mirror of
https://github.com/AUTOMATIC1111/stable-diffusion-webui.git
synced 2024-06-07 21:20:49 +00:00
cccc5a20fc
also useful for keeping settings cache and restore logic together, and nice for code reuse (other third party scripts can import this class)
398 lines
15 KiB
Python
398 lines
15 KiB
Python
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
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from modules.hypernetworks import hypernetwork
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from modules.processing import process_images, Processed, get_correct_sampler, 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 re
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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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# Initally grab the tokens from the prompt, so they can be replaced in order of earliest seen
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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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# Split the prompt up, taking out the tokens
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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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# Rebuild the prompt with the tokens in the order we want
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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 build_samplers_dict(p):
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samplers_dict = {}
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for i, sampler in enumerate(get_correct_sampler(p)):
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samplers_dict[sampler.name.lower()] = i
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for alias in sampler.aliases:
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samplers_dict[alias.lower()] = i
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return samplers_dict
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def apply_sampler(p, x, xs):
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sampler_index = build_samplers_dict(p).get(x.lower(), None)
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if sampler_index is None:
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raise RuntimeError(f"Unknown sampler: {x}")
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p.sampler_index = sampler_index
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def confirm_samplers(p, xs):
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samplers_dict = build_samplers_dict(p)
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for x in xs:
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if x.lower() not in samplers_dict.keys():
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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_hypernetwork(p, x, xs):
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if x.lower() in ["", "none"]:
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name = None
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else:
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name = hypernetwork.find_closest_hypernetwork_name(x)
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if not name:
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raise RuntimeError(f"Unknown hypernetwork: {x}")
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hypernetwork.load_hypernetwork(name)
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def apply_hypernetwork_strength(p, x, xs):
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hypernetwork.apply_strength(x)
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def confirm_hypernetworks(p, xs):
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for x in xs:
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if x.lower() in ["", "none"]:
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continue
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if not hypernetwork.find_closest_hypernetwork_name(x):
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raise RuntimeError(f"Unknown hypernetwork: {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 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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AxisOption = namedtuple("AxisOption", ["label", "type", "apply", "format_value", "confirm"])
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AxisOptionImg2Img = namedtuple("AxisOptionImg2Img", ["label", "type", "apply", "format_value", "confirm"])
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axis_options = [
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AxisOption("Nothing", str, do_nothing, format_nothing, None),
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AxisOption("Seed", int, apply_field("seed"), format_value_add_label, None),
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AxisOption("Var. seed", int, apply_field("subseed"), format_value_add_label, None),
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AxisOption("Var. strength", float, apply_field("subseed_strength"), format_value_add_label, None),
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AxisOption("Steps", int, apply_field("steps"), format_value_add_label, None),
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AxisOption("CFG Scale", float, apply_field("cfg_scale"), format_value_add_label, None),
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AxisOption("Prompt S/R", str, apply_prompt, format_value, None),
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AxisOption("Prompt order", str_permutations, apply_order, format_value_join_list, None),
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AxisOption("Sampler", str, apply_sampler, format_value, confirm_samplers),
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AxisOption("Checkpoint name", str, apply_checkpoint, format_value, confirm_checkpoints),
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AxisOption("Hypernetwork", str, apply_hypernetwork, format_value, confirm_hypernetworks),
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AxisOption("Hypernet str.", float, apply_hypernetwork_strength, format_value_add_label, None),
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AxisOption("Sigma Churn", float, apply_field("s_churn"), format_value_add_label, None),
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AxisOption("Sigma min", float, apply_field("s_tmin"), format_value_add_label, None),
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AxisOption("Sigma max", float, apply_field("s_tmax"), format_value_add_label, None),
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AxisOption("Sigma noise", float, apply_field("s_noise"), format_value_add_label, None),
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AxisOption("Eta", float, apply_field("eta"), format_value_add_label, None),
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AxisOption("Clip skip", int, apply_clip_skip, format_value_add_label, None),
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AxisOption("Denoising", float, apply_field("denoising_strength"), format_value_add_label, None),
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]
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def draw_xy_grid(p, xs, ys, x_labels, y_labels, cell, draw_legend, include_lone_images):
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ver_texts = [[images.GridAnnotation(y)] for y in y_labels]
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hor_texts = [[images.GridAnnotation(x)] for x in x_labels]
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# Temporary list of all the images that are generated to be populated into the grid.
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# Will be filled with empty images for any individual step that fails to process properly
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image_cache = []
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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) * p.n_iter
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for iy, y in enumerate(ys):
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for ix, x in enumerate(xs):
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state.job = f"{ix + iy * len(xs) + 1} out of {len(xs) * len(ys)}"
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processed:Processed = cell(x, y)
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try:
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# this dereference will throw an exception if the image was not processed
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# (this happens in cases such as if the user stops the process from the UI)
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processed_image = processed.images[0]
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if processed_result is None:
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# Use our first valid processed result as a template container to hold our full results
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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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image_cache.append(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.append(Image.new(cell_mode, cell_size))
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if not processed_result:
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print("Unexpected error: draw_xy_grid failed to return even a single processed image")
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return Processed()
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grid = images.image_grid(image_cache, 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)
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processed_result.images[0] = grid
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return processed_result
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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.hypernetwork = opts.sd_hypernetwork
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self.model = shared.sd_model
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def __exit__(self, exc_type, exc_value, tb):
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modules.sd_models.reload_model_weights(self.model)
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hypernetwork.load_hypernetwork(self.hypernetwork)
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hypernetwork.apply_strength()
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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 plot"
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def ui(self, is_img2img):
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current_axis_options = [x for x in axis_options if type(x) == AxisOption or type(x) == AxisOptionImg2Img and is_img2img]
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with gr.Row():
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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")
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x_values = gr.Textbox(label="X values", visible=False, lines=1)
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with gr.Row():
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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")
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y_values = gr.Textbox(label="Y values", visible=False, lines=1)
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draw_legend = gr.Checkbox(label='Draw legend', value=True)
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include_lone_images = gr.Checkbox(label='Include Separate Images', value=False)
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no_fixed_seeds = gr.Checkbox(label='Keep -1 for seeds', value=False)
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return [x_type, x_values, y_type, y_values, draw_legend, include_lone_images, no_fixed_seeds]
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def run(self, p, x_type, x_values, y_type, y_values, draw_legend, include_lone_images, no_fixed_seeds):
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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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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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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
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valslist_ext += 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 == str_permutations:
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valslist = list(permutations(valslist))
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valslist = [opt.type(x) for x in valslist]
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# Confirm options are valid before starting
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if opt.confirm:
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opt.confirm(p, valslist)
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return valslist
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x_opt = axis_options[x_type]
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xs = process_axis(x_opt, x_values)
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y_opt = axis_options[y_type]
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ys = process_axis(y_opt, y_values)
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def fix_axis_seeds(axis_opt, axis_list):
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if axis_opt.label in ['Seed','Var. seed']:
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return [int(random.randrange(4294967294)) if val is None or val == '' or val == -1 else val for val in axis_list]
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else:
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return axis_list
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if not no_fixed_seeds:
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xs = fix_axis_seeds(x_opt, xs)
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ys = fix_axis_seeds(y_opt, ys)
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if x_opt.label == 'Steps':
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total_steps = sum(xs) * len(ys)
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elif y_opt.label == 'Steps':
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total_steps = sum(ys) * len(xs)
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else:
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total_steps = p.steps * len(xs) * len(ys)
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if isinstance(p, StableDiffusionProcessingTxt2Img) and p.enable_hr:
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total_steps *= 2
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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})")
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shared.total_tqdm.updateTotal(total_steps * p.n_iter)
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def cell(x, y):
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pc = copy(p)
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x_opt.apply(pc, x, xs)
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y_opt.apply(pc, y, ys)
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return process_images(pc)
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with SharedSettingsStackHelper():
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processed = draw_xy_grid(
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p,
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xs=xs,
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ys=ys,
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x_labels=[x_opt.format_value(p, x_opt, x) for x in xs],
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y_labels=[y_opt.format_value(p, y_opt, y) for y in ys],
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cell=cell,
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draw_legend=draw_legend,
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include_lone_images=include_lone_images
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)
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if opts.grid_save:
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images.save_image(processed.images[0], p.outpath_grids, "xy_grid", prompt=p.prompt, seed=processed.seed, grid=True, p=p)
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return processed
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