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import copy
import torch
import nodes
from impact import utils
from . import segs_nodes
from thirdparty import noise_nodes
from server import PromptServer
import asyncio
import folder_paths
import os
from comfy_extras import nodes_custom_sampler
import math
class PixelKSampleHook:
cur_step = 0
total_step = 0
def __init__(self):
pass
def set_steps(self, info):
self.cur_step, self.total_step = info
def post_decode(self, pixels):
return pixels
def post_upscale(self, pixels):
return pixels
def post_encode(self, samples):
return samples
def pre_decode(self, samples):
return samples
def pre_ksample(self, model, seed, steps, cfg, sampler_name, scheduler, positive, negative, upscaled_latent,
denoise):
return model, seed, steps, cfg, sampler_name, scheduler, positive, negative, upscaled_latent, denoise
def post_crop_region(self, w, h, item_bbox, crop_region):
return crop_region
def touch_scaled_size(self, w, h):
return w, h
class PixelKSampleHookCombine(PixelKSampleHook):
hook1 = None
hook2 = None
def __init__(self, hook1, hook2):
super().__init__()
self.hook1 = hook1
self.hook2 = hook2
def set_steps(self, info):
self.hook1.set_steps(info)
self.hook2.set_steps(info)
def pre_decode(self, samples):
return self.hook2.pre_decode(self.hook1.pre_decode(samples))
def post_decode(self, pixels):
return self.hook2.post_decode(self.hook1.post_decode(pixels))
def post_upscale(self, pixels):
return self.hook2.post_upscale(self.hook1.post_upscale(pixels))
def post_encode(self, samples):
return self.hook2.post_encode(self.hook1.post_encode(samples))
def post_crop_region(self, w, h, item_bbox, crop_region):
crop_region = self.hook1.post_crop_region(w, h, item_bbox, crop_region)
return self.hook2.post_crop_region(w, h, item_bbox, crop_region)
def touch_scaled_size(self, w, h):
w, h = self.hook1.touch_scaled_size(w, h)
return self.hook2.touch_scaled_size(w, h)
def pre_ksample(self, model, seed, steps, cfg, sampler_name, scheduler, positive, negative, upscaled_latent,
denoise):
model, seed, steps, cfg, sampler_name, scheduler, positive, negative, upscaled_latent, denoise = \
self.hook1.pre_ksample(model, seed, steps, cfg, sampler_name, scheduler, positive, negative,
upscaled_latent, denoise)
return self.hook2.pre_ksample(model, seed, steps, cfg, sampler_name, scheduler, positive, negative,
upscaled_latent, denoise)
class DetailerHookCombine(PixelKSampleHookCombine):
def cycle_latent(self, latent):
latent = self.hook1.cycle_latent(latent)
latent = self.hook2.cycle_latent(latent)
return latent
def post_detection(self, segs):
segs = self.hook1.post_detection(segs)
segs = self.hook2.post_detection(segs)
return segs
def post_paste(self, image):
image = self.hook1.post_paste(image)
image = self.hook2.post_paste(image)
return image
def get_custom_noise(self, seed, noise, is_touched):
noise_1st, is_touched = self.hook1.get_custom_noise(seed, noise, is_touched)
noise_2nd, is_touched = self.hook2.get_custom_noise(seed, noise, is_touched)
return noise, is_touched
class SimpleCfgScheduleHook(PixelKSampleHook):
target_cfg = 0
def __init__(self, target_cfg):
super().__init__()
self.target_cfg = target_cfg
def pre_ksample(self, model, seed, steps, cfg, sampler_name, scheduler, positive, negative, upscaled_latent, denoise):
if self.total_step > 1:
progress = self.cur_step / (self.total_step - 1)
gap = self.target_cfg - cfg
current_cfg = int(cfg + gap * progress)
else:
current_cfg = self.target_cfg
return model, seed, steps, current_cfg, sampler_name, scheduler, positive, negative, upscaled_latent, denoise
class SimpleDenoiseScheduleHook(PixelKSampleHook):
def __init__(self, target_denoise):
super().__init__()
self.target_denoise = target_denoise
def pre_ksample(self, model, seed, steps, cfg, sampler_name, scheduler, positive, negative, upscaled_latent, denoise):
if self.total_step > 1:
progress = self.cur_step / (self.total_step - 1)
gap = self.target_denoise - denoise
current_denoise = denoise + gap * progress
else:
current_denoise = self.target_denoise
return model, seed, steps, cfg, sampler_name, scheduler, positive, negative, upscaled_latent, current_denoise
class SimpleStepsScheduleHook(PixelKSampleHook):
def __init__(self, target_steps):
super().__init__()
self.target_steps = target_steps
def pre_ksample(self, model, seed, steps, cfg, sampler_name, scheduler, positive, negative, upscaled_latent, denoise):
if self.total_step > 1:
progress = self.cur_step / (self.total_step - 1)
gap = self.target_steps - steps
current_steps = int(steps + gap * progress)
else:
current_steps = self.target_steps
return model, seed, current_steps, cfg, sampler_name, scheduler, positive, negative, upscaled_latent, denoise
class DetailerHook(PixelKSampleHook):
def cycle_latent(self, latent):
return latent
def post_detection(self, segs):
return segs
def post_paste(self, image):
return image
def get_custom_noise(self, seed, noise, is_touched):
return noise, is_touched
# class CustomNoiseDetailerHookProvider(DetailerHook):
# def __init__(self, noise):
# super().__init__()
# self.noise = noise
#
# def get_custom_noise(self, seed, noise, is_start):
# return self.noise
class VariationNoiseDetailerHookProvider(DetailerHook):
def __init__(self, variation_seed, variation_strength):
super().__init__()
self.variation_seed = variation_seed
self.variation_strength = variation_strength
def get_custom_noise(self, seed, noise, is_touched):
empty_noise = {'samples': torch.zeros(noise.size())}
if not is_touched:
noise = nodes_custom_sampler.Noise_RandomNoise(seed).generate_noise(empty_noise)
noise_2nd = nodes_custom_sampler.Noise_RandomNoise(self.variation_seed).generate_noise(empty_noise)
mixed_noise = ((1 - self.variation_strength) * noise + self.variation_strength * noise_2nd)
# NOTE: Since the variance of the Gaussian noise in mixed_noise has changed, it must be corrected through scaling.
scale_factor = math.sqrt((1 - self.variation_strength) ** 2 + self.variation_strength ** 2)
corrected_noise = mixed_noise / scale_factor # Scale the noise to maintain variance of 1
return corrected_noise, True
class SimpleDetailerDenoiseSchedulerHook(DetailerHook):
def __init__(self, target_denoise):
super().__init__()
self.target_denoise = target_denoise
def pre_ksample(self, model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent, denoise):
if self.total_step > 1:
progress = self.cur_step / (self.total_step - 1)
gap = self.target_denoise - denoise
current_denoise = denoise + gap * progress
else:
# ignore hook if total cycle <= 1
current_denoise = denoise
return model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent, current_denoise
class CoreMLHook(DetailerHook):
def __init__(self, mode):
super().__init__()
resolution = mode.split('x')
self.w = int(resolution[0])
self.h = int(resolution[1])
self.override_bbox_by_segm = False
def pre_decode(self, samples):
new_samples = copy.deepcopy(samples)
new_samples['samples'] = samples['samples'][0].unsqueeze(0)
return new_samples
def post_encode(self, samples):
new_samples = copy.deepcopy(samples)
new_samples['samples'] = samples['samples'].repeat(2, 1, 1, 1)
return new_samples
def post_crop_region(self, w, h, item_bbox, crop_region):
x1, y1, x2, y2 = crop_region
bx1, by1, bx2, by2 = item_bbox
crop_w = x2-x1
crop_h = y2-y1
crop_ratio = crop_w/crop_h
target_ratio = self.w/self.h
if crop_ratio < target_ratio:
# shrink height
top_gap = by1 - y1
bottom_gap = y2 - by2
gap_ratio = top_gap / bottom_gap
target_height = 1/target_ratio*crop_w
delta_height = crop_h - target_height
new_y1 = int(y1 + delta_height*gap_ratio)
new_y2 = int(new_y1 + target_height)
crop_region = x1, new_y1, x2, new_y2
elif crop_ratio > target_ratio:
# shrink width
left_gap = bx1 - x1
right_gap = x2 - bx2
gap_ratio = left_gap / right_gap
target_width = target_ratio*crop_h
delta_width = crop_w - target_width
new_x1 = int(x1 + delta_width*gap_ratio)
new_x2 = int(new_x1 + target_width)
crop_region = new_x1, y1, new_x2, y2
return crop_region
def touch_scaled_size(self, w, h):
return self.w, self.h
# REQUIREMENTS: BlenderNeko/ComfyUI Noise
class InjectNoiseHook(PixelKSampleHook):
def __init__(self, source, seed, start_strength, end_strength):
super().__init__()
self.source = source
self.seed = seed
self.start_strength = start_strength
self.end_strength = end_strength
def post_encode(self, samples):
cur_step = self.cur_step
size = samples['samples'].shape
seed = cur_step + self.seed + cur_step
if "BNK_NoisyLatentImage" in nodes.NODE_CLASS_MAPPINGS and "BNK_InjectNoise" in nodes.NODE_CLASS_MAPPINGS:
NoisyLatentImage = nodes.NODE_CLASS_MAPPINGS["BNK_NoisyLatentImage"]
InjectNoise = nodes.NODE_CLASS_MAPPINGS["BNK_InjectNoise"]
else:
utils.try_install_custom_node('https://github.com/BlenderNeko/ComfyUI_Noise',
"To use 'NoiseInjectionHookProvider', 'ComfyUI Noise' extension is required.")
raise Exception("'BNK_NoisyLatentImage', 'BNK_InjectNoise' nodes are not installed.")
noise = NoisyLatentImage().create_noisy_latents(self.source, seed, size[3] * 8, size[2] * 8, size[0])[0]
# inj noise
mask = None
if 'noise_mask' in samples:
mask = samples['noise_mask']
strength = self.start_strength + (self.end_strength - self.start_strength) * cur_step / self.total_step
samples = InjectNoise().inject_noise(samples, strength, noise, mask)[0]
print(f"[Impact Pack] InjectNoiseHook: strength = {strength}")
if mask is not None:
samples['noise_mask'] = mask
return samples
class UnsamplerHook(PixelKSampleHook):
def __init__(self, model, steps, start_end_at_step, end_end_at_step, cfg, sampler_name,
scheduler, normalize, positive, negative):
super().__init__()
self.model = model
self.cfg = cfg
self.sampler_name = sampler_name
self.steps = steps
self.start_end_at_step = start_end_at_step
self.end_end_at_step = end_end_at_step
self.scheduler = scheduler
self.normalize = normalize
self.positive = positive
self.negative = negative
def post_encode(self, samples):
cur_step = self.cur_step
Unsampler = noise_nodes.Unsampler
end_at_step = self.start_end_at_step + (self.end_end_at_step - self.start_end_at_step) * cur_step / self.total_step
end_at_step = int(end_at_step)
print(f"[Impact Pack] UnsamplerHook: end_at_step = {end_at_step}")
# inj noise
mask = None
if 'noise_mask' in samples:
mask = samples['noise_mask']
samples = Unsampler().unsampler(self.model, self.cfg, self.sampler_name, self.steps, end_at_step,
self.scheduler, self.normalize, self.positive, self.negative, samples)[0]
if mask is not None:
samples['noise_mask'] = mask
return samples
class InjectNoiseHookForDetailer(DetailerHook):
def __init__(self, source, seed, start_strength, end_strength, from_start=False):
super().__init__()
self.source = source
self.seed = seed
self.start_strength = start_strength
self.end_strength = end_strength
self.from_start = from_start
def inject_noise(self, samples):
cur_step = self.cur_step if self.from_start else self.cur_step - 1
total_step = self.total_step if self.from_start else self.total_step - 1
size = samples['samples'].shape
seed = cur_step + self.seed + cur_step
if "BNK_NoisyLatentImage" in nodes.NODE_CLASS_MAPPINGS and "BNK_InjectNoise" in nodes.NODE_CLASS_MAPPINGS:
NoisyLatentImage = nodes.NODE_CLASS_MAPPINGS["BNK_NoisyLatentImage"]
InjectNoise = nodes.NODE_CLASS_MAPPINGS["BNK_InjectNoise"]
else:
utils.try_install_custom_node('https://github.com/BlenderNeko/ComfyUI_Noise',
"To use 'NoiseInjectionDetailerHookProvider', 'ComfyUI Noise' extension is required.")
raise Exception("'BNK_NoisyLatentImage', 'BNK_InjectNoise' nodes are not installed.")
noise = NoisyLatentImage().create_noisy_latents(self.source, seed, size[3] * 8, size[2] * 8, size[0])[0]
# inj noise
mask = None
if 'noise_mask' in samples:
mask = samples['noise_mask']
strength = self.start_strength + (self.end_strength - self.start_strength) * cur_step / total_step
samples = InjectNoise().inject_noise(samples, strength, noise, mask)[0]
if mask is not None:
samples['noise_mask'] = mask
return samples
def cycle_latent(self, latent):
if self.cur_step == 0 and not self.from_start:
return latent
else:
return self.inject_noise(latent)
class UnsamplerDetailerHook(DetailerHook):
def __init__(self, model, steps, start_end_at_step, end_end_at_step, cfg, sampler_name,
scheduler, normalize, positive, negative, from_start=False):
super().__init__()
self.model = model
self.cfg = cfg
self.sampler_name = sampler_name
self.steps = steps
self.start_end_at_step = start_end_at_step
self.end_end_at_step = end_end_at_step
self.scheduler = scheduler
self.normalize = normalize
self.positive = positive
self.negative = negative
self.from_start = from_start
def unsample(self, samples):
cur_step = self.cur_step if self.from_start else self.cur_step - 1
total_step = self.total_step if self.from_start else self.total_step - 1
Unsampler = noise_nodes.Unsampler
end_at_step = self.start_end_at_step + (self.end_end_at_step - self.start_end_at_step) * cur_step / total_step
end_at_step = int(end_at_step)
# inj noise
mask = None
if 'noise_mask' in samples:
mask = samples['noise_mask']
samples = Unsampler().unsampler(self.model, self.cfg, self.sampler_name, self.steps, end_at_step,
self.scheduler, self.normalize, self.positive, self.negative, samples)[0]
if mask is not None:
samples['noise_mask'] = mask
return samples
def cycle_latent(self, latent):
if self.cur_step == 0 and not self.from_start:
return latent
else:
return self.unsample(latent)
class SEGSOrderedFilterDetailerHook(DetailerHook):
def __init__(self, target, order, take_start, take_count):
super().__init__()
self.target = target
self.order = order
self.take_start = take_start
self.take_count = take_count
def post_detection(self, segs):
return segs_nodes.SEGSOrderedFilter().doit(segs, self.target, self.order, self.take_start, self.take_count)[0]
class SEGSRangeFilterDetailerHook(DetailerHook):
def __init__(self, target, mode, min_value, max_value):
super().__init__()
self.target = target
self.mode = mode
self.min_value = min_value
self.max_value = max_value
def post_detection(self, segs):
return segs_nodes.SEGSRangeFilter().doit(segs, self.target, self.mode, self.min_value, self.max_value)[0]
class SEGSLabelFilterDetailerHook(DetailerHook):
def __init__(self, labels):
super().__init__()
self.labels = labels
def post_detection(self, segs):
return segs_nodes.SEGSLabelFilter().doit(segs, "", self.labels)[0]
class PreviewDetailerHook(DetailerHook):
def __init__(self, node_id, quality):
super().__init__()
self.node_id = node_id
self.quality = quality
async def send(self, image):
if len(image) > 0:
image = image[0].unsqueeze(0)
img = utils.tensor2pil(image)
temp_path = os.path.join(folder_paths.get_temp_directory(), 'pvhook')
if not os.path.exists(temp_path):
os.makedirs(temp_path)
fullpath = os.path.join(temp_path, f"{self.node_id}.webp")
img.save(fullpath, quality=self.quality)
item = {
"filename": f"{self.node_id}.webp",
"subfolder": 'pvhook',
"type": 'temp'
}
PromptServer.instance.send_sync("impact-preview", {'node_id': self.node_id, 'item': item})
def post_paste(self, image):
asyncio.run(self.send(image))
return image
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