import gradio as gr
import torch
import modin.pandas as pd
from PIL import Image
from diffusers import DiffusionPipeline
import os
import random
import torchsde
##########
#
# Original script by:
# https://huggingface.co/Manjushri
#
# This version have been adapted from that person's versions.
#
##########
# Tested with gradio version 4.8.0
# https://www.gradio.app/main/docs/interface
##########
# To launch this script, use the following in the command prompt, taking off
# the # at the start. (You will need to adjust the start of the path if you
# have changed the location)
#cd C:\Diffusers && .venv\Scripts\activate.bat && py .venv\sdxl_and_photoreal\sdxl_and_photoreal.py
##########
# IMPORTANT NOTES:
#
# You must have a NVIDIA graphics card in your computer with Cuda
# installed to use this script. It will not work on just a CPU on
# Windows.
#
# If not using "enable_model_cpu_offload" or
# "enable_sequential_cpu_offload", memory usage will remain high until
# command prompt is closed. (whether image is being created or not)
###############################################################################
###############################################################################
#
#
#
# Begin Configurations
#
#
#
###############################################################################
###############################################################################
#
# Main Directory
#
# This is where everything goes. Your Python virtual environment should
# be here. Model data will be stored here. (unless you change the next
# configuration) If configured, imagery will also be automatically be
# saved here.
#
main_dir = "C:/Diffusers"
####################
#
# Use Custom Hugging Face Cache Directory
#
# The folder where model data is stored can get huge. I choose to add it
# to a place where I am more likely to notice it more often. If you use
# other Hugging Face things however, and will use these models in those
# other things, then you might want to consider not having this here as
# it would duplicate the model data.
#
# If set to 1, the data would be here:
# C:\Diffusers\model_data
#
# If set to 0, the data would be here:
# %USERPROFILE%/.cache/huggingface/hub
# Which would look like this, where {Username} is the username of
# your Windows account:
# C:\Users\{Username}\.cache\huggingface\hub
#
# You need to clean out the folder occasionally as this folder will get
# extremely large. Eventually, it would take up all the space on your
# computer.
#
use_custom_hugging_face_cache_dir = 1
#####
#
# Name of Model Data Folder
#
# This is where all the model data will go. (unless you changed it in the
# previous configuration) This folder will get very large. You need to
# clean it out manually occasionally.
#
cache_directory_folder_name = "model_data"
####################
#
# Default Base Model
#
# This will automatically be SDXL Turbo if you are running this on a CPU.
#
default_base_model = "sdxl"
####################
#
# Use Safety Checker
#
# This can block some NSFW images for the models that allow it.
#
# 0 No
# 1 Yes
#
use_safety_checker = 0
#####
#
# Auto Save Imagery
#
# You can automatically save the image file, and a text file with the
# prompt details.
#
auto_save_imagery = 1
#####
#
# Name of Saved Images Folder
#
# You can change the name of this folder if you want. Imagery will be
# saved in a folder called "saved_images" in the directory configured
# in "main_dir". (the saved images folder will be created
# automatically) A directory for each day will be created in this
# folder. Imagery will then be placed in each folder.
#
saved_images_folder_name = "saved_images"
####################
#
# Auto Open Browser From Command Prompt
#
auto_open_browser = 0
####################
#
# Include Close Command Prompt / Cancel Button
#
# This doesn't work well at all. It just closes the command prompt. I
# might remove this eventually unless someone knows of a way it can work
# better, such as stopping the generation without closing the command
# prompt.
#
enable_close_command_prompt_button = 0
####################
#
# Use Denoising Start In Base Model When Using Refiner
#
# If set to "1", refining will end at the percent (expressed as decimal)
# defined in the denoising start for the refiner. If the steps set are
# 100, and the denoising start value is 0.75, the base model will run for
# 75 steps. The refiner will then run for 25 steps.
#
default_use_denoising_start_in_base_model_when_using_refiner = 1
####################
#
# Base Model Output To Refiner Is In Latent Space
#
# If set to "1", base model output is in latent space instead of PIL
# image when sent to refiner.
#
default_base_model_output_to_refiner_is_in_latent_space = 1
####################
#
# Log Generation Times
#
# Log generation times to saved text output. The initial time it takes to
# load a model is not included in the generation time.
#
log_generation_times = 1
####################
#
# Use Image Gallery
#
use_image_gallery = 1
####################
#
# Show Image Creation Progress Log
#
# This adds the current step that image generation is on.
#
show_image_creation_progress_log = 1
####################
#
# Show Messages In Command Prompt
#
# Messages will be printed in command prompt.
#
show_messages_in_command_prompt = 1
####################
#
# Show Messages In Modal On Page
#
# A popup appears in the top right corner on the page.
#
show_messages_in_modal_on_page = 0
####################
#
# Suppress Hugging Face Hub Offline Status
#
# By default, we add messages about the current setting of
# "HF_HUB_OFFLINE" in
#
suppress_hugging_face_hub_offline_status = 0
####################
#
# Add Seed Into Pipe
#
# To make generation deterministic. I add the option because the online
# configuration for the PhotoReal site doesn't do that and it changes
# things.
#
default_add_seed_into_pipe = 1
####################
#
# Max Queue Size
#
max_queue_size_if_cpu = 1
max_queue_size_if_torch = 20
####################
#
# Allow Online Configurations
#
# This allows matching what was created on these sites:
# https://huggingface.co/spaces/Manjushri/SDXL-1.0
# https://huggingface.co/spaces/Manjushri/PhotoReal-V3.7.5
#
allow_online_configurations = 0
####################
#
# Up Next Is Various Configuration Arrays and Objects
#
####################
base_model_array = [
"sdxl",
"photoreal",
"sdxl_turbo",
"sd_1_5_runwayml"
]
base_model_names_object = {
"sdxl": "Stable Diffusion XL",
"photoreal": "PhotoReal",
"sdxl_turbo": "Stable Diffusion XL Turbo",
"sd_1_5_runwayml": "Stable Diffusion 1.5"
}
####################
#
# "sdxl_default"
#
# - My customized configurations. (subject to change)
#
# "sdxl_2023-11-12"
#
# - Valid from November 12th to present.
# Number of steps in upscaler changed from 5 to 15.
#
# "sdxl_2023-09-05"
#
# - Valid from September 5th to November 12th.
# There were changes on this date.
#
# "photoreal_default"
#
# - My customized configurations. (subject to change)
# "circulus/canvers-real-v3.7.5"
#
# Seeds do not match the online PhotoReal version.
#
# "photoreal_3-8-1"
#
# - My customized configurations. (subject to change)
# "circulus/canvers-real-v3.8.1"
#
# "photoreal_3-8"
#
# - My customized configurations. (subject to change)
# "circulus/canvers-real-v3.8"
#
# "photoreal_3-7-5"
#
# - My customized configurations. (subject to change)
# "circulus/canvers-real-v3.7.5"
#
# "photoreal_3-6"
#
# - My customized configurations. (subject to change)
# "circulus/canvers-realistic-v3.6"
#
# "photoreal_2023-11-12"
#
# - Valid from November 12th to present.
# New base model: "circulus/canvers-real-v3.7.5"
#
# "photoreal_2023-09-01"
#
# - Valid from September 1st to November 12th.
# "circulus/canvers-realistic-v3.6" was already in effect.
# But there were changes on this date.
#
# "sdxl_turbo_default"
#
# - My customized configurations. (subject to change)
#
# "sd_1_5_runwayml_default"
#
# - My customized configurations. (subject to change)
#
base_model_object_of_model_configuration_arrays = {
"sdxl": [
"sdxl_default",
"sdxl_2023-11-12",
"sdxl_2023-09-05"
],
"photoreal": [
"photoreal_default",
"photoreal_3-8-1",
"photoreal_3-8",
"photoreal_3-7-5",
"photoreal_3-6",
"photoreal_2023-11-12",
"photoreal_2023-09-01"
],
"sdxl_turbo": [
"sdxl_turbo_default"
],
"sd_1_5_runwayml": [
"sd_1_5_runwayml_default"
]
}
####################
model_configuration_names_object = {
"sdxl_default": "1.0 - Default",
"sdxl_2023-11-12": "1.0 (2023-11-12 online config)",
"sdxl_2023-09-05": "1.0 (2023-09-05 online config)",
"photoreal_default": "3.6 - Default",
"photoreal_3-8-1": "3.8.1 - Default",
"photoreal_3-8": "3.8 - Default",
"photoreal_3-7-5": "3.7.5 - Default",
"photoreal_3-6": "3.6 - Default",
"photoreal_2023-11-12": "3.7.5 (2023-11-12 online config)",
"photoreal_2023-09-01": "3.6 (2023-09-01 online config)",
"sdxl_turbo_default": "Default",
"sd_1_5_runwayml_default": "1.5 - Default"
}
model_configuration_links_object = {
"sdxl_default": "stabilityai/stable-diffusion-xl-base-1.0",
"sdxl_2023-11-12": "stabilityai/stable-diffusion-xl-base-1.0",
"sdxl_2023-09-05": "stabilityai/stable-diffusion-xl-base-1.0",
"photoreal_default": "circulus/canvers-realistic-v3.6",
"photoreal_3-8-1": "circulus/canvers-real-v3.8.1",
"photoreal_3-8": "circulus/canvers-real-v3.8",
"photoreal_3-7-5": "circulus/canvers-real-v3.7.5",
"photoreal_3-6": "circulus/canvers-realistic-v3.6",
"photoreal_2023-11-12": "circulus/canvers-real-v3.7.5",
"photoreal_2023-09-01": "circulus/canvers-realistic-v3.6",
"sdxl_turbo_default": "stabilityai/sdxl-turbo",
"sd_1_5_runwayml_default": "runwayml/stable-diffusion-v1-5"
}
model_configuration_force_refiner_object = {
"sdxl_2023-11-12": 1,
"sdxl_2023-09-05": 1
}
# For now, the ones that force the refiner also have the "Refiner Number of
# Iterations" available.
model_configuration_include_refiner_number_of_steps_object = model_configuration_force_refiner_object
#model_configuration_include_refiner_number_of_steps_object = {
# "sdxl_2023-11-12": 1,
# "sdxl_2023-09-05": 1
#}
online_configurations_object = {
"sdxl_2023-11-12": 1,
"sdxl_2023-09-05": 1,
"photoreal_2023-11-12": 1,
"photoreal_2023-09-01": 1
}
####################
hugging_face_refiner_partial_path = "stabilityai/stable-diffusion-xl-refiner-1.0"
hugging_face_upscaler_partial_path = "stabilityai/sd-x2-latent-upscaler"
####################
base_model_model_configuration_defaults_object = {
"sdxl": "sdxl_default",
"photoreal": "photoreal_default",
"sdxl_turbo": "sdxl_turbo_default",
"sd_1_5_runwayml": "sd_1_5_runwayml_default"
}
####################
#
# Links:
#
# SD-XL 1.0-base Model Card
# https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0
#
# SD-XL 1.0-refiner Model Card
# https://huggingface.co/stabilityai/stable-diffusion-xl-refiner-1.0
#
# Stable Diffusion x2 latent upscaler model card
# https://huggingface.co/stabilityai/sd-x2-latent-upscaler
#
# PhotoReal
# 3.7.5: https://huggingface.co/circulus/canvers-real-v3.7.5
# 3.6: https://huggingface.co/circulus/canvers-realistic-v3.6
#
# SDXL Turbo
# https://huggingface.co/stabilityai/sdxl-turbo
#
# Stable Diffusion v1-5 (runwayml)
# https://huggingface.co/runwayml/stable-diffusion-v1-5
#
####################
default_scheduler = "model_default"
schedulers_array = [
"model_default",
"ddim",
"ddpm",
"dpm_solver_multistep",
"dpm_solver_multistep_karras_sigmas_true",
"dpm_solver_multistep_algorithm_type_sde-dpmsolver_pp",
"dpm_solver_multistep_karras_sigmas_true_algorithm_type_sde-dpmsolver_pp",
"dpm_solver_singlestep",
"dpm_solver_singlestep_karras_sigmas_true",
"kdpm2_discrete",
"kdpm2_discrete_karras_sigmas_true",
"kdpm2_ancestral_discrete",
"kdpm2_ancestral_discrete_karras_sigmas_true",
"euler_discrete",
"euler_ancestral_discrete",
"heun_discrete",
"lms_discrete",
"lms_discrete_karras_sigmas_true",
"pndm",
"pndm_skip_prk_steps_true",
"deis_multistep",
"dpm_solver_sde",
"uni_pc_multistep"
]
scheduler_long_names_object = {
"model_default": "Model Default",
"ddim": "DDIM",
"ddpm": "DDPM",
"dpm_solver_multistep": "DPM++ 2M (DPMSolverMultistep)",
"dpm_solver_multistep_karras_sigmas_true": "DPM++ 2M Karras (DPMSolverMultistep with use_karras_sigmas=True)",
"dpm_solver_multistep_algorithm_type_sde-dpmsolver_pp": "DPM++ 2M SDE (DPMSolverMultistep with algorithm_type=\"sde-dpmsolver++\")",
"dpm_solver_multistep_karras_sigmas_true_algorithm_type_sde-dpmsolver_pp": "DPM++ 2M SDE Karras (DPMSolverMultistep with use_karras_sigmas=True & algorithm_type=\"sde-dpmsolver++\")",
"dpm_solver_singlestep": "DPM++ SDE (DPMSolverSinglestep)",
"dpm_solver_singlestep_karras_sigmas_true": "DPM++ SDE Karras (DPMSolverSinglestep with use_karras_sigmas=True)",
"kdpm2_discrete": "DPM2 (KDPM2Discrete)",
"kdpm2_discrete_karras_sigmas_true": "DPM2 Karras (KDPM2Discrete with use_karras_sigmas=True)",
"kdpm2_ancestral_discrete": "DPM2 a (KDPM2AncestralDiscrete)",
"kdpm2_ancestral_discrete_karras_sigmas_true": "DPM2 a Karras (KDPM2AncestralDiscrete with use_karras_sigmas=True)",
"euler_discrete": "Euler (EulerDiscrete)",
"euler_ancestral_discrete": "Euler a (EulerAncestralDiscrete)",
"heun_discrete": "Heun (HeunDiscrete)",
"lms_discrete": "LMS (LMSDiscrete)",
"lms_discrete_karras_sigmas_true": "LMS Karras (LMSDiscrete with use_karras_sigmas=True)",
"pndm": "PNDM",
"pndm_skip_prk_steps_true": "PNDM (with skip_prk_steps=True) - Close to PLMS",
"deis_multistep": "DEISMultistep",
"dpm_solver_sde": "DPMSolverSDE",
"uni_pc_multistep": "UniPCMultistep"
}
scheduler_short_names_object = {
"ddim": "DDIM",
"ddpm": "DDPM",
"dpm_solver_multistep": "DPM++ 2M",
"dpm_solver_multistep_karras_sigmas_true": "DPM++ 2M Karras",
"dpm_solver_multistep_algorithm_type_sde-dpmsolver_pp": "DPM++ 2M SDE",
"dpm_solver_multistep_karras_sigmas_true_algorithm_type_sde-dpmsolver_pp": "DPM++ 2M SDE Karras",
"dpm_solver_singlestep": "DPM++ SDE",
"dpm_solver_singlestep_karras_sigmas_true": "DPM++ SDE Karras",
"kdpm2_discrete": "DPM2",
"kdpm2_discrete_karras_sigmas_true": "DPM2 Karras",
"kdpm2_ancestral_discrete": "DPM2 a",
"kdpm2_ancestral_discrete_karras_sigmas_true": "DPM2 a Karras",
"euler_discrete": "Euler",
"euler_ancestral_discrete": "Euler a",
"heun_discrete": "Heun",
"lms_discrete": "LMS",
"lms_discrete_karras_sigmas_true": "LMS Karras",
"pndm": "PNDM",
"pndm_skip_prk_steps_true": "PNDM (with skip_prk_steps=True) - Close to PLMS",
"deis_multistep": "DEISMultistep",
"dpm_solver_sde": "DPMSolverSDE",
"uni_pc_multistep": "UniPCMultistep"
}
scheduler_name_to_identifier_in_app_object = {
"DDIMScheduler": "ddim",
"DDPMScheduler": "ddpm",
"DPMSolverMultistepScheduler": "dpm_solver_multistep",
"DPMSolverSinglestepScheduler": "dpm_solver_singlestep",
"KDPM2DiscreteScheduler": "kdpm2_discrete",
"KDPM2AncestralDiscreteScheduler": "kdpm2_ancestral_discrete",
"EulerDiscreteScheduler": "euler_discrete",
"EulerAncestralDiscreteScheduler": "euler_ancestral_discrete",
"HeunDiscreteScheduler": "heun_discrete",
"LMSDiscreteScheduler": "lms_discrete",
"PNDMScheduler": "pndm",
"DEISMultistepScheduler": "deis_multistep",
"DPMSolverSDEScheduler": "dpm_solver_sde",
"UniPCMultistepScheduler": "uni_pc_multistep"
}
####################
#
# Determine automatically if on CPU or GPU
#
# CPU will not work on Windows.
#
device = "cpu"
if torch.cuda.is_available():
device = "cuda"
PYTORCH_CUDA_ALLOC_CONF = {
"max_split_size_mb": 8000
}
torch.cuda.max_memory_allocated(
device = device
)
torch.cuda.empty_cache()
if device == "cpu":
default_base_model = "sdxl_turbo"
####################
default_prompt = ""
default_negative_prompt = ""
default_width = 768
default_height = 768
default_guidance_scale_value = 7.5
default_seed_value = 0
default_base_model_base_model_num_inference_steps = 50
default_base_model_base_model_num_inference_steps_for_sdxl_turbo = 2
default_seed_maximum = 1000000000000000000
default_seed_value = 876678173805928800
# If you turn off the refiner it will not be available in the display unless
# you select an online configuration option that requires it.
enable_refiner = 1
enable_upscaler = 1
# Selected on form as a default?
default_refiner_selected = 0
default_upscaler_selected = 0
# Accordion visible on load?
#
# 0 If selected as default, will be open. Otherwise, closed.
# 1 Always starts open
default_refiner_accordion_open = 1
default_upscaler_accordion_open = 1
# Upscaler Options
maximum_upscaler_steps = 150
default_upscaler_steps = 50
# xFormers:
#
# https://huggingface.co/docs/diffusers/optimization/xformers
use_xformers = 1
# Scaled dot product attention (SDPA) is used by default for PyTorch 2.0. To
# use default instead, set this to 1.
#
# https://huggingface.co/docs/diffusers/optimization/torch2.0#scaled-dot-product-attention
use_default_attn_processor = 0
display_xformers_usage_in_prompt_info = 1
include_transformers_version_in_prompt_info = 1
display_default_attn_processor_usage_in_prompt_info = 1
# You can't select both sequential and model cpu offloading. If you select
# both, model cpu offloading will be used.
use_sequential_cpu_offload_for_base_model = 1
use_sequential_cpu_offload_for_refiner = 1
use_sequential_cpu_offload_for_upscaler = 1
use_model_cpu_offload_for_base_model = 0
use_model_cpu_offload_for_refiner = 0
use_model_cpu_offload_for_upscaler = 0
if default_base_model == "photoreal":
# PhotoReal
# default_guidance_scale_value = 7
#default_seed_value = 3648905360627576
default_seed_value = 0
elif default_base_model == "sdxl_turbo":
# SDXL Turbo
#default_seed_value = 2725116121543
default_seed_value = 0
elif default_base_model == "sd_1_5_runwayml":
# SD 1.5
default_seed_value = 0
else:
# SDXL
default_width = 1024
default_height = 1024
# default_guidance_scale_value = 10
default_guidance_scale_value = 7.5
#default_seed_value = 876678173805928800
default_seed_value = 0
# Must be multiple of 8
width_and_height_input_slider_steps = 8
opening_html = ""
ending_html = ""
max_queue_size = max_queue_size_if_torch
if device == "cpu":
opening_html = "THIS APP IS EXCEPTIONALLY SLOW!
This app is not running on a GPU. The first time it loads after the space is rebuilt it might take 10 minutes to generate a SDXL Turbo image. It may take 2 to 3 minutes after that point to do two steps. For other models, it may take hours to create a single image."
max_queue_size = max_queue_size_if_cpu
if allow_online_configurations == 1:
ending_html = """This app allows you to try to match images that can be generated using several tools online. (Stable Diffusion XL, PhotoReal with SDXL 1.0 Refiner and SDXL Turbo Unofficial Demo) You can select the base model you want to use in the first dropdown option. The second configuration option involves choosing which version and/or configuration to choose. Certain configurations try to match the version online, taking into account changes that were made over time. Another configuration involves a default configuration I choose and is subject to change while I am still designing this app.
"""
ending_html += """Tokens are not individual characters. If the prompt length is too long, the display will notify you what part of the prompt wasn't used. Changing just the image dimensions alone will change the image generated. For some models, trying to make a large image, such as 1024x1024, may add extra people and come out worse than using smaller dimensions.
The original script for this app was written by Manjushri."""
refiner_on_text = "Refiner is on. "
refiner_off_text = "Refiner is off. "
upscaler_on_text = "Upscaler is on. "
upscaler_off_text = "Upscaler is off. "
number_of_reserved_tokens = 2
###############################################################################
###############################################################################
#
#
#
# End Configurations
#
#
#
###############################################################################
###############################################################################
try:
if (str(os.uname()).find("magicfixeseverything") >= 0):
script_being_run_on_hugging_face = 1
except:
script_being_run_on_hugging_face = 0
if script_being_run_on_hugging_face == 1:
use_custom_hugging_face_cache_dir = 0
auto_save_imagery = 0
show_messages_in_modal_on_page = 0
show_messages_in_command_prompt = 1
allow_online_configurations = 0
if device == "cpu":
show_image_creation_progress_log = 1
default_width = 768
default_height = 768
ending_html = """
If you would like to download this app to run offline on a Windows computer that has a NVIDIA graphics card, click here to download it.
""" + ending_html
# If on HuggingFace, I reduce what is available.
default_width = 768
default_height = 768
if allow_online_configurations == 0:
base_model_array = [
"sdxl",
"photoreal",
"sdxl_turbo"
]
base_model_object_of_model_configuration_arrays = {
"sdxl": [
"sdxl_default"
],
"photoreal": [
"photoreal_default"
],
"sdxl_turbo": [
"sdxl_turbo_default"
]
}
base_model_model_configuration_defaults_object = {
"sdxl": "sdxl_default",
"photoreal": "photoreal_default",
"sdxl_turbo": "sdxl_turbo_default"
}
hugging_face_hub_is_offline = 0
if script_being_run_on_hugging_face == 0:
if (
("HF_HUB_OFFLINE" in os.environ) and
(int(os.environ["HF_HUB_OFFLINE"]) == 1)
):
hugging_face_hub_is_offline = 1
if suppress_hugging_face_hub_offline_status == 1:
if hugging_face_hub_is_offline == 0:
print ("Note: The Hugging Face cache directory does not automatically delete older data. Over time, it could eventually grow to use all the space on the drive it is on. You either need to manually clean out the folder occasionally or see Instructons.txt on how to not automatically update data once you have downloaded everything you need.")
else:
print ("You are working offline. Data will not be downloaded. See \"ai_image_creation.bat\" or \"Instructions.txt\" for more info.")
if device == "cuda":
PYTORCH_CUDA_ALLOC_CONF = {
"max_split_size_mb": 8000
}
torch.cuda.max_memory_allocated(
device = device
)
torch.cuda.empty_cache()
saved_images_dir = main_dir + "/" + saved_images_folder_name
hugging_face_cache_dir = main_dir + "/" + cache_directory_folder_name
if not os.path.exists(hugging_face_cache_dir):
os.makedirs(hugging_face_cache_dir)
if auto_save_imagery == 1:
from datetime import datetime
import time
if (
(log_generation_times == 1) or
(show_image_creation_progress_log == 1)
):
import time
if device == "cpu":
use_sequential_cpu_offload_for_base_model = 0
use_sequential_cpu_offload_for_refiner = 0
use_sequential_cpu_offload_for_upscaler = 0
use_model_cpu_offload_for_base_model = 0
use_model_cpu_offload_for_refiner = 0
use_model_cpu_offload_for_upscaler = 0
use_xformers = 0
if (
(use_sequential_cpu_offload_for_base_model == 1) and
(use_model_cpu_offload_for_base_model == 1)
):
use_sequential_cpu_offload_for_base_model = 0
if (
(use_sequential_cpu_offload_for_refiner == 1) and
(use_model_cpu_offload_for_refiner == 1)
):
use_sequential_cpu_offload_for_refiner = 0
if (
(use_sequential_cpu_offload_for_upscaler == 1) and
(use_model_cpu_offload_for_upscaler == 1)
):
use_sequential_cpu_offload_for_upscaler = 0
def error_function(
text_message
):
print (text_message)
gr.Error(text_message)
exit(1)
additional_prompt_info_html = ""
if auto_save_imagery == 1:
additional_prompt_info_html = " The image, and a text file with generation information, will be saved automatically."
if use_xformers == 1:
from xformers.ops import MemoryEfficientAttentionFlashAttentionOp
if use_default_attn_processor == 1:
from diffusers.models.attention_processor import AttnProcessor
if (
default_base_model and
(default_base_model in base_model_object_of_model_configuration_arrays) and
(default_base_model in base_model_model_configuration_defaults_object)
):
default_model_configuration = base_model_model_configuration_defaults_object[default_base_model]
if default_model_configuration in model_configuration_names_object:
default_model_configuration_choices_array = []
for this_model_configuration in base_model_object_of_model_configuration_arrays[default_base_model]:
if model_configuration_names_object[this_model_configuration]:
default_model_configuration_choices_array.append(
model_configuration_names_object[this_model_configuration]
)
else:
error_function("A default configuration must be properly named in the code.")
else:
error_function("A default configuration must be properly configured in the code.")
else:
error_function("A default base model must be properly configured in the code.")
default_base_model_nicely_named_value = base_model_names_object[default_base_model]
default_model_configuration_nicely_named_value = model_configuration_names_object[default_model_configuration]
if not (
default_scheduler and
default_scheduler in scheduler_long_names_object
):
error_function("A default scheduler must be properly configured in the code.")
default_scheduler_nicely_named_value = scheduler_long_names_object[default_scheduler]
if enable_refiner != 1:
default_refiner_selected = 0
if enable_upscaler != 1:
default_upscaler_selected = 0
model_configuration_requires_refiner = 0
if default_model_configuration in model_configuration_force_refiner_object:
model_configuration_requires_refiner = model_configuration_force_refiner_object[default_model_configuration]
if model_configuration_requires_refiner == 1:
enable_refiner = 1
default_refiner_selected = 1
default_refine_option = "No"
if default_refiner_selected == 1:
default_refine_option = "Yes"
default_upscale_option = "No"
if default_upscaler_selected == 1:
default_upscale_option = "Yes"
is_default_config = 1
if default_model_configuration in online_configurations_object:
is_default_config = 0
default_refiner_and_upscaler_status_text = ""
default_use_denoising_start_in_base_model_when_using_refiner_is_selected = False
if default_use_denoising_start_in_base_model_when_using_refiner == 1:
default_use_denoising_start_in_base_model_when_using_refiner_is_selected = True
default_base_model_output_to_refiner_is_in_latent_space_is_selected = False
if default_base_model_output_to_refiner_is_in_latent_space == 1:
default_base_model_output_to_refiner_is_in_latent_space_is_selected = True
refiner_default_config_accordion_visible = True
if (
(enable_refiner != 1) or
(is_default_config != 1)
):
refiner_default_config_accordion_visible = False
refiner_default_config_accordion_open = False
if (
(default_refiner_accordion_open == 1) or
(
(is_default_config == 1) and
(default_refiner_selected == 1)
)
):
refiner_default_config_accordion_open = True
refiner_online_config_accordion_visible = True
if (
(enable_refiner != 1) or
(is_default_config == 1)
):
refiner_online_config_accordion_visible = False
refiner_online_config_accordion_open = False
if (
(default_refiner_accordion_open == 1) or
(
(is_default_config != 1) and
(default_refiner_selected == 1)
)
):
refiner_online_config_accordion_open = True
refiner_group_visible = False
if enable_refiner == 1:
refiner_group_visible = True
if default_refiner_selected == 1:
default_refiner_and_upscaler_status_text += refiner_on_text
else:
default_refiner_and_upscaler_status_text += refiner_off_text
upscaler_accordion_open = False
if (
(default_upscaler_selected == 1) or
(default_upscaler_accordion_open == 1)
):
upscaler_accordion_open = True
upscaler_group_visible = False
if enable_upscaler == 1:
upscaler_group_visible = True
if default_upscaler_selected == 1:
default_refiner_and_upscaler_status_text += upscaler_on_text
else:
default_refiner_and_upscaler_status_text += upscaler_off_text
default_negative_prompt_field_row_visibility = True
default_negative_prompt_for_sdxl_turbo_field_row_visibility = False
default_base_model_num_inference_steps_field_row_visibility = True
default_base_model_num_inference_steps_field_for_sdxl_turbo_field_row_visibility = False
default_guidance_scale_field_row_visibility = True
default_guidance_scale_for_sdxl_turbo_field_row_visibility = False
if default_base_model == "sdxl_turbo":
default_negative_prompt_field_row_visibility = False
default_negative_prompt_for_sdxl_turbo_field_row_visibility = True
default_base_model_num_inference_steps_field_row_visibility = False
default_base_model_num_inference_steps_field_for_sdxl_turbo_field_row_visibility = True
default_guidance_scale_field_row_visibility = False
default_guidance_scale_for_sdxl_turbo_field_row_visibility = True
default_add_seed_into_pipe_field_row_visibility = False
if is_default_config == 1:
default_add_seed_into_pipe_field_row_visibility = True
default_add_seed_into_pipe_is_selected = False
if default_add_seed_into_pipe == 1:
default_add_seed_into_pipe_is_selected = True
default_base_model_choices_array = []
stored_model_configuration_names_object = {}
for this_base_model in base_model_array:
default_base_model_choices_array.append(
base_model_names_object[this_base_model]
)
stored_model_configuration = base_model_model_configuration_defaults_object[this_base_model]
stored_model_configuration_names_object[this_base_model] = model_configuration_names_object[stored_model_configuration]
default_scheduler_choices_array = []
for this_scheduler in schedulers_array:
default_scheduler_choices_array.append(
scheduler_long_names_object[this_scheduler]
)
make_seed_selection_a_textbox = 1
if default_seed_maximum <= 9007199254740992:
make_seed_selection_a_textbox = 0
###############################################################################
###############################################################################
#
#
#
#
#
#
# Functions
#
#
#
#
#
#
###############################################################################
###############################################################################
#####################
#
# Show Message
#
# Display message to user in model on web form and/or command prompt.
#
#####################
def show_message(
message_to_display
):
if show_messages_in_command_prompt == 1:
print (message_to_display)
if show_messages_in_modal_on_page == 1:
gr.Info(message_to_display)
#####################
#
# Convert Seconds
#
# Convert raw seconds to the numer of hours, minutes and seconds.
#
#####################
def convert_seconds(
seconds
):
# Google AI Code
hours = seconds // 3600
minutes = (seconds % 3600) // 60
seconds = seconds % 60
return hours, minutes, seconds
#####################
#
# Seed Not Valid
#
# Return True if seed is not valid.
#
#####################
def seed_not_valid(seed_num_str):
try:
seed_num = int(seed_num_str)
if (seed_num > 0) and (seed_num <= default_seed_maximum):
return False
else:
return True
except ValueError:
return True
#####################
#
# Numerical Bool
#
# Return 1 for anything that is True/Yes/1. Everything else is False.
#
#####################
def numerical_bool(
original_value
):
new_value = 0
if (
(original_value == 1) or
(original_value == "Yes") or
(original_value == "True") or
(original_value == True)
):
new_value = 1
return new_value
#####################
#
# Truncate Prompt
#
# Truncate a prompt. Get the actual prompt that will be used and save the
# part of the prompt that will not be used.
#
#####################
def truncate_prompt (
pipe,
existing_prompt_text
):
# Only 77 tokens are allowed in the prompt. 2 are reserved, meaning it is
# truncated to 75. This happens automatically, but we want to tell people
# that
tokenizer = pipe.tokenizer
max_token_length_of_model = pipe.tokenizer.model_max_length - number_of_reserved_tokens
prompt_text_words_array = existing_prompt_text.split(" ")
prompt_text_words_array_length = len(prompt_text_words_array)
prompt_text_words_index = 0
prompt_text_substring = ""
prompt_text_not_used_substring = ""
for prompt_text_word in prompt_text_words_array:
prompt_text_words_index += 1
substring_to_test = prompt_text_substring
if prompt_text_words_index > 1:
substring_to_test += " "
substring_to_test += prompt_text_word
token_length_of_substring_to_test = len(tokenizer.tokenize(substring_to_test))
if token_length_of_substring_to_test > max_token_length_of_model:
prompt_text_not_used_substring += prompt_text_word + " "
else:
prompt_text_substring = substring_to_test
return (
prompt_text_substring,
prompt_text_not_used_substring
)
#####################
#
# Construct Pipe
#
# Prepare the base model.
#
#####################
def construct_pipe (
base_model_name_value,
model_configuration_name_value
):
if device == "cuda":
torch.cuda.empty_cache()
base_model_kwargs = {}
if (
(base_model_name_value == "sdxl") or
(base_model_name_value == "photoreal") or
(base_model_name_value == "sdxl_turbo") or
(base_model_name_value == "sd_1_5_runwayml")
):
base_model_kwargs["use_safetensors"] = True
if use_safety_checker == 0:
if (
(base_model_name_value == "photoreal") or
(base_model_name_value == "sd_1_5_runwayml")
):
base_model_kwargs = {
"safety_checker": None,
"requires_safety_checker": False
}
if device == "cuda":
if (
(base_model_name_value == "sdxl") or
(base_model_name_value == "sdxl_turbo") or
(base_model_name_value == "sd_1_5_runwayml")
):
base_model_kwargs["variant"] = "fp16"
base_model_kwargs["torch_dtype"] = torch.float16
if use_custom_hugging_face_cache_dir == 1:
base_model_kwargs["cache_dir"] = hugging_face_cache_dir
pipe = DiffusionPipeline.from_pretrained(
model_configuration_links_object[model_configuration_name_value],
**base_model_kwargs
)
if use_model_cpu_offload_for_base_model == 1:
pipe.enable_model_cpu_offload()
if use_xformers == 1:
pipe.enable_xformers_memory_efficient_attention()
pipe = pipe.to(device)
if use_sequential_cpu_offload_for_base_model == 1:
pipe.enable_sequential_cpu_offload()
if use_default_attn_processor == 1:
pipe.unet.set_default_attn_processor()
if device == "cuda":
torch.cuda.empty_cache()
else:
pipe.unet = torch.compile(
pipe.unet,
mode = "reduce-overhead",
fullgraph = True
)
return (
pipe
)
#####################
#
# Configure Scheduler
#
#####################
def configure_scheduler (
pipe,
scheduler_value
):
scheduler_config = pipe.scheduler.config
scheduler = scheduler_value
if scheduler_value == "model_default":
scheduler_name = pipe.scheduler.config._class_name
if scheduler_name in scheduler_name_to_identifier_in_app_object:
scheduler = scheduler_name_to_identifier_in_app_object[scheduler_name]
scheduler_used = scheduler
if scheduler == "ddim":
from diffusers import DDIMScheduler
pipe.scheduler = DDIMScheduler.from_config(scheduler_config)
elif scheduler == "ddpm":
from diffusers import DDPMScheduler
pipe.scheduler = DDPMScheduler.from_config(scheduler_config)
elif scheduler == "dpm_solver_multistep":
from diffusers import DPMSolverMultistepScheduler
pipe.scheduler = DPMSolverMultistepScheduler.from_config(scheduler_config)
elif scheduler == "dpm_solver_multistep_karras_sigmas_true":
new_scheduler_config = dict(pipe.scheduler.config)
new_scheduler_config.update({"use_karras_sigmas": True})
from diffusers import DPMSolverMultistepScheduler
pipe.scheduler = DPMSolverMultistepScheduler.from_config(new_scheduler_config)
elif scheduler == "dpm_solver_multistep_algorithm_type_sde-dpmsolver_pp":
new_scheduler_config = dict(pipe.scheduler.config)
new_scheduler_config.update({"algorithm_type": "sde-dpmsolver++"})
from diffusers import DPMSolverMultistepScheduler
pipe.scheduler = DPMSolverMultistepScheduler.from_config(new_scheduler_config)
elif scheduler == "dpm_solver_multistep_karras_sigmas_true_algorithm_type_sde-dpmsolver_pp":
new_scheduler_config = dict(pipe.scheduler.config)
new_scheduler_config.update({"use_karras_sigmas": True})
new_scheduler_config.update({"algorithm_type": "sde-dpmsolver++"})
from diffusers import DPMSolverMultistepScheduler
pipe.scheduler = DPMSolverMultistepScheduler.from_config(new_scheduler_config)
elif scheduler == "dpm_solver_singlestep":
from diffusers import DPMSolverSinglestepScheduler
pipe.scheduler = DPMSolverSinglestepScheduler.from_config(scheduler_config)
elif scheduler == "dpm_solver_singlestep_karras_sigmas_true":
new_scheduler_config = dict(pipe.scheduler.config)
new_scheduler_config.update({"use_karras_sigmas": True})
from diffusers import DPMSolverSinglestepScheduler
pipe.scheduler = DPMSolverSinglestepScheduler.from_config(new_scheduler_config)
elif scheduler == "kdpm2_discrete":
from diffusers import KDPM2DiscreteScheduler
pipe.scheduler = KDPM2DiscreteScheduler.from_config(scheduler_config)
elif scheduler == "kdpm2_discrete_karras_sigmas_true":
new_scheduler_config = dict(pipe.scheduler.config)
new_scheduler_config.update({"use_karras_sigmas": True})
from diffusers import KDPM2DiscreteScheduler
pipe.scheduler = KDPM2DiscreteScheduler.from_config(new_scheduler_config)
elif scheduler == "kdpm2_ancestral_discrete":
from diffusers import KDPM2AncestralDiscreteScheduler
pipe.scheduler = KDPM2AncestralDiscreteScheduler.from_config(scheduler_config)
elif scheduler == "kdpm2_ancestral_discrete_karras_sigmas_true":
new_scheduler_config = dict(pipe.scheduler.config)
new_scheduler_config.update({"use_karras_sigmas": True})
from diffusers import KDPM2AncestralDiscreteScheduler
pipe.scheduler = KDPM2AncestralDiscreteScheduler.from_config(new_scheduler_config)
elif scheduler == "euler_discrete":
from diffusers import EulerDiscreteScheduler
pipe.scheduler = EulerDiscreteScheduler.from_config(scheduler_config)
elif scheduler == "euler_ancestral_discrete":
from diffusers import EulerAncestralDiscreteScheduler
pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(scheduler_config)
elif scheduler == "heun_discrete":
from diffusers import HeunDiscreteScheduler
pipe.scheduler = HeunDiscreteScheduler.from_config(scheduler_config)
elif scheduler == "lms_discrete":
from diffusers import LMSDiscreteScheduler
pipe.scheduler = LMSDiscreteScheduler.from_config(scheduler_config)
elif scheduler == "lms_discrete_karras_sigmas_true":
new_scheduler_config = dict(pipe.scheduler.config)
new_scheduler_config.update({"use_karras_sigmas": True})
from diffusers import LMSDiscreteScheduler
pipe.scheduler = LMSDiscreteScheduler.from_config(new_scheduler_config)
elif scheduler == "pndm":
from diffusers import PNDMScheduler
pipe.scheduler = PNDMScheduler.from_config(scheduler_config)
elif scheduler == "pndm_skip_prk_steps_true":
new_scheduler_config = dict(pipe.scheduler.config)
new_scheduler_config.update({"skip_prk_steps": True})
from diffusers import PNDMScheduler
pipe.scheduler = PNDMScheduler.from_config(new_scheduler_config)
elif scheduler == "deis_multistep":
from diffusers import DEISMultistepScheduler
pipe.scheduler = DEISMultistepScheduler.from_config(scheduler_config)
elif scheduler == "dpm_solver_sde":
from diffusers import DPMSolverSDEScheduler
pipe.scheduler = DPMSolverSDEScheduler.from_config(scheduler_config)
elif scheduler == "uni_pc_multistep":
from diffusers import UniPCMultistepScheduler
pipe.scheduler = UniPCMultistepScheduler.from_config(scheduler_config)
else:
from diffusers import PNDMScheduler
pipe.scheduler = PNDMScheduler.from_config(scheduler_config)
scheduler_used = "pndm"
return (
scheduler_used
)
#####################
#
# Construct Refiner
#
# Prepare the refiner.
#
#####################
def construct_refiner ():
refiner_kwargs = {
"use_safetensors": True
}
if device == "cuda":
refiner_kwargs["variant"] = "fp16"
refiner_kwargs["torch_dtype"] = torch.float16
if use_custom_hugging_face_cache_dir == 1:
refiner_kwargs["cache_dir"] = hugging_face_cache_dir
refiner = DiffusionPipeline.from_pretrained(
hugging_face_refiner_partial_path,
**refiner_kwargs
)
if use_model_cpu_offload_for_refiner == 1:
refiner.enable_model_cpu_offload()
if use_xformers == 1:
refiner.enable_xformers_memory_efficient_attention()
refiner = refiner.to(device)
if use_sequential_cpu_offload_for_refiner == 1:
refiner.enable_sequential_cpu_offload()
if use_default_attn_processor == 1:
refiner.unet.set_default_attn_processor()
if device == "cuda":
torch.cuda.empty_cache()
else:
refiner.unet = torch.compile(
refiner.unet,
mode = "reduce-overhead",
fullgraph = True
)
return (
refiner
)
#####################
#
# Construct Upscaler
#
# Prepare the upscaler.
#
#####################
def construct_upscaler ():
upscaler_kwargs = {
"use_safetensors": True
}
if device == "cuda":
upscaler_kwargs["torch_dtype"] = torch.float16
if use_custom_hugging_face_cache_dir == 1:
upscaler_kwargs["cache_dir"] = hugging_face_cache_dir
upscaler = DiffusionPipeline.from_pretrained(
hugging_face_upscaler_partial_path,
**upscaler_kwargs
)
if use_model_cpu_offload_for_upscaler == 1:
upscaler.enable_model_cpu_offload()
if use_xformers == 1:
upscaler.enable_xformers_memory_efficient_attention()
upscaler = upscaler.to(device)
if use_sequential_cpu_offload_for_upscaler == 1:
upscaler.enable_sequential_cpu_offload()
if use_default_attn_processor == 1:
upscaler.unet.set_default_attn_processor()
if device == "cuda":
torch.cuda.empty_cache()
else:
upscaler.unet = torch.compile(
upscaler.unet,
mode = "reduce-overhead",
fullgraph = True
)
return (
upscaler
)
#####################
#
# Update Prompt Info From Gallery
#
# If you select an image in the image gallery, display the prompt
# information for that image.
#
#####################
def update_prompt_info_from_gallery (
gallery_data: gr.SelectData,
image_gallery_array_state_value
):
gallery_data_index = gallery_data.index
output_image_gallery_field_update = gr.Gallery(
selected_index = gallery_data_index
)
output_text_field_update = image_gallery_array_state_value[gallery_data_index]
return {
output_image_gallery_field: output_image_gallery_field_update,
output_text_field: output_text_field_update
}
#####################
#
# Before Create Image Function
#
# This is loaded before the image creation begins.
#
#####################
def before_create_image_function ():
generate_image_btn_update = gr.Button(
value = "Generating...",
variant = "secondary",
interactive = False
)
output_text_field_update = gr.Textbox(
visible = False
)
return {
generate_image_btn: generate_image_btn_update,
output_text_field: output_text_field_update
}
#####################
#
# After Create Image Function
#
# This is loaded once image creation has completed.
#
#####################
def after_create_image_function ():
generate_image_btn_update = gr.Button(
value = "Generate",
variant = "primary",
interactive = True
)
output_text_field_update = gr.Textbox(
visible = True
)
return {
generate_image_btn: generate_image_btn_update,
output_text_field: output_text_field_update
}
#####################
#
# Create Image Function
#
# This is the main image creation function.
#
#####################
def create_image_function (
base_model_field_index,
prompt_text,
negative_prompt_text,
scheduler_index,
image_width,
image_height,
guidance_scale,
base_model_num_inference_steps,
base_model_num_inference_steps_field_for_sdxl_turbo,
actual_seed,
add_seed_into_pipe,
refining_selection_default_config_field_value,
refining_selection_online_config_normal_field_value,
refining_selection_online_config_automatically_selected_field_value,
refining_denoise_start_for_default_config_field_value,
refining_use_denoising_start_in_base_model_when_using_refiner_field_value,
refining_base_model_output_to_refiner_is_in_latent_space_field_value,
refining_denoise_start_for_online_config_field_value,
refining_number_of_iterations_for_online_config_field_value,
upscaling_selection_field_value,
upscaling_num_inference_steps,
image_gallery_array_state_value,
prompt_information_array_state_value,
last_model_configuration_name_selected_state_value,
last_refiner_name_selected_state_value,
last_upscaler_name_selected_state_value,
stored_pipe_state,
stored_refiner_state,
stored_upscaler_state,
*model_configuration_dropdown_fields_array,
progress = gr.Progress()
):
position_in_array = 0
model_configuration_field_object = {}
for model_configuration_field_index in model_configuration_dropdown_fields_array:
this_base_model = base_model_array[position_in_array]
model_configuration_field_object[this_base_model] = model_configuration_field_index
position_in_array += 1
add_seed_into_pipe = numerical_bool(add_seed_into_pipe)
refining_selection_default_config_field_value = numerical_bool(refining_selection_default_config_field_value)
refining_selection_online_config_normal_field_value = numerical_bool(refining_selection_online_config_normal_field_value)
refining_selection_online_config_automatically_selected_field_value = numerical_bool(refining_selection_online_config_automatically_selected_field_value)
refining_use_denoising_start_in_base_model_when_using_refiner_field_value = numerical_bool(refining_use_denoising_start_in_base_model_when_using_refiner_field_value)
refining_base_model_output_to_refiner_is_in_latent_space_field_value = numerical_bool(refining_base_model_output_to_refiner_is_in_latent_space_field_value)
use_upscaler = numerical_bool(upscaling_selection_field_value)
base_model_name_value = base_model_array[base_model_field_index]
model_configuration_field_index = model_configuration_field_object[base_model_name_value]
model_configuration_name_value = base_model_object_of_model_configuration_arrays[base_model_name_value][model_configuration_field_index]
scheduler_value = schedulers_array[scheduler_index]
current_actual_total_base_model_steps = base_model_num_inference_steps
current_actual_total_refiner_steps = 0
is_default_config_state = 1
if model_configuration_name_value in online_configurations_object:
is_default_config_state = 0
use_refiner = 0
if (
(
(is_default_config_state == 1) and
refining_selection_default_config_field_value
) or (
(is_default_config_state != 1) and
(
(
(model_configuration_name_value not in model_configuration_force_refiner_object) and
refining_selection_online_config_normal_field_value
) or (
(model_configuration_name_value in model_configuration_force_refiner_object) and
refining_selection_online_config_automatically_selected_field_value
)
)
)
):
use_refiner = 1
if base_model_name_value == "sdxl_turbo":
negative_prompt_text = ""
base_model_num_inference_steps = base_model_num_inference_steps_field_for_sdxl_turbo
current_actual_total_base_model_steps = base_model_num_inference_steps
guidance_scale = 0
if (
(last_model_configuration_name_selected_state_value == "") or
(model_configuration_name_value != last_model_configuration_name_selected_state_value)
):
if (last_model_configuration_name_selected_state_value != ""):
if "pipe" in globals():
del pipe
progress(
progress = 0,
desc = "Base model is loading"
)
(
pipe
) = construct_pipe(
base_model_name_value,
model_configuration_name_value
)
last_model_configuration_name_selected_state_value = model_configuration_name_value
else:
pipe = stored_pipe_state
(
scheduler_used
) = configure_scheduler(
pipe,
scheduler_value
)
if use_refiner == 1:
if (last_refiner_name_selected_state_value == ""):
progress(
progress = 0,
desc = "Refiner is loading"
)
refiner = construct_refiner()
last_refiner_name_selected_state_value = "refiner"
else:
refiner = stored_refiner_state
else:
refiner = {}
if use_upscaler == 1:
if (last_upscaler_name_selected_state_value == ""):
progress(
progress = 0,
desc = "Upscaler is loading"
)
upscaler = construct_upscaler()
last_upscaler_name_selected_state_value = "upscaler"
else:
upscaler = stored_upscaler_state
else:
upscaler = ""
if log_generation_times == 1:
start_time = time.time()
# Only 77 tokens are allowed in the prompt. 2 are reserved, meaning it is
# truncated to 75. This happens automatically, but we want to tell people
# that
tokenizer = pipe.tokenizer
max_token_length_of_model = pipe.tokenizer.model_max_length - number_of_reserved_tokens
token_length_of_prompt_text = len(tokenizer.tokenize(prompt_text))
token_length_of_negative_prompt_text = len(tokenizer.tokenize(negative_prompt_text))
prompt_text_not_used_substring = ""
message_about_prompt_truncation = ""
if token_length_of_prompt_text > max_token_length_of_model:
(
prompt_text,
prompt_text_not_used_substring
) = truncate_prompt(
pipe,
prompt_text
)
message_about_prompt_truncation += "Your prompt has been truncated because it is too long. This part has been truncated:
" + prompt_text_not_used_substring + ""
negative_prompt_text_not_used_substring = ""
if token_length_of_negative_prompt_text > max_token_length_of_model:
(
negative_prompt_text,
negative_prompt_text_not_used_substring
) = truncate_prompt(
pipe,
negative_prompt_text
)
if len(message_about_prompt_truncation) > 0:
message_about_prompt_truncation += "
"
message_about_prompt_truncation += "Your negative prompt has been truncated because it is too long. This part has been truncated:
" + negative_prompt_text_not_used_substring + ""
prompt_truncated_field_update = gr.HTML(
value = "",
visible = False
)
if len(message_about_prompt_truncation) > 0:
prompt_truncated_field_update = gr.HTML(
value = "Note: " + message_about_prompt_truncation + "
",
visible = True
)
show_message("Note: Part of your prompt has been truncated automatically because it was too long.")
actual_seed = int(actual_seed)
if actual_seed == 0:
default_seed_maximum_for_random = default_seed_maximum
if default_seed_maximum_for_random > 9007199254740992:
# If above this number, seeds may not be able to be entered into slider properly.
default_seed_maximum_for_random = 9007199254740992
actual_seed = int(random.randrange(1, 10**len(str(default_seed_maximum))))
if seed_not_valid(actual_seed):
raise Exception("Seed is not valid.")
generator = torch.manual_seed(actual_seed)
if show_image_creation_progress_log == 1:
current_base_model_generation_start_time = 0
def callback_function_for_base_model_progress(
callback_pipe,
callback_step_index,
callback_timestep,
callback_kwargs
):
global current_base_model_generation_start_time
if int(callback_step_index) == 0:
current_base_model_generation_start_time = time.time()
if int(callback_step_index) > 0:
seconds_per_step = ((time.time() - current_base_model_generation_start_time) / int(callback_step_index))
(
time_per_step_hours,
time_per_step_minutes,
time_per_step_seconds
) = convert_seconds(seconds_per_step)
if time_per_step_hours > 0:
hours_text = "hr"
if time_per_step_hours > 1:
hours_text = "hrs"
nice_time_per_step = str(int(time_per_step_hours)) + " " + hours_text + ". " + str(int(time_per_step_minutes)) + " min. " + str(round(time_per_step_seconds, 1)) + " sec."
elif time_per_step_minutes > 0:
nice_time_per_step = str(int(time_per_step_minutes)) + " min. " + str(round(time_per_step_seconds, 1)) + " sec."
else:
nice_time_per_step = str(round(time_per_step_seconds, 2)) + " seconds"
base_model_progress_text = nice_time_per_step + " per step"
else:
base_model_progress_text = "Base model processing started"
progress(
progress = (
callback_step_index,
current_actual_total_base_model_steps
),
desc = base_model_progress_text,
unit = "base model steps"
)
return {}
callback_to_do_for_base_model_progress = callback_function_for_base_model_progress
current_refiner_generation_start_time = 0
def callback_function_for_refiner_progress(
callback_pipe,
callback_step_index,
callback_timestep,
callback_kwargs
):
global current_refiner_generation_start_time
if int(callback_step_index) == 0:
current_refiner_generation_start_time = time.time()
if int(callback_step_index) > 0:
seconds_per_step = ((time.time() - current_refiner_generation_start_time) / int(callback_step_index))
(
time_per_step_hours,
time_per_step_minutes,
time_per_step_seconds
) = convert_seconds(seconds_per_step)
if time_per_step_hours > 0:
hours_text = "hr"
if time_per_step_hours > 1:
hours_text = "hrs"
nice_time_per_step = str(int(time_per_step_hours)) + " " + hours_text + ". " + str(int(time_per_step_minutes)) + " min. " + str(round(time_per_step_seconds, 1)) + " sec."
elif time_per_step_minutes > 0:
nice_time_per_step = str(int(time_per_step_minutes)) + " min. " + str(round(time_per_step_seconds, 1)) + " sec."
else:
nice_time_per_step = str(round(time_per_step_seconds, 2)) + " seconds"
refiner_progress_text = nice_time_per_step + " per step"
else:
refiner_progress_text = "Refner processing started"
progress(
progress = (
callback_step_index,
current_actual_total_refiner_steps
),
desc = refiner_progress_text,
unit = "refiner steps"
)
return {}
callback_to_do_for_refiner_progress = callback_function_for_refiner_progress
else:
callback_to_do_for_base_model_progress = None
callback_to_do_for_refiner_progress = None
is_sdxl_online_config = 0
is_photoreal_online_config = 0
if (
model_configuration_name_value == "sdxl_2023-11-12" or
model_configuration_name_value == "sdxl_2023-09-05"
):
is_sdxl_online_config = 1
elif (
model_configuration_name_value == "photoreal_2023-11-12" or
model_configuration_name_value == "photoreal_2023-09-01"
):
is_photoreal_online_config = 1
if (
(is_sdxl_online_config == 1) or
(is_photoreal_online_config == 1)
):
#
#
#
# Attempt To Match Online Configurations
#
#
#
prompt = prompt_text
negative_prompt = negative_prompt_text
width = image_width
height = image_height
scale = guidance_scale
steps = base_model_num_inference_steps
refining = use_refiner
if refining == 1:
refining = "Yes"
upscaling = use_upscaler
if upscaling == 1:
upscaling = "Yes"
prompt_2 = ""
negative_prompt_2 = ""
high_noise_frac = refining_denoise_start_for_online_config_field_value
if (is_sdxl_online_config) == 1:
add_seed_into_pipe = 1
n_steps = refining_number_of_iterations_for_online_config_field_value
upscaling_num_inference_steps = 15
if model_configuration_name_value == "sdxl_2023-09-05":
upscaling_num_inference_steps = 5
if show_messages_in_command_prompt == 1:
print ("Initial image creation has begun.");
if show_image_creation_progress_log == 1:
progress(
progress = 0,
desc = "Initial image creation has begun"
)
int_image = pipe(
prompt,
prompt_2=prompt_2,
negative_prompt=negative_prompt,
negative_prompt_2=negative_prompt_2,
num_inference_steps=steps,
height=height,
width=width,
guidance_scale=scale,
num_images_per_prompt=1,
generator=generator,
output_type="latent",
callback_on_step_end=callback_to_do_for_base_model_progress
).images
if show_messages_in_command_prompt == 1:
print ("Refiner steps...");
if show_image_creation_progress_log == 1:
progress(
progress = 0,
desc = "Refining is beginning"
)
current_actual_total_refiner_steps = int(int(n_steps) * float(high_noise_frac))
nice_refiner_denoise_start = str(refining_denoise_start_for_online_config_field_value)
refiner_info_for_info_about_prompt_lines_array = [
"Refiner? Yes"
"Refiner denoise start %: " + nice_refiner_denoise_start,
"Refiner number of iterations: " + str(refining_number_of_iterations_for_online_config_field_value),
"Actual Refining Steps: " + str(current_actual_total_refiner_steps)
]
image = refiner(
prompt=prompt,
prompt_2=prompt_2,
negative_prompt=negative_prompt,
negative_prompt_2=negative_prompt_2,
image=int_image,
num_inference_steps=n_steps,
denoising_start=high_noise_frac,
callback_on_step_end=callback_to_do_for_refiner_progress
).images[0]
if upscaling == 'Yes':
if show_messages_in_command_prompt == 1:
print ("Upscaler steps...");
if show_image_creation_progress_log == 1:
progress(
progress = 0,
desc = "Upscaling in progress (no steps shown)"
)
# Changed
#
# num_inference_steps=15
#
upscaled = upscaler(
prompt=prompt,
negative_prompt=negative_prompt,
image=image,
num_inference_steps=upscaling_num_inference_steps,
guidance_scale=0
).images[0]
if device == "cuda":
torch.cuda.empty_cache()
image_to_return = upscaled
else:
if device == "cuda":
torch.cuda.empty_cache()
image_to_return = image
elif (is_photoreal_online_config == 1):
add_seed_into_pipe = 0
Prompt = prompt
upscale = refining # Not a mistake. This is wrong in code.
if upscale == "Yes":
if show_messages_in_command_prompt == 1:
print ("Initial image creation has begun.");
if show_image_creation_progress_log == 1:
progress(
progress = 0,
desc = "Initial image creation has begun"
)
int_image = pipe(
Prompt,
negative_prompt=negative_prompt,
height=height,
width=width,
num_inference_steps=steps,
guidance_scale=scale,
callback_on_step_end=callback_to_do_for_base_model_progress
).images
if show_messages_in_command_prompt == 1:
print ("Refiner steps...");
if show_image_creation_progress_log == 1:
progress(
progress = 0,
desc = "Refining is beginning"
)
default_steps_in_diffusers = 50
current_actual_total_refiner_steps = int(default_steps_in_diffusers * float(high_noise_frac))
refiner_info_for_info_about_prompt_lines_array = [
"Refiner? Yes"
"Refiner denoise start %: " + nice_refiner_denoise_start,
"Refiner number of iterations: " + str(current_actual_total_refiner_steps),
"Actual Refining Steps: " + str(current_actual_total_refiner_steps)
]
image = refiner(
Prompt,
negative_prompt=negative_prompt,
image=int_image,
num_inference_steps=default_steps_in_diffusers,
denoising_start=high_noise_frac,
callback_on_step_end=callback_to_do_for_refiner_progress
).images[0]
else:
if show_messages_in_command_prompt == 1:
print ("Image creation has begun.");
if show_image_creation_progress_log == 1:
progress(
progress = 0,
desc = "Image creation has begun"
)
image = pipe(
Prompt,
negative_prompt=negative_prompt,
height=height,
width=width,
num_inference_steps=steps,
guidance_scale=scale,
callback_on_step_end=callback_to_do_for_base_model_progress
).images[0]
image_to_return = image
else:
if add_seed_into_pipe == 0:
generator = None
#
#
#
# My Configurations
#
#
#
if use_refiner == 1:
if refining_use_denoising_start_in_base_model_when_using_refiner_field_value == 1:
denoising_end = refining_denoise_start_for_default_config_field_value
current_actual_total_base_model_steps = int(base_model_num_inference_steps * float(refining_denoise_start_for_default_config_field_value))
else:
denoising_end = None
output_type_before_refiner = "pil"
if refining_base_model_output_to_refiner_is_in_latent_space_field_value == 1:
output_type_before_refiner = "latent"
current_actual_total_refiner_steps = (base_model_num_inference_steps - int(base_model_num_inference_steps * float(refining_denoise_start_for_default_config_field_value)))
refiner_info_for_info_about_prompt_lines_array = [
"Refiner? Yes"
]
nice_refiner_denoise_start = str(refining_denoise_start_for_online_config_field_value)
if refining_use_denoising_start_in_base_model_when_using_refiner_field_value == 1:
refiner_info_for_info_about_prompt_lines_array.extend([
"Set \"denoising_end\" in base model generation? Yes",
"Base model denoise end %: " + nice_refiner_denoise_start,
"Actual Base Model Steps: " + str(current_actual_total_base_model_steps)
])
else:
refiner_info_for_info_about_prompt_lines_array.extend([
"Set \"denoising_end\" in base model generation? No",
])
refiner_info_for_info_about_prompt_lines_array.extend([
"Refiner denoise start %: " + nice_refiner_denoise_start,
"Actual Refining Steps: " + str(current_actual_total_refiner_steps)
])
if refining_base_model_output_to_refiner_is_in_latent_space_field_value == 1:
refiner_info_for_info_about_prompt_lines_array.extend([
"Base model output in latent space before refining? Yes",
])
else:
refiner_info_for_info_about_prompt_lines_array.extend([
"Base model output in latent space before refining? No",
])
if use_upscaler == 1:
if show_messages_in_command_prompt == 1:
print ("Will create initial image, then refine and then upscale.");
print ("Initial image steps...");
if show_image_creation_progress_log == 1:
progress(
progress = 0,
desc = "Initial image creation has begun"
)
intitial_image = pipe(
prompt = prompt_text,
negative_prompt = negative_prompt_text,
width = image_width,
height = image_height,
num_inference_steps = base_model_num_inference_steps,
guidance_scale = guidance_scale,
num_images_per_prompt = 1,
generator = generator,
denoising_end = denoising_end,
output_type = output_type_before_refiner,
callback_on_step_end = callback_to_do_for_base_model_progress
).images
if show_messages_in_command_prompt == 1:
print ("Refiner steps...");
if show_image_creation_progress_log == 1:
progress(
progress = 0,
desc = "Refining is beginning"
)
refined_image = refiner(
prompt = prompt_text,
negative_prompt = negative_prompt_text,
image = intitial_image,
num_inference_steps = base_model_num_inference_steps,
denoising_start = refining_denoise_start_for_default_config_field_value,
output_type = "pil",
callback_on_step_end = callback_to_do_for_refiner_progress
).images
if show_messages_in_command_prompt == 1:
print ("Upscaler steps...");
if show_image_creation_progress_log == 1:
progress(
progress = 0,
desc = "Upscaling in progress (no steps shown)"
)
upscaled_image = upscaler(
prompt = prompt_text,
negative_prompt = negative_prompt_text,
image = refined_image,
num_inference_steps = upscaling_num_inference_steps,
guidance_scale = 0
).images[0]
if device == "cuda":
torch.cuda.empty_cache()
image_to_return = upscaled_image
else:
if show_messages_in_command_prompt == 1:
print ("Will create initial image and then refine.");
print ("Initial image steps...");
if show_image_creation_progress_log == 1:
progress(
progress = 0,
desc = "Initial image creation has begun"
)
intitial_image = pipe(
prompt = prompt_text,
negative_prompt = negative_prompt_text,
width = image_width,
height = image_height,
num_inference_steps = base_model_num_inference_steps,
guidance_scale = guidance_scale,
num_images_per_prompt = 1,
generator = generator,
denoising_end = denoising_end,
output_type = output_type_before_refiner,
callback_on_step_end = callback_to_do_for_base_model_progress
).images
if show_messages_in_command_prompt == 1:
print ("Refiner steps...");
if show_image_creation_progress_log == 1:
progress(
progress = 0,
desc = "Refining is beginning"
)
refined_image = refiner(
prompt = prompt_text,
negative_prompt = negative_prompt_text,
image = intitial_image,
num_inference_steps = base_model_num_inference_steps,
denoising_start = refining_denoise_start_for_default_config_field_value,
callback_on_step_end = callback_to_do_for_refiner_progress
).images[0]
if device == "cuda":
torch.cuda.empty_cache()
image_to_return = refined_image
else:
if use_upscaler == 1:
if show_messages_in_command_prompt == 1:
print ("Will create initial image and then upscale.");
print ("Initial image steps...");
if show_image_creation_progress_log == 1:
progress(
progress = 0,
desc = "Initial image creation has begun"
)
intitial_image = pipe(
prompt = prompt_text,
negative_prompt = negative_prompt_text,
width = image_width,
height = image_height,
num_inference_steps = base_model_num_inference_steps,
guidance_scale = guidance_scale,
num_images_per_prompt = 1,
generator = generator,
output_type = "pil",
callback_on_step_end = callback_to_do_for_base_model_progress
).images
if show_messages_in_command_prompt == 1:
print ("Upscaler steps...");
if show_image_creation_progress_log == 1:
progress(
progress = 0,
desc = "Upscaling in progress (no steps shown)"
)
upscaled_image = upscaler(
prompt = prompt_text,
negative_prompt = negative_prompt_text,
image = intitial_image,
num_inference_steps = upscaling_num_inference_steps,
guidance_scale = 0
).images[0]
if device == "cuda":
torch.cuda.empty_cache()
image_to_return = upscaled_image
else:
if show_messages_in_command_prompt == 1:
print ("Will create image (no refining or upscaling).");
print ("Image steps...");
if show_image_creation_progress_log == 1:
progress(
progress = 0,
desc = "Image creation has begun"
)
image = pipe(
prompt = prompt_text,
negative_prompt = negative_prompt_text,
width = image_width,
height = image_height,
num_inference_steps = base_model_num_inference_steps,
guidance_scale = guidance_scale,
num_images_per_prompt = 1,
generator = generator,
callback_on_step_end = callback_to_do_for_base_model_progress
).images[0]
if device == "cuda":
torch.cuda.empty_cache()
image_to_return = image
#
#
#
# Prompt Information
#
#
#
nice_model_name = base_model_names_object[base_model_name_value] + " (" + model_configuration_links_object[model_configuration_name_value] + ")"
info_about_prompt_lines_array = [
"Prompt:\n" + prompt_text
]
if len(negative_prompt_text) > 0:
info_about_prompt_lines_array.extend([
"Negative Prompt:\n" + negative_prompt_text
])
dimensions_title = "Dimensions"
if use_upscaler == 1:
dimensions_title = "Original Dimensions"
info_about_prompt_lines_array.extend([
dimensions_title + ": " + str(image_width) + "x" + str(image_height) + " px"
])
if use_upscaler == 1:
upscaled_image_width = int(image_width * 2)
upscaled_image_height = int(image_height * 2)
info_about_prompt_lines_array.extend([
"Upscaled Dimensions: " + str(upscaled_image_width) + "x" + str(upscaled_image_height) + " px"
])
info_about_prompt_lines_array.extend([
"Seed: " + str(actual_seed)
])
nice_seed_added_to_generation = "No"
if add_seed_into_pipe == 1:
nice_seed_added_to_generation = "Yes"
info_about_prompt_lines_array.extend([
"Seed added to generation? " + nice_seed_added_to_generation
])
if int(guidance_scale) > 0:
info_about_prompt_lines_array.extend([
"Guidance Scale: " + str(guidance_scale)
])
nice_scheduler_name = scheduler_short_names_object[scheduler_used]
if scheduler_value == "model_default":
nice_scheduler_name += " (model default)"
info_about_prompt_lines_array.extend([
"Steps: " + str(base_model_num_inference_steps),
"Model: " + nice_model_name,
"Scheduler/Sampler: " + nice_scheduler_name
])
if use_refiner == 1:
# Default Configuration
info_about_prompt_lines_array.extend(refiner_info_for_info_about_prompt_lines_array)
if use_upscaler == 1:
info_about_prompt_lines_array.extend([
"Upscaled (2x)? Yes",
"Refiner Steps: " + str(upscaling_num_inference_steps)
])
if log_generation_times == 1:
end_time = time.time()
generation_time_in_seconds = (end_time - start_time)
(
generation_partial_hours,
generation_partial_minutes,
generation_partial_seconds
) = convert_seconds(generation_time_in_seconds)
if generation_partial_hours > 0:
hours_text = "hr"
if generation_partial_hours > 1:
hours_text = "hrs"
nice_generation_time = str(int(generation_partial_hours)) + " " + hours_text + ". " + str(int(generation_partial_minutes)) + " min. " + str(round(generation_partial_seconds, 1)) + " sec."
elif generation_partial_minutes > 0:
nice_generation_time = str(int(generation_partial_minutes)) + " min. " + str(round(generation_partial_seconds, 1)) + " sec."
else:
nice_generation_time = str(round(generation_time_in_seconds, 2)) + " sec."
info_about_prompt_lines_array.extend([
"Time: " + nice_generation_time
])
if len(prompt_text_not_used_substring) > 0:
info_about_prompt_lines_array.extend([
"End of Prompt Truncated: " + prompt_text_not_used_substring
])
if len(negative_prompt_text_not_used_substring) > 0:
info_about_prompt_lines_array.extend([
"End of Negative Prompt Truncated: " + negative_prompt_text_not_used_substring
])
if display_xformers_usage_in_prompt_info > 0:
nice_xformers_usage = "No"
if use_xformers == 1:
nice_xformers_usage = "Yes"
if include_transformers_version_in_prompt_info == 1:
import transformers
nice_xformers_usage += " (version " + str(transformers.__version__) + ")"
info_about_prompt_lines_array.extend([
"xFormers Used?: " + nice_xformers_usage
])
if display_default_attn_processor_usage_in_prompt_info > 0:
nice_default_attn_processor_usage = "No"
if use_default_attn_processor == 1:
nice_default_attn_processor_usage = "Yes"
info_about_prompt_lines_array.extend([
"Default AttnProcessor Used? " + nice_default_attn_processor_usage
])
output_text_field_update = '\n'.join(info_about_prompt_lines_array)
if auto_save_imagery == 1:
if not os.path.exists(saved_images_dir):
os.makedirs(saved_images_dir)
yy_mm_dd_date_stamp = datetime.today().strftime('%Y-%m-%d')
saved_images_date_dir = saved_images_dir + "/" + yy_mm_dd_date_stamp + "/"
if not os.path.exists(saved_images_date_dir):
os.makedirs(saved_images_date_dir)
image_count = 1
file_name_without_extension = yy_mm_dd_date_stamp + "-" + ('%04d' % image_count)
saved_image_path_and_file = saved_images_date_dir + file_name_without_extension + ".png"
while os.path.exists(saved_image_path_and_file):
file_name_without_extension = yy_mm_dd_date_stamp + "-" + ('%04d' % image_count)
saved_image_path_and_file = saved_images_date_dir + file_name_without_extension + ".png"
image_count += 1
image_to_return_file = image_to_return.save(saved_image_path_and_file)
saved_text_file_path_and_file = saved_images_date_dir + file_name_without_extension + ".txt"
prompt_info_file_handle = open(saved_text_file_path_and_file, "w")
prompt_info_file_handle.writelines(output_text_field_update)
prompt_info_file_handle.close()
output_image_field_update = gr.Image(
value = image_to_return
)
image_gallery_array_state_value.insert(0, image_to_return)
prompt_information_array_state_value.insert(0, output_text_field_update)
output_image_gallery_field_update = gr.Gallery(
value = image_gallery_array_state_value,
selected_index = 0
)
image_gallery_array_state_update = image_gallery_array_state_value
prompt_information_array_state_update = prompt_information_array_state_value
if show_messages_in_command_prompt == 1:
print ("Image created.")
last_model_configuration_name_selected_state_update = last_model_configuration_name_selected_state_value
last_refiner_name_selected_state_update = last_refiner_name_selected_state_value
last_upscaler_name_selected_state_update = last_upscaler_name_selected_state_value
return (
output_image_field_update,
output_image_gallery_field_update,
output_text_field_update,
prompt_truncated_field_update,
last_model_configuration_name_selected_state_update,
last_refiner_name_selected_state_update,
last_upscaler_name_selected_state_update,
pipe,
refiner,
upscaler
)
#####################
#
# Cancel Image Processing
#
# When running on Windows, this is an attempt at closing the command
# prompt from the web display. It's really not worth having this. You can
# just close the prompt. I would like a nice way to cancel image
# creation, but couldn't figure that out.
#
#####################
def cancel_image_processing():
# I simply don't know how to stop the image generation without closing
# the command prompt. Doing that requires the code below twice for some
# reason.
#
# Method:
# https://stackoverflow.com/questions/67146623/how-to-close-the-command-prompt-from-python-script-directly
gr.Warning("The command prompt window has been closed. Any image generation in progress has been stopped. To generate any other images, you will need to launch the command prompt again.")
os.system('title kill_window')
os.system(f'taskkill /f /fi "WINDOWTITLE eq kill_window"')
os.system(f'taskkill /f /fi "WINDOWTITLE eq kill_window"')
###############################################################################
###############################################################################
#
#
#
#
#
#
# Create Web Display
#
#
#
#
#
#
###############################################################################
###############################################################################
# Hide border when yield is used:
# https://github.com/gradio-app/gradio/issues/5479
# .generating {border: none !important;}
css_to_use = "footer{display:none !important}"
with gr.Blocks(
title = "AI Image Creation",
css = css_to_use,
theme = gr.themes.Default(
spacing_size = gr.themes.sizes.spacing_md,
# spacing_size = gr.themes.sizes.spacing_sm,
radius_size = gr.themes.sizes.radius_none
),
analytics_enabled = False
) as sd_interface:
# Variables to store for user session
image_gallery_array_state = gr.State([])
prompt_information_array_state = gr.State([])
last_model_configuration_name_selected_state = gr.State("")
last_refiner_name_selected_state = gr.State("")
last_upscaler_name_selected_state = gr.State("")
stored_pipe_state = gr.State({})
stored_refiner_state = gr.State({})
stored_upscaler_state = gr.State({})
gr.Markdown(opening_html)
with gr.Row():
with gr.Column(scale = 1):
generate_image_btn = gr.Button(
value = "Generate",
variant = "primary"
)
with gr.Group():
with gr.Row():
prompt_field = gr.Textbox(
label = "Prompt (77 token limit):",
value = default_prompt
)
with gr.Row(
elem_id = "negative_prompt_field_row_id",
visible = default_negative_prompt_field_row_visibility
):
negative_prompt_field = gr.Textbox(
label = "Negative Prompt (77 token limit):",
value = default_negative_prompt
)
with gr.Row(
elem_id = "negative_prompt_for_sdxl_turbo_field_row_id",
visible = default_negative_prompt_for_sdxl_turbo_field_row_visibility
):
negative_prompt_for_sdxl_turbo_field = gr.HTML(
value = "Negative prompt is not used for SDXL Turbo.
"
)
with gr.Group(
visible = refiner_group_visible
):
with gr.Accordion(
elem_id = "refiner_default_config_accordion_id",
label = "Refiner (Default configuration)",
open = refiner_default_config_accordion_open,
visible = refiner_default_config_accordion_visible
) as refiner_default_config_accordion:
#
#
#
# Refiner (Default configuration)
#
#
#
with gr.Row():
gr.Markdown("This can be used if the image has too much noise.")
with gr.Row():
refining_selection_default_config_field = gr.Radio(
choices = ["Yes", "No"],
value = default_refine_option,
show_label = False,
container = False
)
with gr.Row():
refining_denoise_start_for_default_config_field = gr.Slider(
label = "Refiner denoise start %",
minimum = 0.7,
maximum = 0.99,
value = 0.95,
step = 0.01
)
with gr.Row():
refining_use_denoising_start_in_base_model_when_using_refiner_field = gr.Checkbox(
label = "Use \"denoising_start\" value as \"denoising_end\" value in base model generation when using refiner",
value = default_use_denoising_start_in_base_model_when_using_refiner_is_selected,
interactive = True,
container = True
)
with gr.Row():
refining_base_model_output_to_refiner_is_in_latent_space_field = gr.Checkbox(
label = "Base model output in latent space instead of PIL image when using refiner",
value = default_base_model_output_to_refiner_is_in_latent_space_is_selected,
interactive = True,
container = True
)
with gr.Accordion(
elem_id = "refiner_online_config_accordion_id",
label = "Refiner (Online configuration)",
open = refiner_online_config_accordion_open,
visible = refiner_online_config_accordion_visible
) as refiner_online_config_accordion:
#
#
#
# Refiner (Online configuration)
#
#
#
refining_selection_automatically_selected_message_field_visible = False
refining_selection_online_config_normal_field_visible = True
refining_selection_online_config_automatically_selected_field_visible = False
if model_configuration_requires_refiner == 1:
refining_selection_automatically_selected_message_field_visible = True
refining_selection_online_config_normal_field_visible = False
refining_selection_online_config_automatically_selected_field_visible = True
with gr.Row():
refining_selection_automatically_selected_message_field = gr.Markdown(
elem_id = "refining_selection_automatically_selected_message_field_id",
value = "The online configuration you selected automatically uses the refiner.",
visible = refining_selection_automatically_selected_message_field_visible
)
with gr.Row():
refining_selection_online_config_normal_field = gr.Radio(
elem_id = "refining_selection_online_config_normal_field_id",
choices = ["Yes", "No"],
value = default_refine_option,
show_label = False,
container = False,
visible = refining_selection_online_config_normal_field_visible
)
with gr.Row():
refining_selection_online_config_automatically_selected_field = gr.Radio(
elem_id = "refining_selection_online_config_automatically_selected_field_id",
choices = ["Yes"],
value = "Yes",
show_label = False,
container = False,
visible = refining_selection_online_config_automatically_selected_field_visible
)
with gr.Row():
refining_denoise_start_for_online_config_field = gr.Slider(
label = "Refiner denoise start %",
minimum = 0.7,
maximum = 0.99,
value = 0.95,
step = 0.01
)
refining_number_of_iterations_for_online_config_field_visible = False
if default_model_configuration in model_configuration_include_refiner_number_of_steps_object:
refining_number_of_iterations_for_online_config_field_visible = True
with gr.Row(
elem_id = "refining_number_of_iterations_for_online_config_field_row_id",
visible = refining_number_of_iterations_for_online_config_field_visible
):
refining_number_of_iterations_for_online_config_field = gr.Slider(
label = "Refiner number of iterations",
minimum = 1,
maximum = 100,
value = 100,
step = 1
)
with gr.Group(
visible = upscaler_group_visible
):
with gr.Accordion(
label = "Upscaler",
elem_id = "upscaler_accordion_id",
open = upscaler_accordion_open,
visible = upscaler_group_visible
):
#
#
#
# Upscaler
#
#
#
with gr.Row():
gr.Markdown("Upscale by 2x?")
with gr.Row():
upscaling_selection_field = gr.Radio(
choices = ['Yes', 'No'],
value = default_upscale_option,
show_label = False,
container = False
)
with gr.Row():
upscaling_num_inference_steps_field = gr.Slider(
label = "Upscaler number of steps",
minimum = 1,
maximum = maximum_upscaler_steps,
value = default_upscaler_steps,
step = 1
)
if (
(enable_refiner == 1) or
(enable_upscaler == 1)
):
refiner_and_upscaler_text_field = gr.HTML(
value = "" + default_refiner_and_upscaler_status_text + "
"
)
with gr.Column(scale = 1):
with gr.Group():
with gr.Row():
base_model_field = gr.Dropdown(
label = "Base Model:",
choices = default_base_model_choices_array,
value = default_base_model_nicely_named_value,
type = "index",
filterable = False,
#min_width = 240,
interactive = True
)
model_configuration_dropdown_field_values_for_js = ""
model_configuration_dropdown_fields_array = []
for this_base_model in base_model_array:
this_model_configuration_choices_array = []
for this_model_configuration in base_model_object_of_model_configuration_arrays[this_base_model]:
this_model_configuration_choices_array.append(
model_configuration_names_object[this_model_configuration]
)
this_configuration_field_row_visibility = False
if (
(this_base_model == default_base_model) and
(allow_online_configurations == 1)
):
this_configuration_field_row_visibility = True
this_configuration_field_default_value = model_configuration_names_object[base_model_model_configuration_defaults_object[this_base_model]]
this_configuration_field_default_value_for_js = this_configuration_field_default_value
this_configuration_field_default_value_for_js = this_configuration_field_default_value_for_js.replace("\"", "\\\"")
model_configuration_dropdown_field_values_for_js += "\"" + this_base_model + "\": \"" + this_configuration_field_default_value_for_js + "\","
with gr.Row(
elem_id = "model_configuration_field_" + this_base_model + "_row_id",
visible = this_configuration_field_row_visibility
):
this_configuration_field = gr.Dropdown(
label = "Configuration Type:",
choices = this_model_configuration_choices_array,
value = this_configuration_field_default_value,
type = "index",
filterable = False,
#min_width = 240,
interactive = True
)
model_configuration_dropdown_fields_array.append(this_configuration_field)
with gr.Row():
scheduler_field = gr.Dropdown(
elem_id = "scheduler_field_id",
label = "Scheduler / Sampler:",
choices = default_scheduler_choices_array,
value = default_scheduler_nicely_named_value,
type = "index",
filterable = False,
#min_width = 240,
interactive = True
)
with gr.Row():
image_width_field = gr.Slider(
label = "Width:",
minimum = 256,
maximum = 1024,
value = default_width,
step = width_and_height_input_slider_steps,
interactive = True
)
image_height_field = gr.Slider(
label = "Height:",
minimum = 256,
maximum = 1024,
value = default_height,
step = width_and_height_input_slider_steps,
interactive = True
)
with gr.Row(
elem_id = "base_model_num_inference_steps_field_row_id",
visible = default_base_model_num_inference_steps_field_row_visibility
):
base_model_num_inference_steps_field = gr.Slider(
label = "Steps:",
minimum = 1,
maximum = 100,
value = default_base_model_base_model_num_inference_steps,
step = 1,
interactive = True
)
with gr.Row(
elem_id = "base_model_num_inference_steps_field_for_sdxl_turbo_field_row_id",
visible = default_base_model_num_inference_steps_field_for_sdxl_turbo_field_row_visibility
):
base_model_num_inference_steps_field_for_sdxl_turbo_field = gr.Slider(
label = "Steps:",
info = "Try using only 1 or a couple of steps.",
minimum = 1,
maximum = 25,
value = default_base_model_base_model_num_inference_steps_for_sdxl_turbo,
step = 1,
interactive = True
)
with gr.Row(
elem_id = "guidance_scale_field_row_id",
visible = default_guidance_scale_field_row_visibility
):
guidance_scale_field = gr.Slider(
label = "Guidance Scale:",
minimum = 1,
maximum = 15,
value = default_guidance_scale_value,
step = 0.25,
interactive = True
)
with gr.Row(
elem_id = "guidance_scale_for_sdxl_turbo_field_row_id",
visible = default_guidance_scale_for_sdxl_turbo_field_row_visibility
):
guidance_scale_for_sdxl_turbo_field = gr.HTML(
value = "Guidance scale is not used for SDXL Turbo.
"
)
with gr.Row():
# If you use a slider or number field for the seed, some
# seeds can't be duplicated using those fields. If you
# enter a number greater than 9007199254740992, the seed
# won't reliably be used. This is a technical limitation
# as of writing this. See the bug report here:
# https://github.com/gradio-app/gradio/issues/5354
#
# Until this is fixed, we use a textbox if the max seed
# allowed is greater than that number. Using the slider,
# and not entering a number, might be the way to get
# reliable numbers above that number, if you just don't
# then use the up and down arrows in the field to go up
# or down a number.
#
# For now, I do this, but I might eventually have a
# setting on the page to allow the slider.
if make_seed_selection_a_textbox == 1:
seed_field = gr.Textbox(
label = "Seed (0 is random):",
value = default_seed_value,
interactive = True,
info = "Maximum: " + str(default_seed_maximum)
)
else:
seed_field = gr.Slider(
label = "Seed (0 is random):",
minimum = 0,
maximum = default_seed_maximum,
value = default_seed_value,
step = 1,
interactive = True
)
with gr.Row(
elem_id = "add_seed_into_pipe_field_row_id",
visible = default_add_seed_into_pipe_field_row_visibility
):
add_seed_into_pipe_field = gr.Checkbox(
label = "Add seed to generation (to make it deterministic)",
value = default_add_seed_into_pipe_is_selected,
interactive = True,
container = True
)
with gr.Column(scale = 1):
image_field_visibility = True
image_gallery_field_visibility = False
if use_image_gallery == 1:
image_field_visibility = False
image_gallery_field_visibility = True
with gr.Row(
visible = image_field_visibility
):
output_image_field = gr.Image(
label = "Generated Image",
type = "pil"
)
with gr.Row(
visible = image_gallery_field_visibility
):
show_download_button = False
show_download_button = True
output_image_gallery_field = gr.Gallery(
elem_id = "image_gallery_id",
label = "Generated Images",
value = [],
selected_index = 0,
allow_preview = "True",
preview = True,
show_download_button = show_download_button
)
with gr.Row():
output_text_field = gr.Textbox(
label = "Prompt Information:",
value = "After an image is generated, its generation information will appear here." + additional_prompt_info_html,
show_copy_button = True,
lines = 10,
max_lines = 20,
container = True
)
with gr.Row():
prompt_truncated_field = gr.HTML(
value = "",
visible = False
)
if enable_close_command_prompt_button == 1:
cancel_image_btn = gr.Button(
value = "Close Command Prompt / Cancel",
variant = "stop"
)
gr.Markdown("Closing the command prompt will cancel any images in the process of being created. You will need to launch it again, and then likely refresh the page, to create more images.")
if len(ending_html) > 0:
with gr.Accordion(
elem_id = "information_section_id",
label = "Information",
open = True
):
gr.Markdown(ending_html)
#####################
#
# Update Refiner and Upscaler Status Function for Javascript
#
# When the refiner or upscaler is turned on or off, a text message is
# printed on the page. That needs to be updated.
#
#####################
update_refiner_and_upscaler_status_function_js = """
function updateRefinerAndUpscalerStatus(
baseModelFieldFullNameValue,
refiningSelectionDefaultConfigFieldValue,
refiningSelectionOnlineConfigNormalFieldValue,
refiningSelectionOnlineConfigAutomaticallySelectedFieldValue,
upscalingSelectionFieldValue
) {{
"use strict";
var baseModelNamesObject = {0};
var modelConfigurationNamesObject = {1};
var onlineConfigurationsObject = {2};
var modelConfigurationForceRefinerObject = {3};
var refinerOnText = "{4}";
var refinerOffText = "{5}";
var upscalerOnText = "{6}";
var upscalerOffText = "{7}";
var baseModelFullNamesToBaseModelIdConversion = {{}};
Object.keys(baseModelNamesObject).forEach(key => {{
baseModelFullNamesToBaseModelIdConversion[baseModelNamesObject[key]] = key;
}});
var baseModelFieldValue = "";
if (baseModelFullNamesToBaseModelIdConversion.hasOwnProperty(baseModelFieldFullNameValue)) {{
baseModelFieldValue = baseModelFullNamesToBaseModelIdConversion[baseModelFieldFullNameValue];
}}
var modelConfigurationFullNameValue = window.modelConfigurationDropdownFieldValuesObject[baseModelFieldValue];
var modelConfigurationFullNamesToModelConfigurationIdConversion = {{}};
Object.keys(modelConfigurationNamesObject).forEach(key => {{
modelConfigurationFullNamesToModelConfigurationIdConversion[modelConfigurationNamesObject[key]] = key;
}});
var modelConfigurationNameValue = "";
if (modelConfigurationFullNamesToModelConfigurationIdConversion.hasOwnProperty(modelConfigurationFullNameValue)) {{
modelConfigurationNameValue = modelConfigurationFullNamesToModelConfigurationIdConversion[modelConfigurationFullNameValue];
}}
var refinerAndUpscalerInfoMessageHtml = "";
if (
baseModelFieldValue &&
modelConfigurationNameValue
) {{
var isDefaultConfigState = 1;
if (onlineConfigurationsObject[modelConfigurationNameValue]) {{
isDefaultConfigState = 0;
}}
if (
(
(isDefaultConfigState === 1) &&
(refiningSelectionDefaultConfigFieldValue === "Yes")
) || (
(isDefaultConfigState !== 1) &&
(
(
(!Object.keys(modelConfigurationForceRefinerObject).includes(modelConfigurationNameValue)) &&
(refiningSelectionOnlineConfigNormalFieldValue === "Yes")
) || (
(Object.keys(modelConfigurationForceRefinerObject).includes(modelConfigurationNameValue)) &&
(refiningSelectionOnlineConfigAutomaticallySelectedFieldValue === "Yes")
)
)
)
) {{
refinerAndUpscalerInfoMessageHtml += refinerOnText;
}}
else {{
refinerAndUpscalerInfoMessageHtml += refinerOffText;
}}
if (upscalingSelectionFieldValue === "Yes") {{
refinerAndUpscalerInfoMessageHtml += upscalerOnText;
}}
else {{
refinerAndUpscalerInfoMessageHtml += upscalerOffText;
}}
}}
document.getElementById("refiner_and_upscaler_info_message_div_id").innerHTML = refinerAndUpscalerInfoMessageHtml;
}}
""".format(
base_model_names_object,
model_configuration_names_object,
online_configurations_object,
model_configuration_force_refiner_object,
refiner_on_text,
refiner_off_text,
upscaler_on_text,
upscaler_off_text
)
#####################
#
# Model Change Function for Javascript
#
# When the base model or model configuration is changed, we may need
# to show and hide certain fields.
#
#####################
model_change_function_js = """
function modelChange(
baseModelFieldFullNameValue,
possiblyModelConfigurationFullNameValue
) {{
"use strict";
var baseModelNamesObject = {0};
var modelConfigurationNamesObject = {1};
var baseModelArray = {2};
var onlineConfigurationsObject = {3};
var modelConfigurationForceRefinerObject = {4};
var modelConfigurationIncludeRefinerNumberOfStepsObject = {5};
var allowOnlineConfigurations = {6};
var baseModelFullNamesToBaseModelIdConversion = {{}};
Object.keys(baseModelNamesObject).forEach(key => {{
baseModelFullNamesToBaseModelIdConversion[baseModelNamesObject[key]] = key;
}});
var baseModelFieldValue = "";
if (baseModelFullNamesToBaseModelIdConversion.hasOwnProperty(baseModelFieldFullNameValue)) {{
baseModelFieldValue = baseModelFullNamesToBaseModelIdConversion[baseModelFieldFullNameValue];
}}
var modelConfigurationFullNameValue = ""
var isBaseModelDropdownChange = 0
if (baseModelFieldFullNameValue === possiblyModelConfigurationFullNameValue) {{
isBaseModelDropdownChange = 1;
modelConfigurationFullNameValue = window.modelConfigurationDropdownFieldValuesObject[baseModelFieldValue];
}}
else {{
modelConfigurationFullNameValue = possiblyModelConfigurationFullNameValue;
window.modelConfigurationDropdownFieldValuesObject[baseModelFieldValue] = modelConfigurationFullNameValue;
}}
var modelConfigurationFullNamesToModelConfigurationIdConversion = {{}};
Object.keys(modelConfigurationNamesObject).forEach(key => {{
modelConfigurationFullNamesToModelConfigurationIdConversion[modelConfigurationNamesObject[key]] = key;
}});
var modelConfigurationNameValue = "";
if (modelConfigurationFullNamesToModelConfigurationIdConversion.hasOwnProperty(modelConfigurationFullNameValue)) {{
modelConfigurationNameValue = modelConfigurationFullNamesToModelConfigurationIdConversion[modelConfigurationFullNameValue];
}}
for (var thisBaseModel of baseModelArray) {{
var thisModelConfigurationElementId = "model_configuration_field_" + thisBaseModel + "_row_id";
var thisModelConfigurationElementDisplay = "none";
if (
(thisBaseModel === baseModelFieldValue) &&
(allowOnlineConfigurations === 1)
) {{
thisModelConfigurationElementDisplay = "block";
}}
document.getElementById(thisModelConfigurationElementId).style.display = thisModelConfigurationElementDisplay;
}}
var modelConfigurationFullNamesToModelConfigurationIdConversion = {{}};
Object.keys(modelConfigurationNamesObject).forEach(key => {{
modelConfigurationFullNamesToModelConfigurationIdConversion[modelConfigurationNamesObject[key]] = key;
}});
var modelConfigurationNameValue = "";
if (modelConfigurationFullNamesToModelConfigurationIdConversion.hasOwnProperty(modelConfigurationFullNameValue)) {{
modelConfigurationNameValue = modelConfigurationFullNamesToModelConfigurationIdConversion[modelConfigurationFullNameValue];
}}
if (
baseModelFieldValue &&
modelConfigurationNameValue
) {{
var isDefaultConfigState = 1;
if (onlineConfigurationsObject[modelConfigurationNameValue]) {{
isDefaultConfigState = 0;
}}
var negativePromptFieldDisplay = "block";
var negativePromptForSdxlTurboFieldDisplay = "none";
var baseModelNumInferenceStepsFieldDisplay = "block";
var baseModelNumInferenceStepsFieldForSdxlTurboFieldDisplay = "none";
var guidanceScaleFieldDisplay = "block";
var guidanceScaleForSdxlTurboFieldDisplay = "none";
if (baseModelFieldValue === "sdxl_turbo") {{
negativePromptFieldDisplay = "none";
negativePromptForSdxlTurboFieldDisplay = "block";
baseModelNumInferenceStepsFieldDisplay = "none";
baseModelNumInferenceStepsFieldForSdxlTurboFieldDisplay = "block";
guidanceScaleFieldDisplay = "none";
guidanceScaleForSdxlTurboFieldDisplay = "block";
}}
document.getElementById("negative_prompt_field_row_id").style.display = negativePromptFieldDisplay;
document.getElementById("negative_prompt_for_sdxl_turbo_field_row_id").style.display = negativePromptForSdxlTurboFieldDisplay;
document.getElementById("base_model_num_inference_steps_field_row_id").style.display = baseModelNumInferenceStepsFieldDisplay;
document.getElementById("base_model_num_inference_steps_field_for_sdxl_turbo_field_row_id").style.display = baseModelNumInferenceStepsFieldForSdxlTurboFieldDisplay;
document.getElementById("guidance_scale_field_row_id").style.display = guidanceScaleFieldDisplay;
document.getElementById("guidance_scale_for_sdxl_turbo_field_row_id").style.display = guidanceScaleForSdxlTurboFieldDisplay;
var refinerDefaultConfigAccordionDisplay = "none";
var refinerOnlineConfigAccordionDisplay = "block";
var addSeedIntoPipeFieldDisplay = "none";
if (isDefaultConfigState === 1) {{
refinerDefaultConfigAccordionDisplay = "block";
refinerOnlineConfigAccordionDisplay = "none";
addSeedIntoPipeFieldDisplay = "block";
}}
document.getElementById("refiner_default_config_accordion_id").style.display = refinerDefaultConfigAccordionDisplay;
document.getElementById("refiner_online_config_accordion_id").style.display = refinerOnlineConfigAccordionDisplay;
document.getElementById("add_seed_into_pipe_field_row_id").style.display = addSeedIntoPipeFieldDisplay;
var refiningSelectionAutomaticallySelectedMessageFieldDisplay = "none";
var refiningSelectionOnlineConfigNormalFieldDisplay = "block";
var refiningSelectionOnlineConfigAutomaticallySelectedFieldDisplay = "none";
if (Object.keys(modelConfigurationForceRefinerObject).includes(modelConfigurationNameValue)) {{
refiningSelectionAutomaticallySelectedMessageFieldDisplay = "block";
refiningSelectionOnlineConfigNormalFieldDisplay = "none";
refiningSelectionOnlineConfigAutomaticallySelectedFieldDisplay = "block";
}}
var refiningNumberOfIterationsForOnlineConfigFieldDisplay = "none";
if (Object.keys(modelConfigurationIncludeRefinerNumberOfStepsObject).includes(modelConfigurationNameValue)) {{
refiningNumberOfIterationsForOnlineConfigFieldDisplay = "block";
}}
document.getElementById("refining_selection_automatically_selected_message_field_id").style.display = refiningSelectionAutomaticallySelectedMessageFieldDisplay;
document.getElementById("refining_selection_online_config_normal_field_id").style.display = refiningSelectionOnlineConfigNormalFieldDisplay;
document.getElementById("refining_selection_online_config_automatically_selected_field_id").style.display = refiningSelectionOnlineConfigAutomaticallySelectedFieldDisplay;
document.getElementById("refining_number_of_iterations_for_online_config_field_row_id").style.display = refiningNumberOfIterationsForOnlineConfigFieldDisplay;
}}
}}
""".format(
base_model_names_object,
model_configuration_names_object,
base_model_array,
online_configurations_object,
model_configuration_force_refiner_object,
model_configuration_include_refiner_number_of_steps_object,
allow_online_configurations
)
base_model_field.change(
fn = None,
inputs = [
base_model_field
],
outputs = None,
js = model_change_function_js
)
for this_model_configuration_dropdown_field in model_configuration_dropdown_fields_array:
this_model_configuration_dropdown_field.change(
fn = None,
inputs = [
base_model_field,
this_model_configuration_dropdown_field
],
outputs = None,
js = model_change_function_js
)
output_image_gallery_field.select(
fn = update_prompt_info_from_gallery,
inputs = [
prompt_information_array_state
],
outputs = [
output_image_gallery_field,
output_text_field
],
show_progress = "hidden"
)
if (
(enable_refiner == 1) or
(enable_upscaler == 1)
):
triggers_array = []
if enable_refiner == 1:
triggers_array.extend([
base_model_field.change,
refining_selection_default_config_field.change,
refining_selection_online_config_normal_field.change,
refining_selection_online_config_automatically_selected_field.change
])
for this_model_configuration_dropdown_field in model_configuration_dropdown_fields_array:
triggers_array.extend([
this_model_configuration_dropdown_field.change
])
if enable_upscaler == 1:
triggers_array.extend([
upscaling_selection_field.change
])
gr.on(
triggers = triggers_array,
fn = None,
inputs = [
base_model_field,
refining_selection_default_config_field,
refining_selection_online_config_normal_field,
refining_selection_online_config_automatically_selected_field,
upscaling_selection_field
],
outputs = None,
show_progress = "hidden",
queue = False,
js = update_refiner_and_upscaler_status_function_js
)
create_image_function_inputs = [
base_model_field,
prompt_field,
negative_prompt_field,
scheduler_field,
image_width_field,
image_height_field,
guidance_scale_field,
base_model_num_inference_steps_field,
base_model_num_inference_steps_field_for_sdxl_turbo_field,
seed_field,
add_seed_into_pipe_field,
refining_selection_default_config_field,
refining_selection_online_config_normal_field,
refining_selection_online_config_automatically_selected_field,
refining_denoise_start_for_default_config_field,
refining_use_denoising_start_in_base_model_when_using_refiner_field,
refining_base_model_output_to_refiner_is_in_latent_space_field,
refining_denoise_start_for_online_config_field,
refining_number_of_iterations_for_online_config_field,
upscaling_selection_field,
upscaling_num_inference_steps_field,
image_gallery_array_state,
prompt_information_array_state,
last_model_configuration_name_selected_state,
last_refiner_name_selected_state,
last_upscaler_name_selected_state,
stored_pipe_state,
stored_refiner_state,
stored_upscaler_state
]
for this_model_configuration_dropdown_field in model_configuration_dropdown_fields_array:
create_image_function_inputs.append(
this_model_configuration_dropdown_field
)
generate_image_btn_click_event = generate_image_btn.click(
fn = before_create_image_function,
inputs = None,
outputs = [
generate_image_btn,
output_image_field,
output_image_gallery_field,
output_text_field
],
show_progress = "hidden",
queue = True
).then(
fn = create_image_function,
inputs = create_image_function_inputs,
outputs = [
output_image_field,
output_image_gallery_field,
output_text_field,
prompt_truncated_field,
last_model_configuration_name_selected_state,
last_refiner_name_selected_state,
last_upscaler_name_selected_state,
stored_pipe_state,
stored_refiner_state,
stored_upscaler_state
],
show_progress = "full",
queue = True
).then(
fn = after_create_image_function,
inputs = None,
outputs = [
generate_image_btn,
output_text_field
],
show_progress = "hidden",
queue = True
)
if enable_close_command_prompt_button == 1:
# https://github.com/gradio-app/gradio/pull/2433/files
cancel_image_btn.click(
fn = cancel_image_processing,
inputs = None,
outputs = None,
cancels = [generate_image_btn_click_event],
queue = True
)
# Remove last comma
model_configuration_dropdown_field_values_for_js = model_configuration_dropdown_field_values_for_js[:-1]
script_on_load_js = """
function scriptOnLoad() {{
"use strict";
window.modelConfigurationDropdownFieldValuesObject = {{{0}}};
}}
""".format(
model_configuration_dropdown_field_values_for_js
)
sd_interface_load_kwargs = {
"scroll_to_output": False,
"show_progress": "full"
}
sd_interface_continuous = sd_interface.load(
fn = None,
inputs = None,
outputs = None,
js = script_on_load_js,
**sd_interface_load_kwargs
)
sd_interface.queue(
max_size = max_queue_size
)
inbrowser = False
if auto_open_browser == 1:
inbrowser = True
sd_interface.launch(
inbrowser = inbrowser,
share = None,
show_api = False,
quiet = True,
show_error = True,
state_session_capacity = 10000,
max_threads = 40
)