stable-diffusion-webui/scripts/loopback.py
LipeCarmel 5c7ab90a4e
loopback.py Colab compatibility and bug fix
This code (suggested by @abvgdeabvgde2 ) literally does the same thing and it does not break with Python 3.9, making it helpful for Google Colab users (me included).
fixes #8927
Also a partial fix for #8902 but it does not resolve the unresponsive UI problem faced by @Archon332
2023-03-25 14:48:22 -03:00

140 lines
5.2 KiB
Python

import math
import gradio as gr
import modules.scripts as scripts
from modules import deepbooru, images, processing, shared
from modules.processing import Processed
from modules.shared import opts, state
class Script(scripts.Script):
def title(self):
return "Loopback"
def show(self, is_img2img):
return is_img2img
def ui(self, is_img2img):
loops = gr.Slider(minimum=1, maximum=32, step=1, label='Loops', value=4, elem_id=self.elem_id("loops"))
final_denoising_strength = gr.Slider(minimum=0, maximum=1, step=0.01, label='Final denoising strength', 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")
append_interrogation = gr.Dropdown(label="Append interrogated prompt at each iteration", choices=["None", "CLIP", "DeepBooru"], value="None")
return [loops, final_denoising_strength, denoising_curve, append_interrogation]
def run(self, p, loops, final_denoising_strength, denoising_curve, append_interrogation):
processing.fix_seed(p)
batch_count = p.n_iter
p.extra_generation_params = {
"Final denoising strength": final_denoising_strength,
"Denoising curve": denoising_curve
}
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_prompt = p.prompt
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
if append_interrogation != "None":
p.prompt = original_prompt + ", " if original_prompt != "" else ""
if append_interrogation == "CLIP":
p.prompt += shared.interrogator.interrogate(p.init_images[0])
elif append_interrogation == "DeepBooru":
p.prompt += deepbooru.model.tag(p.init_images[0])
state.job = f"Iteration {i + 1}/{loops}, batch {n + 1}/{batch_count}"
processed = processing.process_images(p)
# Generation cancelled.
if state.interrupted:
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]
p.inpainting_fill = 1 # Set "masked content" to "original" for next loop.
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:
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
processed = Processed(p, all_images, initial_seed, initial_info)
return processed