Merge pull request #1752 from Greendayle/dev/deepdanbooru

     - 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 os.path

from concurrent.futures import ProcessPoolExecutor

from multiprocessing import get_context

def _load_tf_and_return_tags(pil_image, threshold):

    import deepdanbooru as dd

    import tensorflow as tf

    import numpy as np

    this_folder = os.path.dirname(__file__)

    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(

        model_path, compile_model=True

    )

    width = model.input_shape[2]

    height = model.input_shape[1]

    image = np.array(pil_image)

    image = tf.image.resize(

        image,

        size=(height, width),

        method=tf.image.ResizeMethod.AREA,

        preserve_aspect_ratio=True,

    )

    image = image.numpy()  # EagerTensor to np.array

    image = dd.image.transform_and_pad_image(image, width, height)

    image = image / 255.0

    image_shape = image.shape

    image = image.reshape((1, image_shape[0], image_shape[1], image_shape[2]))

    y = model.predict(image)[0]

    result_dict = {}

    for i, tag in enumerate(tags):

        result_dict[tag] = y[i]

    result_tags_out = []

    result_tags_print = []

    for tag in tags:

        if result_dict[tag] >= threshold:

            if tag.startswith("rating:"):

                continue

            result_tags_out.append(tag)

            result_tags_print.append(f'{result_dict[tag]} {tag}')

    print('\n'.join(sorted(result_tags_print, reverse=True)))

    return ', '.join(result_tags_out).replace('_', ' ').replace(':', ' ')

def subprocess_init_no_cuda():

    import os

    os.environ["CUDA_VISIBLE_DEVICES"] = "-1"

def get_deepbooru_tags(pil_image, threshold=0.5):

    context = get_context('spawn')

    with ProcessPoolExecutor(initializer=subprocess_init_no_cuda, mp_context=context) as executor:

        f = executor.submit(_load_tf_and_return_tags, pil_image, threshold, )

        ret = f.result()  # will rethrow any exceptions

    return ret

 No newline at end of file

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("--force-enable-xformers", action='store_true', help="enable xformers for cross attention layers regardless of whether the checking code thinks you can run it; do not make bug reports if this fails to work")

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")