File size: 5,317 Bytes
0070fce |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 |
import math
import gradio as gr
from modules import images, processing, scripts
from modules.processing import Processed
from modules.shared import opts, state
class Loopback(scripts.Script):
def title(self):
return "Loopback"
def show(self, is_img2img):
return is_img2img
def ui(self, is_img2img):
with gr.Row():
loops = gr.Slider(
minimum=1,
maximum=8,
step=1,
label="Loops",
value=2,
elem_id=self.elem_id("loops"),
)
final_denoising_strength = gr.Slider(
label="Final Denoising Strength",
minimum=0,
maximum=1,
step=0.01,
value=0.5,
elem_id=self.elem_id("final_denoising_strength"),
)
denoising_curve = gr.Dropdown(
label="Denoising Strength Curve",
choices=("Aggressive", "Linear", "Lazy"),
value="Linear",
elem_id=self.elem_id("denoising_strength_curve"),
)
return [loops, final_denoising_strength, denoising_curve]
def run(self, p, loops: int, final_denoising_strength: float, denoising_curve: str):
processing.fix_seed(p)
p.extra_generation_params = {
"Final Denoising Strength": final_denoising_strength,
"Denoising Strength Curve": denoising_curve,
}
batch_count = p.n_iter
p.batch_size = 1
p.n_iter = 1
info = None
initial_seed = None
initial_info = None
initial_denoising_strength = p.denoising_strength
grids = []
all_images = []
original_init_image = p.init_images
original_inpainting_fill = p.inpainting_fill
state.job_count = loops * batch_count
initial_color_corrections = [
processing.setup_color_correction(p.init_images[0])
]
def calculate_denoising_strength(loop):
strength = initial_denoising_strength
if loops == 1:
return strength
progress = loop / (loops - 1)
if denoising_curve == "Aggressive":
strength = math.sin((progress) * math.pi * 0.5)
elif denoising_curve == "Lazy":
strength = 1 - math.cos((progress) * math.pi * 0.5)
else:
strength = progress
change = (final_denoising_strength - initial_denoising_strength) * strength
return initial_denoising_strength + change
history = []
for n in range(batch_count):
# Reset to original init image at the start of each batch
p.init_images = original_init_image
# Reset to original denoising strength
p.denoising_strength = initial_denoising_strength
last_image = None
for i in range(loops):
p.n_iter = 1
p.batch_size = 1
p.do_not_save_grid = True
if opts.img2img_color_correction:
p.color_corrections = initial_color_corrections
state.job = f"Iteration {i + 1}/{loops}, batch {n + 1}/{batch_count}"
processed = processing.process_images(p)
# Generation cancelled
if state.interrupted or state.stopping_generation:
break
if initial_seed is None:
initial_seed = processed.seed
initial_info = processed.info
p.seed = processed.seed + 1
p.denoising_strength = calculate_denoising_strength(i + 1)
if state.skipped:
break
last_image = processed.images[0]
p.init_images = [last_image]
# Set "masked content" to "original" for next loop
p.inpainting_fill = 1
if batch_count == 1:
history.append(last_image)
all_images.append(last_image)
if batch_count > 1 and not state.skipped and not state.interrupted:
history.append(last_image)
all_images.append(last_image)
p.inpainting_fill = original_inpainting_fill
if state.interrupted or state.stopping_generation:
break
if len(history) > 1:
grid = images.image_grid(history, rows=1)
if opts.grid_save:
images.save_image(
grid,
p.outpath_grids,
"grid",
initial_seed,
p.prompt,
opts.grid_format,
info=info,
short_filename=not opts.grid_extended_filename,
grid=True,
p=p,
)
if opts.return_grid:
grids.append(grid)
all_images = grids + all_images
return Processed(p, all_images, initial_seed, initial_info)
|