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import logging
from os import environ
import modules.scripts as scripts
import gradio as gr
from PIL import Image
import numpy as np
import re
from modules import script_callbacks, prompt_parser
from modules.script_callbacks import CFGDenoiserParams, CFGDenoisedParams, AfterCFGCallbackParams
from modules.prompt_parser import reconstruct_multicond_batch, stack_conds, reconstruct_cond_batch
from modules.processing import StableDiffusionProcessing, decode_latent_batch, txt2img_image_conditioning
#from modules.shared import sd_model, opts
from modules.sd_samplers_cfg_denoiser import pad_cond
from modules import shared, devices, errors, deepbooru
from modules.interrogate import InterrogateModels
from scripts.ui_wrapper import UIWrapper, arg
# from scripts.t2i_zero import SegaExtensionScript
import torch
from torchvision.transforms import ToPILImage
logger = logging.getLogger(__name__)
logger.setLevel(environ.get("SD_WEBUI_LOG_LEVEL", logging.INFO))
"""
!!!
!!! Only semi-functional !!!
!!!
!!! Might conflict with other extensions that modify the prompt !!!
Known conflicts: Dynamic Prompts
Appends a "learned" prompt to the end of your prompt that is optimized to maximize the similarity between the text and image embeddings at the end of the diffusion process.
This is done by masking out words in the prompt that are below a threshold given by CLIP e.g. semantic guidance from the paper.
This is useful as is because it allows you to generate images that are (maybe) more similar to the prompt.
The other methods in the paper are not implemented yet.
I'm not sure how to implement the other methods in the paper because the details of how exactly the "prompt is learned" aren't clear to me. Any insights would be appreciated.
"""
"""
An unofficial and incomplete implementation of Seek for Incantations: Towards Accurate Text-to-Image Diffusion Synthesis
through Prompt Engineering for Automatic1111 WebUI
@misc{yu2024seek,
title={Seek for Incantations: Towards Accurate Text-to-Image Diffusion Synthesis through Prompt Engineering},
author={Chang Yu and Junran Peng and Xiangyu Zhu and Zhaoxiang Zhang and Qi Tian and Zhen Lei},
year={2024},
eprint={2401.06345},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
Author: v0xie
GitHub URL: https://github.com/v0xie/sd-webui-incantations
"""
class Interrogator:
def __init__(self):
pass
def load(self):
pass
def generate_caption(self):
pass
def unload(self):
pass
class InterrogatorCLIP(Interrogator):
def __init__(self):
self.interrogator = shared.interrogator
def load(self):
self.interrogator = shared.interrogator
self.interrogator.load()
def generate_caption(self, pil_image):
self.load()
return self.interrogator.generate_caption(pil_image.to(shared.device))
def unload(self):
self.interrogator.unload()
class InterrogatorDeepbooru(Interrogator):
def __init__(self):
self.interrogator = deepbooru.model
def load(self):
self.interrogator = deepbooru.model
self.interrogator.load()
def generate_caption(self, pil_image):
self.load()
if not shared.opts.interrogate_return_ranks:
print('\nincantations - warning: interrogate_return_ranks should be enabled for Deepbooru Interrogate to work')
threshold = shared.opts.interrogate_deepbooru_score_threshold
if threshold < 0.4:
print('\nincantations - warning: deepbooru score threshold should be lowered for Deepbooru Interrogate to work')
tags = self.interrogator.tag(pil_image)
#prompts = prompt_parser.parse_prompt_attention(tags)
return tags
def unload(self):
pass
class IncantStateParams:
def __init__(self):
self.delim = ''
self.word = '-'
self.coarse = 10
self.fine = 30
self.gamma = 0.25
self.quality = False
self.deepbooru = False
self.prompt = ''
self.prompts = []
self.prompt_tokens = []
self.caption_coarse = []
self.caption_fine = []
self.img_coarse = []
self.img_fine = []
self.emb_img_coarse = []
self.emb_img_fine = []
self.emb_txt_coarse = []
self.emb_txt_fine = []
self.grad_txt = []
self.text_tokens = []
self.grad_img = []
self.matches_coarse = []
self.matches_fine = []
self.masked_prompt = []
self.get_conds_with_caching = None
self.steps = None
self.iteration = None
self.batch_size = 1
self.p = None
self.init_noise = None
self.first_stage_cache = None
self.second_stage = False
self.denoiser = None
self.job = None
# is this loss
self.loss = [] # grad_img * grad txt
self.loss_qual = [] # quality guidance
self.loss_sem = [] # semantic guidance
self.loss_txt_txt = [] # txt - txt
self.loss_txt_img = [] # txt - img
self.loss_spar = [] # sparsity
# hyperparameters
self.qual_scale = 1.0
self.sem_scale = 1.0
self.txt_txt_scale = 1.0
self.txt_img_scale = 1.0
self.spar_scale = 1.0
class IncantExtensionScript(UIWrapper):
def __init__(self):
self.stage_1 = [[]]
self.cached_c = [[None, None],[None, None]]
self.infotext_fields = {}
self.paste_field_names = []
# Extension title in menu UI
def title(self):
return "Seek Incantations"
# Setup menu ui detail
def setup_ui(self, is_img2img):
return self.setup_seek_incantations()
def get_infotext_fields(self):
return self.infotext_fields
def get_paste_field_names(self):
return self.paste_field_names
def setup_seek_incantations(self):
with gr.Accordion('Seek for Incantations', open=False):
inc_active = gr.Checkbox(value=False, default=False, label="Active", elem_id='incant_active')
inc_quality = gr.Checkbox(value=False, default=False, label="Append Generated Caption", elem_id='incant_append_prompt', info="Append interrogated caption to prompt. (Deepbooru is reversed, if disabled, will not append the masked original prompt)")
inc_deepbooru = gr.Checkbox(value=False, default=False, label="Deepbooru Interrogate", elem_id='incant_deepbooru')
inc_coarse_step = gr.Slider(visible=False, value = 10, minimum = 1, maximum = 120, step = 1, label="Coarse Step", elem_id = 'incant_coarse_step')
with gr.Row():
inc_delim = gr.Textbox(value='BREAK', label="Delimiter", elem_id='incant_delim', info="Prompt DELIM Optimized Prompt. Try BREAK, AND, NOT, etc.")
inc_word = gr.Textbox(value='-', label="Word Replacement", elem_id='incant_word', info="Replace masked words with this")
with gr.Row():
inc_gamma = gr.Slider(value = 0.2, minimum = -1.0, maximum = 1.0, step = 0.0001, label="Gamma", elem_id = 'incant_gamma', info="If gamma > 0, mask words with similarity less than gamma percent. If gamma < 0, mask more similar words. For Deepbooru, try higher values > 0.7")
inc_active.do_not_save_to_config = True
inc_quality.do_not_save_to_config = True
inc_deepbooru.do_not_save_to_config = True
inc_delim.do_not_save_to_config = True
inc_word.do_not_save_to_config = True
inc_gamma.do_not_save_to_config = True
inc_coarse_step.do_not_save_to_config = True
self.infotext_fields = [
(inc_active, lambda d: gr.Checkbox.update(value='INCANT Active' in d)),
(inc_quality, 'INCANT Append Prompt'),
(inc_deepbooru, 'INCANT Deepbooru'),
(inc_delim, 'INCANT Delim'),
(inc_word, 'INCANT Word'),
(inc_gamma, 'INCANT Gamma'),
# (inc_coarse_step, 'INCANT Coarse'),
]
self.paste_field_names = [
'incant_active',
'incant_append_prompt',
'incant_deepbooru',
'incant_delim',
'incant_word',
'incant_gamma',
# 'incant_coarse',
]
return [inc_active, inc_quality, inc_deepbooru, inc_delim, inc_word, inc_gamma, inc_coarse_step]
def interrogator(self, deepbooru=False):
if deepbooru:
return InterrogatorDeepbooru()
else:
return shared.interrogator
def before_process(self, p: StableDiffusionProcessing, *args, **kwargs):
self.incant_before_process(p, *args, **kwargs)
def incant_before_process(self, p: StableDiffusionProcessing, inc_active, inc_quality, inc_deepbooru, inc_delim, inc_word, inc_gamma, inc_coarse_step, *args, **kwargs):
inc_active = getattr(p, "incant_active", inc_active)
if inc_active is False:
return
p.n_iter = p.n_iter * 2
def process(self, p: StableDiffusionProcessing, *args, **kwargs):
self.incant_process(p, *args, **kwargs)
def incant_process(self, p: StableDiffusionProcessing, inc_active, inc_quality, inc_deepbooru, inc_delim, inc_word, inc_gamma, inc_coarse_step, *args, **kwargs):
inc_active = getattr(p, "incant_active", inc_active)
if inc_active is False:
return
inc_quality = getattr(p, "incant_append_prompt", inc_quality)
inc_delim = getattr(p, "incant_delim", inc_delim)
inc_word = getattr(p, "incant_word", inc_word)
# modifying the all_prompts* may conflict with extensions that do so
# hr fix untested
if p.iteration == 0:
param_list = [
"all_hr_negative_prompts",
"all_hr_prompts",
"all_negative_prompts",
"all_prompts",
"all_seeds",
"all_subseeds",
]
for param_name in param_list:
run_fn_on_attr(p, param_name, duplicate_alternate_elements, p.batch_size)
# assign
delim_str = f' {inc_delim} ' if len(inc_delim) > 0 else ' '
for n in range(1, p.n_iter, 2):
start_idx = n * p.batch_size
end_idx = (n + 1) * p.batch_size
p.all_prompts[start_idx:end_idx] = [prompt + delim_str + '<<REPLACEME>>' for prompt in p.all_prompts[start_idx:end_idx]]
def before_process_batch(self, p: StableDiffusionProcessing, *args, **kwargs):
self.incant_before_process_batch(p, *args, **kwargs)
def incant_before_process_batch(self, p: StableDiffusionProcessing, inc_active, inc_quality, inc_deepbooru, inc_delim, inc_word, inc_gamma, inc_coarse_step, *args, **kwargs):
inc_active = getattr(p, "incant_active", inc_active)
if inc_active is False:
return
inc_quality = getattr(p, "incant_append_prompt", inc_quality)
inc_deepbooru = getattr(p, "incant_deepbooru", inc_deepbooru)
inc_delim = getattr(p, "incant_delim", inc_delim)
inc_word = getattr(p, "incant_word", inc_word)
fine_step = getattr(p, "incant_fine", None)
inc_gamma = getattr(p, "incant_gamma", inc_gamma)
inc_coarse_step = getattr(p, "incant_coarse", inc_coarse_step)
if fine_step == None:
#print(f"Fine step {fine_step} is greater than total steps {p.steps}, setting to {p.steps-2}")
fine_step = p.steps - 2
interrogator = self.interrogator(inc_deepbooru)
interrogator.load()
n = p.n_iter
if p.iteration % 2 == 1:
n = p.iteration
# batch of images
batch_start_idx = n * p.batch_size
batch_end_idx = (n + 1) * p.batch_size
# mask
mask_start_idx = (n - 1) * p.batch_size
delim_str = f' {inc_delim} ' if len(inc_delim) > 0 else ' '
#add_mask_prompt = self.stage_1.masked_prompt[mask_start_idx]
for idx in range(batch_start_idx, batch_end_idx):
add_mask_prompt = ''
mask_idx = mask_start_idx + (idx - batch_start_idx)
masked_prompts = self.stage_1.masked_prompt[mask_idx]
# if we don't want to append other masked captions, only use the first one
if not inc_quality:
masked_prompts = [masked_prompts[0]]
for masked_prompt_idx, prompt in enumerate(masked_prompts):
if masked_prompt_idx > 0:
add_mask_prompt += delim_str + prompt
else:
add_mask_prompt += prompt
p.all_prompts[idx] = p.all_prompts[idx].replace('<<REPLACEME>>', add_mask_prompt)
kwargs['prompts'][mask_idx] = kwargs['prompts'][mask_idx].replace('<<REPLACEME>>', add_mask_prompt)
# p.steps += fine_step
# TODO: nicely put this into a dict
p.extra_generation_params.update({
"INCANT Active": inc_active,
"INCANT Append Prompt": inc_quality,
"INCANT Delim": inc_delim,
"INCANT Word": inc_word,
"INCANT Deepbooru": inc_deepbooru,
"INCANT Fine": fine_step,
"INCANT Gamma": inc_gamma,
})
self.create_hook(p, inc_active, inc_quality, inc_deepbooru, inc_delim, inc_word, inc_gamma, inc_coarse_step, *args, **kwargs)
def parse_concept_prompt(self, prompt:str) -> list[str]:
"""
Separate prompt by comma into a list of concepts
TODO: parse prompt into a list of concepts using A1111 functions
>>> g = lambda prompt: self.parse_concept_prompt(prompt)
>>> g("")
[]
>>> g("apples")
['apples']
>>> g("apple, banana, carrot")
['apple', 'banana', 'carrot']
"""
if len(prompt) == 0:
return []
return [x.strip() for x in prompt.split(",")]
def create_hook(self, p, active, quality, deepbooru, delim, word, gamma, coarse_step, *args, **kwargs):
import clip
# Create a list of parameters for each concept
incant_params = IncantStateParams()
incant_params.p = p
incant_params.prompt = p.prompt
incant_params.prompts = [pr for pr in p.prompts]
#incant_params.prompt_tokens = clip.tokenize(list(p.prompt), truncate=True).to(devices.device_interrogate)
incant_params.quality = quality
incant_params.delim = delim
incant_params.word = word
incant_params.deepbooru = deepbooru
fine = p.steps
incant_params.fine = p.steps
if incant_params.fine >= p.steps:
print(f"Fine step {fine} is greater than total steps {p.steps}, setting to {p.steps}")
fine = max(p.steps - 2, 1)
incant_params.gamma = gamma
incant_params.coarse = coarse_step
incant_params.qual_scale = 0
incant_params.sem_scale = 0
incant_params.iteration = p.iteration
incant_params.get_conds_with_caching = p.get_conds_with_caching
incant_params.steps = p.steps
incant_params.batch_size = p.batch_size
incant_params.job = shared.state.job
#incant_params.first_stage_cache = self.stage_1[0]
incant_params.second_stage = (p.iteration % 2) == 1
tqdm = shared.total_tqdm
if not hasattr(p, 'incant_params'):
setattr(p, 'incant_params', incant_params)
if p.iteration % 2 == 0:
self.stage_1 = incant_params
else:
# assign old cache to next iteration
incant_params.first_stage_cache = self.stage_1
# Use lambda to call the callback function with the parameters to avoid global variables
y = lambda params: self.on_cfg_denoiser_callback(params, incant_params)
y2 = lambda params: self.cfg_after_cfg_callback(params, incant_params)
#y3 = lambda params: self.cfg_after_cfg_callback(params, incant_params)
logger.debug('Hooked callbacks')
#script_callbacks.on_cfg_denoised(y2)
script_callbacks.on_cfg_denoiser(y)
script_callbacks.on_cfg_after_cfg(y2)
script_callbacks.on_script_unloaded(self.unhook_callbacks)
def calc_masked_prompt(self, incant_params: IncantStateParams, first_stage_cache):
fs = first_stage_cache
repl_word = '-'
prompt = incant_params.p.prompt
repl_threshold = incant_params.gamma * 100.0
word_list = fs.matches_fine[0]
masked_prompt = self.mask_prompt(repl_threshold, word_list, incant_params.p.prompt, incant_params.word)
return masked_prompt
def calc_quality_guidance(self, incant_params: IncantStateParams):
incant_params.loss_qual = []
for i, (grad_img, grad_txt) in enumerate(zip(incant_params.grad_img, incant_params.grad_txt)):
incant_params.loss_qual.append(grad_img * grad_txt)
def loss_sem(self, incant_params: IncantStateParams):
p = incant_params.p
caption = incant_params.prompt
incant_params.loss_sem = []
prompt_list = [caption] * p.batch_size
prompts = prompt_parser.SdConditioning(prompt_list, width=p.width, height=p.height)
c = incant_params.get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, prompts, p.steps, self.cached_c, p.extra_network_data)
masked_prompts = incant_params.masked_prompt
for i, (emb_fine, emb_coarse) in enumerate(zip(incant_params.emb_txt_fine, incant_params.emb_txt_coarse)):
incant_params.loss_sem.append(emb_fine - emb_coarse)
def postprocess_batch(self, p: StableDiffusionProcessing, *args, **kwargs):
return self.incant_postprocess_batch(p, *args, **kwargs)
def incant_postprocess_batch(self, p: StableDiffusionProcessing, inc_active, *args, **kwargs):
inc_active = getattr(p, "incant_active", inc_active)
if inc_active is False:
return
batch_number = kwargs.get('batch_number', -1)
images = kwargs.get('images', None)
incant_params: IncantStateParams = getattr(p, "incant_params", None)
to_pil = ToPILImage()
n = p.iteration
if n % 2 == 0:
#fine_images = self.decode_images(images)
fine_images = []
for img in images:
img = to_pil(img)
incant_params.img_fine.append(img)
self.interrogate_images(incant_params, p)
devices.torch_gc()
# compute masked_prompts
batch_start_idx = n * p.batch_size
batch_end_idx = (n + 1) * p.batch_size
for batch_idx, caption_matches_item in enumerate(incant_params.matches_fine[batch_start_idx:batch_end_idx]):
batch_mask_prompts = []
for caption, matches in caption_matches_item:
batch_mask_prompts.append(self.mask_prompt(incant_params.gamma, matches, caption, incant_params.word))
incant_params.masked_prompt.append(batch_mask_prompts)
self.unhook_callbacks()
def unhook_callbacks(self):
logger.debug('Unhooked callbacks')
interrogator = self.interrogator(False)
interrogator.unload()
script_callbacks.remove_current_script_callbacks()
def on_cfg_denoiser_callback(self, params: CFGDenoiserParams, incant_params: IncantStateParams):
second_stage = incant_params.second_stage
if not second_stage:
return
fs: IncantStateParams = incant_params.first_stage_cache
# TODO: handle batches
p = incant_params.p
sampling_step = params.sampling_step
text_cond = params.text_cond
text_uncond = params.text_uncond
gamma = incant_params.gamma * 100.0
# temp bypass
if incant_params.qual_scale != 0:
# quality guidance
grad_img_batch = []
grad_txt_batch = []
loss_qual_batch = []
for i in range(len(fs.emb_img_fine)):
#for i, (emb_fine, emb_coarse) in enumerate(zip(fs.emb_img_fine, fs.emb_img_coarse)):
img_fine = fs.emb_img_fine[i]
img_norm_fine = torch.norm(img_fine, dim=-1, keepdim=True)
img_coarse = fs.emb_img_coarse[i]
img_norm_coarse = torch.norm(img_coarse, dim=-1, keepdim=True)
grad_img = (img_fine / img_norm_fine**2) - (img_coarse / img_norm_coarse**2)
grad_img_batch.append(grad_img)
# compute the text guidance
#for i, (emb_fine, emb_coarse) in enumerate(zip(fs.emb_txt_fine, fs.emb_txt_coarse)):
#for b in range(incant_params.batch_size):
# not sure what to with batch, when is batch > 0?
b = 0
txt_fine = fs.emb_txt_fine[i].batch[b][0].schedules[0].cond
txt_norm_fine = torch.norm(txt_fine, dim=-1, keepdim=True)
txt_coarse = fs.emb_txt_coarse[i].batch[b][0].schedules[0].cond
txt_norm_coarse = torch.norm(txt_coarse, dim=-1, keepdim=True)
grad_txt = (txt_fine / txt_norm_fine**2) - (txt_coarse / txt_norm_coarse**2)
grad_txt_batch.append(grad_txt)
for i, (grad_img, grad_txt) in enumerate(zip(grad_img_batch, grad_txt_batch)):
loss_qual = grad_img * grad_txt
loss_qual_batch.append(loss_qual)
t = loss_qual * incant_params.qual_scale
print(f'\nloss_qual:{t.norm()}')
t = t.unsqueeze(0)
## TODO: scale t by hyperparameter (and use correct formula)
if t.shape[1] != text_cond.shape[1]:
empty = shared.sd_model.cond_stage_model_empty_prompt
num_repeats = (t.shape[1] - text_cond.shape[1]) // empty.shape[1]
if num_repeats < 0:
t = pad_cond(t, -num_repeats, empty)
elif num_repeats > 0:
t = pad_cond(t, num_repeats, empty)
text_cond[i] += t.squeeze(0)
def mask_prompt(self, gamma, word_list, prompt, word_repl = '-'):
# TODO: refactor out removing <>
regex = r"\b{0}\b"
masked_prompt = prompt
mask_less_similar = gamma > 0
gamma = abs(gamma)
for word, pct in word_list:
word = word.strip(', ')
if len(word) == 0:
continue
condition = (pct < gamma) if mask_less_similar else (pct > gamma)
if condition:
repl_regex = regex.format(word)
# replace word with -
masked_prompt = re.sub(repl_regex, word_repl, masked_prompt)
is_lora = word.startswith('<') and word.endswith('>')
if is_lora:
masked_prompt = masked_prompt.replace(word, '')
# hack: remove text between pairs of brackets like <...>
masked_prompt = re.sub(r'<[^>]*>', '', masked_prompt)
return masked_prompt
def cfg_after_cfg_callback(self, params: AfterCFGCallbackParams, incant_params: IncantStateParams):
p = incant_params.p
coarse_step = incant_params.coarse
second_stage = incant_params.second_stage
x = params.x
## BUG: webui params passes shared.state.sampling_step instead of the internal self.step
# meaning that the sampling step is usually incorrect
step = params.sampling_step
## FIXME: why is the max value of step 2 less than the total steps???
if step == coarse_step and not second_stage:
print(f"\nCoarse step: {step}\n")
coarse_img = self.decode_images(x)
incant_params.img_coarse.extend(coarse_img)
def compute_gradients(self, emb_fine, emb_coarse):
out_gradients = []
# zip together list and iterate
for i, (fine, coarse) in enumerate(zip(emb_fine, emb_coarse)):
# calculate norm of fine and coarse embeddings
norm_fine = torch.norm(fine, dim=-1, keepdim=True)
norm_fine **= 2
norm_coarse = torch.norm(coarse, dim=-1, keepdim=True)
norm_coarse **= 2
grad = (fine/norm_fine) - (coarse/norm_coarse)
out_gradients.append(grad)
return out_gradients
def calculate_embedding_gradients(self, incant_params, p, current_step):
# text embeddings
captions_coarse = incant_params.caption_coarse
captions_fine = incant_params.caption_fine
# txt_emb_coarse = incant_params.emb_txt_coarse
# txt_emb_fine = incant_params.emb_txt_fine
# txt_emb_fine = []
# txt_emb_coarse = []
for i in range(len(captions_coarse)):
out = []
incant_params.grad_txt.append(out)
# image embeddings
img_emb_coarse = incant_params.emb_img_coarse
img_emb_fine = incant_params.emb_img_fine
# incant_params.grad_img = self.compute_gradients(img_emb_fine, img_emb_coarse)
def interrogate_images(self, incant_params, p):
interrogator = self.interrogator(incant_params.deepbooru)
interrogator.load()
# fine features
for batch_idx, pil_image in enumerate(incant_params.img_fine):
# generate caption
caption = interrogator.generate_caption(pil_image)
devices.torch_gc()
# CLIP
if not incant_params.deepbooru:
matches_list = []
# append caption
caption = interrogator.generate_caption(pil_image)
incant_params.caption_fine.append(caption)
# calculate image embeddings
image_features = self.calc_img_embedding(interrogator, pil_image)
incant_params.emb_img_fine.append(image_features)
# calculate image similarity to prompt
prompt_text_array = incant_params.prompt.split()
matches = self.clip_text_image_similarity(interrogator, prompt_text_array, image_features, top_count=len(prompt_text_array))
matches_list.append((incant_params.prompt, matches))
#print(f"\n{batch_idx}-prompt:{caption}\n{batch_idx}-fine:{matches}\n")
# calculate image similarity to generated caption
if incant_params.quality:
caption_text_array = caption.split()
# upcast
try:
matches = self.clip_text_image_similarity(interrogator, caption_text_array, image_features, top_count=len(caption_text_array))
matches_list.append((caption, matches))
except RuntimeError:
print(f"\n{batch_idx}-fine:error computing matches to generated caption\n")
matches_list.append((caption, [(caption, 1.0)]))
print(f"\n{batch_idx}-fine:{matches}\n")
incant_params.matches_fine.append(matches_list)
# deepbooru interrogate
else:
matches_list = []
# TODO: separate options to append generated caption and append masked original prompt
# for deepbooru, if disabled, append generated caption will not append the ORIGINAL prompt
# mask the original prompt
if incant_params.quality:
new_prompt, prompt_matches_list = self.interrogate_deepbooru(incant_params.prompt, incant_params.gamma)
matches_list.append((new_prompt, prompt_matches_list))
print(f"{batch_idx}-prompt:{new_prompt}\n")
new_caption, caption_matches_list = self.interrogate_deepbooru(caption, incant_params.gamma)
matches_list.append((new_caption, caption_matches_list))
print(f"{batch_idx}-caption:{new_caption}\n")
incant_params.caption_fine.append(new_caption)
#incant_params.caption_fine.append(new_prompt)
# append auto generated captions
incant_params.matches_fine.append(matches_list)
devices.torch_gc()
def interrogate_deepbooru(self, caption, gamma):
"""_summary_
Args:
caption (_type_): _description_
matches_list (_type_): _description_
gamma (_type_): _description_
mask_less_similar (_type_): _description_
Returns:
_type_: _description_
"""
mask_less_similar = gamma > 0
gamma = abs(gamma)
matches_list = []
# preprocess caption
# remove lora
caption = re.sub(r'<[^>]*>', '', caption)
matches = prompt_parser.parse_prompt_attention(caption)
if mask_less_similar:
matches = [(tag, strength) for (tag, strength) in matches if strength >= gamma]
else:
matches = [(tag, strength) for (tag, strength) in matches if strength < gamma]
# matches = [(tag, strength) for (tag, strength) in matches]
# split by tags
for tags, strength in matches:
for tag in tags.split(', '):
if len(tag) == 0:
continue
# filter tags
matches_list.append((tag.strip(), strength))
new_caption = ''
for tag, strength in matches_list:
new_caption += f'({tag}:{strength}), '
new_caption.removesuffix(', ')
return new_caption, matches_list
def clip_text_image_similarity(self, interrogator, text_array, image_features, top_count=1) -> list[tuple[str, float]]:
""" Calculate similarity between text and image features using CLIP
Args:
interrogator (): shared.interrogator
text_array (str): text to match similarity
image_features (tensor): image encoded with calc_img_embedding
top_count (int, optional): number of top matches to return
Returns:
list[tuple[str, float]]: _description_
"""
with torch.no_grad(), devices.autocast():
matches = interrogator.rank(image_features, text_array, top_count=top_count)
matches = [(tag, strength/100.0) for (tag, strength) in matches] # rescale to 0-1
return matches
def calc_img_embedding(self, interrogator, pil_image):
clip_image = interrogator.clip_preprocess(pil_image).unsqueeze(0).type(interrogator.dtype).to(devices.device_interrogate)
with torch.no_grad(), devices.autocast():
# calculate image embeddings
image_features = interrogator.clip_model.encode_image(clip_image).type(interrogator.dtype)
image_features /= image_features.norm(dim=-1, keepdim=True)
return image_features
def decode_images(self, x):
batch_images = []
already_decoded = False
x_samples_ddim = decode_latent_batch(shared.sd_model, x, target_device=devices.cpu, check_for_nans=True)
for i, x_sample in enumerate(x_samples_ddim):
x_sample = x_sample.to(torch.float32)
x_sample = torch.clamp((x_sample + 1.0) / 2.0, min=0.0, max=1.0)
x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2)
x_sample = x_sample.astype(np.uint8)
image = Image.fromarray(x_sample)
batch_images.append(image)
return batch_images
def get_xyz_axis_options(self) -> dict:
xyz_grid = [x for x in scripts.scripts_data if x.script_class.__module__ in ("xyz_grid.py", "scripts.xyz_grid")][0].module
extra_axis_options = {
xyz_grid.AxisOption("[Incant] Active", str, incant_apply_override('incant_active', boolean=True), choices=xyz_grid.boolean_choice(reverse=True)),
xyz_grid.AxisOption("[Incant] Append Caption Prompt", str, incant_apply_override('incant_append_prompt', boolean=True), choices=xyz_grid.boolean_choice(reverse=True)),
xyz_grid.AxisOption("[Incant] Deepbooru Interrogate", str, incant_apply_override('incant_deepbooru', boolean=True), choices=xyz_grid.boolean_choice(reverse=True)),
xyz_grid.AxisOption("[Incant] Delimiter", str, incant_apply_field("incant_delim")),
xyz_grid.AxisOption("[Incant] Replacement Word", str, incant_apply_field("incant_word")),
xyz_grid.AxisOption("[Incant] Gamma", float, incant_apply_field("incant_gamma"))
}
return extra_axis_options
def run_fn_on_attr(p, attr_name, fn, *args):
""" Run a function on an attribute of a class if it exists """
try:
attr = getattr(p, attr_name)
setattr(p, attr_name, fn(attr, *args))
except AttributeError:
# No attribute exists
return
except TypeError:
# If the attribute is not iterable, return
return
def duplicate_alternate_elements(input_list: list, batch_size = 1) -> list:
""" Duplicate each element in a list and return a new list
>>> duplicate_list([1, 2, 3, 4], 1)
[1, 1, 3, 3]
>>> duplicate_list([1, 2, 3, 4], 2)
[1, 2, 1, 2]
>>> duplicate_list([1, 2, 3, 4, 5, 6, 7, 8], 4)
[1, 2, 3, 4, 1, 2, 3, 4]
"""
result = []
for i in range(0, len(input_list), batch_size*2):
batch = input_list[i:i + batch_size]
result.extend(batch)
result.extend(batch)
return result
def duplicate_list(input_list: list) -> list:
""" Duplicate each element in a list and return a new list
>>> duplicate_list([1,2,3])
[1, 1, 2, 2, 3, 3]
"""
return [element for item in input_list for element in (item, item)]
# XYZ Plot
# untested
# Based on @mcmonkey4eva's XYZ Plot implementation here: https://github.com/mcmonkeyprojects/sd-dynamic-thresholding/blob/master/scripts/dynamic_thresholding.py
def incant_apply_override(field, boolean: bool = False):
def fun(p, x, xs):
if boolean:
x = True if x.lower() == "true" else False
setattr(p, field, x)
return fun
def incant_apply_field(field):
def fun(p, x, xs):
if not hasattr(p, "incant_active"):
setattr(p, "incant_active", True)
setattr(p, field, x)
return fun
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