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, paths, sd_samplers, processing, sd_models, sd_vae from modules.processing import process_images, Processed, StableDiffusionProcessingTxt2Img from modules.shared import opts, cmd_opts, state import modules.shared as shared import modules.sd_samplers import modules.sd_models import modules.sd_vae import glob import os import re from modules.ui_components import ToolButton fill_values_symbol = "\U0001f4d2" # 📒 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 apply_sampler(p, x, xs): sampler_name = sd_samplers.samplers_map.get(x.lower(), None) if sampler_name is None: raise RuntimeError(f"Unknown sampler: {x}") p.sampler_name = sampler_name def confirm_samplers(p, xs): for x in xs: if x.lower() not in sd_samplers.samplers_map: raise RuntimeError(f"Unknown sampler: {x}") def apply_checkpoint(p, x, xs): info = modules.sd_models.get_closet_checkpoint_match(x) if info is None: raise RuntimeError(f"Unknown checkpoint: {x}") modules.sd_models.reload_model_weights(shared.sd_model, info) def confirm_checkpoints(p, xs): for x in xs: if modules.sd_models.get_closet_checkpoint_match(x) is None: raise RuntimeError(f"Unknown checkpoint: {x}") def apply_clip_skip(p, x, xs): opts.data["CLIP_stop_at_last_layers"] = x def apply_upscale_latent_space(p, x, xs): if x.lower().strip() != '0': opts.data["use_scale_latent_for_hires_fix"] = True else: opts.data["use_scale_latent_for_hires_fix"] = False def find_vae(name: str): if name.lower() in ['auto', 'automatic']: return modules.sd_vae.unspecified if name.lower() == 'none': return None else: choices = [x for x in sorted(modules.sd_vae.vae_dict, key=lambda x: len(x)) if name.lower().strip() in x.lower()] if len(choices) == 0: print(f"No VAE found for {name}; using automatic") return modules.sd_vae.unspecified else: return modules.sd_vae.vae_dict[choices[0]] def apply_vae(p, x, xs): modules.sd_vae.reload_vae_weights(shared.sd_model, vae_file=find_vae(x)) def apply_styles(p: StableDiffusionProcessingTxt2Img, x: str, _): p.styles = x.split(',') 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 class AxisOption: def __init__(self, label, type, apply, format_value=format_value_add_label, confirm=None, cost=0.0, choices=None): self.label = label self.type = type self.apply = apply self.format_value = format_value self.confirm = confirm self.cost = cost self.choices = choices self.is_img2img = False class AxisOptionImg2Img(AxisOption): def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) self.is_img2img = False axis_options = [ AxisOption("Nothing", str, do_nothing, format_value=format_nothing), AxisOption("Seed", int, apply_field("seed")), AxisOption("Var. seed", int, apply_field("subseed")), AxisOption("Var. strength", float, apply_field("subseed_strength")), AxisOption("Steps", int, apply_field("steps")), AxisOption("CFG Scale", float, apply_field("cfg_scale")), AxisOption("Prompt S/R", str, apply_prompt, format_value=format_value), AxisOption("Prompt order", str_permutations, apply_order, format_value=format_value_join_list), AxisOption("Sampler", str, apply_sampler, format_value=format_value, confirm=confirm_samplers, choices=lambda: [x.name for x in sd_samplers.samplers]), AxisOption("Checkpoint name", str, apply_checkpoint, format_value=format_value, confirm=confirm_checkpoints, cost=1.0, choices=lambda: list(sd_models.checkpoints_list)), AxisOption("Sigma Churn", float, apply_field("s_churn")), AxisOption("Sigma min", float, apply_field("s_tmin")), AxisOption("Sigma max", float, apply_field("s_tmax")), AxisOption("Sigma noise", float, apply_field("s_noise")), AxisOption("Eta", float, apply_field("eta")), AxisOption("Clip skip", int, apply_clip_skip), AxisOption("Denoising", float, apply_field("denoising_strength")), AxisOption("Hires upscaler", str, apply_field("hr_upscaler"), choices=lambda: [x.name for x in shared.sd_upscalers]), AxisOption("Cond. Image Mask Weight", float, apply_field("inpainting_mask_weight")), AxisOption("VAE", str, apply_vae, cost=0.7, choices=lambda: list(sd_vae.vae_dict)), AxisOption("Styles", str, apply_styles, choices=lambda: list(shared.prompt_styles.styles)), ] def draw_xy_grid(p, xs, ys, x_labels, y_labels, cell, draw_legend, include_lone_images, swap_axes_processing_order): ver_texts = [[images.GridAnnotation(y)] for y in y_labels] hor_texts = [[images.GridAnnotation(x)] for x in x_labels] # Temporary list of all the images that are generated to be populated into the grid. # Will be filled with empty images for any individual step that fails to process properly image_cache = [None] * (len(xs) * len(ys)) processed_result = None cell_mode = "P" cell_size = (1, 1) state.job_count = len(xs) * len(ys) * p.n_iter def process_cell(x, y, ix, iy): nonlocal image_cache, processed_result, cell_mode, cell_size state.job = f"{ix + iy * len(xs) + 1} out of {len(xs) * len(ys)}" processed: Processed = cell(x, y) try: # this dereference will throw an exception if the image was not processed # (this happens in cases such as if the user stops the process from the UI) processed_image = processed.images[0] if processed_result is None: # Use our first valid processed result as a template container to hold our full results processed_result = copy(processed) cell_mode = processed_image.mode cell_size = processed_image.size processed_result.images = [Image.new(cell_mode, cell_size)] image_cache[ix + iy * len(xs)] = processed_image if include_lone_images: processed_result.images.append(processed_image) processed_result.all_prompts.append(processed.prompt) processed_result.all_seeds.append(processed.seed) processed_result.infotexts.append(processed.infotexts[0]) except: image_cache[ix + iy * len(xs)] = Image.new(cell_mode, cell_size) if swap_axes_processing_order: for ix, x in enumerate(xs): for iy, y in enumerate(ys): process_cell(x, y, ix, iy) else: for iy, y in enumerate(ys): for ix, x in enumerate(xs): process_cell(x, y, ix, iy) if not processed_result: print("Unexpected error: draw_xy_grid failed to return even a single processed image") return Processed(p, []) grid = images.image_grid(image_cache, rows=len(ys)) if draw_legend: grid = images.draw_grid_annotations(grid, cell_size[0], cell_size[1], hor_texts, ver_texts) processed_result.images[0] = grid return processed_result class SharedSettingsStackHelper(object): def __enter__(self): self.CLIP_stop_at_last_layers = opts.CLIP_stop_at_last_layers self.vae = opts.sd_vae def __exit__(self, exc_type, exc_value, tb): opts.data["sd_vae"] = self.vae modules.sd_models.reload_model_weights() modules.sd_vae.reload_vae_weights() opts.data["CLIP_stop_at_last_layers"] = self.CLIP_stop_at_last_layers 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 x.is_img2img and is_img2img] with gr.Row(): with gr.Column(scale=19): 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, type="index", elem_id=self.elem_id("x_type")) x_values = gr.Textbox(label="X values", lines=1, elem_id=self.elem_id("x_values")) fill_x_button = ToolButton(value=fill_values_symbol, elem_id="xy_grid_fill_x_tool_button", visible=False) 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, type="index", elem_id=self.elem_id("y_type")) y_values = gr.Textbox(label="Y values", lines=1, elem_id=self.elem_id("y_values")) fill_y_button = ToolButton(value=fill_values_symbol, elem_id="xy_grid_fill_y_tool_button", visible=False) with gr.Row(variant="compact"): draw_legend = gr.Checkbox(label='Draw legend', value=True, elem_id=self.elem_id("draw_legend")) include_lone_images = gr.Checkbox(label='Include Separate Images', value=False, elem_id=self.elem_id("include_lone_images")) no_fixed_seeds = gr.Checkbox(label='Keep -1 for seeds', value=False, elem_id=self.elem_id("no_fixed_seeds")) swap_axes_button = gr.Button(value="Swap axes", elem_id="xy_grid_swap_axes_button") def swap_axes(x_type, x_values, y_type, y_values): nonlocal current_axis_options return current_axis_options[y_type].label, y_values, current_axis_options[x_type].label, x_values swap_args = [x_type, x_values, y_type, y_values] swap_axes_button.click(swap_axes, inputs=swap_args, outputs=swap_args) def fill(x_type): axis = axis_options[x_type] return ", ".join(axis.choices()) if axis.choices else gr.update() fill_x_button.click(fn=fill, inputs=[x_type], outputs=[x_values]) fill_y_button.click(fn=fill, inputs=[y_type], outputs=[y_values]) def select_axis(x_type): return gr.Button.update(visible=axis_options[x_type].choices is not None) x_type.change(fn=select_axis, inputs=[x_type], outputs=[fill_x_button]) y_type.change(fn=select_axis, inputs=[y_type], outputs=[fill_y_button]) return [x_type, x_values, y_type, y_values, draw_legend, include_lone_images, no_fixed_seeds] def run(self, p, x_type, x_values, y_type, y_values, draw_legend, include_lone_images, no_fixed_seeds): if not no_fixed_seeds: modules.processing.fix_seed(p) if not opts.return_grid: p.batch_size = 1 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.confirm: opt.confirm(p, 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 in ['Seed', '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) if isinstance(p, StableDiffusionProcessingTxt2Img) and p.enable_hr: total_steps *= 2 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) grid_infotext = [None] # If one of the axes is very slow to change between (like SD model # checkpoint), then make sure it is in the outer iteration of the nested # `for` loop. swap_axes_processing_order = x_opt.cost > y_opt.cost def cell(x, y): if shared.state.interrupted: return Processed(p, [], p.seed, "") pc = copy(p) x_opt.apply(pc, x, xs) y_opt.apply(pc, y, ys) res = process_images(pc) if grid_infotext[0] is None: pc.extra_generation_params = copy(pc.extra_generation_params) if x_opt.label != 'Nothing': pc.extra_generation_params["X Type"] = x_opt.label pc.extra_generation_params["X Values"] = x_values if x_opt.label in ["Seed", "Var. seed"] and not no_fixed_seeds: pc.extra_generation_params["Fixed X Values"] = ", ".join([str(x) for x in xs]) if y_opt.label != 'Nothing': pc.extra_generation_params["Y Type"] = y_opt.label pc.extra_generation_params["Y Values"] = y_values if y_opt.label in ["Seed", "Var. seed"] and not no_fixed_seeds: pc.extra_generation_params["Fixed Y Values"] = ", ".join([str(y) for y in ys]) grid_infotext[0] = processing.create_infotext(pc, pc.all_prompts, pc.all_seeds, pc.all_subseeds) return res with SharedSettingsStackHelper(): 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, include_lone_images=include_lone_images, swap_axes_processing_order=swap_axes_processing_order ) if opts.grid_save: images.save_image(processed.images[0], p.outpath_grids, "xy_grid", info=grid_infotext[0], extension=opts.grid_format, prompt=p.prompt, seed=processed.seed, grid=True, p=p) return processed