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stable-diffusion-webui/scripts/txt2img.py
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# This file is part of sygil-webui (https://github.com/Sygil-Dev/sygil-webui/).
# Copyright 2022 Sygil-Dev 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 (
st,
MemUsageMonitor,
server_state,
no_rerun,
get_next_sequence_number,
check_prompt_length,
torch_gc,
save_sample,
generation_callback,
process_images,
KDiffusionSampler,
custom_models_available,
RealESRGAN_available,
GFPGAN_available,
LDSR_available,
load_models,
hc,
seed_to_int,
logger,
set_page_title,
)
# streamlit imports
from streamlit.runtime.scriptrunner import StopException
# streamlit components section
# 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 base64, uuid
import os, sys, datetime, time
from PIL import Image
import requests
from slugify import slugify
from ldm.models.diffusion.ddim import DDIMSampler
from typing import Union
from io import BytesIO
from ldm.models.diffusion.ddim import DDIMSampler
from ldm.models.diffusion.plms import PLMSSampler
# streamlit components
from custom_components import sygil_suggestions
# Temp imports
# end of imports
# ---------------------------------------------------------------------------------------------------------------
sygil_suggestions.init()
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
@logger.catch(reraise=True)
def stable_horde(
outpath,
prompt,
seed,
sampler_name,
save_grid,
batch_size,
n_iter,
steps,
cfg_scale,
width,
height,
prompt_matrix,
use_GFPGAN,
GFPGAN_model,
use_RealESRGAN,
realesrgan_model_name,
use_LDSR,
LDSR_model_name,
ddim_eta,
normalize_prompt_weights,
save_individual_images,
sort_samples,
write_info_files,
jpg_sample,
variant_amount,
variant_seed,
api_key,
nsfw=True,
censor_nsfw=False,
):
log = []
log.append("Generating image with Stable Horde.")
st.session_state["progress_bar_text"].code("\n".join(log), language="")
# start time after garbage collection (or before?)
start_time = time.time()
# We will use this date here later for the folder name, need to start_time if not need
datetime.datetime.now()
mem_mon = MemUsageMonitor("MemMon")
mem_mon.start()
os.makedirs(outpath, exist_ok=True)
sample_path = os.path.join(outpath, "samples")
os.makedirs(sample_path, exist_ok=True)
params = {
"sampler_name": "k_euler",
"toggles": [1, 4],
"cfg_scale": cfg_scale,
"seed": str(seed),
"width": width,
"height": height,
"seed_variation": variant_seed if variant_seed else 1,
"steps": int(steps),
"n": int(n_iter)
# You can put extra params here if you wish
}
final_submit_dict = {
"prompt": prompt,
"params": params,
"nsfw": nsfw,
"censor_nsfw": censor_nsfw,
"trusted_workers": True,
"workers": [],
}
log.append(final_submit_dict)
headers = {"apikey": api_key}
logger.debug(final_submit_dict)
st.session_state["progress_bar_text"].code("\n".join(str(log)), language="")
horde_url = "https://stablehorde.net"
submit_req = requests.post(
f"{horde_url}/api/v2/generate/async", json=final_submit_dict, headers=headers
)
if submit_req.ok:
submit_results = submit_req.json()
logger.debug(submit_results)
log.append(submit_results)
st.session_state["progress_bar_text"].code("".join(str(log)), language="")
req_id = submit_results["id"]
is_done = False
while not is_done:
chk_req = requests.get(f"{horde_url}/api/v2/generate/check/{req_id}")
if not chk_req.ok:
logger.error(chk_req.text)
return
chk_results = chk_req.json()
logger.info(chk_results)
is_done = chk_results["done"]
time.sleep(1)
retrieve_req = requests.get(f"{horde_url}/api/v2/generate/status/{req_id}")
if not retrieve_req.ok:
logger.error(retrieve_req.text)
return
results_json = retrieve_req.json()
# logger.debug(results_json)
results = results_json["generations"]
output_images = []
comments = []
if not st.session_state["defaults"].general.no_verify_input:
try:
check_prompt_length(prompt, comments)
except:
import traceback
logger.info("Error verifying input:", file=sys.stderr)
logger.info(traceback.format_exc(), file=sys.stderr)
all_prompts = batch_size * n_iter * [prompt]
all_seeds = [seed + x for x in range(len(all_prompts))]
for iter in range(len(results)):
b64img = results[iter]["img"]
base64_bytes = b64img.encode("utf-8")
img_bytes = base64.b64decode(base64_bytes)
img = Image.open(BytesIO(img_bytes))
sanitized_prompt = slugify(prompt)
prompts = all_prompts[iter * batch_size : (iter + 1) * batch_size]
# captions = prompt_matrix_parts[n * batch_size:(n + 1) * batch_size]
seeds = all_seeds[iter * batch_size : (iter + 1) * batch_size]
if sort_samples:
full_path = os.path.join(os.getcwd(), sample_path, sanitized_prompt)
sanitized_prompt = sanitized_prompt[: 200 - len(full_path)]
sample_path_i = os.path.join(sample_path, sanitized_prompt)
# print(f"output folder length: {len(os.path.join(os.getcwd(), sample_path_i))}")
# print(os.path.join(os.getcwd(), sample_path_i))
os.makedirs(sample_path_i, exist_ok=True)
base_count = get_next_sequence_number(sample_path_i)
filename = f"{base_count:05}-{steps}_{sampler_name}_{seeds[iter]}"
else:
full_path = os.path.join(os.getcwd(), sample_path)
sample_path_i = sample_path
base_count = get_next_sequence_number(sample_path_i)
filename = (
f"{base_count:05}-{steps}_{sampler_name}_{seed}_{sanitized_prompt}"[
: 200 - len(full_path)
]
) # same as before
save_sample(
img,
sample_path_i,
filename,
jpg_sample,
prompts,
seeds,
width,
height,
steps,
cfg_scale,
normalize_prompt_weights,
use_GFPGAN,
write_info_files,
prompt_matrix,
init_img=None,
denoising_strength=0.75,
resize_mode=None,
uses_loopback=False,
uses_random_seed_loopback=False,
save_grid=save_grid,
sort_samples=sampler_name,
sampler_name=sampler_name,
ddim_eta=ddim_eta,
n_iter=n_iter,
batch_size=batch_size,
i=iter,
save_individual_images=save_individual_images,
model_name="Stable Diffusion v1.5",
)
output_images.append(img)
# update image on the UI so we can see the progress
if "preview_image" in st.session_state:
st.session_state["preview_image"].image(img)
if "progress_bar_text" in st.session_state:
st.session_state["progress_bar_text"].empty()
# if len(results) > 1:
# final_filename = f"{iter}_{filename}"
# img.save(final_filename)
# logger.info(f"Saved {final_filename}")
else:
if "progress_bar_text" in st.session_state:
st.session_state["progress_bar_text"].error(submit_req.text)
logger.error(submit_req.text)
mem_max_used, mem_total = mem_mon.read_and_stop()
time_diff = time.time() - start_time
info = f"""
{prompt}
Steps: {steps}, Sampler: {sampler_name}, CFG scale: {cfg_scale}, Seed: {seed}{', GFPGAN' if use_GFPGAN else ''}{', '+realesrgan_model_name if use_RealESRGAN else ''}
{', Prompt Matrix Mode.' if prompt_matrix else ''}""".strip()
stats = f"""
Took { round(time_diff, 2) }s total ({ round(time_diff/(len(all_prompts)),2) }s per image)
Peak memory usage: { -(mem_max_used // -1_048_576) } MiB / { -(mem_total // -1_048_576) } MiB / { round(mem_max_used/mem_total*100, 3) }%"""
for comment in comments:
info += "\n\n" + comment
# mem_mon.stop()
# del mem_mon
torch_gc()
return output_images, seed, info, stats
#
@logger.catch(reraise=True)
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 = 0.0,
variant_seed: int = None,
ddim_eta: float = 0.0,
write_info_files: bool = True,
use_stable_horde: bool = False,
stable_horde_key: str = "0000000000",
):
outpath = st.session_state["defaults"].general.outdir_txt2img
seed = seed_to_int(seed)
if not use_stable_horde:
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_dpmpp_2m":
sampler = KDiffusionSampler(server_state["model"], "dpmpp_2m")
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
if not server_state["bridge"]
else None,
log_every_t=int(
st.session_state.update_preview_frequency
if not server_state["bridge"]
else 100
),
)
return samples_ddim
if use_stable_horde:
output_images, seed, info, stats = stable_horde(
prompt=prompt,
seed=seed,
outpath=outpath,
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=use_GFPGAN,
GFPGAN_model=GFPGAN_model,
use_RealESRGAN=use_RealESRGAN,
realesrgan_model_name=RealESRGAN_model,
use_LDSR=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,
api_key=stable_horde_key,
)
else:
# 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=use_GFPGAN,
GFPGAN_model=GFPGAN_model,
use_RealESRGAN=use_RealESRGAN,
realesrgan_model_name=RealESRGAN_model,
use_LDSR=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
#
@logger.catch(reraise=True)
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","")
placeholder = "A corgi wearing a top hat as an oil painting."
prompt = st.text_area("Input Text", "", placeholder=placeholder, height=54)
if "defaults" in st.session_state:
if st.session_state["defaults"].general.enable_suggestions:
sygil_suggestions.suggestion_area(placeholder)
if "defaults" in st.session_state:
if st.session_state["defaults"].admin.global_negative_prompt:
prompt += f"### {st.session_state['defaults'].admin.global_negative_prompt}"
# print(prompt)
# creating the page layout using columns
col1, col2, col3 = st.columns([2, 5, 2], 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.number_input(
"CFG (Classifier Free Guidance Scale):",
min_value=st.session_state["defaults"].txt2img.cfg_scale.min_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"] = st.number_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"] = st.number_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.number_input(
"Update Image Preview Frequency",
min_value=0,
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 v1.5",
)
st.session_state.sampling_steps = st.number_input(
"Sampling Steps",
value=st.session_state.defaults.txt2img.sampling_steps.value,
min_value=st.session_state.defaults.txt2img.sampling_steps.min_value,
step=st.session_state["defaults"].txt2img.sampling_steps.step,
help="Set the default number of sampling steps to use. Default is: 30 (with k_euler)",
)
sampler_name_list = [
"k_lms",
"k_euler",
"k_euler_a",
"k_dpm_2",
"k_dpm_2_a",
"k_dpmpp_2m",
"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("Stable Horde"):
use_stable_horde = st.checkbox(
"Use Stable Horde",
value=False,
help="Use the Stable Horde to generate images. More info can be found at https://stablehorde.net/",
)
stable_horde_key = st.text_input(
"Stable Horde Api Key",
value=st.session_state["defaults"].general.stable_horde_api,
type="password",
help="Optional Api Key used for the Stable Horde Bridge, if no api key is added the horde will be used anonymously.",
)
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["use_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(
st.session_state[
"defaults"
].general.upscaling_method
)
if st.session_state[
"defaults"
].general.upscaling_method
in upscaling_method_list
else 0,
)
if st.session_state["RealESRGAN_available"]:
with st.expander("RealESRGAN"):
if (
st.session_state["upscaling_method"]
== "RealESRGAN"
and st.session_state["use_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["use_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"
] = st.number_input(
"Sampling Steps",
value=st.session_state[
"defaults"
].txt2img.LDSR_config.sampling_steps,
help="",
)
st.session_state[
"preDownScale"
] = st.number_input(
"PreDownScale",
value=st.session_state[
"defaults"
].txt2img.LDSR_config.preDownScale,
help="",
)
st.session_state[
"postDownScale"
] = st.number_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 no_rerun:
if not use_stable_horde:
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,
use_stable_horde=use_stable_horde,
stable_horde_key=stable_horde_key,
)
message.success(
"Render Complete: " + info + "; Stats: " + stats, icon="✅"
)
with gallery_tab:
logger.info(seeds)
st.session_state["gallery"].text = ""
sdGallery(output_images)
except (
StopException,
# KeyError
):
print("Received Streamlit StopException")
# reset the page title so the percent doesnt stay on it confusing the user.
set_page_title("Stable Diffusion Playground")
# 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)
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