stable-diffusion-webui/scripts/xy_grid.py

344 lines
12 KiB
Python

from collections import namedtuple
from copy import copy
from itertools import permutations, chain
import random
import csv
from io import StringIO
from PIL import Image
import numpy as np
import modules.scripts as scripts
import gradio as gr
from modules import images
from modules.hypernetworks import hypernetwork
from modules.processing import process_images, Processed, get_correct_sampler
from modules.shared import opts, cmd_opts, state
import modules.shared as shared
import modules.sd_samplers
import modules.sd_models
import re
def apply_field(field):
def fun(p, x, xs):
setattr(p, field, x)
return fun
def apply_prompt(p, x, xs):
if xs[0] not in p.prompt and xs[0] not in p.negative_prompt:
raise RuntimeError(f"Prompt S/R did not find {xs[0]} in prompt or negative prompt.")
p.prompt = p.prompt.replace(xs[0], x)
p.negative_prompt = p.negative_prompt.replace(xs[0], x)
def apply_order(p, x, xs):
token_order = []
# Initally grab the tokens from the prompt, so they can be replaced in order of earliest seen
for token in x:
token_order.append((p.prompt.find(token), token))
token_order.sort(key=lambda t: t[0])
prompt_parts = []
# Split the prompt up, taking out the tokens
for _, token in token_order:
n = p.prompt.find(token)
prompt_parts.append(p.prompt[0:n])
p.prompt = p.prompt[n + len(token):]
# Rebuild the prompt with the tokens in the order we want
prompt_tmp = ""
for idx, part in enumerate(prompt_parts):
prompt_tmp += part
prompt_tmp += x[idx]
p.prompt = prompt_tmp + p.prompt
def build_samplers_dict(p):
samplers_dict = {}
for i, sampler in enumerate(get_correct_sampler(p)):
samplers_dict[sampler.name.lower()] = i
for alias in sampler.aliases:
samplers_dict[alias.lower()] = i
return samplers_dict
def apply_sampler(p, x, xs):
sampler_index = build_samplers_dict(p).get(x.lower(), None)
if sampler_index is None:
raise RuntimeError(f"Unknown sampler: {x}")
p.sampler_index = sampler_index
def apply_checkpoint(p, x, xs):
info = modules.sd_models.get_closet_checkpoint_match(x)
assert info is not None, f'Checkpoint for {x} not found'
modules.sd_models.reload_model_weights(shared.sd_model, info)
def apply_hypernetwork(p, x, xs):
if x.lower() in ["", "none"]:
name = None
else:
name = hypernetwork.find_closest_hypernetwork_name(x)
hypernetwork.load_hypernetwork(name)
def apply_clip_skip(p, x, xs):
opts.data["CLIP_stop_at_last_layers"] = x
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 format_value_join_list(p, opt, x):
return ", ".join(x)
def do_nothing(p, x, xs):
pass
def format_nothing(p, opt, x):
return ""
def str_permutations(x):
"""dummy function for specifying it in AxisOption's type when you want to get a list of permutations"""
return x
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("Prompt order", str_permutations, apply_order, format_value_join_list),
AxisOption("Sampler", str, apply_sampler, format_value),
AxisOption("Checkpoint name", str, apply_checkpoint, format_value),
AxisOption("Hypernetwork", str, apply_hypernetwork, format_value),
AxisOption("Sigma Churn", float, apply_field("s_churn"), format_value_add_label),
AxisOption("Sigma min", float, apply_field("s_tmin"), format_value_add_label),
AxisOption("Sigma max", float, apply_field("s_tmax"), format_value_add_label),
AxisOption("Sigma noise", float, apply_field("s_noise"), format_value_add_label),
AxisOption("Eta", float, apply_field("eta"), format_value_add_label),
AxisOption("Clip skip", int, apply_clip_skip, format_value_add_label),
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(p, xs, ys, x_labels, y_labels, cell, draw_legend):
res = []
ver_texts = [[images.GridAnnotation(y)] for y in y_labels]
hor_texts = [[images.GridAnnotation(x)] for x in x_labels]
first_processed = None
state.job_count = len(xs) * len(ys) * p.n_iter
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_processed is None:
first_processed = processed
try:
res.append(processed.images[0])
except:
res.append(Image.new(res[0].mode, res[0].size))
grid = images.image_grid(res, rows=len(ys))
if draw_legend:
grid = images.draw_grid_annotations(grid, res[0].width, res[0].height, hor_texts, ver_texts)
first_processed.images = [grid]
return first_processed
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*")
re_range_count = re.compile(r"\s*([+-]?\s*\d+)\s*-\s*([+-]?\s*\d+)(?:\s*\[(\d+)\s*\])?\s*")
re_range_count_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[0].label, visible=False, type="index", elem_id="y_type")
y_values = gr.Textbox(label="Y values", visible=False, lines=1)
draw_legend = gr.Checkbox(label='Draw legend', value=True)
no_fixed_seeds = gr.Checkbox(label='Keep -1 for seeds', value=False)
return [x_type, x_values, y_type, y_values, draw_legend, no_fixed_seeds]
def run(self, p, x_type, x_values, y_type, y_values, draw_legend, no_fixed_seeds):
if not no_fixed_seeds:
modules.processing.fix_seed(p)
if not opts.return_grid:
p.batch_size = 1
CLIP_stop_at_last_layers = opts.CLIP_stop_at_last_layers
def process_axis(opt, vals):
if opt.label == 'Nothing':
return [0]
valslist = [x.strip() for x in chain.from_iterable(csv.reader(StringIO(vals)))]
if opt.type == int:
valslist_ext = []
for val in valslist:
m = re_range.fullmatch(val)
mc = re_range_count.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))
elif mc is not None:
start = int(mc.group(1))
end = int(mc.group(2))
num = int(mc.group(3)) if mc.group(3) is not None else 1
valslist_ext += [int(x) for x in np.linspace(start=start, stop=end, num=num).tolist()]
else:
valslist_ext.append(val)
valslist = valslist_ext
elif opt.type == float:
valslist_ext = []
for val in valslist:
m = re_range_float.fullmatch(val)
mc = re_range_count_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()
elif mc is not None:
start = float(mc.group(1))
end = float(mc.group(2))
num = int(mc.group(3)) if mc.group(3) is not None else 1
valslist_ext += np.linspace(start=start, stop=end, num=num).tolist()
else:
valslist_ext.append(val)
valslist = valslist_ext
elif opt.type == str_permutations:
valslist = list(permutations(valslist))
valslist = [opt.type(x) for x in valslist]
# Confirm options are valid before starting
if opt.label == "Sampler":
samplers_dict = build_samplers_dict(p)
for sampler_val in valslist:
if sampler_val.lower() not in samplers_dict.keys():
raise RuntimeError(f"Unknown sampler: {sampler_val}")
elif opt.label == "Checkpoint name":
for ckpt_val in valslist:
if modules.sd_models.get_closet_checkpoint_match(ckpt_val) is None:
raise RuntimeError(f"Checkpoint for {ckpt_val} not found")
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 fix_axis_seeds(axis_opt, axis_list):
if axis_opt.label == 'Seed':
return [int(random.randrange(4294967294)) if val is None or val == '' or val == -1 else val for val in axis_list]
else:
return axis_list
if not no_fixed_seeds:
xs = fix_axis_seeds(x_opt, xs)
ys = fix_axis_seeds(y_opt, ys)
if x_opt.label == 'Steps':
total_steps = sum(xs) * len(ys)
elif y_opt.label == 'Steps':
total_steps = sum(ys) * len(xs)
else:
total_steps = p.steps * len(xs) * len(ys)
print(f"X/Y plot will create {len(xs) * len(ys) * p.n_iter} images on a {len(xs)}x{len(ys)} grid. (Total steps to process: {total_steps * p.n_iter})")
shared.total_tqdm.updateTotal(total_steps * p.n_iter)
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(
p,
xs=xs,
ys=ys,
x_labels=[x_opt.format_value(p, x_opt, x) for x in xs],
y_labels=[y_opt.format_value(p, y_opt, y) for y in ys],
cell=cell,
draw_legend=draw_legend
)
if opts.grid_save:
images.save_image(processed.images[0], p.outpath_grids, "xy_grid", prompt=p.prompt, seed=processed.seed, grid=True, p=p)
# restore checkpoint in case it was changed by axes
modules.sd_models.reload_model_weights(shared.sd_model)
hypernetwork.load_hypernetwork(opts.sd_hypernetwork)
opts.data["CLIP_stop_at_last_layers"] = CLIP_stop_at_last_layers
return processed