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