made deepdanbooru optional, added to readme, automatic download of deepbooru model

     - separate prompts using uppercase `AND`

     - also supports weights for prompts: `a cat :1.2 AND a dog AND a penguin :2.2`

- No token limit for prompts (original stable diffusion lets you use up to 75 tokens)

- DeepDanbooru integration, creates danbooru style tags for anime prompts (add --deepdanbooru to commandline args)

## Installation and Running

Make sure the required [dependencies](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Dependencies) are met and follow the instructions available for both [NVidia](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-NVidia-GPUs) (recommended) and [AMD](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-AMD-GPUs) GPUs.

- Noise generation for outpainting mk2 - https://github.com/parlance-zz/g-diffuser-bot

- CLIP interrogator idea and borrowing some code - https://github.com/pharmapsychotic/clip-interrogator

- Initial Gradio script - posted on 4chan by an Anonymous user. Thank you Anonymous user.

- DeepDanbooru - interrogator for anime diffusors https://github.com/KichangKim/DeepDanbooru

- (You)

args, skip_torch_cuda_test = extract_arg(args, '--skip-torch-cuda-test')

xformers = '--xformers' in args

deepdanbooru = '--deepdanbooru' in args

def repo_dir(name):

    elif platform.system() == "Linux":

        run_pip("install xformers", "xformers")

if not is_installed("deepdanbooru") and deepdanbooru:

    run_pip("install git+https://github.com/KichangKim/DeepDanbooru.git@edf73df4cdaeea2cf00e9ac08bd8a9026b7a7b26#egg=deepdanbooru[tensorflow] tensorflow==2.10.0 tensorflow-io==0.27.0", "deepdanbooru")

os.makedirs(dir_repos, exist_ok=True)

git_clone("https://github.com/CompVis/stable-diffusion.git", repo_dir('stable-diffusion'), "Stable Diffusion", stable_diffusion_commit_hash)

    import numpy as np

    this_folder = os.path.dirname(__file__)

    model_path = os.path.join(this_folder, '..', 'models', 'deepbooru', 'deepdanbooru-v3-20211112-sgd-e28')

    model_good = False

    for path_candidate in [model_path, os.path.dirname(model_path)]:

        if os.path.exists(os.path.join(path_candidate, 'project.json')):

            model_path = path_candidate

            model_good = True

    if not model_good:

        return ("Download https://github.com/KichangKim/DeepDanbooru/releases/download/v3-20211112-sgd-e28/"

                "deepdanbooru-v3-20211112-sgd-e28.zip unpack and put into models/deepbooru")

    model_path = os.path.abspath(os.path.join(this_folder, '..', 'models', 'deepbooru'))

    if not os.path.exists(os.path.join(model_path, 'project.json')):

        # there is no point importing these every time

        import zipfile

        from basicsr.utils.download_util import load_file_from_url

        load_file_from_url(r"https://github.com/KichangKim/DeepDanbooru/releases/download/v3-20211112-sgd-e28/deepdanbooru-v3-20211112-sgd-e28.zip",

                           model_path)

        with zipfile.ZipFile(os.path.join(model_path, "deepdanbooru-v3-20211112-sgd-e28.zip"), "r") as zip_ref:

            zip_ref.extractall(model_path)

        os.remove(os.path.join(model_path, "deepdanbooru-v3-20211112-sgd-e28.zip"))

    tags = dd.project.load_tags_from_project(model_path)

    model = dd.project.load_model_from_project(

parser.add_argument("--swinir-models-path", type=str, help="Path to directory with SwinIR model file(s).", default=os.path.join(models_path, 'SwinIR'))

parser.add_argument("--ldsr-models-path", type=str, help="Path to directory with LDSR model file(s).", default=os.path.join(models_path, 'LDSR'))

parser.add_argument("--xformers", action='store_true', help="enable xformers for cross attention layers")

parser.add_argument("--deepdanbooru", action='store_true', help="enable deepdanbooru interrogator")

parser.add_argument("--opt-split-attention", action='store_true', help="force-enables cross-attention layer optimization. By default, it's on for torch.cuda and off for other torch devices.")

parser.add_argument("--disable-opt-split-attention", action='store_true', help="force-disables cross-attention layer optimization")

parser.add_argument("--opt-split-attention-v1", action='store_true', help="enable older version of split attention optimization that does not consume all the VRAM it can find")

import gradio.routes

from modules import sd_hijack

from modules.deepbooru import get_deepbooru_tags

from modules.paths import script_path

from modules.shared import opts, cmd_opts

if cmd_opts.deepdanbooru:

    from modules.deepbooru import get_deepbooru_tags

import modules.shared as shared

from modules.sd_samplers import samplers, samplers_for_img2img

from modules.sd_hijack import model_hijack

            with gr.Row(scale=1):

                if is_img2img:

                    interrogate = gr.Button('Interrogate\nCLIP', elem_id="interrogate")

                    if cmd_opts.deepdanbooru:

                        deepbooru = gr.Button('Interrogate\nDeepBooru', elem_id="deepbooru")

                    else:

                        deepbooru = None

                else:

                    interrogate = None

                    deepbooru = None

                outputs=[img2img_prompt],

            )

            if cmd_opts.deepdanbooru:

                img2img_deepbooru.click(

                    fn=interrogate_deepbooru,

                    inputs=[init_img],