AIImages / experiments_interrogate.py
Adityadn's picture
Upload 524 files
617d388 verified
raw
history blame
7.73 kB
import os
import sys
root = os.path.dirname(os.path.abspath(__file__))
sys.path.append(root)
os.chdir(root)
try:
import pygit2
pygit2.option(pygit2.GIT_OPT_SET_OWNER_VALIDATION, 0)
repo = pygit2.Repository(os.path.abspath(os.path.dirname(__file__)))
branch_name = repo.head.shorthand
remote_name = 'origin'
remote = repo.remotes[remote_name]
remote.fetch()
local_branch_ref = f'refs/heads/{branch_name}'
local_branch = repo.lookup_reference(local_branch_ref)
remote_reference = f'refs/remotes/{remote_name}/{branch_name}'
remote_commit = repo.revparse_single(remote_reference)
merge_result, _ = repo.merge_analysis(remote_commit.id)
if merge_result & pygit2.GIT_MERGE_ANALYSIS_UP_TO_DATE:
print("Already up-to-date")
elif merge_result & pygit2.GIT_MERGE_ANALYSIS_FASTFORWARD:
local_branch.set_target(remote_commit.id)
repo.head.set_target(remote_commit.id)
repo.checkout_tree(repo.get(remote_commit.id))
repo.reset(local_branch.target, pygit2.GIT_RESET_HARD)
print("Fast-forward merge")
elif merge_result & pygit2.GIT_MERGE_ANALYSIS_NORMAL:
print("Update failed - Did you modify any file?")
except Exception as e:
print('Update failed.')
print(str(e))
import os
import sys
import ssl
print('[System ARGV] ' + str(sys.argv))
root = os.path.dirname(os.path.abspath(__file__))
sys.path.append(root)
os.chdir(root)
os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1"
os.environ["PYTORCH_MPS_HIGH_WATERMARK_RATIO"] = "0.0"
if "GRADIO_SERVER_PORT" not in os.environ:
os.environ["GRADIO_SERVER_PORT"] = "7865"
ssl._create_default_https_context = ssl._create_unverified_context
import platform
import fooocus_version
from build_launcher import build_launcher
from modules.launch_util import is_installed, run, python, run_pip, requirements_met
from modules.model_loader import load_file_from_url
REINSTALL_ALL = False
TRY_INSTALL_XFORMERS = False
def prepare_environment():
torch_index_url = os.environ.get('TORCH_INDEX_URL', "https://download.pytorch.org/whl/cu121")
torch_command = os.environ.get('TORCH_COMMAND',
f"pip install torch==2.1.0 torchvision==0.16.0 --extra-index-url {torch_index_url}")
requirements_file = os.environ.get('REQS_FILE', "requirements_versions.txt")
print(f"Python {sys.version}")
print(f"Fooocus version: {fooocus_version.version}")
if REINSTALL_ALL or not is_installed("torch") or not is_installed("torchvision"):
run(f'"{python}" -m {torch_command}', "Installing torch and torchvision", "Couldn't install torch", live=True)
if TRY_INSTALL_XFORMERS:
if REINSTALL_ALL or not is_installed("xformers"):
xformers_package = os.environ.get('XFORMERS_PACKAGE', 'xformers==0.0.23')
if platform.system() == "Windows":
if platform.python_version().startswith("3.10"):
run_pip(f"install -U -I --no-deps {xformers_package}", "xformers", live=True)
else:
print("Installation of xformers is not supported in this version of Python.")
print(
"You can also check this and build manually: https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Xformers#building-xformers-on-windows-by-duckness")
if not is_installed("xformers"):
exit(0)
elif platform.system() == "Linux":
run_pip(f"install -U -I --no-deps {xformers_package}", "xformers")
if REINSTALL_ALL or not requirements_met(requirements_file):
run_pip(f"install -r \"{requirements_file}\"", "requirements")
return
vae_approx_filenames = [
('xlvaeapp.pth', 'https://huggingface.co/lllyasviel/misc/resolve/main/xlvaeapp.pth'),
('vaeapp_sd15.pth', 'https://huggingface.co/lllyasviel/misc/resolve/main/vaeapp_sd15.pt'),
('xl-to-v1_interposer-v3.1.safetensors',
'https://huggingface.co/lllyasviel/misc/resolve/main/xl-to-v1_interposer-v3.1.safetensors')
]
def ini_args():
from args_manager import args
return args
prepare_environment()
build_launcher()
args = ini_args()
if args.gpu_device_id is not None:
os.environ['CUDA_VISIBLE_DEVICES'] = str(args.gpu_device_id)
print("Set device to:", args.gpu_device_id)
from modules import config
def download_models():
for file_name, url in vae_approx_filenames:
load_file_from_url(url=url, model_dir=config.path_vae_approx, file_name=file_name)
load_file_from_url(
url='https://huggingface.co/lllyasviel/misc/resolve/main/fooocus_expansion.bin',
model_dir=config.path_fooocus_expansion,
file_name='pytorch_model.bin'
)
if args.disable_preset_download:
print('Skipped model download.')
return
if not args.always_download_new_model:
if not os.path.exists(os.path.join(config.paths_checkpoints[0], config.default_base_model_name)):
for alternative_model_name in config.previous_default_models:
if os.path.exists(os.path.join(config.paths_checkpoints[0], alternative_model_name)):
print(f'You do not have [{config.default_base_model_name}] but you have [{alternative_model_name}].')
print(f'Fooocus will use [{alternative_model_name}] to avoid downloading new models, '
f'but you are not using latest models.')
print('Use --always-download-new-model to avoid fallback and always get new models.')
config.checkpoint_downloads = {}
config.default_base_model_name = alternative_model_name
break
for file_name, url in config.checkpoint_downloads.items():
load_file_from_url(url=url, model_dir=config.paths_checkpoints[0], file_name=file_name)
for file_name, url in config.embeddings_downloads.items():
load_file_from_url(url=url, model_dir=config.path_embeddings, file_name=file_name)
for file_name, url in config.lora_downloads.items():
load_file_from_url(url=url, model_dir=config.paths_loras[0], file_name=file_name)
return
download_models()
import gradio as gr
import modules.gradio_hijack as grh
from extras.interrogate import default_interrogator as default_interrogator_photo
from extras.wd14tagger import default_interrogator as default_interrogator_anime
import modules.flags as flags
def interrogatorFunction(img, value):
if value == flags.desc_type_photo: # Menggunakan operator perbandingan '==' untuk memeriksa kesamaan
output = default_interrogator_photo(img)
print(output)
else:
output = default_interrogator_anime(img)
print(output)
return output
describe = gr.Blocks(title="AI Describe Image", css="#component-3, #component-5 {display: grid; align-content: center;}")
with describe:
describe_tab = gr.TabItem(label='Describe')
with describe_tab:
input_column = gr.Row()
with input_column:
with gr.Column():
input_image = grh.Image(label='Input', source='upload', type='numpy')
with gr.Column():
content_type = gr.Radio(
label='Content Type',
choices=[flags.desc_type_photo, flags.desc_type_anime],
value=flags.desc_type_photo
)
desc_btn = gr.Button(value='Describe this Image into Prompt')
outputs=gr.Textbox(type="text", label="Output", show_copy_button=True)
desc_btn.click(interrogatorFunction, inputs=[input_image, content_type], outputs=[outputs])
describe.launch()