mirror of
https://github.com/sd-webui/stable-diffusion-webui.git
synced 2024-12-14 23:02:00 +03:00
472 lines
29 KiB
Python
472 lines
29 KiB
Python
# This file is part of stable-diffusion-webui (https://github.com/sd-webui/stable-diffusion-webui/).
|
|
|
|
# Copyright 2022 sd-webui team.
|
|
# This program is free software: you can redistribute it and/or modify
|
|
# it under the terms of the GNU Affero General Public License as published by
|
|
# the Free Software Foundation, either version 3 of the License, or
|
|
# (at your option) any later version.
|
|
|
|
# This program is distributed in the hope that it will be useful,
|
|
# but WITHOUT ANY WARRANTY; without even the implied warranty of
|
|
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
|
# GNU Affero General Public License for more details.
|
|
|
|
# You should have received a copy of the GNU Affero General Public License
|
|
# along with this program. If not, see <http://www.gnu.org/licenses/>.
|
|
# base webui import and utils.
|
|
from sd_utils import *
|
|
|
|
# streamlit imports
|
|
from streamlit import StopException
|
|
#from streamlit.elements import image as STImage
|
|
import streamlit.components.v1 as components
|
|
from streamlit.runtime.media_file_manager import media_file_manager
|
|
from streamlit.elements.image import image_to_url
|
|
|
|
#other imports
|
|
import uuid
|
|
from typing import Union
|
|
from ldm.models.diffusion.ddim import DDIMSampler
|
|
from ldm.models.diffusion.plms import PLMSSampler
|
|
|
|
# Temp imports
|
|
|
|
|
|
# end of imports
|
|
#---------------------------------------------------------------------------------------------------------------
|
|
|
|
|
|
try:
|
|
# this silences the annoying "Some weights of the model checkpoint were not used when initializing..." message at start.
|
|
from transformers import logging
|
|
|
|
logging.set_verbosity_error()
|
|
except:
|
|
pass
|
|
|
|
#
|
|
# Dev mode (server)
|
|
# _component_func = components.declare_component(
|
|
# "sd-gallery",
|
|
# url="http://localhost:3001",
|
|
# )
|
|
|
|
# Init Vuejs component
|
|
_component_func = components.declare_component(
|
|
"sd-gallery", "./frontend/dists/sd-gallery/dist")
|
|
|
|
def sdGallery(images=[], key=None):
|
|
component_value = _component_func(images=imgsToGallery(images), key=key, default="")
|
|
return component_value
|
|
|
|
def imgsToGallery(images):
|
|
urls = []
|
|
for i in images:
|
|
# random string for id
|
|
random_id = str(uuid.uuid4())
|
|
url = image_to_url(
|
|
image=i,
|
|
image_id= random_id,
|
|
width=i.width,
|
|
clamp=False,
|
|
channels="RGB",
|
|
output_format="PNG"
|
|
)
|
|
# image_io = BytesIO()
|
|
# i.save(image_io, 'PNG')
|
|
# width, height = i.size
|
|
# image_id = "%s" % (str(images.index(i)))
|
|
# (data, mimetype) = STImage._normalize_to_bytes(image_io.getvalue(), width, 'auto')
|
|
# this_file = media_file_manager.add(data, mimetype, image_id)
|
|
# img_str = this_file.url
|
|
urls.append(url)
|
|
|
|
return urls
|
|
|
|
|
|
class plugin_info():
|
|
plugname = "txt2img"
|
|
description = "Text to Image"
|
|
isTab = True
|
|
displayPriority = 1
|
|
|
|
#
|
|
def txt2img(prompt: str, ddim_steps: int, sampler_name: str, n_iter: int, batch_size: int, cfg_scale: float, seed: Union[int, str, None],
|
|
height: int, width: int, separate_prompts:bool = False, normalize_prompt_weights:bool = True,
|
|
save_individual_images: bool = True, save_grid: bool = True, group_by_prompt: bool = True,
|
|
save_as_jpg: bool = True, use_GFPGAN: bool = True, GFPGAN_model: str = 'GFPGANv1.3', use_RealESRGAN: bool = False,
|
|
RealESRGAN_model: str = "RealESRGAN_x4plus_anime_6B", use_LDSR: bool = True, LDSR_model: str = "model",
|
|
fp = None, variant_amount: float = None,
|
|
variant_seed: int = None, ddim_eta:float = 0.0, write_info_files:bool = True):
|
|
|
|
outpath = st.session_state['defaults'].general.outdir_txt2img
|
|
|
|
seed = seed_to_int(seed)
|
|
|
|
if sampler_name == 'PLMS':
|
|
sampler = PLMSSampler(server_state["model"])
|
|
elif sampler_name == 'DDIM':
|
|
sampler = DDIMSampler(server_state["model"])
|
|
elif sampler_name == 'k_dpm_2_a':
|
|
sampler = KDiffusionSampler(server_state["model"],'dpm_2_ancestral')
|
|
elif sampler_name == 'k_dpm_2':
|
|
sampler = KDiffusionSampler(server_state["model"],'dpm_2')
|
|
elif sampler_name == 'k_euler_a':
|
|
sampler = KDiffusionSampler(server_state["model"],'euler_ancestral')
|
|
elif sampler_name == 'k_euler':
|
|
sampler = KDiffusionSampler(server_state["model"],'euler')
|
|
elif sampler_name == 'k_heun':
|
|
sampler = KDiffusionSampler(server_state["model"],'heun')
|
|
elif sampler_name == 'k_lms':
|
|
sampler = KDiffusionSampler(server_state["model"],'lms')
|
|
else:
|
|
raise Exception("Unknown sampler: " + sampler_name)
|
|
|
|
def init():
|
|
pass
|
|
|
|
def sample(init_data, x, conditioning, unconditional_conditioning, sampler_name):
|
|
samples_ddim, _ = sampler.sample(S=ddim_steps, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=cfg_scale,
|
|
unconditional_conditioning=unconditional_conditioning, eta=ddim_eta, x_T=x, img_callback=generation_callback,
|
|
log_every_t=int(st.session_state.update_preview_frequency))
|
|
|
|
return samples_ddim
|
|
|
|
#try:
|
|
output_images, seed, info, stats = process_images(
|
|
outpath=outpath,
|
|
func_init=init,
|
|
func_sample=sample,
|
|
prompt=prompt,
|
|
seed=seed,
|
|
sampler_name=sampler_name,
|
|
save_grid=save_grid,
|
|
batch_size=batch_size,
|
|
n_iter=n_iter,
|
|
steps=ddim_steps,
|
|
cfg_scale=cfg_scale,
|
|
width=width,
|
|
height=height,
|
|
prompt_matrix=separate_prompts,
|
|
use_GFPGAN=st.session_state["use_GFPGAN"],
|
|
GFPGAN_model=st.session_state["GFPGAN_model"],
|
|
use_RealESRGAN=st.session_state["use_RealESRGAN"],
|
|
realesrgan_model_name=RealESRGAN_model,
|
|
use_LDSR=st.session_state["use_LDSR"],
|
|
LDSR_model_name=LDSR_model,
|
|
ddim_eta=ddim_eta,
|
|
normalize_prompt_weights=normalize_prompt_weights,
|
|
save_individual_images=save_individual_images,
|
|
sort_samples=group_by_prompt,
|
|
write_info_files=write_info_files,
|
|
jpg_sample=save_as_jpg,
|
|
variant_amount=variant_amount,
|
|
variant_seed=variant_seed,
|
|
)
|
|
|
|
del sampler
|
|
|
|
return output_images, seed, info, stats
|
|
|
|
#except RuntimeError as e:
|
|
#err = e
|
|
#err_msg = f'CRASHED:<br><textarea rows="5" style="color:white;background: black;width: -webkit-fill-available;font-family: monospace;font-size: small;font-weight: bold;">{str(e)}</textarea><br><br>Please wait while the program restarts.'
|
|
#stats = err_msg
|
|
#return [], seed, 'err', stats
|
|
|
|
#
|
|
def layout():
|
|
with st.form("txt2img-inputs"):
|
|
st.session_state["generation_mode"] = "txt2img"
|
|
|
|
input_col1, generate_col1 = st.columns([10,1])
|
|
|
|
with input_col1:
|
|
#prompt = st.text_area("Input Text","")
|
|
prompt = st.text_input("Input Text","", placeholder="A corgi wearing a top hat as an oil painting.")
|
|
|
|
# creating the page layout using columns
|
|
col1, col2, col3 = st.columns([1,2,1], gap="large")
|
|
|
|
with col1:
|
|
width = st.slider("Width:", min_value=st.session_state['defaults'].txt2img.width.min_value, max_value=st.session_state['defaults'].txt2img.width.max_value,
|
|
value=st.session_state['defaults'].txt2img.width.value, step=st.session_state['defaults'].txt2img.width.step)
|
|
height = st.slider("Height:", min_value=st.session_state['defaults'].txt2img.height.min_value, max_value=st.session_state['defaults'].txt2img.height.max_value,
|
|
value=st.session_state['defaults'].txt2img.height.value, step=st.session_state['defaults'].txt2img.height.step)
|
|
cfg_scale = st.slider("CFG (Classifier Free Guidance Scale):", min_value=st.session_state['defaults'].txt2img.cfg_scale.min_value,
|
|
max_value=st.session_state['defaults'].txt2img.cfg_scale.max_value,
|
|
value=st.session_state['defaults'].txt2img.cfg_scale.value, step=st.session_state['defaults'].txt2img.cfg_scale.step,
|
|
help="How strongly the image should follow the prompt.")
|
|
seed = st.text_input("Seed:", value=st.session_state['defaults'].txt2img.seed, help=" The seed to use, if left blank a random seed will be generated.")
|
|
|
|
with st.expander("Batch Options"):
|
|
#batch_count = st.slider("Batch count.", min_value=st.session_state['defaults'].txt2img.batch_count.min_value, max_value=st.session_state['defaults'].txt2img.batch_count.max_value,
|
|
#value=st.session_state['defaults'].txt2img.batch_count.value, step=st.session_state['defaults'].txt2img.batch_count.step,
|
|
#help="How many iterations or batches of images to generate in total.")
|
|
|
|
#batch_size = st.slider("Batch size", min_value=st.session_state['defaults'].txt2img.batch_size.min_value, max_value=st.session_state['defaults'].txt2img.batch_size.max_value,
|
|
#value=st.session_state.defaults.txt2img.batch_size.value, step=st.session_state.defaults.txt2img.batch_size.step,
|
|
#help="How many images are at once in a batch.\
|
|
#It increases the VRAM usage a lot but if you have enough VRAM it can reduce the time it takes to finish generation as more images are generated at once.\
|
|
#Default: 1")
|
|
|
|
st.session_state["batch_count"] = int(st.text_input("Batch count.", value=st.session_state['defaults'].txt2img.batch_count.value,
|
|
help="How many iterations or batches of images to generate in total."))
|
|
|
|
st.session_state["batch_size"] = int(st.text_input("Batch size", value=st.session_state.defaults.txt2img.batch_size.value,
|
|
help="How many images are at once in a batch.\
|
|
It increases the VRAM usage a lot but if you have enough VRAM it can reduce the time it takes \
|
|
to finish generation as more images are generated at once.\
|
|
Default: 1") )
|
|
|
|
with st.expander("Preview Settings"):
|
|
|
|
st.session_state["update_preview"] = st.session_state["defaults"].general.update_preview
|
|
st.session_state["update_preview_frequency"] = st.text_input("Update Image Preview Frequency", value=st.session_state['defaults'].txt2img.update_preview_frequency,
|
|
help="Frequency in steps at which the the preview image is updated. By default the frequency \
|
|
is set to 10 step.")
|
|
|
|
with col2:
|
|
preview_tab, gallery_tab = st.tabs(["Preview", "Gallery"])
|
|
|
|
with preview_tab:
|
|
#st.write("Image")
|
|
#Image for testing
|
|
#image = Image.open(requests.get("https://icon-library.com/images/image-placeholder-icon/image-placeholder-icon-13.jpg", stream=True).raw).convert('RGB')
|
|
#new_image = image.resize((175, 240))
|
|
#preview_image = st.image(image)
|
|
|
|
# create an empty container for the image, progress bar, etc so we can update it later and use session_state to hold them globally.
|
|
st.session_state["preview_image"] = st.empty()
|
|
|
|
|
|
st.session_state["progress_bar_text"] = st.empty()
|
|
st.session_state["progress_bar_text"].info("Nothing but crickets here, try generating something first.")
|
|
|
|
st.session_state["progress_bar"] = st.empty()
|
|
|
|
message = st.empty()
|
|
|
|
with gallery_tab:
|
|
st.session_state["gallery"] = st.empty()
|
|
st.session_state["gallery"].info("Nothing but crickets here, try generating something first.")
|
|
|
|
with col3:
|
|
# If we have custom models available on the "models/custom"
|
|
#folder then we show a menu to select which model we want to use, otherwise we use the main model for SD
|
|
custom_models_available()
|
|
|
|
if server_state["CustomModel_available"]:
|
|
st.session_state["custom_model"] = st.selectbox("Custom Model:", server_state["custom_models"],
|
|
index=server_state["custom_models"].index(st.session_state['defaults'].general.default_model),
|
|
help="Select the model you want to use. This option is only available if you have custom models \
|
|
on your 'models/custom' folder. The model name that will be shown here is the same as the name\
|
|
the file for the model has on said folder, it is recommended to give the .ckpt file a name that \
|
|
will make it easier for you to distinguish it from other models. Default: Stable Diffusion v1.4")
|
|
|
|
st.session_state.sampling_steps = st.slider("Sampling Steps", value=st.session_state.defaults.txt2img.sampling_steps.value,
|
|
min_value=st.session_state.defaults.txt2img.sampling_steps.min_value,
|
|
max_value=st.session_state['defaults'].txt2img.sampling_steps.max_value,
|
|
step=st.session_state['defaults'].txt2img.sampling_steps.step)
|
|
|
|
sampler_name_list = ["k_lms", "k_euler", "k_euler_a", "k_dpm_2", "k_dpm_2_a", "k_heun", "PLMS", "DDIM"]
|
|
sampler_name = st.selectbox("Sampling method", sampler_name_list,
|
|
index=sampler_name_list.index(st.session_state['defaults'].txt2img.default_sampler), help="Sampling method to use. Default: k_euler")
|
|
|
|
with st.expander("Advanced"):
|
|
with st.expander("Output Settings"):
|
|
separate_prompts = st.checkbox("Create Prompt Matrix.", value=st.session_state['defaults'].txt2img.separate_prompts,
|
|
help="Separate multiple prompts using the `|` character, and get all combinations of them.")
|
|
|
|
normalize_prompt_weights = st.checkbox("Normalize Prompt Weights.", value=st.session_state['defaults'].txt2img.normalize_prompt_weights,
|
|
help="Ensure the sum of all weights add up to 1.0")
|
|
|
|
save_individual_images = st.checkbox("Save individual images.", value=st.session_state['defaults'].txt2img.save_individual_images,
|
|
help="Save each image generated before any filter or enhancement is applied.")
|
|
|
|
save_grid = st.checkbox("Save grid",value=st.session_state['defaults'].txt2img.save_grid, help="Save a grid with all the images generated into a single image.")
|
|
group_by_prompt = st.checkbox("Group results by prompt", value=st.session_state['defaults'].txt2img.group_by_prompt,
|
|
help="Saves all the images with the same prompt into the same folder. When using a prompt matrix each prompt combination will have its own folder.")
|
|
|
|
write_info_files = st.checkbox("Write Info file", value=st.session_state['defaults'].txt2img.write_info_files,
|
|
help="Save a file next to the image with informartion about the generation.")
|
|
|
|
save_as_jpg = st.checkbox("Save samples as jpg", value=st.session_state['defaults'].txt2img.save_as_jpg, help="Saves the images as jpg instead of png.")
|
|
|
|
# check if GFPGAN, RealESRGAN and LDSR are available.
|
|
if "GFPGAN_available" not in st.session_state:
|
|
GFPGAN_available()
|
|
|
|
if "RealESRGAN_available" not in st.session_state:
|
|
RealESRGAN_available()
|
|
|
|
if "LDSR_available" not in st.session_state:
|
|
LDSR_available()
|
|
|
|
if st.session_state["GFPGAN_available"] or st.session_state["RealESRGAN_available"] or st.session_state["LDSR_available"]:
|
|
with st.expander("Post-Processing"):
|
|
face_restoration_tab, upscaling_tab = st.tabs(["Face Restoration", "Upscaling"])
|
|
with face_restoration_tab:
|
|
# GFPGAN used for face restoration
|
|
if st.session_state["GFPGAN_available"]:
|
|
#with st.expander("Face Restoration"):
|
|
#if st.session_state["GFPGAN_available"]:
|
|
#with st.expander("GFPGAN"):
|
|
st.session_state["use_GFPGAN"] = st.checkbox("Use GFPGAN", value=st.session_state['defaults'].txt2img.use_GFPGAN,
|
|
help="Uses the GFPGAN model to improve faces after the generation.\
|
|
This greatly improve the quality and consistency of faces but uses\
|
|
extra VRAM. Disable if you need the extra VRAM.")
|
|
|
|
st.session_state["GFPGAN_model"] = st.selectbox("GFPGAN model", st.session_state["GFPGAN_models"],
|
|
index=st.session_state["GFPGAN_models"].index(st.session_state['defaults'].general.GFPGAN_model))
|
|
|
|
#st.session_state["GFPGAN_strenght"] = st.slider("Effect Strenght", min_value=1, max_value=100, value=1, step=1, help='')
|
|
|
|
else:
|
|
st.session_state["use_GFPGAN"] = False
|
|
|
|
with upscaling_tab:
|
|
st.session_state['us_upscaling'] = st.checkbox("Use Upscaling", value=st.session_state['defaults'].txt2img.use_upscaling)
|
|
|
|
# RealESRGAN and LDSR used for upscaling.
|
|
if st.session_state["RealESRGAN_available"] or st.session_state["LDSR_available"]:
|
|
|
|
upscaling_method_list = []
|
|
if st.session_state["RealESRGAN_available"]:
|
|
upscaling_method_list.append("RealESRGAN")
|
|
if st.session_state["LDSR_available"]:
|
|
upscaling_method_list.append("LDSR")
|
|
|
|
#print (st.session_state["RealESRGAN_available"])
|
|
st.session_state["upscaling_method"] = st.selectbox("Upscaling Method", upscaling_method_list,
|
|
index=upscaling_method_list.index(str(st.session_state['defaults'].general.upscaling_method)))
|
|
|
|
if st.session_state["RealESRGAN_available"]:
|
|
with st.expander("RealESRGAN"):
|
|
if st.session_state["upscaling_method"] == "RealESRGAN" and st.session_state['us_upscaling']:
|
|
st.session_state["use_RealESRGAN"] = True
|
|
else:
|
|
st.session_state["use_RealESRGAN"] = False
|
|
|
|
st.session_state["RealESRGAN_model"] = st.selectbox("RealESRGAN model", st.session_state["RealESRGAN_models"],
|
|
index=st.session_state["RealESRGAN_models"].index(st.session_state['defaults'].general.RealESRGAN_model))
|
|
else:
|
|
st.session_state["use_RealESRGAN"] = False
|
|
st.session_state["RealESRGAN_model"] = "RealESRGAN_x4plus"
|
|
|
|
|
|
#
|
|
if st.session_state["LDSR_available"]:
|
|
with st.expander("LDSR"):
|
|
if st.session_state["upscaling_method"] == "LDSR" and st.session_state['us_upscaling']:
|
|
st.session_state["use_LDSR"] = True
|
|
else:
|
|
st.session_state["use_LDSR"] = False
|
|
|
|
st.session_state["LDSR_model"] = st.selectbox("LDSR model", st.session_state["LDSR_models"],
|
|
index=st.session_state["LDSR_models"].index(st.session_state['defaults'].general.LDSR_model))
|
|
|
|
st.session_state["ldsr_sampling_steps"] = int(st.text_input("Sampling Steps", value=st.session_state['defaults'].txt2img.LDSR_config.sampling_steps,
|
|
help=""))
|
|
|
|
st.session_state["preDownScale"] = int(st.text_input("PreDownScale", value=st.session_state['defaults'].txt2img.LDSR_config.preDownScale,
|
|
help=""))
|
|
|
|
st.session_state["postDownScale"] = int(st.text_input("postDownScale", value=st.session_state['defaults'].txt2img.LDSR_config.postDownScale,
|
|
help=""))
|
|
|
|
downsample_method_list = ['Nearest', 'Lanczos']
|
|
st.session_state["downsample_method"] = st.selectbox("Downsample Method", downsample_method_list,
|
|
index=downsample_method_list.index(st.session_state['defaults'].txt2img.LDSR_config.downsample_method))
|
|
|
|
else:
|
|
st.session_state["use_LDSR"] = False
|
|
st.session_state["LDSR_model"] = "model"
|
|
|
|
with st.expander("Variant"):
|
|
variant_amount = st.slider("Variant Amount:", value=st.session_state['defaults'].txt2img.variant_amount.value,
|
|
min_value=st.session_state['defaults'].txt2img.variant_amount.min_value, max_value=st.session_state['defaults'].txt2img.variant_amount.max_value,
|
|
step=st.session_state['defaults'].txt2img.variant_amount.step)
|
|
variant_seed = st.text_input("Variant Seed:", value=st.session_state['defaults'].txt2img.seed,
|
|
help="The seed to use when generating a variant, if left blank a random seed will be generated.")
|
|
|
|
#galleryCont = st.empty()
|
|
|
|
# Every form must have a submit button, the extra blank spaces is a temp way to align it with the input field. Needs to be done in CSS or some other way.
|
|
generate_col1.write("")
|
|
generate_col1.write("")
|
|
generate_button = generate_col1.form_submit_button("Generate")
|
|
|
|
#
|
|
if generate_button:
|
|
|
|
with col2:
|
|
with hc.HyLoader('Loading Models...', hc.Loaders.standard_loaders,index=[0]):
|
|
load_models(use_LDSR=st.session_state["use_LDSR"], LDSR_model=st.session_state["LDSR_model"],
|
|
use_GFPGAN=st.session_state["use_GFPGAN"], GFPGAN_model=st.session_state["GFPGAN_model"] ,
|
|
use_RealESRGAN=st.session_state["use_RealESRGAN"], RealESRGAN_model=st.session_state["RealESRGAN_model"],
|
|
CustomModel_available=server_state["CustomModel_available"], custom_model=st.session_state["custom_model"])
|
|
|
|
|
|
#print(st.session_state['use_RealESRGAN'])
|
|
#print(st.session_state['use_LDSR'])
|
|
#try:
|
|
#
|
|
|
|
output_images, seeds, info, stats = txt2img(prompt, st.session_state.sampling_steps, sampler_name, st.session_state["batch_count"], st.session_state["batch_size"],
|
|
cfg_scale, seed, height, width, separate_prompts, normalize_prompt_weights, save_individual_images,
|
|
save_grid, group_by_prompt, save_as_jpg, st.session_state["use_GFPGAN"], st.session_state['GFPGAN_model'],
|
|
use_RealESRGAN=st.session_state["use_RealESRGAN"], RealESRGAN_model=st.session_state["RealESRGAN_model"],
|
|
use_LDSR=st.session_state["use_LDSR"], LDSR_model=st.session_state["LDSR_model"],
|
|
variant_amount=variant_amount, variant_seed=variant_seed, write_info_files=write_info_files)
|
|
|
|
message.success('Render Complete: ' + info + '; Stats: ' + stats, icon="✅")
|
|
|
|
#history_tab,col1,col2,col3,PlaceHolder,col1_cont,col2_cont,col3_cont = st.session_state['historyTab']
|
|
|
|
#if 'latestImages' in st.session_state:
|
|
#for i in output_images:
|
|
##push the new image to the list of latest images and remove the oldest one
|
|
##remove the last index from the list\
|
|
#st.session_state['latestImages'].pop()
|
|
##add the new image to the start of the list
|
|
#st.session_state['latestImages'].insert(0, i)
|
|
#PlaceHolder.empty()
|
|
#with PlaceHolder.container():
|
|
#col1, col2, col3 = st.columns(3)
|
|
#col1_cont = st.container()
|
|
#col2_cont = st.container()
|
|
#col3_cont = st.container()
|
|
#images = st.session_state['latestImages']
|
|
#with col1_cont:
|
|
#with col1:
|
|
#[st.image(images[index]) for index in [0, 3, 6] if index < len(images)]
|
|
#with col2_cont:
|
|
#with col2:
|
|
#[st.image(images[index]) for index in [1, 4, 7] if index < len(images)]
|
|
#with col3_cont:
|
|
#with col3:
|
|
#[st.image(images[index]) for index in [2, 5, 8] if index < len(images)]
|
|
#historyGallery = st.empty()
|
|
|
|
## check if output_images length is the same as seeds length
|
|
#with gallery_tab:
|
|
#st.markdown(createHTMLGallery(output_images,seeds), unsafe_allow_html=True)
|
|
|
|
|
|
#st.session_state['historyTab'] = [history_tab,col1,col2,col3,PlaceHolder,col1_cont,col2_cont,col3_cont]
|
|
|
|
with gallery_tab:
|
|
print(seeds)
|
|
sdGallery(output_images)
|
|
|
|
|
|
#except (StopException, KeyError):
|
|
#print(f"Received Streamlit StopException")
|
|
|
|
# this will render all the images at the end of the generation but its better if its moved to a second tab inside col2 and shown as a gallery.
|
|
# use the current col2 first tab to show the preview_img and update it as its generated.
|
|
#preview_image.image(output_images)
|
|
|
|
|