mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
moved server.py into right location
This commit is contained in:
Lincoln Stein
parent
7c485a1a4a
commit
ac02a775e4
@ -1,548 +1,92 @@
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#!/usr/bin/env python3
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import json
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# Copyright (c) 2022 Lincoln D. Stein (https://github.com/lstein)
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import base64
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import mimetypes
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import argparse
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import shlex
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import os
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import os
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import sys
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from http.server import BaseHTTPRequestHandler, ThreadingHTTPServer
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import copy
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import warnings
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import ldm.dream.readline
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from ldm.dream.pngwriter import PngWriter, PromptFormatter
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from dream_server import DreamServer, ThreadingDreamServer
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def main():
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class DreamServer(BaseHTTPRequestHandler):
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"""Initialize command-line parsers and the diffusion model"""
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model = None
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arg_parser = create_argv_parser()
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opt = arg_parser.parse_args()
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if opt.laion400m:
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# defaults suitable to the older latent diffusion weights
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width = 256
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height = 256
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config = 'configs/latent-diffusion/txt2img-1p4B-eval.yaml'
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weights = 'models/ldm/text2img-large/model.ckpt'
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else:
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# some defaults suitable for stable diffusion weights
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width = 512
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height = 512
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config = 'configs/stable-diffusion/v1-inference.yaml'
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weights = 'models/ldm/stable-diffusion-v1/model.ckpt'
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print('* Initializing, be patient...\n')
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def do_GET(self):
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sys.path.append('.')
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if self.path == "/":
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from pytorch_lightning import logging
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self.send_response(200)
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from ldm.simplet2i import T2I
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self.send_header("Content-type", "text/html")
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self.end_headers()
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# these two lines prevent a horrible warning message from appearing
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with open("./static/dream_web/index.html", "rb") as content:
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# when the frozen CLIP tokenizer is imported
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self.wfile.write(content.read())
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import transformers
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elif os.path.exists("." + self.path):
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mime_type = mimetypes.guess_type(self.path)[0]
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transformers.logging.set_verbosity_error()
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if mime_type is not None:
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self.send_response(200)
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# creating a simple text2image object with a handful of
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self.send_header("Content-type", mime_type)
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# defaults passed on the command line.
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self.end_headers()
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# additional parameters will be added (or overriden) during
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with open("." + self.path, "rb") as content:
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# the user input loop
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self.wfile.write(content.read())
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t2i = T2I(
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width=width,
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height=height,
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sampler_name=opt.sampler_name,
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weights=weights,
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full_precision=opt.full_precision,
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config=config,
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latent_diffusion_weights=opt.laion400m, # this is solely for recreating the prompt
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embedding_path=opt.embedding_path,
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device=opt.device,
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)
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# make sure the output directory exists
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if not os.path.exists(opt.outdir):
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os.makedirs(opt.outdir)
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# gets rid of annoying messages about random seed
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logging.getLogger('pytorch_lightning').setLevel(logging.ERROR)
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# load the infile as a list of lines
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infile = None
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if opt.infile:
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try:
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if os.path.isfile(opt.infile):
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infile = open(opt.infile,'r')
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elif opt.infile=='-': # stdin
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infile = sys.stdin
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else:
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else:
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raise FileNotFoundError(f'{opt.infile} not found.')
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self.send_response(404)
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except (FileNotFoundError,IOError) as e:
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print(f'{e}. Aborting.')
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sys.exit(-1)
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# preload the model
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t2i.load_model()
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# load GFPGAN if requested
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if opt.use_gfpgan:
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print('\n* --gfpgan was specified, loading gfpgan...')
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with warnings.catch_warnings():
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warnings.filterwarnings('ignore', category=DeprecationWarning)
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try:
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model_path = os.path.join(
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opt.gfpgan_dir, opt.gfpgan_model_path
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)
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if not os.path.isfile(model_path):
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raise Exception(
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'GFPGAN model not found at path ' + model_path
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)
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sys.path.append(os.path.abspath(opt.gfpgan_dir))
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from gfpgan import GFPGANer
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bg_upsampler = load_gfpgan_bg_upsampler(
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opt.gfpgan_bg_upsampler, opt.gfpgan_bg_tile
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)
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t2i.gfpgan = GFPGANer(
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model_path=model_path,
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upscale=opt.gfpgan_upscale,
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arch='clean',
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channel_multiplier=2,
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bg_upsampler=bg_upsampler,
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)
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except Exception:
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import traceback
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print('Error loading GFPGAN:', file=sys.stderr)
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print(traceback.format_exc(), file=sys.stderr)
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log_path = os.path.join(opt.outdir, 'dream_log.txt')
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with open(log_path, 'a') as log:
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cmd_parser = create_cmd_parser()
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if opt.web:
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dream_server_loop(t2i)
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else:
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else:
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main_loop(t2i, opt.outdir, cmd_parser, log_path, infile)
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self.send_response(404)
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log.close()
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def main_loop(t2i, outdir, parser, log_path, infile):
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def do_POST(self):
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print(
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self.send_response(200)
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"\n* Initialization done! Awaiting your command (-h for help, 'q' to quit, 'cd' to change output dir, 'pwd' to print output dir)..."
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self.send_header("Content-type", "application/json")
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)
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self.end_headers()
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"""prompt/read/execute loop"""
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done = False
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last_seeds = []
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while not done:
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content_length = int(self.headers['Content-Length'])
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try:
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post_data = json.loads(self.rfile.read(content_length))
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command = get_next_command(infile)
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prompt = post_data['prompt']
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except EOFError:
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initimg = post_data['initimg']
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done = True
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iterations = int(post_data['iterations'])
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break
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steps = int(post_data['steps'])
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width = int(post_data['width'])
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height = int(post_data['height'])
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cfgscale = float(post_data['cfgscale'])
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gfpgan_strength = float(post_data['gfpgan_strength'])
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seed = None if int(post_data['seed']) == -1 else int(post_data['seed'])
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# skip empty lines
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print(f"Request to generate with prompt: {prompt}")
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if not command.strip():
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continue
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if command.startswith(('#', '//')):
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outputs = []
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continue
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if initimg is None:
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# Run txt2img
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# before splitting, escape single quotes so as not to mess
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outputs = self.model.txt2img(prompt,
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# up the parser
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iterations=iterations,
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command = command.replace("'", "\\'")
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cfg_scale = cfgscale,
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width = width,
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try:
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height = height,
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elements = shlex.split(command)
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seed = seed,
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except ValueError as e:
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steps = steps,
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print(str(e))
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gfpgan_strength = gfpgan_strength)
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continue
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if elements[0] == 'q':
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done = True
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break
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if elements[0].startswith(
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'!dream'
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): # in case a stored prompt still contains the !dream command
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elements.pop(0)
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# rearrange the arguments to mimic how it works in the Dream bot.
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switches = ['']
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switches_started = False
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for el in elements:
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if el[0] == '-' and not switches_started:
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switches_started = True
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if switches_started:
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switches.append(el)
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else:
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switches[0] += el
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switches[0] += ' '
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switches[0] = switches[0][: len(switches[0]) - 1]
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try:
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opt = parser.parse_args(switches)
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except SystemExit:
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parser.print_help()
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continue
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if len(opt.prompt) == 0:
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print('Try again with a prompt!')
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continue
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if opt.seed is not None and opt.seed < 0: # retrieve previous value!
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try:
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opt.seed = last_seeds[opt.seed]
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print(f'reusing previous seed {opt.seed}')
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except IndexError:
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print(f'No previous seed at position {opt.seed} found')
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opt.seed = None
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normalized_prompt = PromptFormatter(t2i, opt).normalize_prompt()
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individual_images = not opt.grid
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if opt.outdir:
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if not os.path.exists(opt.outdir):
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os.makedirs(opt.outdir)
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current_outdir = opt.outdir
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else:
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else:
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current_outdir = outdir
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# Decode initimg as base64 to temp file
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with open("./img2img-tmp.png", "wb") as f:
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initimg = initimg.split(",")[1] # Ignore mime type
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f.write(base64.b64decode(initimg))
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# Here is where the images are actually generated!
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# Run img2img
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try:
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outputs = self.model.img2img(prompt,
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file_writer = PngWriter(current_outdir, normalized_prompt, opt.batch_size)
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init_img = "./img2img-tmp.png",
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callback = file_writer.write_image if individual_images else None
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iterations = iterations,
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image_list = t2i.prompt2image(image_callback=callback, **vars(opt))
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cfg_scale = cfgscale,
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results = (
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seed = seed,
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file_writer.files_written if individual_images else image_list
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steps = steps)
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)
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# Remove the temp file
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os.remove("./img2img-tmp.png")
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if opt.grid and len(results) > 0:
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print(f"Prompt generated with output: {outputs}")
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grid_img = file_writer.make_grid([r[0] for r in results])
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filename = file_writer.unique_filename(results[0][1])
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seeds = [a[1] for a in results]
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results = [[filename, seeds]]
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metadata_prompt = f'{normalized_prompt} -S{results[0][1]}'
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file_writer.save_image_and_prompt_to_png(
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grid_img, metadata_prompt, filename
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)
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last_seeds = [r[1] for r in results]
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post_data['initimg'] = '' # Don't send init image back
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except AssertionError as e:
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# Append post_data to log
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print(e)
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with open("./outputs/img-samples/dream_web_log.txt", "a") as log:
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continue
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for output in outputs:
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log.write(f"{output[0]}: {json.dumps(post_data)}\n")
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except OSError as e:
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outputs = [x + [post_data] for x in outputs] # Append config to each output
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print(e)
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result = {'outputs': outputs}
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continue
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self.wfile.write(bytes(json.dumps(result), "utf-8"))
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print('Outputs:')
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write_log_message(t2i, normalized_prompt, results, log_path)
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print('goodbye!')
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def get_next_command(infile=None) -> 'command string':
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if infile is None:
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command = input("dream> ")
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else:
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command = infile.readline()
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if not command:
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raise EOFError
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else:
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command = command.strip()
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print(f'#{command}')
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return command
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def dream_server_loop(t2i):
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print('\n* --web was specified, starting web server...')
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# Change working directory to the stable-diffusion directory
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os.chdir(
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os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))
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)
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# Start server
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DreamServer.model = t2i
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dream_server = ThreadingDreamServer(("0.0.0.0", 9090))
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print("\nStarted Stable Diffusion dream server!")
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print("Point your browser at http://localhost:9090 or use the host's DNS name or IP address.")
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try:
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dream_server.serve_forever()
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except KeyboardInterrupt:
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pass
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dream_server.server_close()
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def load_gfpgan_bg_upsampler(bg_upsampler, bg_tile=400):
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import torch
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if bg_upsampler == 'realesrgan':
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if not torch.cuda.is_available(): # CPU
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import warnings
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warnings.warn(
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'The unoptimized RealESRGAN is slow on CPU. We do not use it. '
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'If you really want to use it, please modify the corresponding codes.'
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)
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bg_upsampler = None
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else:
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from basicsr.archs.rrdbnet_arch import RRDBNet
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from realesrgan import RealESRGANer
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model = RRDBNet(
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num_in_ch=3,
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num_out_ch=3,
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num_feat=64,
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num_block=23,
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num_grow_ch=32,
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scale=2,
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)
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bg_upsampler = RealESRGANer(
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scale=2,
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model_path='https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth',
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model=model,
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tile=bg_tile,
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tile_pad=10,
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pre_pad=0,
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half=True,
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) # need to set False in CPU mode
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else:
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bg_upsampler = None
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return bg_upsampler
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# variant generation is going to be superseded by a generalized
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class ThreadingDreamServer(ThreadingHTTPServer):
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# "prompt-morph" functionality
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def __init__(self, server_address):
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# def generate_variants(t2i,outdir,opt,previous_gens):
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super(ThreadingDreamServer, self).__init__(server_address, DreamServer)
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# variants = []
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# print(f"Generating {opt.variants} variant(s)...")
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# newopt = copy.deepcopy(opt)
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# newopt.iterations = 1
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# newopt.variants = None
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# for r in previous_gens:
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# newopt.init_img = r[0]
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# prompt = PromptFormatter(t2i,newopt).normalize_prompt()
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# print(f"] generating variant for {newopt.init_img}")
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# for j in range(0,opt.variants):
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# try:
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# file_writer = PngWriter(outdir,prompt,newopt.batch_size)
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# callback = file_writer.write_image
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# t2i.prompt2image(image_callback=callback,**vars(newopt))
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# results = file_writer.files_written
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# variants.append([prompt,results])
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# except AssertionError as e:
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# print(e)
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# continue
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# print(f'{opt.variants} variants generated')
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# return variants
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### the t2i variable doesn't seem to be necessary here. maybe remove it?
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def write_log_message(t2i, prompt, results, log_path):
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"""logs the name of the output image, its prompt and seed to the terminal, log file, and a Dream text chunk in the PNG metadata"""
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log_lines = [f"{r[0]}: {prompt} -S{r[1]}\n" for r in results]
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|
||||||
print(*log_lines, sep="")
|
|
||||||
|
|
||||||
with open(log_path, "a") as file:
|
|
||||||
file.writelines(log_lines)
|
|
||||||
|
|
||||||
|
|
||||||
def create_argv_parser():
|
|
||||||
parser = argparse.ArgumentParser(
|
|
||||||
description="Parse script's command line args"
|
|
||||||
)
|
|
||||||
parser.add_argument(
|
|
||||||
'--laion400m',
|
|
||||||
'--latent_diffusion',
|
|
||||||
'-l',
|
|
||||||
dest='laion400m',
|
|
||||||
action='store_true',
|
|
||||||
help='fallback to the latent diffusion (laion400m) weights and config',
|
|
||||||
)
|
|
||||||
parser.add_argument(
|
|
||||||
'--from_file',
|
|
||||||
dest='infile',
|
|
||||||
type=str,
|
|
||||||
help='if specified, load prompts from this file',
|
|
||||||
)
|
|
||||||
parser.add_argument(
|
|
||||||
'-n',
|
|
||||||
'--iterations',
|
|
||||||
type=int,
|
|
||||||
default=1,
|
|
||||||
help='number of images to generate',
|
|
||||||
)
|
|
||||||
parser.add_argument(
|
|
||||||
'-F',
|
|
||||||
'--full_precision',
|
|
||||||
dest='full_precision',
|
|
||||||
action='store_true',
|
|
||||||
help='use slower full precision math for calculations',
|
|
||||||
)
|
|
||||||
parser.add_argument(
|
|
||||||
'--sampler',
|
|
||||||
'-m',
|
|
||||||
dest='sampler_name',
|
|
||||||
choices=[
|
|
||||||
'ddim',
|
|
||||||
'k_dpm_2_a',
|
|
||||||
'k_dpm_2',
|
|
||||||
'k_euler_a',
|
|
||||||
'k_euler',
|
|
||||||
'k_heun',
|
|
||||||
'k_lms',
|
|
||||||
'plms',
|
|
||||||
],
|
|
||||||
default='k_lms',
|
|
||||||
help='which sampler to use (k_lms) - can only be set on command line',
|
|
||||||
)
|
|
||||||
parser.add_argument(
|
|
||||||
'--outdir',
|
|
||||||
'-o',
|
|
||||||
type=str,
|
|
||||||
default='outputs/img-samples',
|
|
||||||
help='directory in which to place generated images and a log of prompts and seeds (outputs/img-samples',
|
|
||||||
)
|
|
||||||
parser.add_argument(
|
|
||||||
'--embedding_path',
|
|
||||||
type=str,
|
|
||||||
help='Path to a pre-trained embedding manager checkpoint - can only be set on command line',
|
|
||||||
)
|
|
||||||
parser.add_argument(
|
|
||||||
'--device',
|
|
||||||
'-d',
|
|
||||||
type=str,
|
|
||||||
default='cuda',
|
|
||||||
help='device to run stable diffusion on. defaults to cuda `torch.cuda.current_device()` if avalible',
|
|
||||||
)
|
|
||||||
# GFPGAN related args
|
|
||||||
parser.add_argument(
|
|
||||||
'--gfpgan',
|
|
||||||
dest='use_gfpgan',
|
|
||||||
action='store_true',
|
|
||||||
help='load gfpgan for use in the dreambot. Note: Enabling GFPGAN will require more GPU memory',
|
|
||||||
)
|
|
||||||
parser.add_argument(
|
|
||||||
'--gfpgan_upscale',
|
|
||||||
type=int,
|
|
||||||
default=2,
|
|
||||||
help='The final upsampling scale of the image. Default: 2. Only used if --gfpgan is specified',
|
|
||||||
)
|
|
||||||
parser.add_argument(
|
|
||||||
'--gfpgan_bg_upsampler',
|
|
||||||
type=str,
|
|
||||||
default='realesrgan',
|
|
||||||
help='Background upsampler. Default: None. Options: realesrgan, none. Only used if --gfpgan is specified',
|
|
||||||
)
|
|
||||||
parser.add_argument(
|
|
||||||
'--gfpgan_bg_tile',
|
|
||||||
type=int,
|
|
||||||
default=400,
|
|
||||||
help='Tile size for background sampler, 0 for no tile during testing. Default: 400. Only used if --gfpgan is specified',
|
|
||||||
)
|
|
||||||
parser.add_argument(
|
|
||||||
'--gfpgan_model_path',
|
|
||||||
type=str,
|
|
||||||
default='experiments/pretrained_models/GFPGANv1.3.pth',
|
|
||||||
help='indicates the path to the GFPGAN model, relative to --gfpgan_dir. Only used if --gfpgan is specified',
|
|
||||||
)
|
|
||||||
parser.add_argument(
|
|
||||||
'--gfpgan_dir',
|
|
||||||
type=str,
|
|
||||||
default='../GFPGAN',
|
|
||||||
help='indicates the directory containing the GFPGAN code. Only used if --gfpgan is specified',
|
|
||||||
)
|
|
||||||
parser.add_argument(
|
|
||||||
'--web',
|
|
||||||
dest='web',
|
|
||||||
action='store_true',
|
|
||||||
help='start in web server mode.',
|
|
||||||
)
|
|
||||||
return parser
|
|
||||||
|
|
||||||
|
|
||||||
def create_cmd_parser():
|
|
||||||
parser = argparse.ArgumentParser(
|
|
||||||
description='Example: dream> a fantastic alien landscape -W1024 -H960 -s100 -n12'
|
|
||||||
)
|
|
||||||
parser.add_argument('prompt')
|
|
||||||
parser.add_argument('-s', '--steps', type=int, help='number of steps')
|
|
||||||
parser.add_argument(
|
|
||||||
'-S',
|
|
||||||
'--seed',
|
|
||||||
type=int,
|
|
||||||
help='image seed; a +ve integer, or use -1 for the previous seed, -2 for the one before that, etc',
|
|
||||||
)
|
|
||||||
parser.add_argument(
|
|
||||||
'-n',
|
|
||||||
'--iterations',
|
|
||||||
type=int,
|
|
||||||
default=1,
|
|
||||||
help='number of samplings to perform (slower, but will provide seeds for individual images)',
|
|
||||||
)
|
|
||||||
parser.add_argument(
|
|
||||||
'-b',
|
|
||||||
'--batch_size',
|
|
||||||
type=int,
|
|
||||||
default=1,
|
|
||||||
help='number of images to produce per sampling (will not provide seeds for individual images!)',
|
|
||||||
)
|
|
||||||
parser.add_argument(
|
|
||||||
'-W', '--width', type=int, help='image width, multiple of 64'
|
|
||||||
)
|
|
||||||
parser.add_argument(
|
|
||||||
'-H', '--height', type=int, help='image height, multiple of 64'
|
|
||||||
)
|
|
||||||
parser.add_argument(
|
|
||||||
'-C',
|
|
||||||
'--cfg_scale',
|
|
||||||
default=7.5,
|
|
||||||
type=float,
|
|
||||||
help='prompt configuration scale',
|
|
||||||
)
|
|
||||||
parser.add_argument(
|
|
||||||
'-g', '--grid', action='store_true', help='generate a grid'
|
|
||||||
)
|
|
||||||
parser.add_argument(
|
|
||||||
'--outdir',
|
|
||||||
'-o',
|
|
||||||
type=str,
|
|
||||||
default=None,
|
|
||||||
help='directory in which to place generated images and a log of prompts and seeds (outputs/img-samples',
|
|
||||||
)
|
|
||||||
parser.add_argument(
|
|
||||||
'-i',
|
|
||||||
'--individual',
|
|
||||||
action='store_true',
|
|
||||||
help='generate individual files (default)',
|
|
||||||
)
|
|
||||||
parser.add_argument(
|
|
||||||
'-I',
|
|
||||||
'--init_img',
|
|
||||||
type=str,
|
|
||||||
help='path to input image for img2img mode (supersedes width and height)',
|
|
||||||
)
|
|
||||||
parser.add_argument(
|
|
||||||
'-f',
|
|
||||||
'--strength',
|
|
||||||
default=0.75,
|
|
||||||
type=float,
|
|
||||||
help='strength for noising/unnoising. 0.0 preserves image exactly, 1.0 replaces it completely',
|
|
||||||
)
|
|
||||||
parser.add_argument(
|
|
||||||
'-G',
|
|
||||||
'--gfpgan_strength',
|
|
||||||
default=None,
|
|
||||||
type=float,
|
|
||||||
help='The strength at which to apply the GFPGAN model to the result, in order to improve faces.',
|
|
||||||
)
|
|
||||||
# variants is going to be superseded by a generalized "prompt-morph" function
|
|
||||||
# parser.add_argument('-v','--variants',type=int,help="in img2img mode, the first generated image will get passed back to img2img to generate the requested number of variants")
|
|
||||||
parser.add_argument(
|
|
||||||
'-x',
|
|
||||||
'--skip_normalize',
|
|
||||||
action='store_true',
|
|
||||||
help='skip subprompt weight normalization',
|
|
||||||
)
|
|
||||||
return parser
|
|
||||||
|
|
||||||
|
|
||||||
if __name__ == '__main__':
|
|
||||||
main()
|
|
||||||
|
|||||||
Loading…
Reference in New Issue
Block a user