mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
725 lines
20 KiB
Python
725 lines
20 KiB
Python
import mimetypes
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import transformers
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import json
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import os
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import traceback
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import eventlet
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import glob
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import shlex
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import math
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import shutil
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from flask_socketio import SocketIO
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from flask import Flask, send_from_directory, url_for, jsonify
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from pathlib import Path
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from PIL import Image
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from pytorch_lightning import logging
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from threading import Event
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from uuid import uuid4
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from send2trash import send2trash
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from ldm.gfpgan.gfpgan_tools import real_esrgan_upscale
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from ldm.gfpgan.gfpgan_tools import run_gfpgan
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from ldm.generate import Generate
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from ldm.dream.pngwriter import PngWriter, retrieve_metadata
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from ldm.dream.args import APP_ID, APP_VERSION, calculate_init_img_hash
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from ldm.dream.conditioning import split_weighted_subprompts
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from modules.parameters import parameters_to_command
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"""
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USER CONFIG
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"""
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output_dir = "outputs/" # Base output directory for images
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# host = 'localhost' # Web & socket.io host
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host = "localhost" # Web & socket.io host
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port = 9090 # Web & socket.io port
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verbose = False # enables copious socket.io logging
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additional_allowed_origins = [
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"http://localhost:5173"
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] # additional CORS allowed origins
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model = "stable-diffusion-1.4"
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"""
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END USER CONFIG
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"""
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"""
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SERVER SETUP
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"""
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# fix missing mimetypes on windows due to registry wonkiness
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mimetypes.add_type("application/javascript", ".js")
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mimetypes.add_type("text/css", ".css")
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app = Flask(__name__, static_url_path="", static_folder="../frontend/dist/")
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app.config["OUTPUTS_FOLDER"] = "../outputs"
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@app.route("/outputs/<path:filename>")
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def outputs(filename):
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return send_from_directory(app.config["OUTPUTS_FOLDER"], filename)
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@app.route("/", defaults={"path": ""})
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def serve(path):
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return send_from_directory(app.static_folder, "index.html")
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logger = True if verbose else False
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engineio_logger = True if verbose else False
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# default 1,000,000, needs to be higher for socketio to accept larger images
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max_http_buffer_size = 10000000
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cors_allowed_origins = [f"http://{host}:{port}"] + additional_allowed_origins
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socketio = SocketIO(
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app,
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logger=logger,
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engineio_logger=engineio_logger,
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max_http_buffer_size=max_http_buffer_size,
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cors_allowed_origins=cors_allowed_origins,
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)
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"""
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END SERVER SETUP
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"""
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"""
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APP SETUP
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"""
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class CanceledException(Exception):
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pass
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canceled = Event()
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# reduce logging outputs to error
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transformers.logging.set_verbosity_error()
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logging.getLogger("pytorch_lightning").setLevel(logging.ERROR)
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# Initialize and load model
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generate = Generate(model)
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generate.load_model()
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# location for "finished" images
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result_path = os.path.join(output_dir, "img-samples/")
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# temporary path for intermediates
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intermediate_path = os.path.join(result_path, "intermediates/")
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# path for user-uploaded init images and masks
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init_image_path = os.path.join(result_path, "init-images/")
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mask_image_path = os.path.join(result_path, "mask-images/")
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# txt log
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log_path = os.path.join(result_path, "dream_log.txt")
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# make all output paths
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[
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os.makedirs(path, exist_ok=True)
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for path in [result_path, intermediate_path, init_image_path, mask_image_path]
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]
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"""
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END APP SETUP
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"""
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"""
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SOCKET.IO LISTENERS
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"""
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@socketio.on("requestSystemConfig")
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def handle_request_capabilities():
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print(f">> System config requested")
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config = get_system_config()
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socketio.emit("systemConfig", config)
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@socketio.on("requestAllImages")
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def handle_request_all_images():
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print(f">> All images requested")
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paths = list(filter(os.path.isfile, glob.glob(result_path + "*.png")))
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paths.sort(key=lambda x: os.path.getmtime(x))
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image_array = []
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for path in paths:
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metadata = retrieve_metadata(path)
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image_array.append({"url": path, "metadata": metadata["sd-metadata"]})
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socketio.emit("galleryImages", {"images": image_array})
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eventlet.sleep(0)
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@socketio.on("generateImage")
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def handle_generate_image_event(
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generation_parameters, esrgan_parameters, gfpgan_parameters
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):
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print(
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f">> Image generation requested: {generation_parameters}\nESRGAN parameters: {esrgan_parameters}\nGFPGAN parameters: {gfpgan_parameters}"
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)
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generate_images(generation_parameters, esrgan_parameters, gfpgan_parameters)
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@socketio.on("runESRGAN")
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def handle_run_esrgan_event(original_image, esrgan_parameters):
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print(
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f'>> ESRGAN upscale requested for "{original_image["url"]}": {esrgan_parameters}'
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)
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progress = {
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"currentStep": 1,
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"totalSteps": 1,
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"currentIteration": 1,
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"totalIterations": 1,
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"currentStatus": "Preparing",
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"isProcessing": True,
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"currentStatusHasSteps": False,
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}
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socketio.emit("progressUpdate", progress)
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eventlet.sleep(0)
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image = Image.open(original_image["url"])
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seed = (
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original_image["metadata"]["seed"]
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if "seed" in original_image["metadata"]
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else "unknown_seed"
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)
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progress["currentStatus"] = "Upscaling"
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socketio.emit("progressUpdate", progress)
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eventlet.sleep(0)
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image = real_esrgan_upscale(
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image=image,
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upsampler_scale=esrgan_parameters["upscale"][0],
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strength=esrgan_parameters["upscale"][1],
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seed=seed,
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)
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progress["currentStatus"] = "Saving image"
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socketio.emit("progressUpdate", progress)
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eventlet.sleep(0)
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esrgan_parameters["seed"] = seed
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metadata = parameters_to_post_processed_image_metadata(
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parameters=esrgan_parameters,
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original_image_path=original_image["url"],
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type="esrgan",
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)
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command = parameters_to_command(esrgan_parameters)
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path = save_image(image, command, metadata, result_path, postprocessing="esrgan")
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write_log_message(f'[Upscaled] "{original_image["url"]}" > "{path}": {command}')
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progress["currentStatus"] = "Finished"
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progress["currentStep"] = 0
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progress["totalSteps"] = 0
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progress["currentIteration"] = 0
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progress["totalIterations"] = 0
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progress["isProcessing"] = False
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socketio.emit("progressUpdate", progress)
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eventlet.sleep(0)
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socketio.emit(
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"esrganResult",
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{
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"url": os.path.relpath(path),
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"metadata": metadata,
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},
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)
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@socketio.on("runGFPGAN")
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def handle_run_gfpgan_event(original_image, gfpgan_parameters):
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print(
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f'>> GFPGAN face fix requested for "{original_image["url"]}": {gfpgan_parameters}'
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)
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progress = {
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"currentStep": 1,
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"totalSteps": 1,
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"currentIteration": 1,
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"totalIterations": 1,
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"currentStatus": "Preparing",
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"isProcessing": True,
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"currentStatusHasSteps": False,
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}
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socketio.emit("progressUpdate", progress)
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eventlet.sleep(0)
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image = Image.open(original_image["url"])
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seed = (
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original_image["metadata"]["seed"]
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if "seed" in original_image["metadata"]
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else "unknown_seed"
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)
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progress["currentStatus"] = "Fixing faces"
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socketio.emit("progressUpdate", progress)
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eventlet.sleep(0)
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image = run_gfpgan(
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image=image,
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strength=gfpgan_parameters["gfpgan_strength"],
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seed=seed,
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upsampler_scale=1,
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)
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progress["currentStatus"] = "Saving image"
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socketio.emit("progressUpdate", progress)
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eventlet.sleep(0)
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gfpgan_parameters["seed"] = seed
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metadata = parameters_to_post_processed_image_metadata(
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parameters=gfpgan_parameters,
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original_image_path=original_image["url"],
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type="gfpgan",
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)
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command = parameters_to_command(gfpgan_parameters)
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path = save_image(image, command, metadata, result_path, postprocessing="gfpgan")
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write_log_message(f'[Fixed faces] "{original_image["url"]}" > "{path}": {command}')
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progress["currentStatus"] = "Finished"
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progress["currentStep"] = 0
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progress["totalSteps"] = 0
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progress["currentIteration"] = 0
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progress["totalIterations"] = 0
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progress["isProcessing"] = False
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socketio.emit("progressUpdate", progress)
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eventlet.sleep(0)
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socketio.emit(
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"gfpganResult",
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{
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"url": os.path.relpath(path),
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"metadata": metadata,
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},
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)
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@socketio.on("cancel")
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def handle_cancel():
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print(f">> Cancel processing requested")
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canceled.set()
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socketio.emit("processingCanceled")
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# TODO: I think this needs a safety mechanism.
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@socketio.on("deleteImage")
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def handle_delete_image(path, uuid):
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print(f'>> Delete requested "{path}"')
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send2trash(path)
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socketio.emit("imageDeleted", {"url": path, "uuid": uuid})
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# TODO: I think this needs a safety mechanism.
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@socketio.on("uploadInitialImage")
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def handle_upload_initial_image(bytes, name):
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print(f'>> Init image upload requested "{name}"')
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uuid = uuid4().hex
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split = os.path.splitext(name)
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name = f"{split[0]}.{uuid}{split[1]}"
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file_path = os.path.join(init_image_path, name)
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os.makedirs(os.path.dirname(file_path), exist_ok=True)
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newFile = open(file_path, "wb")
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newFile.write(bytes)
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socketio.emit("initialImageUploaded", {"url": file_path, "uuid": ""})
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# TODO: I think this needs a safety mechanism.
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@socketio.on("uploadMaskImage")
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def handle_upload_mask_image(bytes, name):
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print(f'>> Mask image upload requested "{name}"')
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uuid = uuid4().hex
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split = os.path.splitext(name)
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name = f"{split[0]}.{uuid}{split[1]}"
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file_path = os.path.join(mask_image_path, name)
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os.makedirs(os.path.dirname(file_path), exist_ok=True)
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newFile = open(file_path, "wb")
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newFile.write(bytes)
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socketio.emit("maskImageUploaded", {"url": file_path, "uuid": ""})
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"""
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END SOCKET.IO LISTENERS
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"""
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"""
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ADDITIONAL FUNCTIONS
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"""
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def get_system_config():
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return {
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"model": "stable diffusion",
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"model_id": model,
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"model_hash": generate.model_hash,
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"app_id": APP_ID,
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"app_version": APP_VERSION,
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}
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def parameters_to_post_processed_image_metadata(parameters, original_image_path, type):
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# top-level metadata minus `image` or `images`
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metadata = get_system_config()
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orig_hash = calculate_init_img_hash(original_image_path)
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image = {"orig_path": original_image_path, "orig_hash": orig_hash}
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if type == "esrgan":
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image["type"] = "esrgan"
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image["scale"] = parameters["upscale"][0]
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image["strength"] = parameters["upscale"][1]
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elif type == "gfpgan":
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image["type"] = "gfpgan"
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image["strength"] = parameters["gfpgan_strength"]
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else:
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raise TypeError(f"Invalid type: {type}")
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metadata["image"] = image
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return metadata
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def parameters_to_generated_image_metadata(parameters):
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# top-level metadata minus `image` or `images`
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metadata = get_system_config()
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# remove any image keys not mentioned in RFC #266
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rfc266_img_fields = [
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"type",
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"postprocessing",
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"sampler",
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"prompt",
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"seed",
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"variations",
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"steps",
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"cfg_scale",
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"step_number",
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"width",
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"height",
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"extra",
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"seamless",
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]
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rfc_dict = {}
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for item in parameters.items():
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key, value = item
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if key in rfc266_img_fields:
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rfc_dict[key] = value
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postprocessing = []
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# 'postprocessing' is either null or an
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if "gfpgan_strength" in parameters:
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postprocessing.append(
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{"type": "gfpgan", "strength": float(parameters["gfpgan_strength"])}
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)
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if "upscale" in parameters:
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postprocessing.append(
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{
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"type": "esrgan",
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"scale": int(parameters["upscale"][0]),
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"strength": float(parameters["upscale"][1]),
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}
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)
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rfc_dict["postprocessing"] = postprocessing if len(postprocessing) > 0 else None
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# semantic drift
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rfc_dict["sampler"] = parameters["sampler_name"]
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# display weighted subprompts (liable to change)
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subprompts = split_weighted_subprompts(parameters["prompt"])
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subprompts = [{"prompt": x[0], "weight": x[1]} for x in subprompts]
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rfc_dict["prompt"] = subprompts
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# 'variations' should always exist and be an array, empty or consisting of {'seed': seed, 'weight': weight} pairs
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variations = []
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if "with_variations" in parameters:
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variations = [
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{"seed": x[0], "weight": x[1]} for x in parameters["with_variations"]
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]
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rfc_dict["variations"] = variations
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if "init_img" in parameters:
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rfc_dict["type"] = "img2img"
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rfc_dict["strength"] = parameters["strength"]
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rfc_dict["fit"] = parameters["fit"] # TODO: Noncompliant
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rfc_dict["orig_hash"] = calculate_init_img_hash(parameters["init_img"])
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rfc_dict["init_image_path"] = parameters["init_img"] # TODO: Noncompliant
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rfc_dict["sampler"] = "ddim" # TODO: FIX ME WHEN IMG2IMG SUPPORTS ALL SAMPLERS
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if "init_mask" in parameters:
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rfc_dict["mask_hash"] = calculate_init_img_hash(
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parameters["init_mask"]
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) # TODO: Noncompliant
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rfc_dict["mask_image_path"] = parameters["init_mask"] # TODO: Noncompliant
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else:
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rfc_dict["type"] = "txt2img"
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metadata["image"] = rfc_dict
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return metadata
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def make_unique_init_image_filename(name):
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uuid = uuid4().hex
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split = os.path.splitext(name)
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name = f"{split[0]}.{uuid}{split[1]}"
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return name
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def write_log_message(message, log_path=log_path):
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"""Logs the filename and parameters used to generate or process that image to log file"""
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message = f"{message}\n"
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with open(log_path, "a", encoding="utf-8") as file:
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file.writelines(message)
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def save_image(
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image, command, metadata, output_dir, step_index=None, postprocessing=False
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):
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pngwriter = PngWriter(output_dir)
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prefix = pngwriter.unique_prefix()
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seed = "unknown_seed"
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if "image" in metadata:
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if "seed" in metadata["image"]:
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seed = metadata["image"]["seed"]
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filename = f"{prefix}.{seed}"
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if step_index:
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filename += f".{step_index}"
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if postprocessing:
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filename += f".postprocessed"
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filename += ".png"
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path = pngwriter.save_image_and_prompt_to_png(
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image=image, dream_prompt=command, metadata=metadata, name=filename
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)
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return path
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def calculate_real_steps(steps, strength, has_init_image):
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return math.floor(strength * steps) if has_init_image else steps
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def generate_images(generation_parameters, esrgan_parameters, gfpgan_parameters):
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canceled.clear()
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step_index = 1
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"""
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If a result image is used as an init image, and then deleted, we will want to be
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able to use it as an init image in the future. Need to copy it.
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If the init/mask image doesn't exist in the init_image_path/mask_image_path,
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make a unique filename for it and copy it there.
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"""
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if "init_img" in generation_parameters:
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filename = os.path.basename(generation_parameters["init_img"])
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if not os.path.exists(os.path.join(init_image_path, filename)):
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unique_filename = make_unique_init_image_filename(filename)
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new_path = os.path.join(init_image_path, unique_filename)
|
|
shutil.copy(generation_parameters["init_img"], new_path)
|
|
generation_parameters["init_img"] = new_path
|
|
if "init_mask" in generation_parameters:
|
|
filename = os.path.basename(generation_parameters["init_mask"])
|
|
if not os.path.exists(os.path.join(mask_image_path, filename)):
|
|
unique_filename = make_unique_init_image_filename(filename)
|
|
new_path = os.path.join(init_image_path, unique_filename)
|
|
shutil.copy(generation_parameters["init_img"], new_path)
|
|
generation_parameters["init_mask"] = new_path
|
|
|
|
totalSteps = calculate_real_steps(
|
|
steps=generation_parameters["steps"],
|
|
strength=generation_parameters["strength"]
|
|
if "strength" in generation_parameters
|
|
else None,
|
|
has_init_image="init_img" in generation_parameters,
|
|
)
|
|
|
|
progress = {
|
|
"currentStep": 1,
|
|
"totalSteps": totalSteps,
|
|
"currentIteration": 1,
|
|
"totalIterations": generation_parameters["iterations"],
|
|
"currentStatus": "Preparing",
|
|
"isProcessing": True,
|
|
"currentStatusHasSteps": False,
|
|
}
|
|
|
|
socketio.emit("progressUpdate", progress)
|
|
eventlet.sleep(0)
|
|
|
|
def image_progress(sample, step):
|
|
if canceled.is_set():
|
|
raise CanceledException
|
|
|
|
nonlocal step_index
|
|
nonlocal generation_parameters
|
|
nonlocal progress
|
|
|
|
progress["currentStep"] = step + 1
|
|
progress["currentStatus"] = "Generating"
|
|
progress["currentStatusHasSteps"] = True
|
|
|
|
if (
|
|
generation_parameters["progress_images"]
|
|
and step % 5 == 0
|
|
and step < generation_parameters["steps"] - 1
|
|
):
|
|
image = generate.sample_to_image(sample)
|
|
path = save_image(
|
|
image, generation_parameters, intermediate_path, step_index
|
|
)
|
|
|
|
step_index += 1
|
|
socketio.emit(
|
|
"intermediateResult",
|
|
{"url": os.path.relpath(path), "metadata": generation_parameters},
|
|
)
|
|
socketio.emit("progressUpdate", progress)
|
|
eventlet.sleep(0)
|
|
|
|
def image_done(image, seed):
|
|
nonlocal generation_parameters
|
|
nonlocal esrgan_parameters
|
|
nonlocal gfpgan_parameters
|
|
nonlocal progress
|
|
|
|
step_index = 1
|
|
|
|
progress["currentStatus"] = "Generation complete"
|
|
socketio.emit("progressUpdate", progress)
|
|
eventlet.sleep(0)
|
|
|
|
all_parameters = generation_parameters
|
|
postprocessing = False
|
|
|
|
if esrgan_parameters:
|
|
progress["currentStatus"] = "Upscaling"
|
|
progress["currentStatusHasSteps"] = False
|
|
socketio.emit("progressUpdate", progress)
|
|
eventlet.sleep(0)
|
|
|
|
image = real_esrgan_upscale(
|
|
image=image,
|
|
strength=esrgan_parameters["strength"],
|
|
upsampler_scale=esrgan_parameters["level"],
|
|
seed=seed,
|
|
)
|
|
postprocessing = True
|
|
all_parameters["upscale"] = [
|
|
esrgan_parameters["level"],
|
|
esrgan_parameters["strength"],
|
|
]
|
|
|
|
if gfpgan_parameters:
|
|
progress["currentStatus"] = "Fixing faces"
|
|
progress["currentStatusHasSteps"] = False
|
|
socketio.emit("progressUpdate", progress)
|
|
eventlet.sleep(0)
|
|
|
|
image = run_gfpgan(
|
|
image=image,
|
|
strength=gfpgan_parameters["strength"],
|
|
seed=seed,
|
|
upsampler_scale=1,
|
|
)
|
|
postprocessing = True
|
|
all_parameters["gfpgan_strength"] = gfpgan_parameters["strength"]
|
|
|
|
all_parameters["seed"] = seed
|
|
progress["currentStatus"] = "Saving image"
|
|
socketio.emit("progressUpdate", progress)
|
|
eventlet.sleep(0)
|
|
|
|
metadata = parameters_to_generated_image_metadata(all_parameters)
|
|
command = parameters_to_command(all_parameters)
|
|
|
|
path = save_image(
|
|
image, command, metadata, result_path, postprocessing=postprocessing
|
|
)
|
|
|
|
print(f'>> Image generated: "{path}"')
|
|
write_log_message(f'[Generated] "{path}": {command}')
|
|
|
|
if progress["totalIterations"] > progress["currentIteration"]:
|
|
progress["currentStep"] = 1
|
|
progress["currentIteration"] += 1
|
|
progress["currentStatus"] = "Iteration finished"
|
|
progress["currentStatusHasSteps"] = False
|
|
else:
|
|
progress["currentStep"] = 0
|
|
progress["totalSteps"] = 0
|
|
progress["currentIteration"] = 0
|
|
progress["totalIterations"] = 0
|
|
progress["currentStatus"] = "Finished"
|
|
progress["isProcessing"] = False
|
|
|
|
socketio.emit("progressUpdate", progress)
|
|
eventlet.sleep(0)
|
|
|
|
socketio.emit(
|
|
"generationResult",
|
|
{"url": os.path.relpath(path), "metadata": metadata},
|
|
)
|
|
eventlet.sleep(0)
|
|
|
|
try:
|
|
generate.prompt2image(
|
|
**generation_parameters,
|
|
step_callback=image_progress,
|
|
image_callback=image_done,
|
|
)
|
|
|
|
except KeyboardInterrupt:
|
|
raise
|
|
except CanceledException:
|
|
pass
|
|
except Exception as e:
|
|
socketio.emit("error", {"message": (str(e))})
|
|
print("\n")
|
|
traceback.print_exc()
|
|
print("\n")
|
|
|
|
|
|
"""
|
|
END ADDITIONAL FUNCTIONS
|
|
"""
|
|
|
|
|
|
if __name__ == "__main__":
|
|
print(f">> Starting server at http://{host}:{port}")
|
|
socketio.run(app, host=host, port=port)
|