602 lines
32 KiB
Python
602 lines
32 KiB
Python
'''
|
|
# ------------------------------------------------------------------------
|
|
#
|
|
# Tiled Diffusion for Automatic1111 WebUI
|
|
#
|
|
# Introducing revolutionary large image drawing methods:
|
|
# MultiDiffusion and Mixture of Diffusers!
|
|
#
|
|
# Techniques is not originally proposed by me, please refer to
|
|
#
|
|
# MultiDiffusion: https://multidiffusion.github.io
|
|
# Mixture of Diffusers: https://github.com/albarji/mixture-of-diffusers
|
|
#
|
|
# The script contains a few optimizations including:
|
|
# - symmetric tiling bboxes
|
|
# - cached tiling weights
|
|
# - batched denoising
|
|
# - advanced prompt control for each tile
|
|
#
|
|
# ------------------------------------------------------------------------
|
|
#
|
|
# This script hooks into the original sampler and decomposes the latent
|
|
# image, sampled separately and run weighted average to merge them back.
|
|
#
|
|
# Advantages:
|
|
# - Allows for super large resolutions (2k~8k) for both txt2img and img2img.
|
|
# - The merged output is completely seamless without any post-processing.
|
|
# - Training free. No need to train a new model, and you can control the
|
|
# text prompt for specific regions.
|
|
#
|
|
# Drawbacks:
|
|
# - Depending on your parameter settings, the process can be very slow,
|
|
# especially when overlap is relatively large.
|
|
# - The gradient calculation is not compatible with this hack. It
|
|
# will break any backward() or torch.autograd.grad() that passes UNet.
|
|
#
|
|
# How it works:
|
|
# 1. The latent image is split into tiles.
|
|
# 2. In MultiDiffusion:
|
|
# 1. The UNet predicts the noise of each tile.
|
|
# 2. The tiles are denoised by the original sampler for one time step.
|
|
# 3. The tiles are added together but divided by how many times each pixel is added.
|
|
# 3. In Mixture of Diffusers:
|
|
# 1. The UNet predicts the noise of each tile
|
|
# 2. All noises are fused with a gaussian weight mask.
|
|
# 3. The denoiser denoises the whole image for one time step using fused noises.
|
|
# 4. Repeat 2-3 until all timesteps are completed.
|
|
#
|
|
# Enjoy!
|
|
#
|
|
# @author: LI YI @ Nanyang Technological University - Singapore
|
|
# @date: 2023-03-03
|
|
# @license: CC BY-NC-SA 4.0
|
|
#
|
|
# Please give me a star if you like this project!
|
|
#
|
|
# ------------------------------------------------------------------------
|
|
'''
|
|
|
|
import os
|
|
import json
|
|
import torch
|
|
import numpy as np
|
|
import gradio as gr
|
|
|
|
from modules import sd_samplers, images, shared, devices, processing, scripts
|
|
from modules.shared import opts
|
|
from modules.processing import opt_f, get_fixed_seed
|
|
from modules.ui import gr_show
|
|
|
|
from tile_methods.abstractdiffusion import TiledDiffusion
|
|
from tile_methods.multidiffusion import MultiDiffusion
|
|
from tile_methods.mixtureofdiffusers import MixtureOfDiffusers
|
|
from tile_utils.utils import *
|
|
from tile_utils.typing import *
|
|
|
|
CFG_PATH = os.path.join(scripts.basedir(), 'region_configs')
|
|
BBOX_MAX_NUM = min(getattr(shared.cmd_opts, 'md_max_regions', 8), 16)
|
|
|
|
|
|
class Script(scripts.Script):
|
|
|
|
def __init__(self):
|
|
self.controlnet_script: ModuleType = None
|
|
self.stablesr_script: ModuleType = None
|
|
self.delegate: TiledDiffusion = None
|
|
self.noise_inverse_cache: NoiseInverseCache = None
|
|
|
|
def title(self):
|
|
return 'Tiled Diffusion'
|
|
|
|
def show(self, is_img2img):
|
|
return scripts.AlwaysVisible
|
|
|
|
def ui(self, is_img2img):
|
|
tab = 't2i' if not is_img2img else 'i2i'
|
|
is_t2i = 'true' if not is_img2img else 'false'
|
|
|
|
def uid(name):
|
|
return f'MD-{tab}-{name}'
|
|
|
|
with gr.Accordion('Tiled Diffusion', open=False):
|
|
with gr.Row(variant='compact') as tab_enable:
|
|
enabled = gr.Checkbox(label='Enable Tiled Diffusion', value=False, elem_id=uid('enabled'))
|
|
overwrite_size = gr.Checkbox(label='Overwrite image size', value=False, visible=not is_img2img, elem_id=uid('overwrite-image-size'))
|
|
keep_input_size = gr.Checkbox(label='Keep input image size', value=True, visible=is_img2img, elem_id=uid('keep-input-size'))
|
|
|
|
with gr.Row(variant='compact', visible=False) as tab_size:
|
|
image_width = gr.Slider(minimum=256, maximum=16384, step=16, label='Image width', value=1024, elem_id=f'MD-overwrite-width-{tab}')
|
|
image_height = gr.Slider(minimum=256, maximum=16384, step=16, label='Image height', value=1024, elem_id=f'MD-overwrite-height-{tab}')
|
|
overwrite_size.change(fn=gr_show, inputs=overwrite_size, outputs=tab_size, show_progress=False)
|
|
|
|
with gr.Row(variant='compact') as tab_param:
|
|
method = gr.Dropdown(label='Method', choices=[e.value for e in Method], value=Method.MULTI_DIFF.value if is_t2i else Method.MIX_DIFF.value, elem_id=uid('method'))
|
|
control_tensor_cpu = gr.Checkbox(label='Move ControlNet tensor to CPU (if applicable)', value=False, elem_id=uid('control-tensor-cpu'))
|
|
reset_status = gr.Button(value='Free GPU', variant='tool')
|
|
reset_status.click(fn=self.reset_and_gc, show_progress=False)
|
|
|
|
with gr.Group() as tab_tile:
|
|
with gr.Row(variant='compact'):
|
|
tile_width = gr.Slider(minimum=16, maximum=256, step=16, label='Latent tile width', value=96, elem_id=uid('latent-tile-width'))
|
|
tile_height = gr.Slider(minimum=16, maximum=256, step=16, label='Latent tile height', value=96, elem_id=uid('latent-tile-height'))
|
|
|
|
with gr.Row(variant='compact'):
|
|
overlap = gr.Slider(minimum=0, maximum=256, step=4, label='Latent tile overlap', value=48 if is_t2i else 8, elem_id=uid('latent-tile-overlap'))
|
|
batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Latent tile batch size', value=4, elem_id=uid('latent-tile-batch-size'))
|
|
|
|
with gr.Row(variant='compact', visible=is_img2img) as tab_upscale:
|
|
upscaler_name = gr.Dropdown(label='Upscaler', choices=[x.name for x in shared.sd_upscalers], value='None', elem_id=uid('upscaler-index'))
|
|
scale_factor = gr.Slider(minimum=1.0, maximum=8.0, step=0.05, label='Scale Factor', value=2.0, elem_id=uid('upscaler-factor'))
|
|
|
|
with gr.Accordion('Noise Inversion', open=True, visible=is_img2img) as tab_noise_inv:
|
|
with gr.Row(variant='compact'):
|
|
noise_inverse = gr.Checkbox(label='Enable Noise Inversion', value=False, elem_id=uid('noise-inverse'))
|
|
noise_inverse_steps = gr.Slider(minimum=1, maximum=200, step=1, label='Inversion steps', value=10, elem_id=uid('noise-inverse-steps'))
|
|
gr.HTML('<p>Please test on small images before actual upscale. Default params require denoise <= 0.6</p>')
|
|
with gr.Row(variant='compact'):
|
|
noise_inverse_retouch = gr.Slider(minimum=1, maximum=100, step=0.1, label='Retouch', value=1, elem_id=uid('noise-inverse-retouch'))
|
|
noise_inverse_renoise_strength = gr.Slider(minimum=0, maximum=2, step=0.01, label='Renoise strength', value=1, elem_id=uid('noise-inverse-renoise-strength'))
|
|
noise_inverse_renoise_kernel = gr.Slider(minimum=2, maximum=512, step=1, label='Renoise kernel size', value=64, elem_id=uid('noise-inverse-renoise-kernel'))
|
|
|
|
# The control includes txt2img and img2img, we use t2i and i2i to distinguish them
|
|
with gr.Group(elem_id=f'MD-bbox-control-{tab}') as tab_bbox:
|
|
with gr.Accordion('Region Prompt Control', open=False):
|
|
with gr.Row(variant='compact'):
|
|
enable_bbox_control = gr.Checkbox(label='Enable Control', value=False, elem_id=uid('enable-bbox-control'))
|
|
draw_background = gr.Checkbox(label='Draw full canvas background', value=False, elem_id=uid('draw-background'))
|
|
causal_layers = gr.Checkbox(label='Causalize layers', value=False, visible=False, elem_id='MD-causal-layers') # NOTE: currently not used
|
|
|
|
with gr.Row(variant='compact'):
|
|
create_button = gr.Button(value="Create txt2img canvas" if not is_img2img else "From img2img", elem_id='MD-create-canvas')
|
|
|
|
bbox_controls: List[Component] = [] # control set for each bbox
|
|
with gr.Row(variant='compact'):
|
|
ref_image = gr.Image(label='Ref image (for conviently locate regions)', image_mode=None, elem_id=f'MD-bbox-ref-{tab}', interactive=True)
|
|
if not is_img2img:
|
|
# gradio has a serious bug: it cannot accept multiple inputs when you use both js and fn.
|
|
# to workaround this, we concat the inputs into a single string and parse it in js
|
|
def create_t2i_ref(string):
|
|
w, h = [int(x) for x in string.split('x')]
|
|
w = max(w, opt_f)
|
|
h = max(h, opt_f)
|
|
return np.zeros(shape=(h, w, 3), dtype=np.uint8) + 255
|
|
create_button.click(
|
|
fn=create_t2i_ref,
|
|
inputs=overwrite_size,
|
|
outputs=ref_image,
|
|
_js='onCreateT2IRefClick',
|
|
show_progress=False)
|
|
else:
|
|
create_button.click(fn=None, outputs=ref_image, _js='onCreateI2IRefClick', show_progress=False)
|
|
|
|
with gr.Row(variant='compact'):
|
|
cfg_name = gr.Textbox(label='Custom Config File', value='config.json', elem_id=uid('cfg-name'))
|
|
cfg_dump = gr.Button(value='💾 Save', variant='tool')
|
|
cfg_load = gr.Button(value='⚙️ Load', variant='tool')
|
|
|
|
with gr.Row(variant='compact'):
|
|
cfg_tip = gr.HTML(value='', visible=False)
|
|
|
|
for i in range(BBOX_MAX_NUM):
|
|
# Only when displaying & png generate info we use index i+1, in other cases we use i
|
|
with gr.Accordion(f'Region {i+1}', open=False, elem_id=f'MD-accordion-{tab}-{i}'):
|
|
with gr.Row(variant='compact'):
|
|
e = gr.Checkbox(label=f'Enable Region {i+1}', value=False, elem_id=f'MD-bbox-{tab}-{i}-enable')
|
|
e.change(fn=None, inputs=e, outputs=e, _js=f'e => onBoxEnableClick({is_t2i}, {i}, e)', show_progress=False)
|
|
|
|
blend_mode = gr.Dropdown(label='Type', choices=[e.value for e in BlendMode], value=BlendMode.BACKGROUND.value, elem_id=f'MD-{tab}-{i}-blend-mode')
|
|
feather_ratio = gr.Slider(label='Feather', value=0.2, minimum=0, maximum=1, step=0.05, visible=False, elem_id=f'MD-{tab}-{i}-feather')
|
|
|
|
blend_mode.change(fn=lambda x: gr_show(x==BlendMode.FOREGROUND.value), inputs=blend_mode, outputs=feather_ratio, show_progress=False)
|
|
|
|
with gr.Row(variant='compact'):
|
|
x = gr.Slider(label='x', value=0.4, minimum=0.0, maximum=1.0, step=0.0001, elem_id=f'MD-{tab}-{i}-x')
|
|
y = gr.Slider(label='y', value=0.4, minimum=0.0, maximum=1.0, step=0.0001, elem_id=f'MD-{tab}-{i}-y')
|
|
|
|
with gr.Row(variant='compact'):
|
|
w = gr.Slider(label='w', value=0.2, minimum=0.0, maximum=1.0, step=0.0001, elem_id=f'MD-{tab}-{i}-w')
|
|
h = gr.Slider(label='h', value=0.2, minimum=0.0, maximum=1.0, step=0.0001, elem_id=f'MD-{tab}-{i}-h')
|
|
|
|
x.change(fn=None, inputs=x, outputs=x, _js=f'v => onBoxChange({is_t2i}, {i}, "x", v)', show_progress=False)
|
|
y.change(fn=None, inputs=y, outputs=y, _js=f'v => onBoxChange({is_t2i}, {i}, "y", v)', show_progress=False)
|
|
w.change(fn=None, inputs=w, outputs=w, _js=f'v => onBoxChange({is_t2i}, {i}, "w", v)', show_progress=False)
|
|
h.change(fn=None, inputs=h, outputs=h, _js=f'v => onBoxChange({is_t2i}, {i}, "h", v)', show_progress=False)
|
|
|
|
prompt = gr.Text(show_label=False, placeholder=f'Prompt, will append to your {tab} prompt', max_lines=2, elem_id=f'MD-{tab}-{i}-prompt')
|
|
neg_prompt = gr.Text(show_label=False, placeholder='Negative Prompt, will also be appended', max_lines=1, elem_id=f'MD-{tab}-{i}-neg-prompt')
|
|
with gr.Row(variant='compact'):
|
|
seed = gr.Number(label='Seed', value=-1, visible=True, elem_id=f'MD-{tab}-{i}-seed')
|
|
random_seed = gr.Button(value='🎲', variant='tool', elem_id=f'MD-{tab}-{i}-random_seed')
|
|
reuse_seed = gr.Button(value='♻️', variant='tool', elem_id=f'MD-{tab}-{i}-reuse_seed')
|
|
random_seed.click(fn=lambda: -1, outputs=seed, show_progress=False)
|
|
reuse_seed.click(fn=None, inputs=seed, outputs=seed, _js=f'e => getSeedInfo({is_t2i}, {i+1}, e)', show_progress=False)
|
|
|
|
control = [e, x, y, w, h, prompt, neg_prompt, blend_mode, feather_ratio, seed]
|
|
assert len(control) == NUM_BBOX_PARAMS
|
|
bbox_controls.extend(control)
|
|
|
|
# NOTE: dynamically hard coded!!
|
|
load_regions_js = '''
|
|
function onBoxChangeAll(ref_image, cfg_name, ...args) {
|
|
const is_t2i = %s;
|
|
const n_bbox = %d;
|
|
const n_ctrl = %d;
|
|
for (let i=0; i<n_bbox; i++) {
|
|
onBoxEnableClick(is_t2i, i, args[i * n_ctrl + 0])
|
|
onBoxChange(is_t2i, i, "x", args[i * n_ctrl + 1]);
|
|
onBoxChange(is_t2i, i, "y", args[i * n_ctrl + 2]);
|
|
onBoxChange(is_t2i, i, "w", args[i * n_ctrl + 3]);
|
|
onBoxChange(is_t2i, i, "h", args[i * n_ctrl + 4]);
|
|
}
|
|
updateBoxes(true);
|
|
updateBoxes(false);
|
|
return args_to_array(arguments);
|
|
}
|
|
''' % (is_t2i, BBOX_MAX_NUM, NUM_BBOX_PARAMS)
|
|
cfg_dump.click(fn=self.dump_regions, inputs=[cfg_name, *bbox_controls], outputs=cfg_tip, show_progress=False)
|
|
cfg_load.click(fn=self.load_regions, _js=load_regions_js, inputs=[ref_image, cfg_name, *bbox_controls], outputs=[*bbox_controls, cfg_tip], show_progress=False)
|
|
|
|
return [
|
|
enabled, method,
|
|
overwrite_size, keep_input_size, image_width, image_height,
|
|
tile_width, tile_height, overlap, batch_size,
|
|
upscaler_name, scale_factor,
|
|
noise_inverse, noise_inverse_steps, noise_inverse_retouch, noise_inverse_renoise_strength, noise_inverse_renoise_kernel,
|
|
control_tensor_cpu,
|
|
enable_bbox_control, draw_background, causal_layers,
|
|
*bbox_controls,
|
|
]
|
|
|
|
def process(self, p: Processing,
|
|
enabled: bool, method: str,
|
|
overwrite_size: bool, keep_input_size: bool, image_width: int, image_height: int,
|
|
tile_width: int, tile_height: int, overlap: int, tile_batch_size: int,
|
|
upscaler_name: str, scale_factor: float,
|
|
noise_inverse: bool, noise_inverse_steps: int, noise_inverse_retouch: float, noise_inverse_renoise_strength: float, noise_inverse_renoise_kernel: int,
|
|
control_tensor_cpu: bool,
|
|
enable_bbox_control: bool, draw_background: bool, causal_layers: bool,
|
|
*bbox_control_states: List[Any],
|
|
):
|
|
|
|
# unhijack & unhook, in case it broke at last time
|
|
self.reset()
|
|
|
|
if not enabled: return
|
|
|
|
''' upscale '''
|
|
# store canvas size settings
|
|
if hasattr(p, "init_images"):
|
|
p.init_images_original_md = [img.copy() for img in p.init_images]
|
|
p.width_original_md = p.width
|
|
p.height_original_md = p.height
|
|
|
|
is_img2img = hasattr(p, "init_images") and len(p.init_images) > 0
|
|
if is_img2img: # img2img, TODO: replace with `images.resize_image()`
|
|
idx = [x.name for x in shared.sd_upscalers].index(upscaler_name)
|
|
upscaler = shared.sd_upscalers[idx]
|
|
init_img = p.init_images[0]
|
|
init_img = images.flatten(init_img, opts.img2img_background_color)
|
|
if upscaler.name != "None":
|
|
print(f"[Tiled Diffusion] upscaling image with {upscaler.name}...")
|
|
image = upscaler.scaler.upscale(init_img, scale_factor, upscaler.data_path)
|
|
p.extra_generation_params["Tiled Diffusion upscaler"] = upscaler.name
|
|
p.extra_generation_params["Tiled Diffusion scale factor"] = scale_factor
|
|
# For webui folder based batch processing, the length of init_images is not 1
|
|
# We need to replace all images with the upsampled one
|
|
for i in range(len(p.init_images)):
|
|
p.init_images[i] = image
|
|
else:
|
|
image = init_img
|
|
|
|
# decide final canvas size
|
|
if keep_input_size:
|
|
p.width = image.width
|
|
p.height = image.height
|
|
elif upscaler.name != "None":
|
|
p.width = int(scale_factor * p.width_original_md)
|
|
p.height = int(scale_factor * p.height_original_md)
|
|
elif overwrite_size: # txt2img
|
|
p.width = image_width
|
|
p.height = image_height
|
|
|
|
''' sanitiy check '''
|
|
chks = [
|
|
splitable(p.width, p.height, tile_width, tile_height, overlap),
|
|
enable_bbox_control,
|
|
is_img2img and noise_inverse,
|
|
]
|
|
if not any(chks):
|
|
print("[Tiled Diffusion] ignore tiling when there's only 1 tile or nothing to do :)")
|
|
return
|
|
|
|
bbox_settings = build_bbox_settings(bbox_control_states) if enable_bbox_control else {}
|
|
|
|
if 'png info':
|
|
info = {}
|
|
p.extra_generation_params["Tiled Diffusion"] = info
|
|
|
|
info['Method'] = method
|
|
info['Tile tile width'] = tile_width
|
|
info['Tile tile height'] = tile_height
|
|
info['Tile Overlap'] = overlap
|
|
info['Tile batch size'] = tile_batch_size
|
|
|
|
if is_img2img:
|
|
if upscaler.name != "None":
|
|
info['Upscaler'] = upscaler.name
|
|
info['Upscale factor'] = scale_factor
|
|
if keep_input_size:
|
|
info['Keep input size'] = keep_input_size
|
|
if noise_inverse:
|
|
info['NoiseInv'] = noise_inverse
|
|
info['NoiseInv Steps'] = noise_inverse_steps
|
|
info['NoiseInv Retouch'] = noise_inverse_retouch
|
|
info['NoiseInv Renoise strength'] = noise_inverse_renoise_strength
|
|
info['NoiseInv Kernel size'] = noise_inverse_renoise_kernel
|
|
|
|
''' ControlNet hackin '''
|
|
try:
|
|
from scripts.cldm import ControlNet
|
|
|
|
for script in p.scripts.scripts + p.scripts.alwayson_scripts:
|
|
if hasattr(script, "latest_network") and script.title().lower() == "controlnet":
|
|
self.controlnet_script = script
|
|
print("[Tiled Diffusion] ControlNet found, support is enabled.")
|
|
break
|
|
except ImportError:
|
|
pass
|
|
|
|
''' StableSR hackin '''
|
|
for script in p.scripts.scripts:
|
|
if hasattr(script, "stablesr_model") and script.title().lower() == "stablesr":
|
|
if script.stablesr_model is not None:
|
|
self.stablesr_script = script
|
|
print("[Tiled Diffusion] StableSR found, support is enabled.")
|
|
break
|
|
|
|
''' hijack inner APIs '''
|
|
if getattr(Script, "create_sampler_original_md", None) is None:
|
|
Script.create_sampler_original_md = sd_samplers.create_sampler
|
|
sd_samplers.create_sampler = lambda name, model: self.create_sampler_hijack(
|
|
name, model, p, Method(method),
|
|
tile_width, tile_height, overlap, tile_batch_size,
|
|
noise_inverse, noise_inverse_steps, noise_inverse_retouch,
|
|
noise_inverse_renoise_strength, noise_inverse_renoise_kernel,
|
|
control_tensor_cpu,
|
|
enable_bbox_control, draw_background, causal_layers,
|
|
bbox_settings,
|
|
)
|
|
|
|
if enable_bbox_control:
|
|
region_info = { f'Region {i+1}': v._asdict() for i, v in bbox_settings.items() }
|
|
info["Region control"] = region_info
|
|
processing.create_random_tensors_original_md = processing.create_random_tensors
|
|
processing.create_random_tensors = lambda *args, **kwargs: self.create_random_tensors_hijack(
|
|
bbox_settings, region_info,
|
|
*args, **kwargs,
|
|
)
|
|
|
|
def postprocess_batch(self, p: Processing, enabled, *args, **kwargs):
|
|
if not enabled: return
|
|
|
|
if self.delegate is not None: self.delegate.reset_controlnet_tensors()
|
|
|
|
def postprocess(self, p: Processing, processed, enabled, *args):
|
|
if not enabled: return
|
|
|
|
# unhijack & unhook
|
|
self.reset()
|
|
|
|
# restore canvas size settings
|
|
if hasattr(p, 'init_images') and hasattr(p, 'init_images_original_md'):
|
|
p.init_images.clear() # NOTE: do NOT change the list object, compatible with shallow copy of XYZ-plot
|
|
p.init_images.extend(p.init_images_original_md)
|
|
del p.init_images_original_md
|
|
p.width = p.width_original_md ; del p.width_original_md
|
|
p.height = p.height_original_md ; del p.height_original_md
|
|
|
|
# clean up noise inverse latent for folder-based processing
|
|
if hasattr(p, 'noise_inverse_latent'):
|
|
del p.noise_inverse_latent
|
|
|
|
''' ↓↓↓ inner API hijack ↓↓↓ '''
|
|
|
|
def create_sampler_hijack(
|
|
self, name: str, model: LatentDiffusion, p: Processing, method: Method,
|
|
tile_width: int, tile_height: int, overlap: int, tile_batch_size: int,
|
|
noise_inverse: bool, noise_inverse_steps: int, noise_inverse_retouch:float,
|
|
noise_inverse_renoise_strength: float, noise_inverse_renoise_kernel: int,
|
|
control_tensor_cpu: bool,
|
|
enable_bbox_control: bool, draw_background: bool, causal_layers: bool,
|
|
bbox_settings: Dict[int, BBoxSettings]
|
|
):
|
|
|
|
if self.delegate is not None:
|
|
# samplers are stateless, we reuse it if possible
|
|
if self.delegate.sampler_name == name:
|
|
# before we reuse the sampler, we refresh the control tensor
|
|
# so that we are compatible with ControlNet batch processing
|
|
if self.controlnet_script:
|
|
self.delegate.prepare_controlnet_tensors(refresh=True)
|
|
return self.delegate.sampler_raw
|
|
else:
|
|
self.reset()
|
|
|
|
flag_noise_inverse = hasattr(p, "init_images") and len(p.init_images) > 0 and noise_inverse
|
|
if flag_noise_inverse:
|
|
print('warn: noise inversion only supports the Euler sampler, switch to it sliently...')
|
|
name = 'Euler'
|
|
p.sampler_name = 'Euler'
|
|
if name is None: print('>> name is empty')
|
|
if model is None: print('>> model is empty')
|
|
sampler = Script.create_sampler_original_md(name, model)
|
|
if method == Method.MULTI_DIFF: delegate_cls = MultiDiffusion
|
|
elif method == Method.MIX_DIFF: delegate_cls = MixtureOfDiffusers
|
|
else: raise NotImplementedError(f"Method {method} not implemented.")
|
|
|
|
# delegate hacks into the `sampler` with context of `p`
|
|
delegate = delegate_cls(p, sampler)
|
|
|
|
# setup **optional** supports through `init_*`, make everything relatively pluggable!!
|
|
if flag_noise_inverse:
|
|
get_cache_callback = self.noise_inverse_get_cache
|
|
set_cache_callback = lambda x0, xt, prompts: self.noise_inverse_set_cache(p, x0, xt, prompts, noise_inverse_steps, noise_inverse_retouch)
|
|
delegate.init_noise_inverse(noise_inverse_steps, noise_inverse_retouch, get_cache_callback, set_cache_callback, noise_inverse_renoise_strength, noise_inverse_renoise_kernel)
|
|
if not enable_bbox_control or draw_background:
|
|
delegate.init_grid_bbox(tile_width, tile_height, overlap, tile_batch_size)
|
|
if enable_bbox_control:
|
|
delegate.init_custom_bbox(bbox_settings, draw_background, causal_layers)
|
|
if self.controlnet_script:
|
|
delegate.init_controlnet(self.controlnet_script, control_tensor_cpu)
|
|
if self.stablesr_script:
|
|
delegate.init_stablesr(self.stablesr_script)
|
|
|
|
# init everything done, perform sanity check & pre-computations
|
|
delegate.init_done()
|
|
# hijack the behaviours
|
|
delegate.hook()
|
|
|
|
self.delegate = delegate
|
|
|
|
info = (
|
|
f"{method.value} hooked into {name!r} sampler, " +
|
|
f"Tile size: {tile_width}x{tile_height}, " +
|
|
f"Tile batches: {len(self.delegate.batched_bboxes)}, " +
|
|
f"Batch size: {tile_batch_size}."
|
|
)
|
|
exts = [
|
|
f"NoiseInv" if flag_noise_inverse else None,
|
|
f"RegionCtrl" if enable_bbox_control else None,
|
|
f"ContrlNet" if self.controlnet_script else None,
|
|
]
|
|
ext_info = ', '.join([e for e in exts if e])
|
|
if ext_info: ext_info = f' (ext: {ext_info})'
|
|
print(info + ext_info)
|
|
|
|
return delegate.sampler_raw
|
|
|
|
def create_random_tensors_hijack(
|
|
self, bbox_settings: Dict, region_info: Dict,
|
|
shape, seeds, subseeds=None, subseed_strength=0.0, seed_resize_from_h=0, seed_resize_from_w=0, p=None,
|
|
):
|
|
org_random_tensors = processing.create_random_tensors_original_md(shape, seeds, subseeds, subseed_strength, seed_resize_from_h, seed_resize_from_w, p)
|
|
height, width = shape[1], shape[2]
|
|
background_noise = torch.zeros_like(org_random_tensors)
|
|
background_noise_count = torch.zeros((1, 1, height, width), device=org_random_tensors.device)
|
|
foreground_noise = torch.zeros_like(org_random_tensors)
|
|
foreground_noise_count = torch.zeros((1, 1, height, width), device=org_random_tensors.device)
|
|
|
|
for i, v in bbox_settings.items():
|
|
seed = get_fixed_seed(v.seed)
|
|
x, y, w, h = v.x, v.y, v.w, v.h
|
|
# convert to pixel
|
|
x = int(x * width)
|
|
y = int(y * height)
|
|
w = math.ceil(w * width)
|
|
h = math.ceil(h * height)
|
|
# clamp
|
|
x = max(0, x)
|
|
y = max(0, y)
|
|
w = min(width - x, w)
|
|
h = min(height - y, h)
|
|
# create random tensor
|
|
torch.manual_seed(seed)
|
|
rand_tensor = torch.randn((1, org_random_tensors.shape[1], h, w), device=devices.cpu)
|
|
if BlendMode(v.blend_mode) == BlendMode.BACKGROUND:
|
|
background_noise [:, :, y:y+h, x:x+w] += rand_tensor.to(background_noise.device)
|
|
background_noise_count[:, :, y:y+h, x:x+w] += 1
|
|
elif BlendMode(v.blend_mode) == BlendMode.FOREGROUND:
|
|
foreground_noise [:, :, y:y+h, x:x+w] += rand_tensor.to(foreground_noise.device)
|
|
foreground_noise_count[:, :, y:y+h, x:x+w] += 1
|
|
else:
|
|
raise NotImplementedError
|
|
region_info['Region ' + str(i+1)]['seed'] = seed
|
|
|
|
# average
|
|
background_noise = torch.where(background_noise_count > 1, background_noise / background_noise_count, background_noise)
|
|
foreground_noise = torch.where(foreground_noise_count > 1, foreground_noise / foreground_noise_count, foreground_noise)
|
|
# paste two layers to original random tensor
|
|
org_random_tensors = torch.where(background_noise_count > 0, background_noise, org_random_tensors)
|
|
org_random_tensors = torch.where(foreground_noise_count > 0, foreground_noise, org_random_tensors)
|
|
return org_random_tensors
|
|
|
|
''' ↓↓↓ helper methods ↓↓↓ '''
|
|
|
|
def dump_regions(self, cfg_name, *bbox_controls):
|
|
if not cfg_name: return gr_value(f'<span style="color:red">Config file name cannot be empty.</span>', visible=True)
|
|
|
|
bbox_settings = build_bbox_settings(bbox_controls)
|
|
data = {'bbox_controls': [v._asdict() for v in bbox_settings.values()]}
|
|
|
|
if not os.path.exists(CFG_PATH): os.makedirs(CFG_PATH)
|
|
fp = os.path.join(CFG_PATH, cfg_name)
|
|
with open(fp, 'w', encoding='utf-8') as fh:
|
|
json.dump(data, fh, indent=2, ensure_ascii=False)
|
|
|
|
return gr_value(f'Config saved to {fp}.', visible=True)
|
|
|
|
def load_regions(self, ref_image, cfg_name, *bbox_controls):
|
|
if ref_image is None:
|
|
return [gr_value(v) for v in bbox_controls] + [gr_value(f'<span style="color:red">Please create or upload a ref image first.</span>', visible=True)]
|
|
fp = os.path.join(CFG_PATH, cfg_name)
|
|
if not os.path.exists(fp):
|
|
return [gr_value(v) for v in bbox_controls] + [gr_value(f'<span style="color:red">Config {fp} not found.</span>', visible=True)]
|
|
|
|
try:
|
|
with open(fp, 'r', encoding='utf-8') as fh:
|
|
data = json.load(fh)
|
|
except Exception as e:
|
|
return [gr_value(v) for v in bbox_controls] + [gr_value(f'<span style="color:red">Failed to load config {fp}: {e}</span>', visible=True)]
|
|
|
|
num_boxes = len(data['bbox_controls'])
|
|
data_list = []
|
|
for i in range(BBOX_MAX_NUM):
|
|
if i < num_boxes:
|
|
for k in BBoxSettings._fields:
|
|
if k in data['bbox_controls'][i]:
|
|
data_list.append(data['bbox_controls'][i][k])
|
|
else:
|
|
data_list.append(None)
|
|
else:
|
|
data_list.extend(DEFAULT_BBOX_SETTINGS)
|
|
|
|
return [gr_value(v) for v in data_list] + [gr_value(f'Config loaded from {fp}.', visible=True)]
|
|
|
|
def noise_inverse_set_cache(self, p: ProcessingImg2Img, x0: Tensor, xt: Tensor, prompts: List[str], steps: int, retouch:float):
|
|
self.noise_inverse_cache = NoiseInverseCache(p.sd_model.sd_model_hash, x0, xt, steps, retouch, prompts)
|
|
|
|
def noise_inverse_get_cache(self):
|
|
return self.noise_inverse_cache
|
|
|
|
def reset(self):
|
|
''' unhijack inner APIs '''
|
|
if hasattr(processing, "create_random_original_md"):
|
|
sd_samplers.create_sampler = Script.create_sampler_original_md
|
|
if hasattr(processing, "create_random_tensors_original_md"):
|
|
processing.create_random_tensors = processing.create_random_tensors_original_md
|
|
del processing.create_random_tensors_original_md
|
|
MultiDiffusion .unhook()
|
|
MixtureOfDiffusers.unhook()
|
|
self.delegate = None
|
|
|
|
def reset_and_gc(self):
|
|
self.reset()
|
|
self.noise_inverse_cache = None
|
|
|
|
import gc; gc.collect()
|
|
devices.torch_gc()
|
|
|
|
try:
|
|
import os
|
|
import psutil
|
|
mem = psutil.Process(os.getpid()).memory_info()
|
|
print(f'[Mem] rss: {mem.rss/2**30:.3f} GB, vms: {mem.vms/2**30:.3f} GB')
|
|
from modules.shared import mem_mon as vram_mon
|
|
from modules.memmon import MemUsageMonitor
|
|
vram_mon: MemUsageMonitor
|
|
free, total = vram_mon.cuda_mem_get_info()
|
|
print(f'[VRAM] free: {free/2**30:.3f} GB, total: {total/2**30:.3f} GB')
|
|
except:
|
|
pass
|