mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
Lincoln Stein
parent
00d2d0e90e
commit
cbac95b02a
1
.gitignore
vendored
1
.gitignore
vendored
@ -191,3 +191,4 @@ checkpoints
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.scratch/
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.vscode/
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gfpgan/
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models/ldm/stable-diffusion-v1/model.sha256
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@ -51,6 +51,7 @@ We thank them for all of their time and hard work.
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- [Any Winter](https://github.com/any-winter-4079)
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- [Doggettx](https://github.com/doggettx)
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- [Matthias Wild](https://github.com/mauwii)
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- [Kyle Schouviller](https://github.com/kyle0654)
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## __Original CompVis Authors:__
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@ -33,11 +33,12 @@ class PngWriter:
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# saves image named _image_ to outdir/name, writing metadata from prompt
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# returns full path of output
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def save_image_and_prompt_to_png(self, image, dream_prompt, metadata, name):
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def save_image_and_prompt_to_png(self, image, dream_prompt, name, metadata=None):
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path = os.path.join(self.outdir, name)
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info = PngImagePlugin.PngInfo()
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info.add_text('Dream', dream_prompt)
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info.add_text('sd-metadata', json.dumps(metadata))
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if metadata: # TODO: merge command line app's method of writing metadata and always just write metadata
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info.add_text('sd-metadata', json.dumps(metadata))
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image.save(path, 'PNG', pnginfo=info)
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return path
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@ -230,7 +230,7 @@ class DreamServer(BaseHTTPRequestHandler):
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image = self.model.sample_to_image(sample)
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name = f'{prefix}.{opt.seed}.{step_index}.png'
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metadata = f'{opt.prompt} -S{opt.seed} [intermediate]'
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path = step_writer.save_image_and_prompt_to_png(image, metadata, name)
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path = step_writer.save_image_and_prompt_to_png(image, dream_prompt=metadata, name=name)
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step_index += 1
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self.wfile.write(bytes(json.dumps(
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{'event': 'step', 'step': step + 1, 'url': path}
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@ -181,7 +181,7 @@ class Generate:
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for image, seed in results:
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name = f'{prefix}.{seed}.png'
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path = pngwriter.save_image_and_prompt_to_png(
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image, f'{prompt} -S{seed}', name)
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image, dream_prompt=f'{prompt} -S{seed}', name=name)
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outputs.append([path, seed])
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return outputs
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@ -22,6 +22,11 @@ test-tube
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torch-fidelity
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torchmetrics
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transformers
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flask==2.1.3
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flask_socketio==5.3.0
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flask_cors==3.0.10
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dependency_injector==4.40.0
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eventlet
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git+https://github.com/openai/CLIP.git@main#egg=clip
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git+https://github.com/Birch-san/k-diffusion.git@mps#egg=k-diffusion
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git+https://github.com/lstein/GFPGAN@fix-dark-cast-images#egg=gfpgan
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@ -7,9 +7,10 @@ import os
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import sys
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from flask import Flask
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from flask_cors import CORS
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from flask_socketio import SocketIO, join_room, leave_room
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from flask_socketio import SocketIO
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from omegaconf import OmegaConf
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from dependency_injector.wiring import inject, Provide
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from ldm.dream.args import Args
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from server import views
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from server.containers import Container
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from server.services import GeneratorService, SignalService
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@ -58,6 +59,8 @@ def run_app(config, host, port) -> Flask:
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# TODO: Get storage root from config
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app.add_url_rule('/api/images/<string:dreamId>', view_func=views.ApiImages.as_view('api_images', '../'))
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app.add_url_rule('/api/images/<string:dreamId>/metadata', view_func=views.ApiImagesMetadata.as_view('api_images_metadata', '../'))
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app.add_url_rule('/api/images', view_func=views.ApiImagesList.as_view('api_images_list'))
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app.add_url_rule('/api/intermediates/<string:dreamId>/<string:step>', view_func=views.ApiIntermediates.as_view('api_intermediates', '../'))
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app.static_folder = os.path.abspath(os.path.join(os.path.dirname(__file__), '../static/dream_web/'))
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@ -79,30 +82,28 @@ def run_app(config, host, port) -> Flask:
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def main():
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"""Initialize command-line parsers and the diffusion model"""
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from scripts.dream import create_argv_parser
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arg_parser = create_argv_parser()
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arg_parser = Args()
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opt = arg_parser.parse_args()
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if opt.laion400m:
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print('--laion400m flag has been deprecated. Please use --model laion400m instead.')
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sys.exit(-1)
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if opt.weights != 'model':
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print('--weights argument has been deprecated. Please configure ./configs/models.yaml, and call it using --model instead.')
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sys.exit(-1)
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print('--laion400m flag has been deprecated. Please use --model laion400m instead.')
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sys.exit(-1)
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if opt.weights:
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print('--weights argument has been deprecated. Please edit ./configs/models.yaml, and select the weights using --model instead.')
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sys.exit(-1)
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try:
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models = OmegaConf.load(opt.config)
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width = models[opt.model].width
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height = models[opt.model].height
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config = models[opt.model].config
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weights = models[opt.model].weights
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except (FileNotFoundError, IOError, KeyError) as e:
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print(f'{e}. Aborting.')
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sys.exit(-1)
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# try:
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# models = OmegaConf.load(opt.config)
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# width = models[opt.model].width
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# height = models[opt.model].height
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# config = models[opt.model].config
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# weights = models[opt.model].weights
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# except (FileNotFoundError, IOError, KeyError) as e:
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# print(f'{e}. Aborting.')
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# sys.exit(-1)
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print('* Initializing, be patient...\n')
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#print('* Initializing, be patient...\n')
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sys.path.append('.')
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from pytorch_lightning import logging
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# these two lines prevent a horrible warning message from appearing
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# when the frozen CLIP tokenizer is imported
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@ -110,26 +111,28 @@ def main():
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transformers.logging.set_verbosity_error()
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appConfig = {
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"model": {
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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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"grid": opt.grid,
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"latent_diffusion_weights": opt.laion400m,
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"embedding_path": opt.embedding_path,
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"device_type": opt.device
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}
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}
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appConfig = opt.__dict__
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# appConfig = {
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# "model": {
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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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# "grid": opt.grid,
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# "latent_diffusion_weights": opt.laion400m,
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# "embedding_path": opt.embedding_path
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# }
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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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from pytorch_lightning import logging
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logging.getLogger('pytorch_lightning').setLevel(logging.ERROR)
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print('\n* starting api server...')
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@ -17,18 +17,24 @@ class Container(containers.DeclarativeContainer):
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app = None
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)
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# TODO: Add a model provider service that provides model(s) dynamically
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model_singleton = providers.ThreadSafeSingleton(
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Generate,
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width = config.model.width,
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height = config.model.height,
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sampler_name = config.model.sampler_name,
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weights = config.model.weights,
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full_precision = config.model.full_precision,
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config = config.model.config,
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grid = config.model.grid,
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seamless = config.model.seamless,
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embedding_path = config.model.embedding_path,
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device_type = config.model.device_type
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model = config.model,
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sampler_name = config.sampler_name,
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embedding_path = config.embedding_path,
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full_precision = config.full_precision
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# config = config.model.config,
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# width = config.model.width,
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# height = config.model.height,
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# sampler_name = config.model.sampler_name,
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# weights = config.model.weights,
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# full_precision = config.model.full_precision,
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# grid = config.model.grid,
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# seamless = config.model.seamless,
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# embedding_path = config.model.embedding_path,
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# device_type = config.model.device_type
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)
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# TODO: get location from config
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229
server/models.py
229
server/models.py
@ -1,77 +1,182 @@
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# Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
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from base64 import urlsafe_b64encode
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import json
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import string
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from copy import deepcopy
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from datetime import datetime, timezone
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from enum import Enum
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from typing import Any, Dict, List, Union
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from uuid import uuid4
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class DreamRequest():
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prompt: string
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initimg: string
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strength: float
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iterations: int
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steps: int
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width: int
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height: int
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fit = None
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cfgscale: float
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sampler_name: string
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gfpgan_strength: float
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upscale_level: int
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upscale_strength: float
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class DreamBase():
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# Id
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id: str
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# Initial Image
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enable_init_image: bool
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initimg: string = None
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# Img2Img
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enable_img2img: bool # TODO: support this better
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strength: float = 0 # TODO: name this something related to img2img to make it clearer?
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fit = None # Fit initial image dimensions
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# Generation
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enable_generate: bool
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prompt: string = ""
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seed: int = 0 # 0 is random
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steps: int = 10
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width: int = 512
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height: int = 512
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cfg_scale: float = 7.5
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sampler_name: string = 'klms'
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seamless: bool = False
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model: str = None # The model to use (currently unused)
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embeddings = None # The embeddings to use (currently unused)
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progress_images: bool = False
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# GFPGAN
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enable_gfpgan: bool
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gfpgan_strength: float = 0
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# Upscale
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enable_upscale: bool
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upscale: None
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progress_images = None
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seed: int
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upscale_level: int = None
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upscale_strength: float = 0.75
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# Embiggen
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enable_embiggen: bool
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embiggen: Union[None, List[float]] = None
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embiggen_tiles: Union[None, List[int]] = None
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# Metadata
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time: int
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def __init__(self):
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self.id = urlsafe_b64encode(uuid4().bytes).decode('ascii')
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def parse_json(self, j, new_instance=False):
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# Id
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if 'id' in j and not new_instance:
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self.id = j.get('id')
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# Initial Image
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self.enable_init_image = 'enable_init_image' in j and bool(j.get('enable_init_image'))
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if self.enable_init_image:
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self.initimg = j.get('initimg')
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# Img2Img
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self.enable_img2img = 'enable_img2img' in j and bool(j.get('enable_img2img'))
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if self.enable_img2img:
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self.strength = float(j.get('strength'))
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self.fit = 'fit' in j
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# Generation
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self.enable_generate = 'enable_generate' in j and bool(j.get('enable_generate'))
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if self.enable_generate:
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self.prompt = j.get('prompt')
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self.seed = int(j.get('seed'))
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self.steps = int(j.get('steps'))
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self.width = int(j.get('width'))
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self.height = int(j.get('height'))
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self.cfg_scale = float(j.get('cfgscale') or j.get('cfg_scale'))
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self.sampler_name = j.get('sampler') or j.get('sampler_name')
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# model: str = None # The model to use (currently unused)
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# embeddings = None # The embeddings to use (currently unused)
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self.seamless = 'seamless' in j
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self.progress_images = 'progress_images' in j
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# GFPGAN
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self.enable_gfpgan = 'enable_gfpgan' in j and bool(j.get('enable_gfpgan'))
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if self.enable_gfpgan:
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self.gfpgan_strength = float(j.get('gfpgan_strength'))
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# Upscale
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self.enable_upscale = 'enable_upscale' in j and bool(j.get('enable_upscale'))
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if self.enable_upscale:
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self.upscale_level = j.get('upscale_level')
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self.upscale_strength = j.get('upscale_strength')
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self.upscale = None if self.upscale_level in {None,''} else [int(self.upscale_level),float(self.upscale_strength)]
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# Embiggen
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self.enable_embiggen = 'enable_embiggen' in j and bool(j.get('enable_embiggen'))
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if self.enable_embiggen:
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self.embiggen = j.get('embiggen')
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self.embiggen_tiles = j.get('embiggen_tiles')
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# Metadata
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self.time = int(j.get('time')) if ('time' in j and not new_instance) else int(datetime.now(timezone.utc).timestamp())
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class DreamResult(DreamBase):
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# Result
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has_upscaled: False
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has_gfpgan: False
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# TODO: use something else for state tracking
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images_generated: int = 0
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images_upscaled: int = 0
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def id(self, seed = None, upscaled = False) -> str:
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return f"{self.time}.{seed or self.seed}{'.u' if upscaled else ''}"
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def __init__(self):
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super().__init__()
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# TODO: handle this more cleanly (probably by splitting this into a Job and Result class)
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# TODO: Set iterations to 1 or remove it from the dream result? And just keep it on the job?
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def clone_without_image(self, seed = None):
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data = deepcopy(self)
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data.initimg = None
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if seed:
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data.seed = seed
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def clone_without_img(self):
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copy = deepcopy(self)
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copy.initimg = None
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return copy
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return data
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def to_json(self, seed: int = None):
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copy = self.clone_without_image(seed)
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return json.dumps(copy.__dict__)
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def to_json(self):
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copy = deepcopy(self)
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copy.initimg = None
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j = json.dumps(copy.__dict__)
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return j
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@staticmethod
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def from_json(j, newTime: bool = False):
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d = DreamRequest()
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d.prompt = j.get('prompt')
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d.initimg = j.get('initimg')
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d.strength = float(j.get('strength'))
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d.iterations = int(j.get('iterations'))
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d.steps = int(j.get('steps'))
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d.width = int(j.get('width'))
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d.height = int(j.get('height'))
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d.fit = 'fit' in j
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d.seamless = 'seamless' in j
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d.cfgscale = float(j.get('cfgscale'))
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d.sampler_name = j.get('sampler')
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d.variation_amount = float(j.get('variation_amount'))
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d.with_variations = j.get('with_variations')
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d.gfpgan_strength = float(j.get('gfpgan_strength'))
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d.upscale_level = j.get('upscale_level')
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d.upscale_strength = j.get('upscale_strength')
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d.upscale = [int(d.upscale_level),float(d.upscale_strength)] if d.upscale_level != '' else None
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d.progress_images = 'progress_images' in j
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d.seed = int(j.get('seed'))
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d.time = int(datetime.now(timezone.utc).timestamp()) if newTime else int(j.get('time'))
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d = DreamResult()
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d.parse_json(j)
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return d
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# TODO: switch this to a pipelined request, with pluggable steps
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# Will likely require generator code changes to accomplish
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class JobRequest(DreamBase):
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# Iteration
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iterations: int = 1
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variation_amount = None
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with_variations = None
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# Results
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results: List[DreamResult] = []
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def __init__(self):
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super().__init__()
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def newDreamResult(self) -> DreamResult:
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result = DreamResult()
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result.parse_json(self.__dict__, new_instance=True)
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return result
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@staticmethod
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def from_json(j):
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job = JobRequest()
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job.parse_json(j)
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# Metadata
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job.time = int(j.get('time')) if ('time' in j) else int(datetime.now(timezone.utc).timestamp())
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# Iteration
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if job.enable_generate:
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job.iterations = int(j.get('iterations'))
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job.variation_amount = float(j.get('variation_amount'))
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job.with_variations = j.get('with_variations')
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return job
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class ProgressType(Enum):
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GENERATION = 1
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UPSCALING_STARTED = 2
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@ -102,11 +207,11 @@ class Signal():
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# TODO: use a result id or something? Like a sub-job
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@staticmethod
|
||||
def image_result(jobId: str, dreamId: str, dreamRequest: DreamRequest):
|
||||
def image_result(jobId: str, dreamId: str, dreamResult: DreamResult):
|
||||
return Signal('dream_result', {
|
||||
'jobId': jobId,
|
||||
'dreamId': dreamId,
|
||||
'dreamRequest': dreamRequest.__dict__
|
||||
'dreamRequest': dreamResult.clone_without_img().__dict__
|
||||
}, room=jobId, broadcast=True)
|
||||
|
||||
@staticmethod
|
||||
@ -126,3 +231,21 @@ class Signal():
|
||||
return Signal('job_canceled', {
|
||||
'jobId': jobId
|
||||
}, room=jobId, broadcast=True)
|
||||
|
||||
|
||||
class PaginatedItems():
|
||||
items: List[Any]
|
||||
page: int # Current Page
|
||||
pages: int # Total number of pages
|
||||
per_page: int # Number of items per page
|
||||
total: int # Total number of items in result
|
||||
|
||||
def __init__(self, items: List[Any], page: int, pages: int, per_page: int, total: int):
|
||||
self.items = items
|
||||
self.page = page
|
||||
self.pages = pages
|
||||
self.per_page = per_page
|
||||
self.total = total
|
||||
|
||||
def to_json(self):
|
||||
return json.dumps(self.__dict__)
|
||||
|
||||
@ -1,25 +1,33 @@
|
||||
# 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 import app, url_for
|
||||
from flask_socketio import SocketIO, join_room, leave_room
|
||||
from ldm.dream.args import Args
|
||||
from ldm.dream.generator import embiggen
|
||||
from PIL import Image
|
||||
|
||||
from ldm.dream.pngwriter import PngWriter
|
||||
from ldm.dream.server import CanceledException
|
||||
from ldm.generate import Generate
|
||||
from server.models import DreamRequest, ProgressType, Signal
|
||||
from server.models import DreamResult, JobRequest, PaginatedItems, ProgressType, Signal
|
||||
|
||||
class JobQueueService:
|
||||
__queue: Queue = Queue()
|
||||
|
||||
def push(self, dreamRequest: DreamRequest):
|
||||
def push(self, dreamRequest: DreamResult):
|
||||
self.__queue.put(dreamRequest)
|
||||
|
||||
def get(self, timeout: float = None) -> DreamRequest:
|
||||
def get(self, timeout: float = None) -> DreamResult:
|
||||
return self.__queue.get(timeout= timeout)
|
||||
|
||||
class SignalQueueService:
|
||||
@ -85,31 +93,116 @@ class LogService:
|
||||
self.__location = location
|
||||
self.__logFile = file
|
||||
|
||||
def log(self, dreamRequest: DreamRequest, seed = None, upscaled = False):
|
||||
def log(self, dreamResult: DreamResult, seed = None, upscaled = False):
|
||||
with open(os.path.join(self.__location, self.__logFile), "a") as log:
|
||||
log.write(f"{dreamRequest.id(seed, upscaled)}: {dreamRequest.to_json(seed)}\n")
|
||||
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, dreamRequest, seed = None, upscaled = False, postfix: str = '', metadataPostfix: str = '') -> str:
|
||||
name = self.__getName(dreamRequest.id(seed, upscaled), postfix)
|
||||
path = self.__pngWriter.save_image_and_prompt_to_png(image, f'{dreamRequest.prompt} -S{seed or dreamRequest.seed}{metadataPostfix}', name)
|
||||
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:
|
||||
@ -144,13 +237,11 @@ class GeneratorService:
|
||||
# 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(f'>> model loaded in', '%4.2fs' % (time.time() - tic))
|
||||
|
||||
print('Started generation queue processor')
|
||||
try:
|
||||
@ -162,103 +253,136 @@ class GeneratorService:
|
||||
print('Generation queue processor stopped')
|
||||
|
||||
|
||||
def __start(self, dreamRequest: DreamRequest):
|
||||
if dreamRequest.start_callback:
|
||||
dreamRequest.start_callback()
|
||||
self.__signal_service.emit(Signal.job_started(dreamRequest.id()))
|
||||
def __on_start(self, jobRequest: JobRequest):
|
||||
self.__signal_service.emit(Signal.job_started(jobRequest.id))
|
||||
|
||||
|
||||
def __done(self, dreamRequest: DreamRequest, image, seed, upscaled=False):
|
||||
self.__imageStorage.save(image, dreamRequest, seed, upscaled)
|
||||
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(dreamRequest, seed, upscaled)
|
||||
self.__log.log(dreamResult)
|
||||
|
||||
self.__signal_service.emit(Signal.image_result(dreamRequest.id(), dreamRequest.id(seed, upscaled), dreamRequest.clone_without_image(seed)))
|
||||
# Send result signal
|
||||
self.__signal_service.emit(Signal.image_result(jobRequest.id, dreamResult.id, dreamResult))
|
||||
|
||||
upscaling_requested = dreamRequest.upscale or dreamRequest.gfpgan_strength>0
|
||||
upscaling_requested = dreamResult.enable_upscale or dreamResult.enable_gfpgan
|
||||
|
||||
if upscaled:
|
||||
dreamRequest.images_upscaled += 1
|
||||
else:
|
||||
dreamRequest.images_generated +=1
|
||||
if upscaling_requested:
|
||||
# action = None
|
||||
if dreamRequest.images_generated >= dreamRequest.iterations:
|
||||
progressType = ProgressType.UPSCALING_DONE
|
||||
if dreamRequest.images_upscaled < dreamRequest.iterations:
|
||||
progressType = ProgressType.UPSCALING_STARTED
|
||||
self.__signal_service.emit(Signal.image_progress(dreamRequest.id(), dreamRequest.id(seed), dreamRequest.images_upscaled+1, dreamRequest.iterations, progressType))
|
||||
# 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 __progress(self, dreamRequest, sample, step):
|
||||
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
|
||||
if dreamRequest.progress_images and step % 5 == 0 and step < dreamRequest.steps - 1:
|
||||
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)
|
||||
self.__intermediateStorage.save(image, dreamRequest, self.__model.seed, postfix=f'.{step}', metadataPostfix=f' [intermediate]')
|
||||
|
||||
# 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(dreamRequest.id(), dreamRequest.id(self.__model.seed), step, dreamRequest.steps, ProgressType.GENERATION, hasProgressImage))
|
||||
self.__signal_service.emit(Signal.image_progress(jobRequest.id, f'{jobRequest.id}.{s}', step, jobRequest.steps, ProgressType.GENERATION, hasProgressImage))
|
||||
|
||||
|
||||
def __generate(self, dreamRequest: DreamRequest):
|
||||
def __generate(self, jobRequest: JobRequest):
|
||||
try:
|
||||
initimgfile = None
|
||||
if dreamRequest.initimg is not None:
|
||||
with open("./img2img-tmp.png", "wb") as f:
|
||||
initimg = dreamRequest.initimg.split(",")[1] # Ignore mime type
|
||||
f.write(base64.b64decode(initimg))
|
||||
initimgfile = "./img2img-tmp.png"
|
||||
# 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"
|
||||
|
||||
# Get a random seed if we don't have one yet
|
||||
# TODO: handle "previous" seed usage?
|
||||
if dreamRequest.seed == -1:
|
||||
dreamRequest.seed = self.__model.seed
|
||||
# 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:
|
||||
dreamRequest.gfpgan_strength = 0
|
||||
jobRequest.enable_gfpgan = False
|
||||
|
||||
self.__start(dreamRequest)
|
||||
# Signal start
|
||||
self.__on_start(jobRequest)
|
||||
|
||||
self.__model.prompt2image(
|
||||
prompt = dreamRequest.prompt,
|
||||
init_img = initimgfile, # TODO: ensure this works
|
||||
strength = None if initimgfile is None else dreamRequest.strength,
|
||||
fit = None if initimgfile is None else dreamRequest.fit,
|
||||
iterations = dreamRequest.iterations,
|
||||
cfg_scale = dreamRequest.cfgscale,
|
||||
width = dreamRequest.width,
|
||||
height = dreamRequest.height,
|
||||
seed = dreamRequest.seed,
|
||||
steps = dreamRequest.steps,
|
||||
variation_amount = dreamRequest.variation_amount,
|
||||
with_variations = dreamRequest.with_variations,
|
||||
gfpgan_strength = dreamRequest.gfpgan_strength,
|
||||
upscale = dreamRequest.upscale,
|
||||
sampler_name = dreamRequest.sampler_name,
|
||||
seamless = dreamRequest.seamless,
|
||||
step_callback = lambda sample, step: self.__progress(dreamRequest, sample, step),
|
||||
image_callback = lambda image, seed, upscaled=False: self.__done(dreamRequest, image, seed, upscaled))
|
||||
# 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
|
||||
gfpgan_strength = 0 if not jobRequest.enable_gfpgan else jobRequest.gfpgan_strength
|
||||
|
||||
if not jobRequest.enable_generate:
|
||||
# If not generating, check if we're upscaling or running gfpgan
|
||||
if not upscale and not gfpgan_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 = gfpgan_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,
|
||||
width = jobRequest.width,
|
||||
height = jobRequest.height,
|
||||
seed = jobRequest.seed,
|
||||
steps = jobRequest.steps,
|
||||
variation_amount = jobRequest.variation_amount,
|
||||
with_variations = jobRequest.with_variations,
|
||||
gfpgan_strength = gfpgan_strength,
|
||||
upscale = upscale,
|
||||
sampler_name = jobRequest.sampler_name,
|
||||
seamless = jobRequest.seamless,
|
||||
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:
|
||||
if dreamRequest.cancelled_callback:
|
||||
dreamRequest.cancelled_callback()
|
||||
|
||||
self.__signal_service.emit(Signal.job_canceled(dreamRequest.id()))
|
||||
self.__signal_service.emit(Signal.job_canceled(jobRequest.id))
|
||||
|
||||
finally:
|
||||
if dreamRequest.done_callback:
|
||||
dreamRequest.done_callback()
|
||||
self.__signal_service.emit(Signal.job_done(dreamRequest.id()))
|
||||
self.__signal_service.emit(Signal.job_done(jobRequest.id))
|
||||
|
||||
# Remove the temp file
|
||||
if (initimgfile is not None):
|
||||
|
||||
@ -8,7 +8,7 @@ from flask import current_app, jsonify, request, Response, send_from_directory,
|
||||
from flask.views import MethodView
|
||||
from dependency_injector.wiring import inject, Provide
|
||||
|
||||
from server.models import DreamRequest
|
||||
from server.models import DreamResult, JobRequest
|
||||
from server.services import GeneratorService, ImageStorageService, JobQueueService
|
||||
from server.containers import Container
|
||||
|
||||
@ -16,23 +16,14 @@ class ApiJobs(MethodView):
|
||||
|
||||
@inject
|
||||
def post(self, job_queue_service: JobQueueService = Provide[Container.generation_queue_service]):
|
||||
dreamRequest = DreamRequest.from_json(request.json, newTime = True)
|
||||
jobRequest = JobRequest.from_json(request.json)
|
||||
|
||||
#self.canceled.clear()
|
||||
print(f">> Request to generate with prompt: {dreamRequest.prompt}")
|
||||
|
||||
q = Queue()
|
||||
|
||||
dreamRequest.start_callback = None
|
||||
dreamRequest.image_callback = None
|
||||
dreamRequest.progress_callback = None
|
||||
dreamRequest.cancelled_callback = None
|
||||
dreamRequest.done_callback = None
|
||||
print(f">> Request to generate with prompt: {jobRequest.prompt}")
|
||||
|
||||
# Push the request
|
||||
job_queue_service.push(dreamRequest)
|
||||
job_queue_service.push(jobRequest)
|
||||
|
||||
return { 'dreamId': dreamRequest.id() }
|
||||
return { 'jobId': jobRequest.id }
|
||||
|
||||
|
||||
class WebIndex(MethodView):
|
||||
@ -68,6 +59,7 @@ class ApiCancel(MethodView):
|
||||
return Response(status=204)
|
||||
|
||||
|
||||
# TODO: Combine all image storage access
|
||||
class ApiImages(MethodView):
|
||||
init_every_request = False
|
||||
__pathRoot = None
|
||||
@ -82,6 +74,27 @@ class ApiImages(MethodView):
|
||||
name = self.__storage.path(dreamId)
|
||||
fullpath=os.path.join(self.__pathRoot, name)
|
||||
return send_from_directory(os.path.dirname(fullpath), os.path.basename(fullpath))
|
||||
|
||||
def delete(self, dreamId):
|
||||
result = self.__storage.delete(dreamId)
|
||||
return Response(status=204) if result else Response(status=404)
|
||||
|
||||
|
||||
class ApiImagesMetadata(MethodView):
|
||||
init_every_request = False
|
||||
__pathRoot = None
|
||||
__storage: ImageStorageService
|
||||
|
||||
@inject
|
||||
def __init__(self, pathBase, storage: ImageStorageService = Provide[Container.image_storage_service]):
|
||||
self.__pathRoot = os.path.abspath(os.path.join(os.path.dirname(__file__), pathBase))
|
||||
self.__storage = storage
|
||||
|
||||
def get(self, dreamId):
|
||||
meta = self.__storage.getMetadata(dreamId)
|
||||
j = {} if meta is None else meta.__dict__
|
||||
return j
|
||||
|
||||
|
||||
class ApiIntermediates(MethodView):
|
||||
init_every_request = False
|
||||
@ -97,3 +110,23 @@ class ApiIntermediates(MethodView):
|
||||
name = self.__storage.path(dreamId, postfix=f'.{step}')
|
||||
fullpath=os.path.join(self.__pathRoot, name)
|
||||
return send_from_directory(os.path.dirname(fullpath), os.path.basename(fullpath))
|
||||
|
||||
def delete(self, dreamId):
|
||||
result = self.__storage.delete(dreamId)
|
||||
return Response(status=204) if result else Response(status=404)
|
||||
|
||||
|
||||
class ApiImagesList(MethodView):
|
||||
init_every_request = False
|
||||
__storage: ImageStorageService
|
||||
|
||||
@inject
|
||||
def __init__(self, storage: ImageStorageService = Provide[Container.image_storage_service]):
|
||||
self.__storage = storage
|
||||
|
||||
def get(self):
|
||||
page = request.args.get("page", default=0, type=int)
|
||||
perPage = request.args.get("per_page", default=10, type=int)
|
||||
|
||||
result = self.__storage.list_files(page, perPage)
|
||||
return result.__dict__
|
||||
|
||||
@ -1,3 +1,8 @@
|
||||
:root {
|
||||
--fields-dark:#DCDCDC;
|
||||
--fields-light:#F5F5F5;
|
||||
}
|
||||
|
||||
* {
|
||||
font-family: 'Arial';
|
||||
font-size: 100%;
|
||||
@ -18,15 +23,26 @@ fieldset {
|
||||
border: none;
|
||||
line-height: 2.2em;
|
||||
}
|
||||
fieldset > legend {
|
||||
width: auto;
|
||||
margin-left: 0;
|
||||
margin-right: auto;
|
||||
font-weight:bold;
|
||||
}
|
||||
select, input {
|
||||
margin-right: 10px;
|
||||
padding: 2px;
|
||||
}
|
||||
input:disabled {
|
||||
cursor:auto;
|
||||
}
|
||||
input[type=submit] {
|
||||
cursor: pointer;
|
||||
background-color: #666;
|
||||
color: white;
|
||||
}
|
||||
input[type=checkbox] {
|
||||
cursor: pointer;
|
||||
margin-right: 0px;
|
||||
width: 20px;
|
||||
height: 20px;
|
||||
@ -87,11 +103,11 @@ header h1 {
|
||||
}
|
||||
#results img {
|
||||
border-radius: 5px;
|
||||
object-fit: cover;
|
||||
object-fit: contain;
|
||||
background-color: var(--fields-dark);
|
||||
}
|
||||
#fieldset-config {
|
||||
line-height:2em;
|
||||
background-color: #F0F0F0;
|
||||
}
|
||||
input[type="number"] {
|
||||
width: 60px;
|
||||
@ -118,35 +134,46 @@ label {
|
||||
#progress-image {
|
||||
width: 30vh;
|
||||
height: 30vh;
|
||||
object-fit: contain;
|
||||
background-color: var(--fields-dark);
|
||||
}
|
||||
#cancel-button {
|
||||
cursor: pointer;
|
||||
color: red;
|
||||
}
|
||||
#basic-parameters {
|
||||
background-color: #EEEEEE;
|
||||
}
|
||||
#txt2img {
|
||||
background-color: #DCDCDC;
|
||||
background-color: var(--fields-dark);
|
||||
}
|
||||
#variations {
|
||||
background-color: #EEEEEE;
|
||||
background-color: var(--fields-light);
|
||||
}
|
||||
#initimg {
|
||||
background-color: var(--fields-dark);
|
||||
}
|
||||
#img2img {
|
||||
background-color: #DCDCDC;
|
||||
background-color: var(--fields-light);
|
||||
}
|
||||
#gfpgan {
|
||||
background-color: #EEEEEE;
|
||||
#initimg > :not(legend) {
|
||||
background-color: var(--fields-light);
|
||||
margin: .5em;
|
||||
}
|
||||
|
||||
#postprocess, #initimg {
|
||||
display:flex;
|
||||
flex-wrap:wrap;
|
||||
padding: 0;
|
||||
margin-top: 1em;
|
||||
background-color: var(--fields-dark);
|
||||
}
|
||||
#postprocess > fieldset, #initimg > * {
|
||||
flex-grow: 1;
|
||||
}
|
||||
#postprocess > fieldset {
|
||||
background-color: var(--fields-dark);
|
||||
}
|
||||
#progress-section {
|
||||
background-color: #F5F5F5;
|
||||
}
|
||||
.section-header {
|
||||
text-align: left;
|
||||
font-weight: bold;
|
||||
padding: 0 0 0 0;
|
||||
background-color: var(--fields-light);
|
||||
}
|
||||
#no-results-message:not(:only-child) {
|
||||
display: none;
|
||||
}
|
||||
|
||||
|
||||
@ -1,104 +1,152 @@
|
||||
<html lang="en">
|
||||
<head>
|
||||
<title>Stable Diffusion Dream Server</title>
|
||||
<meta charset="utf-8">
|
||||
<link rel="icon" href="data:,">
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1.0">
|
||||
|
||||
<link rel="stylesheet" href="index.css">
|
||||
<script src="config.js"></script>
|
||||
<script src="https://cdnjs.cloudflare.com/ajax/libs/socket.io/4.0.1/socket.io.js" integrity="sha512-q/dWJ3kcmjBLU4Qc47E4A9kTB4m3wuTY7vkFJDTZKjTs8jhyGQnaUrxa0Ytd0ssMZhbNua9hE+E7Qv1j+DyZwA==" crossorigin="anonymous"></script>
|
||||
<script src="index.js"></script>
|
||||
</head>
|
||||
<body>
|
||||
<header>
|
||||
<h1>Stable Diffusion Dream Server</h1>
|
||||
<div id="about">
|
||||
For news and support for this web service, visit our <a href="http://github.com/lstein/stable-diffusion">GitHub site</a>
|
||||
<head>
|
||||
<title>Stable Diffusion Dream Server</title>
|
||||
<meta charset="utf-8">
|
||||
<link rel="icon" type="image/x-icon" href="static/dream_web/favicon.ico" />
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1.0">
|
||||
|
||||
<script src="config.js"></script>
|
||||
<script src="https://cdnjs.cloudflare.com/ajax/libs/socket.io/4.0.1/socket.io.js"
|
||||
integrity="sha512-q/dWJ3kcmjBLU4Qc47E4A9kTB4m3wuTY7vkFJDTZKjTs8jhyGQnaUrxa0Ytd0ssMZhbNua9hE+E7Qv1j+DyZwA=="
|
||||
crossorigin="anonymous"></script>
|
||||
<link rel="stylesheet" href="index.css">
|
||||
<script src="index.js"></script>
|
||||
</head>
|
||||
|
||||
<body>
|
||||
<header>
|
||||
<h1>Stable Diffusion Dream Server</h1>
|
||||
<div id="about">
|
||||
For news and support for this web service, visit our <a href="http://github.com/lstein/stable-diffusion">GitHub
|
||||
site</a>
|
||||
</div>
|
||||
</header>
|
||||
|
||||
<main>
|
||||
<!--
|
||||
<div id="dropper" style="background-color:red;width:200px;height:200px;">
|
||||
</div>
|
||||
</header>
|
||||
|
||||
<main>
|
||||
<form id="generate-form" method="post" action="api/jobs">
|
||||
<fieldset id="txt2img">
|
||||
<div id="search-box">
|
||||
<textarea rows="3" id="prompt" name="prompt"></textarea>
|
||||
<input type="submit" id="submit" value="Generate">
|
||||
</div>
|
||||
</fieldset>
|
||||
<fieldset id="fieldset-config">
|
||||
<div class="section-header">Basic options</div>
|
||||
<label for="iterations">Images to generate:</label>
|
||||
<input value="1" type="number" id="iterations" name="iterations" size="4">
|
||||
<label for="steps">Steps:</label>
|
||||
<input value="50" type="number" id="steps" name="steps">
|
||||
<label for="cfgscale">Cfg Scale:</label>
|
||||
<input value="7.5" type="number" id="cfgscale" name="cfgscale" step="any">
|
||||
<label for="sampler">Sampler:</label>
|
||||
<select id="sampler" name="sampler" value="k_lms">
|
||||
<option value="ddim">DDIM</option>
|
||||
<option value="plms">PLMS</option>
|
||||
<option value="k_lms" selected>KLMS</option>
|
||||
<option value="k_dpm_2">KDPM_2</option>
|
||||
<option value="k_dpm_2_a">KDPM_2A</option>
|
||||
<option value="k_euler">KEULER</option>
|
||||
<option value="k_euler_a">KEULER_A</option>
|
||||
<option value="k_heun">KHEUN</option>
|
||||
</select>
|
||||
<input type="checkbox" name="seamless" id="seamless">
|
||||
<label for="seamless">Seamless circular tiling</label>
|
||||
<br>
|
||||
<label title="Set to multiple of 64" for="width">Width:</label>
|
||||
<select id="width" name="width" value="512">
|
||||
<option value="64">64</option> <option value="128">128</option>
|
||||
<option value="192">192</option> <option value="256">256</option>
|
||||
<option value="320">320</option> <option value="384">384</option>
|
||||
<option value="448">448</option> <option value="512" selected>512</option>
|
||||
<option value="576">576</option> <option value="640">640</option>
|
||||
<option value="704">704</option> <option value="768">768</option>
|
||||
<option value="832">832</option> <option value="896">896</option>
|
||||
<option value="960">960</option> <option value="1024">1024</option>
|
||||
</select>
|
||||
<label title="Set to multiple of 64" for="height">Height:</label>
|
||||
<select id="height" name="height" value="512">
|
||||
<option value="64">64</option> <option value="128">128</option>
|
||||
<option value="192">192</option> <option value="256">256</option>
|
||||
<option value="320">320</option> <option value="384">384</option>
|
||||
<option value="448">448</option> <option value="512" selected>512</option>
|
||||
<option value="576">576</option> <option value="640">640</option>
|
||||
<option value="704">704</option> <option value="768">768</option>
|
||||
<option value="832">832</option> <option value="896">896</option>
|
||||
<option value="960">960</option> <option value="1024">1024</option>
|
||||
</select>
|
||||
<label title="Set to 0 for random seed" for="seed">Seed:</label>
|
||||
<input value="0" type="number" id="seed" name="seed">
|
||||
<button type="button" id="reset-seed">↺</button>
|
||||
<input type="checkbox" name="progress_images" id="progress_images">
|
||||
<label for="progress_images">Display in-progress images (slower)</label>
|
||||
<button type="button" id="reset-all">Reset to Defaults</button>
|
||||
<span id="variations">
|
||||
<label title="If > 0, generates variations on the initial seed instead of random seeds per iteration. Must be between 0 and 1. Higher values will be more different." for="variation_amount">Variation amount (0 to disable):</label>
|
||||
<input value="0" type="number" id="variation_amount" name="variation_amount" step="0.01" min="0" max="1">
|
||||
<label title="list of variations to apply, in the format `seed:weight,seed:weight,..." for="with_variations">With variations (seed:weight,seed:weight,...):</label>
|
||||
<input value="" type="text" id="with_variations" name="with_variations">
|
||||
</span>
|
||||
</fieldset>
|
||||
<fieldset id="img2img">
|
||||
<div class="section-header">Image-to-image options</div>
|
||||
-->
|
||||
<form id="generate-form" method="post" action="api/jobs">
|
||||
<fieldset id="txt2img">
|
||||
<legend>
|
||||
<input type="checkbox" name="enable_generate" id="enable_generate" checked>
|
||||
<label for="enable_generate">Generate</label>
|
||||
</legend>
|
||||
<div id="search-box">
|
||||
<textarea rows="3" id="prompt" name="prompt"></textarea>
|
||||
</div>
|
||||
<label for="iterations">Images to generate:</label>
|
||||
<input value="1" type="number" id="iterations" name="iterations" size="4">
|
||||
<label for="steps">Steps:</label>
|
||||
<input value="50" type="number" id="steps" name="steps">
|
||||
<label for="cfg_scale">Cfg Scale:</label>
|
||||
<input value="7.5" type="number" id="cfg_scale" name="cfg_scale" step="any">
|
||||
<label for="sampler_name">Sampler:</label>
|
||||
<select id="sampler_name" name="sampler_name" value="k_lms">
|
||||
<option value="ddim">DDIM</option>
|
||||
<option value="plms">PLMS</option>
|
||||
<option value="k_lms" selected>KLMS</option>
|
||||
<option value="k_dpm_2">KDPM_2</option>
|
||||
<option value="k_dpm_2_a">KDPM_2A</option>
|
||||
<option value="k_euler">KEULER</option>
|
||||
<option value="k_euler_a">KEULER_A</option>
|
||||
<option value="k_heun">KHEUN</option>
|
||||
</select>
|
||||
<input type="checkbox" name="seamless" id="seamless">
|
||||
<label for="seamless">Seamless circular tiling</label>
|
||||
<br>
|
||||
<label title="Set to multiple of 64" for="width">Width:</label>
|
||||
<select id="width" name="width" value="512">
|
||||
<option value="64">64</option>
|
||||
<option value="128">128</option>
|
||||
<option value="192">192</option>
|
||||
<option value="256">256</option>
|
||||
<option value="320">320</option>
|
||||
<option value="384">384</option>
|
||||
<option value="448">448</option>
|
||||
<option value="512" selected>512</option>
|
||||
<option value="576">576</option>
|
||||
<option value="640">640</option>
|
||||
<option value="704">704</option>
|
||||
<option value="768">768</option>
|
||||
<option value="832">832</option>
|
||||
<option value="896">896</option>
|
||||
<option value="960">960</option>
|
||||
<option value="1024">1024</option>
|
||||
</select>
|
||||
<label title="Set to multiple of 64" for="height">Height:</label>
|
||||
<select id="height" name="height" value="512">
|
||||
<option value="64">64</option>
|
||||
<option value="128">128</option>
|
||||
<option value="192">192</option>
|
||||
<option value="256">256</option>
|
||||
<option value="320">320</option>
|
||||
<option value="384">384</option>
|
||||
<option value="448">448</option>
|
||||
<option value="512" selected>512</option>
|
||||
<option value="576">576</option>
|
||||
<option value="640">640</option>
|
||||
<option value="704">704</option>
|
||||
<option value="768">768</option>
|
||||
<option value="832">832</option>
|
||||
<option value="896">896</option>
|
||||
<option value="960">960</option>
|
||||
<option value="1024">1024</option>
|
||||
</select>
|
||||
<label title="Set to 0 for random seed" for="seed">Seed:</label>
|
||||
<input value="0" type="number" id="seed" name="seed">
|
||||
<button type="button" id="reset-seed">↺</button>
|
||||
<input type="checkbox" name="progress_images" id="progress_images">
|
||||
<label for="progress_images">Display in-progress images (slower)</label>
|
||||
<button type="button" id="reset-all">Reset to Defaults</button>
|
||||
<div id="variations">
|
||||
<label
|
||||
title="If > 0, generates variations on the initial seed instead of random seeds per iteration. Must be between 0 and 1. Higher values will be more different."
|
||||
for="variation_amount">Variation amount (0 to disable):</label>
|
||||
<input value="0" type="number" id="variation_amount" name="variation_amount" step="0.01" min="0" max="1">
|
||||
<label title="list of variations to apply, in the format `seed:weight,seed:weight,..."
|
||||
for="with_variations">With variations (seed:weight,seed:weight,...):</label>
|
||||
<input value="" type="text" id="with_variations" name="with_variations">
|
||||
</div>
|
||||
</fieldset>
|
||||
<fieldset id="initimg">
|
||||
<legend>
|
||||
<input type="checkbox" name="enable_init_image" id="enable_init_image" checked>
|
||||
<label for="enable_init_image">Enable init image</label>
|
||||
</legend>
|
||||
<div>
|
||||
<label title="Upload an image to use img2img" for="initimg">Initial image:</label>
|
||||
<input type="file" id="initimg" name="initimg" accept=".jpg, .jpeg, .png">
|
||||
<button type="button" id="remove-image">Remove Image</button>
|
||||
<br>
|
||||
<label for="strength">Img2Img Strength:</label>
|
||||
<input value="0.75" type="number" id="strength" name="strength" step="0.01" min="0" max="1">
|
||||
<input type="checkbox" id="fit" name="fit" checked>
|
||||
<label title="Rescale image to fit within requested width and height" for="fit">Fit to width/height</label>
|
||||
</fieldset>
|
||||
</div>
|
||||
<fieldset id="img2img">
|
||||
<legend>
|
||||
<input type="checkbox" name="enable_img2img" id="enable_img2img" checked>
|
||||
<label for="enable_img2img">Enable Img2Img</label>
|
||||
</legend>
|
||||
<label for="strength">Img2Img Strength:</label>
|
||||
<input value="0.75" type="number" id="strength" name="strength" step="0.01" min="0" max="1">
|
||||
<input type="checkbox" id="fit" name="fit" checked>
|
||||
<label title="Rescale image to fit within requested width and height" for="fit">Fit to width/height:</label>
|
||||
</fieldset>
|
||||
</fieldset>
|
||||
<div id="postprocess">
|
||||
<fieldset id="gfpgan">
|
||||
<div class="section-header">Post-processing options</div>
|
||||
<label title="Strength of the gfpgan (face fixing) algorithm." for="gfpgan_strength">GPFGAN Strength (0 to disable):</label>
|
||||
<input value="0.0" min="0" max="1" type="number" id="gfpgan_strength" name="gfpgan_strength" step="0.1">
|
||||
<label title="Upscaling to perform using ESRGAN." for="upscale_level">Upscaling Level</label>
|
||||
<legend>
|
||||
<input type="checkbox" name="enable_gfpgan" id="enable_gfpgan">
|
||||
<label for="enable_gfpgan">Enable gfpgan</label>
|
||||
</legend>
|
||||
<label title="Strength of the gfpgan (face fixing) algorithm." for="gfpgan_strength">GPFGAN Strength:</label>
|
||||
<input value="0.8" min="0" max="1" type="number" id="gfpgan_strength" name="gfpgan_strength" step="0.05">
|
||||
</fieldset>
|
||||
<fieldset id="upscale">
|
||||
<legend>
|
||||
<input type="checkbox" name="enable_upscale" id="enable_upscale">
|
||||
<label for="enable_upscale">Enable Upscaling</label>
|
||||
</legend>
|
||||
<label title="Upscaling to perform using ESRGAN." for="upscale_level">Upscaling Level:</label>
|
||||
<select id="upscale_level" name="upscale_level" value="">
|
||||
<option value="" selected>None</option>
|
||||
<option value="2">2x</option>
|
||||
@ -107,25 +155,25 @@
|
||||
<label title="Strength of the esrgan (upscaling) algorithm." for="upscale_strength">Upscale Strength:</label>
|
||||
<input value="0.75" min="0" max="1" type="number" id="upscale_strength" name="upscale_strength" step="0.05">
|
||||
</fieldset>
|
||||
</form>
|
||||
<br>
|
||||
<section id="progress-section">
|
||||
<div id="progress-container">
|
||||
<progress id="progress-bar" value="0" max="1"></progress>
|
||||
<span id="cancel-button" title="Cancel">✖</span>
|
||||
<br>
|
||||
<img id="progress-image" src='data:image/svg+xml,<svg xmlns="http://www.w3.org/2000/svg"/>'>
|
||||
<div id="scaling-inprocess-message">
|
||||
<i><span>Postprocessing...</span><span id="processing_cnt">1</span>/<span id="processing_total">3</span></i>
|
||||
</div>
|
||||
</span>
|
||||
</section>
|
||||
|
||||
<div id="results">
|
||||
<div id="no-results-message">
|
||||
<i><p>No results...</p></i>
|
||||
</div>
|
||||
<input type="submit" id="submit" value="Generate">
|
||||
</form>
|
||||
<br>
|
||||
<section id="progress-section">
|
||||
<div id="progress-container">
|
||||
<progress id="progress-bar" value="0" max="1"></progress>
|
||||
<span id="cancel-button" title="Cancel">✖</span>
|
||||
<br>
|
||||
<img id="progress-image" src='data:image/svg+xml,<svg xmlns="http://www.w3.org/2000/svg"/>'>
|
||||
<div id="scaling-inprocess-message">
|
||||
<i><span>Postprocessing...</span><span id="processing_cnt">1</span>/<span id="processing_total">3</span></i>
|
||||
</div>
|
||||
</div>
|
||||
</main>
|
||||
</body>
|
||||
</section>
|
||||
|
||||
<div id="results">
|
||||
</div>
|
||||
</main>
|
||||
</body>
|
||||
|
||||
</html>
|
||||
|
||||
@ -1,5 +1,73 @@
|
||||
const socket = io();
|
||||
|
||||
var priorResultsLoadState = {
|
||||
page: 0,
|
||||
pages: 1,
|
||||
per_page: 10,
|
||||
total: 20,
|
||||
offset: 0, // number of items generated since last load
|
||||
loading: false,
|
||||
initialized: false
|
||||
};
|
||||
|
||||
function loadPriorResults() {
|
||||
// Fix next page by offset
|
||||
let offsetPages = priorResultsLoadState.offset / priorResultsLoadState.per_page;
|
||||
priorResultsLoadState.page += offsetPages;
|
||||
priorResultsLoadState.pages += offsetPages;
|
||||
priorResultsLoadState.total += priorResultsLoadState.offset;
|
||||
priorResultsLoadState.offset = 0;
|
||||
|
||||
if (priorResultsLoadState.loading) {
|
||||
return;
|
||||
}
|
||||
|
||||
if (priorResultsLoadState.page >= priorResultsLoadState.pages) {
|
||||
return; // Nothing more to load
|
||||
}
|
||||
|
||||
// Load
|
||||
priorResultsLoadState.loading = true
|
||||
let url = new URL('/api/images', document.baseURI);
|
||||
url.searchParams.append('page', priorResultsLoadState.initialized ? priorResultsLoadState.page + 1 : priorResultsLoadState.page);
|
||||
url.searchParams.append('per_page', priorResultsLoadState.per_page);
|
||||
fetch(url.href, {
|
||||
method: 'GET',
|
||||
headers: new Headers({'content-type': 'application/json'})
|
||||
})
|
||||
.then(response => response.json())
|
||||
.then(data => {
|
||||
priorResultsLoadState.page = data.page;
|
||||
priorResultsLoadState.pages = data.pages;
|
||||
priorResultsLoadState.per_page = data.per_page;
|
||||
priorResultsLoadState.total = data.total;
|
||||
|
||||
data.items.forEach(function(dreamId, index) {
|
||||
let src = 'api/images/' + dreamId;
|
||||
fetch('/api/images/' + dreamId + '/metadata', {
|
||||
method: 'GET',
|
||||
headers: new Headers({'content-type': 'application/json'})
|
||||
})
|
||||
.then(response => response.json())
|
||||
.then(metadata => {
|
||||
let seed = metadata.seed || 0; // TODO: Parse old metadata
|
||||
appendOutput(src, seed, metadata, true);
|
||||
});
|
||||
});
|
||||
|
||||
// Load until page is full
|
||||
if (!priorResultsLoadState.initialized) {
|
||||
if (document.body.scrollHeight <= window.innerHeight) {
|
||||
loadPriorResults();
|
||||
}
|
||||
}
|
||||
})
|
||||
.finally(() => {
|
||||
priorResultsLoadState.loading = false;
|
||||
priorResultsLoadState.initialized = true;
|
||||
});
|
||||
}
|
||||
|
||||
function resetForm() {
|
||||
var form = document.getElementById('generate-form');
|
||||
form.querySelector('fieldset').removeAttribute('disabled');
|
||||
@ -45,48 +113,64 @@ function toBase64(file) {
|
||||
});
|
||||
}
|
||||
|
||||
function appendOutput(src, seed, config) {
|
||||
function ondragdream(event) {
|
||||
let dream = event.target.dataset.dream;
|
||||
event.dataTransfer.setData("dream", dream);
|
||||
}
|
||||
|
||||
function seedClick(event) {
|
||||
// Get element
|
||||
var image = event.target.closest('figure').querySelector('img');
|
||||
var dream = JSON.parse(decodeURIComponent(image.dataset.dream));
|
||||
|
||||
let form = document.querySelector("#generate-form");
|
||||
for (const [k, v] of new FormData(form)) {
|
||||
if (k == 'initimg') { continue; }
|
||||
let formElem = form.querySelector(`*[name=${k}]`);
|
||||
formElem.value = dream[k] !== undefined ? dream[k] : formElem.defaultValue;
|
||||
}
|
||||
|
||||
document.querySelector("#seed").value = dream.seed;
|
||||
document.querySelector('#iterations').value = 1; // Reset to 1 iteration since we clicked a single image (not a full job)
|
||||
|
||||
// NOTE: leaving this manual for the user for now - it was very confusing with this behavior
|
||||
// document.querySelector("#with_variations").value = variations || '';
|
||||
// if (document.querySelector("#variation_amount").value <= 0) {
|
||||
// document.querySelector("#variation_amount").value = 0.2;
|
||||
// }
|
||||
|
||||
saveFields(document.querySelector("#generate-form"));
|
||||
}
|
||||
|
||||
function appendOutput(src, seed, config, toEnd=false) {
|
||||
let outputNode = document.createElement("figure");
|
||||
let altText = seed.toString() + " | " + config.prompt;
|
||||
|
||||
// img needs width and height for lazy loading to work
|
||||
// TODO: store the full config in a data attribute on the image?
|
||||
const figureContents = `
|
||||
<a href="${src}" target="_blank">
|
||||
<img src="${src}" alt="${altText}" title="${altText}">
|
||||
<img src="${src}"
|
||||
alt="${altText}"
|
||||
title="${altText}"
|
||||
loading="lazy"
|
||||
width="256"
|
||||
height="256"
|
||||
draggable="true"
|
||||
ondragstart="ondragdream(event, this)"
|
||||
data-dream="${encodeURIComponent(JSON.stringify(config))}"
|
||||
data-dreamId="${encodeURIComponent(config.dreamId)}">
|
||||
</a>
|
||||
<figcaption>${seed}</figcaption>
|
||||
<figcaption onclick="seedClick(event, this)">${seed}</figcaption>
|
||||
`;
|
||||
|
||||
outputNode.innerHTML = figureContents;
|
||||
let figcaption = outputNode.querySelector('figcaption')
|
||||
|
||||
// Reload image config
|
||||
figcaption.addEventListener('click', () => {
|
||||
let form = document.querySelector("#generate-form");
|
||||
for (const [k, v] of new FormData(form)) {
|
||||
if (k == 'initimg') { continue; }
|
||||
form.querySelector(`*[name=${k}]`).value = config[k];
|
||||
}
|
||||
if (config.variation_amount > 0 || config.with_variations != '') {
|
||||
document.querySelector("#seed").value = config.seed;
|
||||
} else {
|
||||
document.querySelector("#seed").value = seed;
|
||||
}
|
||||
|
||||
if (config.variation_amount > 0) {
|
||||
let oldVarAmt = document.querySelector("#variation_amount").value
|
||||
let oldVariations = document.querySelector("#with_variations").value
|
||||
let varSep = ''
|
||||
document.querySelector("#variation_amount").value = 0;
|
||||
if (document.querySelector("#with_variations").value != '') {
|
||||
varSep = ","
|
||||
}
|
||||
document.querySelector("#with_variations").value = oldVariations + varSep + seed + ':' + config.variation_amount
|
||||
}
|
||||
|
||||
saveFields(document.querySelector("#generate-form"));
|
||||
});
|
||||
|
||||
document.querySelector("#results").prepend(outputNode);
|
||||
if (toEnd) {
|
||||
document.querySelector("#results").append(outputNode);
|
||||
} else {
|
||||
document.querySelector("#results").prepend(outputNode);
|
||||
}
|
||||
document.querySelector("#no-results-message")?.remove();
|
||||
}
|
||||
|
||||
@ -119,14 +203,33 @@ async function generateSubmit(form) {
|
||||
// Convert file data to base64
|
||||
// TODO: Should probably uplaod files with formdata or something, and store them in the backend?
|
||||
let formData = Object.fromEntries(new FormData(form));
|
||||
if (!formData.enable_generate && !formData.enable_init_image) {
|
||||
gen_label = document.querySelector("label[for=enable_generate]").innerHTML;
|
||||
initimg_label = document.querySelector("label[for=enable_init_image]").innerHTML;
|
||||
alert(`Error: one of "${gen_label}" or "${initimg_label}" must be set`);
|
||||
}
|
||||
|
||||
|
||||
formData.initimg_name = formData.initimg.name
|
||||
formData.initimg = formData.initimg.name !== '' ? await toBase64(formData.initimg) : null;
|
||||
|
||||
// Evaluate all checkboxes
|
||||
let checkboxes = form.querySelectorAll('input[type=checkbox]');
|
||||
checkboxes.forEach(function (checkbox) {
|
||||
if (checkbox.checked) {
|
||||
formData[checkbox.name] = 'true';
|
||||
}
|
||||
});
|
||||
|
||||
let strength = formData.strength;
|
||||
let totalSteps = formData.initimg ? Math.floor(strength * formData.steps) : formData.steps;
|
||||
let showProgressImages = formData.progress_images;
|
||||
|
||||
// Set enabling flags
|
||||
|
||||
|
||||
// Initialize the progress bar
|
||||
initProgress(totalSteps);
|
||||
initProgress(totalSteps, showProgressImages);
|
||||
|
||||
// POST, use response to listen for events
|
||||
fetch(form.action, {
|
||||
@ -136,13 +239,19 @@ async function generateSubmit(form) {
|
||||
})
|
||||
.then(response => response.json())
|
||||
.then(data => {
|
||||
var dreamId = data.dreamId;
|
||||
socket.emit('join_room', { 'room': dreamId });
|
||||
var jobId = data.jobId;
|
||||
socket.emit('join_room', { 'room': jobId });
|
||||
});
|
||||
|
||||
form.querySelector('fieldset').setAttribute('disabled','');
|
||||
}
|
||||
|
||||
function fieldSetEnableChecked(event) {
|
||||
cb = event.target;
|
||||
fields = cb.closest('fieldset');
|
||||
fields.disabled = !cb.checked;
|
||||
}
|
||||
|
||||
// Socket listeners
|
||||
socket.on('job_started', (data) => {})
|
||||
|
||||
@ -152,6 +261,7 @@ socket.on('dream_result', (data) => {
|
||||
var dreamRequest = data.dreamRequest;
|
||||
var src = 'api/images/' + dreamId;
|
||||
|
||||
priorResultsLoadState.offset += 1;
|
||||
appendOutput(src, dreamRequest.seed, dreamRequest);
|
||||
|
||||
resetProgress(false);
|
||||
@ -193,7 +303,13 @@ socket.on('job_done', (data) => {
|
||||
resetProgress();
|
||||
})
|
||||
|
||||
window.onload = () => {
|
||||
window.onload = async () => {
|
||||
document.querySelector("#prompt").addEventListener("keydown", (e) => {
|
||||
if (e.key === "Enter" && !e.shiftKey) {
|
||||
const form = e.target.form;
|
||||
generateSubmit(form);
|
||||
}
|
||||
});
|
||||
document.querySelector("#generate-form").addEventListener('submit', (e) => {
|
||||
e.preventDefault();
|
||||
const form = e.target;
|
||||
@ -216,12 +332,65 @@ window.onload = () => {
|
||||
loadFields(document.querySelector("#generate-form"));
|
||||
|
||||
document.querySelector('#cancel-button').addEventListener('click', () => {
|
||||
fetch('/cancel').catch(e => {
|
||||
fetch('/api/cancel').catch(e => {
|
||||
console.error(e);
|
||||
});
|
||||
});
|
||||
document.documentElement.addEventListener('keydown', (e) => {
|
||||
if (e.key === "Escape")
|
||||
fetch('/api/cancel').catch(err => {
|
||||
console.error(err);
|
||||
});
|
||||
});
|
||||
|
||||
if (!config.gfpgan_model_exists) {
|
||||
document.querySelector("#gfpgan").style.display = 'none';
|
||||
}
|
||||
|
||||
window.addEventListener("scroll", () => {
|
||||
if ((window.innerHeight + window.pageYOffset) >= document.body.offsetHeight) {
|
||||
loadPriorResults();
|
||||
}
|
||||
});
|
||||
|
||||
|
||||
|
||||
// Enable/disable forms by checkboxes
|
||||
document.querySelectorAll("legend > input[type=checkbox]").forEach(function(cb) {
|
||||
cb.addEventListener('change', fieldSetEnableChecked);
|
||||
fieldSetEnableChecked({ target: cb})
|
||||
});
|
||||
|
||||
|
||||
// Load some of the previous results
|
||||
loadPriorResults();
|
||||
|
||||
// Image drop/upload WIP
|
||||
/*
|
||||
let drop = document.getElementById('dropper');
|
||||
function ondrop(event) {
|
||||
let dreamData = event.dataTransfer.getData('dream');
|
||||
if (dreamData) {
|
||||
var dream = JSON.parse(decodeURIComponent(dreamData));
|
||||
alert(dream.dreamId);
|
||||
}
|
||||
};
|
||||
|
||||
function ondragenter(event) {
|
||||
event.preventDefault();
|
||||
};
|
||||
|
||||
function ondragover(event) {
|
||||
event.preventDefault();
|
||||
};
|
||||
|
||||
function ondragleave(event) {
|
||||
|
||||
}
|
||||
|
||||
drop.addEventListener('drop', ondrop);
|
||||
drop.addEventListener('dragenter', ondragenter);
|
||||
drop.addEventListener('dragover', ondragover);
|
||||
drop.addEventListener('dragleave', ondragleave);
|
||||
*/
|
||||
};
|
||||
|
||||
Loading…
Reference in New Issue
Block a user