diff --git a/ldm/invoke/pngwriter.py b/ldm/invoke/pngwriter.py
index 882aaa844d..feee907d7f 100644
--- a/ldm/invoke/pngwriter.py
+++ b/ldm/invoke/pngwriter.py
@@ -66,3 +66,43 @@ def write_metadata(img_path:str, meta:dict):
     info = PngImagePlugin.PngInfo()
     info.add_text('sd-metadata', json.dumps(meta))
     im.save(img_path,'PNG',pnginfo=info)
+
+class PromptFormatter:
+    def __init__(self, t2i, opt):
+        self.t2i = t2i
+        self.opt = opt
+
+    # note: the t2i object should provide all these values.
+    # there should be no need to or against opt values
+    def normalize_prompt(self):
+        """Normalize the prompt and switches"""
+        t2i = self.t2i
+        opt = self.opt
+
+        switches = list()
+        switches.append(f'"{opt.prompt}"')
+        switches.append(f'-s{opt.steps        or t2i.steps}')
+        switches.append(f'-W{opt.width        or t2i.width}')
+        switches.append(f'-H{opt.height       or t2i.height}')
+        switches.append(f'-C{opt.cfg_scale    or t2i.cfg_scale}')
+        switches.append(f'-A{opt.sampler_name or t2i.sampler_name}')
+# to do: put model name into the t2i object
+#        switches.append(f'--model{t2i.model_name}')
+        if opt.seamless or t2i.seamless:
+            switches.append(f'--seamless')
+        if opt.init_img:
+            switches.append(f'-I{opt.init_img}')
+        if opt.fit:
+            switches.append(f'--fit')
+        if opt.strength and opt.init_img is not None:
+            switches.append(f'-f{opt.strength or t2i.strength}')
+        if opt.gfpgan_strength:
+            switches.append(f'-G{opt.gfpgan_strength}')
+        if opt.upscale:
+            switches.append(f'-U {" ".join([str(u) for u in opt.upscale])}')
+        if opt.variation_amount > 0:
+            switches.append(f'-v{opt.variation_amount}')
+        if opt.with_variations:
+            formatted_variations = ','.join(f'{seed}:{weight}' for seed, weight in opt.with_variations)
+            switches.append(f'-V{formatted_variations}')
+        return ' '.join(switches)
diff --git a/ldm/invoke/server_legacy.py b/ldm/invoke/server_legacy.py
new file mode 100644
index 0000000000..8c0d7a857d
--- /dev/null
+++ b/ldm/invoke/server_legacy.py
@@ -0,0 +1,246 @@
+import argparse
+import json
+import base64
+import mimetypes
+import os
+from http.server import BaseHTTPRequestHandler, ThreadingHTTPServer
+from ldm.invoke.pngwriter import PngWriter, PromptFormatter
+from threading import Event
+
+def build_opt(post_data, seed, gfpgan_model_exists):
+    opt = argparse.Namespace()
+    setattr(opt, 'prompt', post_data['prompt'])
+    setattr(opt, 'init_img', post_data['initimg'])
+    setattr(opt, 'strength', float(post_data['strength']))
+    setattr(opt, 'iterations', int(post_data['iterations']))
+    setattr(opt, 'steps', int(post_data['steps']))
+    setattr(opt, 'width', int(post_data['width']))
+    setattr(opt, 'height', int(post_data['height']))
+    setattr(opt, 'seamless', 'seamless' in post_data)
+    setattr(opt, 'fit', 'fit' in post_data)
+    setattr(opt, 'mask', 'mask' in post_data)
+    setattr(opt, 'invert_mask', 'invert_mask' in post_data)
+    setattr(opt, 'cfg_scale', float(post_data['cfg_scale']))
+    setattr(opt, 'sampler_name', post_data['sampler_name'])
+    setattr(opt, 'gfpgan_strength', float(post_data['gfpgan_strength']) if gfpgan_model_exists else 0)
+    setattr(opt, 'upscale', [int(post_data['upscale_level']), float(post_data['upscale_strength'])] if post_data['upscale_level'] != '' else None)
+    setattr(opt, 'progress_images', 'progress_images' in post_data)
+    setattr(opt, 'seed', None if int(post_data['seed']) == -1 else int(post_data['seed']))
+    setattr(opt, 'variation_amount', float(post_data['variation_amount']) if int(post_data['seed']) != -1 else 0)
+    setattr(opt, 'with_variations', [])
+
+    broken = False
+    if int(post_data['seed']) != -1 and post_data['with_variations'] != '':
+        for part in post_data['with_variations'].split(','):
+            seed_and_weight = part.split(':')
+            if len(seed_and_weight) != 2:
+                print(f'could not parse with_variation part "{part}"')
+                broken = True
+                break
+            try:
+                seed = int(seed_and_weight[0])
+                weight = float(seed_and_weight[1])
+            except ValueError:
+                print(f'could not parse with_variation part "{part}"')
+                broken = True
+                break
+            opt.with_variations.append([seed, weight])
+    
+    if broken:
+        raise CanceledException
+
+    if len(opt.with_variations) == 0:
+        opt.with_variations = None
+
+    return opt
+
+class CanceledException(Exception):
+    pass
+
+class DreamServer(BaseHTTPRequestHandler):
+    model = None
+    outdir = None
+    canceled = Event()
+
+    def do_GET(self):
+        if self.path == "/":
+            self.send_response(200)
+            self.send_header("Content-type", "text/html")
+            self.end_headers()
+            with open("./static/dream_web/index.html", "rb") as content:
+                self.wfile.write(content.read())
+        elif self.path == "/config.js":
+            # unfortunately this import can't be at the top level, since that would cause a circular import
+            from ldm.gfpgan.gfpgan_tools import gfpgan_model_exists
+            self.send_response(200)
+            self.send_header("Content-type", "application/javascript")
+            self.end_headers()
+            config = {
+                'gfpgan_model_exists': gfpgan_model_exists
+            }
+            self.wfile.write(bytes("let config = " + json.dumps(config) + ";\n", "utf-8"))
+        elif self.path == "/run_log.json":
+            self.send_response(200)
+            self.send_header("Content-type", "application/json")
+            self.end_headers()
+            output = []
+            
+            log_file = os.path.join(self.outdir, "dream_web_log.txt")
+            if os.path.exists(log_file):
+                with open(log_file, "r") as log:
+                    for line in log:
+                        url, config = line.split(": {", maxsplit=1)
+                        config = json.loads("{" + config)
+                        config["url"] = url.lstrip(".")
+                        if os.path.exists(url):
+                            output.append(config)
+
+            self.wfile.write(bytes(json.dumps({"run_log": output}), "utf-8"))
+        elif self.path == "/cancel":
+            self.canceled.set()
+            self.send_response(200)
+            self.send_header("Content-type", "application/json")
+            self.end_headers()
+            self.wfile.write(bytes('{}', 'utf8'))
+        else:
+            path = "." + self.path
+            cwd = os.path.realpath(os.getcwd())
+            is_in_cwd = os.path.commonprefix((os.path.realpath(path), cwd)) == cwd
+            if not (is_in_cwd and os.path.exists(path)):
+                self.send_response(404)
+                return
+            mime_type = mimetypes.guess_type(path)[0]
+            if mime_type is not None:
+                self.send_response(200)
+                self.send_header("Content-type", mime_type)
+                self.end_headers()
+                with open("." + self.path, "rb") as content:
+                    self.wfile.write(content.read())
+            else:
+                self.send_response(404)
+
+    def do_POST(self):
+        self.send_response(200)
+        self.send_header("Content-type", "application/json")
+        self.end_headers()
+
+        # unfortunately this import can't be at the top level, since that would cause a circular import
+        # TODO temporarily commented out, import fails for some reason
+        # from ldm.gfpgan.gfpgan_tools import gfpgan_model_exists
+        gfpgan_model_exists = False
+
+        content_length = int(self.headers['Content-Length'])
+        post_data = json.loads(self.rfile.read(content_length))
+        opt = build_opt(post_data, self.model.seed, gfpgan_model_exists)
+
+        self.canceled.clear()
+        print(f">> Request to generate with prompt: {opt.prompt}")
+        # In order to handle upscaled images, the PngWriter needs to maintain state
+        # across images generated by each call to prompt2img(), so we define it in
+        # the outer scope of image_done()
+        config = post_data.copy() # Shallow copy
+        config['initimg'] = config.pop('initimg_name', '')
+
+        images_generated = 0    # helps keep track of when upscaling is started
+        images_upscaled = 0     # helps keep track of when upscaling is completed
+        pngwriter = PngWriter(self.outdir)
+
+        prefix = pngwriter.unique_prefix()
+        # if upscaling is requested, then this will be called twice, once when
+        # the images are first generated, and then again when after upscaling
+        # is complete. The upscaling replaces the original file, so the second
+        # entry should not be inserted into the image list.
+        def image_done(image, seed, upscaled=False, first_seed=-1, use_prefix=None):
+            print(f'First seed: {first_seed}')
+            name = f'{prefix}.{seed}.png'
+            iter_opt = argparse.Namespace(**vars(opt)) # copy
+            if opt.variation_amount > 0:
+                this_variation = [[seed, opt.variation_amount]]
+                if opt.with_variations is None:
+                    iter_opt.with_variations = this_variation
+                else:
+                    iter_opt.with_variations = opt.with_variations + this_variation
+                iter_opt.variation_amount = 0
+            elif opt.with_variations is None:
+                iter_opt.seed = seed
+            normalized_prompt = PromptFormatter(self.model, iter_opt).normalize_prompt()
+            path = pngwriter.save_image_and_prompt_to_png(image, f'{normalized_prompt} -S{iter_opt.seed}', name)
+
+            if int(config['seed']) == -1:
+                config['seed'] = seed
+            # Append post_data to log, but only once!
+            if not upscaled:
+                with open(os.path.join(self.outdir, "dream_web_log.txt"), "a") as log:
+                    log.write(f"{path}: {json.dumps(config)}\n")
+
+                self.wfile.write(bytes(json.dumps(
+                    {'event': 'result', 'url': path, 'seed': seed, 'config': config}
+                ) + '\n',"utf-8"))
+
+            # control state of the "postprocessing..." message
+            upscaling_requested = opt.upscale or opt.gfpgan_strength > 0
+            nonlocal images_generated # NB: Is this bad python style? It is typical usage in a perl closure.
+            nonlocal images_upscaled  # NB: Is this bad python style? It is typical usage in a perl closure.
+            if upscaled:
+                images_upscaled += 1
+            else:
+                images_generated += 1
+            if upscaling_requested:
+                action = None
+                if images_generated >= opt.iterations:
+                    if images_upscaled < opt.iterations:
+                        action = 'upscaling-started'
+                    else:
+                        action = 'upscaling-done'
+                if action:
+                    x = images_upscaled + 1
+                    self.wfile.write(bytes(json.dumps(
+                        {'event': action, 'processed_file_cnt': f'{x}/{opt.iterations}'}
+                    ) + '\n',"utf-8"))
+
+        step_writer = PngWriter(os.path.join(self.outdir, "intermediates"))
+        step_index = 1
+        def image_progress(sample, step):
+            if self.canceled.is_set():
+                self.wfile.write(bytes(json.dumps({'event':'canceled'}) + '\n', 'utf-8'))
+                raise CanceledException
+            path = None
+            # since rendering images is moderately expensive, only render every 5th image
+            # and don't bother with the last one, since it'll render anyway
+            nonlocal step_index
+            if opt.progress_images and step % 5 == 0 and step < opt.steps - 1:
+                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)
+                step_index += 1
+            self.wfile.write(bytes(json.dumps(
+                {'event': 'step', 'step': step + 1, 'url': path}
+            ) + '\n',"utf-8"))
+
+        try:
+            if opt.init_img is None:
+                # Run txt2img
+                self.model.prompt2image(**vars(opt), step_callback=image_progress, image_callback=image_done)
+            else:
+                # Decode initimg as base64 to temp file
+                with open("./img2img-tmp.png", "wb") as f:
+                    initimg = opt.init_img.split(",")[1] # Ignore mime type
+                    f.write(base64.b64decode(initimg))
+                opt1 = argparse.Namespace(**vars(opt))
+                opt1.init_img = "./img2img-tmp.png"
+
+                try:
+                    # Run img2img
+                    self.model.prompt2image(**vars(opt1), step_callback=image_progress, image_callback=image_done)
+                finally:
+                    # Remove the temp file
+                    os.remove("./img2img-tmp.png")
+        except CanceledException:
+            print(f"Canceled.")
+            return
+
+
+class ThreadingDreamServer(ThreadingHTTPServer):
+    def __init__(self, server_address):
+        super(ThreadingDreamServer, self).__init__(server_address, DreamServer)
diff --git a/scripts/legacy_api.py b/scripts/legacy_api.py
new file mode 100755
index 0000000000..47545cd0ea
--- /dev/null
+++ b/scripts/legacy_api.py
@@ -0,0 +1,685 @@
+#!/usr/bin/env python3
+# Copyright (c) 2022 Lincoln D. Stein (https://github.com/lstein)
+
+import argparse
+import shlex
+import os
+import re
+import sys
+import copy
+import warnings
+import time
+import ldm.invoke.readline
+from ldm.invoke.pngwriter import PngWriter, PromptFormatter
+from ldm.invoke.server_legacy import DreamServer, ThreadingDreamServer
+from ldm.invoke.image_util import make_grid
+from omegaconf import OmegaConf
+
+# Placeholder to be replaced with proper class that tracks the
+# outputs and associates with the prompt that generated them.
+# Just want to get the formatting look right for now.
+output_cntr = 0
+
+
+def main():
+    """Initialize command-line parsers and the diffusion model"""
+    arg_parser = create_argv_parser()
+    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.')
+        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')
+    sys.path.append('.')
+    from pytorch_lightning import logging
+    from ldm.generate import Generate
+
+    # these two lines prevent a horrible warning message from appearing
+    # when the frozen CLIP tokenizer is imported
+    import transformers
+
+    transformers.logging.set_verbosity_error()
+
+    # creating a simple text2image object with a handful of
+    # defaults passed on the command line.
+    # additional parameters will be added (or overriden) during
+    # the user input loop
+    t2i = Generate(
+        # width=width,
+        # height=height,
+        sampler_name=opt.sampler_name,
+        weights=weights,
+        full_precision=opt.full_precision,
+        config=config,
+        # grid=opt.grid,
+        # this is solely for recreating the prompt
+        # seamless=opt.seamless,
+        embedding_path=opt.embedding_path,
+        # device_type=opt.device,
+        # ignore_ctrl_c=opt.infile is None,
+    )
+
+    # 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
+    logging.getLogger('pytorch_lightning').setLevel(logging.ERROR)
+
+    # load the infile as a list of lines
+    infile = None
+    if opt.infile:
+        try:
+            if os.path.isfile(opt.infile):
+                infile = open(opt.infile, 'r', encoding='utf-8')
+            elif opt.infile == '-':  # stdin
+                infile = sys.stdin
+            else:
+                raise FileNotFoundError(f'{opt.infile} not found.')
+        except (FileNotFoundError, IOError) as e:
+            print(f'{e}. Aborting.')
+            sys.exit(-1)
+
+    if opt.seamless:
+        print(">> changed to seamless tiling mode")
+
+    # preload the model
+    t2i.load_model()
+
+    if not infile:
+        print(
+            "\n* Initialization done! Awaiting your command (-h for help, 'q' to quit)"
+        )
+
+    cmd_parser = create_cmd_parser()
+    if opt.web:
+        dream_server_loop(t2i, opt.host, opt.port, opt.outdir)
+    else:
+        main_loop(t2i, opt.outdir, opt.prompt_as_dir, cmd_parser, infile)
+
+
+def main_loop(t2i, outdir, prompt_as_dir, parser, infile):
+    """prompt/read/execute loop"""
+    done = False
+    path_filter = re.compile(r'[<>:"/\\|?*]')
+    last_results = list()
+
+    # os.pathconf is not available on Windows
+    if hasattr(os, 'pathconf'):
+        path_max = os.pathconf(outdir, 'PC_PATH_MAX')
+        name_max = os.pathconf(outdir, 'PC_NAME_MAX')
+    else:
+        path_max = 260
+        name_max = 255
+
+    while not done:
+        try:
+            command = get_next_command(infile)
+        except EOFError:
+            done = True
+            continue
+        except KeyboardInterrupt:
+            done = True
+            continue
+
+        # skip empty lines
+        if not command.strip():
+            continue
+
+        if command.startswith(('#', '//')):
+            continue
+
+        # 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:
+            print(str(e))
+            continue
+
+        if elements[0] == 'q':
+            done = True
+            break
+
+        if elements[0].startswith(
+            '!dream'
+        ):   # in case a stored prompt still contains the !dream command
+            elements.pop(0)
+
+        # 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 = parser.parse_args(switches)
+        except SystemExit:
+            parser.print_help()
+            continue
+        if len(opt.prompt) == 0:
+            print('Try again with a prompt!')
+            continue
+        # retrieve previous value!
+        if opt.init_img is not None and re.match('^-\\d+$', opt.init_img):
+            try:
+                opt.init_img = last_results[int(opt.init_img)][0]
+                print(f'>> Reusing previous image {opt.init_img}')
+            except IndexError:
+                print(
+                    f'>> No previous initial image at position {opt.init_img} found')
+                opt.init_img = None
+                continue
+
+        if opt.seed is not None and opt.seed < 0:   # retrieve previous value!
+            try:
+                opt.seed = last_results[opt.seed][1]
+                print(f'>> Reusing previous seed {opt.seed}')
+            except IndexError:
+                print(f'>> No previous seed at position {opt.seed} found')
+                opt.seed = None
+                continue
+
+        do_grid = opt.grid or t2i.grid
+
+        if opt.with_variations is not None:
+            # shotgun parsing, woo
+            parts = []
+            broken = False  # python doesn't have labeled loops...
+            for part in opt.with_variations.split(','):
+                seed_and_weight = part.split(':')
+                if len(seed_and_weight) != 2:
+                    print(f'could not parse with_variation part "{part}"')
+                    broken = True
+                    break
+                try:
+                    seed = int(seed_and_weight[0])
+                    weight = float(seed_and_weight[1])
+                except ValueError:
+                    print(f'could not parse with_variation part "{part}"')
+                    broken = True
+                    break
+                parts.append([seed, weight])
+            if broken:
+                continue
+            if len(parts) > 0:
+                opt.with_variations = parts
+            else:
+                opt.with_variations = None
+
+        if opt.outdir:
+            if not os.path.exists(opt.outdir):
+                os.makedirs(opt.outdir)
+            current_outdir = opt.outdir
+        elif prompt_as_dir:
+            # sanitize the prompt to a valid folder name
+            subdir = path_filter.sub('_', opt.prompt)[:name_max].rstrip(' .')
+
+            # truncate path to maximum allowed length
+            # 27 is the length of '######.##########.##.png', plus two separators and a NUL
+            subdir = subdir[:(path_max - 27 - len(os.path.abspath(outdir)))]
+            current_outdir = os.path.join(outdir, subdir)
+
+            print('Writing files to directory: "' + current_outdir + '"')
+
+            # make sure the output directory exists
+            if not os.path.exists(current_outdir):
+                os.makedirs(current_outdir)
+        else:
+            current_outdir = outdir
+
+        # Here is where the images are actually generated!
+        last_results = []
+        try:
+            file_writer = PngWriter(current_outdir)
+            prefix = file_writer.unique_prefix()
+            results = []  # list of filename, prompt pairs
+            grid_images = dict()  # seed -> Image, only used if `do_grid`
+
+            def image_writer(image, seed, upscaled=False):
+                path = None
+                if do_grid:
+                    grid_images[seed] = image
+                else:
+                    if upscaled and opt.save_original:
+                        filename = f'{prefix}.{seed}.postprocessed.png'
+                    else:
+                        filename = f'{prefix}.{seed}.png'
+                    if opt.variation_amount > 0:
+                        iter_opt = argparse.Namespace(**vars(opt))  # copy
+                        this_variation = [[seed, opt.variation_amount]]
+                        if opt.with_variations is None:
+                            iter_opt.with_variations = this_variation
+                        else:
+                            iter_opt.with_variations = opt.with_variations + this_variation
+                        iter_opt.variation_amount = 0
+                        normalized_prompt = PromptFormatter(
+                            t2i, iter_opt).normalize_prompt()
+                        metadata_prompt = f'{normalized_prompt} -S{iter_opt.seed}'
+                    elif opt.with_variations is not None:
+                        normalized_prompt = PromptFormatter(
+                            t2i, opt).normalize_prompt()
+                        # use the original seed - the per-iteration value is the last variation-seed
+                        metadata_prompt = f'{normalized_prompt} -S{opt.seed}'
+                    else:
+                        normalized_prompt = PromptFormatter(
+                            t2i, opt).normalize_prompt()
+                        metadata_prompt = f'{normalized_prompt} -S{seed}'
+                    path = file_writer.save_image_and_prompt_to_png(
+                        image, metadata_prompt, filename)
+                    if (not upscaled) or opt.save_original:
+                        # only append to results if we didn't overwrite an earlier output
+                        results.append([path, metadata_prompt])
+                last_results.append([path, seed])
+
+            t2i.prompt2image(image_callback=image_writer, **vars(opt))
+
+            if do_grid and len(grid_images) > 0:
+                grid_img   = make_grid(list(grid_images.values()))
+                grid_seeds = list(grid_images.keys())
+                first_seed = last_results[0][1]
+                filename = f'{prefix}.{first_seed}.png'
+                # TODO better metadata for grid images
+                normalized_prompt = PromptFormatter(
+                    t2i, opt).normalize_prompt()
+                metadata_prompt = f'{normalized_prompt} -S{first_seed} --grid -n{len(grid_images)} # {grid_seeds}'
+                path = file_writer.save_image_and_prompt_to_png(
+                    grid_img, metadata_prompt, filename
+                )
+                results = [[path, metadata_prompt]]
+
+        except AssertionError as e:
+            print(e)
+            continue
+
+        except OSError as e:
+            print(e)
+            continue
+
+        print('Outputs:')
+        log_path = os.path.join(current_outdir, 'dream_log.txt')
+        write_log_message(results, log_path)
+        print()
+
+    print('goodbye!')
+
+
+def get_next_command(infile=None) -> str:  # command string
+    if infile is None:
+        command = input('dream> ')
+    else:
+        command = infile.readline()
+        if not command:
+            raise EOFError
+        else:
+            command = command.strip()
+        print(f'#{command}')
+    return command
+
+
+def dream_server_loop(t2i, host, port, outdir):
+    print('\n* --web was specified, starting web server...')
+    # Change working directory to the stable-diffusion directory
+    os.chdir(
+        os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))
+    )
+
+    # Start server
+    DreamServer.model = t2i
+    DreamServer.outdir = outdir
+    dream_server = ThreadingDreamServer((host, port))
+    print(">> Started Stable Diffusion dream server!")
+    if host == '0.0.0.0':
+        print(
+            f"Point your browser at http://localhost:{port} or use the host's DNS name or IP address.")
+    else:
+        print(">> Default host address now 127.0.0.1 (localhost). Use --host 0.0.0.0 to bind any address.")
+        print(f">> Point your browser at http://{host}:{port}.")
+
+    try:
+        dream_server.serve_forever()
+    except KeyboardInterrupt:
+        pass
+
+    dream_server.server_close()
+
+
+def write_log_message(results, log_path):
+    """logs the name of the output image, prompt, and prompt args to the terminal and log file"""
+    global output_cntr
+    log_lines = [f'{path}: {prompt}\n' for path, prompt in results]
+    for l in log_lines:
+        output_cntr += 1
+        print(f'[{output_cntr}] {l}',end='')
+
+
+    with open(log_path, 'a', encoding='utf-8') as file:
+        file.writelines(log_lines)
+
+
+SAMPLER_CHOICES = [
+    'ddim',
+    'k_dpm_2_a',
+    'k_dpm_2',
+    'k_euler_a',
+    'k_euler',
+    'k_heun',
+    'k_lms',
+    'plms',
+]
+
+
+def create_argv_parser():
+    parser = argparse.ArgumentParser(
+        description="""Generate images using Stable Diffusion.
+        Use --web to launch the web interface. 
+        Use --from_file to load prompts from a file path or standard input ("-").
+        Otherwise you will be dropped into an interactive command prompt (type -h for help.)
+        Other command-line arguments are defaults that can usually be overridden
+        prompt the command prompt.
+"""
+    )
+    parser.add_argument(
+        '--laion400m',
+        '--latent_diffusion',
+        '-l',
+        dest='laion400m',
+        action='store_true',
+        help='Fallback to the latent diffusion (laion400m) weights and config',
+    )
+    parser.add_argument(
+        '--from_file',
+        dest='infile',
+        type=str,
+        help='If specified, load prompts from this file',
+    )
+    parser.add_argument(
+        '-n',
+        '--iterations',
+        type=int,
+        default=1,
+        help='Number of images to generate',
+    )
+    parser.add_argument(
+        '-F',
+        '--full_precision',
+        dest='full_precision',
+        action='store_true',
+        help='Use more memory-intensive full precision math for calculations',
+    )
+    parser.add_argument(
+        '-g',
+        '--grid',
+        action='store_true',
+        help='Generate a grid instead of individual images',
+    )
+    parser.add_argument(
+        '-A',
+        '-m',
+        '--sampler',
+        dest='sampler_name',
+        choices=SAMPLER_CHOICES,
+        metavar='SAMPLER_NAME',
+        default='k_lms',
+        help=f'Set the initial sampler. Default: k_lms. Supported samplers: {", ".join(SAMPLER_CHOICES)}',
+    )
+    parser.add_argument(
+        '--outdir',
+        '-o',
+        type=str,
+        default='outputs/img-samples',
+        help='Directory to save generated images and a log of prompts and seeds. Default: outputs/img-samples',
+    )
+    parser.add_argument(
+        '--seamless',
+        action='store_true',
+        help='Change the model to seamless tiling (circular) mode',
+    )
+    parser.add_argument(
+        '--embedding_path',
+        type=str,
+        help='Path to a pre-trained embedding manager checkpoint - can only be set on command line',
+    )
+    parser.add_argument(
+        '--prompt_as_dir',
+        '-p',
+        action='store_true',
+        help='Place images in subdirectories named after the prompt.',
+    )
+    # GFPGAN related args
+    parser.add_argument(
+        '--gfpgan_bg_upsampler',
+        type=str,
+        default='realesrgan',
+        help='Background upsampler. Default: realesrgan. Options: realesrgan, none.',
+
+    )
+    parser.add_argument(
+        '--gfpgan_bg_tile',
+        type=int,
+        default=400,
+        help='Tile size for background sampler, 0 for no tile during testing. Default: 400.',
+    )
+    parser.add_argument(
+        '--gfpgan_model_path',
+        type=str,
+        default='experiments/pretrained_models/GFPGANv1.3.pth',
+        help='Indicates the path to the GFPGAN model, relative to --gfpgan_dir.',
+    )
+    parser.add_argument(
+        '--gfpgan_dir',
+        type=str,
+        default='./src/gfpgan',
+        help='Indicates the directory containing the GFPGAN code.',
+    )
+    parser.add_argument(
+        '--web',
+        dest='web',
+        action='store_true',
+        help='Start in web server mode.',
+    )
+    parser.add_argument(
+        '--host',
+        type=str,
+        default='127.0.0.1',
+        help='Web server: Host or IP to listen on. Set to 0.0.0.0 to accept traffic from other devices on your network.'
+    )
+    parser.add_argument(
+        '--port',
+        type=int,
+        default='9090',
+        help='Web server: Port to listen on'
+    )
+    parser.add_argument(
+        '--weights',
+        default='model',
+        help='Indicates the Stable Diffusion model to use.',
+    )
+    parser.add_argument(
+        '--device',
+        '-d',
+        type=str,
+        default='cuda',
+        help="device to run stable diffusion on. defaults to cuda `torch.cuda.current_device()` if available"
+    )
+    parser.add_argument(
+        '--model',
+        default='stable-diffusion-1.4',
+        help='Indicates which diffusion model to load. (currently "stable-diffusion-1.4" (default) or "laion400m")',
+    )
+    parser.add_argument(
+        '--config',
+        default='configs/models.yaml',
+        help='Path to configuration file for alternate models.',
+    )
+    return parser
+
+
+def create_cmd_parser():
+    parser = argparse.ArgumentParser(
+        description='Example: dream> a fantastic alien landscape -W1024 -H960 -s100 -n12'
+    )
+    parser.add_argument('prompt')
+    parser.add_argument('-s', '--steps', type=int, help='Number of steps')
+    parser.add_argument(
+        '-S',
+        '--seed',
+        type=int,
+        help='Image seed; a +ve integer, or use -1 for the previous seed, -2 for the one before that, etc',
+    )
+    parser.add_argument(
+        '-n',
+        '--iterations',
+        type=int,
+        default=1,
+        help='Number of samplings to perform (slower, but will provide seeds for individual images)',
+    )
+    parser.add_argument(
+        '-W', '--width', type=int, help='Image width, multiple of 64'
+    )
+    parser.add_argument(
+        '-H', '--height', type=int, help='Image height, multiple of 64'
+    )
+    parser.add_argument(
+        '-C',
+        '--cfg_scale',
+        default=7.5,
+        type=float,
+        help='Classifier free guidance (CFG) scale - higher numbers cause generator to "try" harder.',
+    )
+    parser.add_argument(
+        '-g', '--grid', action='store_true', help='generate a grid'
+    )
+    parser.add_argument(
+        '--outdir',
+        '-o',
+        type=str,
+        default=None,
+        help='Directory to save generated images and a log of prompts and seeds',
+    )
+    parser.add_argument(
+        '--seamless',
+        action='store_true',
+        help='Change the model to seamless tiling (circular) mode',
+    )
+    parser.add_argument(
+        '-i',
+        '--individual',
+        action='store_true',
+        help='Generate individual files (default)',
+    )
+    parser.add_argument(
+        '-I',
+        '--init_img',
+        type=str,
+        help='Path to input image for img2img mode (supersedes width and height)',
+    )
+    parser.add_argument(
+        '-M',
+        '--init_mask',
+        type=str,
+        help='Path to input mask for inpainting mode (supersedes width and height)',
+    )
+    parser.add_argument(
+        '-T',
+        '-fit',
+        '--fit',
+        action='store_true',
+        help='If specified, will resize the input image to fit within the dimensions of width x height (512x512 default)',
+    )
+    parser.add_argument(
+        '-f',
+        '--strength',
+        default=0.75,
+        type=float,
+        help='Strength for noising/unnoising. 0.0 preserves image exactly, 1.0 replaces it completely',
+    )
+    parser.add_argument(
+        '-G',
+        '--gfpgan_strength',
+        default=0,
+        type=float,
+        help='The strength at which to apply the GFPGAN model to the result, in order to improve faces.',
+    )
+    parser.add_argument(
+        '-U',
+        '--upscale',
+        nargs='+',
+        default=None,
+        type=float,
+        help='Scale factor (2, 4) for upscaling followed by upscaling strength (0-1.0). If strength not specified, defaults to 0.75'
+    )
+    parser.add_argument(
+        '-save_orig',
+        '--save_original',
+        action='store_true',
+        help='Save original. Use it when upscaling to save both versions.',
+    )
+    # variants is going to be superseded by a generalized "prompt-morph" function
+    #    parser.add_argument('-v','--variants',type=int,help="in img2img mode, the first generated image will get passed back to img2img to generate the requested number of variants")
+    parser.add_argument(
+        '-x',
+        '--skip_normalize',
+        action='store_true',
+        help='Skip subprompt weight normalization',
+    )
+    parser.add_argument(
+        '-A',
+        '-m',
+        '--sampler',
+        dest='sampler_name',
+        default=None,
+        type=str,
+        choices=SAMPLER_CHOICES,
+        metavar='SAMPLER_NAME',
+        help=f'Switch to a different sampler. Supported samplers: {", ".join(SAMPLER_CHOICES)}',
+    )
+    parser.add_argument(
+        '-t',
+        '--log_tokenization',
+        action='store_true',
+        help='shows how the prompt is split into tokens'
+    )
+    parser.add_argument(
+        '-v',
+        '--variation_amount',
+        default=0.0,
+        type=float,
+        help='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.'
+    )
+    parser.add_argument(
+        '-V',
+        '--with_variations',
+        default=None,
+        type=str,
+        help='list of variations to apply, in the format `seed:weight,seed:weight,...'
+    )
+    return parser
+
+
+if __name__ == '__main__':
+    main()
diff --git a/tests/legacy_tests.sh b/tests/legacy_tests.sh
new file mode 100755
index 0000000000..a171a47c2b
--- /dev/null
+++ b/tests/legacy_tests.sh
@@ -0,0 +1,46 @@
+#! /usr/bin/env bash
+
+# This file contains bunch of compatibility tests that ensures
+# that the API interface of `scripts/legacy-api.py` remains stable
+
+set -e
+
+OUTDIR=$(mktemp -d)
+
+echo "Using directory $OUTDIR"
+
+# Start API
+python -u scripts/legacy_api.py --web --host=localhost --port=3333 --outdir=$OUTDIR &> $OUTDIR/sd.log &
+APP_PID=$!
+
+echo "Wait for server to startup"
+
+tail -f -n0 $OUTDIR/sd.log | grep -qe "Point your browser at"
+
+echo "Started, continuing"
+
+if [ $? == 1 ]; then
+    echo "Search terminated without finding the pattern"
+fi
+
+# Generate image
+RESULT=$(curl -v -X POST -d '{"index":0,"variation_amount":0,"with_variations":"","steps":25,"width":512,"seed":"1337","prompt":"A cat wearing a hat","strength":0.5,"initimg":null,"cfg_scale":2,"iterations":1,"upscale_level":0,"upscale_strength":0,"sampler_name":"k_euler","height":512}' localhost:3333 | grep result)
+
+# Test 01 - Image contents
+FILENAME=$(echo $RESULT | jq -r .url)
+
+ACTUAL_CHECKSUM=$(sha256sum $FILENAME)
+EXPECTED_CHECKSUM="a77799226a4dfc62a1674498e575c775da042959a4b90b13e26f666c302f079f"
+
+if [ "$ACTUAL_CHECKSUM" != "$EXPECTED_CHECKSUM" ]; then
+    echo "Expected hash $EXPECTED_CHECKSUM but got hash $ACTUAL_CHECKSUM"
+    kill $APP_PID
+    # rm -r $OUTDIR
+    exit 33
+fi
+
+# Assert output
+
+# Cleanup
+kill $APP_PID
+# rm -r $OUTDIR