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sygil-webui/scripts/Settings.py

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First implementation of the Settings page.
2022-09-24 15:30:16 +03:00
# base webui import and utils.
Modified the way the defaults settings are loaded from the config file so we only load them on the webui_strealit.py file and use st.session_state to access them from anywhere else, this makes it so the config can be modified externally like before the code split and the changes will be updated on next rerun of the UI.
2022-09-14 16:40:56 +03:00
from webui_streamlit import st
First implementation of the Settings page.
2022-09-24 15:30:16 +03:00
from sd_utils import *
-Added the Update Image Preview option to be part of the current tab options under Preview Settings. - Added Dynamic Preview Frequency option for the txt2vid tab which tries to find the lowest value for update_preview_frequency at which we can update the preview image during generation while at the same time minimizing the impact it has in performance. - Added option to save a video file on the outputs/txt2vid-samples folder after the generation is complete similar to how the save_grid option works on other tabs. - Added a video preview which shows a video on the txt2vid tab when the generation is completed. - Formated some lines of code to make it use less space and fit on the a single screen. - Added a script called Settings.py to the script folder in which Settings for the Setting page will be placed. Empty for now.
2022-09-12 21:44:00 +03:00
First implementation of the Settings page.
2022-09-24 15:30:16 +03:00
# streamlit imports
#streamlit components section
import streamlit_nested_layout
#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 = st.tabs(['General', "Text-To-Image",
"Image-To-Image", "Text-To-Video",
"Textual Inversion"])
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 st.session_state.CustomModel_available:
st.session_state.default_model = st.selectbox("Default Model:", st.session_state.custom_models,
index=st.session_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.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")
with txt2img_tab:
st.title("Text To Image")
with img2img_tab:
st.title("Image To Image")
with txt2vid_tab:
st.title("Text To Video")
with textual_inversion_tab:
st.title("Textual Inversion")
# 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:
#userconfig_streamlit = OmegaConf.to_yaml(st.session_state.defaults)
#OmegaConf.save("configs/webui/userconfig_streamlit.yaml")
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 reset_button:
st.session_state["defaults"] = OmegaConf.load("configs/webui/webui_streamlit.yaml")
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