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

This commit is contained in:
Greendayle 2022-10-08 18:02:56 +02:00
parent 5329d0aba0
commit 01f8cb4447
7 changed files with 29 additions and 23 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:
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)

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@ -9,16 +9,16 @@ def _load_tf_and_return_tags(pil_image, threshold):
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(

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@ -44,6 +44,7 @@ parser.add_argument("--scunet-models-path", type=str, help="Path to directory wi
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")

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@ -23,9 +23,10 @@ import gradio.utils
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
@ -437,7 +438,10 @@ def create_toprow(is_img2img):
with gr.Row(scale=1):
if is_img2img:
interrogate = gr.Button('Interrogate\nCLIP', elem_id="interrogate")
deepbooru = gr.Button('Interrogate\nDeepBooru', elem_id="deepbooru")
if cmd_opts.deepdanbooru:
deepbooru = gr.Button('Interrogate\nDeepBooru', elem_id="deepbooru")
else:
deepbooru = None
else:
interrogate = None
deepbooru = None
@ -782,11 +786,12 @@ def create_ui(wrap_gradio_gpu_call):
outputs=[img2img_prompt],
)
img2img_deepbooru.click(
fn=interrogate_deepbooru,
inputs=[init_img],
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),

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@ -23,7 +23,4 @@ resize-right
torchdiffeq
kornia
lark
deepdanbooru
tensorflow
tensorflow-io
functorch

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@ -22,7 +22,4 @@ resize-right==0.0.2
torchdiffeq==0.2.3
kornia==0.6.7
lark==1.1.2
git+https://github.com/KichangKim/DeepDanbooru.git@edf73df4cdaeea2cf00e9ac08bd8a9026b7a7b26#egg=deepdanbooru[tensorflow]
tensorflow==2.10.0
tensorflow-io==0.27.0
functorch==0.2.1