height' in optd) or (optd.get('height') is None):
optd['height'] = image.height
if (not 'steps' in optd) or (optd.get('steps') is None):
optd['steps'] = 10 # No way around this unfortunately - seems like it wasn't storing this previously
optd['time'] = os.path.getmtime(path) # Set timestamp manually (won't be exactly correct though)
return DreamResult.from_json(optd)
except:
return None
else:
return None
def __parseLegacyMetadata(self, command: str) -> DreamResult:
# before splitting, escape single quotes so as not to mess
# up the parser
command = command.replace("'", "\\'")
try:
elements = shlex.split(command)
except ValueError as e:
return None
# rearrange the arguments to mimic how it works in the Dream bot.
switches = ['']
switches_started = False
for el in elements:
if el[0] == '-' and not switches_started:
switches_started = True
if switches_started:
switches.append(el)
else:
switches[0] += el
switches[0] += ' '
switches[0] = switches[0][: len(switches[0]) - 1]
try:
opt = self.__legacyParser.parse_cmd(switches)
return opt
except SystemExit:
return None
def list_files(self, page: int, perPage: int) -> PaginatedItems:
files = sorted(glob.glob(os.path.join(self.__location,'*.png')), key=os.path.getmtime, reverse=True)
count = len(files)
startId = page * perPage
pageCount = int(count / perPage) + 1
endId = min(startId + perPage, count)
items = [] if startId >= count else files[startId:endId]
items = list(map(lambda f: Path(f).stem, items))
return PaginatedItems(items, page, pageCount, perPage, count)
class GeneratorService:
__model: Generate
__queue: JobQueueService
__imageStorage: ImageStorageService
__intermediateStorage: ImageStorageService
__log: LogService
__thread: Thread
__cancellationRequested: bool = False
__signal_service: SignalService
def __init__(self, model: Generate, queue: JobQueueService, imageStorage: ImageStorageService, intermediateStorage: ImageStorageService, log: LogService, signal_service: SignalService):
self.__model = model
self.__queue = queue
self.__imageStorage = imageStorage
self.__intermediateStorage = intermediateStorage
self.__log = log
self.__signal_service = signal_service
# Create the background thread
self.__thread = Thread(target=self.__process, name = "GeneratorService")
self.__thread.daemon = True
self.__thread.start()
# Request cancellation of the current job
def cancel(self):
self.__cancellationRequested = True
# TODO: Consider moving this to its own service if there's benefit in separating the generator
def __process(self):
# preload the model
# TODO: support multiple models
print('Preloading model')
tic = time.time()
self.__model.load_model()
print(f'>> model loaded in', '%4.2fs' % (time.time() - tic))
print('Started generation queue processor')
try:
while True:
dreamRequest = self.__queue.get()
self.__generate(dreamRequest)
except KeyboardInterrupt:
print('Generation queue processor stopped')
def __on_start(self, jobRequest: JobRequest):
self.__signal_service.emit(Signal.job_started(jobRequest.id))
def __on_image_result(self, jobRequest: JobRequest, image, seed, upscaled=False):
dreamResult = jobRequest.newDreamResult()
dreamResult.seed = seed
dreamResult.has_upscaled = upscaled
dreamResult.iterations = 1
jobRequest.results.append(dreamResult)
# TODO: Separate status of GFPGAN?
self.__imageStorage.save(image, dreamResult)
# TODO: handle upscaling logic better (this is appending data to log, but only on first generation)
if not upscaled:
self.__log.log(dreamResult)
# Send result signal
self.__signal_service.emit(Signal.image_result(jobRequest.id, dreamResult.id, dreamResult))
upscaling_requested = dreamResult.enable_upscale or dreamResult.enable_gfpgan
# Report upscaling status
# TODO: this is very coupled to logic inside the generator. Fix that.
if upscaling_requested and any(result.has_upscaled for result in jobRequest.results):
progressType = ProgressType.UPSCALING_STARTED if len(jobRequest.results) < 2 * jobRequest.iterations else ProgressType.UPSCALING_DONE
upscale_count = sum(1 for i in jobRequest.results if i.has_upscaled)
self.__signal_service.emit(Signal.image_progress(jobRequest.id, dreamResult.id, upscale_count, jobRequest.iterations, progressType))
def __on_progress(self, jobRequest: JobRequest, sample, step):
if self.__cancellationRequested:
self.__cancellationRequested = False
raise CanceledException
# TODO: Progress per request will be easier once the seeds (and ids) can all be pre-generated
hasProgressImage = False
s = str(len(jobRequest.results))
if jobRequest.progress_images and step % 5 == 0 and step < jobRequest.steps - 1:
image = self.__model._sample_to_image(sample)
# TODO: clean this up, use a pre-defined dream result
result = DreamResult()
result.parse_json(jobRequest.__dict__, new_instance=False)
self.__intermediateStorage.save(image, result, postfix=f'.{s}.{step}')
hasProgressImage = True
self.__signal_service.emit(Signal.image_progress(jobRequest.id, f'{jobRequest.id}.{s}', step, jobRequest.steps, ProgressType.GENERATION, hasProgressImage))
def __generate(self, jobRequest: JobRequest):
try:
# TODO: handle this file a file service for init images
initimgfile = None # TODO: support this on the model directly?
if (jobRequest.enable_init_image):
if jobRequest.initimg is not None:
with open("./img2img-tmp.png", "wb") as f:
initimg = jobRequest.initimg.split(",")[1] # Ignore mime type
f.write(base64.b64decode(initimg))
initimgfile = "./img2img-tmp.png"
# Use previous seed if set to -1
initSeed = jobRequest.seed
if initSeed == -1:
initSeed = self.__model.seed
# Zero gfpgan strength if the model doesn't exist
# TODO: determine if this could be at the top now? Used to cause circular import
from ldm.gfpgan.gfpgan_tools import gfpgan_model_exists
if not gfpgan_model_exists:
jobRequest.enable_gfpgan = False
# Signal start
self.__on_start(jobRequest)
# Generate in model
# TODO: Split job generation requests instead of fitting all parameters here
# TODO: Support no generation (just upscaling/gfpgan)
upscale = None if not jobRequest.enable_upscale else jobRequest.upscale
facetool_strength = 0 if not jobRequest.enable_gfpgan else jobRequest.facetool_strength
if not jobRequest.enable_generate:
# If not generating, check if we're upscaling or running gfpgan
if not upscale and not facetool_strength:
# Invalid settings (TODO: Add message to help user)
raise CanceledException()
image = Image.open(initimgfile)
# TODO: support progress for upscale?
self.__model.upscale_and_reconstruct(
image_list = [[image,0]],
upscale = upscale,
strength = facetool_strength,
save_original = False,
image_callback = lambda image, seed, upscaled=False: self.__on_image_result(jobRequest, image, seed, upscaled))
else:
# Generating - run the generation
init_img = None if (not jobRequest.enable_img2img or jobRequest.strength == 0) else initimgfile
self.__model.prompt2image(
prompt = jobRequest.prompt,
init_img = init_img, # TODO: ensure this works
strength = None if init_img is None else jobRequest.strength,
fit = None if init_img is None else jobRequest.fit,
iterations = jobRequest.iterations,
cfg_scale = jobRequest.cfg_scale,
threshold = jobRequest.threshold,
perlin = jobRequest.perlin,
width = jobRequest.width,
height = jobRequest.height,
seed = jobRequest.seed,
steps = jobRequest.steps,
variation_amount = jobRequest.variation_amount,
with_variations = jobRequest.with_variations,
facetool_strength = facetool_strength,
upscale = upscale,
sampler_name = jobRequest.sampler_name,
seamless = jobRequest.seamless,
hires_fix = jobRequest.hires_fix,
embiggen = jobRequest.embiggen,
embiggen_tiles = jobRequest.embiggen_tiles,
step_callback = lambda sample, step: self.__on_progress(jobRequest, sample, step),
image_callback = lambda image, seed, upscaled=False: self.__on_image_result(jobRequest, image, seed, upscaled))
except CanceledException:
self.__signal_service.emit(Signal.job_canceled(jobRequest.id))
finally:
self.__signal_service.emit(Signal.job_done(jobRequest.id))
# Remove the temp file
if (initimgfile is not None):
os.remove("./img2img-tmp.png")