sd-webui/stable-diffusion-webui
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- Added the functions to load the optimized models, this "should" make it so optimized and turbo mode work now but needs to be tested more.

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- Added function to load LDSR.
This commit is contained in:
ZeroCool940711 2022-09-14 02:45:18 -07:00
parent 2e684f948f
commit 374b144e77
2 changed files with 471 additions and 122 deletions
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1
configs/webui/webui_streamlit.yaml
View File

@ -13,6 +13,7 @@ general:
GFPGAN_dir: "./src/gfpgan"
RealESRGAN_dir: "./src/realesrgan"
RealESRGAN_model: "RealESRGAN_x4plus"
LDSR_dir: "./src/latent-diffusion"
outdir_txt2img: outputs/txt2img-samples
outdir_img2img: outputs/img2img-samples
gfpgan_cpu: False

592
scripts/sd_utils.py
View File

@ -1,21 +1,20 @@
import warnings
# base webui import and utils.
from webui_streamlit import st
import piexif
import piexif.helper
# streamlit imports
#other imports
import warnings
import json
import streamlit as st
from streamlit import StopException
#streamlit components section
from st_on_hover_tabs import on_hover_tabs
import base64, cv2
import os, sys, re, random, datetime, timeit
from PIL import Image, ImageFont, ImageDraw, ImageFilter, ImageOps
import base64
import os, sys, re, random, datetime, time, math
from PIL import Image, ImageFont, ImageDraw, ImageFilter
from PIL.PngImagePlugin import PngInfo
from scipy import integrate
import pandas as pd
import torch
from torchdiffeq import odeint
import k_diffusion as K
@ -24,36 +23,28 @@ import mimetypes
import numpy as np
import pynvml
import threading
import time, inspect
import torch
from torch import autocast
from torchvision import transforms
import torch.nn as nn
from omegaconf import OmegaConf
import yaml
from typing import Union
from pathlib import Path
#from tqdm import tqdm
from contextlib import nullcontext
from einops import rearrange
from omegaconf import OmegaConf
from io import StringIO
from ldm.models.diffusion.ddim import DDIMSampler
from ldm.models.diffusion.plms import PLMSSampler
from ldm.util import instantiate_from_config
from retry import retry
import find_noise_for_image
import matched_noise
# these are for testing txt2vid, should be removed and we should use things from our own code.
from diffusers import StableDiffusionPipeline
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
#will be used for saving and reading a video made by the txt2vid function
import imageio, io
# we use python-slugify to make the filenames safe for windows and linux, its better than doing it manually
# install it with 'pip install python-slugify'
from slugify import slugify
import skimage
import piexif
import piexif.helper
from tqdm import trange
# Temp imports
# end of imports
#---------------------------------------------------------------------------------------------------------------
try:
# this silences the annoying "Some weights of the model checkpoint were not used when initializing..." message at start.
@ -241,58 +232,6 @@ def load_sd_from_config(ckpt, verbose=False):
print(f"Global Step: {pl_sd['global_step']}")
sd = pl_sd["state_dict"]
return sd
#
@retry(tries=5)
def generation_callback(img, i=0):
try:
if i == 0:
if img['i']: i = img['i']
except TypeError:
pass
if i % int(defaults.general.update_preview_frequency) == 0 and defaults.general.update_preview:
#print (img)
#print (type(img))
# The following lines will convert the tensor we got on img to an actual image we can render on the UI.
# It can probably be done in a better way for someone who knows what they're doing. I don't.
#print (img,isinstance(img, torch.Tensor))
if isinstance(img, torch.Tensor):
x_samples_ddim = (st.session_state["model"] if not defaults.general.optimized else modelFS).decode_first_stage(img)
else:
# When using the k Diffusion samplers they return a dict instead of a tensor that look like this:
# {'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}
x_samples_ddim = (st.session_state["model"] if not defaults.general.optimized else modelFS).decode_first_stage(img["denoised"])
x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
pil_image = transforms.ToPILImage()(x_samples_ddim.squeeze_(0))
# update image on the UI so we can see the progress
st.session_state["preview_image"].image(pil_image)
# Show a progress bar so we can keep track of the progress even when the image progress is not been shown,
# Dont worry, it doesnt affect the performance.
if st.session_state["generation_mode"] == "txt2img":
percent = int(100 * float(i+1 if i+1 < st.session_state.sampling_steps else st.session_state.sampling_steps)/float(st.session_state.sampling_steps))
st.session_state["progress_bar_text"].text(
f"Running step: {i+1 if i+1 < st.session_state.sampling_steps else st.session_state.sampling_steps}/{st.session_state.sampling_steps} {percent if percent < 100 else 100}%")
else:
if st.session_state["generation_mode"] == "img2img":
round_sampling_steps = round(st.session_state.sampling_steps * st.session_state["denoising_strength"])
percent = int(100 * float(i+1 if i+1 < round_sampling_steps else round_sampling_steps)/float(round_sampling_steps))
st.session_state["progress_bar_text"].text(
f"""Running step: {i+1 if i+1 < round_sampling_steps else round_sampling_steps}/{round_sampling_steps} {percent if percent < 100 else 100}%""")
else:
if st.session_state["generation_mode"] == "txt2vid":
percent = int(100 * float(i+1 if i+1 < st.session_state.sampling_steps else st.session_state.sampling_steps)/float(st.session_state.sampling_steps))
st.session_state["progress_bar_text"].text(
f"Running step: {i+1 if i+1 < st.session_state.sampling_steps else st.session_state.sampling_steps}/{st.session_state.sampling_steps}"
f"{percent if percent < 100 else 100}%")
st.session_state["progress_bar"].progress(percent if percent < 100 else 100)
class MemUsageMonitor(threading.Thread):
@ -379,6 +318,204 @@ def get_sigmas_karras(n, sigma_min, sigma_max, rho=7., device='cpu'):
sigmas = (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
return append_zero(sigmas).to(device)
#
# helper fft routines that keep ortho normalization and auto-shift before and after fft
def _fft2(data):
if data.ndim > 2: # has channels
out_fft = np.zeros((data.shape[0], data.shape[1], data.shape[2]), dtype=np.complex128)
for c in range(data.shape[2]):
c_data = data[:,:,c]
out_fft[:,:,c] = np.fft.fft2(np.fft.fftshift(c_data),norm="ortho")
out_fft[:,:,c] = np.fft.ifftshift(out_fft[:,:,c])
else: # one channel
out_fft = np.zeros((data.shape[0], data.shape[1]), dtype=np.complex128)
out_fft[:,:] = np.fft.fft2(np.fft.fftshift(data),norm="ortho")
out_fft[:,:] = np.fft.ifftshift(out_fft[:,:])
return out_fft
def _ifft2(data):
if data.ndim > 2: # has channels
out_ifft = np.zeros((data.shape[0], data.shape[1], data.shape[2]), dtype=np.complex128)
for c in range(data.shape[2]):
c_data = data[:,:,c]
out_ifft[:,:,c] = np.fft.ifft2(np.fft.fftshift(c_data),norm="ortho")
out_ifft[:,:,c] = np.fft.ifftshift(out_ifft[:,:,c])
else: # one channel
out_ifft = np.zeros((data.shape[0], data.shape[1]), dtype=np.complex128)
out_ifft[:,:] = np.fft.ifft2(np.fft.fftshift(data),norm="ortho")
out_ifft[:,:] = np.fft.ifftshift(out_ifft[:,:])
return out_ifft
def _get_gaussian_window(width, height, std=3.14, mode=0):
window_scale_x = float(width / min(width, height))
window_scale_y = float(height / min(width, height))
window = np.zeros((width, height))
x = (np.arange(width) / width * 2. - 1.) * window_scale_x
for y in range(height):
fy = (y / height * 2. - 1.) * window_scale_y
if mode == 0:
window[:, y] = np.exp(-(x**2+fy**2) * std)
else:
window[:, y] = (1/((x**2+1.) * (fy**2+1.))) ** (std/3.14) # hey wait a minute that's not gaussian
return window
def _get_masked_window_rgb(np_mask_grey, hardness=1.):
np_mask_rgb = np.zeros((np_mask_grey.shape[0], np_mask_grey.shape[1], 3))
if hardness != 1.:
hardened = np_mask_grey[:] ** hardness
else:
hardened = np_mask_grey[:]
for c in range(3):
np_mask_rgb[:,:,c] = hardened[:]
return np_mask_rgb
def get_matched_noise(_np_src_image, np_mask_rgb, noise_q, color_variation):
"""
Explanation:
Getting good results in/out-painting with stable diffusion can be challenging.
Although there are simpler effective solutions for in-painting, out-painting can be especially challenging because there is no color data
in the masked area to help prompt the generator. Ideally, even for in-painting we'd like work effectively without that data as well.
Provided here is my take on a potential solution to this problem.
By taking a fourier transform of the masked src img we get a function that tells us the presence and orientation of each feature scale in the unmasked src.
Shaping the init/seed noise for in/outpainting to the same distribution of feature scales, orientations, and positions increases output coherence
by helping keep features aligned. This technique is applicable to any continuous generation task such as audio or video, each of which can
be conceptualized as a series of out-painting steps where the last half of the input "frame" is erased. For multi-channel data such as color
or stereo sound the "color tone" or histogram of the seed noise can be matched to improve quality (using scikit-image currently)
This method is quite robust and has the added benefit of being fast independently of the size of the out-painted area.
The effects of this method include things like helping the generator integrate the pre-existing view distance and camera angle.
Carefully managing color and brightness with histogram matching is also essential to achieving good coherence.
noise_q controls the exponent in the fall-off of the distribution can be any positive number, lower values means higher detail (range > 0, default 1.)
color_variation controls how much freedom is allowed for the colors/palette of the out-painted area (range 0..1, default 0.01)
This code is provided as is under the Unlicense (https://unlicense.org/)
Although you have no obligation to do so, if you found this code helpful please find it in your heart to credit me [parlance-zz].
Questions or comments can be sent to parlance@fifth-harmonic.com (https://github.com/parlance-zz/)
This code is part of a new branch of a discord bot I am working on integrating with diffusers (https://github.com/parlance-zz/g-diffuser-bot)
"""
global DEBUG_MODE
global TMP_ROOT_PATH
width = _np_src_image.shape[0]
height = _np_src_image.shape[1]
num_channels = _np_src_image.shape[2]
np_src_image = _np_src_image[:] * (1. - np_mask_rgb)
np_mask_grey = (np.sum(np_mask_rgb, axis=2)/3.)
np_src_grey = (np.sum(np_src_image, axis=2)/3.)
all_mask = np.ones((width, height), dtype=bool)
img_mask = np_mask_grey > 1e-6
ref_mask = np_mask_grey < 1e-3
windowed_image = _np_src_image * (1.-_get_masked_window_rgb(np_mask_grey))
windowed_image /= np.max(windowed_image)
windowed_image += np.average(_np_src_image) * np_mask_rgb# / (1.-np.average(np_mask_rgb)) # rather than leave the masked area black, we get better results from fft by filling the average unmasked color
#windowed_image += np.average(_np_src_image) * (np_mask_rgb * (1.- np_mask_rgb)) / (1.-np.average(np_mask_rgb)) # compensate for darkening across the mask transition area
#_save_debug_img(windowed_image, "windowed_src_img")
src_fft = _fft2(windowed_image) # get feature statistics from masked src img
src_dist = np.absolute(src_fft)
src_phase = src_fft / src_dist
#_save_debug_img(src_dist, "windowed_src_dist")
noise_window = _get_gaussian_window(width, height, mode=1) # start with simple gaussian noise
noise_rgb = np.random.random_sample((width, height, num_channels))
noise_grey = (np.sum(noise_rgb, axis=2)/3.)
noise_rgb *= color_variation # the colorfulness of the starting noise is blended to greyscale with a parameter
for c in range(num_channels):
noise_rgb[:,:,c] += (1. - color_variation) * noise_grey
noise_fft = _fft2(noise_rgb)
for c in range(num_channels):
noise_fft[:,:,c] *= noise_window
noise_rgb = np.real(_ifft2(noise_fft))
shaped_noise_fft = _fft2(noise_rgb)
shaped_noise_fft[:,:,:] = np.absolute(shaped_noise_fft[:,:,:])**2 * (src_dist ** noise_q) * src_phase # perform the actual shaping
brightness_variation = 0.#color_variation # todo: temporarily tieing brightness variation to color variation for now
contrast_adjusted_np_src = _np_src_image[:] * (brightness_variation + 1.) - brightness_variation * 2.
# scikit-image is used for histogram matching, very convenient!
shaped_noise = np.real(_ifft2(shaped_noise_fft))
shaped_noise -= np.min(shaped_noise)
shaped_noise /= np.max(shaped_noise)
shaped_noise[img_mask,:] = skimage.exposure.match_histograms(shaped_noise[img_mask,:]**1., contrast_adjusted_np_src[ref_mask,:], channel_axis=1)
shaped_noise = _np_src_image[:] * (1. - np_mask_rgb) + shaped_noise * np_mask_rgb
#_save_debug_img(shaped_noise, "shaped_noise")
matched_noise = np.zeros((width, height, num_channels))
matched_noise = shaped_noise[:]
#matched_noise[all_mask,:] = skimage.exposure.match_histograms(shaped_noise[all_mask,:], _np_src_image[ref_mask,:], channel_axis=1)
#matched_noise = _np_src_image[:] * (1. - np_mask_rgb) + matched_noise * np_mask_rgb
#_save_debug_img(matched_noise, "matched_noise")
"""
todo:
color_variation doesnt have to be a single number, the overall color tone of the out-painted area could be param controlled
"""
return np.clip(matched_noise, 0., 1.)
#
def find_noise_for_image(model, device, init_image, prompt, steps=200, cond_scale=2.0, verbose=False, normalize=False, generation_callback=None):
image = np.array(init_image).astype(np.float32) / 255.0
image = image[None].transpose(0, 3, 1, 2)
image = torch.from_numpy(image)
image = 2. * image - 1.
image = image.to(device)
x = model.get_first_stage_encoding(model.encode_first_stage(image))
uncond = model.get_learned_conditioning([''])
cond = model.get_learned_conditioning([prompt])
s_in = x.new_ones([x.shape[0]])
dnw = K.external.CompVisDenoiser(model)
sigmas = dnw.get_sigmas(steps).flip(0)
if verbose:
print(sigmas)
for i in trange(1, len(sigmas)):
x_in = torch.cat([x] * 2)
sigma_in = torch.cat([sigmas[i - 1] * s_in] * 2)
cond_in = torch.cat([uncond, cond])
c_out, c_in = [K.utils.append_dims(k, x_in.ndim) for k in dnw.get_scalings(sigma_in)]
if i == 1:
t = dnw.sigma_to_t(torch.cat([sigmas[i] * s_in] * 2))
else:
t = dnw.sigma_to_t(sigma_in)
eps = model.apply_model(x_in * c_in, t, cond=cond_in)
denoised_uncond, denoised_cond = (x_in + eps * c_out).chunk(2)
denoised = denoised_uncond + (denoised_cond - denoised_uncond) * cond_scale
if i == 1:
d = (x - denoised) / (2 * sigmas[i])
else:
d = (x - denoised) / sigmas[i - 1]
if generation_callback is not None:
generation_callback(x, i)
dt = sigmas[i] - sigmas[i - 1]
x = x + d * dt
return x / sigmas[-1]
def get_sigmas_exponential(n, sigma_min, sigma_max, device='cpu'):
"""Constructs an exponential noise schedule."""
@ -511,6 +648,190 @@ def load_RealESRGAN(model_name: str):
return instance
#
def load_LDSR(checking=False):
model_name = 'model'
yaml_name = 'project'
model_path = os.path.join(defaults.general.LDSR_dir, 'experiments/pretrained_models', model_name + '.ckpt')
yaml_path = os.path.join(defaults.general.LDSR_dir, 'experiments/pretrained_models', yaml_name + '.yaml')
if not os.path.isfile(model_path):
raise Exception("LDSR model not found at path "+model_path)
if not os.path.isfile(yaml_path):
raise Exception("LDSR model not found at path "+yaml_path)
if checking == True:
return True
sys.path.append(os.path.abspath(defaults.general.LDSR_dir))
from LDSR import LDSR
LDSRObject = LDSR(model_path, yaml_path)
return LDSRObject
#
LDSR = None
def try_loading_LDSR(model_name: str,checking=False):
global LDSR
if os.path.exists(defaults.general.LDSR_dir):
try:
LDSR = load_LDSR(checking=True) # TODO: Should try to load both models before giving up
if checking == True:
print("Found LDSR")
return True
print("Latent Diffusion Super Sampling (LDSR) model loaded")
except Exception:
import traceback
print("Error loading LDSR:", file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)
else:
print("LDSR not found at path, please make sure you have cloned the LDSR repo to ./src/latent-diffusion/")
#try_loading_LDSR('model',checking=True)
def load_SD_model():
if defaults.general.optimized:
sd = load_sd_from_config(defaults.general.default_model_path)
li, lo = [], []
for key, v_ in sd.items():
sp = key.split('.')
if(sp[0]) == 'model':
if('input_blocks' in sp):
li.append(key)
elif('middle_block' in sp):
li.append(key)
elif('time_embed' in sp):
li.append(key)
else:
lo.append(key)
for key in li:
sd['model1.' + key[6:]] = sd.pop(key)
for key in lo:
sd['model2.' + key[6:]] = sd.pop(key)
config = OmegaConf.load("optimizedSD/v1-inference.yaml")
device = torch.device(f"cuda:{opt.gpu}") if torch.cuda.is_available() else torch.device("cpu")
model = instantiate_from_config(config.modelUNet)
_, _ = model.load_state_dict(sd, strict=False)
model.cuda()
model.eval()
model.turbo = defaults.general.optimized_turbo
modelCS = instantiate_from_config(config.modelCondStage)
_, _ = modelCS.load_state_dict(sd, strict=False)
modelCS.cond_stage_model.device = device
modelCS.eval()
modelFS = instantiate_from_config(config.modelFirstStage)
_, _ = modelFS.load_state_dict(sd, strict=False)
modelFS.eval()
del sd
if not defaults.general.no_half:
model = model.half()
modelCS = modelCS.half()
modelFS = modelFS.half()
return model,modelCS,modelFS,device, config
else:
config = OmegaConf.load(defaults.general.default_model_config)
model = load_model_from_config(config, defaults.general.default_model_path)
device = torch.device(f"cuda:{opt.gpu}") if torch.cuda.is_available() else torch.device("cpu")
model = (model if defaults.general.no_half else model.half()).to(device)
return model, device,config
#
#
def ModelLoader(models,load=False,unload=False,imgproc_realesrgan_model_name='RealESRGAN_x4plus'):
#get global variables
global_vars = globals()
#check if m is in globals
if unload:
for m in models:
if m in global_vars:
#if it is, delete it
del global_vars[m]
if defaults.general.optimized:
if m == 'model':
del global_vars[m+'FS']
del global_vars[m+'CS']
if m =='model':
m='Stable Diffusion'
print('Unloaded ' + m)
if load:
for m in models:
if m not in global_vars or m in global_vars and type(global_vars[m]) == bool:
#if it isn't, load it
if m == 'GFPGAN':
global_vars[m] = load_GFPGAN()
elif m == 'model':
sdLoader = load_sd_from_config()
global_vars[m] = sdLoader[0]
if defaults.general.optimized:
global_vars[m+'CS'] = sdLoader[1]
global_vars[m+'FS'] = sdLoader[2]
elif m == 'RealESRGAN':
global_vars[m] = load_RealESRGAN(imgproc_realesrgan_model_name)
elif m == 'LDSR':
global_vars[m] = load_LDSR()
if m =='model':
m='Stable Diffusion'
print('Loaded ' + m)
torch_gc()
#
@retry(tries=5)
def generation_callback(img, i=0):
try:
if i == 0:
if img['i']: i = img['i']
except TypeError:
pass
if i % int(defaults.general.update_preview_frequency) == 0 and defaults.general.update_preview:
#print (img)
#print (type(img))
# The following lines will convert the tensor we got on img to an actual image we can render on the UI.
# It can probably be done in a better way for someone who knows what they're doing. I don't.
#print (img,isinstance(img, torch.Tensor))
if isinstance(img, torch.Tensor):
x_samples_ddim = (st.session_state["model"] if not defaults.general.optimized else modelFS).decode_first_stage(img)
else:
# When using the k Diffusion samplers they return a dict instead of a tensor that look like this:
# {'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}
x_samples_ddim = (st.session_state["model"] if not defaults.general.optimized else modelFS).decode_first_stage(img["denoised"])
x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
pil_image = transforms.ToPILImage()(x_samples_ddim.squeeze_(0))
# update image on the UI so we can see the progress
st.session_state["preview_image"].image(pil_image)
# Show a progress bar so we can keep track of the progress even when the image progress is not been shown,
# Dont worry, it doesnt affect the performance.
if st.session_state["generation_mode"] == "txt2img":
percent = int(100 * float(i+1 if i+1 < st.session_state.sampling_steps else st.session_state.sampling_steps)/float(st.session_state.sampling_steps))
st.session_state["progress_bar_text"].text(
f"Running step: {i+1 if i+1 < st.session_state.sampling_steps else st.session_state.sampling_steps}/{st.session_state.sampling_steps} {percent if percent < 100 else 100}%")
else:
if st.session_state["generation_mode"] == "img2img":
round_sampling_steps = round(st.session_state.sampling_steps * st.session_state["denoising_strength"])
percent = int(100 * float(i+1 if i+1 < round_sampling_steps else round_sampling_steps)/float(round_sampling_steps))
st.session_state["progress_bar_text"].text(
f"""Running step: {i+1 if i+1 < round_sampling_steps else round_sampling_steps}/{round_sampling_steps} {percent if percent < 100 else 100}%""")
else:
if st.session_state["generation_mode"] == "txt2vid":
percent = int(100 * float(i+1 if i+1 < st.session_state.sampling_steps else st.session_state.sampling_steps)/float(st.session_state.sampling_steps))
st.session_state["progress_bar_text"].text(
f"Running step: {i+1 if i+1 < st.session_state.sampling_steps else st.session_state.sampling_steps}/{st.session_state.sampling_steps}"
f"{percent if percent < 100 else 100}%")
st.session_state["progress_bar"].progress(percent if percent < 100 else 100)
prompt_parser = re.compile("""
(?P<prompt> # capture group for 'prompt'
[^:]+ # match one or more non ':' characters
@ -574,44 +895,6 @@ def optimize_update_preview_frequency(current_chunk_speed, previous_chunk_speed,
return current_chunk_speed, previous_chunk_speed, update_preview_frequency
#
def ModelLoader(models,load=False,unload=False,imgproc_realesrgan_model_name='RealESRGAN_x4plus'):
#get global variables
global_vars = globals()
#check if m is in globals
if unload:
for m in models:
if m in global_vars:
#if it is, delete it
del global_vars[m]
if defaults.general.optimized:
if m == 'model':
del global_vars[m+'FS']
del global_vars[m+'CS']
if m =='model':
m='Stable Diffusion'
print('Unloaded ' + m)
if load:
for m in models:
if m not in global_vars or m in global_vars and type(global_vars[m]) == bool:
#if it isn't, load it
if m == 'GFPGAN':
global_vars[m] = load_GFPGAN()
elif m == 'model':
sdLoader = load_sd_from_config()
global_vars[m] = sdLoader[0]
if defaults.general.optimized:
global_vars[m+'CS'] = sdLoader[1]
global_vars[m+'FS'] = sdLoader[2]
elif m == 'RealESRGAN':
global_vars[m] = load_RealESRGAN(imgproc_realesrgan_model_name)
elif m == 'LDSR':
global_vars[m] = load_LDSR()
if m =='model':
m='Stable Diffusion'
print('Loaded ' + m)
torch_gc()
def get_font(fontsize):
@ -679,6 +962,71 @@ def seed_to_int(s):
n = n >> 32
return n
#
def draw_prompt_matrix(im, width, height, all_prompts):
def wrap(text, d, font, line_length):
lines = ['']
for word in text.split():
line = f'{lines[-1]} {word}'.strip()
if d.textlength(line, font=font) <= line_length:
lines[-1] = line
else:
lines.append(word)
return '\n'.join(lines)
def draw_texts(pos, x, y, texts, sizes):
for i, (text, size) in enumerate(zip(texts, sizes)):
active = pos & (1 << i) != 0
if not active:
text = '\u0336'.join(text) + '\u0336'
d.multiline_text((x, y + size[1] / 2), text, font=fnt, fill=color_active if active else color_inactive, anchor="mm", align="center")
y += size[1] + line_spacing
fontsize = (width + height) // 25
line_spacing = fontsize // 2
fnt = get_font(fontsize)
color_active = (0, 0, 0)
color_inactive = (153, 153, 153)
pad_top = height // 4
pad_left = width * 3 // 4 if len(all_prompts) > 2 else 0
cols = im.width // width
rows = im.height // height
prompts = all_prompts[1:]
result = Image.new("RGB", (im.width + pad_left, im.height + pad_top), "white")
result.paste(im, (pad_left, pad_top))
d = ImageDraw.Draw(result)
boundary = math.ceil(len(prompts) / 2)
prompts_horiz = [wrap(x, d, fnt, width) for x in prompts[:boundary]]
prompts_vert = [wrap(x, d, fnt, pad_left) for x in prompts[boundary:]]
sizes_hor = [(x[2] - x[0], x[3] - x[1]) for x in [d.multiline_textbbox((0, 0), x, font=fnt) for x in prompts_horiz]]
sizes_ver = [(x[2] - x[0], x[3] - x[1]) for x in [d.multiline_textbbox((0, 0), x, font=fnt) for x in prompts_vert]]
hor_text_height = sum([x[1] + line_spacing for x in sizes_hor]) - line_spacing
ver_text_height = sum([x[1] + line_spacing for x in sizes_ver]) - line_spacing
for col in range(cols):
x = pad_left + width * col + width / 2
y = pad_top / 2 - hor_text_height / 2
draw_texts(col, x, y, prompts_horiz, sizes_hor)
for row in range(rows):
x = pad_left / 2
y = pad_top + height * row + height / 2 - ver_text_height / 2
draw_texts(row, x, y, prompts_vert, sizes_ver)
return result
def check_prompt_length(prompt, comments):
"""this function tests if prompt is too long, and if so, adds a message to comments"""