stable-diffusion-webui/scripts/xy_grid.py

198 lines
6.3 KiB
Python

from collections import namedtuple
from copy import copy
import random
import numpy as np
import modules.scripts as scripts
import gradio as gr
from modules import images
from modules.processing import process_images, Processed
from modules.shared import opts, cmd_opts, state
import modules.sd_samplers
import re
def apply_field(field):
def fun(p, x, xs):
setattr(p, field, x)
return fun
def apply_prompt(p, x, xs):
p.prompt = p.prompt.replace(xs[0], x)
p.negative_prompt = p.negative_prompt.replace(xs[0], x)
samplers_dict = {}
for i, sampler in enumerate(modules.sd_samplers.samplers):
samplers_dict[sampler.name.lower()] = i
for alias in sampler.aliases:
samplers_dict[alias.lower()] = i
def apply_sampler(p, x, xs):
sampler_index = samplers_dict.get(x.lower(), None)
if sampler_index is None:
raise RuntimeError(f"Unknown sampler: {x}")
p.sampler_index = sampler_index
def format_value_add_label(p, opt, x):
if type(x) == float:
x = round(x, 8)
return f"{opt.label}: {x}"
def format_value(p, opt, x):
if type(x) == float:
x = round(x, 8)
return x
def do_nothing(p, x, xs):
pass
def format_nothing(p, opt, x):
return ""
AxisOption = namedtuple("AxisOption", ["label", "type", "apply", "format_value"])
AxisOptionImg2Img = namedtuple("AxisOptionImg2Img", ["label", "type", "apply", "format_value"])
axis_options = [
AxisOption("Nothing", str, do_nothing, format_nothing),
AxisOption("Seed", int, apply_field("seed"), format_value_add_label),
AxisOption("Var. seed", int, apply_field("subseed"), format_value_add_label),
AxisOption("Var. strength", float, apply_field("subseed_strength"), format_value_add_label),
AxisOption("Steps", int, apply_field("steps"), format_value_add_label),
AxisOption("CFG Scale", float, apply_field("cfg_scale"), format_value_add_label),
AxisOption("Prompt S/R", str, apply_prompt, format_value),
AxisOption("Sampler", str, apply_sampler, format_value),
AxisOptionImg2Img("Denoising", float, apply_field("denoising_strength"), format_value_add_label), # as it is now all AxisOptionImg2Img items must go after AxisOption ones
]
def draw_xy_grid(xs, ys, x_label, y_label, cell):
res = []
ver_texts = [[images.GridAnnotation(y_label(y))] for y in ys]
hor_texts = [[images.GridAnnotation(x_label(x))] for x in xs]
first_pocessed = None
state.job_count = len(xs) * len(ys)
for iy, y in enumerate(ys):
for ix, x in enumerate(xs):
state.job = f"{ix + iy * len(xs) + 1} out of {len(xs) * len(ys)}"
processed = cell(x, y)
if first_pocessed is None:
first_pocessed = processed
res.append(processed.images[0])
grid = images.image_grid(res, rows=len(ys))
grid = images.draw_grid_annotations(grid, res[0].width, res[0].height, hor_texts, ver_texts)
first_pocessed.images = [grid]
return first_pocessed
re_range = re.compile(r"\s*([+-]?\s*\d+)\s*-\s*([+-]?\s*\d+)(?:\s*\(([+-]\d+)\s*\))?\s*")
re_range_float = re.compile(r"\s*([+-]?\s*\d+(?:.\d*)?)\s*-\s*([+-]?\s*\d+(?:.\d*)?)(?:\s*\(([+-]\d+(?:.\d*)?)\s*\))?\s*")
class Script(scripts.Script):
def title(self):
return "X/Y plot"
def ui(self, is_img2img):
current_axis_options = [x for x in axis_options if type(x) == AxisOption or type(x) == AxisOptionImg2Img and is_img2img]
with gr.Row():
x_type = gr.Dropdown(label="X type", choices=[x.label for x in current_axis_options], value=current_axis_options[1].label, visible=False, type="index", elem_id="x_type")
x_values = gr.Textbox(label="X values", visible=False, lines=1)
with gr.Row():
y_type = gr.Dropdown(label="Y type", choices=[x.label for x in current_axis_options], value=current_axis_options[4].label, visible=False, type="index", elem_id="y_type")
y_values = gr.Textbox(label="Y values", visible=False, lines=1)
return [x_type, x_values, y_type, y_values]
def run(self, p, x_type, x_values, y_type, y_values):
modules.processing.fix_seed(p)
p.batch_size = 1
p.batch_count = 1
def process_axis(opt, vals):
valslist = [x.strip() for x in vals.split(",")]
if opt.type == int:
valslist_ext = []
for val in valslist:
m = re_range.fullmatch(val)
if m is not None:
start = int(m.group(1))
end = int(m.group(2))+1
step = int(m.group(3)) if m.group(3) is not None else 1
valslist_ext += list(range(start, end, step))
else:
valslist_ext.append(val)
valslist = valslist_ext
elif opt.type == float:
valslist_ext = []
for val in valslist:
m = re_range_float.fullmatch(val)
if m is not None:
start = float(m.group(1))
end = float(m.group(2))
step = float(m.group(3)) if m.group(3) is not None else 1
valslist_ext += np.arange(start, end + step, step).tolist()
else:
valslist_ext.append(val)
valslist = valslist_ext
valslist = [opt.type(x) for x in valslist]
return valslist
x_opt = axis_options[x_type]
xs = process_axis(x_opt, x_values)
y_opt = axis_options[y_type]
ys = process_axis(y_opt, y_values)
def cell(x, y):
pc = copy(p)
x_opt.apply(pc, x, xs)
y_opt.apply(pc, y, ys)
return process_images(pc)
processed = draw_xy_grid(
xs=xs,
ys=ys,
x_label=lambda x: x_opt.format_value(p, x_opt, x),
y_label=lambda y: y_opt.format_value(p, y_opt, y),
cell=cell
)
if opts.grid_save:
images.save_image(processed.images[0], p.outpath_grids, "xy_grid", prompt=p.prompt, seed=processed.seed)
return processed