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