Merge with PR #602

- New and improved web api
- Author: @Kyle0654

.gitignore

@@ -191,3 +191,4 @@ checkpoints
 .scratch/
 .vscode/
 gfpgan/
+models/ldm/stable-diffusion-v1/model.sha256

docs/other/CONTRIBUTORS.md

@@ -51,6 +51,7 @@ We thank them for all of their time and hard work.
 - [Any Winter](https://github.com/any-winter-4079)
 - [Doggettx](https://github.com/doggettx)
 - [Matthias Wild](https://github.com/mauwii)
+- [Kyle Schouviller](https://github.com/kyle0654)
 
 ## __Original CompVis Authors:__
 

ldm/dream/pngwriter.py

@@ -33,10 +33,11 @@ class PngWriter:
 
     # saves image named _image_ to outdir/name, writing metadata from prompt
     # returns full path of output
-    def save_image_and_prompt_to_png(self, image, dream_prompt, metadata, name):
+    def save_image_and_prompt_to_png(self, image, dream_prompt, name, metadata=None):
         path = os.path.join(self.outdir, name)
         info = PngImagePlugin.PngInfo()
         info.add_text('Dream', dream_prompt)
+        if metadata: # TODO: merge command line app's method of writing metadata and always just write metadata
           info.add_text('sd-metadata', json.dumps(metadata))
         image.save(path, 'PNG', pnginfo=info)
         return path

ldm/dream/server.py

@@ -230,7 +230,7 @@ class DreamServer(BaseHTTPRequestHandler):
                 image = self.model.sample_to_image(sample)
                 name = f'{prefix}.{opt.seed}.{step_index}.png'
                 metadata = f'{opt.prompt} -S{opt.seed} [intermediate]'
-                path = step_writer.save_image_and_prompt_to_png(image, metadata, name)
+                path = step_writer.save_image_and_prompt_to_png(image, dream_prompt=metadata, name=name)
                 step_index += 1
             self.wfile.write(bytes(json.dumps(
                 {'event': 'step', 'step': step + 1, 'url': path}

ldm/generate.py

@@ -181,7 +181,7 @@ class Generate:
         for image, seed in results:
             name = f'{prefix}.{seed}.png'
             path = pngwriter.save_image_and_prompt_to_png(
-                image, f'{prompt} -S{seed}', name)
+                image, dream_prompt=f'{prompt} -S{seed}', name=name)
             outputs.append([path, seed])
         return outputs
 

requirements.txt

@@ -22,6 +22,11 @@ test-tube
 torch-fidelity
 torchmetrics
 transformers
+flask==2.1.3
+flask_socketio==5.3.0
+flask_cors==3.0.10
+dependency_injector==4.40.0
+eventlet
 git+https://github.com/openai/CLIP.git@main#egg=clip
 git+https://github.com/Birch-san/k-diffusion.git@mps#egg=k-diffusion
 git+https://github.com/lstein/GFPGAN@fix-dark-cast-images#egg=gfpgan

server/application.py

@@ -7,9 +7,10 @@ import os
 import sys
 from flask import Flask
 from flask_cors import CORS
-from flask_socketio import SocketIO, join_room, leave_room
+from flask_socketio import SocketIO
 from omegaconf import OmegaConf
 from dependency_injector.wiring import inject, Provide
+from ldm.dream.args import Args
 from server import views
 from server.containers import Container
 from server.services import GeneratorService, SignalService
@@ -58,6 +59,8 @@ def run_app(config, host, port) -> Flask:
 
   # TODO: Get storage root from config
   app.add_url_rule('/api/images/<string:dreamId>', view_func=views.ApiImages.as_view('api_images', '../'))
+  app.add_url_rule('/api/images/<string:dreamId>/metadata', view_func=views.ApiImagesMetadata.as_view('api_images_metadata', '../'))
+  app.add_url_rule('/api/images', view_func=views.ApiImagesList.as_view('api_images_list'))
   app.add_url_rule('/api/intermediates/<string:dreamId>/<string:step>', view_func=views.ApiIntermediates.as_view('api_intermediates', '../'))
 
   app.static_folder = os.path.abspath(os.path.join(os.path.dirname(__file__), '../static/dream_web/')) 
@@ -79,30 +82,28 @@ def run_app(config, host, port) -> Flask:
 
 def main():
   """Initialize command-line parsers and the diffusion model"""
-  from scripts.dream import create_argv_parser
-  arg_parser = create_argv_parser()
+  arg_parser = Args()
   opt = arg_parser.parse_args()
 
   if opt.laion400m:
       print('--laion400m flag has been deprecated. Please use --model laion400m instead.')
       sys.exit(-1)
-  if opt.weights != 'model':
-    print('--weights argument has been deprecated. Please configure ./configs/models.yaml, and call it using --model instead.')
+  if opt.weights:
+      print('--weights argument has been deprecated. Please edit ./configs/models.yaml, and select the weights using --model instead.')
       sys.exit(-1)
       
-  try:
-    models  = OmegaConf.load(opt.config)
-    width   = models[opt.model].width
-    height  = models[opt.model].height
-    config  = models[opt.model].config
-    weights = models[opt.model].weights
-  except (FileNotFoundError, IOError, KeyError) as e:
-    print(f'{e}. Aborting.')
-    sys.exit(-1)
+  # try:
+  #   models  = OmegaConf.load(opt.config)
+  #   width   = models[opt.model].width
+  #   height  = models[opt.model].height
+  #   config  = models[opt.model].config
+  #   weights = models[opt.model].weights
+  # except (FileNotFoundError, IOError, KeyError) as e:
+  #   print(f'{e}. Aborting.')
+  #   sys.exit(-1)
 
-  print('* Initializing, be patient...\n')
+  #print('* Initializing, be patient...\n')
   sys.path.append('.')
-  from pytorch_lightning import logging
 
   # these two lines prevent a horrible warning message from appearing
   # when the frozen CLIP tokenizer is imported
@@ -110,26 +111,28 @@ def main():
 
   transformers.logging.set_verbosity_error()
 
-  appConfig = {
-    "model": {
-      "width": width,
-      "height": height,
-      "sampler_name": opt.sampler_name,
-      "weights": weights,
-      "full_precision": opt.full_precision,
-      "config": config,
-      "grid": opt.grid,
-      "latent_diffusion_weights": opt.laion400m,
-      "embedding_path": opt.embedding_path,
-      "device_type": opt.device
-    }
-  }
+  appConfig = opt.__dict__
+
+  # appConfig = {
+  #   "model": {
+  #     "width": width,
+  #     "height": height,
+  #     "sampler_name": opt.sampler_name,
+  #     "weights": weights,
+  #     "full_precision": opt.full_precision,
+  #     "config": config,
+  #     "grid": opt.grid,
+  #     "latent_diffusion_weights": opt.laion400m,
+  #     "embedding_path": opt.embedding_path
+  #   }
+  # }
 
   # make sure the output directory exists
   if not os.path.exists(opt.outdir):
     os.makedirs(opt.outdir)
 
   # gets rid of annoying messages about random seed
+  from pytorch_lightning import logging
   logging.getLogger('pytorch_lightning').setLevel(logging.ERROR)
 
   print('\n* starting api server...')

server/containers.py

@@ -17,18 +17,24 @@ class Container(containers.DeclarativeContainer):
     app = None
   )
 
+  # TODO: Add a model provider service that provides model(s) dynamically
   model_singleton = providers.ThreadSafeSingleton(
     Generate,
-    width = config.model.width,
-    height = config.model.height,
-    sampler_name = config.model.sampler_name,
-    weights = config.model.weights,
-    full_precision = config.model.full_precision,
-    config = config.model.config,
-    grid = config.model.grid,
-    seamless = config.model.seamless,
-    embedding_path = config.model.embedding_path,
-    device_type = config.model.device_type
+    model          = config.model,
+    sampler_name   = config.sampler_name,
+    embedding_path = config.embedding_path,
+    full_precision = config.full_precision
+    # config = config.model.config,
+
+    # width = config.model.width,
+    # height = config.model.height,
+    # sampler_name = config.model.sampler_name,
+    # weights = config.model.weights,
+    # full_precision = config.model.full_precision,
+    # grid = config.model.grid,
+    # seamless = config.model.seamless,
+    # embedding_path = config.model.embedding_path,
+    # device_type = config.model.device_type
   )
 
   # TODO: get location from config

server/models.py

@@ -1,77 +1,182 @@
 # Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
 
+from base64 import urlsafe_b64encode
 import json
 import string
 from copy import deepcopy
 from datetime import datetime, timezone
 from enum import Enum
+from typing import Any, Dict, List, Union
+from uuid import uuid4
 
-class DreamRequest():
-  prompt: string
-  initimg: string
-  strength: float
-  iterations: int
-  steps: int
-  width: int
-  height: int
-  fit = None
-  cfgscale: float
-  sampler_name: string
-  gfpgan_strength: float
-  upscale_level: int
-  upscale_strength: float
+
+class DreamBase():
+  # Id
+  id: str
+
+  # Initial Image
+  enable_init_image: bool
+  initimg: string = None
+
+  # Img2Img
+  enable_img2img: bool # TODO: support this better
+  strength: float = 0 # TODO: name this something related to img2img to make it clearer?
+  fit = None # Fit initial image dimensions
+
+  # Generation
+  enable_generate: bool
+  prompt: string = ""
+  seed: int = 0 # 0 is random
+  steps: int = 10
+  width: int = 512
+  height: int = 512
+  cfg_scale: float = 7.5
+  sampler_name: string = 'klms'
+  seamless: bool = False
+  model: str = None # The model to use (currently unused)
+  embeddings = None # The embeddings to use (currently unused)
+  progress_images: bool = False
+
+  # GFPGAN
+  enable_gfpgan: bool
+  gfpgan_strength: float = 0
+
+  # Upscale
+  enable_upscale: bool
   upscale: None
-  progress_images = None
-  seed: int
+  upscale_level: int = None
+  upscale_strength: float = 0.75
+
+  # Embiggen
+  enable_embiggen: bool
+  embiggen: Union[None, List[float]] = None
+  embiggen_tiles: Union[None, List[int]] = None
+
+  # Metadata
   time: int
 
+  def __init__(self):
+    self.id = urlsafe_b64encode(uuid4().bytes).decode('ascii')
+
+  def parse_json(self, j, new_instance=False):
+    # Id
+    if 'id' in j and not new_instance:
+      self.id = j.get('id')
+
+    # Initial Image
+    self.enable_init_image = 'enable_init_image' in j and bool(j.get('enable_init_image'))
+    if self.enable_init_image:
+      self.initimg = j.get('initimg')
+
+      # Img2Img
+      self.enable_img2img = 'enable_img2img' in j and bool(j.get('enable_img2img'))
+      if self.enable_img2img:
+        self.strength = float(j.get('strength'))
+        self.fit    = 'fit' in j
+
+    # Generation
+    self.enable_generate = 'enable_generate' in j and bool(j.get('enable_generate'))
+    if self.enable_generate:
+      self.prompt = j.get('prompt')
+      self.seed = int(j.get('seed'))
+      self.steps = int(j.get('steps'))
+      self.width = int(j.get('width'))
+      self.height = int(j.get('height'))
+      self.cfg_scale = float(j.get('cfgscale') or j.get('cfg_scale'))
+      self.sampler_name  = j.get('sampler') or j.get('sampler_name')
+      # model: str = None # The model to use (currently unused)
+      # embeddings = None # The embeddings to use (currently unused)
+      self.seamless = 'seamless' in j
+      self.progress_images = 'progress_images' in j
+
+    # GFPGAN
+    self.enable_gfpgan = 'enable_gfpgan' in j and bool(j.get('enable_gfpgan'))
+    if self.enable_gfpgan:
+      self.gfpgan_strength = float(j.get('gfpgan_strength'))
+
+    # Upscale
+    self.enable_upscale = 'enable_upscale' in j and bool(j.get('enable_upscale'))
+    if self.enable_upscale:
+      self.upscale_level    = j.get('upscale_level')
+      self.upscale_strength = j.get('upscale_strength')
+      self.upscale = None if self.upscale_level in {None,''} else [int(self.upscale_level),float(self.upscale_strength)]
+
+    # Embiggen
+    self.enable_embiggen = 'enable_embiggen' in j and bool(j.get('enable_embiggen'))
+    if self.enable_embiggen:
+      self.embiggen       = j.get('embiggen')
+      self.embiggen_tiles = j.get('embiggen_tiles')
+
+    # Metadata
+    self.time = int(j.get('time')) if ('time' in j and not new_instance) else int(datetime.now(timezone.utc).timestamp())
+
+
+class DreamResult(DreamBase):
+  # Result
+  has_upscaled: False
+  has_gfpgan: False
+
   # TODO: use something else for state tracking
   images_generated: int = 0
   images_upscaled: int = 0
 
-  def id(self, seed = None, upscaled = False) -> str:
-    return f"{self.time}.{seed or self.seed}{'.u' if upscaled else ''}"
+  def __init__(self):
+    super().__init__()
 
-  # TODO: handle this more cleanly (probably by splitting this into a Job and Result class)
-  # TODO: Set iterations to 1 or remove it from the dream result? And just keep it on the job?
-  def clone_without_image(self, seed = None):
-    data = deepcopy(self)
-    data.initimg = None
-    if seed:
-      data.seed = seed
+  def clone_without_img(self):
+    copy = deepcopy(self)
+    copy.initimg = None
+    return copy
 
-    return data
-
-  def to_json(self, seed: int = None):
-    copy = self.clone_without_image(seed)
-    return json.dumps(copy.__dict__)
+  def to_json(self):
+    copy = deepcopy(self)
+    copy.initimg = None
+    j = json.dumps(copy.__dict__)
+    return j
 
   @staticmethod
   def from_json(j, newTime: bool = False):
-    d = DreamRequest()
-    d.prompt = j.get('prompt')
-    d.initimg = j.get('initimg')
-    d.strength = float(j.get('strength'))
-    d.iterations = int(j.get('iterations'))
-    d.steps = int(j.get('steps'))
-    d.width = int(j.get('width'))
-    d.height = int(j.get('height'))
-    d.fit    = 'fit' in j
-    d.seamless = 'seamless' in j
-    d.cfgscale = float(j.get('cfgscale'))
-    d.sampler_name  = j.get('sampler')
-    d.variation_amount = float(j.get('variation_amount'))
-    d.with_variations = j.get('with_variations')
-    d.gfpgan_strength = float(j.get('gfpgan_strength'))
-    d.upscale_level    = j.get('upscale_level')
-    d.upscale_strength = j.get('upscale_strength')
-    d.upscale = [int(d.upscale_level),float(d.upscale_strength)] if d.upscale_level != '' else None
-    d.progress_images = 'progress_images' in j
-    d.seed = int(j.get('seed'))
-    d.time = int(datetime.now(timezone.utc).timestamp()) if newTime else int(j.get('time'))
+    d = DreamResult()
+    d.parse_json(j)
     return d
 
 
+# TODO: switch this to a pipelined request, with pluggable steps
+# Will likely require generator code changes to accomplish
+class JobRequest(DreamBase):
+  # Iteration
+  iterations: int = 1
+  variation_amount = None
+  with_variations = None
+
+  # Results
+  results: List[DreamResult] = []
+
+  def __init__(self):
+    super().__init__()
+
+  def newDreamResult(self) -> DreamResult:
+    result = DreamResult()
+    result.parse_json(self.__dict__, new_instance=True)
+    return result
+
+  @staticmethod
+  def from_json(j):
+    job = JobRequest()
+    job.parse_json(j)
+
+    # Metadata
+    job.time = int(j.get('time')) if ('time' in j) else int(datetime.now(timezone.utc).timestamp())
+
+    # Iteration
+    if job.enable_generate:
+      job.iterations = int(j.get('iterations'))
+      job.variation_amount = float(j.get('variation_amount'))
+      job.with_variations = j.get('with_variations')
+
+    return job
+
+
 class ProgressType(Enum):
   GENERATION = 1
   UPSCALING_STARTED = 2
@@ -102,11 +207,11 @@ class Signal():
 
   # TODO: use a result id or something? Like a sub-job
   @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__)

server/services.py

@@ -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:
@@ -85,25 +93,28 @@ 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:
@@ -111,6 +122,88 @@ class ImageStorageService:
     path = os.path.join(self.__location, name)
     return path
   
+  # Returns true if found, false if not found or error
+  def delete(self, dreamId: str, postfix: str = '') -> bool:
+    path = self.path(dreamId, postfix)
+    if (os.path.exists(path)):
+      os.remove(path)
+      return True
+    else:
+      return False
+  
+  def getMetadata(self, dreamId: str, postfix: str = '') -> DreamResult:
+    path = self.path(dreamId, postfix)
+    image = Image.open(path)
+    text = image.text
+    if text.__contains__('Dream'):
+      dreamMeta = text.get('Dream')
+      try:
+        j = json.loads(dreamMeta)
+        return DreamResult.from_json(j)
+      except ValueError:
+        # Try to parse command-line format (legacy metadata format)
+        try:
+          opt = self.__parseLegacyMetadata(dreamMeta)
+          optd = opt.__dict__
+          if (not 'width' in optd) or (optd.get('width') is None):
+            optd['width'] = image.width
+          if (not 'height' in optd) or (optd.get('height') is None):
+            optd['height'] = image.height
+          if (not 'steps' in optd) or (optd.get('steps') is None):
+            optd['steps'] = 10 # No way around this unfortunately - seems like it wasn't storing this previously
+
+          optd['time'] = os.path.getmtime(path) # Set timestamp manually (won't be exactly correct though)
+
+          return DreamResult.from_json(optd)
+
+        except:
+          return None
+    else:
+      return None
+
+  def __parseLegacyMetadata(self, command: str) -> DreamResult:
+    # before splitting, escape single quotes so as not to mess
+    # up the parser
+    command = command.replace("'", "\\'")
+
+    try:
+        elements = shlex.split(command)
+    except ValueError as e:
+        return None
+
+    # rearrange the arguments to mimic how it works in the Dream bot.
+    switches = ['']
+    switches_started = False
+
+    for el in elements:
+        if el[0] == '-' and not switches_started:
+            switches_started = True
+        if switches_started:
+            switches.append(el)
+        else:
+            switches[0] += el
+            switches[0] += ' '
+    switches[0] = switches[0][: len(switches[0]) - 1]
+
+    try:
+        opt = self.__legacyParser.parse_cmd(switches)
+        return opt
+    except SystemExit:
+        return None
+
+  def list_files(self, page: int, perPage: int) -> PaginatedItems:
+    files = sorted(glob.glob(os.path.join(self.__location,'*.png')), key=os.path.getmtime, reverse=True)
+    count = len(files)
+
+    startId = page * perPage
+    pageCount = int(count / perPage) + 1
+    endId = min(startId + perPage, count)
+    items = [] if startId >= count else files[startId:endId]
+
+    items = list(map(lambda f: Path(f).stem, items))
+
+    return PaginatedItems(items, page, pageCount, perPage, count)
+
 
 class GeneratorService:
   __model: Generate
@@ -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:
+      # 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 = dreamRequest.initimg.split(",")[1] # Ignore mime type
+            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
+
+      # Signal start
+      self.__on_start(jobRequest)
+
+      # Generate in model
+      # TODO: Split job generation requests instead of fitting all parameters here
+      # TODO: Support no generation (just upscaling/gfpgan)
+
+      upscale = None if not jobRequest.enable_upscale else jobRequest.upscale
+      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.__start(dreamRequest)
 
         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))
+          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):

server/views.py

@@ -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
@@ -83,6 +75,27 @@ class ApiImages(MethodView):
     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
   __pathRoot = None
@@ -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__

static/dream_web/index.css

@@ -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;
 }
-

static/dream_web/index.html

@@ -1,41 +1,50 @@
 <html lang="en">
-  <head>
+
+<head>
   <title>Stable Diffusion Dream Server</title>
   <meta charset="utf-8">
-    <link rel="icon" href="data:,">
+  <link rel="icon" type="image/x-icon" href="static/dream_web/favicon.ico" />
   <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="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>
+</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>
+      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>
+      -->
     <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>
-            <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">
+        <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>
@@ -50,25 +59,41 @@
         <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>
+          <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>
+          <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">
@@ -76,29 +101,52 @@
         <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>
+        <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>
+          <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>
+        </div>
       </fieldset>
-	<fieldset id="img2img">
-	  <div class="section-header">Image-to-image options</div>
+      <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>
+        </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>
+          <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>
@@ -107,6 +155,8 @@
           <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>
+      </div>
+      <input type="submit" id="submit" value="Generate">
     </form>
     <br>
     <section id="progress-section">
@@ -118,14 +168,12 @@
         <div id="scaling-inprocess-message">
           <i><span>Postprocessing...</span><span id="processing_cnt">1</span>/<span id="processing_total">3</span></i>
         </div>
-        </span>
+      </div>
     </section>
 
     <div id="results">
-        <div id="no-results-message">
-          <i><p>No results...</p></i>
-        </div>
     </div>
   </main>
-  </body>
+</body>
+
 </html>

static/dream_web/index.js

@@ -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) {
-    let outputNode = document.createElement("figure");
-    let altText = seed.toString() + " | " + config.prompt;
+function ondragdream(event) {
+  let dream = event.target.dataset.dream;
+  event.dataTransfer.setData("dream", dream);
+}
 
-    const figureContents = `
-        <a href="${src}" target="_blank">
-            <img src="${src}" alt="${altText}" title="${altText}">
-        </a>
-        <figcaption>${seed}</figcaption>
-    `;
+function seedClick(event) {
+    // Get element
+    var image = event.target.closest('figure').querySelector('img');
+    var dream = JSON.parse(decodeURIComponent(image.dataset.dream));
 
-    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=${k}]`).value = config[k];
-        }
-        if (config.variation_amount > 0 || config.with_variations != '') {
-            document.querySelector("#seed").value = config.seed;
-        } else {
-            document.querySelector("#seed").value = seed;
+        let formElem = form.querySelector(`*[name=${k}]`);
+        formElem.value = dream[k] !== undefined ? dream[k] : formElem.defaultValue;
     }
 
-        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
-        }
+    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}"
+                 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 onclick="seedClick(event, this)">${seed}</figcaption>
+    `;
+
+    outputNode.innerHTML = figureContents;
+
+    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);
+    */
 };