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https://github.com/Sygil-Dev/sygil-webui.git
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model manager
model manager
This commit is contained in:
hlky
parent
1e7bdfe3f3
commit
3851fcc537
@ -322,55 +322,82 @@ model_manager:
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stable_diffusion:
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model_name: "Stable Diffusion v1.4"
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save_location: "./models/ldm/stable-diffusion-v1"
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download_link: "https://huggingface.co/CompVis/stable-diffusion-v-1-4-original"
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files:
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model_ckpt:
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file_name: "model.ckpt"
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download_link: "https://www.googleapis.com/storage/v1/b/aai-blog-files/o/sd-v1-4.ckpt?alt=media"
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gfpgan:
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model_name: "GFPGAN v1.4"
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model_name: "GFPGAN"
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save_location: "./models/gfpgan"
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files:
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gfpgan_1_4:
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file_name: "GFPGANv1.4.pth"
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download_link: "https://github.com/TencentARC/GFPGAN/releases/download/v1.3.4/GFPGANv1.4.pth"
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resnet_50:
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file_name: "detection_Resnet50_Final.pth"
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save_location: "./gfpgan/weights"
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download_link: "https://github.com/xinntao/facexlib/releases/download/v0.1.0/detection_Resnet50_Final.pth"
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parsing_parsenet:
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file_name: "parsing_parsenet.pth"
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save_location: "./gfpgan/weights"
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download_link: "https://github.com/xinntao/facexlib/releases/download/v0.2.2/parsing_parsenet.pth"
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realesrgan_x4plus:
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model_name: "RealESRGAN_x4plus"
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realesrgan:
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model_name: "RealESRGAN"
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save_location: "./models/realesrgan"
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files:
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x4plus:
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file_name: "RealESRGAN_x4plus.pth"
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download_link: "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth"
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realesrgan_x4plus_anime_6b:
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model_name: "RealESRGAN_x4plus_anime_6B"
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save_location: "./models/realesrgan"
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x4plus_anime_6b:
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file_name: "RealESRGAN_x4plus_anime_6B.pth"
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download_link: "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth"
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waifu_diffusion:
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model_name: "Waifu Diffusion v1.2"
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save_location: "./models/custom"
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download_link: "https://huggingface.co/hakurei/waifu-diffusion"
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files:
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waifu_diffusion:
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file_name: "waifu-diffusion.ckpt"
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download_link: "https://huggingface.co/crumb/pruned-waifu-diffusion/resolve/main/model-pruned.ckpt"
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waifu-diffusion_pruned:
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model_name: "Waifu Diffusion v1.2 Pruned"
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save_location: "./models/custom"
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download_link: "https://huggingface.co/crumb/pruned-waifu-diffusion"
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trinart_stable_diffusion:
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model_name: "TrinArt Stable Diffusion v2"
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save_location: "./models/custom"
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download_link: "https://huggingface.co/naclbit/trinart_stable_diffusion_v2"
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files:
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trinart:
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file_name: "trinart.ckpt"
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download_link: "https://huggingface.co/naclbit/trinart_stable_diffusion_v2/resolve/main/trinart2_step95000.ckpt"
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stable_diffusion_concept_library:
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model_name: "Stable Diffusion Concept Library"
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save_location: "./models/custom/sd-concepts-library"
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save_location: "./models/custom/sd-concepts-library/"
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files:
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concept_library:
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file_name: ""
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download_link: "https://github.com/sd-webui/sd-concepts-library"
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blip_model:
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model_name: "Blip Model"
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save_location: "./models/blip"
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files:
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blip:
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file_name: "model__base_caption.pth"
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download_link: "https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model*_base_caption.pth"
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lds_project_file:
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model_name: "LDSR `project.yaml`"
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ldsr:
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model_name: "Latent Diffusion Super Resolution (LDSR)"
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save_location: "./models/ldsr"
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files:
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project_yaml:
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file_name: "project.yaml"
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download_link: "https://heibox.uni-heidelberg.de/f/31a76b13ea27482981b4/?dl=1"
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ldsr_model:
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model_name: "LDSR `model.cpkt`"
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save_location: "./models/ldsr"
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file_name: "model.ckpt"
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download_link: "https://heibox.uni-heidelberg.de/f/578df07c8fc04ffbadf3/?dl=1"
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@ -15,7 +15,7 @@
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# along with this program. If not, see <http://www.gnu.org/licenses/>.
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# base webui import and utils.
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from sd_utils import *
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import wget
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# streamlit imports
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@ -26,6 +26,22 @@ from sd_utils import *
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# end of imports
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#---------------------------------------------------------------------------------------------------------------
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def download_file(file_name, file_path, file_url):
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if not os.path.exists(file_path):
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os.makedirs(file_path)
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if not os.path.exists(file_path + '/' + file_name):
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print('Downloading ' + file_name + '...')
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# TODO - add progress bar in streamlit
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wget.download(url=file_url, out=file_path + '/' + file_name)
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else:
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print(file_name + ' already exists.')
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def download_model(models, model_name):
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""" Download all files from model_list[model_name] """
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for file in models[model_name]:
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download_file(file['file_name'], file['file_path'], file['file_url'])
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return
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def layout():
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#search = st.text_input(label="Search", placeholder="Type the name of the model you want to search for.", help="")
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@ -44,4 +60,29 @@ def layout():
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col1.write(x) # index
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col2.write(models[model_name]['model_name'])
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col3.write(models[model_name]['save_location'])
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col4.write(models[model_name]['download_link'])
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with col4:
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files_exist = 0
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for file in models[model_name]['files']:
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if "save_location" in models[model_name]['files'][file]:
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os.path.exists(models[model_name]['files'][file]['save_location'] + '/' + models[model_name]['files'][file]['file_name'])
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files_exist += 1
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elif os.path.exists(models[model_name]['save_location'] + '/' + models[model_name]['files'][file]['file_name']):
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files_exist += 1
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files_needed = []
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for file in models[model_name]['files']:
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if "save_location" in models[model_name]['files'][file]:
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if not os.path.exists(models[model_name]['files'][file]['save_location'] + '/' + models[model_name]['files'][file]['file_name']):
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files_needed.append(file)
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elif not os.path.exists(models[model_name]['save_location'] + '/' + models[model_name]['files'][file]['file_name']):
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files_needed.append(file)
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if len(files_needed) > 0:
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if st.button('Download', key=models[model_name]['model_name'], help='Download ' + models[model_name]['model_name']):
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for file in files_needed:
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if "save_location" in models[model_name]['files'][file]:
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download_file(models[model_name]['files'][file]['file_name'], models[model_name]['files'][file]['save_location'], models[model_name]['files'][file]['download_link'])
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else:
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download_file(models[model_name]['files'][file]['file_name'], models[model_name]['save_location'], models[model_name]['files'][file]['download_link'])
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else:
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st.empty()
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else:
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st.write('✅')
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@ -83,10 +83,10 @@ def layout():
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help="Default model path. Default: 'models/ldm/stable-diffusion-v1/model.ckpt'")
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st.session_state['defaults'].general.GFPGAN_dir = st.text_input("Default GFPGAN directory", value=st.session_state['defaults'].general.GFPGAN_dir,
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help="Default GFPGAN directory. Default: './src/gfpgan'")
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help="Default GFPGAN directory. Default: './models/gfpgan'")
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st.session_state['defaults'].general.RealESRGAN_dir = st.text_input("Default RealESRGAN directory", value=st.session_state['defaults'].general.RealESRGAN_dir,
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help="Default GFPGAN directory. Default: './src/realesrgan'")
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help="Default GFPGAN directory. Default: './models/realesrgan'")
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RealESRGAN_model_list = ["RealESRGAN_x4plus", "RealESRGAN_x4plus_anime_6B"]
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st.session_state['defaults'].general.RealESRGAN_model = st.selectbox("RealESRGAN model", RealESRGAN_model_list,
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@ -52,7 +52,7 @@ def img2img(prompt: str = '', init_info: any = None, init_info_mask: any = None,
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variant_amount: float = None, variant_seed: int = None, ddim_eta:float = 0.0,
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write_info_files:bool = True, separate_prompts:bool = False, normalize_prompt_weights:bool = True,
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save_individual_images: bool = True, save_grid: bool = True, group_by_prompt: bool = True,
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save_as_jpg: bool = True, use_GFPGAN: bool = True, GFPGAN_model: str = 'GFPGANv1.3',
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save_as_jpg: bool = True, use_GFPGAN: bool = True, GFPGAN_model: str = 'GFPGANv1.4',
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use_RealESRGAN: bool = True, RealESRGAN_model: str = "RealESRGAN_x4plus_anime_6B",
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use_LDSR: bool = True, LDSR_model: str = "model",
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loopback: bool = False,
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@ -167,7 +167,7 @@ def img2img(prompt: str = '', init_info: any = None, init_info_mask: any = None,
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init_image = 2. * image - 1.
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init_image = init_image.to(server_state["device"])
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init_latent = (server_state["model"] if not st.session_state['defaults'].general.optimized else server_state["modelFS"]).get_first_stage_encoding((server_state["model"] if not st.session_state['defaults'].general.optimized else modelFS).encode_first_stage(init_image)) # move to latent space
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init_latent = (server_state["model"] if not st.session_state['defaults'].general.optimized else server_state["modelFS"]).get_first_stage_encoding((server_state["model"] if not st.session_state['defaults'].general.optimized else server_state["modelFS"]).encode_first_stage(init_image)) # move to latent space
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if st.session_state['defaults'].general.optimized:
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mem = torch.cuda.memory_allocated()/1e6
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@ -226,11 +226,11 @@ def interrogate(image, models):
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if model_name not in server_state["clip_models"]:
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if model_name == 'ViT-H-14':
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server_state["clip_models"][model_name], _, server_state["preprocesses"][model_name] = open_clip.create_model_and_transforms(model_name, pretrained='laion2b_s32b_b79k', cache_dir='user_data/model_cache/clip')
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server_state["clip_models"][model_name], _, server_state["preprocesses"][model_name] = open_clip.create_model_and_transforms(model_name, pretrained='laion2b_s32b_b79k', cache_dir='models/clip')
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elif model_name == 'ViT-g-14':
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server_state["clip_models"][model_name], _, server_state["preprocesses"][model_name] = open_clip.create_model_and_transforms(model_name, pretrained='laion2b_s12b_b42k', cache_dir='user_data/model_cache/clip')
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server_state["clip_models"][model_name], _, server_state["preprocesses"][model_name] = open_clip.create_model_and_transforms(model_name, pretrained='laion2b_s12b_b42k', cache_dir='models/clip')
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else:
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server_state["clip_models"][model_name], server_state["preprocesses"][model_name] = clip.load(model_name, device=device, download_root='user_data/model_cache/clip')
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server_state["clip_models"][model_name], server_state["preprocesses"][model_name] = clip.load(model_name, device=device, download_root='models/clip')
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server_state["clip_models"][model_name] = server_state["clip_models"][model_name].cuda().eval()
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images = server_state["preprocesses"][model_name](image).unsqueeze(0).cuda()
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@ -15,6 +15,7 @@
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# along with this program. If not, see <http://www.gnu.org/licenses/>.
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# base webui import and utils.
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#from webui_streamlit import st
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import gfpgan
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import hydralit as st
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@ -219,7 +220,7 @@ def human_readable_size(size, decimal_places=3):
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return f"{size:.{decimal_places}f}{unit}"
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def load_models(use_LDSR = False, LDSR_model='model', use_GFPGAN=False, GFPGAN_model='GFPGANv1.3', use_RealESRGAN=False, RealESRGAN_model="RealESRGAN_x4plus",
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def load_models(use_LDSR = False, LDSR_model='model', use_GFPGAN=False, GFPGAN_model='GFPGANv1.4', use_RealESRGAN=False, RealESRGAN_model="RealESRGAN_x4plus",
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CustomModel_available=False, custom_model="Stable Diffusion v1.4"):
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"""Load the different models. We also reuse the models that are already in memory to speed things up instead of loading them again. """
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@ -1193,7 +1194,6 @@ def load_GFPGAN(model_name='GFPGANv1.4'):
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sys.path.append(os.path.abspath(st.session_state['defaults'].general.GFPGAN_dir))
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from gfpgan import GFPGANer
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with server_state_lock['GFPGAN']:
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if st.session_state['defaults'].general.gfpgan_cpu or st.session_state['defaults'].general.extra_models_cpu:
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server_state['GFPGAN'] = GFPGANer(model_path=model_path, upscale=1, arch='clean',
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@ -1221,7 +1221,7 @@ def load_RealESRGAN(model_name: str):
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'RealESRGAN_x4plus_anime_6B': RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=6, num_grow_ch=32, scale=4)
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}
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model_path = os.path.join(st.session_state['defaults'].general.RealESRGAN_dir, 'experiments/pretrained_models', model_name + '.pth')
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model_path = os.path.join(st.session_state['defaults'].general.RealESRGAN_dir, model_name + '.pth')
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if not os.path.isfile(model_path):
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model_path = os.path.join(st.session_state['defaults'].general.RealESRGAN_dir, model_name + '.pth')
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@ -1756,15 +1756,19 @@ def GFPGAN_available():
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# Allow for custom models to be used instead of the default one,
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# an example would be Waifu-Diffusion or any other fine tune of stable diffusion
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st.session_state["GFPGAN_models"]:sorted = []
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model = st.session_state["defaults"].model_manager.models.gfpgan
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files_available = 0
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for file in model['files']:
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if "save_location" in model['files'][file]:
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if os.path.exists(os.path.join(model['files'][file]['save_location'], model['files'][file]['file_name'] )):
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files_available += 1
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elif os.path.exists(os.path.join(model['save_location'], model['files'][file]['file_name'] )):
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base_name = os.path.splitext(model['files'][file]['file_name'])[0]
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if "GFPGANv" in base_name:
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st.session_state["GFPGAN_models"].append(base_name)
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files_available += 1
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for root, dirs, files in os.walk(st.session_state['defaults'].general.GFPGAN_dir):
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for file in files:
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if os.path.splitext(file)[1] == '.pth':
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st.session_state["GFPGAN_models"].append(os.path.splitext(file)[0])
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#print (len(st.session_state["GFPGAN_models"]))
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#with server_state_lock["GFPGAN_available"]:
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if len(st.session_state["GFPGAN_models"]) > 0:
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if len(st.session_state["GFPGAN_models"]) > 0 and files_available == len(model['files']):
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st.session_state["GFPGAN_available"] = True
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else:
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st.session_state["GFPGAN_available"] = False
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@ -1776,13 +1780,12 @@ def RealESRGAN_available():
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# Allow for custom models to be used instead of the default one,
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# an example would be Waifu-Diffusion or any other fine tune of stable diffusion
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st.session_state["RealESRGAN_models"]:sorted = []
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model = st.session_state["defaults"].model_manager.models.realesrgan
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for file in model['files']:
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if os.path.exists(os.path.join(model['save_location'], model['files'][file]['file_name'] )):
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base_name = os.path.splitext(model['files'][file]['file_name'])[0]
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st.session_state["RealESRGAN_models"].append(base_name)
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for root, dirs, files in os.walk(st.session_state['defaults'].general.RealESRGAN_dir):
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for file in files:
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if os.path.splitext(file)[1] == '.pth':
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st.session_state["RealESRGAN_models"].append(os.path.splitext(file)[0])
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#with server_state_lock["RealESRGAN_available"]:
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if len(st.session_state["RealESRGAN_models"]) > 0:
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st.session_state["RealESRGAN_available"] = True
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else:
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@ -1794,21 +1797,24 @@ def LDSR_available():
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# Allow for custom models to be used instead of the default one,
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# an example would be Waifu-Diffusion or any other fine tune of stable diffusion
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st.session_state["LDSR_models"]:sorted = []
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for root, dirs, files in os.walk(st.session_state['defaults'].general.LDSR_dir):
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for file in files:
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if os.path.splitext(file)[1] == '.ckpt':
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st.session_state["LDSR_models"].append(os.path.splitext(file)[0])
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#print (st.session_state['defaults'].general.LDSR_dir)
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#print (st.session_state["LDSR_models"])
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#with server_state_lock["LDSR_available"]:
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if len(st.session_state["LDSR_models"]) > 0:
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files_available = 0
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model = st.session_state["defaults"].model_manager.models.ldsr
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for file in model['files']:
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if os.path.exists(os.path.join(model['save_location'], model['files'][file]['file_name'] )):
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base_name = os.path.splitext(model['files'][file]['file_name'])[0]
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extension = os.path.splitext(model['files'][file]['file_name'])[1]
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if extension == ".ckpt":
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st.session_state["LDSR_models"].append(base_name)
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files_available += 1
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if files_available == len(model['files']):
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st.session_state["LDSR_available"] = True
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else:
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st.session_state["LDSR_available"] = False
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def save_sample(image, sample_path_i, filename, jpg_sample, prompts, seeds, width, height, steps, cfg_scale,
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normalize_prompt_weights, use_GFPGAN, write_info_files, prompt_matrix, init_img, uses_loopback, uses_random_seed_loopback,
|
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save_grid, sort_samples, sampler_name, ddim_eta, n_iter, batch_size, i, denoising_strength, resize_mode, save_individual_images, model_name):
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@ -59,17 +59,6 @@ class plugin_info():
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isTab = True
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displayPriority = 1
|
||||
|
||||
|
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if os.path.exists(os.path.join(st.session_state['defaults'].general.GFPGAN_dir, "experiments", "pretrained_models", "GFPGANv1.3.pth")):
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server_state["GFPGAN_available"] = True
|
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else:
|
||||
server_state["GFPGAN_available"] = False
|
||||
|
||||
if os.path.exists(os.path.join(st.session_state['defaults'].general.RealESRGAN_dir, "experiments","pretrained_models", f"{st.session_state['defaults'].txt2vid.RealESRGAN_model}.pth")):
|
||||
server_state["RealESRGAN_available"] = True
|
||||
else:
|
||||
server_state["RealESRGAN_available"] = False
|
||||
|
||||
#
|
||||
# -----------------------------------------------------------------------------
|
||||
|
||||
@ -235,11 +224,11 @@ def load_diffusers_model(weights_path,torch_device):
|
||||
del st.session_state["weights_path"]
|
||||
|
||||
st.session_state["weights_path"] = weights_path
|
||||
# if folder "user_data/model_cache/stable-diffusion-v1-4" exists, load the model from there
|
||||
# if folder "models/diffusers/stable-diffusion-v1-4" exists, load the model from there
|
||||
if weights_path == "CompVis/stable-diffusion-v1-4":
|
||||
model_path = os.path.join("user_data", "model_cache", "stable-diffusion-v1-4")
|
||||
model_path = os.path.join("models", "diffusers", "stable-diffusion-v1-4")
|
||||
elif weights_path == "hakurei/waifu-diffusion":
|
||||
model_path = os.path.join("user_data", "model_cache", "waifu-diffusion")
|
||||
model_path = os.path.join("models", "diffusers", "waifu-diffusion")
|
||||
|
||||
if not os.path.exists(model_path + "/model_index.json"):
|
||||
server_state["pipe"] = StableDiffusionPipeline.from_pretrained(
|
||||
|
||||
@ -369,8 +369,8 @@ def torch_gc():
|
||||
def load_LDSR(checking=False):
|
||||
model_name = 'model'
|
||||
yaml_name = 'project'
|
||||
model_path = os.path.join(LDSR_dir, 'experiments/pretrained_models', model_name + '.ckpt')
|
||||
yaml_path = os.path.join(LDSR_dir, 'experiments/pretrained_models', yaml_name + '.yaml')
|
||||
model_path = os.path.join(LDSR_dir, model_name + '.ckpt')
|
||||
yaml_path = os.path.join(LDSR_dir, yaml_name + '.yaml')
|
||||
if not os.path.isfile(model_path):
|
||||
raise Exception("LDSR model not found at path "+model_path)
|
||||
if not os.path.isfile(yaml_path):
|
||||
@ -384,7 +384,7 @@ def load_LDSR(checking=False):
|
||||
return LDSRObject
|
||||
def load_GFPGAN(checking=False):
|
||||
model_name = 'GFPGANv1.3'
|
||||
model_path = os.path.join(GFPGAN_dir, 'experiments/pretrained_models', model_name + '.pth')
|
||||
model_path = os.path.join(GFPGAN_dir, model_name + '.pth')
|
||||
if not os.path.isfile(model_path):
|
||||
raise Exception("GFPGAN model not found at path "+model_path)
|
||||
if checking == True:
|
||||
@ -407,7 +407,7 @@ def load_RealESRGAN(model_name: str, checking = False):
|
||||
'RealESRGAN_x4plus_anime_6B': RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=6, num_grow_ch=32, scale=4)
|
||||
}
|
||||
|
||||
model_path = os.path.join(RealESRGAN_dir, 'experiments/pretrained_models', model_name + '.pth')
|
||||
model_path = os.path.join(RealESRGAN_dir, model_name + '.pth')
|
||||
if not os.path.isfile(model_path):
|
||||
raise Exception(model_name+".pth not found at path "+model_path)
|
||||
if checking == True:
|
||||
|
||||
@ -100,13 +100,13 @@ def layout():
|
||||
|
||||
# check if the models exist on their respective folders
|
||||
with server_state_lock["GFPGAN_available"]:
|
||||
if os.path.exists(os.path.join(st.session_state["defaults"].general.GFPGAN_dir, "experiments", "pretrained_models", "GFPGANv1.3.pth")):
|
||||
if os.path.exists(os.path.join(st.session_state["defaults"].general.GFPGAN_dir, f"{st.session_state['defaults'].general.GFPGAN_model}.pth")):
|
||||
server_state["GFPGAN_available"] = True
|
||||
else:
|
||||
server_state["GFPGAN_available"] = False
|
||||
|
||||
with server_state_lock["RealESRGAN_available"]:
|
||||
if os.path.exists(os.path.join(st.session_state["defaults"].general.RealESRGAN_dir, "experiments","pretrained_models", f"{st.session_state['defaults'].general.RealESRGAN_model}.pth")):
|
||||
if os.path.exists(os.path.join(st.session_state["defaults"].general.RealESRGAN_dir, f"{st.session_state['defaults'].general.RealESRGAN_model}.pth")):
|
||||
server_state["RealESRGAN_available"] = True
|
||||
else:
|
||||
server_state["RealESRGAN_available"] = False
|
||||
|
||||
Loading…
Reference in New Issue
Block a user