Merge pull request #1752 from Greendayle/dev/deepdanbooru
Added DeepDanbooru interrogator
This commit is contained in:
AUTOMATIC1111
GitHub
commit
e00b4df7c6
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@ -66,6 +66,7 @@ Check the [custom scripts](https://github.com/AUTOMATIC1111/stable-diffusion-web
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- separate prompts using uppercase `AND`
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- also supports weights for prompts: `a cat :1.2 AND a dog AND a penguin :2.2`
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- No token limit for prompts (original stable diffusion lets you use up to 75 tokens)
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- DeepDanbooru integration, creates danbooru style tags for anime prompts (add --deepdanbooru to commandline args)
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## Installation and Running
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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.
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@ -123,4 +124,5 @@ The documentation was moved from this README over to the project's [wiki](https:
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- Noise generation for outpainting mk2 - https://github.com/parlance-zz/g-diffuser-bot
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- CLIP interrogator idea and borrowing some code - https://github.com/pharmapsychotic/clip-interrogator
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- Initial Gradio script - posted on 4chan by an Anonymous user. Thank you Anonymous user.
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- DeepDanbooru - interrogator for anime diffusors https://github.com/KichangKim/DeepDanbooru
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- (You)
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@ -33,6 +33,7 @@ def extract_arg(args, name):
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args, skip_torch_cuda_test = extract_arg(args, '--skip-torch-cuda-test')
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xformers = '--xformers' in args
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deepdanbooru = '--deepdanbooru' in args
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def repo_dir(name):
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@ -132,6 +133,9 @@ if not is_installed("xformers") and xformers and platform.python_version().start
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elif platform.system() == "Linux":
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run_pip("install xformers", "xformers")
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if not is_installed("deepdanbooru") and deepdanbooru:
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run_pip("install git+https://github.com/KichangKim/DeepDanbooru.git@edf73df4cdaeea2cf00e9ac08bd8a9026b7a7b26#egg=deepdanbooru[tensorflow] tensorflow==2.10.0 tensorflow-io==0.27.0", "deepdanbooru")
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os.makedirs(dir_repos, exist_ok=True)
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git_clone("https://github.com/CompVis/stable-diffusion.git", repo_dir('stable-diffusion'), "Stable Diffusion", stable_diffusion_commit_hash)
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73
modules/deepbooru.py
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73
modules/deepbooru.py
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@ -0,0 +1,73 @@
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import os.path
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from concurrent.futures import ProcessPoolExecutor
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from multiprocessing import get_context
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def _load_tf_and_return_tags(pil_image, threshold):
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import deepdanbooru as dd
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import tensorflow as tf
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import numpy as np
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this_folder = os.path.dirname(__file__)
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model_path = os.path.abspath(os.path.join(this_folder, '..', 'models', 'deepbooru'))
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if not os.path.exists(os.path.join(model_path, 'project.json')):
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# there is no point importing these every time
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import zipfile
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from basicsr.utils.download_util import load_file_from_url
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load_file_from_url(r"https://github.com/KichangKim/DeepDanbooru/releases/download/v3-20211112-sgd-e28/deepdanbooru-v3-20211112-sgd-e28.zip",
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model_path)
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with zipfile.ZipFile(os.path.join(model_path, "deepdanbooru-v3-20211112-sgd-e28.zip"), "r") as zip_ref:
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zip_ref.extractall(model_path)
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os.remove(os.path.join(model_path, "deepdanbooru-v3-20211112-sgd-e28.zip"))
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tags = dd.project.load_tags_from_project(model_path)
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model = dd.project.load_model_from_project(
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model_path, compile_model=True
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)
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width = model.input_shape[2]
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height = model.input_shape[1]
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image = np.array(pil_image)
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image = tf.image.resize(
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image,
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size=(height, width),
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method=tf.image.ResizeMethod.AREA,
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preserve_aspect_ratio=True,
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)
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image = image.numpy() # EagerTensor to np.array
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image = dd.image.transform_and_pad_image(image, width, height)
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image = image / 255.0
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image_shape = image.shape
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image = image.reshape((1, image_shape[0], image_shape[1], image_shape[2]))
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y = model.predict(image)[0]
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result_dict = {}
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for i, tag in enumerate(tags):
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result_dict[tag] = y[i]
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result_tags_out = []
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result_tags_print = []
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for tag in tags:
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if result_dict[tag] >= threshold:
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if tag.startswith("rating:"):
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continue
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result_tags_out.append(tag)
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result_tags_print.append(f'{result_dict[tag]} {tag}')
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print('\n'.join(sorted(result_tags_print, reverse=True)))
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return ', '.join(result_tags_out).replace('_', ' ').replace(':', ' ')
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def subprocess_init_no_cuda():
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import os
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os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
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def get_deepbooru_tags(pil_image, threshold=0.5):
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context = get_context('spawn')
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with ProcessPoolExecutor(initializer=subprocess_init_no_cuda, mp_context=context) as executor:
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f = executor.submit(_load_tf_and_return_tags, pil_image, threshold, )
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ret = f.result() # will rethrow any exceptions
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return ret
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@ -45,6 +45,7 @@ parser.add_argument("--swinir-models-path", type=str, help="Path to directory wi
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parser.add_argument("--ldsr-models-path", type=str, help="Path to directory with LDSR model file(s).", default=os.path.join(models_path, 'LDSR'))
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parser.add_argument("--xformers", action='store_true', help="enable xformers for cross attention layers")
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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")
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parser.add_argument("--deepdanbooru", action='store_true', help="enable deepdanbooru interrogator")
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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.")
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parser.add_argument("--disable-opt-split-attention", action='store_true', help="force-disables cross-attention layer optimization")
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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
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from modules import sd_hijack
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from modules.paths import script_path
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from modules.shared import opts, cmd_opts
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if cmd_opts.deepdanbooru:
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from modules.deepbooru import get_deepbooru_tags
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import modules.shared as shared
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from modules.sd_samplers import samplers, samplers_for_img2img
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from modules.sd_hijack import model_hijack
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@ -308,6 +310,11 @@ def interrogate(image):
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return gr_show(True) if prompt is None else prompt
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def interrogate_deepbooru(image):
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prompt = get_deepbooru_tags(image)
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return gr_show(True) if prompt is None else prompt
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def create_seed_inputs():
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with gr.Row():
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with gr.Box():
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@ -444,15 +451,20 @@ def create_toprow(is_img2img):
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outputs=[],
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)
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with gr.Row():
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with gr.Row(scale=1):
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if is_img2img:
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interrogate = gr.Button('Interrogate', elem_id="interrogate")
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interrogate = gr.Button('Interrogate\nCLIP', elem_id="interrogate")
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if cmd_opts.deepdanbooru:
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deepbooru = gr.Button('Interrogate\nDeepBooru', elem_id="deepbooru")
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else:
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deepbooru = None
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else:
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interrogate = None
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deepbooru = None
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prompt_style_apply = gr.Button('Apply style', elem_id="style_apply")
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save_style = gr.Button('Create style', elem_id="style_create")
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return prompt, roll, prompt_style, negative_prompt, prompt_style2, submit, interrogate, prompt_style_apply, save_style, paste, token_counter, token_button
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return prompt, roll, prompt_style, negative_prompt, prompt_style2, submit, interrogate, deepbooru, prompt_style_apply, save_style, paste, token_counter, token_button
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def setup_progressbar(progressbar, preview, id_part, textinfo=None):
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@ -481,7 +493,7 @@ def create_ui(wrap_gradio_gpu_call):
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import modules.txt2img
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with gr.Blocks(analytics_enabled=False) as txt2img_interface:
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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)
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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)
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dummy_component = gr.Label(visible=False)
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with gr.Row(elem_id='txt2img_progress_row'):
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@ -641,7 +653,7 @@ def create_ui(wrap_gradio_gpu_call):
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token_button.click(fn=update_token_counter, inputs=[txt2img_prompt, steps], outputs=[token_counter])
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with gr.Blocks(analytics_enabled=False) as img2img_interface:
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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)
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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)
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with gr.Row(elem_id='img2img_progress_row'):
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with gr.Column(scale=1):
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@ -804,6 +816,13 @@ def create_ui(wrap_gradio_gpu_call):
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outputs=[img2img_prompt],
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)
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if cmd_opts.deepdanbooru:
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img2img_deepbooru.click(
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fn=interrogate_deepbooru,
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inputs=[init_img],
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outputs=[img2img_prompt],
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)
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save.click(
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fn=wrap_gradio_call(save_files),
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_js="(x, y, z, w) => [x, y, z, selected_gallery_index()]",
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