import torch
from torch import Tensor
import comfy.utils
import comfy.model_patcher
import comfy.model_management
from nodes import ImageScale
from comfy.model_base import BaseModel
from comfy.model_patcher import ModelPatcher
from comfy.controlnet import ControlNet, T2IAdapter
from typing import List, Union, Tuple, Dict
from weakref import WeakSet
opt_C = 4
opt_f = 8
def ceildiv(big, small):
# Correct ceiling division that avoids floating-point errors and importing math.ceil.
return -(big // -small)
from enum import Enum
class BlendMode(Enum): # i.e. LayerType
FOREGROUND = 'Foreground'
BACKGROUND = 'Background'
class Processing: ...
class Device: ...
devices = Device()
devices.device = comfy.model_management.get_torch_device()
def null_decorator(fn):
def wrapper(*args, **kwargs):
return fn(*args, **kwargs)
return wrapper
keep_signature = null_decorator
controlnet = null_decorator
stablesr = null_decorator
grid_bbox = null_decorator
custom_bbox = null_decorator
noise_inverse = null_decorator
class BBox:
''' grid bbox '''
def __init__(self, x:int, y:int, w:int, h:int):
self.x = x
self.y = y
self.w = w
self.h = h
self.box = [x, y, x+w, y+h]
self.slicer = slice(None), slice(None), slice(y, y+h), slice(x, x+w)
def __getitem__(self, idx:int) -> int:
return self.box[idx]
def split_bboxes(w:int, h:int, tile_w:int, tile_h:int, overlap:int=16, init_weight:Union[Tensor, float]=1.0) -> Tuple[List[BBox], Tensor]:
cols = ceildiv((w - overlap) , (tile_w - overlap))
rows = ceildiv((h - overlap) , (tile_h - overlap))
dx = (w - tile_w) / (cols - 1) if cols > 1 else 0
dy = (h - tile_h) / (rows - 1) if rows > 1 else 0
bbox_list: List[BBox] = []
weight = torch.zeros((1, 1, h, w), device=devices.device, dtype=torch.float32)
for row in range(rows):
y = min(int(row * dy), h - tile_h)
for col in range(cols):
x = min(int(col * dx), w - tile_w)
bbox = BBox(x, y, tile_w, tile_h)
bbox_list.append(bbox)
weight[bbox.slicer] += init_weight
return bbox_list, weight
class CustomBBox(BBox):
''' region control bbox '''
pass
class AbstractDiffusion:
def __init__(self):
self.method = self.__class__.__name__
self.pbar = None
self.w: int = 0
self.h: int = 0
self.tile_width: int = None
self.tile_height: int = None
self.tile_overlap: int = None
self.tile_batch_size: int = None
# cache. final result of current sampling step, [B, C=4, H//8, W//8]
# avoiding overhead of creating new tensors and weight summing
self.x_buffer: Tensor = None
# self.w: int = int(self.p.width // opt_f) # latent size
# self.h: int = int(self.p.height // opt_f)
# weights for background & grid bboxes
self._weights: Tensor = None
# self.weights: Tensor = torch.zeros((1, 1, self.h, self.w), device=devices.device, dtype=torch.float32)
self._init_grid_bbox = None
self._init_done = None
# count the step correctly
self.step_count = 0
self.inner_loop_count = 0
self.kdiff_step = -1
# ext. Grid tiling painting (grid bbox)
self.enable_grid_bbox: bool = False
self.tile_w: int = None
self.tile_h: int = None
self.tile_bs: int = None
self.num_tiles: int = None
self.num_batches: int = None
self.batched_bboxes: List[List[BBox]] = []
# ext. Region Prompt Control (custom bbox)
self.enable_custom_bbox: bool = False
self.custom_bboxes: List[CustomBBox] = []
# self.cond_basis: Cond = None
# self.uncond_basis: Uncond = None
# self.draw_background: bool = True # by default we draw major prompts in grid tiles
# self.causal_layers: bool = None
# ext. ControlNet
self.enable_controlnet: bool = False
# self.controlnet_script: ModuleType = None
self.control_tensor_batch_dict = {}
self.control_tensor_batch: List[List[Tensor]] = [[]]
# self.control_params: Dict[str, Tensor] = None # {}
self.control_params: Dict[Tuple, List[List[Tensor]]] = {}
self.control_tensor_cpu: bool = None
self.control_tensor_custom: List[List[Tensor]] = []
self.draw_background: bool = True # by default we draw major prompts in grid tiles
self.control_tensor_cpu = False
self.weights = None
self.imagescale = ImageScale()
def reset(self):
tile_width = self.tile_width
tile_height = self.tile_height
tile_overlap = self.tile_overlap
tile_batch_size = self.tile_batch_size
self.__init__()
self.tile_width = tile_width
self.tile_height = tile_height
self.tile_overlap = tile_overlap
self.tile_batch_size = tile_batch_size
def repeat_tensor(self, x:Tensor, n:int, concat=False, concat_to=0) -> Tensor:
''' repeat the tensor on it's first dim '''
if n == 1: return x
B = x.shape[0]
r_dims = len(x.shape) - 1
if B == 1: # batch_size = 1 (not `tile_batch_size`)
shape = [n] + [-1] * r_dims # [N, -1, ...]
return x.expand(shape) # `expand` is much lighter than `tile`
else:
if concat:
return torch.cat([x for _ in range(n)], dim=0)[:concat_to]
shape = [n] + [1] * r_dims # [N, 1, ...]
return x.repeat(shape)
def update_pbar(self):
if self.pbar.n >= self.pbar.total:
self.pbar.close()
else:
# self.pbar.update()
sampling_step = 20
if self.step_count == sampling_step:
self.inner_loop_count += 1
if self.inner_loop_count < self.total_bboxes:
self.pbar.update()
else:
self.step_count = sampling_step
self.inner_loop_count = 0
def reset_buffer(self, x_in:Tensor):
# Judge if the shape of x_in is the same as the shape of x_buffer
if self.x_buffer is None or self.x_buffer.shape != x_in.shape:
self.x_buffer = torch.zeros_like(x_in, device=x_in.device, dtype=x_in.dtype)
else:
self.x_buffer.zero_()
@grid_bbox
def init_grid_bbox(self, tile_w:int, tile_h:int, overlap:int, tile_bs:int):
# if self._init_grid_bbox is not None: return
# self._init_grid_bbox = True
self.weights = torch.zeros((1, 1, self.h, self.w), device=devices.device, dtype=torch.float32)
self.enable_grid_bbox = True
self.tile_w = min(tile_w, self.w)
self.tile_h = min(tile_h, self.h)
overlap = max(0, min(overlap, min(tile_w, tile_h) - 4))
# split the latent into overlapped tiles, then batching
# weights basically indicate how many times a pixel is painted
bboxes, weights = split_bboxes(self.w, self.h, self.tile_w, self.tile_h, overlap, self.get_tile_weights())
self.weights += weights
self.num_tiles = len(bboxes)
self.num_batches = ceildiv(self.num_tiles , tile_bs)
self.tile_bs = ceildiv(len(bboxes) , self.num_batches) # optimal_batch_size
self.batched_bboxes = [bboxes[i*self.tile_bs:(i+1)*self.tile_bs] for i in range(self.num_batches)]
@grid_bbox
def get_tile_weights(self) -> Union[Tensor, float]:
return 1.0
@noise_inverse
def init_noise_inverse(self, steps:int, retouch:float, get_cache_callback, set_cache_callback, renoise_strength:float, renoise_kernel:int):
self.noise_inverse_enabled = True
self.noise_inverse_steps = steps
self.noise_inverse_retouch = float(retouch)
self.noise_inverse_renoise_strength = float(renoise_strength)
self.noise_inverse_renoise_kernel = int(renoise_kernel)
self.noise_inverse_set_cache = set_cache_callback
self.noise_inverse_get_cache = get_cache_callback
def init_done(self):
'''
Call this after all `init_*`, settings are done, now perform:
- settings sanity check
- pre-computations, cache init
- anything thing needed before denoising starts
'''
# if self._init_done is not None: return
# self._init_done = True
self.total_bboxes = 0
if self.enable_grid_bbox: self.total_bboxes += self.num_batches
if self.enable_custom_bbox: self.total_bboxes += len(self.custom_bboxes)
assert self.total_bboxes > 0, "Nothing to paint! No background to draw and no custom bboxes were provided."
# sampling_steps = _steps
# self.pbar = tqdm(total=(self.total_bboxes) * sampling_steps, desc=f"{self.method} Sampling: ")
@controlnet
def prepare_controlnet_tensors(self, refresh:bool=False, tensor=None):
''' Crop the control tensor into tiles and cache them '''
if not refresh:
if self.control_tensor_batch is not None or self.control_params is not None: return
tensors = [tensor]
self.org_control_tensor_batch = tensors
self.control_tensor_batch = []
for i in range(len(tensors)):
control_tile_list = []
control_tensor = tensors[i]
for bboxes in self.batched_bboxes:
single_batch_tensors = []
for bbox in bboxes:
if len(control_tensor.shape) == 3:
control_tensor.unsqueeze_(0)
control_tile = control_tensor[:, :, bbox[1]*opt_f:bbox[3]*opt_f, bbox[0]*opt_f:bbox[2]*opt_f]
single_batch_tensors.append(control_tile)
control_tile = torch.cat(single_batch_tensors, dim=0)
if self.control_tensor_cpu:
control_tile = control_tile.cpu()
control_tile_list.append(control_tile)
self.control_tensor_batch.append(control_tile_list)
if len(self.custom_bboxes) > 0:
custom_control_tile_list = []
for bbox in self.custom_bboxes:
if len(control_tensor.shape) == 3:
control_tensor.unsqueeze_(0)
control_tile = control_tensor[:, :, bbox[1]*opt_f:bbox[3]*opt_f, bbox[0]*opt_f:bbox[2]*opt_f]
if self.control_tensor_cpu:
control_tile = control_tile.cpu()
custom_control_tile_list.append(control_tile)
self.control_tensor_custom.append(custom_control_tile_list)
@controlnet
def switch_controlnet_tensors(self, batch_id:int, x_batch_size:int, tile_batch_size:int, is_denoise=False):
# if not self.enable_controlnet: return
if self.control_tensor_batch is None: return
# self.control_params = [0]
# for param_id in range(len(self.control_params)):
for param_id in range(len(self.control_tensor_batch)):
# tensor that was concatenated in `prepare_controlnet_tensors`
control_tile = self.control_tensor_batch[param_id][batch_id]
# broadcast to latent batch size
if x_batch_size > 1: # self.is_kdiff:
all_control_tile = []
for i in range(tile_batch_size):
this_control_tile = [control_tile[i].unsqueeze(0)] * x_batch_size
all_control_tile.append(torch.cat(this_control_tile, dim=0))
control_tile = torch.cat(all_control_tile, dim=0) # [:x_tile.shape[0]]
self.control_tensor_batch[param_id][batch_id] = control_tile
# else:
# control_tile = control_tile.repeat([x_batch_size if is_denoise else x_batch_size * 2, 1, 1, 1])
# self.control_params[param_id].hint_cond = control_tile.to(devices.device)
def process_controlnet(self, x_shape, x_dtype, c_in: dict, cond_or_uncond: List, bboxes, batch_size: int, batch_id: int):
control: ControlNet = c_in['control']
param_id = -1 # current controlnet & previous_controlnets
tuple_key = tuple(cond_or_uncond) + tuple(x_shape)
while control is not None:
param_id += 1
PH, PW = self.h*8, self.w*8
if tuple_key not in self.control_params:
self.control_params[tuple_key] = [[None]]
while len(self.control_params[tuple_key]) <= param_id:
self.control_params[tuple_key].append([None])
while len(self.control_params[tuple_key][param_id]) <= batch_id:
self.control_params[tuple_key][param_id].append(None)
# Below is taken from comfy.controlnet.py, but we need to additionally tile the cnets.
# if statement: eager eval. first time when cond_hint is None.
if self.refresh or control.cond_hint is None or not isinstance(self.control_params[tuple_key][param_id][batch_id], Tensor):
dtype = getattr(control, 'manual_cast_dtype', None)
if dtype is None: dtype = getattr(getattr(control, 'control_model', None), 'dtype', None)
if dtype is None: dtype = x_dtype
if isinstance(control, T2IAdapter):
width, height = control.scale_image_to(PW, PH)
control.cond_hint = comfy.utils.common_upscale(control.cond_hint_original, width, height, 'nearest-exact', "center").float().to(control.device)
if control.channels_in == 1 and control.cond_hint.shape[1] > 1:
control.cond_hint = torch.mean(control.cond_hint, 1, keepdim=True)
elif control.__class__.__name__ == 'ControlLLLiteAdvanced':
if control.sub_idxs is not None and control.cond_hint_original.shape[0] >= control.full_latent_length:
control.cond_hint = comfy.utils.common_upscale(control.cond_hint_original[control.sub_idxs], PW, PH, 'nearest-exact', "center").to(dtype=dtype, device=control.device)
else:
if (PH, PW) == (control.cond_hint_original.shape[-2], control.cond_hint_original.shape[-1]):
control.cond_hint = control.cond_hint_original.clone().to(dtype=dtype, device=control.device)
else:
control.cond_hint = comfy.utils.common_upscale(control.cond_hint_original, PW, PH, 'nearest-exact', "center").to(dtype=dtype, device=control.device)
else:
if (PH, PW) == (control.cond_hint_original.shape[-2], control.cond_hint_original.shape[-1]):
control.cond_hint = control.cond_hint_original.clone().to(dtype=dtype, device=control.device)
else:
control.cond_hint = comfy.utils.common_upscale(control.cond_hint_original, PW, PH, 'nearest-exact', 'center').to(dtype=dtype, device=control.device)
# Broadcast then tile
#
# Below can be in the parent's if clause because self.refresh will trigger on resolution change, e.g. cause of ConditioningSetArea
# so that particular case isn't cached atm.
cond_hint_pre_tile = control.cond_hint
if control.cond_hint.shape[0] < batch_size :
cond_hint_pre_tile = self.repeat_tensor(control.cond_hint, ceildiv(batch_size, control.cond_hint.shape[0]))[:batch_size]
cns = [cond_hint_pre_tile[:, :, bbox[1]*opt_f:bbox[3]*opt_f, bbox[0]*opt_f:bbox[2]*opt_f] for bbox in bboxes]
control.cond_hint = torch.cat(cns, dim=0)
self.control_params[tuple_key][param_id][batch_id]=control.cond_hint
else:
control.cond_hint = self.control_params[tuple_key][param_id][batch_id]
control = control.previous_controlnet
import numpy as np
from numpy import pi, exp, sqrt
def gaussian_weights(tile_w:int, tile_h:int) -> Tensor:
'''
Copy from the original implementation of Mixture of Diffusers
https://github.com/albarji/mixture-of-diffusers/blob/master/mixdiff/tiling.py
This generates gaussian weights to smooth the noise of each tile.
This is critical for this method to work.
'''
f = lambda x, midpoint, var=0.01: exp(-(x-midpoint)*(x-midpoint) / (tile_w*tile_w) / (2*var)) / sqrt(2*pi*var)
x_probs = [f(x, (tile_w - 1) / 2) for x in range(tile_w)] # -1 because index goes from 0 to latent_width - 1
y_probs = [f(y, tile_h / 2) for y in range(tile_h)]
w = np.outer(y_probs, x_probs)
return torch.from_numpy(w).to(devices.device, dtype=torch.float32)
class CondDict: ...
class MultiDiffusion(AbstractDiffusion):
@torch.inference_mode()
def __call__(self, model_function: BaseModel.apply_model, args: dict):
x_in: Tensor = args["input"]
t_in: Tensor = args["timestep"]
c_in: dict = args["c"]
cond_or_uncond: List = args["cond_or_uncond"]
c_crossattn: Tensor = c_in['c_crossattn']
N, C, H, W = x_in.shape
# comfyui can feed in a latent that's a different size cause of SetArea, so we'll refresh in that case.
self.refresh = False
if self.weights is None or self.h != H or self.w != W:
self.h, self.w = H, W
self.refresh = True
self.init_grid_bbox(self.tile_width, self.tile_height, self.tile_overlap, self.tile_batch_size)
# init everything done, perform sanity check & pre-computations
self.init_done()
self.h, self.w = H, W
# clear buffer canvas
self.reset_buffer(x_in)
# Background sampling (grid bbox)
if self.draw_background:
for batch_id, bboxes in enumerate(self.batched_bboxes):
if comfy.model_management.processing_interrupted():
# self.pbar.close()
return x_in
# batching & compute tiles
x_tile = torch.cat([x_in[bbox.slicer] for bbox in bboxes], dim=0) # [TB, C, TH, TW]
n_rep = len(bboxes)
ts_tile = self.repeat_tensor(t_in, n_rep)
cond_tile = self.repeat_tensor(c_crossattn, n_rep)
c_tile = c_in.copy()
c_tile['c_crossattn'] = cond_tile
if 'time_context' in c_in:
c_tile['time_context'] = self.repeat_tensor(c_in['time_context'], n_rep)
for key in c_tile:
if key in ['y', 'c_concat']:
icond = c_tile[key]
if icond.shape[2:] == (self.h, self.w):
c_tile[key] = torch.cat([icond[bbox.slicer] for bbox in bboxes])
else:
c_tile[key] = self.repeat_tensor(icond, n_rep)
# controlnet tiling
# self.switch_controlnet_tensors(batch_id, N, len(bboxes))
if 'control' in c_in:
control=c_in['control']
self.process_controlnet(x_tile.shape, x_tile.dtype, c_in, cond_or_uncond, bboxes, N, batch_id)
c_tile['control'] = control.get_control_orig(x_tile, ts_tile, c_tile, len(cond_or_uncond))
# stablesr tiling
# self.switch_stablesr_tensors(batch_id)
x_tile_out = model_function(x_tile, ts_tile, **c_tile)
for i, bbox in enumerate(bboxes):
self.x_buffer[bbox.slicer] += x_tile_out[i*N:(i+1)*N, :, :, :]
del x_tile_out, x_tile, ts_tile, c_tile
# update progress bar
# self.update_pbar()
# Averaging background buffer
x_out = torch.where(self.weights > 1, self.x_buffer / self.weights, self.x_buffer)
return x_out
class MixtureOfDiffusers(AbstractDiffusion):
"""
Mixture-of-Diffusers Implementation
https://github.com/albarji/mixture-of-diffusers
"""
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
# weights for custom bboxes
self.custom_weights: List[Tensor] = []
self.get_weight = gaussian_weights
def init_done(self):
super().init_done()
# The original gaussian weights can be extremely small, so we rescale them for numerical stability
self.rescale_factor = 1 / self.weights
# Meanwhile, we rescale the custom weights in advance to save time of slicing
for bbox_id, bbox in enumerate(self.custom_bboxes):
if bbox.blend_mode == BlendMode.BACKGROUND:
self.custom_weights[bbox_id] *= self.rescale_factor[bbox.slicer]
@grid_bbox
def get_tile_weights(self) -> Tensor:
# weights for grid bboxes
# if not hasattr(self, 'tile_weights'):
# x_in can change sizes cause of ConditioningSetArea, so we have to recalcualte each time
self.tile_weights = self.get_weight(self.tile_w, self.tile_h)
return self.tile_weights
@torch.inference_mode()
def __call__(self, model_function: BaseModel.apply_model, args: dict):
x_in: Tensor = args["input"]
t_in: Tensor = args["timestep"]
c_in: dict = args["c"]
cond_or_uncond: List= args["cond_or_uncond"]
c_crossattn: Tensor = c_in['c_crossattn']
N, C, H, W = x_in.shape
self.refresh = False
# self.refresh = True
if self.weights is None or self.h != H or self.w != W:
self.h, self.w = H, W
self.refresh = True
self.init_grid_bbox(self.tile_width, self.tile_height, self.tile_overlap, self.tile_batch_size)
# init everything done, perform sanity check & pre-computations
self.init_done()
self.h, self.w = H, W
# clear buffer canvas
self.reset_buffer(x_in)
# self.pbar = tqdm(total=(self.total_bboxes) * sampling_steps, desc=f"{self.method} Sampling: ")
# self.pbar = tqdm(total=len(self.batched_bboxes), desc=f"{self.method} Sampling: ")
# Global sampling
if self.draw_background:
for batch_id, bboxes in enumerate(self.batched_bboxes): # batch_id is the `Latent tile batch size`
if comfy.model_management.processing_interrupted():
# self.pbar.close()
return x_in
# batching
x_tile_list = []
t_tile_list = []
icond_map = {}
# tcond_tile_list = []
# icond_tile_list = []
# vcond_tile_list = []
# control_list = []
for bbox in bboxes:
x_tile_list.append(x_in[bbox.slicer])
t_tile_list.append(t_in)
if isinstance(c_in, dict):
# tcond
# tcond_tile = c_crossattn #self.get_tcond(c_in) # cond, [1, 77, 768]
# tcond_tile_list.append(tcond_tile)
# present in sdxl
for key in ['y', 'c_concat']:
if key in c_in:
icond=c_in[key] # self.get_icond(c_in)
if icond.shape[2:] == (self.h, self.w):
icond = icond[bbox.slicer]
if icond_map.get(key, None) is None:
icond_map[key] = []
icond_map[key].append(icond)
# # vcond:
# vcond = self.get_vcond(c_in)
# vcond_tile_list.append(vcond)
else:
print('>> [WARN] not supported, make an issue on github!!')
n_rep = len(bboxes)
x_tile = torch.cat(x_tile_list, dim=0) # differs each
t_tile = self.repeat_tensor(t_in, n_rep) # just repeat
tcond_tile = self.repeat_tensor(c_crossattn, n_rep) # just repeat
c_tile = c_in.copy()
c_tile['c_crossattn'] = tcond_tile
if 'time_context' in c_in:
c_tile['time_context'] = self.repeat_tensor(c_in['time_context'], n_rep) # just repeat
for key in c_tile:
if key in ['y', 'c_concat']:
icond_tile = torch.cat(icond_map[key], dim=0) # differs each
c_tile[key] = icond_tile
# vcond_tile = torch.cat(vcond_tile_list, dim=0) if None not in vcond_tile_list else None # just repeat
# controlnet
# self.switch_controlnet_tensors(batch_id, N, len(bboxes), is_denoise=True)
if 'control' in c_in:
control=c_in['control']
self.process_controlnet(x_tile.shape, x_tile.dtype, c_in, cond_or_uncond, bboxes, N, batch_id)
c_tile['control'] = control.get_control_orig(x_tile, t_tile, c_tile, len(cond_or_uncond))
# stablesr
# self.switch_stablesr_tensors(batch_id)
# denoising: here the x is the noise
x_tile_out = model_function(x_tile, t_tile, **c_tile)
# de-batching
for i, bbox in enumerate(bboxes):
# These weights can be calcluated in advance, but will cost a lot of vram
# when you have many tiles. So we calculate it here.
w = self.tile_weights * self.rescale_factor[bbox.slicer]
self.x_buffer[bbox.slicer] += x_tile_out[i*N:(i+1)*N, :, :, :] * w
del x_tile_out, x_tile, t_tile, c_tile
# self.update_pbar()
# self.pbar.update()
# self.pbar.close()
x_out = self.x_buffer
return x_out
MAX_RESOLUTION=8192
class TiledDiffusion():
@classmethod
def INPUT_TYPES(s):
return {"required": {"model": ("MODEL", ),
"method": (["MultiDiffusion", "Mixture of Diffusers"], {"default": "Mixture of Diffusers"}),
# "tile_width": ("INT", {"default": 96, "min": 16, "max": 256, "step": 16}),
"tile_width": ("INT", {"default": 96*opt_f, "min": 16, "max": MAX_RESOLUTION, "step": 16}),
# "tile_height": ("INT", {"default": 96, "min": 16, "max": 256, "step": 16}),
"tile_height": ("INT", {"default": 96*opt_f, "min": 16, "max": MAX_RESOLUTION, "step": 16}),
"tile_overlap": ("INT", {"default": 8*opt_f, "min": 0, "max": 256*opt_f, "step": 4*opt_f}),
"tile_batch_size": ("INT", {"default": 4, "min": 1, "max": MAX_RESOLUTION, "step": 1}),
}}
RETURN_TYPES = ("MODEL",)
FUNCTION = "apply"
CATEGORY = "_for_testing"
instances = WeakSet()
@classmethod
def IS_CHANGED(s, *args, **kwargs):
for o in s.instances:
o.impl.reset()
return ""
def __init__(self) -> None:
self.__class__.instances.add(self)
def apply(self, model: ModelPatcher, method, tile_width, tile_height, tile_overlap, tile_batch_size):
if method == "Mixture of Diffusers":
self.impl = MixtureOfDiffusers()
else:
self.impl = MultiDiffusion()
# if noise_inversion:
# get_cache_callback = self.noise_inverse_get_cache
# set_cache_callback = None # lambda x0, xt, prompts: self.noise_inverse_set_cache(p, x0, xt, prompts, steps, retouch)
# self.impl.init_noise_inverse(steps, retouch, get_cache_callback, set_cache_callback, renoise_strength, renoise_kernel_size)
self.impl.tile_width = tile_width // opt_f
self.impl.tile_height = tile_height // opt_f
self.impl.tile_overlap = tile_overlap // opt_f
self.impl.tile_batch_size = tile_batch_size
# self.impl.init_grid_bbox(tile_width, tile_height, tile_overlap, tile_batch_size)
# # init everything done, perform sanity check & pre-computations
# self.impl.init_done()
# hijack the behaviours
# self.impl.hook()
model = model.clone()
model.set_model_unet_function_wrapper(self.impl)
model.model_options['tiled_diffusion'] = True
return (model,)
class NoiseInversion():
@classmethod
def INPUT_TYPES(s):
return {"required": {"model": ("MODEL", ),
"positive": ("CONDITIONING", ),
"negative": ("CONDITIONING", ),
"latent_image": ("LATENT", ),
"image": ("IMAGE", ),
"steps": ("INT", {"default": 10, "min": 1, "max": 208, "step": 1}),
"retouch": ("FLOAT", {"default": 1, "min": 1, "max": 100, "step": 0.1}),
"renoise_strength": ("FLOAT", {"default": 1, "min": 1, "max": 2, "step": 0.01}),
"renoise_kernel_size": ("INT", {"default": 2, "min": 2, "max": 512, "step": 1}),
}}
RETURN_TYPES = ("LATENT",)
FUNCTION = "sample"
CATEGORY = "sampling"
def sample(self, model: ModelPatcher, positive, negative,
latent_image, image, steps, retouch, renoise_strength, renoise_kernel_size):
return (latent_image,)
NODE_CLASS_MAPPINGS = {
"TiledDiffusion": TiledDiffusion,
# "NoiseInversion": NoiseInversion,
}
NODE_DISPLAY_NAME_MAPPINGS = {
"TiledDiffusion": "Tiled Diffusion",
# "NoiseInversion": "Noise Inversion",
}