mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
chore: remove old web server code and python deps
This commit is contained in:
psychedelicious
parent
d3d8b71c67
commit
549d2e0485
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"""
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Initialization file for the web backend.
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"""
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from .invoke_ai_web_server import InvokeAIWebServer
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import argparse
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import os
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from ...args import PRECISION_CHOICES
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def create_cmd_parser():
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parser = argparse.ArgumentParser(description="InvokeAI web UI")
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parser.add_argument(
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"--host",
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type=str,
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help="The host to serve on",
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default="localhost",
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)
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parser.add_argument("--port", type=int, help="The port to serve on", default=9090)
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parser.add_argument(
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"--cors",
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nargs="*",
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type=str,
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help="Additional allowed origins, comma-separated",
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)
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parser.add_argument(
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"--embedding_path",
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type=str,
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help="Path to a pre-trained embedding manager checkpoint - can only be set on command line",
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)
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# TODO: Can't get flask to serve images from any dir (saving to the dir does work when specified)
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# parser.add_argument(
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# "--output_dir",
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# default="outputs/",
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# type=str,
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# help="Directory for output images",
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# )
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parser.add_argument(
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"-v",
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"--verbose",
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action="store_true",
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help="Enables verbose logging",
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)
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parser.add_argument(
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"--precision",
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dest="precision",
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type=str,
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choices=PRECISION_CHOICES,
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metavar="PRECISION",
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help=f'Set model precision. Defaults to auto selected based on device. Options: {", ".join(PRECISION_CHOICES)}',
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default="auto",
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)
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parser.add_argument(
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"--free_gpu_mem",
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dest="free_gpu_mem",
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action="store_true",
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help="Force free gpu memory before final decoding",
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)
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return parser
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from typing import Literal, Union
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from PIL import Image, ImageChops
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from PIL.Image import Image as ImageType
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# https://stackoverflow.com/questions/43864101/python-pil-check-if-image-is-transparent
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def check_for_any_transparency(img: Union[ImageType, str]) -> bool:
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if type(img) is str:
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img = Image.open(str)
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if img.info.get("transparency", None) is not None:
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return True
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if img.mode == "P":
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transparent = img.info.get("transparency", -1)
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for _, index in img.getcolors():
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if index == transparent:
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return True
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elif img.mode == "RGBA":
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extrema = img.getextrema()
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if extrema[3][0] < 255:
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return True
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return False
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def get_canvas_generation_mode(
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init_img: Union[ImageType, str], init_mask: Union[ImageType, str]
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) -> Literal["txt2img", "outpainting", "inpainting", "img2img",]:
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if type(init_img) is str:
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init_img = Image.open(init_img)
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if type(init_mask) is str:
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init_mask = Image.open(init_mask)
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init_img = init_img.convert("RGBA")
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# Get alpha from init_img
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init_img_alpha = init_img.split()[-1]
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init_img_alpha_mask = init_img_alpha.convert("L")
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init_img_has_transparency = check_for_any_transparency(init_img)
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if init_img_has_transparency:
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init_img_is_fully_transparent = True if init_img_alpha_mask.getbbox() is None else False
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"""
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Mask images are white in areas where no change should be made, black where changes
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should be made.
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"""
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# Fit the mask to init_img's size and convert it to greyscale
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init_mask = init_mask.resize(init_img.size).convert("L")
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"""
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PIL.Image.getbbox() returns the bounding box of non-zero areas of the image, so we first
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invert the mask image so that masked areas are white and other areas black == zero.
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getbbox() now tells us if the are any masked areas.
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"""
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init_mask_bbox = ImageChops.invert(init_mask).getbbox()
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init_mask_exists = False if init_mask_bbox is None else True
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if init_img_has_transparency:
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if init_img_is_fully_transparent:
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return "txt2img"
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else:
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return "outpainting"
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else:
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if init_mask_exists:
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return "inpainting"
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else:
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return "img2img"
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def main():
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# Testing
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init_img_opaque = "test_images/init-img_opaque.png"
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init_img_partial_transparency = "test_images/init-img_partial_transparency.png"
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init_img_full_transparency = "test_images/init-img_full_transparency.png"
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init_mask_no_mask = "test_images/init-mask_no_mask.png"
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init_mask_has_mask = "test_images/init-mask_has_mask.png"
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print(
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"OPAQUE IMAGE, NO MASK, expect img2img, got ",
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get_canvas_generation_mode(init_img_opaque, init_mask_no_mask),
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)
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print(
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"IMAGE WITH TRANSPARENCY, NO MASK, expect outpainting, got ",
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get_canvas_generation_mode(init_img_partial_transparency, init_mask_no_mask),
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)
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print(
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"FULLY TRANSPARENT IMAGE NO MASK, expect txt2img, got ",
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get_canvas_generation_mode(init_img_full_transparency, init_mask_no_mask),
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)
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print(
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"OPAQUE IMAGE, WITH MASK, expect inpainting, got ",
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get_canvas_generation_mode(init_img_opaque, init_mask_has_mask),
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)
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print(
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"IMAGE WITH TRANSPARENCY, WITH MASK, expect outpainting, got ",
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get_canvas_generation_mode(init_img_partial_transparency, init_mask_has_mask),
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)
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print(
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"FULLY TRANSPARENT IMAGE WITH MASK, expect txt2img, got ",
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get_canvas_generation_mode(init_img_full_transparency, init_mask_has_mask),
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)
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if __name__ == "__main__":
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main()
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@ -1,82 +0,0 @@
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import argparse
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from .parse_seed_weights import parse_seed_weights
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SAMPLER_CHOICES = [
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"ddim",
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"ddpm",
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"deis",
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"lms",
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"lms_k",
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"pndm",
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"heun",
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"heun_k",
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"euler",
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"euler_k",
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"euler_a",
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"kdpm_2",
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"kdpm_2_a",
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"dpmpp_2s",
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"dpmpp_2s_k",
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"dpmpp_2m",
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"dpmpp_2m_k",
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"dpmpp_2m_sde",
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"dpmpp_2m_sde_k",
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"dpmpp_sde",
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"dpmpp_sde_k",
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"unipc",
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]
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def parameters_to_command(params):
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"""
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Converts dict of parameters into a `invoke.py` REPL command.
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"""
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switches = list()
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if "prompt" in params:
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switches.append(f'"{params["prompt"]}"')
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if "steps" in params:
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switches.append(f'-s {params["steps"]}')
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if "seed" in params:
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switches.append(f'-S {params["seed"]}')
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if "width" in params:
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switches.append(f'-W {params["width"]}')
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if "height" in params:
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switches.append(f'-H {params["height"]}')
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if "cfg_scale" in params:
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switches.append(f'-C {params["cfg_scale"]}')
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if "sampler_name" in params:
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switches.append(f'-A {params["sampler_name"]}')
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if "seamless" in params and params["seamless"] == True:
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switches.append(f"--seamless")
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if "hires_fix" in params and params["hires_fix"] == True:
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switches.append(f"--hires")
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if "init_img" in params and len(params["init_img"]) > 0:
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switches.append(f'-I {params["init_img"]}')
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if "init_mask" in params and len(params["init_mask"]) > 0:
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switches.append(f'-M {params["init_mask"]}')
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if "init_color" in params and len(params["init_color"]) > 0:
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switches.append(f'--init_color {params["init_color"]}')
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if "strength" in params and "init_img" in params:
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switches.append(f'-f {params["strength"]}')
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if "fit" in params and params["fit"] == True:
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switches.append(f"--fit")
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if "facetool" in params:
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switches.append(f'-ft {params["facetool"]}')
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if "facetool_strength" in params and params["facetool_strength"]:
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switches.append(f'-G {params["facetool_strength"]}')
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elif "gfpgan_strength" in params and params["gfpgan_strength"]:
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switches.append(f'-G {params["gfpgan_strength"]}')
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if "codeformer_fidelity" in params:
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switches.append(f'-cf {params["codeformer_fidelity"]}')
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if "upscale" in params and params["upscale"]:
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switches.append(f'-U {params["upscale"][0]} {params["upscale"][1]}')
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if "variation_amount" in params and params["variation_amount"] > 0:
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switches.append(f'-v {params["variation_amount"]}')
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if "with_variations" in params:
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seed_weight_pairs = ",".join(f"{seed}:{weight}" for seed, weight in params["with_variations"])
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switches.append(f"-V {seed_weight_pairs}")
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return " ".join(switches)
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@ -1,47 +0,0 @@
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def parse_seed_weights(seed_weights):
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"""
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Accepts seed weights as string in "12345:0.1,23456:0.2,3456:0.3" format
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Validates them
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If valid: returns as [[12345, 0.1], [23456, 0.2], [3456, 0.3]]
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If invalid: returns False
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"""
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# Must be a string
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if not isinstance(seed_weights, str):
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return False
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# String must not be empty
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if len(seed_weights) == 0:
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return False
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pairs = []
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for pair in seed_weights.split(","):
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split_values = pair.split(":")
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# Seed and weight are required
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if len(split_values) != 2:
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return False
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if len(split_values[0]) == 0 or len(split_values[1]) == 1:
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return False
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# Try casting the seed to int and weight to float
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try:
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seed = int(split_values[0])
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weight = float(split_values[1])
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except ValueError:
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return False
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# Seed must be 0 or above
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if not seed >= 0:
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return False
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# Weight must be between 0 and 1
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if not (weight >= 0 and weight <= 1):
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return False
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# This pair is valid
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pairs.append([seed, weight])
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# All pairs are valid
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return pairs
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@ -45,15 +45,10 @@ dependencies = [
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"dynamicprompts",
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"easing-functions",
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"einops",
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"eventlet",
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"facexlib",
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"fastapi==0.88.0",
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"fastapi-events==0.8.0",
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"fastapi-socketio==0.0.10",
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"flask==2.1.3",
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"flask_cors==3.0.10",
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"flask_socketio==5.3.0",
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"flaskwebgui==1.0.3",
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"huggingface-hub>=0.11.1",
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"invisible-watermark~=0.2.0", # needed to install SDXL base and refiner using their repo_ids
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"matplotlib", # needed for plotting of Penner easing functions
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