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 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) 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 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*") 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 = modules.processing.set_seed(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: 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 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