mirror of
https://github.com/vladmandic/automatic
synced 2026-09-19 09:14:35 +02:00
global xyz-grid
This commit is contained in:
Vladimir Mandic
@@ -0,0 +1,830 @@
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# pylint: disable=unused-argument
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import os
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import re
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import csv
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import random
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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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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 gradio as gr
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from modules import shared, errors, scripts, images, sd_samplers, processing, sd_models, sd_vae, ipadapter
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from modules.ui_components import ToolButton
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import modules.ui_symbols as symbols
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active = False
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cache = None
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def apply_field(field):
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def fun(p, x, xs):
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shared.log.debug(f'XYZ grid apply field: {field}={x}')
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setattr(p, field, x)
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return fun
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def apply_task_args(field):
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def fun(p, x, xs):
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shared.log.debug(f'XYZ grid apply task-arg: {field}={x}')
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p.task_args[field] = x
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return fun
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def apply_setting(field):
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def fun(p, x, xs):
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shared.log.debug(f'XYZ grid apply setting: {field}={x}')
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shared.opts.data[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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shared.log.warning(f"XYZ grid: prompt S/R did not find {xs[0]} in prompt or negative prompt.")
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else:
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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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shared.log.debug(f'XYZ grid apply prompt: "{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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shared.log.warning(f"XYZ grid: unknown sampler: {x}")
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else:
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p.sampler_name = sampler_name
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shared.log.debug(f'XYZ grid apply sampler: "{x}"')
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def apply_hr_sampler_name(p, x, xs):
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hr_sampler_name = sd_samplers.samplers_map.get(x.lower(), None)
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if hr_sampler_name is None:
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shared.log.warning(f"XYZ grid: unknown sampler: {x}")
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else:
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p.hr_sampler_name = hr_sampler_name
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shared.log.debug(f'XYZ grid apply HR sampler: "{x}"')
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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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shared.log.warning(f"XYZ grid: unknown sampler: {x}")
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def apply_checkpoint(p, x, xs):
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if x == shared.opts.sd_model_checkpoint:
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return
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info = sd_models.get_closet_checkpoint_match(x)
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if info is None:
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shared.log.warning(f"XYZ grid: apply checkpoint unknown checkpoint: {x}")
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else:
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sd_models.reload_model_weights(shared.sd_model, info)
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p.override_settings['sd_model_checkpoint'] = info.name
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shared.log.debug(f'XYZ grid apply checkpoint: "{x}"')
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def apply_refiner(p, x, xs):
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if x == shared.opts.sd_model_refiner:
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return
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if x == 'None':
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return
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info = sd_models.get_closet_checkpoint_match(x)
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if info is None:
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shared.log.warning(f"XYZ grid: apply refiner unknown checkpoint: {x}")
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else:
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sd_models.reload_model_weights(shared.sd_refiner, info)
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p.override_settings['sd_model_refiner'] = info.name
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shared.log.debug(f'XYZ grid apply refiner: "{x}"')
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def apply_dict(p, x, xs):
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if x == shared.opts.sd_model_dict:
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return
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info_dict = sd_models.get_closet_checkpoint_match(x)
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info_ckpt = sd_models.get_closet_checkpoint_match(shared.opts.sd_model_checkpoint)
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if info_dict is None or info_ckpt is None:
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shared.log.warning(f"XYZ grid: apply dict unknown checkpoint: {x}")
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else:
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shared.opts.sd_model_dict = info_dict.name # this will trigger reload_model_weights via onchange handler
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p.override_settings['sd_model_checkpoint'] = info_ckpt.name
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p.override_settings['sd_model_dict'] = info_dict.name
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shared.log.debug(f'XYZ grid apply model dict: "{x}"')
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def apply_clip_skip(p, x, xs):
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p.clip_skip = x
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shared.opts.data["clip_skip"] = x
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shared.log.debug(f'XYZ grid apply clip-skip: "{x}"')
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def find_vae(name: str):
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if name.lower() in ['auto', 'automatic']:
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return 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(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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shared.log.warning(f"No VAE found for {name}; using automatic")
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return sd_vae.unspecified
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else:
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return sd_vae.vae_dict[choices[0]]
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def apply_vae(p, x, xs):
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sd_vae.reload_vae_weights(shared.sd_model, vae_file=find_vae(x))
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shared.log.debug(f'XYZ grid apply VAE: "{x}"')
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def list_lora():
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import sys
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lora = [v for k, v in sys.modules.items() if k == 'networks'][0]
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loras = [v.fullname for v in lora.available_networks.values()]
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return ['None'] + loras
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def apply_lora(p, x, xs):
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if x == 'None':
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return
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x = os.path.basename(x)
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p.prompt = p.prompt + f" <lora:{x}:{shared.opts.extra_networks_default_multiplier}>"
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shared.log.debug(f'XYZ grid apply LoRA: "{x}"')
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def apply_te(p, x, xs):
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shared.opts.data["sd_text_encoder"] = x
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sd_models.reload_text_encoder()
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shared.log.debug(f'XYZ grid apply text-encoder: "{x}"')
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def apply_styles(p: processing.StableDiffusionProcessingTxt2Img, x: str, _):
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p.styles.extend(x.split(','))
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shared.log.debug(f'XYZ grid apply style: "{x}"')
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def apply_upscaler(p: processing.StableDiffusionProcessingTxt2Img, opt, x):
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p.enable_hr = True
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p.hr_force = True
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p.denoising_strength = 0.0
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p.hr_upscaler = opt
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shared.log.debug(f'XYZ grid apply upscaler: "{x}"')
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def apply_context(p: processing.StableDiffusionProcessingTxt2Img, opt, x):
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p.resize_mode = 5
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p.resize_context = opt
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shared.log.debug(f'XYZ grid apply resize-context: "{x}"')
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def apply_face_restore(p, opt, x):
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opt = opt.lower()
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if opt == 'codeformer':
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is_active = True
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p.face_restoration_model = 'CodeFormer'
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elif opt == 'gfpgan':
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is_active = True
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p.face_restoration_model = 'GFPGAN'
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else:
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is_active = opt in ('true', 'yes', 'y', '1')
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p.restore_faces = is_active
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shared.log.debug(f'XYZ grid apply face-restore: "{x}"')
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def apply_override(field):
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def fun(p, x, xs):
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p.override_settings[field] = x
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shared.log.debug(f'XYZ grid apply override: "{field}"="{x}"')
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return fun
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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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def list_to_csv_string(data_list):
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with StringIO() as o:
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csv.writer(o).writerow(data_list)
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return o.getvalue().strip()
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class AxisOption:
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def __init__(self, label, tipe, apply, fmt=format_value_add_label, confirm=None, cost=0.0, choices=None):
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self.label = label
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self.type = tipe
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self.apply = apply
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self.format_value = fmt
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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, fmt=format_nothing),
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AxisOption("Prompt S/R", str, apply_prompt, fmt=format_value),
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AxisOption("Model", str, apply_checkpoint, fmt=format_value, cost=1.0, choices=lambda: sorted(sd_models.checkpoints_list)),
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AxisOption("VAE", str, apply_vae, cost=0.7, choices=lambda: ['None'] + list(sd_vae.vae_dict)),
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AxisOption("LoRA", str, apply_lora, cost=0.5, choices=list_lora),
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AxisOption("LoRA strength", float, apply_setting('extra_networks_default_multiplier')),
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AxisOption("Text encoder", str, apply_te, cost=0.7, choices=lambda: ['None', 'T5 FP4', 'T5 FP8', 'T5 FP16']),
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AxisOption("Styles", str, apply_styles, choices=lambda: [s.name for s in shared.prompt_styles.styles.values()]),
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AxisOption("Seed", int, apply_field("seed")),
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AxisOption("Steps", int, apply_field("steps")),
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AxisOption("CFG scale", float, apply_field("cfg_scale")),
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AxisOption("Guidance end", float, apply_field("cfg_end")),
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AxisOption("Variation seed", int, apply_field("subseed")),
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AxisOption("Variation strength", float, apply_field("subseed_strength")),
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AxisOption("Clip skip", float, apply_clip_skip),
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AxisOption("Denoising strength", float, apply_field("denoising_strength")),
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AxisOption("Prompt order", str_permutations, apply_order, fmt=format_value_join_list),
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AxisOption("Model dictionary", str, apply_dict, fmt=format_value, cost=1.0, choices=lambda: ['None'] + list(sd_models.checkpoints_list)),
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AxisOptionImg2Img("Image mask weight", float, apply_field("inpainting_mask_weight")),
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AxisOptionTxt2Img("[Sampler] Name", str, apply_sampler, fmt=format_value, confirm=confirm_samplers, choices=lambda: [x.name for x in sd_samplers.samplers]),
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AxisOptionImg2Img("[Sampler] Name", str, apply_sampler, fmt=format_value, confirm=confirm_samplers, choices=lambda: [x.name for x in sd_samplers.samplers_for_img2img]),
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AxisOption("[Sampler] Timestep spacing", str, apply_setting("schedulers_timestep_spacing"), choices=lambda: ['default', 'linspace', 'leading', 'trailing']),
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AxisOption("[Sampler] Sigma min", float, apply_field("s_min")),
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AxisOption("[Sampler] Sigma max", float, apply_field("s_max")),
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AxisOption("[Sampler] Sigma tmin", float, apply_field("s_tmin")),
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AxisOption("[Sampler] Sigma tmax", float, apply_field("s_tmax")),
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AxisOption("[Sampler] Sigma churn", float, apply_field("s_churn")),
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AxisOption("[Sampler] Sigma noise", float, apply_field("s_noise")),
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AxisOption("[Sampler] Shift", float, apply_setting("schedulers_shift")),
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AxisOption("[Sampler] ETA", float, apply_setting("scheduler_eta")),
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AxisOption("[Sampler] Solver order", int, apply_setting("schedulers_solver_order")),
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AxisOption("[Second pass] Upscaler", str, apply_field("hr_upscaler"), choices=lambda: [*shared.latent_upscale_modes, *[x.name for x in shared.sd_upscalers]]),
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AxisOption("[Second pass] Sampler", str, apply_hr_sampler_name, fmt=format_value, confirm=confirm_samplers, choices=lambda: [x.name for x in sd_samplers.samplers]),
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AxisOption("[Second pass] Denoising strength", float, apply_field("denoising_strength")),
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AxisOption("[Second pass] Hires steps", int, apply_field("hr_second_pass_steps")),
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AxisOption("[Second pass] CFG scale", float, apply_field("image_cfg_scale")),
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AxisOption("[Second pass] Guidance rescale", float, apply_field("diffusers_guidance_rescale")),
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AxisOption("[Refiner] Model", str, apply_refiner, fmt=format_value, cost=1.0, choices=lambda: ['None'] + sorted(sd_models.checkpoints_list)),
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AxisOption("[Refiner] Refiner start", float, apply_field("refiner_start")),
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AxisOption("[Refiner] Refiner steps", float, apply_field("refiner_steps")),
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AxisOption("[Postprocess] Upscaler", str, apply_upscaler, choices=lambda: [x.name for x in shared.sd_upscalers][1:]),
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AxisOption("[Postprocess] Context", str, apply_context, choices=lambda: ["Add with forward", "Remove with forward", "Add with backward", "Remove with backward"]),
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AxisOption("[Postprocess] Face restore", str, apply_face_restore, fmt=format_value),
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AxisOption("[HDR] Mode", int, apply_field("hdr_mode")),
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AxisOption("[HDR] Brightness", float, apply_field("hdr_brightness")),
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AxisOption("[HDR] Color", float, apply_field("hdr_color")),
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AxisOption("[HDR] Sharpen", float, apply_field("hdr_sharpen")),
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AxisOption("[HDR] Clamp boundary", float, apply_field("hdr_boundary")),
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AxisOption("[HDR] Clamp threshold", float, apply_field("hdr_threshold")),
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AxisOption("[HDR] Maximize center shift", float, apply_field("hdr_max_center")),
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AxisOption("[HDR] Maximize boundary", float, apply_field("hdr_max_boundry")),
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AxisOption("[HDR] Tint color hex", str, apply_field("hdr_color_picker")),
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AxisOption("[HDR] Tint ratio", float, apply_field("hdr_tint_ratio")),
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AxisOption("[Token Merging] ToMe ratio", float, apply_setting('tome_ratio')),
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AxisOption("[Token Merging] ToDo ratio", float, apply_setting('todo_ratio')),
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AxisOption("[FreeU] 1st stage backbone factor", float, apply_setting('freeu_b1')),
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AxisOption("[FreeU] 2nd stage backbone factor", float, apply_setting('freeu_b2')),
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AxisOption("[FreeU] 1st stage skip factor", float, apply_setting('freeu_s1')),
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AxisOption("[FreeU] 2nd stage skip factor", float, apply_setting('freeu_s2')),
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AxisOption("[IP adapter] Name", str, apply_field('ip_adapter_names'), cost=1.0, choices=lambda: list(ipadapter.ADAPTERS)),
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AxisOption("[IP adapter] Scale", float, apply_field('ip_adapter_scales')),
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AxisOption("[IP adapter] Starts", float, apply_field('ip_adapter_starts')),
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AxisOption("[IP adapter] Ends", float, apply_field('ip_adapter_ends')),
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AxisOption("[HiDiffusion] T1", float, apply_override('hidiffusion_t1')),
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AxisOption("[HiDiffusion] T2", float, apply_override('hidiffusion_t2')),
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AxisOption("[HiDiffusion] Agression step", float, apply_field('hidiffusion_steps')),
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AxisOption("[PAG] Attention scale", float, apply_field('pag_scale')),
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AxisOption("[PAG] Adaptive scaling", float, apply_field('pag_adaptive')),
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AxisOption("[PAG] Applied layers", str, apply_setting('pag_apply_layers')),
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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, no_grid):
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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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list_size = (len(xs) * len(ys) * len(zs))
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processed_result = None
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shared.state.job_count = list_size * p.n_iter
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def process_cell(x, y, z, ix, iy, iz):
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nonlocal processed_result
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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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shared.state.job = 'grid'
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processed: processing.Processed = cell(x, y, z, ix, iy, iz)
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if processed_result is None:
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processed_result = copy(processed)
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if processed_result is None:
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shared.log.error('XYZ grid: no processing results')
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return processing.Processed(p, [])
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processed_result.images = [None] * list_size
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processed_result.all_prompts = [None] * list_size
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processed_result.all_seeds = [None] * list_size
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processed_result.infotexts = [None] * list_size
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processed_result.index_of_first_image = 1
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idx = index(ix, iy, iz)
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if processed is not None and processed.images:
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processed_result.images[idx] = processed.images[0]
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processed_result.all_prompts[idx] = processed.prompt
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processed_result.all_seeds[idx] = processed.seed
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processed_result.infotexts[idx] = processed.infotexts[0]
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else:
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cell_mode = "P"
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cell_size = (processed_result.width, processed_result.height)
|
||||
if processed_result.images[0] is not None:
|
||||
cell_mode = processed_result.images[0].mode
|
||||
cell_size = processed_result.images[0].size
|
||||
processed_result.images[idx] = Image.new(cell_mode, cell_size)
|
||||
|
||||
if first_axes_processed == 'x':
|
||||
for ix, x in enumerate(xs):
|
||||
if second_axes_processed == 'y':
|
||||
for iy, y in enumerate(ys):
|
||||
for iz, z in enumerate(zs):
|
||||
process_cell(x, y, z, ix, iy, iz)
|
||||
else:
|
||||
for iz, z in enumerate(zs):
|
||||
for iy, y in enumerate(ys):
|
||||
process_cell(x, y, z, ix, iy, iz)
|
||||
elif first_axes_processed == 'y':
|
||||
for iy, y in enumerate(ys):
|
||||
if second_axes_processed == 'x':
|
||||
for ix, x in enumerate(xs):
|
||||
for iz, z in enumerate(zs):
|
||||
process_cell(x, y, z, ix, iy, iz)
|
||||
else:
|
||||
for iz, z in enumerate(zs):
|
||||
for ix, x in enumerate(xs):
|
||||
process_cell(x, y, z, ix, iy, iz)
|
||||
elif first_axes_processed == 'z':
|
||||
for iz, z in enumerate(zs):
|
||||
if second_axes_processed == 'x':
|
||||
for ix, x in enumerate(xs):
|
||||
for iy, y in enumerate(ys):
|
||||
process_cell(x, y, z, ix, iy, iz)
|
||||
else:
|
||||
for iy, y in enumerate(ys):
|
||||
for ix, x in enumerate(xs):
|
||||
process_cell(x, y, z, ix, iy, iz)
|
||||
|
||||
if not processed_result:
|
||||
shared.log.error("XYZ grid: Failed to initialize processing")
|
||||
return processing.Processed(p, [])
|
||||
elif not any(processed_result.images):
|
||||
shared.log.error("XYZ grid: Failed to return processed image")
|
||||
return processing.Processed(p, [])
|
||||
|
||||
z_count = len(zs)
|
||||
for i in range(z_count):
|
||||
start_index = (i * len(xs) * len(ys)) + i
|
||||
end_index = start_index + len(xs) * len(ys)
|
||||
if (not no_grid or include_sub_grids) and images.check_grid_size(processed_result.images[start_index:end_index]):
|
||||
grid = images.image_grid(processed_result.images[start_index:end_index], rows=len(ys))
|
||||
if draw_legend:
|
||||
grid = images.draw_grid_annotations(grid, processed_result.images[start_index].size[0], processed_result.images[start_index].size[1], hor_texts, ver_texts, margin_size, title=title_texts[i])
|
||||
processed_result.images.insert(i, grid)
|
||||
processed_result.all_prompts.insert(i, processed_result.all_prompts[start_index])
|
||||
processed_result.all_seeds.insert(i, processed_result.all_seeds[start_index])
|
||||
processed_result.infotexts.insert(i, processed_result.infotexts[start_index])
|
||||
sub_grid_size = processed_result.images[0].size
|
||||
if not no_grid and images.check_grid_size(processed_result.images[:z_count]):
|
||||
z_grid = images.image_grid(processed_result.images[:z_count], rows=1)
|
||||
if draw_legend:
|
||||
z_grid = images.draw_grid_annotations(z_grid, sub_grid_size[0], sub_grid_size[1], [[images.GridAnnotation()] for _ in z_labels], [[images.GridAnnotation()]])
|
||||
processed_result.images.insert(0, z_grid)
|
||||
#processed_result.all_prompts.insert(0, processed_result.all_prompts[0])
|
||||
#processed_result.all_seeds.insert(0, processed_result.all_seeds[0])
|
||||
processed_result.infotexts.insert(0, processed_result.infotexts[0])
|
||||
return processed_result
|
||||
|
||||
|
||||
class SharedSettingsStackHelper(object):
|
||||
vae = None
|
||||
schedulers_solver_order = None
|
||||
tome_ratio = None
|
||||
todo_ratio = None
|
||||
sd_model_checkpoint = None
|
||||
sd_model_dict = None
|
||||
sd_vae_checkpoint = None
|
||||
|
||||
def __enter__(self):
|
||||
#Save overridden settings so they can be restored later.
|
||||
self.vae = shared.opts.sd_vae
|
||||
self.schedulers_solver_order = shared.opts.schedulers_solver_order
|
||||
self.tome_ratio = shared.opts.tome_ratio
|
||||
self.todo_ratio = shared.opts.todo_ratio
|
||||
self.sd_model_checkpoint = shared.opts.sd_model_checkpoint
|
||||
self.sd_model_dict = shared.opts.sd_model_dict
|
||||
self.sd_vae_checkpoint = shared.opts.sd_vae
|
||||
|
||||
def __exit__(self, exc_type, exc_value, tb):
|
||||
#Restore overriden settings after plot generation.
|
||||
shared.opts.data["sd_vae"] = self.vae
|
||||
shared.opts.data["schedulers_solver_order"] = self.schedulers_solver_order
|
||||
shared.opts.data["tome_ratio"] = self.tome_ratio
|
||||
shared.opts.data["todo_ratio"] = self.todo_ratio
|
||||
if self.sd_model_dict != shared.opts.sd_model_dict:
|
||||
shared.opts.data["sd_model_dict"] = self.sd_model_dict
|
||||
if self.sd_model_checkpoint != shared.opts.sd_model_checkpoint:
|
||||
shared.opts.data["sd_model_checkpoint"] = self.sd_model_checkpoint
|
||||
sd_models.reload_model_weights()
|
||||
if self.sd_vae_checkpoint != shared.opts.sd_vae:
|
||||
shared.opts.data["sd_vae"] = self.sd_vae_checkpoint
|
||||
sd_vae.reload_vae_weights()
|
||||
|
||||
|
||||
re_range = re.compile(r'([-+]?[0-9]*\.?[0-9]+)-([-+]?[0-9]*\.?[0-9]+):?([0-9]+)?')
|
||||
|
||||
class Script(scripts.Script):
|
||||
current_axis_options = []
|
||||
|
||||
def show(self, is_img2img):
|
||||
return scripts.AlwaysVisible
|
||||
|
||||
def title(self):
|
||||
return "XYZ Grid"
|
||||
|
||||
def ui(self, is_img2img):
|
||||
self.current_axis_options = [x for x in axis_options if type(x) == AxisOption or x.is_img2img == is_img2img]
|
||||
|
||||
with gr.Accordion('XYZ Grid', open = False, elem_id='xyz_grid'):
|
||||
with gr.Row():
|
||||
enabled = gr.Checkbox(label = 'Enabled', value = False)
|
||||
with gr.Row():
|
||||
with gr.Column():
|
||||
with gr.Row(variant='compact'):
|
||||
x_type = gr.Dropdown(label="X type", container=True, choices=[x.label for x in self.current_axis_options], value=self.current_axis_options[0].label, type="index", elem_id=self.elem_id("x_type"))
|
||||
x_values = gr.Textbox(label="X values", container=True, lines=1, elem_id=self.elem_id("x_values"))
|
||||
x_values_dropdown = gr.Dropdown(label="X values", container=True, visible=False, multiselect=True, interactive=True)
|
||||
fill_x_button = ToolButton(value=symbols.fill, elem_id="xyz_grid_fill_x_tool_button", visible=False)
|
||||
with gr.Row(variant='compact'):
|
||||
y_type = gr.Dropdown(label="Y type", container=True, choices=[x.label for x in self.current_axis_options], value=self.current_axis_options[0].label, type="index", elem_id=self.elem_id("y_type"))
|
||||
y_values = gr.Textbox(label="Y values", container=True, lines=1, elem_id=self.elem_id("y_values"))
|
||||
y_values_dropdown = gr.Dropdown(label="Y values", container=True, visible=False, multiselect=True, interactive=True)
|
||||
fill_y_button = ToolButton(value=symbols.fill, elem_id="xyz_grid_fill_y_tool_button", visible=False)
|
||||
with gr.Row(variant='compact'):
|
||||
z_type = gr.Dropdown(label="Z type", container=True, 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"))
|
||||
z_values = gr.Textbox(label="Z values", container=True, lines=1, elem_id=self.elem_id("z_values"))
|
||||
z_values_dropdown = gr.Dropdown(label="Z values", container=True, visible=False, multiselect=True, interactive=True)
|
||||
fill_z_button = ToolButton(value=symbols.fill, elem_id="xyz_grid_fill_z_tool_button", visible=False)
|
||||
with gr.Row():
|
||||
with gr.Column():
|
||||
csv_mode = gr.Checkbox(label='Text inputs', value=False, elem_id=self.elem_id("csv_mode"), container=False)
|
||||
draw_legend = gr.Checkbox(label='Legend', value=True, elem_id=self.elem_id("draw_legend"), container=False)
|
||||
no_fixed_seeds = gr.Checkbox(label='Random seeds', value=False, elem_id=self.elem_id("no_fixed_seeds"), container=False)
|
||||
with gr.Column():
|
||||
no_grid = gr.Checkbox(label='Skip grid', value=False, elem_id=self.elem_id("no_xyz_grid"), container=False)
|
||||
include_lone_images = gr.Checkbox(label='Sub-images', value=False, elem_id=self.elem_id("include_lone_images"), container=False)
|
||||
include_sub_grids = gr.Checkbox(label='Sub-grids', value=False, elem_id=self.elem_id("include_sub_grids"), container=False)
|
||||
with gr.Row():
|
||||
margin_size = gr.Slider(label="Grid margins", minimum=0, maximum=500, value=0, step=2, elem_id=self.elem_id("margin_size"))
|
||||
with gr.Row():
|
||||
swap_xy_axes_button = gr.Button(value="Swap X/Y", elem_id="xy_grid_swap_axes_button", variant="secondary")
|
||||
swap_yz_axes_button = gr.Button(value="Swap Y/Z", elem_id="yz_grid_swap_axes_button", variant="secondary")
|
||||
swap_xz_axes_button = gr.Button(value="Swap X/Z", elem_id="xz_grid_swap_axes_button", variant="secondary")
|
||||
|
||||
def swap_axes(axis1_type, axis1_values, axis1_values_dropdown, axis2_type, axis2_values, axis2_values_dropdown):
|
||||
return self.current_axis_options[axis2_type].label, axis2_values, axis2_values_dropdown, self.current_axis_options[axis1_type].label, axis1_values, axis1_values_dropdown
|
||||
|
||||
xy_swap_args = [x_type, x_values, x_values_dropdown, y_type, y_values, y_values_dropdown]
|
||||
swap_xy_axes_button.click(swap_axes, inputs=xy_swap_args, outputs=xy_swap_args)
|
||||
yz_swap_args = [y_type, y_values, y_values_dropdown, z_type, z_values, z_values_dropdown]
|
||||
swap_yz_axes_button.click(swap_axes, inputs=yz_swap_args, outputs=yz_swap_args)
|
||||
xz_swap_args = [x_type, x_values, x_values_dropdown, z_type, z_values, z_values_dropdown]
|
||||
swap_xz_axes_button.click(swap_axes, inputs=xz_swap_args, outputs=xz_swap_args)
|
||||
|
||||
def fill(axis_type, csv_mode):
|
||||
axis = self.current_axis_options[axis_type]
|
||||
if axis.choices:
|
||||
if csv_mode:
|
||||
return list_to_csv_string(axis.choices()), gr.update()
|
||||
else:
|
||||
return gr.update(), axis.choices()
|
||||
else:
|
||||
return gr.update(), gr.update()
|
||||
|
||||
fill_x_button.click(fn=fill, inputs=[x_type, csv_mode], outputs=[x_values, x_values_dropdown])
|
||||
fill_y_button.click(fn=fill, inputs=[y_type, csv_mode], outputs=[y_values, y_values_dropdown])
|
||||
fill_z_button.click(fn=fill, inputs=[z_type, csv_mode], outputs=[z_values, z_values_dropdown])
|
||||
|
||||
def select_axis(axis_type, axis_values, axis_values_dropdown, csv_mode):
|
||||
choices = self.current_axis_options[axis_type].choices
|
||||
has_choices = choices is not None
|
||||
current_values = axis_values
|
||||
current_dropdown_values = axis_values_dropdown
|
||||
if has_choices:
|
||||
choices = choices()
|
||||
if csv_mode:
|
||||
current_dropdown_values = list(filter(lambda x: x in choices, current_dropdown_values))
|
||||
current_values = list_to_csv_string(current_dropdown_values)
|
||||
else:
|
||||
current_dropdown_values = [x.strip() for x in chain.from_iterable(csv.reader(StringIO(axis_values)))]
|
||||
current_dropdown_values = list(filter(lambda x: x in choices, current_dropdown_values))
|
||||
|
||||
return (gr.Button.update(visible=has_choices), gr.Textbox.update(visible=not has_choices or csv_mode, value=current_values),
|
||||
gr.update(choices=choices if has_choices else None, visible=has_choices and not csv_mode, value=current_dropdown_values))
|
||||
|
||||
x_type.change(fn=select_axis, inputs=[x_type, x_values, x_values_dropdown, csv_mode], outputs=[fill_x_button, x_values, x_values_dropdown])
|
||||
y_type.change(fn=select_axis, inputs=[y_type, y_values, y_values_dropdown, csv_mode], outputs=[fill_y_button, y_values, y_values_dropdown])
|
||||
z_type.change(fn=select_axis, inputs=[z_type, z_values, z_values_dropdown, csv_mode], outputs=[fill_z_button, z_values, z_values_dropdown])
|
||||
|
||||
def change_choice_mode(csv_mode, x_type, x_values, x_values_dropdown, y_type, y_values, y_values_dropdown, z_type, z_values, z_values_dropdown):
|
||||
_fill_x_button, _x_values, _x_values_dropdown = select_axis(x_type, x_values, x_values_dropdown, csv_mode)
|
||||
_fill_y_button, _y_values, _y_values_dropdown = select_axis(y_type, y_values, y_values_dropdown, csv_mode)
|
||||
_fill_z_button, _z_values, _z_values_dropdown = select_axis(z_type, z_values, z_values_dropdown, csv_mode)
|
||||
return _fill_x_button, _x_values, _x_values_dropdown, _fill_y_button, _y_values, _y_values_dropdown, _fill_z_button, _z_values, _z_values_dropdown
|
||||
|
||||
csv_mode.change(fn=change_choice_mode, inputs=[csv_mode, x_type, x_values, x_values_dropdown, y_type, y_values, y_values_dropdown, z_type, z_values, z_values_dropdown], outputs=[fill_x_button, x_values, x_values_dropdown, fill_y_button, y_values, y_values_dropdown, fill_z_button, z_values, z_values_dropdown])
|
||||
|
||||
def get_dropdown_update_from_params(axis,params):
|
||||
val_key = f"{axis} Values"
|
||||
vals = params.get(val_key,"")
|
||||
valslist = [x.strip() for x in chain.from_iterable(csv.reader(StringIO(vals))) if x]
|
||||
return gr.update(value = valslist)
|
||||
|
||||
self.infotext_fields = (
|
||||
(x_type, "X Type"),
|
||||
(x_values, "X Values"),
|
||||
(x_values_dropdown, lambda params:get_dropdown_update_from_params("X",params)),
|
||||
(y_type, "Y Type"),
|
||||
(y_values, "Y Values"),
|
||||
(y_values_dropdown, lambda params:get_dropdown_update_from_params("Y",params)),
|
||||
(z_type, "Z Type"),
|
||||
(z_values, "Z Values"),
|
||||
(z_values_dropdown, lambda params:get_dropdown_update_from_params("Z",params)),
|
||||
)
|
||||
|
||||
return [enabled, x_type, x_values, x_values_dropdown, y_type, y_values, y_values_dropdown, z_type, z_values, z_values_dropdown, csv_mode, draw_legend, no_fixed_seeds, no_grid, include_lone_images, include_sub_grids, margin_size]
|
||||
|
||||
def process(self, p, enabled, x_type, x_values, x_values_dropdown, y_type, y_values, y_values_dropdown, z_type, z_values, z_values_dropdown, csv_mode, draw_legend, no_fixed_seeds, no_grid, include_lone_images, include_sub_grids, margin_size): # pylint: disable=W0221
|
||||
global active, cache # pylint: disable=W0603
|
||||
if not enabled or active:
|
||||
return
|
||||
active = True
|
||||
shared.log.debug(f'xyzgrid: x_type={x_type}|x_values={x_values}|x_values_dropdown={x_values_dropdown}|y_type={y_type}|{y_values}={y_values}|{y_values_dropdown}={y_values_dropdown}|z_type={z_type}|z_values={z_values}|z_values_dropdown={z_values_dropdown}|draw_legend={draw_legend}|include_lone_images={include_lone_images}|include_sub_grids={include_sub_grids}|no_grid={no_grid}|margin_size={margin_size}')
|
||||
if not no_fixed_seeds:
|
||||
processing.fix_seed(p)
|
||||
if not shared.opts.return_grid:
|
||||
p.batch_size = 1
|
||||
|
||||
def process_axis(opt, vals, vals_dropdown):
|
||||
if opt.label == 'Nothing':
|
||||
return [0]
|
||||
if opt.choices is not None and not csv_mode:
|
||||
valslist = vals_dropdown
|
||||
else:
|
||||
valslist = [x.strip() for x in chain.from_iterable(csv.reader(StringIO(vals))) if x]
|
||||
if opt.type == int:
|
||||
valslist_ext = []
|
||||
for val in valslist:
|
||||
m = re_range.fullmatch(val)
|
||||
if m is not None:
|
||||
start_val = int(m.group(1)) if m.group(1) is not None else val
|
||||
end_val = int(m.group(2)) if m.group(2) is not None else val
|
||||
num = int(m.group(3)) if m.group(3) is not None else int(end_val-start_val)
|
||||
valslist_ext += [int(x) for x in np.linspace(start=start_val, stop=end_val, num=max(2, num)).tolist()]
|
||||
shared.log.debug(f'XYZ grid range: start={start_val} end={end_val} num={max(2, num)} list={valslist}')
|
||||
else:
|
||||
valslist_ext.append(int(val))
|
||||
valslist.clear()
|
||||
valslist = [x for x in valslist_ext if x not in valslist]
|
||||
elif opt.type == float:
|
||||
valslist_ext = []
|
||||
for val in valslist:
|
||||
m = re_range.fullmatch(val)
|
||||
if m is not None:
|
||||
start_val = float(m.group(1)) if m.group(1) is not None else val
|
||||
end_val = float(m.group(2)) if m.group(2) is not None else val
|
||||
num = int(m.group(3)) if m.group(3) is not None else int(end_val-start_val)
|
||||
valslist_ext += [round(float(x), 2) for x in np.linspace(start=start_val, stop=end_val, num=max(2, num)).tolist()]
|
||||
shared.log.debug(f'XYZ grid range: start={start_val} end={end_val} num={max(2, num)} list={valslist}')
|
||||
else:
|
||||
valslist_ext.append(float(val))
|
||||
valslist.clear()
|
||||
valslist = [x for x in valslist_ext if x not in valslist]
|
||||
elif opt.type == str_permutations: # pylint: disable=comparison-with-callable
|
||||
valslist = list(permutations(valslist))
|
||||
valslist = [opt.type(x) for x in valslist]
|
||||
# Confirm options are valid before starting
|
||||
if opt.confirm:
|
||||
opt.confirm(p, valslist)
|
||||
return valslist
|
||||
|
||||
x_opt = self.current_axis_options[x_type]
|
||||
if x_opt.choices is not None and not csv_mode:
|
||||
x_values = list_to_csv_string(x_values_dropdown)
|
||||
xs = process_axis(x_opt, x_values, x_values_dropdown)
|
||||
y_opt = self.current_axis_options[y_type]
|
||||
if y_opt.choices is not None and not csv_mode:
|
||||
y_values = list_to_csv_string(y_values_dropdown)
|
||||
ys = process_axis(y_opt, y_values, y_values_dropdown)
|
||||
z_opt = self.current_axis_options[z_type]
|
||||
if z_opt.choices is not None and not csv_mode:
|
||||
z_values = list_to_csv_string(z_values_dropdown)
|
||||
zs = process_axis(z_opt, z_values, z_values_dropdown)
|
||||
Image.MAX_IMAGE_PIXELS = None # disable check in Pillow and rely on check below to allow large custom image sizes
|
||||
|
||||
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, processing.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
|
||||
shared.log.info(f"XYZ grid: images={len(xs)*len(ys)*len(zs)*image_cell_count} grid={len(zs)} {len(xs)}x{len(ys)} cells={len(zs)} steps={total_steps}")
|
||||
AxisInfo = namedtuple('AxisInfo', ['axis', 'values'])
|
||||
shared.state.xyz_plot_x = AxisInfo(x_opt, xs)
|
||||
shared.state.xyz_plot_y = AxisInfo(y_opt, ys)
|
||||
shared.state.xyz_plot_z = AxisInfo(z_opt, zs)
|
||||
# If one of the axes is very slow to change between (like SD model checkpoint), then make sure it is in the outer iteration of the nested `for` loop.
|
||||
first_axes_processed = 'z'
|
||||
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'
|
||||
grid_infotext = [None] * (1 + len(zs))
|
||||
|
||||
def cell(x, y, z, ix, iy, iz):
|
||||
if shared.state.interrupted:
|
||||
return processing.Processed(p, [], p.seed, "")
|
||||
pc = copy(p)
|
||||
pc.override_settings_restore_afterwards = False
|
||||
pc.styles = pc.styles[:]
|
||||
x_opt.apply(pc, x, xs)
|
||||
y_opt.apply(pc, y, ys)
|
||||
z_opt.apply(pc, z, zs)
|
||||
try:
|
||||
res = processing.process_images(pc)
|
||||
except Exception as e:
|
||||
shared.log.error(f"XYZ grid: Failed to process image: {e}")
|
||||
errors.display(e, 'XYZ grid')
|
||||
res = None
|
||||
subgrid_index = 1 + iz # Sets subgrid infotexts
|
||||
if grid_infotext[subgrid_index] is None and ix == 0 and iy == 0:
|
||||
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])
|
||||
grid_infotext[subgrid_index] = processing.create_infotext(pc, pc.all_prompts, pc.all_seeds, pc.all_subseeds)
|
||||
if grid_infotext[0] is None and ix == 0 and iy == 0 and iz == 0: # Sets main grid infotext
|
||||
pc.extra_generation_params = copy(pc.extra_generation_params)
|
||||
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 = 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,
|
||||
no_grid=no_grid,
|
||||
)
|
||||
|
||||
if not processed.images:
|
||||
active = False
|
||||
return processed # It broke, no further handling needed.
|
||||
z_count = len(zs)
|
||||
processed.infotexts[:1+z_count] = grid_infotext[:1+z_count] # Set the grid infotexts to the real ones with extra_generation_params (1 main grid + z_count sub-grids)
|
||||
if not include_lone_images:
|
||||
if no_grid and include_sub_grids:
|
||||
processed.images = processed.images[:z_count] # we don't have the main grid image, and need zero additional sub-images
|
||||
else:
|
||||
processed.images = processed.images[:z_count+1] # we either have the main grid image, or need one sub-images
|
||||
if shared.opts.grid_save: # Auto-save main and sub-grids:
|
||||
grid_count = z_count + ( 1 if not no_grid and z_count > 1 else 0 )
|
||||
for g in range(grid_count):
|
||||
adj_g = g-1 if g > 0 else g
|
||||
images.save_image(processed.images[g], p.outpath_grids, "xyz_grid", info=processed.infotexts[g], extension=shared.opts.grid_format, prompt=processed.all_prompts[adj_g], seed=processed.all_seeds[adj_g], grid=True, p=processed)
|
||||
if not include_sub_grids: # Done with sub-grids, drop all related information:
|
||||
for _sg in range(z_count):
|
||||
del processed.images[1]
|
||||
del processed.all_prompts[1]
|
||||
del processed.all_seeds[1]
|
||||
del processed.infotexts[1]
|
||||
elif no_grid:
|
||||
del processed.infotexts[0]
|
||||
active = False
|
||||
cache = processed
|
||||
return processed
|
||||
|
||||
def process_images(self, p, enabled, x_type, x_values, x_values_dropdown, y_type, y_values, y_values_dropdown, z_type, z_values, z_values_dropdown, csv_mode, draw_legend, no_fixed_seeds, no_grid, include_lone_images, include_sub_grids, margin_size): # pylint: disable=W0221
|
||||
global cache # pylint: disable=W0603
|
||||
if cache is not None and hasattr(cache, 'images'):
|
||||
samples = cache.images.copy()
|
||||
cache = None
|
||||
return samples
|
||||
return None
|
||||
Reference in New Issue
Block a user