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/xy_grid.py | 498 ----------------------------------------------------- 1 file changed, 498 deletions(-) delete mode 100644 scripts/xy_grid.py (limited to 'scripts/xy_grid.py') diff --git a/scripts/xy_grid.py b/scripts/xy_grid.py deleted file mode 100644 index 1a452355..00000000 --- a/scripts/xy_grid.py +++ /dev/null @@ -1,498 +0,0 @@ -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