156 lines
5 KiB
Python
156 lines
5 KiB
Python
from collections import namedtuple
|
|
from copy import copy
|
|
import random
|
|
|
|
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
|
|
|
|
|
|
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)
|
|
|
|
|
|
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):
|
|
return f"{opt.label}: {x}"
|
|
|
|
|
|
def format_value(p, opt, x):
|
|
return x
|
|
|
|
|
|
AxisOption = namedtuple("AxisOption", ["label", "type", "apply", "format_value"])
|
|
AxisOptionImg2Img = namedtuple("AxisOptionImg2Img", ["label", "type", "apply", "format_value"])
|
|
|
|
|
|
axis_options = [
|
|
AxisOption("Seed", int, apply_field("seed"), 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
|
|
|
|
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
|
|
|
|
|
|
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[0].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[1].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):
|
|
p.seed = int(random.randrange(4294967294) if p.seed == -1 else p.seed)
|
|
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:
|
|
if "-" in val:
|
|
s = val.split("-")
|
|
start = int(s[0])
|
|
end = int(s[1])+1
|
|
step = 1 if len(s) < 3 else int(s[2])
|
|
valslist_ext += list(range(start, end, step))
|
|
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
|