mirror of
https://github.com/vladmandic/automatic
synced 2026-09-19 09:14:35 +02:00
refactor xyzgrid
This commit is contained in:
Vladimir Mandic
+5
-475
@@ -1,7 +1,4 @@
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# pylint: disable=unused-argument
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import os
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import re
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# xyz grid that shows as selectable script
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import csv
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import random
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from collections import namedtuple
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@@ -11,477 +8,14 @@ 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 scripts.xyz_grid_shared import str_permutations, list_to_csv_string, re_range # pylint: disable=no-name-in-module
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from scripts.xyz_grid_classes import axis_options, AxisOption, SharedSettingsStackHelper # pylint: disable=no-name-in-module
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from scripts.xyz_grid_draw import draw_xyz_grid # pylint: disable=no-name-in-module
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from modules import shared, errors, scripts, images, processing
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from modules.ui_components import ToolButton
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import modules.ui_symbols as symbols
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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
|
||||
processed_result.all_seeds[idx] = processed.seed
|
||||
processed_result.infotexts[idx] = processed.infotexts[0]
|
||||
else:
|
||||
cell_mode = "P"
|
||||
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 = []
|
||||
|
||||
@@ -699,7 +233,6 @@ class Script(scripts.Script):
|
||||
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:
|
||||
@@ -803,8 +336,5 @@ class Script(scripts.Script):
|
||||
del processed.all_seeds[1]
|
||||
del processed.infotexts[1]
|
||||
elif no_grid:
|
||||
# del processed.images[0]
|
||||
# del processed.all_prompts[0]
|
||||
# del processed.all_seeds[0]
|
||||
del processed.infotexts[0]
|
||||
return processed
|
||||
|
||||
@@ -0,0 +1,151 @@
|
||||
from scripts.xyz_grid_shared import apply_field, apply_task_args, apply_setting, apply_prompt, apply_order, apply_sampler, apply_hr_sampler_name, confirm_samplers, apply_checkpoint, apply_refiner, apply_unet, apply_dict, apply_clip_skip, apply_vae, list_lora, apply_lora, apply_te, apply_styles, apply_upscaler, apply_context, apply_face_restore, apply_override, apply_processing, format_value_add_label, format_value, format_value_join_list, do_nothing, format_nothing, str_permutations # pylint: disable=no-name-in-module
|
||||
from modules import shared, sd_samplers, ipadapter, sd_models, sd_vae, sd_unet
|
||||
|
||||
|
||||
class AxisOption:
|
||||
def __init__(self, label, tipe, apply, fmt=format_value_add_label, confirm=None, cost=0.0, choices=None):
|
||||
self.label = label
|
||||
self.type = tipe
|
||||
self.apply = apply
|
||||
self.format_value = fmt
|
||||
self.confirm = confirm
|
||||
self.cost = cost
|
||||
self.choices = choices
|
||||
|
||||
|
||||
class AxisOptionImg2Img(AxisOption):
|
||||
def __init__(self, *args, **kwargs):
|
||||
super().__init__(*args, **kwargs)
|
||||
self.is_img2img = True
|
||||
|
||||
class AxisOptionTxt2Img(AxisOption):
|
||||
def __init__(self, *args, **kwargs):
|
||||
super().__init__(*args, **kwargs)
|
||||
self.is_img2img = False
|
||||
|
||||
|
||||
class SharedSettingsStackHelper(object):
|
||||
vae = None
|
||||
schedulers_solver_order = None
|
||||
tome_ratio = None
|
||||
todo_ratio = None
|
||||
sd_model_checkpoint = None
|
||||
sd_model_refiner = None
|
||||
sd_model_dict = None
|
||||
sd_vae = None
|
||||
sd_unet = None
|
||||
sd_text_encoder = 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_refiner = shared.opts.sd_model_refiner
|
||||
self.sd_model_dict = shared.opts.sd_model_dict
|
||||
self.sd_vae = shared.opts.sd_vae
|
||||
self.sd_unet = shared.opts.sd_unet
|
||||
self.sd_text_encoder = shared.opts.sd_text_encoder
|
||||
|
||||
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_checkpoint != shared.opts.sd_model_checkpoint:
|
||||
shared.opts.data["sd_model_checkpoint"] = self.sd_model_checkpoint
|
||||
sd_models.reload_model_weights(op='model')
|
||||
if self.sd_model_refiner != shared.opts.sd_model_refiner:
|
||||
shared.opts.data["sd_model_refiner"] = self.sd_model_refiner
|
||||
sd_models.reload_model_weights(op='refiner')
|
||||
if self.sd_model_dict != shared.opts.sd_model_dict:
|
||||
shared.opts.data["sd_model_dict"] = self.sd_model_dict
|
||||
sd_models.reload_model_weights(op='dict')
|
||||
if self.sd_vae != shared.opts.sd_vae:
|
||||
shared.opts.data["sd_vae"] = self.sd_vae
|
||||
sd_vae.reload_vae_weights()
|
||||
if self.sd_text_encoder != shared.opts.sd_text_encoder:
|
||||
shared.opts.data["sd_text_encoder"] = self.sd_text_encoder
|
||||
sd_models.reload_text_encoder()
|
||||
if self.sd_unet != shared.opts.sd_unet:
|
||||
shared.opts.data["sd_unet"] = self.sd_unet
|
||||
sd_unet.load_unet(shared.sd_model)
|
||||
|
||||
|
||||
axis_options = [
|
||||
AxisOption("Nothing", str, do_nothing, fmt=format_nothing),
|
||||
AxisOption("Prompt S/R", str, apply_prompt, fmt=format_value),
|
||||
AxisOption("Model", str, apply_checkpoint, fmt=format_value, cost=1.0, choices=lambda: sorted(sd_models.checkpoints_list)),
|
||||
AxisOption("UNET", str, apply_unet, cost=0.9, choices=lambda: ['None'] + list(sd_unet.unet_dict)),
|
||||
AxisOption("VAE", str, apply_vae, cost=0.7, choices=lambda: ['None'] + list(sd_vae.vae_dict)),
|
||||
AxisOption("LoRA", str, apply_lora, cost=0.5, choices=list_lora),
|
||||
AxisOption("LoRA strength", float, apply_setting('extra_networks_default_multiplier')),
|
||||
AxisOption("Text encoder", str, apply_te, cost=0.7, choices=lambda: ['None', 'T5 FP4', 'T5 FP8', 'T5 FP16']),
|
||||
AxisOption("Styles", str, apply_styles, choices=lambda: [s.name for s in shared.prompt_styles.styles.values()]),
|
||||
AxisOption("Seed", int, apply_field("seed")),
|
||||
AxisOption("Steps", int, apply_field("steps")),
|
||||
AxisOption("CFG scale", float, apply_field("cfg_scale")),
|
||||
AxisOption("Guidance end", float, apply_field("cfg_end")),
|
||||
AxisOption("Variation seed", int, apply_field("subseed")),
|
||||
AxisOption("Variation strength", float, apply_field("subseed_strength")),
|
||||
AxisOption("Clip skip", float, apply_clip_skip),
|
||||
AxisOption("Denoising strength", float, apply_field("denoising_strength")),
|
||||
AxisOption("Prompt order", str_permutations, apply_order, fmt=format_value_join_list),
|
||||
AxisOption("Model dictionary", str, apply_dict, fmt=format_value, cost=1.0, choices=lambda: ['None'] + list(sd_models.checkpoints_list)),
|
||||
AxisOption("Model args", str, apply_task_args),
|
||||
AxisOption("Processing args", str, apply_processing),
|
||||
AxisOptionImg2Img("Image mask weight", float, apply_field("inpainting_mask_weight")),
|
||||
AxisOptionTxt2Img("[Sampler] Name", str, apply_sampler, fmt=format_value, confirm=confirm_samplers, choices=lambda: [x.name for x in sd_samplers.samplers]),
|
||||
AxisOptionImg2Img("[Sampler] Name", str, apply_sampler, fmt=format_value, confirm=confirm_samplers, choices=lambda: [x.name for x in sd_samplers.samplers_for_img2img]),
|
||||
AxisOption("[Sampler] Timestep spacing", str, apply_setting("schedulers_timestep_spacing"), choices=lambda: ['default', 'linspace', 'leading', 'trailing']),
|
||||
AxisOption("[Sampler] Sigma min", float, apply_field("s_min")),
|
||||
AxisOption("[Sampler] Sigma max", float, apply_field("s_max")),
|
||||
AxisOption("[Sampler] Sigma tmin", float, apply_field("s_tmin")),
|
||||
AxisOption("[Sampler] Sigma tmax", float, apply_field("s_tmax")),
|
||||
AxisOption("[Sampler] Sigma churn", float, apply_field("s_churn")),
|
||||
AxisOption("[Sampler] Sigma noise", float, apply_field("s_noise")),
|
||||
AxisOption("[Sampler] Shift", float, apply_setting("schedulers_shift")),
|
||||
AxisOption("[Sampler] ETA", float, apply_setting("scheduler_eta")),
|
||||
AxisOption("[Sampler] Solver order", int, apply_setting("schedulers_solver_order")),
|
||||
AxisOption("[Second pass] Upscaler", str, apply_field("hr_upscaler"), choices=lambda: [*shared.latent_upscale_modes, *[x.name for x in shared.sd_upscalers]]),
|
||||
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]),
|
||||
AxisOption("[Second pass] Denoising strength", float, apply_field("denoising_strength")),
|
||||
AxisOption("[Second pass] Hires steps", int, apply_field("hr_second_pass_steps")),
|
||||
AxisOption("[Second pass] CFG scale", float, apply_field("image_cfg_scale")),
|
||||
AxisOption("[Second pass] Guidance rescale", float, apply_field("diffusers_guidance_rescale")),
|
||||
AxisOption("[Refiner] Model", str, apply_refiner, fmt=format_value, cost=1.0, choices=lambda: ['None'] + sorted(sd_models.checkpoints_list)),
|
||||
AxisOption("[Refiner] Refiner start", float, apply_field("refiner_start")),
|
||||
AxisOption("[Refiner] Refiner steps", float, apply_field("refiner_steps")),
|
||||
AxisOption("[Postprocess] Upscaler", str, apply_upscaler, choices=lambda: [x.name for x in shared.sd_upscalers][1:]),
|
||||
AxisOption("[Postprocess] Context", str, apply_context, choices=lambda: ["Add with forward", "Remove with forward", "Add with backward", "Remove with backward"]),
|
||||
AxisOption("[Postprocess] Face restore", str, apply_face_restore, fmt=format_value),
|
||||
AxisOption("[HDR] Mode", int, apply_field("hdr_mode")),
|
||||
AxisOption("[HDR] Brightness", float, apply_field("hdr_brightness")),
|
||||
AxisOption("[HDR] Color", float, apply_field("hdr_color")),
|
||||
AxisOption("[HDR] Sharpen", float, apply_field("hdr_sharpen")),
|
||||
AxisOption("[HDR] Clamp boundary", float, apply_field("hdr_boundary")),
|
||||
AxisOption("[HDR] Clamp threshold", float, apply_field("hdr_threshold")),
|
||||
AxisOption("[HDR] Maximize center shift", float, apply_field("hdr_max_center")),
|
||||
AxisOption("[HDR] Maximize boundary", float, apply_field("hdr_max_boundry")),
|
||||
AxisOption("[HDR] Tint color hex", str, apply_field("hdr_color_picker")),
|
||||
AxisOption("[HDR] Tint ratio", float, apply_field("hdr_tint_ratio")),
|
||||
AxisOption("[Token Merging] ToMe ratio", float, apply_setting('tome_ratio')),
|
||||
AxisOption("[Token Merging] ToDo ratio", float, apply_setting('todo_ratio')),
|
||||
AxisOption("[FreeU] 1st stage backbone factor", float, apply_setting('freeu_b1')),
|
||||
AxisOption("[FreeU] 2nd stage backbone factor", float, apply_setting('freeu_b2')),
|
||||
AxisOption("[FreeU] 1st stage skip factor", float, apply_setting('freeu_s1')),
|
||||
AxisOption("[FreeU] 2nd stage skip factor", float, apply_setting('freeu_s2')),
|
||||
AxisOption("[IP adapter] Name", str, apply_field('ip_adapter_names'), cost=1.0, choices=lambda: list(ipadapter.ADAPTERS)),
|
||||
AxisOption("[IP adapter] Scale", float, apply_field('ip_adapter_scales')),
|
||||
AxisOption("[IP adapter] Starts", float, apply_field('ip_adapter_starts')),
|
||||
AxisOption("[IP adapter] Ends", float, apply_field('ip_adapter_ends')),
|
||||
AxisOption("[HiDiffusion] T1", float, apply_override('hidiffusion_t1')),
|
||||
AxisOption("[HiDiffusion] T2", float, apply_override('hidiffusion_t2')),
|
||||
AxisOption("[HiDiffusion] Agression step", float, apply_field('hidiffusion_steps')),
|
||||
AxisOption("[PAG] Attention scale", float, apply_field('pag_scale')),
|
||||
AxisOption("[PAG] Adaptive scaling", float, apply_field('pag_adaptive')),
|
||||
AxisOption("[PAG] Applied layers", str, apply_setting('pag_apply_layers')),
|
||||
]
|
||||
@@ -0,0 +1,105 @@
|
||||
from copy import copy
|
||||
from PIL import Image
|
||||
from modules import shared, images, processing
|
||||
|
||||
|
||||
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): # pylint: disable=unused-argument
|
||||
hor_texts = [[images.GridAnnotation(x)] for x in x_labels]
|
||||
ver_texts = [[images.GridAnnotation(y)] for y in y_labels]
|
||||
title_texts = [[images.GridAnnotation(z)] for z in z_labels]
|
||||
list_size = (len(xs) * len(ys) * len(zs))
|
||||
processed_result = None
|
||||
shared.state.job_count = list_size * p.n_iter
|
||||
|
||||
def process_cell(x, y, z, ix, iy, iz):
|
||||
nonlocal processed_result
|
||||
|
||||
def index(ix, iy, iz):
|
||||
return ix + iy * len(xs) + iz * len(xs) * len(ys)
|
||||
|
||||
shared.state.job = 'grid'
|
||||
processed: processing.Processed = cell(x, y, z, ix, iy, iz)
|
||||
if processed_result is None:
|
||||
processed_result = copy(processed)
|
||||
if processed_result is None:
|
||||
shared.log.error('XYZ grid: no processing results')
|
||||
return processing.Processed(p, [])
|
||||
processed_result.images = [None] * list_size
|
||||
processed_result.all_prompts = [None] * list_size
|
||||
processed_result.all_seeds = [None] * list_size
|
||||
processed_result.infotexts = [None] * list_size
|
||||
processed_result.index_of_first_image = 1
|
||||
idx = index(ix, iy, iz)
|
||||
if processed is not None and processed.images:
|
||||
processed_result.images[idx] = processed.images[0]
|
||||
processed_result.all_prompts[idx] = processed.prompt
|
||||
processed_result.all_seeds[idx] = processed.seed
|
||||
processed_result.infotexts[idx] = processed.infotexts[0]
|
||||
else:
|
||||
cell_mode = "P"
|
||||
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
|
||||
+6
-474
@@ -1,7 +1,4 @@
|
||||
# pylint: disable=unused-argument
|
||||
|
||||
import os
|
||||
import re
|
||||
# xyz grid that shows up as alwayson script
|
||||
import csv
|
||||
import random
|
||||
from collections import namedtuple
|
||||
@@ -11,7 +8,10 @@ from io import StringIO
|
||||
from PIL import Image
|
||||
import numpy as np
|
||||
import gradio as gr
|
||||
from modules import shared, errors, scripts, images, sd_samplers, processing, sd_models, sd_vae, ipadapter
|
||||
from scripts.xyz_grid_shared import str_permutations, list_to_csv_string, re_range # pylint: disable=no-name-in-module
|
||||
from scripts.xyz_grid_classes import axis_options, AxisOption, SharedSettingsStackHelper # pylint: disable=no-name-in-module
|
||||
from scripts.xyz_grid_draw import draw_xyz_grid # pylint: disable=no-name-in-module
|
||||
from modules import shared, errors, scripts, images, processing
|
||||
from modules.ui_components import ToolButton
|
||||
import modules.ui_symbols as symbols
|
||||
|
||||
@@ -20,474 +20,6 @@ active = False
|
||||
cache = None
|
||||
|
||||
|
||||
def apply_field(field):
|
||||
def fun(p, x, xs):
|
||||
shared.log.debug(f'XYZ grid apply field: {field}={x}')
|
||||
setattr(p, field, x)
|
||||
return fun
|
||||
|
||||
|
||||
def apply_task_args(field):
|
||||
def fun(p, x, xs):
|
||||
shared.log.debug(f'XYZ grid apply task-arg: {field}={x}')
|
||||
p.task_args[field] = x
|
||||
return fun
|
||||
|
||||
|
||||
def apply_setting(field):
|
||||
def fun(p, x, xs):
|
||||
shared.log.debug(f'XYZ grid apply setting: {field}={x}')
|
||||
shared.opts.data[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:
|
||||
shared.log.warning(f"XYZ grid: prompt S/R did not find {xs[0]} in prompt or negative prompt.")
|
||||
else:
|
||||
p.prompt = p.prompt.replace(xs[0], x)
|
||||
p.all_prompts = p.batch_size * [p.prompt]
|
||||
p.negative_prompt = p.negative_prompt.replace(xs[0], x)
|
||||
p.all_negative_prompts = p.batch_size * [p.negative_prompt]
|
||||
shared.log.debug(f'XYZ grid apply prompt: "{xs[0]}"="{x}"')
|
||||
|
||||
|
||||
def apply_order(p, x, xs):
|
||||
token_order = []
|
||||
for token in x:
|
||||
token_order.append((p.prompt.find(token), token))
|
||||
token_order.sort(key=lambda t: t[0])
|
||||
prompt_parts = []
|
||||
for _, token in token_order:
|
||||
n = p.prompt.find(token)
|
||||
prompt_parts.append(p.prompt[0:n])
|
||||
p.prompt = p.prompt[n + len(token):]
|
||||
prompt_tmp = ""
|
||||
for idx, part in enumerate(prompt_parts):
|
||||
prompt_tmp += part
|
||||
prompt_tmp += x[idx]
|
||||
p.prompt = prompt_tmp + p.prompt
|
||||
|
||||
|
||||
def apply_sampler(p, x, xs):
|
||||
sampler_name = sd_samplers.samplers_map.get(x.lower(), None)
|
||||
if sampler_name is None:
|
||||
shared.log.warning(f"XYZ grid: unknown sampler: {x}")
|
||||
else:
|
||||
p.sampler_name = sampler_name
|
||||
shared.log.debug(f'XYZ grid apply sampler: "{x}"')
|
||||
|
||||
|
||||
def apply_hr_sampler_name(p, x, xs):
|
||||
hr_sampler_name = sd_samplers.samplers_map.get(x.lower(), None)
|
||||
if hr_sampler_name is None:
|
||||
shared.log.warning(f"XYZ grid: unknown sampler: {x}")
|
||||
else:
|
||||
p.hr_sampler_name = hr_sampler_name
|
||||
shared.log.debug(f'XYZ grid apply HR sampler: "{x}"')
|
||||
|
||||
|
||||
def confirm_samplers(p, xs):
|
||||
for x in xs:
|
||||
if x.lower() not in sd_samplers.samplers_map:
|
||||
shared.log.warning(f"XYZ grid: unknown sampler: {x}")
|
||||
|
||||
|
||||
def apply_checkpoint(p, x, xs):
|
||||
if x == shared.opts.sd_model_checkpoint:
|
||||
return
|
||||
info = sd_models.get_closet_checkpoint_match(x)
|
||||
if info is None:
|
||||
shared.log.warning(f"XYZ grid: apply checkpoint unknown checkpoint: {x}")
|
||||
else:
|
||||
sd_models.reload_model_weights(shared.sd_model, info)
|
||||
p.override_settings['sd_model_checkpoint'] = info.name
|
||||
shared.log.debug(f'XYZ grid apply checkpoint: "{x}"')
|
||||
|
||||
|
||||
def apply_refiner(p, x, xs):
|
||||
if x == shared.opts.sd_model_refiner:
|
||||
return
|
||||
if x == 'None':
|
||||
return
|
||||
info = sd_models.get_closet_checkpoint_match(x)
|
||||
if info is None:
|
||||
shared.log.warning(f"XYZ grid: apply refiner unknown checkpoint: {x}")
|
||||
else:
|
||||
sd_models.reload_model_weights(shared.sd_refiner, info)
|
||||
p.override_settings['sd_model_refiner'] = info.name
|
||||
shared.log.debug(f'XYZ grid apply refiner: "{x}"')
|
||||
|
||||
|
||||
def apply_dict(p, x, xs):
|
||||
if x == shared.opts.sd_model_dict:
|
||||
return
|
||||
info_dict = sd_models.get_closet_checkpoint_match(x)
|
||||
info_ckpt = sd_models.get_closet_checkpoint_match(shared.opts.sd_model_checkpoint)
|
||||
if info_dict is None or info_ckpt is None:
|
||||
shared.log.warning(f"XYZ grid: apply dict unknown checkpoint: {x}")
|
||||
else:
|
||||
shared.opts.sd_model_dict = info_dict.name # this will trigger reload_model_weights via onchange handler
|
||||
p.override_settings['sd_model_checkpoint'] = info_ckpt.name
|
||||
p.override_settings['sd_model_dict'] = info_dict.name
|
||||
shared.log.debug(f'XYZ grid apply model dict: "{x}"')
|
||||
|
||||
|
||||
def apply_clip_skip(p, x, xs):
|
||||
p.clip_skip = x
|
||||
shared.opts.data["clip_skip"] = x
|
||||
shared.log.debug(f'XYZ grid apply clip-skip: "{x}"')
|
||||
|
||||
|
||||
def find_vae(name: str):
|
||||
if name.lower() in ['auto', 'automatic']:
|
||||
return sd_vae.unspecified
|
||||
if name.lower() == 'none':
|
||||
return None
|
||||
else:
|
||||
choices = [x for x in sorted(sd_vae.vae_dict, key=lambda x: len(x)) if name.lower().strip() in x.lower()]
|
||||
if len(choices) == 0:
|
||||
shared.log.warning(f"No VAE found for {name}; using automatic")
|
||||
return sd_vae.unspecified
|
||||
else:
|
||||
return sd_vae.vae_dict[choices[0]]
|
||||
|
||||
|
||||
def apply_vae(p, x, xs):
|
||||
sd_vae.reload_vae_weights(shared.sd_model, vae_file=find_vae(x))
|
||||
shared.log.debug(f'XYZ grid apply VAE: "{x}"')
|
||||
|
||||
|
||||
def list_lora():
|
||||
import sys
|
||||
lora = [v for k, v in sys.modules.items() if k == 'networks'][0]
|
||||
loras = [v.fullname for v in lora.available_networks.values()]
|
||||
return ['None'] + loras
|
||||
|
||||
|
||||
def apply_lora(p, x, xs):
|
||||
if x == 'None':
|
||||
return
|
||||
x = os.path.basename(x)
|
||||
p.prompt = p.prompt + f" <lora:{x}:{shared.opts.extra_networks_default_multiplier}>"
|
||||
shared.log.debug(f'XYZ grid apply LoRA: "{x}"')
|
||||
|
||||
|
||||
def apply_te(p, x, xs):
|
||||
shared.opts.data["sd_text_encoder"] = x
|
||||
sd_models.reload_text_encoder()
|
||||
shared.log.debug(f'XYZ grid apply text-encoder: "{x}"')
|
||||
|
||||
|
||||
def apply_styles(p: processing.StableDiffusionProcessingTxt2Img, x: str, _):
|
||||
p.styles.extend(x.split(','))
|
||||
shared.log.debug(f'XYZ grid apply style: "{x}"')
|
||||
|
||||
|
||||
def apply_upscaler(p: processing.StableDiffusionProcessingTxt2Img, opt, x):
|
||||
p.enable_hr = True
|
||||
p.hr_force = True
|
||||
p.denoising_strength = 0.0
|
||||
p.hr_upscaler = opt
|
||||
shared.log.debug(f'XYZ grid apply upscaler: "{x}"')
|
||||
|
||||
|
||||
def apply_context(p: processing.StableDiffusionProcessingTxt2Img, opt, x):
|
||||
p.resize_mode = 5
|
||||
p.resize_context = opt
|
||||
shared.log.debug(f'XYZ grid apply resize-context: "{x}"')
|
||||
|
||||
|
||||
def apply_face_restore(p, opt, x):
|
||||
opt = opt.lower()
|
||||
if opt == 'codeformer':
|
||||
is_active = True
|
||||
p.face_restoration_model = 'CodeFormer'
|
||||
elif opt == 'gfpgan':
|
||||
is_active = True
|
||||
p.face_restoration_model = 'GFPGAN'
|
||||
else:
|
||||
is_active = opt in ('true', 'yes', 'y', '1')
|
||||
p.restore_faces = is_active
|
||||
shared.log.debug(f'XYZ grid apply face-restore: "{x}"')
|
||||
|
||||
|
||||
def apply_override(field):
|
||||
def fun(p, x, xs):
|
||||
p.override_settings[field] = x
|
||||
shared.log.debug(f'XYZ grid apply override: "{field}"="{x}"')
|
||||
return fun
|
||||
|
||||
|
||||
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
|
||||
|
||||
|
||||
def list_to_csv_string(data_list):
|
||||
with StringIO() as o:
|
||||
csv.writer(o).writerow(data_list)
|
||||
return o.getvalue().strip()
|
||||
|
||||
|
||||
class AxisOption:
|
||||
def __init__(self, label, tipe, apply, fmt=format_value_add_label, confirm=None, cost=0.0, choices=None):
|
||||
self.label = label
|
||||
self.type = tipe
|
||||
self.apply = apply
|
||||
self.format_value = fmt
|
||||
self.confirm = confirm
|
||||
self.cost = cost
|
||||
self.choices = choices
|
||||
|
||||
|
||||
class AxisOptionImg2Img(AxisOption):
|
||||
def __init__(self, *args, **kwargs):
|
||||
super().__init__(*args, **kwargs)
|
||||
self.is_img2img = True
|
||||
|
||||
class AxisOptionTxt2Img(AxisOption):
|
||||
def __init__(self, *args, **kwargs):
|
||||
super().__init__(*args, **kwargs)
|
||||
self.is_img2img = False
|
||||
|
||||
|
||||
axis_options = [
|
||||
AxisOption("Nothing", str, do_nothing, fmt=format_nothing),
|
||||
AxisOption("Prompt S/R", str, apply_prompt, fmt=format_value),
|
||||
AxisOption("Model", str, apply_checkpoint, fmt=format_value, cost=1.0, choices=lambda: sorted(sd_models.checkpoints_list)),
|
||||
AxisOption("VAE", str, apply_vae, cost=0.7, choices=lambda: ['None'] + list(sd_vae.vae_dict)),
|
||||
AxisOption("LoRA", str, apply_lora, cost=0.5, choices=list_lora),
|
||||
AxisOption("LoRA strength", float, apply_setting('extra_networks_default_multiplier')),
|
||||
AxisOption("Text encoder", str, apply_te, cost=0.7, choices=lambda: ['None', 'T5 FP4', 'T5 FP8', 'T5 FP16']),
|
||||
AxisOption("Styles", str, apply_styles, choices=lambda: [s.name for s in shared.prompt_styles.styles.values()]),
|
||||
AxisOption("Seed", int, apply_field("seed")),
|
||||
AxisOption("Steps", int, apply_field("steps")),
|
||||
AxisOption("CFG scale", float, apply_field("cfg_scale")),
|
||||
AxisOption("Guidance end", float, apply_field("cfg_end")),
|
||||
AxisOption("Variation seed", int, apply_field("subseed")),
|
||||
AxisOption("Variation strength", float, apply_field("subseed_strength")),
|
||||
AxisOption("Clip skip", float, apply_clip_skip),
|
||||
AxisOption("Denoising strength", float, apply_field("denoising_strength")),
|
||||
AxisOption("Prompt order", str_permutations, apply_order, fmt=format_value_join_list),
|
||||
AxisOption("Model dictionary", str, apply_dict, fmt=format_value, cost=1.0, choices=lambda: ['None'] + list(sd_models.checkpoints_list)),
|
||||
AxisOptionImg2Img("Image mask weight", float, apply_field("inpainting_mask_weight")),
|
||||
AxisOptionTxt2Img("[Sampler] Name", str, apply_sampler, fmt=format_value, confirm=confirm_samplers, choices=lambda: [x.name for x in sd_samplers.samplers]),
|
||||
AxisOptionImg2Img("[Sampler] Name", str, apply_sampler, fmt=format_value, confirm=confirm_samplers, choices=lambda: [x.name for x in sd_samplers.samplers_for_img2img]),
|
||||
AxisOption("[Sampler] Timestep spacing", str, apply_setting("schedulers_timestep_spacing"), choices=lambda: ['default', 'linspace', 'leading', 'trailing']),
|
||||
AxisOption("[Sampler] Sigma min", float, apply_field("s_min")),
|
||||
AxisOption("[Sampler] Sigma max", float, apply_field("s_max")),
|
||||
AxisOption("[Sampler] Sigma tmin", float, apply_field("s_tmin")),
|
||||
AxisOption("[Sampler] Sigma tmax", float, apply_field("s_tmax")),
|
||||
AxisOption("[Sampler] Sigma churn", float, apply_field("s_churn")),
|
||||
AxisOption("[Sampler] Sigma noise", float, apply_field("s_noise")),
|
||||
AxisOption("[Sampler] Shift", float, apply_setting("schedulers_shift")),
|
||||
AxisOption("[Sampler] ETA", float, apply_setting("scheduler_eta")),
|
||||
AxisOption("[Sampler] Solver order", int, apply_setting("schedulers_solver_order")),
|
||||
AxisOption("[Second pass] Upscaler", str, apply_field("hr_upscaler"), choices=lambda: [*shared.latent_upscale_modes, *[x.name for x in shared.sd_upscalers]]),
|
||||
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]),
|
||||
AxisOption("[Second pass] Denoising strength", float, apply_field("denoising_strength")),
|
||||
AxisOption("[Second pass] Hires steps", int, apply_field("hr_second_pass_steps")),
|
||||
AxisOption("[Second pass] CFG scale", float, apply_field("image_cfg_scale")),
|
||||
AxisOption("[Second pass] Guidance rescale", float, apply_field("diffusers_guidance_rescale")),
|
||||
AxisOption("[Refiner] Model", str, apply_refiner, fmt=format_value, cost=1.0, choices=lambda: ['None'] + sorted(sd_models.checkpoints_list)),
|
||||
AxisOption("[Refiner] Refiner start", float, apply_field("refiner_start")),
|
||||
AxisOption("[Refiner] Refiner steps", float, apply_field("refiner_steps")),
|
||||
AxisOption("[Postprocess] Upscaler", str, apply_upscaler, choices=lambda: [x.name for x in shared.sd_upscalers][1:]),
|
||||
AxisOption("[Postprocess] Context", str, apply_context, choices=lambda: ["Add with forward", "Remove with forward", "Add with backward", "Remove with backward"]),
|
||||
AxisOption("[Postprocess] Face restore", str, apply_face_restore, fmt=format_value),
|
||||
AxisOption("[HDR] Mode", int, apply_field("hdr_mode")),
|
||||
AxisOption("[HDR] Brightness", float, apply_field("hdr_brightness")),
|
||||
AxisOption("[HDR] Color", float, apply_field("hdr_color")),
|
||||
AxisOption("[HDR] Sharpen", float, apply_field("hdr_sharpen")),
|
||||
AxisOption("[HDR] Clamp boundary", float, apply_field("hdr_boundary")),
|
||||
AxisOption("[HDR] Clamp threshold", float, apply_field("hdr_threshold")),
|
||||
AxisOption("[HDR] Maximize center shift", float, apply_field("hdr_max_center")),
|
||||
AxisOption("[HDR] Maximize boundary", float, apply_field("hdr_max_boundry")),
|
||||
AxisOption("[HDR] Tint color hex", str, apply_field("hdr_color_picker")),
|
||||
AxisOption("[HDR] Tint ratio", float, apply_field("hdr_tint_ratio")),
|
||||
AxisOption("[Token Merging] ToMe ratio", float, apply_setting('tome_ratio')),
|
||||
AxisOption("[Token Merging] ToDo ratio", float, apply_setting('todo_ratio')),
|
||||
AxisOption("[FreeU] 1st stage backbone factor", float, apply_setting('freeu_b1')),
|
||||
AxisOption("[FreeU] 2nd stage backbone factor", float, apply_setting('freeu_b2')),
|
||||
AxisOption("[FreeU] 1st stage skip factor", float, apply_setting('freeu_s1')),
|
||||
AxisOption("[FreeU] 2nd stage skip factor", float, apply_setting('freeu_s2')),
|
||||
AxisOption("[IP adapter] Name", str, apply_field('ip_adapter_names'), cost=1.0, choices=lambda: list(ipadapter.ADAPTERS)),
|
||||
AxisOption("[IP adapter] Scale", float, apply_field('ip_adapter_scales')),
|
||||
AxisOption("[IP adapter] Starts", float, apply_field('ip_adapter_starts')),
|
||||
AxisOption("[IP adapter] Ends", float, apply_field('ip_adapter_ends')),
|
||||
AxisOption("[HiDiffusion] T1", float, apply_override('hidiffusion_t1')),
|
||||
AxisOption("[HiDiffusion] T2", float, apply_override('hidiffusion_t2')),
|
||||
AxisOption("[HiDiffusion] Agression step", float, apply_field('hidiffusion_steps')),
|
||||
AxisOption("[PAG] Attention scale", float, apply_field('pag_scale')),
|
||||
AxisOption("[PAG] Adaptive scaling", float, apply_field('pag_adaptive')),
|
||||
AxisOption("[PAG] Applied layers", str, apply_setting('pag_apply_layers')),
|
||||
]
|
||||
|
||||
|
||||
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):
|
||||
hor_texts = [[images.GridAnnotation(x)] for x in x_labels]
|
||||
ver_texts = [[images.GridAnnotation(y)] for y in y_labels]
|
||||
title_texts = [[images.GridAnnotation(z)] for z in z_labels]
|
||||
list_size = (len(xs) * len(ys) * len(zs))
|
||||
processed_result = None
|
||||
shared.state.job_count = list_size * p.n_iter
|
||||
|
||||
def process_cell(x, y, z, ix, iy, iz):
|
||||
nonlocal processed_result
|
||||
|
||||
def index(ix, iy, iz):
|
||||
return ix + iy * len(xs) + iz * len(xs) * len(ys)
|
||||
|
||||
shared.state.job = 'grid'
|
||||
processed: processing.Processed = cell(x, y, z, ix, iy, iz)
|
||||
if processed_result is None:
|
||||
processed_result = copy(processed)
|
||||
if processed_result is None:
|
||||
shared.log.error('XYZ grid: no processing results')
|
||||
return processing.Processed(p, [])
|
||||
processed_result.images = [None] * list_size
|
||||
processed_result.all_prompts = [None] * list_size
|
||||
processed_result.all_seeds = [None] * list_size
|
||||
processed_result.infotexts = [None] * list_size
|
||||
processed_result.index_of_first_image = 1
|
||||
idx = index(ix, iy, iz)
|
||||
if processed is not None and processed.images:
|
||||
processed_result.images[idx] = processed.images[0]
|
||||
processed_result.all_prompts[idx] = processed.prompt
|
||||
processed_result.all_seeds[idx] = processed.seed
|
||||
processed_result.infotexts[idx] = processed.infotexts[0]
|
||||
else:
|
||||
cell_mode = "P"
|
||||
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 = []
|
||||
|
||||
@@ -823,7 +355,7 @@ class Script(scripts.Script):
|
||||
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
|
||||
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, W0613
|
||||
global cache # pylint: disable=W0603
|
||||
if cache is not None and hasattr(cache, 'images'):
|
||||
samples = cache.images.copy()
|
||||
|
||||
@@ -0,0 +1,270 @@
|
||||
# pylint: disable=unused-argument
|
||||
|
||||
import os
|
||||
import re
|
||||
import csv
|
||||
from io import StringIO
|
||||
from modules import shared, processing, sd_samplers, sd_models, sd_vae, sd_unet
|
||||
|
||||
|
||||
re_range = re.compile(r'([-+]?[0-9]*\.?[0-9]+)-([-+]?[0-9]*\.?[0-9]+):?([0-9]+)?')
|
||||
|
||||
|
||||
def apply_field(field):
|
||||
def fun(p, x, xs):
|
||||
shared.log.debug(f'XYZ grid apply field: {field}={x}')
|
||||
setattr(p, field, x)
|
||||
return fun
|
||||
|
||||
|
||||
def apply_task_args(p, x, xs):
|
||||
for section in x.split(';'):
|
||||
k, v = section.split('=')
|
||||
k, v = k.strip(), v.strip()
|
||||
if v.replace('.','',1).isdigit():
|
||||
v = float(v) if '.' in v else int(v)
|
||||
p.task_args[k] = v
|
||||
shared.log.debug(f'XYZ grid apply task-arg: {k}={type(v)}:{v}')
|
||||
|
||||
|
||||
def apply_processing(p, x, xs):
|
||||
for section in x.split(';'):
|
||||
k, v = section.split('=')
|
||||
k, v = k.strip(), v.strip()
|
||||
if v.replace('.','',1).isdigit():
|
||||
v = float(v) if '.' in v else int(v)
|
||||
found = 'existing' if hasattr(p, k) else 'new'
|
||||
setattr(p, k, v)
|
||||
shared.log.debug(f'XYZ grid apply processing-arg: type={found} {k}={type(v)}:{v} ')
|
||||
|
||||
|
||||
def apply_setting(field):
|
||||
def fun(p, x, xs):
|
||||
shared.log.debug(f'XYZ grid apply setting: {field}={x}')
|
||||
shared.opts.data[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:
|
||||
shared.log.warning(f"XYZ grid: prompt S/R did not find {xs[0]} in prompt or negative prompt.")
|
||||
else:
|
||||
p.prompt = p.prompt.replace(xs[0], x)
|
||||
for i in range(len(p.all_prompts)):
|
||||
p.all_prompts[i] = p.all_prompts[i].replace(xs[0], x)
|
||||
p.negative_prompt = p.negative_prompt.replace(xs[0], x)
|
||||
for i in range(len(p.all_negative_prompts)):
|
||||
p.all_negative_prompts[i] = p.all_negative_prompts[i].replace(xs[0], x)
|
||||
shared.log.debug(f'XYZ grid apply prompt: "{xs[0]}"="{x}"')
|
||||
|
||||
|
||||
def apply_order(p, x, xs):
|
||||
token_order = []
|
||||
for token in x:
|
||||
token_order.append((p.prompt.find(token), token))
|
||||
token_order.sort(key=lambda t: t[0])
|
||||
prompt_parts = []
|
||||
for _, token in token_order:
|
||||
n = p.prompt.find(token)
|
||||
prompt_parts.append(p.prompt[0:n])
|
||||
p.prompt = p.prompt[n + len(token):]
|
||||
prompt_tmp = ""
|
||||
for idx, part in enumerate(prompt_parts):
|
||||
prompt_tmp += part
|
||||
prompt_tmp += x[idx]
|
||||
p.prompt = prompt_tmp + p.prompt
|
||||
|
||||
|
||||
def apply_sampler(p, x, xs):
|
||||
sampler_name = sd_samplers.samplers_map.get(x.lower(), None)
|
||||
if sampler_name is None:
|
||||
shared.log.warning(f"XYZ grid: unknown sampler: {x}")
|
||||
else:
|
||||
p.sampler_name = sampler_name
|
||||
shared.log.debug(f'XYZ grid apply sampler: "{x}"')
|
||||
|
||||
|
||||
def apply_hr_sampler_name(p, x, xs):
|
||||
hr_sampler_name = sd_samplers.samplers_map.get(x.lower(), None)
|
||||
if hr_sampler_name is None:
|
||||
shared.log.warning(f"XYZ grid: unknown sampler: {x}")
|
||||
else:
|
||||
p.hr_sampler_name = hr_sampler_name
|
||||
shared.log.debug(f'XYZ grid apply HR sampler: "{x}"')
|
||||
|
||||
|
||||
def confirm_samplers(p, xs):
|
||||
for x in xs:
|
||||
if x.lower() not in sd_samplers.samplers_map:
|
||||
shared.log.warning(f"XYZ grid: unknown sampler: {x}")
|
||||
|
||||
|
||||
def apply_checkpoint(p, x, xs):
|
||||
if x == shared.opts.sd_model_checkpoint:
|
||||
return
|
||||
info = sd_models.get_closet_checkpoint_match(x)
|
||||
if info is None:
|
||||
shared.log.warning(f"XYZ grid: apply checkpoint unknown checkpoint: {x}")
|
||||
else:
|
||||
sd_models.reload_model_weights(shared.sd_model, info)
|
||||
p.override_settings['sd_model_checkpoint'] = info.name
|
||||
shared.log.debug(f'XYZ grid apply checkpoint: "{x}"')
|
||||
|
||||
|
||||
def apply_refiner(p, x, xs):
|
||||
if x == shared.opts.sd_model_refiner:
|
||||
return
|
||||
if x == 'None':
|
||||
return
|
||||
info = sd_models.get_closet_checkpoint_match(x)
|
||||
if info is None:
|
||||
shared.log.warning(f"XYZ grid: apply refiner unknown checkpoint: {x}")
|
||||
else:
|
||||
sd_models.reload_model_weights(shared.sd_refiner, info)
|
||||
p.override_settings['sd_model_refiner'] = info.name
|
||||
shared.log.debug(f'XYZ grid apply refiner: "{x}"')
|
||||
|
||||
|
||||
def apply_unet(p, x, xs):
|
||||
if x == shared.opts.sd_unet:
|
||||
return
|
||||
if x == 'None':
|
||||
return
|
||||
p.override_settings['sd_unet'] = x
|
||||
sd_unet.load_unet(shared.sd_model)
|
||||
shared.log.debug(f'XYZ grid apply unet: "{x}"')
|
||||
|
||||
|
||||
def apply_dict(p, x, xs):
|
||||
if x == shared.opts.sd_model_dict:
|
||||
return
|
||||
info_dict = sd_models.get_closet_checkpoint_match(x)
|
||||
info_ckpt = sd_models.get_closet_checkpoint_match(shared.opts.sd_model_checkpoint)
|
||||
if info_dict is None or info_ckpt is None:
|
||||
shared.log.warning(f"XYZ grid: apply dict unknown checkpoint: {x}")
|
||||
else:
|
||||
shared.opts.sd_model_dict = info_dict.name # this will trigger reload_model_weights via onchange handler
|
||||
p.override_settings['sd_model_checkpoint'] = info_ckpt.name
|
||||
p.override_settings['sd_model_dict'] = info_dict.name
|
||||
shared.log.debug(f'XYZ grid apply model dict: "{x}"')
|
||||
|
||||
|
||||
def apply_clip_skip(p, x, xs):
|
||||
p.clip_skip = x
|
||||
shared.opts.data["clip_skip"] = x
|
||||
shared.log.debug(f'XYZ grid apply clip-skip: "{x}"')
|
||||
|
||||
|
||||
def find_vae(name: str):
|
||||
if name.lower() in ['auto', 'automatic']:
|
||||
return sd_vae.unspecified
|
||||
if name.lower() == 'none':
|
||||
return None
|
||||
else:
|
||||
choices = [x for x in sorted(sd_vae.vae_dict, key=lambda x: len(x)) if name.lower().strip() in x.lower()]
|
||||
if len(choices) == 0:
|
||||
shared.log.warning(f"No VAE found for {name}; using automatic")
|
||||
return sd_vae.unspecified
|
||||
else:
|
||||
return sd_vae.vae_dict[choices[0]]
|
||||
|
||||
|
||||
def apply_vae(p, x, xs):
|
||||
sd_vae.reload_vae_weights(shared.sd_model, vae_file=find_vae(x))
|
||||
shared.log.debug(f'XYZ grid apply VAE: "{x}"')
|
||||
|
||||
|
||||
def list_lora():
|
||||
import sys
|
||||
lora = [v for k, v in sys.modules.items() if k == 'networks'][0]
|
||||
loras = [v.fullname for v in lora.available_networks.values()]
|
||||
return ['None'] + loras
|
||||
|
||||
|
||||
def apply_lora(p, x, xs):
|
||||
if x == 'None':
|
||||
return
|
||||
x = os.path.basename(x)
|
||||
p.prompt = p.prompt + f" <lora:{x}:{shared.opts.extra_networks_default_multiplier}>"
|
||||
shared.log.debug(f'XYZ grid apply LoRA: "{x}"')
|
||||
|
||||
|
||||
def apply_te(p, x, xs):
|
||||
shared.opts.data["sd_text_encoder"] = x
|
||||
sd_models.reload_text_encoder()
|
||||
shared.log.debug(f'XYZ grid apply text-encoder: "{x}"')
|
||||
|
||||
|
||||
def apply_styles(p: processing.StableDiffusionProcessingTxt2Img, x: str, _):
|
||||
p.styles.extend(x.split(','))
|
||||
shared.log.debug(f'XYZ grid apply style: "{x}"')
|
||||
|
||||
|
||||
def apply_upscaler(p: processing.StableDiffusionProcessingTxt2Img, opt, x):
|
||||
p.enable_hr = True
|
||||
p.hr_force = True
|
||||
p.denoising_strength = 0.0
|
||||
p.hr_upscaler = opt
|
||||
shared.log.debug(f'XYZ grid apply upscaler: "{x}"')
|
||||
|
||||
|
||||
def apply_context(p: processing.StableDiffusionProcessingTxt2Img, opt, x):
|
||||
p.resize_mode = 5
|
||||
p.resize_context = opt
|
||||
shared.log.debug(f'XYZ grid apply resize-context: "{x}"')
|
||||
|
||||
|
||||
def apply_face_restore(p, opt, x):
|
||||
opt = opt.lower()
|
||||
if opt == 'codeformer':
|
||||
is_active = True
|
||||
p.face_restoration_model = 'CodeFormer'
|
||||
elif opt == 'gfpgan':
|
||||
is_active = True
|
||||
p.face_restoration_model = 'GFPGAN'
|
||||
else:
|
||||
is_active = opt in ('true', 'yes', 'y', '1')
|
||||
p.restore_faces = is_active
|
||||
shared.log.debug(f'XYZ grid apply face-restore: "{x}"')
|
||||
|
||||
|
||||
def apply_override(field):
|
||||
def fun(p, x, xs):
|
||||
p.override_settings[field] = x
|
||||
shared.log.debug(f'XYZ grid apply override: "{field}"="{x}"')
|
||||
return fun
|
||||
|
||||
|
||||
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
|
||||
|
||||
|
||||
def list_to_csv_string(data_list):
|
||||
with StringIO() as o:
|
||||
csv.writer(o).writerow(data_list)
|
||||
return o.getvalue().strip()
|
||||
Reference in New Issue
Block a user