InvokeAI/ldm/dream/server.py

import argparse
import json
import base64
import mimetypes
import os
from http.server import BaseHTTPRequestHandler, ThreadingHTTPServer
from ldm.dream.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'])

    # embiggen not practical at this point because we have no way of feeding images back into img2img
    # however, this code is here against that eventuality
    setattr(opt, 'embiggen', None)
    setattr(opt, 'embiggen_tiles', None)

    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_dir = os.path.dirname(self.path)
            out_dir  = os.path.realpath(self.outdir.rstrip('/'))
            if self.path.startswith('/static/dream_web/'):
                path = '.' + self.path
            elif out_dir.replace('\\', '/').endswith(path_dir):
                file = os.path.basename(self.path)
                path = os.path.join(self.outdir,file)
            else:
                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(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
        from ldm.gfpgan.gfpgan_tools import gfpgan_model_exists

        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):
            name = f'{prefix}.{seed}.png'
            iter_opt = argparse.Namespace(**vars(opt)) # copy
            print(f'iter_opt = {iter_opt}')
            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
        except Exception as e:
            print("Error happened")
            print(e)
            self.wfile.write(bytes(json.dumps(
                {'event': 'error',
                 'message': str(e),
                 'type': e.__class__.__name__}
            ) + '\n',"utf-8"))
            raise e


class ThreadingDreamServer(ThreadingHTTPServer):
    def __init__(self, server_address):
        super(ThreadingDreamServer, self).__init__(server_address, DreamServer)