Merge pull request #8799 from JaRail/master

Loopback Script Updates
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AUTOMATIC1111 2023-03-25 10:41:24 +03:00 committed by GitHub
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2 changed files with 75 additions and 29 deletions

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@ -40,7 +40,6 @@ titles = {
"Inpaint at full resolution": "Upscale masked region to target resolution, do inpainting, downscale back and paste into original image", "Inpaint at full resolution": "Upscale masked region to target resolution, do inpainting, downscale back and paste into original image",
"Denoising strength": "Determines how little respect the algorithm should have for image's content. At 0, nothing will change, and at 1 you'll get an unrelated image. With values below 1.0, processing will take less steps than the Sampling Steps slider specifies.", "Denoising strength": "Determines how little respect the algorithm should have for image's content. At 0, nothing will change, and at 1 you'll get an unrelated image. With values below 1.0, processing will take less steps than the Sampling Steps slider specifies.",
"Denoising strength change factor": "In loopback mode, on each loop the denoising strength is multiplied by this value. <1 means decreasing variety so your sequence will converge on a fixed picture. >1 means increasing variety so your sequence will become more and more chaotic.",
"Skip": "Stop processing current image and continue processing.", "Skip": "Stop processing current image and continue processing.",
"Interrupt": "Stop processing images and return any results accumulated so far.", "Interrupt": "Stop processing images and return any results accumulated so far.",
@ -71,8 +70,10 @@ titles = {
"Directory name pattern": "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg],[prompt_hash], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [model_name], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]; leave empty for default.", "Directory name pattern": "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg],[prompt_hash], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [model_name], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]; leave empty for default.",
"Max prompt words": "Set the maximum number of words to be used in the [prompt_words] option; ATTENTION: If the words are too long, they may exceed the maximum length of the file path that the system can handle", "Max prompt words": "Set the maximum number of words to be used in the [prompt_words] option; ATTENTION: If the words are too long, they may exceed the maximum length of the file path that the system can handle",
"Loopback": "Process an image, use it as an input, repeat.", "Loopback": "Performs img2img processing multiple times. Output images are used as input for the next loop.",
"Loops": "How many times to repeat processing an image and using it as input for the next iteration", "Loops": "How many times to process an image. Each output is used as the input of the next loop. If set to 1, behavior will be as if this script were not used.",
"Final denoising strength": "The denoising strength for the final loop of each image in the batch.",
"Denoising strength curve": "The denoising curve controls the rate of denoising strength change each loop. Aggressive: Most of the change will happen towards the start of the loops. Linear: Change will be constant through all loops. Lazy: Most of the change will happen towards the end of the loops.",
"Style 1": "Style to apply; styles have components for both positive and negative prompts and apply to both", "Style 1": "Style to apply; styles have components for both positive and negative prompts and apply to both",
"Style 2": "Style to apply; styles have components for both positive and negative prompts and apply to both", "Style 2": "Style to apply; styles have components for both positive and negative prompts and apply to both",

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@ -1,14 +1,10 @@
import numpy as np import math
from tqdm import trange
import modules.scripts as scripts
import gradio as gr import gradio as gr
import modules.scripts as scripts
from modules import processing, shared, sd_samplers, images from modules import deepbooru, images, processing, shared
from modules.processing import Processed from modules.processing import Processed
from modules.sd_samplers import samplers from modules.shared import opts, state
from modules.shared import opts, cmd_opts, state
from modules import deepbooru
class Script(scripts.Script): class Script(scripts.Script):
@ -20,39 +16,68 @@ class Script(scripts.Script):
def ui(self, 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")) loops = gr.Slider(minimum=1, maximum=32, step=1, label='Loops', value=4, elem_id=self.elem_id("loops"))
denoising_strength_change_factor = gr.Slider(minimum=0.9, maximum=1.1, step=0.01, label='Denoising strength change factor', value=1, elem_id=self.elem_id("denoising_strength_change_factor")) 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") append_interrogation = gr.Dropdown(label="Append interrogated prompt at each iteration", choices=["None", "CLIP", "DeepBooru"], value="None")
return [loops, denoising_strength_change_factor, append_interrogation] return [loops, final_denoising_strength, denoising_curve, append_interrogation]
def run(self, p, loops, denoising_strength_change_factor, append_interrogation): def run(self, p, loops, final_denoising_strength, denoising_curve, append_interrogation):
processing.fix_seed(p) processing.fix_seed(p)
batch_count = p.n_iter batch_count = p.n_iter
p.extra_generation_params = { p.extra_generation_params = {
"Denoising strength change factor": denoising_strength_change_factor, "Final denoising strength": final_denoising_strength,
"Denoising curve": denoising_curve
} }
p.batch_size = 1 p.batch_size = 1
p.n_iter = 1 p.n_iter = 1
output_images, info = None, None info = None
initial_seed = None initial_seed = None
initial_info = None initial_info = None
initial_denoising_strength = p.denoising_strength
grids = [] grids = []
all_images = [] all_images = []
original_init_image = p.init_images original_init_image = p.init_images
original_prompt = p.prompt original_prompt = p.prompt
original_inpainting_fill = p.inpainting_fill
state.job_count = loops * batch_count state.job_count = loops * batch_count
initial_color_corrections = [processing.setup_color_correction(p.init_images[0])] initial_color_corrections = [processing.setup_color_correction(p.init_images[0])]
for n in range(batch_count): def calculate_denoising_strength(loop):
strength = initial_denoising_strength
if loops == 1:
return strength
progress = loop / (loops - 1)
match denoising_curve:
case "Aggressive":
strength = math.sin((progress) * math.pi * 0.5)
case "Lazy":
strength = 1 - math.cos((progress) * math.pi * 0.5)
case _:
strength = progress
change = (final_denoising_strength - initial_denoising_strength) * strength
return initial_denoising_strength + change
history = [] history = []
for n in range(batch_count):
# Reset to original init image at the start of each batch # Reset to original init image at the start of each batch
p.init_images = original_init_image 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): for i in range(loops):
p.n_iter = 1 p.n_iter = 1
p.batch_size = 1 p.batch_size = 1
@ -72,25 +97,45 @@ class Script(scripts.Script):
processed = processing.process_images(p) processed = processing.process_images(p)
# Generation cancelled.
if state.interrupted:
break
if initial_seed is None: if initial_seed is None:
initial_seed = processed.seed initial_seed = processed.seed
initial_info = processed.info initial_info = processed.info
init_img = processed.images[0]
p.init_images = [init_img]
p.seed = processed.seed + 1 p.seed = processed.seed + 1
p.denoising_strength = min(max(p.denoising_strength * denoising_strength_change_factor, 0.1), 1) p.denoising_strength = calculate_denoising_strength(i + 1)
history.append(processed.images[0])
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) grid = images.image_grid(history, rows=1)
if opts.grid_save: 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) 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)
grids.append(grid)
all_images += history
if opts.return_grid: if opts.return_grid:
grids.append(grid)
all_images = grids + all_images all_images = grids + all_images
processed = Processed(p, all_images, initial_seed, initial_info) processed = Processed(p, all_images, initial_seed, initial_info)