From d30ac02f28bf5fa1ca5d4ba444180ba9e50b4860 Mon Sep 17 00:00:00 2001 From: EllangoK Date: Tue, 24 Jan 2023 02:21:32 -0500 Subject: renamed xy to xyz grid this is mostly just so git can detect it properly --- scripts/xyz_grid.py | 498 ++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 498 insertions(+) create mode 100644 scripts/xyz_grid.py (limited to 'scripts/xyz_grid.py') diff --git a/scripts/xyz_grid.py b/scripts/xyz_grid.py new file mode 100644 index 00000000..1a452355 --- /dev/null +++ b/scripts/xyz_grid.py @@ -0,0 +1,498 @@ +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 + + +class AxisOptionImg2Img(AxisOption): + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + self.is_img2img = True + +class AxisOptionTxt2Img(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")), + AxisOptionTxt2Img("Hires steps", int, apply_field("hr_second_pass_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), + AxisOptionTxt2Img("Sampler", str, apply_sampler, format_value=format_value, confirm=confirm_samplers, choices=lambda: [x.name for x in sd_samplers.samplers]), + AxisOptionImg2Img("Sampler", str, apply_sampler, format_value=format_value, confirm=confirm_samplers, choices=lambda: [x.name for x in sd_samplers.samplers_for_img2img]), + 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")), + AxisOptionTxt2Img("Hires upscaler", str, apply_field("hr_upscaler"), choices=lambda: [*shared.latent_upscale_modes, *[x.name for x in shared.sd_upscalers]]), + AxisOptionImg2Img("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): + self.current_axis_options = [x for x in axis_options if type(x) == AxisOption or x.is_img2img == 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 self.current_axis_options], value=self.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 self.current_axis_options], value=self.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", elem_id="axis_options"): + 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): + return self.current_axis_options[y_type].label, y_values, self.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 = self.current_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=self.current_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 = self.current_axis_options[x_type] + xs = process_axis(x_opt, x_values) + + y_opt = self.current_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: + if x_opt.label == "Hires steps": + total_steps += sum(xs) * len(ys) + elif y_opt.label == "Hires steps": + total_steps += sum(ys) * len(xs) + elif p.hr_second_pass_steps: + total_steps += p.hr_second_pass_steps * len(xs) * len(ys) + else: + total_steps *= 2 + + total_steps *= p.n_iter + + image_cell_count = p.n_iter * p.batch_size + cell_console_text = f"; {image_cell_count} images per cell" if image_cell_count > 1 else "" + print(f"X/Y plot will create {len(xs) * len(ys) * image_cell_count} images on a {len(xs)}x{len(ys)} grid{cell_console_text}. (Total steps to process: {total_steps})") + shared.total_tqdm.updateTotal(total_steps) + + 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 -- cgit v1.2.3