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refactor xyzgrid

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This commit is contained in:
Vladimir Mandic
2024-09-13 19:22:02 -04:00
parent bf870c7644
commit fe93ad6929
14 changed files with 626 additions and 984 deletions
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CHANGELOG.md
+15
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@@ -1,5 +1,20 @@
# Change Log for SD.Next
## Update for 2024-09-14
- flux avoid unet load if unchanged
- flux mark specific unet as unavailable if load failed
- xyz grid full refactor
- xyz grid multi-mode: *selectable-script* and *alwayson-script*
- xyz grid allow usage combined with other scripts
- xyz grid allow **unet** selection
- xyz grid allow passing **model args** directly:
allowed params will be checked against models call signature
example: `width=768; height=512, width=512; height=768`
- xyz grid allow passing **processing args** directly:
params are set directly on main processing object and can be known or new params
example: `steps=10, steps=20; test=unknown`
## Update for 2024-09-13
### Highlights for 2024-09-13
javascript/sdnext.css
+1 -1
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@@ -45,7 +45,7 @@ input[type='color'] { width: 64px; height: 32px; }
.gradio-dropdown ul.options { z-index: 1000; min-width: fit-content; max-height: 50vh !important; white-space: nowrap; }
.gradio-dropdown ul.options li.item { padding: var(--spacing-xs); }
.gradio-dropdown ul.options li.item:not(:has(.hide)) { background-color: var(--primary-500); }
.gradio-dropdown .token { padding: var(--spacing-xs) !important; }
.gradio-dropdown .token { padding: var(--spacing-xs) !important; overflow-x: hidden; }
.gradio-html { color: var(--body-text-color); }
.gradio-html .min { min-height: 0; }
.gradio-html div.wrap { height: 100%; }
launch.py
+6
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@@ -169,6 +169,12 @@ def start_server(immediate=True, server=None):
uvicorn = None
if args.test:
installer.log.info("Test only")
installer.log.critical('Logging: level=critical')
installer.log.error('Logging: level=error')
installer.log.warning('Logging: level=warning')
installer.log.info('Logging: level=info')
installer.log.debug('Logging: level=debug')
installer.log.trace('Logging: level=trace')
server.wants_restart = False
else:
if args.api_only:
modules/devices.py
+1 -1
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@@ -67,7 +67,7 @@ def get_gpu_info():
}
elif torch.version.cuda:
return {
'device': f'{torch.cuda.get_device_name(torch.cuda.current_device())} n={torch.cuda.device_count()} arch={torch.cuda.get_arch_list()[-1]} cap={torch.cuda.get_device_capability(device)}',
'device': f'{torch.cuda.get_device_name(torch.cuda.current_device())} n={torch.cuda.device_count()} arch={torch.cuda.get_arch_list()[-1]} capability={torch.cuda.get_device_capability(device)}',
'cuda': torch.version.cuda,
'cudnn': torch.backends.cudnn.version(),
'driver': get_driver(),
modules/loader.py
+5
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@@ -39,6 +39,9 @@ timer.startup.record("torch")
import transformers # pylint: disable=W0611,C0411
timer.startup.record("transformers")
import accelerate # pylint: disable=W0611,C0411
timer.startup.record("accelerate")
import onnxruntime # pylint: disable=W0611,C0411
onnxruntime.set_default_logger_severity(3)
timer.startup.record("onnx")
@@ -86,6 +89,8 @@ def get_packages():
"torch": getattr(torch, "__long_version__", torch.__version__),
"diffusers": diffusers.__version__,
"gradio": gradio.__version__,
"transformers": transformers.__version__,
"accelerate": accelerate.__version__,
}
errors.log.info(f'Load packages: {get_packages()}')
modules/model_flux.py
+28 -15
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@@ -107,17 +107,27 @@ def load_flux_bnb(checkpoint_info, diffusers_load_config): # pylint: disable=unu
install('bitsandbytes', quiet=True)
from diffusers import FluxTransformer2DModel
quant = get_quant(repo_path)
if quant == 'fp8':
quantization_config = transformers.BitsAndBytesConfig(load_in_8bit=True)
if transformer is None:
try:
if quant == 'fp8':
quantization_config = transformers.BitsAndBytesConfig(load_in_8bit=True, bnb_4bit_compute_dtype=devices.dtype)
debug(f'Quantization: {quantization_config}')
transformer = FluxTransformer2DModel.from_single_file(repo_path, **diffusers_load_config, quantization_config=quantization_config)
elif quant == 'fp4':
quantization_config = transformers.BitsAndBytesConfig(load_in_4bit=True)
if transformer is None:
elif quant == 'fp4':
quantization_config = transformers.BitsAndBytesConfig(load_in_4bit=True, bnb_4bit_compute_dtype=devices.dtype, bnb_4bit_quant_type= 'fp4')
debug(f'Quantization: {quantization_config}')
transformer = FluxTransformer2DModel.from_single_file(repo_path, **diffusers_load_config, quantization_config=quantization_config)
else:
if transformer is None:
elif quant == 'nf4':
quantization_config = transformers.BitsAndBytesConfig(load_in_4bit=True, bnb_4bit_compute_dtype=devices.dtype, bnb_4bit_quant_type= 'nf4')
debug(f'Quantization: {quantization_config}')
transformer = FluxTransformer2DModel.from_single_file(repo_path, **diffusers_load_config, quantization_config=quantization_config)
else:
transformer = FluxTransformer2DModel.from_single_file(repo_path, **diffusers_load_config)
except Exception as e:
shared.log.error(f"Loading FLUX: Failed to load BnB transformer: {e}")
transformer, text_encoder_2 = None, None
if debug:
from modules import errors
errors.display(e, 'FLUX:')
return transformer, text_encoder_2
@@ -130,19 +140,19 @@ def load_transformer(file_path): # triggered by opts.sd_unet change
"cache_dir": shared.opts.hfcache_dir,
}
shared.log.info(f'Loading UNet: type=FLUX file="{file_path}" offload={shared.opts.diffusers_offload_mode} quant={quant} dtype={devices.dtype}')
if 'nf4' in quant:
from modules.model_flux_nf4 import load_flux_nf4
_transformer, _text_encoder_2 = load_flux_nf4(file_path)
if _transformer is not None:
transformer = _transformer
elif quant == 'qint8' or quant == 'qint4':
if quant == 'qint8' or quant == 'qint4':
_transformer, _text_encoder_2 = load_flux_quanto(file_path)
if _transformer is not None:
transformer = _transformer
elif quant == 'fp8' or quant == 'fp4':
elif quant == 'fp8' or quant == 'fp4' or quant == 'nf4':
_transformer, _text_encoder_2 = load_flux_bnb(file_path, diffusers_load_config)
if _transformer is not None:
transformer = _transformer
elif 'nf4' in quant: # TODO right now this is not working for civitai published nf4 models
from modules.model_flux_nf4 import load_flux_nf4
_transformer, _text_encoder_2 = load_flux_nf4(file_path)
if _transformer is not None:
transformer = _transformer
else:
from diffusers import FluxTransformer2DModel
transformer = FluxTransformer2DModel.from_single_file(file_path, **diffusers_load_config)
@@ -169,7 +179,10 @@ def load_flux(checkpoint_info, diffusers_load_config): # triggered by opts.sd_ch
from modules import sd_unet
_transformer = load_transformer(sd_unet.unet_dict[shared.opts.sd_unet])
if _transformer is not None:
sd_unet.loaded_unet = shared.opts.sd_unet
transformer = _transformer
else:
sd_unet.failed_unet.append(shared.opts.sd_unet)
except Exception as e:
shared.log.error(f"Loading FLUX: Failed to load UNet: {e}")
if debug:
modules/model_flux_nf4.py
+17 -10
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@@ -198,16 +198,23 @@ def load_flux_nf4(checkpoint_info):
_replace_with_bnb_linear(transformer, "nf4")
for param_name, param in converted_state_dict.items():
if param_name not in expected_state_dict_keys:
continue
is_param_float8_e4m3fn = hasattr(torch, "float8_e4m3fn") and param.dtype == torch.float8_e4m3fn
if torch.is_floating_point(param) and not is_param_float8_e4m3fn:
param = param.to(devices.dtype)
if not check_quantized_param(transformer, param_name):
set_module_tensor_to_device(transformer, param_name, device=0, value=param)
else:
create_quantized_param(transformer, param, param_name, target_device=0, state_dict=original_state_dict, pre_quantized=True)
try:
for param_name, param in converted_state_dict.items():
if param_name not in expected_state_dict_keys:
continue
is_param_float8_e4m3fn = hasattr(torch, "float8_e4m3fn") and param.dtype == torch.float8_e4m3fn
if torch.is_floating_point(param) and not is_param_float8_e4m3fn:
param = param.to(devices.dtype)
if not check_quantized_param(transformer, param_name):
set_module_tensor_to_device(transformer, param_name, device=0, value=param)
else:
create_quantized_param(transformer, param, param_name, target_device=0, state_dict=original_state_dict, pre_quantized=True)
except Exception as e:
transformer, text_encoder_2 = None, None
shared.log.error(f"Loading FLUX: Failed to load UNET: {e}")
if debug:
from modules import errors
errors.display(e, 'FLUX:')
del original_state_dict
devices.torch_gc(force=True)
modules/sd_unet.py
+12 -2
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@@ -3,10 +3,13 @@ from modules import shared, devices, files_cache, sd_models
unet_dict = {}
loaded_unet = None
failed_unet = []
debug = os.environ.get('SD_LOAD_DEBUG', None) is not None
def load_unet(model):
global loaded_unet # pylint: disable=global-statement
if shared.opts.sd_unet == 'None':
return
if shared.opts.sd_unet not in list(unet_dict):
@@ -19,16 +22,21 @@ def load_unet(model):
config = None
config_file = 'default'
try:
if "StableCascade" in model.__class__.__name__:
if shared.opts.sd_unet == loaded_unet or shared.opts.sd_unet in failed_unet:
pass
elif "StableCascade" in model.__class__.__name__:
from modules.model_stablecascade import load_prior
prior_unet, prior_text_encoder = load_prior(unet_dict[shared.opts.sd_unet], config_file=config_file)
loaded_unet = shared.opts.sd_unet
if prior_unet is not None:
model.prior_pipe.prior = None # Prevent OOM
model.prior_pipe.prior = prior_unet.to(devices.device, dtype=devices.dtype_unet)
if prior_text_encoder is not None:
model.prior_pipe.text_encoder = None # Prevent OOM
model.prior_pipe.text_encoder = prior_text_encoder.to(devices.device, dtype=devices.dtype)
if "Flux" in model.__class__.__name__:
elif "Flux" in model.__class__.__name__:
sd_models.load_diffuser() # TODO forcing reloading entire flux as loading transformers only leads to massive memory usage
"""
from modules.model_flux import load_transformer
transformer = load_transformer(unet_dict[shared.opts.sd_unet])
if transformer is not None:
@@ -36,8 +44,10 @@ def load_unet(model):
if shared.opts.diffusers_offload_mode == 'none':
sd_models.move_model(transformer, devices.device)
model.transformer = transformer
loaded_unet = shared.opts.sd_unet
from modules.sd_models import set_diffuser_offload
set_diffuser_offload(model, 'model')
"""
else:
if not hasattr(model, 'unet') or model.unet is None:
shared.log.error('UNet not found in current model')
scripts/xyz_grid.py
+5 -475
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@@ -1,7 +1,4 @@
# pylint: disable=unused-argument
import os
import re
# xyz grid that shows as selectable script
import csv
import random
from collections import namedtuple
@@ -11,477 +8,14 @@ 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
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.negative_prompt = p.negative_prompt.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_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 = []
@@ -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
scripts/xyz_grid_classes.py
+151
View File
@@ -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')),
]
scripts/xyz_grid_draw.py
+105
View File
@@ -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
scripts/xyz_grid_on.py
+6 -474
View File
@@ -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()
scripts/xyz_grid_shared.py
+270
View File
@@ -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()
webui.py
+4 -6
View File
@@ -117,11 +117,6 @@ def initialize():
modelloader.load_upscalers()
timer.startup.record("upscalers")
shared.opts.onchange("sd_vae", wrap_queued_call(lambda: modules.sd_vae.reload_vae_weights()), call=False)
shared.opts.onchange("sd_unet", wrap_queued_call(lambda: modules.sd_unet.load_unet(shared.sd_model)), call=False)
shared.opts.onchange("temp_dir", gr_tempdir.on_tmpdir_changed)
timer.startup.record("onchange")
shared.reload_hypernetworks()
shared.prompt_styles.reload()
@@ -170,13 +165,16 @@ def load_model():
shared.state.end()
thread_model.join()
thread_refiner.join()
timer.startup.record("checkpoint")
shared.opts.onchange("sd_model_checkpoint", wrap_queued_call(lambda: modules.sd_models.reload_model_weights(op='model')), call=False)
shared.opts.onchange("sd_model_refiner", wrap_queued_call(lambda: modules.sd_models.reload_model_weights(op='refiner')), call=False)
shared.opts.onchange("sd_text_encoder", wrap_queued_call(lambda: modules.sd_models.reload_text_encoder()), call=False)
shared.opts.onchange("sd_model_dict", wrap_queued_call(lambda: modules.sd_models.reload_model_weights(op='dict')), call=False)
shared.opts.onchange("sd_vae", wrap_queued_call(lambda: modules.sd_vae.reload_vae_weights()), call=False)
shared.opts.onchange("sd_unet", wrap_queued_call(lambda: modules.sd_unet.load_unet(shared.sd_model)), call=False)
shared.opts.onchange("sd_backend", wrap_queued_call(lambda: modules.sd_models.change_backend()), call=False)
timer.startup.record("checkpoint")
shared.opts.onchange("temp_dir", gr_tempdir.on_tmpdir_changed)
timer.startup.record("onchange")
def create_api(app):