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 os import modules.scripts as scripts import gradio as gr from modules import images, sd_samplers from modules.paths import models_path from modules.hypernetworks import hypernetwork 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 re 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 edit_prompt(p,x,z): p.prompt = z + " " + 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 build_samplers_dict(): samplers_dict = {} for i, sampler in enumerate(sd_samplers.all_samplers): samplers_dict[sampler.name.lower()] = i for alias in sampler.aliases: samplers_dict[alias.lower()] = i return samplers_dict def apply_sampler(p, x, xs): sampler_index = build_samplers_dict().get(x.lower(), None) if sampler_index is None: raise RuntimeError(f"Unknown sampler: {x}") p.sampler_index = sampler_index def confirm_samplers(p, xs): samplers_dict = build_samplers_dict() for x in xs: if x.lower() not in samplers_dict.keys(): 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) p.sd_model = shared.sd_model 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_hypernetwork(p, x, xs): if x.lower() in ["", "none"]: name = None else: name = hypernetwork.find_closest_hypernetwork_name(x) if not name: raise RuntimeError(f"Unknown hypernetwork: {x}") hypernetwork.load_hypernetwork(name) def apply_hypernetwork_strength(p, x, xs): hypernetwork.apply_strength(x) def confirm_hypernetworks(p, xs): for x in xs: if x.lower() in ["", "none"]: continue if not hypernetwork.find_closest_hypernetwork_name(x): raise RuntimeError(f"Unknown hypernetwork: {x}") def apply_clip_skip(p, x, xs): opts.data["CLIP_stop_at_last_layers"] = x 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 # AxisOption = namedtuple("AxisOption", ["label", "type", "apply", "format_value", "confirm"]) # AxisOptionImg2Img = namedtuple("AxisOptionImg2Img", ["label", "type", "apply", "format_value", "confirm"]) def draw_xy_grid(p, xs, ys, zs, x_labels, y_labels, cell, draw_legend, include_lone_images): 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 = [] processed_result = None cell_mode = "P" cell_size = (1,1) state.job_count = len(xs) * len(ys) * p.n_iter for iy, y in enumerate(ys): for ix, x in enumerate(xs): state.job = f"{ix + iy * len(xs) + 1} out of {len(xs) * len(ys)}" z = zs[iy] processed:Processed = cell(x, y, z) 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.append(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.append(Image.new(cell_mode, cell_size)) if not processed_result: print("Unexpected error: draw_xy_grid failed to return even a single processed image") return Processed() 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.hypernetwork = opts.sd_hypernetwork self.model = shared.sd_model def __exit__(self, exc_type, exc_value, tb): modules.sd_models.reload_model_weights(self.model) hypernetwork.load_hypernetwork(self.hypernetwork) hypernetwork.apply_strength() 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 "Generate Model Grid" def ui(self, is_img2img): filenames = [] for path in os.listdir(os.path.join(models_path, 'Stable-diffusion')): if path.endswith('.ckpt'): filenames.append(path) with gr.Row(): x_values = gr.Textbox(label="Prompts, separated with &", lines=1) with gr.Row(): y_values = gr.CheckboxGroup(filenames, label="Checkpoint file names, including file ending", lines=1) with gr.Row(): z_values = gr.Textbox(label="Model tokens", lines=1) draw_legend = gr.Checkbox(label='Draw legend', value=True) include_lone_images = gr.Checkbox(label='Include Separate Images', value=False) no_fixed_seeds = gr.Checkbox(label='Keep -1 for seeds', value=False) return [x_values, y_values, z_values, draw_legend, include_lone_images, no_fixed_seeds] def run(self, p, x_values, y_values, z_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 xs = [x.strip() for x in chain.from_iterable(csv.reader(StringIO(x_values), delimiter='&'))] ys = [x.strip() for x in chain.from_iterable(csv.reader(StringIO(y_values)))] zs = [x.strip() for x in chain.from_iterable(csv.reader(StringIO(z_values)))] def cell(x, y, z): pc = copy(p) edit_prompt(pc, x, z) confirm_checkpoints(pc,ys) apply_checkpoint(pc, y, ys) return process_images(pc) with SharedSettingsStackHelper(): processed = draw_xy_grid( p, xs=xs, ys=ys, zs=zs, x_labels=xs, y_labels=ys, cell=cell, draw_legend=draw_legend, include_lone_images=include_lone_images ) if opts.grid_save: images.save_image(processed.images[0], p.outpath_grids, "xy_grid", prompt=p.prompt, seed=processed.seed, grid=True, p=p) return processed