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from collections import namedtuple |
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from copy import copy |
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from itertools import permutations, chain |
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import random |
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import csv |
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from io import StringIO |
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from PIL import Image |
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import numpy as np |
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import modules.scripts as scripts |
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import gradio as gr |
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from modules import images, paths, sd_samplers, processing, sd_models, sd_vae |
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from modules.processing import process_images, Processed, StableDiffusionProcessingTxt2Img |
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from modules.shared import opts, cmd_opts, state |
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import modules.shared as shared |
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import modules.sd_samplers |
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import modules.sd_models |
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import modules.sd_vae |
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import glob |
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import os |
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import re |
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from modules.ui_components import ToolButton |
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fill_values_symbol = "\U0001f4d2" |
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AxisInfo = namedtuple('AxisInfo', ['axis', 'values']) |
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def apply_field(field): |
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def fun(p, x, xs): |
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setattr(p, field, x) |
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return fun |
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def apply_prompt(p, x, xs): |
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if xs[0] not in p.prompt and xs[0] not in p.negative_prompt: |
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raise RuntimeError(f"Prompt S/R did not find {xs[0]} in prompt or negative prompt.") |
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p.prompt = p.prompt.replace(xs[0], x) |
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p.negative_prompt = p.negative_prompt.replace(xs[0], x) |
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def apply_order(p, x, xs): |
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token_order = [] |
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for token in x: |
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token_order.append((p.prompt.find(token), token)) |
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token_order.sort(key=lambda t: t[0]) |
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prompt_parts = [] |
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for _, token in token_order: |
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n = p.prompt.find(token) |
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prompt_parts.append(p.prompt[0:n]) |
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p.prompt = p.prompt[n + len(token):] |
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prompt_tmp = "" |
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for idx, part in enumerate(prompt_parts): |
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prompt_tmp += part |
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prompt_tmp += x[idx] |
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p.prompt = prompt_tmp + p.prompt |
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def apply_sampler(p, x, xs): |
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sampler_name = sd_samplers.samplers_map.get(x.lower(), None) |
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if sampler_name is None: |
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raise RuntimeError(f"Unknown sampler: {x}") |
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p.sampler_name = sampler_name |
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def confirm_samplers(p, xs): |
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for x in xs: |
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if x.lower() not in sd_samplers.samplers_map: |
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raise RuntimeError(f"Unknown sampler: {x}") |
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def apply_checkpoint(p, x, xs): |
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info = modules.sd_models.get_closet_checkpoint_match(x) |
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if info is None: |
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raise RuntimeError(f"Unknown checkpoint: {x}") |
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modules.sd_models.reload_model_weights(shared.sd_model, info) |
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def confirm_checkpoints(p, xs): |
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for x in xs: |
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if modules.sd_models.get_closet_checkpoint_match(x) is None: |
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raise RuntimeError(f"Unknown checkpoint: {x}") |
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def apply_clip_skip(p, x, xs): |
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opts.data["CLIP_stop_at_last_layers"] = x |
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def apply_upscale_latent_space(p, x, xs): |
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if x.lower().strip() != '0': |
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opts.data["use_scale_latent_for_hires_fix"] = True |
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else: |
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opts.data["use_scale_latent_for_hires_fix"] = False |
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def find_vae(name: str): |
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if name.lower() in ['auto', 'automatic']: |
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return modules.sd_vae.unspecified |
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if name.lower() == 'none': |
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return None |
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else: |
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choices = [x for x in sorted(modules.sd_vae.vae_dict, key=lambda x: len(x)) if name.lower().strip() in x.lower()] |
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if len(choices) == 0: |
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print(f"No VAE found for {name}; using automatic") |
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return modules.sd_vae.unspecified |
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else: |
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return modules.sd_vae.vae_dict[choices[0]] |
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def apply_vae(p, x, xs): |
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modules.sd_vae.reload_vae_weights(shared.sd_model, vae_file=find_vae(x)) |
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def apply_styles(p: StableDiffusionProcessingTxt2Img, x: str, _): |
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p.styles.extend(x.split(',')) |
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def format_value_add_label(p, opt, x): |
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if type(x) == float: |
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x = round(x, 8) |
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return f"{opt.label}: {x}" |
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def format_value(p, opt, x): |
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if type(x) == float: |
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x = round(x, 8) |
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return x |
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def format_value_join_list(p, opt, x): |
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return ", ".join(x) |
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def do_nothing(p, x, xs): |
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pass |
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def format_nothing(p, opt, x): |
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return "" |
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def str_permutations(x): |
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"""dummy function for specifying it in AxisOption's type when you want to get a list of permutations""" |
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return x |
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class AxisOption: |
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def __init__(self, label, type, apply, format_value=format_value_add_label, confirm=None, cost=0.0, choices=None): |
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self.label = label |
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self.type = type |
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self.apply = apply |
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self.format_value = format_value |
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self.confirm = confirm |
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self.cost = cost |
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self.choices = choices |
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class AxisOptionImg2Img(AxisOption): |
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def __init__(self, *args, **kwargs): |
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super().__init__(*args, **kwargs) |
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self.is_img2img = True |
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class AxisOptionTxt2Img(AxisOption): |
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def __init__(self, *args, **kwargs): |
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super().__init__(*args, **kwargs) |
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self.is_img2img = False |
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axis_options = [ |
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AxisOption("Nothing", str, do_nothing, format_value=format_nothing), |
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AxisOption("Seed", int, apply_field("seed")), |
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AxisOption("Var. seed", int, apply_field("subseed")), |
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AxisOption("Var. strength", float, apply_field("subseed_strength")), |
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AxisOption("Steps", int, apply_field("steps")), |
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AxisOptionTxt2Img("Hires steps", int, apply_field("hr_second_pass_steps")), |
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AxisOption("CFG Scale", float, apply_field("cfg_scale")), |
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AxisOptionImg2Img("Image CFG Scale", float, apply_field("image_cfg_scale")), |
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AxisOption("Prompt S/R", str, apply_prompt, format_value=format_value), |
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AxisOption("Prompt order", str_permutations, apply_order, format_value=format_value_join_list), |
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AxisOptionTxt2Img("Sampler", str, apply_sampler, format_value=format_value, confirm=confirm_samplers, choices=lambda: [x.name for x in sd_samplers.samplers]), |
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AxisOptionImg2Img("Sampler", str, apply_sampler, format_value=format_value, confirm=confirm_samplers, choices=lambda: [x.name for x in sd_samplers.samplers_for_img2img]), |
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AxisOption("Checkpoint name", str, apply_checkpoint, format_value=format_value, confirm=confirm_checkpoints, cost=1.0, choices=lambda: list(sd_models.checkpoints_list)), |
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AxisOption("Sigma Churn", float, apply_field("s_churn")), |
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AxisOption("Sigma min", float, apply_field("s_tmin")), |
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AxisOption("Sigma max", float, apply_field("s_tmax")), |
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AxisOption("Sigma noise", float, apply_field("s_noise")), |
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AxisOption("Eta", float, apply_field("eta")), |
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AxisOption("Clip skip", int, apply_clip_skip), |
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AxisOption("Denoising", float, apply_field("denoising_strength")), |
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AxisOptionTxt2Img("Hires upscaler", str, apply_field("hr_upscaler"), choices=lambda: [*shared.latent_upscale_modes, *[x.name for x in shared.sd_upscalers]]), |
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AxisOptionImg2Img("Cond. Image Mask Weight", float, apply_field("inpainting_mask_weight")), |
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AxisOption("VAE", str, apply_vae, cost=0.7, choices=lambda: list(sd_vae.vae_dict)), |
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AxisOption("Styles", str, apply_styles, choices=lambda: list(shared.prompt_styles.styles)), |
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] |
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def draw_xyz_grid(p, xs, ys, zs, x_labels, y_labels, z_labels, cell, draw_legend, include_lone_images, include_sub_grids, first_axes_processed, second_axes_processed, margin_size): |
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hor_texts = [[images.GridAnnotation(x)] for x in x_labels] |
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ver_texts = [[images.GridAnnotation(y)] for y in y_labels] |
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title_texts = [[images.GridAnnotation(z)] for z in z_labels] |
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image_cache = [None] * (len(xs) * len(ys) * len(zs)) |
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processed_result = None |
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cell_mode = "P" |
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cell_size = (1, 1) |
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state.job_count = len(xs) * len(ys) * len(zs) * p.n_iter |
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def process_cell(x, y, z, ix, iy, iz): |
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nonlocal image_cache, processed_result, cell_mode, cell_size |
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def index(ix, iy, iz): |
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return ix + iy * len(xs) + iz * len(xs) * len(ys) |
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state.job = f"{index(ix, iy, iz) + 1} out of {len(xs) * len(ys) * len(zs)}" |
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processed: Processed = cell(x, y, z) |
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try: |
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processed_image = processed.images[0] |
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if processed_result is None: |
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processed_result = copy(processed) |
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cell_mode = processed_image.mode |
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cell_size = processed_image.size |
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processed_result.images = [Image.new(cell_mode, cell_size)] |
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processed_result.all_prompts = [processed.prompt] |
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processed_result.all_seeds = [processed.seed] |
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processed_result.infotexts = [processed.infotexts[0]] |
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image_cache[index(ix, iy, iz)] = processed_image |
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if include_lone_images: |
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processed_result.images.append(processed_image) |
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processed_result.all_prompts.append(processed.prompt) |
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processed_result.all_seeds.append(processed.seed) |
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processed_result.infotexts.append(processed.infotexts[0]) |
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except: |
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image_cache[index(ix, iy, iz)] = Image.new(cell_mode, cell_size) |
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if first_axes_processed == 'x': |
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for ix, x in enumerate(xs): |
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if second_axes_processed == 'y': |
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for iy, y in enumerate(ys): |
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for iz, z in enumerate(zs): |
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process_cell(x, y, z, ix, iy, iz) |
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else: |
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for iz, z in enumerate(zs): |
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for iy, y in enumerate(ys): |
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process_cell(x, y, z, ix, iy, iz) |
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elif first_axes_processed == 'y': |
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for iy, y in enumerate(ys): |
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if second_axes_processed == 'x': |
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for ix, x in enumerate(xs): |
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for iz, z in enumerate(zs): |
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process_cell(x, y, z, ix, iy, iz) |
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else: |
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for iz, z in enumerate(zs): |
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for ix, x in enumerate(xs): |
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process_cell(x, y, z, ix, iy, iz) |
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elif first_axes_processed == 'z': |
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for iz, z in enumerate(zs): |
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if second_axes_processed == 'x': |
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for ix, x in enumerate(xs): |
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for iy, y in enumerate(ys): |
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process_cell(x, y, z, ix, iy, iz) |
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else: |
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for iy, y in enumerate(ys): |
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for ix, x in enumerate(xs): |
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process_cell(x, y, z, ix, iy, iz) |
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if not processed_result: |
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print("Unexpected error: draw_xyz_grid failed to return even a single processed image") |
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return Processed(p, []) |
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sub_grids = [None] * len(zs) |
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for i in range(len(zs)): |
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start_index = i * len(xs) * len(ys) |
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end_index = start_index + len(xs) * len(ys) |
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grid = images.image_grid(image_cache[start_index:end_index], rows=len(ys)) |
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if draw_legend: |
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grid = images.draw_grid_annotations(grid, cell_size[0], cell_size[1], hor_texts, ver_texts, margin_size) |
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sub_grids[i] = grid |
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if include_sub_grids and len(zs) > 1: |
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processed_result.images.insert(i+1, grid) |
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sub_grid_size = sub_grids[0].size |
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z_grid = images.image_grid(sub_grids, rows=1) |
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if draw_legend: |
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z_grid = images.draw_grid_annotations(z_grid, sub_grid_size[0], sub_grid_size[1], title_texts, [[images.GridAnnotation()]]) |
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processed_result.images[0] = z_grid |
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return processed_result, sub_grids |
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class SharedSettingsStackHelper(object): |
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def __enter__(self): |
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self.CLIP_stop_at_last_layers = opts.CLIP_stop_at_last_layers |
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self.vae = opts.sd_vae |
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def __exit__(self, exc_type, exc_value, tb): |
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opts.data["sd_vae"] = self.vae |
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modules.sd_models.reload_model_weights() |
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modules.sd_vae.reload_vae_weights() |
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opts.data["CLIP_stop_at_last_layers"] = self.CLIP_stop_at_last_layers |
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re_range = re.compile(r"\s*([+-]?\s*\d+)\s*-\s*([+-]?\s*\d+)(?:\s*\(([+-]\d+)\s*\))?\s*") |
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re_range_float = re.compile(r"\s*([+-]?\s*\d+(?:.\d*)?)\s*-\s*([+-]?\s*\d+(?:.\d*)?)(?:\s*\(([+-]\d+(?:.\d*)?)\s*\))?\s*") |
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re_range_count = re.compile(r"\s*([+-]?\s*\d+)\s*-\s*([+-]?\s*\d+)(?:\s*\[(\d+)\s*\])?\s*") |
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re_range_count_float = re.compile(r"\s*([+-]?\s*\d+(?:.\d*)?)\s*-\s*([+-]?\s*\d+(?:.\d*)?)(?:\s*\[(\d+(?:.\d*)?)\s*\])?\s*") |
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class Script(scripts.Script): |
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def title(self): |
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return "X/Y/Z plot" |
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def ui(self, is_img2img): |
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self.current_axis_options = [x for x in axis_options if type(x) == AxisOption or x.is_img2img == is_img2img] |
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with gr.Row(): |
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with gr.Column(scale=19): |
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with gr.Row(): |
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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")) |
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x_values = gr.Textbox(label="X values", lines=1, elem_id=self.elem_id("x_values")) |
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fill_x_button = ToolButton(value=fill_values_symbol, elem_id="xyz_grid_fill_x_tool_button", visible=False) |
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with gr.Row(): |
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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")) |
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y_values = gr.Textbox(label="Y values", lines=1, elem_id=self.elem_id("y_values")) |
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fill_y_button = ToolButton(value=fill_values_symbol, elem_id="xyz_grid_fill_y_tool_button", visible=False) |
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with gr.Row(): |
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z_type = gr.Dropdown(label="Z 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("z_type")) |
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z_values = gr.Textbox(label="Z values", lines=1, elem_id=self.elem_id("z_values")) |
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fill_z_button = ToolButton(value=fill_values_symbol, elem_id="xyz_grid_fill_z_tool_button", visible=False) |
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with gr.Row(variant="compact", elem_id="axis_options"): |
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with gr.Column(): |
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draw_legend = gr.Checkbox(label='Draw legend', value=True, elem_id=self.elem_id("draw_legend")) |
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no_fixed_seeds = gr.Checkbox(label='Keep -1 for seeds', value=False, elem_id=self.elem_id("no_fixed_seeds")) |
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with gr.Column(): |
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include_lone_images = gr.Checkbox(label='Include Sub Images', value=False, elem_id=self.elem_id("include_lone_images")) |
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include_sub_grids = gr.Checkbox(label='Include Sub Grids', value=False, elem_id=self.elem_id("include_sub_grids")) |
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with gr.Column(): |
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margin_size = gr.Slider(label="Grid margins (px)", minimum=0, maximum=500, value=0, step=2, elem_id=self.elem_id("margin_size")) |
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with gr.Row(variant="compact", elem_id="swap_axes"): |
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swap_xy_axes_button = gr.Button(value="Swap X/Y axes", elem_id="xy_grid_swap_axes_button") |
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swap_yz_axes_button = gr.Button(value="Swap Y/Z axes", elem_id="yz_grid_swap_axes_button") |
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swap_xz_axes_button = gr.Button(value="Swap X/Z axes", elem_id="xz_grid_swap_axes_button") |
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def swap_axes(axis1_type, axis1_values, axis2_type, axis2_values): |
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return self.current_axis_options[axis2_type].label, axis2_values, self.current_axis_options[axis1_type].label, axis1_values |
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xy_swap_args = [x_type, x_values, y_type, y_values] |
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swap_xy_axes_button.click(swap_axes, inputs=xy_swap_args, outputs=xy_swap_args) |
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yz_swap_args = [y_type, y_values, z_type, z_values] |
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swap_yz_axes_button.click(swap_axes, inputs=yz_swap_args, outputs=yz_swap_args) |
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xz_swap_args = [x_type, x_values, z_type, z_values] |
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swap_xz_axes_button.click(swap_axes, inputs=xz_swap_args, outputs=xz_swap_args) |
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def fill(x_type): |
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axis = self.current_axis_options[x_type] |
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return ", ".join(axis.choices()) if axis.choices else gr.update() |
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fill_x_button.click(fn=fill, inputs=[x_type], outputs=[x_values]) |
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fill_y_button.click(fn=fill, inputs=[y_type], outputs=[y_values]) |
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fill_z_button.click(fn=fill, inputs=[z_type], outputs=[z_values]) |
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def select_axis(x_type): |
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return gr.Button.update(visible=self.current_axis_options[x_type].choices is not None) |
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x_type.change(fn=select_axis, inputs=[x_type], outputs=[fill_x_button]) |
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y_type.change(fn=select_axis, inputs=[y_type], outputs=[fill_y_button]) |
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z_type.change(fn=select_axis, inputs=[z_type], outputs=[fill_z_button]) |
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self.infotext_fields = ( |
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(x_type, "X Type"), |
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(x_values, "X Values"), |
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(y_type, "Y Type"), |
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(y_values, "Y Values"), |
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(z_type, "Z Type"), |
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(z_values, "Z Values"), |
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) |
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return [x_type, x_values, y_type, y_values, z_type, z_values, draw_legend, include_lone_images, include_sub_grids, no_fixed_seeds, margin_size] |
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def run(self, p, x_type, x_values, y_type, y_values, z_type, z_values, draw_legend, include_lone_images, include_sub_grids, no_fixed_seeds, margin_size): |
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if not no_fixed_seeds: |
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modules.processing.fix_seed(p) |
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if not opts.return_grid: |
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p.batch_size = 1 |
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def process_axis(opt, vals): |
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if opt.label == 'Nothing': |
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return [0] |
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valslist = [x.strip() for x in chain.from_iterable(csv.reader(StringIO(vals)))] |
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if opt.type == int: |
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valslist_ext = [] |
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for val in valslist: |
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m = re_range.fullmatch(val) |
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mc = re_range_count.fullmatch(val) |
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if m is not None: |
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start = int(m.group(1)) |
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end = int(m.group(2))+1 |
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step = int(m.group(3)) if m.group(3) is not None else 1 |
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valslist_ext += list(range(start, end, step)) |
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elif mc is not None: |
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start = int(mc.group(1)) |
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end = int(mc.group(2)) |
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num = int(mc.group(3)) if mc.group(3) is not None else 1 |
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valslist_ext += [int(x) for x in np.linspace(start=start, stop=end, num=num).tolist()] |
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else: |
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valslist_ext.append(val) |
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valslist = valslist_ext |
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elif opt.type == float: |
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valslist_ext = [] |
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|
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for val in valslist: |
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m = re_range_float.fullmatch(val) |
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mc = re_range_count_float.fullmatch(val) |
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if m is not None: |
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start = float(m.group(1)) |
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end = float(m.group(2)) |
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step = float(m.group(3)) if m.group(3) is not None else 1 |
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|
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valslist_ext += np.arange(start, end + step, step).tolist() |
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elif mc is not None: |
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start = float(mc.group(1)) |
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end = float(mc.group(2)) |
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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] |
|
|
|
|
|
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) |
|
|
|
z_opt = self.current_axis_options[z_type] |
|
zs = process_axis(z_opt, z_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) |
|
zs = fix_axis_seeds(z_opt, zs) |
|
|
|
if x_opt.label == 'Steps': |
|
total_steps = sum(xs) * len(ys) * len(zs) |
|
elif y_opt.label == 'Steps': |
|
total_steps = sum(ys) * len(xs) * len(zs) |
|
elif z_opt.label == 'Steps': |
|
total_steps = sum(zs) * len(xs) * len(ys) |
|
else: |
|
total_steps = p.steps * len(xs) * len(ys) * len(zs) |
|
|
|
if isinstance(p, StableDiffusionProcessingTxt2Img) and p.enable_hr: |
|
if x_opt.label == "Hires steps": |
|
total_steps += sum(xs) * len(ys) * len(zs) |
|
elif y_opt.label == "Hires steps": |
|
total_steps += sum(ys) * len(xs) * len(zs) |
|
elif z_opt.label == "Hires steps": |
|
total_steps += sum(zs) * len(xs) * len(ys) |
|
elif p.hr_second_pass_steps: |
|
total_steps += p.hr_second_pass_steps * len(xs) * len(ys) * len(zs) |
|
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 "" |
|
plural_s = 's' if len(zs) > 1 else '' |
|
print(f"X/Y/Z plot will create {len(xs) * len(ys) * len(zs) * image_cell_count} images on {len(zs)} {len(xs)}x{len(ys)} grid{plural_s}{cell_console_text}. (Total steps to process: {total_steps})") |
|
shared.total_tqdm.updateTotal(total_steps) |
|
|
|
grid_infotext = [None] |
|
|
|
state.xyz_plot_x = AxisInfo(x_opt, xs) |
|
state.xyz_plot_y = AxisInfo(y_opt, ys) |
|
state.xyz_plot_z = AxisInfo(z_opt, zs) |
|
|
|
|
|
|
|
|
|
first_axes_processed = 'x' |
|
second_axes_processed = 'y' |
|
if x_opt.cost > y_opt.cost and x_opt.cost > z_opt.cost: |
|
first_axes_processed = 'x' |
|
if y_opt.cost > z_opt.cost: |
|
second_axes_processed = 'y' |
|
else: |
|
second_axes_processed = 'z' |
|
elif y_opt.cost > x_opt.cost and y_opt.cost > z_opt.cost: |
|
first_axes_processed = 'y' |
|
if x_opt.cost > z_opt.cost: |
|
second_axes_processed = 'x' |
|
else: |
|
second_axes_processed = 'z' |
|
elif z_opt.cost > x_opt.cost and z_opt.cost > y_opt.cost: |
|
first_axes_processed = 'z' |
|
if x_opt.cost > y_opt.cost: |
|
second_axes_processed = 'x' |
|
else: |
|
second_axes_processed = 'y' |
|
|
|
def cell(x, y, z): |
|
if shared.state.interrupted: |
|
return Processed(p, [], p.seed, "") |
|
|
|
pc = copy(p) |
|
pc.styles = pc.styles[:] |
|
x_opt.apply(pc, x, xs) |
|
y_opt.apply(pc, y, ys) |
|
z_opt.apply(pc, z, zs) |
|
|
|
res = process_images(pc) |
|
|
|
if grid_infotext[0] is None: |
|
pc.extra_generation_params = copy(pc.extra_generation_params) |
|
pc.extra_generation_params['Script'] = self.title() |
|
|
|
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]) |
|
|
|
if z_opt.label != 'Nothing': |
|
pc.extra_generation_params["Z Type"] = z_opt.label |
|
pc.extra_generation_params["Z Values"] = z_values |
|
if z_opt.label in ["Seed", "Var. seed"] and not no_fixed_seeds: |
|
pc.extra_generation_params["Fixed Z Values"] = ", ".join([str(z) for z in zs]) |
|
|
|
grid_infotext[0] = processing.create_infotext(pc, pc.all_prompts, pc.all_seeds, pc.all_subseeds) |
|
|
|
return res |
|
|
|
with SharedSettingsStackHelper(): |
|
processed, sub_grids = draw_xyz_grid( |
|
p, |
|
xs=xs, |
|
ys=ys, |
|
zs=zs, |
|
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], |
|
z_labels=[z_opt.format_value(p, z_opt, z) for z in zs], |
|
cell=cell, |
|
draw_legend=draw_legend, |
|
include_lone_images=include_lone_images, |
|
include_sub_grids=include_sub_grids, |
|
first_axes_processed=first_axes_processed, |
|
second_axes_processed=second_axes_processed, |
|
margin_size=margin_size |
|
) |
|
|
|
if opts.grid_save and len(sub_grids) > 1: |
|
for sub_grid in sub_grids: |
|
images.save_image(sub_grid, p.outpath_grids, "xyz_grid", info=grid_infotext[0], extension=opts.grid_format, prompt=p.prompt, seed=processed.seed, grid=True, p=p) |
|
|
|
if opts.grid_save: |
|
images.save_image(processed.images[0], p.outpath_grids, "xyz_grid", info=grid_infotext[0], extension=opts.grid_format, prompt=p.prompt, seed=processed.seed, grid=True, p=p) |
|
|
|
return processed |
|
|
