InvokeAI/ldm/invoke/server_legacy.py
CapableWeb 6c0dd9b5ef Add back old dream.py as legacy_api.py
This commit "reverts" the new API changes by extracting the old
functionality into new files.

The work is based on the commit `803a51d5adca7e6e28491fc414fd3937bee7cb79`

PngWriter regained PromptFormatter as old server used that.

`server_legacy.py` is the old server that `dream.py` used.

Finally `legacy_api.py` is what `dream.py` used to be at the mentioned
commit.

One manually run test has been added in order to be able to test
compatibility with the old API, currently just testing that the API
endpoint works the same way + the image hash is the same as it used to
be before.
2022-10-12 20:35:56 -04:00

247 lines
11 KiB
Python

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)