from collections import namedtuple from copy import copy import random from PIL import Image 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.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): 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 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 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), AxisOption("Checkpoint name", str, apply_checkpoint, 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), 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_pocessed = 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_pocessed is None: first_pocessed = 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_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*") 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[4].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): modules.processing.fix_seed(p) p.batch_size = 1 def process_axis(opt, vals): if opt.label == 'Nothing': return [0] valslist = [x.strip() for x in vals.split(",")] 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 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 fix_axis_seeds(axis_opt, axis_list): if axis_opt.label == 'Seed' or 'Var. 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) if not x_opt.label == 'Nothing': p.extra_generation_params["XY Plot X Type"] = x_opt.label p.extra_generation_params["X Values"] = '{' + x_values + '}' if x_opt.label in ["Seed","Var. seed"] and not no_fixed_seeds: p.extra_generation_params["Fixed X Values"] = '{' + ", ".join([str(x) for x in xs])+ '}' if not y_opt.label == 'Nothing': p.extra_generation_params["XY Plot Y Type"] = y_opt.label p.extra_generation_params["Y Values"] = '{' + y_values + '}' if y_opt.label in ["Seed","Var. seed"] and not no_fixed_seeds: p.extra_generation_params["Fixed Y Values"] = '{' + ", ".join([str(y) for y in ys])+ '}' 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) return processed