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https://github.com/sd-webui/stable-diffusion-webui.git
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35ec438682
Added option on the settings page to add your huggingface token. Daisi app fix 3 Daisi app fix 2 Daisi app fix
259 lines
17 KiB
Python
259 lines
17 KiB
Python
# base webui import and utils.
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from webui_streamlit import st
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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, \
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txt2vid_tab, textual_inversion_tab, concepts_library_tab = st.tabs(['General', "Text-To-Image",
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"Image-To-Image", "Text-To-Video",
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"Textual Inversion",
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"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 st.session_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: './src/gfpgan'")
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st.session_state['defaults'].general.RealESRGAN_dir = st.text_input("Default RealESRGAN directory", value=st.session_state['defaults'].general.RealESRGAN_dir,
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help="Default GFPGAN directory. Default: './src/realesrgan'")
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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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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. Default: True")
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if st.session_state["defaults"].general.update_preview:
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st.session_state["defaults"].general.update_preview_frequency = int(st.text_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: './src/latent-diffusion'")
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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 = int(st.text_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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st.title("Text To Image")
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st.info("Under Construction. :construction_worker:")
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with img2img_tab:
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st.title("Image To Image")
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st.info("Under Construction. :construction_worker:")
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with txt2vid_tab:
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st.title("Text To Video")
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st.info("Under Construction. :construction_worker:")
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with textual_inversion_tab:
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st.title("Textual Inversion")
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st.info("Under Construction. :construction_worker:")
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with concepts_library_tab:
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st.title("Concepts Library")
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#st.info("Under Construction. :construction_worker:")
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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.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,
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help="Number of concepts per page to show on the Concepts Library. Default: '12'"))
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# add space for the buttons at the bottom
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st.markdown("---")
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# We need a submit button to save the Settings
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# as well as one to reset them to the defaults, just in case.
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_, _, save_button_col, reset_button_col, _, _ = st.columns([1,1,1,1,1,1], gap="large")
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with save_button_col:
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save_button = st.form_submit_button("Save")
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with reset_button_col:
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reset_button = st.form_submit_button("Reset")
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if save_button:
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OmegaConf.save(config=st.session_state.defaults, f="configs/webui/userconfig_streamlit.yaml")
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loaded = OmegaConf.load("configs/webui/userconfig_streamlit.yaml")
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assert st.session_state.defaults == loaded
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#
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if (os.path.exists(".streamlit/config.toml")):
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with open(".streamlit/config.toml", "w") as toml_file:
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toml.dump(st.session_state["streamlit_config"], toml_file)
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if reset_button:
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st.session_state["defaults"] = OmegaConf.load("configs/webui/webui_streamlit.yaml") |