# This file is part of stable-diffusion-webui (https://github.com/sd-webui/stable-diffusion-webui/). # Copyright 2022 sd-webui team. # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU Affero General Public License as published by # the Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Affero General Public License for more details. # You should have received a copy of the GNU Affero General Public License # along with this program. If not, see . # base webui import and utils. from sd_utils import * # streamlit imports #streamlit components section import streamlit_nested_layout from streamlit_server_state import server_state, server_state_lock #other imports from omegaconf import OmegaConf # end of imports #--------------------------------------------------------------------------------------------------------------- def layout(): st.header("Settings") with st.form("Settings"): general_tab, txt2img_tab, img2img_tab, \ txt2vid_tab, textual_inversion_tab, concepts_library_tab = st.tabs(['General', "Text-To-Image", "Image-To-Image", "Text-To-Video", "Textual Inversion", "Concepts Library"]) with general_tab: col1, col2, col3, col4, col5 = st.columns(5, gap='large') device_list = [] device_properties = [(i, torch.cuda.get_device_properties(i)) for i in range(torch.cuda.device_count())] for device in device_properties: id = device[0] name = device[1].name total_memory = device[1].total_memory device_list.append(f"{id}: {name} ({human_readable_size(total_memory, decimal_places=0)})") with col1: st.title("General") st.session_state['defaults'].general.gpu = int(st.selectbox("GPU", device_list, help=f"Select which GPU to use. Default: {device_list[0]}").split(":")[0]) st.session_state['defaults'].general.outdir = str(st.text_input("Output directory", value=st.session_state['defaults'].general.outdir, help="Relative directory on which the output images after a generation will be placed. Default: 'outputs'")) # If we have custom models available on the "models/custom" # folder then we show a menu to select which model we want to use, otherwise we use the main model for SD custom_models_available() if server_state["CustomModel_available"]: st.session_state.default_model = st.selectbox("Default Model:", server_state["custom_models"], index=server_state["custom_models"].index(st.session_state['defaults'].general.default_model), help="Select the model you want to use. If you have placed custom models \ on your 'models/custom' folder they will be shown here as well. The model name that will be shown here \ is the same as the name the file for the model has on said folder, \ it is recommended to give the .ckpt file a name that \ will make it easier for you to distinguish it from other models. Default: Stable Diffusion v1.4") else: st.session_state.default_model = st.selectbox("Default Model:", [st.session_state['defaults'].general.default_model], help="Select the model you want to use. If you have placed custom models \ on your 'models/custom' folder they will be shown here as well. \ The model name that will be shown here is the same as the name\ the file for the model has on said folder, it is recommended to give the .ckpt file a name that \ will make it easier for you to distinguish it from other models. Default: Stable Diffusion v1.4") st.session_state['defaults'].general.default_model_config = st.text_input("Default Model Config", value=st.session_state['defaults'].general.default_model_config, help="Default model config file for inference. Default: 'configs/stable-diffusion/v1-inference.yaml'") st.session_state['defaults'].general.default_model_path = st.text_input("Default Model Config", value=st.session_state['defaults'].general.default_model_path, help="Default model path. Default: 'models/ldm/stable-diffusion-v1/model.ckpt'") st.session_state['defaults'].general.GFPGAN_dir = st.text_input("Default GFPGAN directory", value=st.session_state['defaults'].general.GFPGAN_dir, help="Default GFPGAN directory. Default: './src/gfpgan'") st.session_state['defaults'].general.RealESRGAN_dir = st.text_input("Default RealESRGAN directory", value=st.session_state['defaults'].general.RealESRGAN_dir, help="Default GFPGAN directory. Default: './src/realesrgan'") RealESRGAN_model_list = ["RealESRGAN_x4plus", "RealESRGAN_x4plus_anime_6B"] st.session_state['defaults'].general.RealESRGAN_model = st.selectbox("RealESRGAN model", RealESRGAN_model_list, index=RealESRGAN_model_list.index(st.session_state['defaults'].general.RealESRGAN_model), help="Default RealESRGAN model. Default: 'RealESRGAN_x4plus'") with col2: st.title("Performance") st.session_state["defaults"].general.gfpgan_cpu = st.checkbox("GFPGAN - CPU", value=st.session_state['defaults'].general.gfpgan_cpu, help="Run GFPGAN on the cpu. Default: False") st.session_state["defaults"].general.esrgan_cpu = st.checkbox("ESRGAN - CPU", value=st.session_state['defaults'].general.esrgan_cpu, help="Run ESRGAN on the cpu. Default: False") st.session_state["defaults"].general.extra_models_cpu = st.checkbox("Extra Models - CPU", value=st.session_state['defaults'].general.extra_models_cpu, help="Run extra models (GFGPAN/ESRGAN) on cpu. Default: False") st.session_state["defaults"].general.extra_models_gpu = st.checkbox("Extra Models - GPU", value=st.session_state['defaults'].general.extra_models_gpu, help="Run extra models (GFGPAN/ESRGAN) on gpu. \ Check and save in order to be able to select the GPU that each model will use. Default: False") if st.session_state["defaults"].general.extra_models_gpu: st.session_state['defaults'].general.gfpgan_gpu = int(st.selectbox("GFGPAN GPU", device_list, index=st.session_state['defaults'].general.gfpgan_gpu, help=f"Select which GPU to use. Default: {device_list[st.session_state['defaults'].general.gfpgan_gpu]}", key="gfpgan_gpu").split(":")[0]) st.session_state["defaults"].general.esrgan_gpu = int(st.selectbox("ESRGAN - GPU", device_list, index=st.session_state['defaults'].general.esrgan_gpu, help=f"Select which GPU to use. Default: {device_list[st.session_state['defaults'].general.esrgan_gpu]}", key="esrgan_gpu").split(":")[0]) st.session_state["defaults"].general.no_half = st.checkbox("No Half", value=st.session_state['defaults'].general.no_half, help="DO NOT switch the model to 16-bit floats. Default: False") st.session_state["defaults"].general.use_float16 = st.checkbox("Use float16", value=st.session_state['defaults'].general.use_float16, help="Switch the model to 16-bit floats. Default: False") precision_list = ['full','autocast'] st.session_state["defaults"].general.precision = st.selectbox("Precision", precision_list, index=precision_list.index(st.session_state['defaults'].general.precision), help="Evaluates at this precision. Default: autocast") st.session_state["defaults"].general.optimized = st.checkbox("Optimized Mode", value=st.session_state['defaults'].general.optimized, help="Loads the model onto the device piecemeal instead of all at once to reduce VRAM usage\ at the cost of performance. Default: False") st.session_state["defaults"].general.optimized_turbo = st.checkbox("Optimized Turbo Mode", value=st.session_state['defaults'].general.optimized_turbo, help="Alternative optimization mode that does not save as much VRAM but \ runs siginificantly faster. Default: False") st.session_state["defaults"].general.optimized_config = st.text_input("Optimized Config", value=st.session_state['defaults'].general.optimized_config, help=f"Loads alternative optimized configuration for inference. \ Default: optimizedSD/v1-inference.yaml") st.session_state["defaults"].general.enable_attention_slicing = st.checkbox("Enable Attention Slicing", value=st.session_state['defaults'].general.enable_attention_slicing, help="Enable sliced attention computation. When this option is enabled, the attention module will \ split the input tensor in slices, to compute attention in several steps. This is useful to save some \ memory in exchange for a small speed decrease. Only works the txt2vid tab right now. Default: False") 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, help="Moves only unet to fp16 and to CUDA, while keepping lighter models on CPUs \ (Not properly implemented and currently not working, check this \ link 'https://github.com/huggingface/diffusers/pull/537' for more information on it ). Default: False") st.session_state["defaults"].general.update_preview = st.checkbox("Update Preview Image", value=st.session_state['defaults'].general.update_preview, help="Enables the preview image to be updated and shown to the user on the UI during the generation.\ If checked, once you save the settings an option to specify the frequency at which the image is updated\ in steps will be shown, this is helpful to reduce the negative effect this option has on performance. Default: True") if st.session_state["defaults"].general.update_preview: st.session_state["defaults"].general.update_preview_frequency = int(st.text_input("Update Preview Frequency", value=st.session_state['defaults'].general.update_preview_frequency, help="Specify the frequency at which the image is updated in steps, this is helpful to reduce the \ negative effect updating the preview image has on performance. Default: 10")) with col3: st.title("Others") 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, help="Use the embeds Concepts Library, if checked, once the settings are saved an option will\ appear to specify the directory where the concepts are stored. Default: True)") if st.session_state["defaults"].general.use_sd_concepts_library: st.session_state['defaults'].general.sd_concepts_library_folder = st.text_input("Concepts Library Folder", value=st.session_state['defaults'].general.sd_concepts_library_folder, help="Relative folder on which the concepts library embeds are stored. \ Default: 'models/custom/sd-concepts-library'") st.session_state['defaults'].general.LDSR_dir = st.text_input("LDSR Folder", value=st.session_state['defaults'].general.LDSR_dir, help="Folder where LDSR is located. Default: './src/latent-diffusion'") st.session_state["defaults"].general.save_metadata = st.checkbox("Save Metadata", value=st.session_state['defaults'].general.save_metadata, help="Save metadata on the output image. Default: True") save_format_list = ["png"] 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), help="Format that will be used whens saving the output images. Default: 'png'") st.session_state["defaults"].general.skip_grid = st.checkbox("Skip Grid", value=st.session_state['defaults'].general.skip_grid, help="Skip saving the grid output image. Default: False") if not st.session_state["defaults"].general.skip_grid: st.session_state["defaults"].general.grid_format = st.text_input("Grid Format", value=st.session_state['defaults'].general.grid_format, help="Format for saving the grid output image. Default: 'jpg:95'") st.session_state["defaults"].general.skip_save = st.checkbox("Skip Save", value=st.session_state['defaults'].general.skip_save, help="Skip saving the output image. Default: False") st.session_state["defaults"].general.n_rows = int(st.text_input("Number of Grid Rows", value=st.session_state['defaults'].general.n_rows, help="Number of rows the grid wil have when saving the grid output image. Default: '-1'")) st.session_state["defaults"].general.no_verify_input = st.checkbox("Do not Verify Input", value=st.session_state['defaults'].general.no_verify_input, help="Do not verify input to check if it's too long. Default: False") 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, help="Specify if we are running on app.Daisi.io . Default: False") with col4: st.title("Streamlit Config") st.session_state["defaults"].general.streamlit_telemetry = st.checkbox("Enable Telemetry", value=st.session_state['defaults'].general.streamlit_telemetry, help="Enables or Disables streamlit telemetry. Default: False") st.session_state["streamlit_config"]["browser"]["gatherUsageStats"] = st.session_state["defaults"].general.streamlit_telemetry default_theme_list = ["light", "dark"] 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), help="Defaut theme to use as base for streamlit. Default: dark") st.session_state["streamlit_config"]["theme"]["base"] = st.session_state["defaults"].general.default_theme with col5: st.title("Huggingface") st.session_state["defaults"].general.huggingface_token = st.text_input("Huggingface Token", value=st.session_state['defaults'].general.huggingface_token, type="password", help="Your Huggingface Token, it's used to download the model for the diffusers library which \ is used on the Text To Video tab. This token will be saved to your user config file\ and WILL NOT be share with us or anyone. You can get your access token \ at https://huggingface.co/settings/tokens. Default: None") with txt2img_tab: col1, col2, col3 = st.columns(3, gap='large') with col1: st.title("Slider Parameters") # Width st.session_state['defaults'].txt2img.width.value = st.session_state["defaults"].txt2img.width.value = int(st.text_input("Default Image Width", value=st.session_state['defaults'].txt2img.width.value, help="Set the default width for the generated image. Default is: 512")) st.session_state['defaults'].txt2img.width.min_value = st.session_state["defaults"].txt2img.width.min_value = int(st.text_input("Minimum Image Width", value=st.session_state['defaults'].txt2img.width.min_value, help="Set the default minimum value for the width slider. Default is: 64")) st.session_state['defaults'].txt2img.width.max_value = st.session_state["defaults"].txt2img.width.max_value = int(st.text_input("Maximum Image Width", value=st.session_state['defaults'].txt2img.width.max_value, help="Set the default maximum value for the width slider. Default is: 2048")) # Height st.session_state['defaults'].txt2img.height.value = st.session_state["defaults"].txt2img.height.value = int(st.text_input("Default Image Height", value=st.session_state['defaults'].txt2img.height.value, help="Set the default height for the generated image. Default is: 512")) st.session_state['defaults'].txt2img.height.min_value = st.session_state["defaults"].txt2img.height.min_value = int(st.text_input("Minimum Image Height", value=st.session_state['defaults'].txt2img.height.min_value, help="Set the default minimum value for the height slider. Default is: 64")) st.session_state['defaults'].txt2img.height.max_value = st.session_state["defaults"].txt2img.height.max_value = int(st.text_input("Maximum Image Height", value=st.session_state['defaults'].txt2img.height.max_value, help="Set the default maximum value for the height slider. Default is: 2048")) # CFG st.session_state['defaults'].txt2img.cfg_scale.value = st.session_state["defaults"].txt2img.cfg_scale.value = float(st.text_input("Default CFG Scale", value=st.session_state['defaults'].txt2img.cfg_scale.value, help="Set the default value for the CFG Scale. Default is: 7.5")) st.session_state['defaults'].txt2img.cfg_scale.min_value = st.session_state["defaults"].txt2img.cfg_scale.min_value = float(st.text_input("Minimum CFG Scale Value", value=st.session_state['defaults'].txt2img.cfg_scale.min_value, help="Set the default minimum value for the CFG scale slider. Default is: 1")) st.session_state['defaults'].txt2img.cfg_scale.max_value = st.session_state["defaults"].txt2img.cfg_scale.max_value = float(st.text_input("Maximum CFG Scale Value", value=st.session_state['defaults'].txt2img.cfg_scale.max_value, help="Set the default maximum value for the CFG scale slider. Default is: 30")) st.session_state['defaults'].txt2img.cfg_scale.step = st.session_state["defaults"].txt2img.cfg_scale.step = float(st.text_input("CFG Slider Steps", value=st.session_state['defaults'].txt2img.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'].txt2img.sampling_steps.value = st.session_state["defaults"].txt2img.sampling_steps.value = int(st.text_input("Default Sampling Steps", value=st.session_state['defaults'].txt2img.sampling_steps.value, help="Set the default number of sampling steps to use. Default is: 30 (with k_euler)")) st.session_state['defaults'].txt2img.sampling_steps.min_value = st.session_state["defaults"].txt2img.sampling_steps.min_value = int(st.text_input("Minimum Sampling Steps", value=st.session_state['defaults'].txt2img.sampling_steps.min_value, help="Set the default minimum value for the sampling steps slider. Default is: 1")) st.session_state['defaults'].txt2img.sampling_steps.max_value = st.session_state["defaults"].txt2img.sampling_steps.max_value = int(st.text_input("Maximum Sampling Steps", value=st.session_state['defaults'].txt2img.sampling_steps.max_value, help="Set the default maximum value for the sampling steps slider. Default is: 250")) st.session_state['defaults'].txt2img.sampling_steps.step = st.session_state["defaults"].txt2img.sampling_steps.step = int(st.text_input("Sampling Slider Steps", value=st.session_state['defaults'].txt2img.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'].txt2img.batch_count.value = st.session_state["defaults"].txt2img.batch_count.value = int(st.text_input("Default Batch Count", value=st.session_state['defaults'].txt2img.batch_count.value, help="Set the default batch count to use. Default is: 1")) st.session_state['defaults'].txt2img.batch_count.min_value = st.session_state["defaults"].txt2img.batch_count.min_value = int(st.text_input("Minimum Batch Count", value=st.session_state['defaults'].txt2img.batch_count.min_value, help="Set the default minimum value for the batch count slider. Default is: 1")) st.session_state['defaults'].txt2img.batch_count.max_value = st.session_state["defaults"].txt2img.batch_count.max_value = int(st.text_input("Maximum Batch Count", value=st.session_state['defaults'].txt2img.batch_count.max_value, help="Set the default maximum value for the batch count slider. Default is: 100")) st.session_state['defaults'].txt2img.batch_count.step = st.session_state["defaults"].txt2img.batch_count.step = int(st.text_input("Batch Count Slider Steps", value=st.session_state['defaults'].txt2img.batch_count.step, help="Set the default value for the number of steps on the batch count slider. Default is: 10")) # Batch Size st.session_state['defaults'].txt2img.batch_size.value = st.session_state["defaults"].txt2img.batch_size.value = int(st.text_input("Default Batch Size", value=st.session_state['defaults'].txt2img.batch_size.value, help="Set the default batch size to use. Default is: 1")) st.session_state['defaults'].txt2img.batch_size.min_value = st.session_state["defaults"].txt2img.batch_size.min_value = int(st.text_input("Minimum Batch Size", value=st.session_state['defaults'].txt2img.batch_size.min_value, help="Set the default minimum value for the batch size slider. Default is: 1")) st.session_state['defaults'].txt2img.batch_size.max_value = st.session_state["defaults"].txt2img.batch_size.max_value = int(st.text_input("Maximum Batch Size", value=st.session_state['defaults'].txt2img.batch_size.max_value, help="Set the default maximum value for the batch size slider. Default is: 5")) st.session_state['defaults'].txt2img.batch_size.step = st.session_state["defaults"].txt2img.batch_size.step = int(st.text_input("Batch Size Slider Steps", value=st.session_state['defaults'].txt2img.batch_size.step, help="Set the default value for the number of steps on the batch size slider. Default is: 1")) with col2: 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"].txt2img.default_sampler = st.selectbox("Default Sampler", default_sampler_list, index=default_sampler_list.index(st.session_state['defaults'].txt2img.default_sampler), help="Defaut sampler to use for txt2img. Default: k_euler") st.session_state['defaults'].txt2img.seed = st.text_input("Default Seed", value=st.session_state['defaults'].txt2img.seed, help="Default seed.") # THIS THROWS AN ERROR, I HAVE NO IDEA WHY THOUGH #st.session_state["defaults"].txt2img.separate_prompts = st.checkbox("Separate Prompts", value=st.session_state['defaults'].separate_prompts, help="Separate Prompts. Default: False") st.session_state["defaults"].txt2img.normalize_prompt_weights = st.checkbox("Normalize Prompt Weights", value=st.session_state['defaults'].txt2img.normalize_prompt_weights, help="Choose to normalize prompt weights. Default: True") st.session_state["defaults"].txt2img.save_individual_images = st.checkbox("Save Individual Images", value=st.session_state['defaults'].txt2img.save_individual_images, help="Choose to save individual images. Default: True") st.session_state["defaults"].txt2img.save_grid = st.checkbox("Save Grid Images", value=st.session_state['defaults'].txt2img.save_grid, help="Choose to save the grid images. Default: True") st.session_state["defaults"].txt2img.group_by_prompt = st.checkbox("Group By Prompt", value=st.session_state['defaults'].txt2img.group_by_prompt, help="Choose to save images grouped by their prompt. Default: False") st.session_state["defaults"].txt2img.save_as_jpg = st.checkbox("Save As JPG", value=st.session_state['defaults'].txt2img.save_as_jpg, help="Choose to save images as jpegs. Default: False") st.session_state["defaults"].txt2img.write_info_files = st.checkbox("Write Info Files For Images", value=st.session_state['defaults'].txt2img.write_info_files, help="Choose to write the info files along with the generated images. Default: True") 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") st.session_state["defaults"].txt2img.use_RealESRGAN = st.checkbox("Use RealESRGAN", value=st.session_state['defaults'].txt2img.use_RealESRGAN, help="Choose to use RealESRGAN. Default: False") st.session_state["defaults"].txt2img.update_preview = st.checkbox("Update Preview Image", value=st.session_state['defaults'].txt2img.update_preview, help="Choose to update the preview image during generation. Default: True") st.session_state['defaults'].txt2img.update_preview_frequency = st.session_state["defaults"].txt2img.update_preview_frequency = int(st.text_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 col3: st.title("Variation Parameters") st.session_state['defaults'].txt2img.variant_amount.value = st.session_state["defaults"].txt2img.variant_amount.value = float(st.text_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.session_state["defaults"].txt2img.variant_amount.min_value = float(st.text_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.session_state["defaults"].txt2img.variant_amount.max_value = float(st.text_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.session_state["defaults"].txt2img.variant_amount.step = float(st.text_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: st.title("Image To Image") st.info("Under Construction. :construction_worker:") with txt2vid_tab: st.title("Text To Video") st.info("Under Construction. :construction_worker:") with textual_inversion_tab: st.title("Textual Inversion") st.info("Under Construction. :construction_worker:") 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 = int(st.text_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")