492052b5df
Adds upscale latent space for hires., VAE, and Styles as new axis options to the X/Y plot.
430 lines
16 KiB
Python
430 lines
16 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, paths, sd_samplers
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from modules.hypernetworks import hypernetwork
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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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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 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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p.sd_model = shared.sd_model
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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 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', 'none']:
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return name
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else:
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vae_path = os.path.abspath(os.path.join(paths.models_path, 'VAE'))
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found = glob.glob(os.path.join(vae_path, f'**/{name}.*pt'), recursive=True)
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if found:
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return found[0]
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else:
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return 'auto'
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def apply_vae(p, x, xs):
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if x.lower().strip() == 'none':
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modules.sd_vae.reload_vae_weights(shared.sd_model, vae_file='None')
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else:
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found = find_vae(x)
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if found:
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v = modules.sd_vae.reload_vae_weights(shared.sd_model, vae_file=found)
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def apply_styles(p: StableDiffusionProcessingTxt2Img, x: str, _):
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p.styles = 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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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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AxisOption("Upscale latent space for hires.", str, apply_upscale_latent_space, format_value_add_label, None),
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AxisOption("Cond. Image Mask Weight", float, apply_field("inpainting_mask_weight"), format_value_add_label, None),
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AxisOption("VAE", str, apply_vae, format_value_add_label, None),
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AxisOption("Styles", str, apply_styles, 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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self.use_scale_latent_for_hires_fix = opts.use_scale_latent_for_hires_fix
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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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modules.sd_models.reload_model_weights(self.model)
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modules.sd_vae.reload_vae_weights(self.model, vae_file=find_vae(self.vae))
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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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opts.data["use_scale_latent_for_hires_fix"] = self.use_scale_latent_for_hires_fix
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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, type="index", elem_id="x_type")
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x_values = gr.Textbox(label="X values", 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, type="index", elem_id="y_type")
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y_values = gr.Textbox(label="Y values", 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", extension=opts.grid_format, prompt=p.prompt, seed=processed.seed, grid=True, p=p)
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return processed
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