Merge pull request #1752 from Greendayle/dev/deepdanbooru

Added DeepDanbooru interrogator
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AUTOMATIC1111 2022-10-09 10:52:21 +03:00 committed by GitHub
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7 changed files with 110 additions and 6 deletions

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@ -66,6 +66,7 @@ Check the [custom scripts](https://github.com/AUTOMATIC1111/stable-diffusion-web
- 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.
@ -123,4 +124,5 @@ The documentation was moved from this README over to the project's [wiki](https:
- 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)

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@ -33,6 +33,7 @@ def extract_arg(args, name):
args, skip_torch_cuda_test = extract_arg(args, '--skip-torch-cuda-test')
xformers = '--xformers' in args
deepdanbooru = '--deepdanbooru' in args
def repo_dir(name):
@ -132,6 +133,9 @@ if not is_installed("xformers") and xformers and platform.python_version().start
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)

73
modules/deepbooru.py Normal file
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@ -0,0 +1,73 @@
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

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@ -45,6 +45,7 @@ parser.add_argument("--swinir-models-path", type=str, help="Path to directory wi
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")

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@ -25,6 +25,8 @@ import gradio.routes
from modules import sd_hijack
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
@ -308,6 +310,11 @@ def interrogate(image):
return gr_show(True) if prompt is None else prompt
def interrogate_deepbooru(image):
prompt = get_deepbooru_tags(image)
return gr_show(True) if prompt is None else prompt
def create_seed_inputs():
with gr.Row():
with gr.Box():
@ -444,15 +451,20 @@ def create_toprow(is_img2img):
outputs=[],
)
with gr.Row():
with gr.Row(scale=1):
if is_img2img:
interrogate = gr.Button('Interrogate', elem_id="interrogate")
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
prompt_style_apply = gr.Button('Apply style', elem_id="style_apply")
save_style = gr.Button('Create style', elem_id="style_create")
return prompt, roll, prompt_style, negative_prompt, prompt_style2, submit, interrogate, prompt_style_apply, save_style, paste, token_counter, token_button
return prompt, roll, prompt_style, negative_prompt, prompt_style2, submit, interrogate, deepbooru, prompt_style_apply, save_style, paste, token_counter, token_button
def setup_progressbar(progressbar, preview, id_part, textinfo=None):
@ -481,7 +493,7 @@ def create_ui(wrap_gradio_gpu_call):
import modules.txt2img
with gr.Blocks(analytics_enabled=False) as txt2img_interface:
txt2img_prompt, roll, txt2img_prompt_style, txt2img_negative_prompt, txt2img_prompt_style2, submit, _, txt2img_prompt_style_apply, txt2img_save_style, paste, token_counter, token_button = create_toprow(is_img2img=False)
txt2img_prompt, roll, txt2img_prompt_style, txt2img_negative_prompt, txt2img_prompt_style2, submit, _, _, txt2img_prompt_style_apply, txt2img_save_style, paste, token_counter, token_button = create_toprow(is_img2img=False)
dummy_component = gr.Label(visible=False)
with gr.Row(elem_id='txt2img_progress_row'):
@ -641,7 +653,7 @@ def create_ui(wrap_gradio_gpu_call):
token_button.click(fn=update_token_counter, inputs=[txt2img_prompt, steps], outputs=[token_counter])
with gr.Blocks(analytics_enabled=False) as img2img_interface:
img2img_prompt, roll, img2img_prompt_style, img2img_negative_prompt, img2img_prompt_style2, submit, img2img_interrogate, img2img_prompt_style_apply, img2img_save_style, paste, token_counter, token_button = create_toprow(is_img2img=True)
img2img_prompt, roll, img2img_prompt_style, img2img_negative_prompt, img2img_prompt_style2, submit, img2img_interrogate, img2img_deepbooru, img2img_prompt_style_apply, img2img_save_style, paste, token_counter, token_button = create_toprow(is_img2img=True)
with gr.Row(elem_id='img2img_progress_row'):
with gr.Column(scale=1):
@ -804,6 +816,13 @@ def create_ui(wrap_gradio_gpu_call):
outputs=[img2img_prompt],
)
if cmd_opts.deepdanbooru:
img2img_deepbooru.click(
fn=interrogate_deepbooru,
inputs=[init_img],
outputs=[img2img_prompt],
)
save.click(
fn=wrap_gradio_call(save_files),
_js="(x, y, z, w) => [x, y, z, selected_gallery_index()]",

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@ -103,7 +103,12 @@
#style_apply, #style_create, #interrogate{
margin: 0.75em 0.25em 0.25em 0.25em;
min-width: 3em;
min-width: 5em;
}
#style_apply, #style_create, #deepbooru{
margin: 0.75em 0.25em 0.25em 0.25em;
min-width: 5em;
}
#style_pos_col, #style_neg_col{