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830 lines
56 KiB
Python
830 lines
56 KiB
Python
# This file is part of stable-diffusion-webui (https://github.com/sd-webui/stable-diffusion-webui/).
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# Copyright 2022 sd-webui team.
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# This program is free software: you can redistribute it and/or modify
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# it under the terms of the GNU Affero General Public License as published by
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# the Free Software Foundation, either version 3 of the License, or
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# (at your option) any later version.
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# This program is distributed in the hope that it will be useful,
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# but WITHOUT ANY WARRANTY; without even the implied warranty of
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# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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# GNU Affero General Public License for more details.
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# You should have received a copy of the GNU Affero General Public License
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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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# streamlit imports
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#streamlit components section
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import streamlit_nested_layout
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from streamlit_server_state import server_state, server_state_lock
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#other imports
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from omegaconf import OmegaConf
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# end of imports
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#---------------------------------------------------------------------------------------------------------------
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def layout():
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st.header("Settings")
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with st.form("Settings"):
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general_tab, txt2img_tab, img2img_tab, img2txt_tab, txt2vid_tab, image_processing, textual_inversion_tab, concepts_library_tab = st.tabs(
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['General', "Text-To-Image", "Image-To-Image", "Image-To-Text", "Text-To-Video", "Image processing", "Textual Inversion", "Concepts Library"])
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with general_tab:
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col1, col2, col3, col4, col5 = st.columns(5, gap='large')
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device_list = []
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device_properties = [(i, torch.cuda.get_device_properties(i)) for i in range(torch.cuda.device_count())]
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for device in device_properties:
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id = device[0]
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name = device[1].name
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total_memory = device[1].total_memory
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device_list.append(f"{id}: {name} ({human_readable_size(total_memory, decimal_places=0)})")
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with col1:
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st.title("General")
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st.session_state['defaults'].general.gpu = int(st.selectbox("GPU", device_list,
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help=f"Select which GPU to use. Default: {device_list[0]}").split(":")[0])
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st.session_state['defaults'].general.outdir = str(st.text_input("Output directory", value=st.session_state['defaults'].general.outdir,
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help="Relative directory on which the output images after a generation will be placed. Default: 'outputs'"))
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# If we have custom models available on the "models/custom"
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# folder then we show a menu to select which model we want to use, otherwise we use the main model for SD
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custom_models_available()
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if server_state["CustomModel_available"]:
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st.session_state.default_model = st.selectbox("Default Model:", server_state["custom_models"],
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index=server_state["custom_models"].index(st.session_state['defaults'].general.default_model),
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help="Select the model you want to use. If you have placed custom models \
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on your 'models/custom' folder they will be shown here as well. The model name that will be shown here \
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is the same as the name the file for the model has on said folder, \
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it is recommended to give the .ckpt file a name that \
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will make it easier for you to distinguish it from other models. Default: Stable Diffusion v1.4")
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else:
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st.session_state.default_model = st.selectbox("Default Model:", [st.session_state['defaults'].general.default_model],
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help="Select the model you want to use. If you have placed custom models \
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on your 'models/custom' folder they will be shown here as well. \
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The model name that will be shown here is the same as the name\
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the file for the model has on said folder, it is recommended to give the .ckpt file a name that \
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will make it easier for you to distinguish it from other models. Default: Stable Diffusion v1.4")
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st.session_state['defaults'].general.default_model_config = st.text_input("Default Model Config", value=st.session_state['defaults'].general.default_model_config,
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help="Default model config file for inference. Default: 'configs/stable-diffusion/v1-inference.yaml'")
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st.session_state['defaults'].general.default_model_path = st.text_input("Default Model Config", value=st.session_state['defaults'].general.default_model_path,
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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: './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: './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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index=RealESRGAN_model_list.index(st.session_state['defaults'].general.RealESRGAN_model),
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help="Default RealESRGAN model. Default: 'RealESRGAN_x4plus'")
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Upscaler_list = ["RealESRGAN", "LDSR"]
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st.session_state['defaults'].general.upscaling_method = st.selectbox("Upscaler", Upscaler_list, index=Upscaler_list.index(st.session_state['defaults'].general.upscaling_method), help="Default upscaling method. Default: 'RealESRGAN'")
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with col2:
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st.title("Performance")
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st.session_state["defaults"].general.gfpgan_cpu = st.checkbox("GFPGAN - CPU", value=st.session_state['defaults'].general.gfpgan_cpu,
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help="Run GFPGAN on the cpu. Default: False")
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st.session_state["defaults"].general.esrgan_cpu = st.checkbox("ESRGAN - CPU", value=st.session_state['defaults'].general.esrgan_cpu,
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help="Run ESRGAN on the cpu. Default: False")
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st.session_state["defaults"].general.extra_models_cpu = st.checkbox("Extra Models - CPU", value=st.session_state['defaults'].general.extra_models_cpu,
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help="Run extra models (GFGPAN/ESRGAN) on cpu. Default: False")
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st.session_state["defaults"].general.extra_models_gpu = st.checkbox("Extra Models - GPU", value=st.session_state['defaults'].general.extra_models_gpu,
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help="Run extra models (GFGPAN/ESRGAN) on gpu. \
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Check and save in order to be able to select the GPU that each model will use. Default: False")
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if st.session_state["defaults"].general.extra_models_gpu:
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st.session_state['defaults'].general.gfpgan_gpu = int(st.selectbox("GFGPAN GPU", device_list, index=st.session_state['defaults'].general.gfpgan_gpu,
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help=f"Select which GPU to use. Default: {device_list[st.session_state['defaults'].general.gfpgan_gpu]}",
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key="gfpgan_gpu").split(":")[0])
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st.session_state["defaults"].general.esrgan_gpu = int(st.selectbox("ESRGAN - GPU", device_list, index=st.session_state['defaults'].general.esrgan_gpu,
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help=f"Select which GPU to use. Default: {device_list[st.session_state['defaults'].general.esrgan_gpu]}",
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key="esrgan_gpu").split(":")[0])
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st.session_state["defaults"].general.no_half = st.checkbox("No Half", value=st.session_state['defaults'].general.no_half,
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help="DO NOT switch the model to 16-bit floats. Default: False")
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st.session_state["defaults"].general.use_float16 = st.checkbox("Use float16", value=st.session_state['defaults'].general.use_float16,
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help="Switch the model to 16-bit floats. Default: False")
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precision_list = ['full','autocast']
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st.session_state["defaults"].general.precision = st.selectbox("Precision", precision_list, index=precision_list.index(st.session_state['defaults'].general.precision),
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help="Evaluates at this precision. Default: autocast")
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st.session_state["defaults"].general.optimized = st.checkbox("Optimized Mode", value=st.session_state['defaults'].general.optimized,
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help="Loads the model onto the device piecemeal instead of all at once to reduce VRAM usage\
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at the cost of performance. Default: False")
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st.session_state["defaults"].general.optimized_turbo = st.checkbox("Optimized Turbo Mode", value=st.session_state['defaults'].general.optimized_turbo,
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help="Alternative optimization mode that does not save as much VRAM but \
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runs siginificantly faster. Default: False")
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st.session_state["defaults"].general.optimized_config = st.text_input("Optimized Config", value=st.session_state['defaults'].general.optimized_config,
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help=f"Loads alternative optimized configuration for inference. \
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Default: optimizedSD/v1-inference.yaml")
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st.session_state["defaults"].general.enable_attention_slicing = st.checkbox("Enable Attention Slicing", value=st.session_state['defaults'].general.enable_attention_slicing,
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help="Enable sliced attention computation. When this option is enabled, the attention module will \
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split the input tensor in slices, to compute attention in several steps. This is useful to save some \
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memory in exchange for a small speed decrease. Only works the txt2vid tab right now. Default: False")
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st.session_state["defaults"].general.enable_minimal_memory_usage = st.checkbox("Enable Minimal Memory Usage", value=st.session_state['defaults'].general.enable_minimal_memory_usage,
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help="Moves only unet to fp16 and to CUDA, while keepping lighter models on CPUs \
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(Not properly implemented and currently not working, check this \
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link 'https://github.com/huggingface/diffusers/pull/537' for more information on it ). Default: False")
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#st.session_state["defaults"].general.update_preview = st.checkbox("Update Preview Image", value=st.session_state['defaults'].general.update_preview,
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#help="Enables the preview image to be updated and shown to the user on the UI during the generation.\
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#If checked, once you save the settings an option to specify the frequency at which the image is updated\
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#in steps will be shown, this is helpful to reduce the negative effect this option has on performance. \
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#Default: True")
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st.session_state["defaults"].general.update_preview = True
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st.session_state["defaults"].general.update_preview_frequency = st.number_input("Update Preview Frequency", value=st.session_state['defaults'].general.update_preview_frequency,
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help="Specify the frequency at which the image is updated in steps, this is helpful to reduce the \
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negative effect updating the preview image has on performance. Default: 10")
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with col3:
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st.title("Others")
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st.session_state["defaults"].general.use_sd_concepts_library = st.checkbox("Use the Concepts Library", value=st.session_state['defaults'].general.use_sd_concepts_library,
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help="Use the embeds Concepts Library, if checked, once the settings are saved an option will\
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appear to specify the directory where the concepts are stored. Default: True)")
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if st.session_state["defaults"].general.use_sd_concepts_library:
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st.session_state['defaults'].general.sd_concepts_library_folder = st.text_input("Concepts Library Folder",
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value=st.session_state['defaults'].general.sd_concepts_library_folder,
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help="Relative folder on which the concepts library embeds are stored. \
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Default: 'models/custom/sd-concepts-library'")
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st.session_state['defaults'].general.LDSR_dir = st.text_input("LDSR Folder", value=st.session_state['defaults'].general.LDSR_dir,
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help="Folder where LDSR is located. Default: './models/ldsr'")
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st.session_state["defaults"].general.save_metadata = st.checkbox("Save Metadata", value=st.session_state['defaults'].general.save_metadata,
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help="Save metadata on the output image. Default: True")
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save_format_list = ["png"]
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st.session_state["defaults"].general.save_format = st.selectbox("Save Format",save_format_list, index=save_format_list.index(st.session_state['defaults'].general.save_format),
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help="Format that will be used whens saving the output images. Default: 'png'")
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st.session_state["defaults"].general.skip_grid = st.checkbox("Skip Grid", value=st.session_state['defaults'].general.skip_grid,
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help="Skip saving the grid output image. Default: False")
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if not st.session_state["defaults"].general.skip_grid:
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st.session_state["defaults"].general.grid_format = st.text_input("Grid Format", value=st.session_state['defaults'].general.grid_format,
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help="Format for saving the grid output image. Default: 'jpg:95'")
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st.session_state["defaults"].general.skip_save = st.checkbox("Skip Save", value=st.session_state['defaults'].general.skip_save,
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help="Skip saving the output image. Default: False")
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st.session_state["defaults"].general.n_rows = st.number_input("Number of Grid Rows", value=st.session_state['defaults'].general.n_rows,
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help="Number of rows the grid wil have when saving the grid output image. Default: '-1'")
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st.session_state["defaults"].general.no_verify_input = st.checkbox("Do not Verify Input", value=st.session_state['defaults'].general.no_verify_input,
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help="Do not verify input to check if it's too long. Default: False")
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st.session_state["defaults"].daisi_app.running_on_daisi_io = st.checkbox("Running on Daisi.io?", value=st.session_state['defaults'].daisi_app.running_on_daisi_io,
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help="Specify if we are running on app.Daisi.io . Default: False")
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with col4:
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st.title("Streamlit Config")
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st.session_state["defaults"].general.streamlit_telemetry = st.checkbox("Enable Telemetry", value=st.session_state['defaults'].general.streamlit_telemetry,
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help="Enables or Disables streamlit telemetry. Default: False")
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st.session_state["streamlit_config"]["browser"]["gatherUsageStats"] = st.session_state["defaults"].general.streamlit_telemetry
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default_theme_list = ["light", "dark"]
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st.session_state["defaults"].general.default_theme = st.selectbox("Default Theme", default_theme_list, index=default_theme_list.index(st.session_state['defaults'].general.default_theme),
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help="Defaut theme to use as base for streamlit. Default: dark")
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st.session_state["streamlit_config"]["theme"]["base"] = st.session_state["defaults"].general.default_theme
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with col5:
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st.title("Huggingface")
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st.session_state["defaults"].general.huggingface_token = st.text_input("Huggingface Token", value=st.session_state['defaults'].general.huggingface_token, type="password",
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help="Your Huggingface Token, it's used to download the model for the diffusers library which \
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is used on the Text To Video tab. This token will be saved to your user config file\
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and WILL NOT be share with us or anyone. You can get your access token \
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at https://huggingface.co/settings/tokens. Default: None")
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with txt2img_tab:
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col1, col2, col3, col4, col5 = st.columns(5, gap='medium')
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with col1:
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st.title("Slider Parameters")
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# Width
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st.session_state["defaults"].txt2img.width.value = st.number_input("Default Image Width", value=st.session_state['defaults'].txt2img.width.value,
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help="Set the default width for the generated image. Default is: 512")
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st.session_state["defaults"].txt2img.width.min_value = st.number_input("Minimum Image Width", value=st.session_state['defaults'].txt2img.width.min_value,
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help="Set the default minimum value for the width slider. Default is: 64")
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st.session_state["defaults"].txt2img.width.max_value = st.number_input("Maximum Image Width", value=st.session_state['defaults'].txt2img.width.max_value,
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help="Set the default maximum value for the width slider. Default is: 2048")
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# Height
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st.session_state["defaults"].txt2img.height.value = st.number_input("Default Image Height", value=st.session_state['defaults'].txt2img.height.value,
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help="Set the default height for the generated image. Default is: 512")
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st.session_state["defaults"].txt2img.height.min_value = st.number_input("Minimum Image Height", value=st.session_state['defaults'].txt2img.height.min_value,
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help="Set the default minimum value for the height slider. Default is: 64")
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st.session_state["defaults"].txt2img.height.max_value = st.number_input("Maximum Image Height", value=st.session_state['defaults'].txt2img.height.max_value,
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help="Set the default maximum value for the height slider. Default is: 2048")
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with col2:
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# CFG
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st.session_state["defaults"].txt2img.cfg_scale.value = st.number_input("Default CFG Scale", value=st.session_state['defaults'].txt2img.cfg_scale.value,
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help="Set the default value for the CFG Scale. Default is: 7.5")
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st.session_state["defaults"].txt2img.cfg_scale.min_value = st.number_input("Minimum CFG Scale Value", value=st.session_state['defaults'].txt2img.cfg_scale.min_value,
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help="Set the default minimum value for the CFG scale slider. Default is: 1")
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st.session_state["defaults"].txt2img.cfg_scale.max_value = st.number_input("Maximum CFG Scale Value",
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value=st.session_state['defaults'].txt2img.cfg_scale.max_value,
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help="Set the default maximum value for the CFG scale slider. Default is: 30")
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st.session_state["defaults"].txt2img.cfg_scale.step = st.number_input("CFG Slider Steps", value=st.session_state['defaults'].txt2img.cfg_scale.step,
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help="Set the default value for the number of steps on the CFG scale slider. Default is: 0.5")
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# Sampling Steps
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st.session_state["defaults"].txt2img.sampling_steps.value = st.number_input("Default Sampling Steps", value=st.session_state['defaults'].txt2img.sampling_steps.value,
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help="Set the default number of sampling steps to use. Default is: 30 (with k_euler)")
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st.session_state["defaults"].txt2img.sampling_steps.min_value = st.number_input("Minimum Sampling Steps",
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value=st.session_state['defaults'].txt2img.sampling_steps.min_value,
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help="Set the default minimum value for the sampling steps slider. Default is: 1")
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st.session_state["defaults"].txt2img.sampling_steps.step = st.text_input("Sampling Slider Steps",
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value=st.session_state['defaults'].txt2img.sampling_steps.step,
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help="Set the default value for the number of steps on the sampling steps slider. Default is: 10")
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with col3:
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st.title("General Parameters")
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# Batch Count
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st.session_state["defaults"].txt2img.batch_count.value = st.number_input("Batch count", value=st.session_state['defaults'].txt2img.batch_count.value,
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help="How many iterations or batches of images to generate in total.")
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st.session_state["defaults"].txt2img.batch_size.value = st.number_input("Batch size", value=st.session_state.defaults.txt2img.batch_size.value,
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help="How many images are at once in a batch.\
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It increases the VRAM usage a lot but if you have enough VRAM it can reduce the time it \
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takes to finish generation as more images are generated at once.\
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Default: 1")
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default_sampler_list = ["k_lms", "k_euler", "k_euler_a", "k_dpm_2", "k_dpm_2_a", "k_heun", "PLMS", "DDIM"]
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st.session_state["defaults"].txt2img.default_sampler = st.selectbox("Default Sampler",
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default_sampler_list, index=default_sampler_list.index(st.session_state['defaults'].txt2img.default_sampler),
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help="Defaut sampler to use for txt2img. Default: k_euler")
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st.session_state['defaults'].txt2img.seed = st.text_input("Default Seed", value=st.session_state['defaults'].txt2img.seed, help="Default seed.")
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with col4:
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st.session_state["defaults"].txt2img.separate_prompts = st.checkbox("Separate Prompts",
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value=st.session_state['defaults'].txt2img.separate_prompts, help="Separate Prompts. Default: False")
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st.session_state["defaults"].txt2img.normalize_prompt_weights = st.checkbox("Normalize Prompt Weights",
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value=st.session_state['defaults'].txt2img.normalize_prompt_weights,
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help="Choose to normalize prompt weights. Default: True")
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st.session_state["defaults"].txt2img.save_individual_images = st.checkbox("Save Individual Images",
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value=st.session_state['defaults'].txt2img.save_individual_images,
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help="Choose to save individual images. Default: True")
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st.session_state["defaults"].txt2img.save_grid = st.checkbox("Save Grid Images", value=st.session_state['defaults'].txt2img.save_grid,
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help="Choose to save the grid images. Default: True")
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st.session_state["defaults"].txt2img.group_by_prompt = st.checkbox("Group By Prompt", value=st.session_state['defaults'].txt2img.group_by_prompt,
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help="Choose to save images grouped by their prompt. Default: False")
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st.session_state["defaults"].txt2img.save_as_jpg = st.checkbox("Save As JPG", value=st.session_state['defaults'].txt2img.save_as_jpg,
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help="Choose to save images as jpegs. Default: False")
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st.session_state["defaults"].txt2img.write_info_files = st.checkbox("Write Info Files For Images", value=st.session_state['defaults'].txt2img.write_info_files,
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help="Choose to write the info files along with the generated images. Default: True")
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st.session_state["defaults"].txt2img.use_GFPGAN = st.checkbox("Use GFPGAN", value=st.session_state['defaults'].txt2img.use_GFPGAN, help="Choose to use GFPGAN. Default: False")
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st.session_state["defaults"].txt2img.use_upscaling = st.checkbox("Use Upscaling", value=st.session_state['defaults'].txt2img.use_upscaling,
|
|
help="Choose to turn on upscaling by default. Default: False")
|
|
|
|
st.session_state["defaults"].txt2img.update_preview = True
|
|
st.session_state["defaults"].txt2img.update_preview_frequency = st.number_input("Preview Image Update Frequency",
|
|
value=st.session_state['defaults'].txt2img.update_preview_frequency,
|
|
help="Set the default value for the frrquency of the preview image updates. Default is: 10")
|
|
|
|
with col5:
|
|
st.title("Variation Parameters")
|
|
|
|
st.session_state["defaults"].txt2img.variant_amount.value = st.number_input("Default Variation Amount",
|
|
value=st.session_state['defaults'].txt2img.variant_amount.value,
|
|
help="Set the default variation to use. Default is: 0.0")
|
|
|
|
st.session_state["defaults"].txt2img.variant_amount.min_value = st.number_input("Minimum Variation Amount",
|
|
value=st.session_state['defaults'].txt2img.variant_amount.min_value,
|
|
help="Set the default minimum value for the variation slider. Default is: 0.0")
|
|
|
|
st.session_state["defaults"].txt2img.variant_amount.max_value = st.number_input("Maximum Variation Amount",
|
|
value=st.session_state['defaults'].txt2img.variant_amount.max_value,
|
|
help="Set the default maximum value for the variation slider. Default is: 1.0")
|
|
|
|
st.session_state["defaults"].txt2img.variant_amount.step = st.number_input("Variation Slider Steps",
|
|
value=st.session_state['defaults'].txt2img.variant_amount.step,
|
|
help="Set the default value for the number of steps on the variation slider. Default is: 1")
|
|
|
|
st.session_state['defaults'].txt2img.variant_seed = st.text_input("Default Variation Seed", value=st.session_state['defaults'].txt2img.variant_seed,
|
|
help="Default variation seed.")
|
|
|
|
with img2img_tab:
|
|
col1, col2, col3, col4, col5 = st.columns(5, gap='medium')
|
|
|
|
with col1:
|
|
st.title("Image Editing")
|
|
|
|
# Denoising
|
|
st.session_state["defaults"].img2img.denoising_strength.value = st.number_input("Default Denoising Amount",
|
|
value=st.session_state['defaults'].img2img.denoising_strength.value,
|
|
help="Set the default denoising to use. Default is: 0.75")
|
|
|
|
st.session_state["defaults"].img2img.denoising_strength.min_value = st.number_input("Minimum Denoising Amount",
|
|
value=st.session_state['defaults'].img2img.denoising_strength.min_value,
|
|
help="Set the default minimum value for the denoising slider. Default is: 0.0")
|
|
|
|
st.session_state["defaults"].img2img.denoising_strength.max_value = st.number_input("Maximum Denoising Amount",
|
|
value=st.session_state['defaults'].img2img.denoising_strength.max_value,
|
|
help="Set the default maximum value for the denoising slider. Default is: 1.0")
|
|
|
|
st.session_state["defaults"].img2img.denoising_strength.step = st.number_input("Denoising Slider Steps",
|
|
value=st.session_state['defaults'].img2img.denoising_strength.step,
|
|
help="Set the default value for the number of steps on the denoising slider. Default is: 0.01")
|
|
|
|
# Masking
|
|
st.session_state["defaults"].img2img.mask_mode = st.number_input("Default Mask Mode", value=st.session_state['defaults'].img2img.mask_mode,
|
|
help="Set the default mask mode to use. 0 = Keep Masked Area, 1 = Regenerate Masked Area. Default is: 0")
|
|
|
|
st.session_state["defaults"].img2img.mask_restore = st.checkbox("Default Mask Restore", value=st.session_state['defaults'].img2img.mask_restore,
|
|
help="Mask Restore. Default: False")
|
|
|
|
st.session_state["defaults"].img2img.resize_mode = st.number_input("Default Resize Mode", value=st.session_state['defaults'].img2img.resize_mode,
|
|
help="Set the default resizing mode. 0 = Just Resize, 1 = Crop and Resize, 3 = Resize and Fill. Default is: 0")
|
|
|
|
with col2:
|
|
st.title("Slider Parameters")
|
|
|
|
# Width
|
|
st.session_state["defaults"].img2img.width.value = st.number_input("Default Outputted Image Width", value=st.session_state['defaults'].img2img.width.value,
|
|
help="Set the default width for the generated image. Default is: 512")
|
|
|
|
st.session_state["defaults"].img2img.width.min_value = st.number_input("Minimum Outputted Image Width", value=st.session_state['defaults'].img2img.width.min_value,
|
|
help="Set the default minimum value for the width slider. Default is: 64")
|
|
|
|
st.session_state["defaults"].img2img.width.max_value = st.number_input("Maximum Outputted Image Width", value=st.session_state['defaults'].img2img.width.max_value,
|
|
help="Set the default maximum value for the width slider. Default is: 2048")
|
|
|
|
# Height
|
|
st.session_state["defaults"].img2img.height.value = st.number_input("Default Outputted Image Height", value=st.session_state['defaults'].img2img.height.value,
|
|
help="Set the default height for the generated image. Default is: 512")
|
|
|
|
st.session_state["defaults"].img2img.height.min_value = st.number_input("Minimum Outputted Image Height", value=st.session_state['defaults'].img2img.height.min_value,
|
|
help="Set the default minimum value for the height slider. Default is: 64")
|
|
|
|
st.session_state["defaults"].img2img.height.max_value = st.number_input("Maximum Outputted Image Height", value=st.session_state['defaults'].img2img.height.max_value,
|
|
help="Set the default maximum value for the height slider. Default is: 2048")
|
|
|
|
# CFG
|
|
st.session_state["defaults"].img2img.cfg_scale.value = st.number_input("Default Img2Img CFG Scale", value=st.session_state['defaults'].img2img.cfg_scale.value,
|
|
help="Set the default value for the CFG Scale. Default is: 7.5")
|
|
|
|
st.session_state["defaults"].img2img.cfg_scale.min_value = st.number_input("Minimum Img2Img CFG Scale Value",
|
|
value=st.session_state['defaults'].img2img.cfg_scale.min_value,
|
|
help="Set the default minimum value for the CFG scale slider. Default is: 1")
|
|
|
|
st.session_state["defaults"].img2img.cfg_scale.max_value = st.number_input("Maximum Img2Img CFG Scale Value",
|
|
value=st.session_state['defaults'].img2img.cfg_scale.max_value,
|
|
help="Set the default maximum value for the CFG scale slider. Default is: 30")
|
|
|
|
with col3:
|
|
st.session_state["defaults"].img2img.cfg_scale.step = st.number_input("Img2Img CFG Slider Steps",
|
|
value=st.session_state['defaults'].img2img.cfg_scale.step,
|
|
help="Set the default value for the number of steps on the CFG scale slider. Default is: 0.5")
|
|
|
|
# Sampling Steps
|
|
st.session_state["defaults"].img2img.sampling_steps.value = st.number_input("Default Img2Img Sampling Steps",
|
|
value=st.session_state['defaults'].img2img.sampling_steps.value,
|
|
help="Set the default number of sampling steps to use. Default is: 30 (with k_euler)")
|
|
|
|
st.session_state["defaults"].img2img.sampling_steps.min_value = st.number_input("Minimum Img2Img Sampling Steps",
|
|
value=st.session_state['defaults'].img2img.sampling_steps.min_value,
|
|
help="Set the default minimum value for the sampling steps slider. Default is: 1")
|
|
|
|
|
|
st.session_state["defaults"].img2img.sampling_steps.step = st.number_input("Img2Img Sampling Slider Steps",
|
|
value=st.session_state['defaults'].img2img.sampling_steps.step,
|
|
help="Set the default value for the number of steps on the sampling steps slider. Default is: 10")
|
|
|
|
# Batch Count
|
|
st.session_state["defaults"].img2img.batch_count.value = st.number_input("Img2img Batch count", value=st.session_state["defaults"].img2img.batch_count.value,
|
|
help="How many iterations or batches of images to generate in total.")
|
|
|
|
st.session_state["defaults"].img2img.batch_size.value = st.number_input("Img2img Batch size", value=st.session_state["defaults"].img2img.batch_size.value,
|
|
help="How many images are at once in a batch.\
|
|
It increases the VRAM usage a lot but if you have enough VRAM it can reduce the time it \
|
|
takes to finish generation as more images are generated at once.\
|
|
Default: 1")
|
|
with col4:
|
|
# Inference Steps
|
|
st.session_state["defaults"].img2img.num_inference_steps.value = st.number_input("Default Inference Steps",
|
|
value=st.session_state['defaults'].img2img.num_inference_steps.value,
|
|
help="Set the default number of inference steps to use. Default is: 200")
|
|
|
|
st.session_state["defaults"].img2img.num_inference_steps.min_value = st.number_input("Minimum Sampling Steps",
|
|
value=st.session_state['defaults'].img2img.num_inference_steps.min_value,
|
|
help="Set the default minimum value for the inference steps slider. Default is: 10")
|
|
|
|
st.session_state["defaults"].img2img.num_inference_steps.max_value = st.number_input("Maximum Sampling Steps",
|
|
value=st.session_state['defaults'].img2img.num_inference_steps.max_value,
|
|
help="Set the default maximum value for the inference steps slider. Default is: 500")
|
|
|
|
st.session_state["defaults"].img2img.num_inference_steps.step = st.number_input("Inference Slider Steps",
|
|
value=st.session_state['defaults'].img2img.num_inference_steps.step,
|
|
help="Set the default value for the number of steps on the inference steps slider.\
|
|
Default is: 10")
|
|
|
|
# Find Noise Steps
|
|
st.session_state["defaults"].img2img.find_noise_steps.value = st.number_input("Default Find Noise Steps",
|
|
value=st.session_state['defaults'].img2img.find_noise_steps.value,
|
|
help="Set the default number of find noise steps to use. Default is: 100")
|
|
|
|
st.session_state["defaults"].img2img.find_noise_steps.min_value = st.number_input("Minimum Find Noise Steps",
|
|
value=st.session_state['defaults'].img2img.find_noise_steps.min_value,
|
|
help="Set the default minimum value for the find noise steps slider. Default is: 0")
|
|
|
|
st.session_state["defaults"].img2img.find_noise_steps.max_value = st.number_input("Maximum Find Noise Steps",
|
|
value=st.session_state['defaults'].img2img.find_noise_steps.max_value,
|
|
help="Set the default maximum value for the find noise steps slider. Default is: 500")
|
|
|
|
st.session_state["defaults"].img2img.find_noise_steps.step = st.number_input("Find Noise Slider Steps",
|
|
value=st.session_state['defaults'].img2img.find_noise_steps.step,
|
|
help="Set the default value for the number of steps on the find noise steps slider. \
|
|
Default is: 10")
|
|
|
|
with col5:
|
|
st.title("General Parameters")
|
|
|
|
default_sampler_list = ["k_lms", "k_euler", "k_euler_a", "k_dpm_2", "k_dpm_2_a", "k_heun", "PLMS", "DDIM"]
|
|
st.session_state["defaults"].img2img.sampler_name = st.selectbox("Default Img2Img Sampler", default_sampler_list,
|
|
index=default_sampler_list.index(st.session_state['defaults'].img2img.sampler_name),
|
|
help="Defaut sampler to use for img2img. Default: k_euler")
|
|
|
|
st.session_state['defaults'].img2img.seed = st.text_input("Default Img2Img Seed", value=st.session_state['defaults'].img2img.seed, help="Default seed.")
|
|
|
|
st.session_state["defaults"].img2img.separate_prompts = st.checkbox("Separate Img2Img Prompts", value=st.session_state['defaults'].img2img.separate_prompts,
|
|
help="Separate Prompts. Default: False")
|
|
|
|
st.session_state["defaults"].img2img.normalize_prompt_weights = st.checkbox("Normalize Img2Img Prompt Weights",
|
|
value=st.session_state['defaults'].img2img.normalize_prompt_weights,
|
|
help="Choose to normalize prompt weights. Default: True")
|
|
|
|
st.session_state["defaults"].img2img.save_individual_images = st.checkbox("Save Individual Img2Img Images",
|
|
value=st.session_state['defaults'].img2img.save_individual_images,
|
|
help="Choose to save individual images. Default: True")
|
|
|
|
st.session_state["defaults"].img2img.save_grid = st.checkbox("Save Img2Img Grid Images",
|
|
value=st.session_state['defaults'].img2img.save_grid, help="Choose to save the grid images. Default: True")
|
|
|
|
st.session_state["defaults"].img2img.group_by_prompt = st.checkbox("Group By Img2Img Prompt",
|
|
value=st.session_state['defaults'].img2img.group_by_prompt,
|
|
help="Choose to save images grouped by their prompt. Default: False")
|
|
|
|
st.session_state["defaults"].img2img.save_as_jpg = st.checkbox("Save Img2Img As JPG", value=st.session_state['defaults'].img2img.save_as_jpg,
|
|
help="Choose to save images as jpegs. Default: False")
|
|
|
|
st.session_state["defaults"].img2img.write_info_files = st.checkbox("Write Info Files For Img2Img Images",
|
|
value=st.session_state['defaults'].img2img.write_info_files,
|
|
help="Choose to write the info files along with the generated images. Default: True")
|
|
|
|
st.session_state["defaults"].img2img.use_GFPGAN = st.checkbox("Img2Img Use GFPGAN", value=st.session_state['defaults'].img2img.use_GFPGAN, help="Choose to use GFPGAN. Default: False")
|
|
|
|
st.session_state["defaults"].img2img.use_RealESRGAN = st.checkbox("Img2Img Use RealESRGAN", value=st.session_state['defaults'].img2img.use_RealESRGAN,
|
|
help="Choose to use RealESRGAN. Default: False")
|
|
|
|
st.session_state["defaults"].img2img.update_preview = True
|
|
st.session_state["defaults"].img2img.update_preview_frequency = st.number_input("Img2Img Preview Image Update Frequency",
|
|
value=st.session_state['defaults'].img2img.update_preview_frequency,
|
|
help="Set the default value for the frrquency of the preview image updates. Default is: 10")
|
|
|
|
st.title("Variation Parameters")
|
|
|
|
st.session_state["defaults"].img2img.variant_amount = st.number_input("Default Img2Img Variation Amount",
|
|
value=st.session_state['defaults'].img2img.variant_amount,
|
|
help="Set the default variation to use. Default is: 0.0")
|
|
|
|
# I THINK THESE ARE MISSING FROM THE CONFIG FILE
|
|
# st.session_state["defaults"].img2img.variant_amount.min_value = st.number_input("Minimum Img2Img Variation Amount",
|
|
# value=st.session_state['defaults'].img2img.variant_amount.min_value, help="Set the default minimum value for the variation slider. Default is: 0.0"))
|
|
|
|
# st.session_state["defaults"].img2img.variant_amount.max_value = st.number_input("Maximum Img2Img Variation Amount",
|
|
# value=st.session_state['defaults'].img2img.variant_amount.max_value, help="Set the default maximum value for the variation slider. Default is: 1.0"))
|
|
|
|
# st.session_state["defaults"].img2img.variant_amount.step = st.number_input("Img2Img Variation Slider Steps",
|
|
# value=st.session_state['defaults'].img2img.variant_amount.step, help="Set the default value for the number of steps on the variation slider. Default is: 1"))
|
|
|
|
st.session_state['defaults'].img2img.variant_seed = st.text_input("Default Img2Img Variation Seed",
|
|
value=st.session_state['defaults'].img2img.variant_seed, help="Default variation seed.")
|
|
|
|
with img2txt_tab:
|
|
col1 = st.columns(1, gap="large")
|
|
|
|
st.title("Image-To-Text")
|
|
|
|
st.session_state["defaults"].img2txt.batch_size = st.number_input("Default Img2Txt Batch Size", value=st.session_state['defaults'].img2txt.batch_size,
|
|
help="Set the default batch size for Img2Txt. Default is: 420?")
|
|
|
|
st.session_state["defaults"].img2txt.blip_image_eval_size = st.number_input("Default Blip Image Size Evaluation",
|
|
value=st.session_state['defaults'].img2txt.blip_image_eval_size,
|
|
help="Set the default value for the blip image evaluation size. Default is: 512")
|
|
|
|
with txt2vid_tab:
|
|
col1, col2, col3, col4, col5 = st.columns(5, gap="medium")
|
|
|
|
with col1:
|
|
st.title("Slider Parameters")
|
|
|
|
# Width
|
|
st.session_state["defaults"].txt2vid.width.value = st.number_input("Default txt2vid Image Width",
|
|
value=st.session_state['defaults'].txt2vid.width.value,
|
|
help="Set the default width for the generated image. Default is: 512")
|
|
|
|
st.session_state["defaults"].txt2vid.width.min_value = st.number_input("Minimum txt2vid Image Width",
|
|
value=st.session_state['defaults'].txt2vid.width.min_value,
|
|
help="Set the default minimum value for the width slider. Default is: 64")
|
|
|
|
st.session_state["defaults"].txt2vid.width.max_value = st.number_input("Maximum txt2vid Image Width",
|
|
value=st.session_state['defaults'].txt2vid.width.max_value,
|
|
help="Set the default maximum value for the width slider. Default is: 2048")
|
|
|
|
# Height
|
|
st.session_state["defaults"].txt2vid.height.value = st.number_input("Default txt2vid Image Height",
|
|
value=st.session_state['defaults'].txt2vid.height.value,
|
|
help="Set the default height for the generated image. Default is: 512")
|
|
|
|
st.session_state["defaults"].txt2vid.height.min_value = st.number_input("Minimum txt2vid Image Height",
|
|
value=st.session_state['defaults'].txt2vid.height.min_value,
|
|
help="Set the default minimum value for the height slider. Default is: 64")
|
|
|
|
st.session_state["defaults"].txt2vid.height.max_value = st.number_input("Maximum txt2vid Image Height",
|
|
value=st.session_state['defaults'].txt2vid.height.max_value,
|
|
help="Set the default maximum value for the height slider. Default is: 2048")
|
|
|
|
# CFG
|
|
st.session_state["defaults"].txt2vid.cfg_scale.value = st.number_input("Default txt2vid CFG Scale",
|
|
value=st.session_state['defaults'].txt2vid.cfg_scale.value,
|
|
help="Set the default value for the CFG Scale. Default is: 7.5")
|
|
|
|
st.session_state["defaults"].txt2vid.cfg_scale.min_value = st.number_input("Minimum txt2vid CFG Scale Value",
|
|
value=st.session_state['defaults'].txt2vid.cfg_scale.min_value,
|
|
help="Set the default minimum value for the CFG scale slider. Default is: 1")
|
|
|
|
st.session_state["defaults"].txt2vid.cfg_scale.max_value = st.number_input("Maximum txt2vid CFG Scale Value",
|
|
value=st.session_state['defaults'].txt2vid.cfg_scale.max_value,
|
|
help="Set the default maximum value for the CFG scale slider. Default is: 30")
|
|
|
|
st.session_state["defaults"].txt2vid.cfg_scale.step = st.number_input("txt2vid CFG Slider Steps",
|
|
value=st.session_state['defaults'].txt2vid.cfg_scale.step,
|
|
help="Set the default value for the number of steps on the CFG scale slider. Default is: 0.5")
|
|
|
|
with col2:
|
|
# Sampling Steps
|
|
st.session_state["defaults"].txt2vid.sampling_steps.value = st.number_input("Default txt2vid Sampling Steps",
|
|
value=st.session_state['defaults'].txt2vid.sampling_steps.value,
|
|
help="Set the default number of sampling steps to use. Default is: 30 (with k_euler)")
|
|
|
|
st.session_state["defaults"].txt2vid.sampling_steps.min_value = st.number_input("Minimum txt2vid Sampling Steps",
|
|
value=st.session_state['defaults'].txt2vid.sampling_steps.min_value,
|
|
help="Set the default minimum value for the sampling steps slider. Default is: 1")
|
|
|
|
st.session_state["defaults"].txt2vid.sampling_steps.step = st.number_input("txt2vid Sampling Slider Steps",
|
|
value=st.session_state['defaults'].txt2vid.sampling_steps.step,
|
|
help="Set the default value for the number of steps on the sampling steps slider. Default is: 10")
|
|
|
|
# Batch Count
|
|
st.session_state["defaults"].txt2vid.batch_count.value = st.number_input("txt2vid Batch count", value=st.session_state['defaults'].txt2vid.batch_count.value,
|
|
help="How many iterations or batches of images to generate in total.")
|
|
|
|
st.session_state["defaults"].txt2vid.batch_size.value = st.number_input("txt2vid Batch size", value=st.session_state.defaults.txt2vid.batch_size.value,
|
|
help="How many images are at once in a batch.\
|
|
It increases the VRAM usage a lot but if you have enough VRAM it can reduce the time it \
|
|
takes to finish generation as more images are generated at once.\
|
|
Default: 1")
|
|
|
|
# Inference Steps
|
|
st.session_state["defaults"].txt2vid.num_inference_steps.value = st.number_input("Default Txt2Vid Inference Steps",
|
|
value=st.session_state['defaults'].txt2vid.num_inference_steps.value,
|
|
help="Set the default number of inference steps to use. Default is: 200")
|
|
|
|
st.session_state["defaults"].txt2vid.num_inference_steps.min_value = st.number_input("Minimum Txt2Vid Sampling Steps",
|
|
value=st.session_state['defaults'].txt2vid.num_inference_steps.min_value,
|
|
help="Set the default minimum value for the inference steps slider. Default is: 10")
|
|
|
|
st.session_state["defaults"].txt2vid.num_inference_steps.max_value = st.number_input("Maximum Txt2Vid Sampling Steps",
|
|
value=st.session_state['defaults'].txt2vid.num_inference_steps.max_value,
|
|
help="Set the default maximum value for the inference steps slider. Default is: 500")
|
|
st.session_state["defaults"].txt2vid.num_inference_steps.step = st.number_input("Txt2Vid Inference Slider Steps",
|
|
value=st.session_state['defaults'].txt2vid.num_inference_steps.step,
|
|
help="Set the default value for the number of steps on the inference steps slider. Default is: 10")
|
|
|
|
with col3:
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st.title("General Parameters")
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st.session_state['defaults'].txt2vid.default_model = st.text_input("Default Txt2Vid Model", value=st.session_state['defaults'].txt2vid.default_model,
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help="Default: CompVis/stable-diffusion-v1-4")
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# INSERT CUSTOM_MODELS_LIST HERE
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default_sampler_list = ["k_lms", "k_euler", "k_euler_a", "k_dpm_2", "k_dpm_2_a", "k_heun", "PLMS", "DDIM"]
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st.session_state["defaults"].txt2vid.default_sampler = st.selectbox("Default txt2vid Sampler", default_sampler_list,
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index=default_sampler_list.index(st.session_state['defaults'].txt2vid.default_sampler),
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help="Defaut sampler to use for txt2vid. Default: k_euler")
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st.session_state['defaults'].txt2vid.seed = st.text_input("Default txt2vid Seed", value=st.session_state['defaults'].txt2vid.seed, help="Default seed.")
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st.session_state['defaults'].txt2vid.scheduler_name = st.text_input("Default Txt2Vid Scheduler",
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value=st.session_state['defaults'].txt2vid.scheduler_name, help="Default scheduler.")
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st.session_state["defaults"].txt2vid.separate_prompts = st.checkbox("Separate txt2vid Prompts",
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value=st.session_state['defaults'].txt2vid.separate_prompts, help="Separate Prompts. Default: False")
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st.session_state["defaults"].txt2vid.normalize_prompt_weights = st.checkbox("Normalize txt2vid Prompt Weights",
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value=st.session_state['defaults'].txt2vid.normalize_prompt_weights,
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help="Choose to normalize prompt weights. Default: True")
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st.session_state["defaults"].txt2vid.save_individual_images = st.checkbox("Save Individual txt2vid Images",
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value=st.session_state['defaults'].txt2vid.save_individual_images,
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help="Choose to save individual images. Default: True")
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st.session_state["defaults"].txt2vid.save_video = st.checkbox("Save Txt2Vid Video", value=st.session_state['defaults'].txt2vid.save_video,
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help="Choose to save the Txt2Vid video. Default: True")
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st.session_state["defaults"].txt2vid.group_by_prompt = st.checkbox("Group By txt2vid Prompt", value=st.session_state['defaults'].txt2vid.group_by_prompt,
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help="Choose to save images grouped by their prompt. Default: False")
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st.session_state["defaults"].txt2vid.save_as_jpg = st.checkbox("Save txt2vid As JPG", value=st.session_state['defaults'].txt2vid.save_as_jpg,
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help="Choose to save images as jpegs. Default: False")
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# Need more info for the Help dialog...
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st.session_state["defaults"].txt2vid.do_loop = st.checkbox("Loop Generations", value=st.session_state['defaults'].txt2vid.do_loop,
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help="Choose to loop or something, IDK.... Default: False")
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st.session_state["defaults"].txt2vid.max_frames = st.number_input("Txt2Vid Max Video Frames", value=st.session_state['defaults'].txt2vid.max_frames,
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help="Set the default value for the number of video frames generated. Default is: 100")
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st.session_state["defaults"].txt2vid.write_info_files = st.checkbox("Write Info Files For txt2vid Images", value=st.session_state['defaults'].txt2vid.write_info_files,
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help="Choose to write the info files along with the generated images. Default: True")
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st.session_state["defaults"].txt2vid.use_GFPGAN = st.checkbox("txt2vid Use GFPGAN", value=st.session_state['defaults'].txt2vid.use_GFPGAN,
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help="Choose to use GFPGAN. Default: False")
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st.session_state["defaults"].txt2vid.use_RealESRGAN = st.checkbox("txt2vid Use RealESRGAN", value=st.session_state['defaults'].txt2vid.use_RealESRGAN,
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help="Choose to use RealESRGAN. Default: False")
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st.session_state["defaults"].txt2vid.update_preview = True
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st.session_state["defaults"].txt2vid.update_preview_frequency = st.number_input("txt2vid Preview Image Update Frequency",
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value=st.session_state['defaults'].txt2vid.update_preview_frequency,
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help="Set the default value for the frrquency of the preview image updates. Default is: 10")
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with col4:
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st.title("Variation Parameters")
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st.session_state["defaults"].txt2vid.variant_amount.value = st.number_input("Default txt2vid Variation Amount",
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value=st.session_state['defaults'].txt2vid.variant_amount.value,
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help="Set the default variation to use. Default is: 0.0")
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st.session_state["defaults"].txt2vid.variant_amount.min_value = st.number_input("Minimum txt2vid Variation Amount",
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value=st.session_state['defaults'].txt2vid.variant_amount.min_value,
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help="Set the default minimum value for the variation slider. Default is: 0.0")
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st.session_state["defaults"].txt2vid.variant_amount.max_value = st.number_input("Maximum txt2vid Variation Amount",
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value=st.session_state['defaults'].txt2vid.variant_amount.max_value,
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help="Set the default maximum value for the variation slider. Default is: 1.0")
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st.session_state["defaults"].txt2vid.variant_amount.step = st.number_input("txt2vid Variation Slider Steps",
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value=st.session_state['defaults'].txt2vid.variant_amount.step,
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help="Set the default value for the number of steps on the variation slider. Default is: 1")
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st.session_state['defaults'].txt2vid.variant_seed = st.text_input("Default txt2vid Variation Seed",
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value=st.session_state['defaults'].txt2vid.variant_seed, help="Default variation seed.")
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with col5:
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st.title("Beta Parameters")
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# Beta Start
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st.session_state["defaults"].txt2vid.beta_start.value = st.number_input("Default txt2vid Beta Start Value",
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value=st.session_state['defaults'].txt2vid.beta_start.value,
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help="Set the default variation to use. Default is: 0.0")
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st.session_state["defaults"].txt2vid.beta_start.min_value = st.number_input("Minimum txt2vid Beta Start Amount",
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value=st.session_state['defaults'].txt2vid.beta_start.min_value,
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help="Set the default minimum value for the variation slider. Default is: 0.0")
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st.session_state["defaults"].txt2vid.beta_start.max_value = st.number_input("Maximum txt2vid Beta Start Amount",
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value=st.session_state['defaults'].txt2vid.beta_start.max_value,
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|
help="Set the default maximum value for the variation slider. Default is: 1.0")
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st.session_state["defaults"].txt2vid.beta_start.step = st.number_input("txt2vid Beta Start Slider Steps", value=st.session_state['defaults'].txt2vid.beta_start.step,
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|
help="Set the default value for the number of steps on the variation slider. Default is: 1")
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st.session_state["defaults"].txt2vid.beta_start.format = st.text_input("Default txt2vid Beta Start Format", value=st.session_state['defaults'].txt2vid.beta_start.format,
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help="Set the default Beta Start Format. Default is: %.5\f")
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# Beta End
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st.session_state["defaults"].txt2vid.beta_end.value = st.number_input("Default txt2vid Beta End Value", value=st.session_state['defaults'].txt2vid.beta_end.value,
|
|
help="Set the default variation to use. Default is: 0.0")
|
|
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|
st.session_state["defaults"].txt2vid.beta_end.min_value = st.number_input("Minimum txt2vid Beta End Amount", value=st.session_state['defaults'].txt2vid.beta_end.min_value,
|
|
help="Set the default minimum value for the variation slider. Default is: 0.0")
|
|
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st.session_state["defaults"].txt2vid.beta_end.max_value = st.number_input("Maximum txt2vid Beta End Amount", value=st.session_state['defaults'].txt2vid.beta_end.max_value,
|
|
help="Set the default maximum value for the variation slider. Default is: 1.0")
|
|
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st.session_state["defaults"].txt2vid.beta_end.step = st.number_input("txt2vid Beta End Slider Steps", value=st.session_state['defaults'].txt2vid.beta_end.step,
|
|
help="Set the default value for the number of steps on the variation slider. Default is: 1")
|
|
|
|
st.session_state["defaults"].txt2vid.beta_end.format = st.text_input("Default txt2vid Beta End Format", value=st.session_state['defaults'].txt2vid.beta_start.format,
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|
help="Set the default Beta Start Format. Default is: %.5\f")
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with image_processing:
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|
col1, col2, col3, col4, col5 = st.columns(5, gap="large")
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with col1:
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st.title("GFPGAN")
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|
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st.session_state["defaults"].gfpgan.strength = st.number_input("Default Img2Txt Batch Size", value=st.session_state['defaults'].gfpgan.strength,
|
|
help="Set the default global strength for GFPGAN. Default is: 100")
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|
with col2:
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|
st.title("GoBig")
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|
with col3:
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|
st.title("RealESRGAN")
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|
with col4:
|
|
st.title("LDSR")
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|
with col5:
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|
st.title("GoLatent")
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|
|
with textual_inversion_tab:
|
|
st.title("Textual Inversion")
|
|
|
|
st.session_state['defaults'].textual_inversion.pretrained_model_name_or_path = st.text_input("Default Textual Inversion Model Path",
|
|
value=st.session_state['defaults'].textual_inversion.pretrained_model_name_or_path,
|
|
help="Default: models/ldm/stable-diffusion-v1-4")
|
|
|
|
st.session_state['defaults'].textual_inversion.tokenizer_name = st.text_input("Default Img2Img Variation Seed", value=st.session_state['defaults'].textual_inversion.tokenizer_name,
|
|
help="Default tokenizer seed.")
|
|
|
|
with concepts_library_tab:
|
|
st.title("Concepts Library")
|
|
#st.info("Under Construction. :construction_worker:")
|
|
col1, col2, col3, col4, col5 = st.columns(5, gap='large')
|
|
with col1:
|
|
st.session_state["defaults"].concepts_library.concepts_per_page = st.number_input("Concepts Per Page", value=st.session_state['defaults'].concepts_library.concepts_per_page,
|
|
help="Number of concepts per page to show on the Concepts Library. Default: '12'")
|
|
|
|
# add space for the buttons at the bottom
|
|
st.markdown("---")
|
|
|
|
# We need a submit button to save the Settings
|
|
# as well as one to reset them to the defaults, just in case.
|
|
_, _, save_button_col, reset_button_col, _, _ = st.columns([1,1,1,1,1,1], gap="large")
|
|
with save_button_col:
|
|
save_button = st.form_submit_button("Save")
|
|
|
|
with reset_button_col:
|
|
reset_button = st.form_submit_button("Reset")
|
|
|
|
if save_button:
|
|
OmegaConf.save(config=st.session_state.defaults, f="configs/webui/userconfig_streamlit.yaml")
|
|
loaded = OmegaConf.load("configs/webui/userconfig_streamlit.yaml")
|
|
assert st.session_state.defaults == loaded
|
|
|
|
#
|
|
if (os.path.exists(".streamlit/config.toml")):
|
|
with open(".streamlit/config.toml", "w") as toml_file:
|
|
toml.dump(st.session_state["streamlit_config"], toml_file)
|
|
|
|
if reset_button:
|
|
st.session_state["defaults"] = OmegaConf.load("configs/webui/webui_streamlit.yaml") |