InvokeAI/ldm/dream/server.py

import json
import base64
import mimetypes
import os
from http.server import BaseHTTPRequestHandler, ThreadingHTTPServer
from ldm.dream.pngwriter import PngWriter

class DreamServer(BaseHTTPRequestHandler):
    model = None

    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"))
        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
        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))
        prompt = post_data['prompt']
        initimg = post_data['initimg']
        iterations = int(post_data['iterations'])
        steps = int(post_data['steps'])
        width = int(post_data['width'])
        height = int(post_data['height'])
        cfgscale = float(post_data['cfgscale'])
        sampler_name  = post_data['sampler']
        gfpgan_strength = float(post_data['gfpgan_strength']) if gfpgan_model_exists else 0
        upscale_level    = post_data['upscale_level']
        upscale_strength = post_data['upscale_strength']
        upscale = [int(upscale_level),float(upscale_strength)] if upscale_level != '' else None
        seed = None if int(post_data['seed']) == -1 else int(post_data['seed'])

        print(f"Request to generate with prompt: {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'] = ''

        images_generated = 0    # helps keep track of when upscaling is started
        images_upscaled = 0     # helps keep track of when upscaling is completed
        pngwriter = PngWriter(
            "./outputs/img-samples/", config['prompt'], 1
        )

        # 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):
            pngwriter.write_image(image, seed, upscaled)

            # Append post_data to log, but only once!
            if not upscaled:
                current_image = pngwriter.files_written[-1]
                with open("./outputs/img-samples/dream_web_log.txt", "a") as log:
                    log.write(f"{current_image[0]}: {json.dumps(config)}\n")
                self.wfile.write(bytes(json.dumps(
                    {'event':'result', 'files':current_image, 'config':config}
                ) + '\n',"utf-8"))

            # control state of the "postprocessing..." message
            upscaling_requested = upscale or 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 >= iterations:
                    if images_upscaled < 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}/{iterations}'}
                    ) + '\n',"utf-8"))

        def image_progress(image, step):
            self.wfile.write(bytes(json.dumps(
                {'event':'step', 'step':step}
            ) + '\n',"utf-8"))

        if initimg is None:
            # Run txt2img
            self.model.prompt2image(prompt,
                                    iterations=iterations,
                                    cfg_scale = cfgscale,
                                    width = width,
                                    height = height,
                                    seed = seed,
                                    steps = steps,
                                    gfpgan_strength = gfpgan_strength,
                                    upscale         = upscale,
                                    sampler_name    = sampler_name,
                                    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 = initimg.split(",")[1] # Ignore mime type
                f.write(base64.b64decode(initimg))

            # Run img2img
            self.model.prompt2image(prompt,
                                    init_img = "./img2img-tmp.png",
                                    iterations = iterations,
                                    cfg_scale = cfgscale,
                                    seed = seed,
                                    steps = steps,
                                    sampler_name    = sampler_name,
                                    gfpgan_strength=gfpgan_strength,
                                    upscale         = upscale,
                                    step_callback=image_progress,
                                    image_callback=image_done)

            # Remove the temp file
            os.remove("./img2img-tmp.png")

        print(f"Prompt generated!")


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