nodes/ltdrdata-ComfyUI-Impact-Pack
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feat: LamaRemoverDetailerHookProvider is added

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Dr.Lt.Data
2025-06-17 23:40:50 +09:00
parent f57d309932
commit 6adfb0fec9
7 changed files with 119 additions and 56 deletions
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README.md
+5
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@@ -190,6 +190,9 @@ NOTE: The UltralyticsDetectorProvider node is not part of the ComfyUI-Impact-Pac
* Since this is the hook applied when pasting onto the original image, it has no effect on nodes like `SEGSDetailer`.
* `VariationNoiseDetailerHookProvider` - Apply variation seed to the detailer. It can be applied in multiple stages through combine.
* `CustomSamplerDetailerHookProvider` - Apply a hook that allows you to use a custom sampler in the Detailer nodes. When using `DetailerHookCombine`, the sampler from the first hook is applied.
* `LamaRemoverDetailerHookProvider` – Applies Lama Remover to the upscaled image during the detailing stage. If `skip_sampling` is set to True, Lama Remover can be used alone without the detailing stage, allowing it to simply remove detected regions.
* Not applicable for **AnimateDiff** detailers. When using `DetailerHookCombine`, `skip_sampling` is only applied if it is set to `True` for all hooks.
* To use this node, the node pack at [Layer-norm/comfyui-lama-remover](https://github.com/Layer-norm/comfyui-lama-remover) must be installed.
### Iterative Upscale nodes
* `Iterative Upscale (Latent/on Pixel Space)` - The upscaler takes the input upscaler and splits the scale_factor into steps, then iteratively performs upscaling.
@@ -489,3 +492,5 @@ BlenderNeok/[ComfyUI_Noise](https://github.com/BlenderNeko/ComfyUI_Noise) - The
WASasquatch/[was-node-suite-comfyui](https://github.com/WASasquatch/was-node-suite-comfyui) - A powerful custom node extensions of ComfyUI.
Trung0246/[ComfyUI-0246](https://github.com/Trung0246/ComfyUI-0246) - Nice bypass hack!
Layer-norm/[comfyui-lama-remover](https://github.com/Layer-norm/comfyui-lama-remover) - Required for using `LamaRemoverDetailerHook`.
__init__.py
+1
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@@ -123,6 +123,7 @@ NODE_CLASS_MAPPINGS = {
"CoreMLDetailerHookProvider": CoreMLDetailerHookProvider,
"PreviewDetailerHookProvider": PreviewDetailerHookProvider,
"CustomSamplerDetailerHookProvider": CustomSamplerDetailerHookProvider,
"LamaRemoverDetailerHookProvider": LamaRemoverDetailerHookProvider,
"DetailerHookCombine": DetailerHookCombine,
"NoiseInjectionDetailerHookProvider": NoiseInjectionDetailerHookProvider,
modules/impact/config.py
+1 -1
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@@ -1,7 +1,7 @@
import configparser
import os
version_code = [8, 16, 1]
version_code = [8, 17]
version = f"V{version_code[0]}.{version_code[1]}" + (f'.{version_code[2]}' if len(version_code) > 2 else '')
dependency_version = 24
modules/impact/core.py
+57 -50
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@@ -320,6 +320,9 @@ def enhance_detail(image, model, clip, vae, guide_size, guide_size_for_bbox, max
# upscale
upscaled_image = tensor_resize(image, new_w, new_h)
if detailer_hook is not None:
upscaled_image = detailer_hook.post_upscale(upscaled_image, noise_mask)
cnet_pils = None
if control_net_wrapper is not None:
positive, negative, cnet_pils = control_net_wrapper.apply(positive, negative, upscaled_image, noise_mask)
@@ -327,65 +330,69 @@ def enhance_detail(image, model, clip, vae, guide_size, guide_size_for_bbox, max
cnet_pils.extend(cnet_pils2)
# prepare mask
if noise_mask is not None and inpaint_model:
imc_encode = nodes.InpaintModelConditioning().encode
if 'noise_mask' in inspect.signature(imc_encode).parameters:
positive, negative, latent_image = imc_encode(positive, negative, upscaled_image, vae, mask=noise_mask, noise_mask=True)
if detailer_hook is None or not detailer_hook.get_skip_sampling():
if noise_mask is not None and inpaint_model:
imc_encode = nodes.InpaintModelConditioning().encode
if 'noise_mask' in inspect.signature(imc_encode).parameters:
positive, negative, latent_image = imc_encode(positive, negative, upscaled_image, vae, mask=noise_mask, noise_mask=True)
else:
print(f"[Impact Pack] ComfyUI is an outdated version.")
positive, negative, latent_image = imc_encode(positive, negative, upscaled_image, vae, noise_mask)
else:
print(f"[Impact Pack] ComfyUI is an outdated version.")
positive, negative, latent_image = imc_encode(positive, negative, upscaled_image, vae, noise_mask)
else:
latent_image = to_latent_image(upscaled_image, vae, vae_tiled_encode=vae_tiled_encode)
if noise_mask is not None:
latent_image['noise_mask'] = noise_mask
latent_image = to_latent_image(upscaled_image, vae, vae_tiled_encode=vae_tiled_encode)
if noise_mask is not None:
latent_image['noise_mask'] = noise_mask
if detailer_hook is not None:
latent_image = detailer_hook.post_encode(latent_image)
refined_latent = latent_image
sampler_opt=None
if detailer_hook is not None:
sampler_opt = detailer_hook.get_custom_sampler()
# ksampler
for i in range(0, cycle):
if detailer_hook is not None:
latent_image = detailer_hook.post_encode(latent_image)
refined_latent = latent_image
sampler_opt=None
if detailer_hook is not None:
sampler_opt = detailer_hook.get_custom_sampler()
# ksampler
for i in range(0, cycle):
if detailer_hook is not None:
detailer_hook.set_steps((i, cycle))
if detailer_hook is not None:
detailer_hook.set_steps((i, cycle))
refined_latent = detailer_hook.cycle_latent(refined_latent)
refined_latent = detailer_hook.cycle_latent(refined_latent)
model2, seed2, steps2, cfg2, sampler_name2, scheduler2, positive2, negative2, upscaled_latent2, denoise2 = \
detailer_hook.pre_ksample(model, seed+i, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise)
noise, is_touched = detailer_hook.get_custom_noise(seed+i, torch.zeros(latent_image['samples'].size()), is_touched=False)
if not is_touched:
model2, seed2, steps2, cfg2, sampler_name2, scheduler2, positive2, negative2, upscaled_latent2, denoise2 = \
detailer_hook.pre_ksample(model, seed+i, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise)
noise, is_touched = detailer_hook.get_custom_noise(seed+i, torch.zeros(latent_image['samples'].size()), is_touched=False)
if not is_touched:
noise = None
else:
model2, seed2, steps2, cfg2, sampler_name2, scheduler2, positive2, negative2, upscaled_latent2, denoise2 = \
model, seed + i, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise
noise = None
refined_latent = impact_sampling.ksampler_wrapper(model2, seed2, steps2, cfg2, sampler_name2, scheduler2, positive2, negative2,
refined_latent, denoise2, refiner_ratio, refiner_model, refiner_clip, refiner_positive, refiner_negative,
noise=noise, scheduler_func=scheduler_func, sampler_opt=sampler_opt)
if detailer_hook is not None:
refined_latent = detailer_hook.pre_decode(refined_latent)
# non-latent downscale - latent downscale cause bad quality
start = time.time()
if vae_tiled_decode:
(refined_image,) = nodes.VAEDecodeTiled().decode(vae, refined_latent, 512) # using default settings
print(f"[Impact Pack] vae decoded (tiled) in {time.time() - start:.1f}s")
else:
model2, seed2, steps2, cfg2, sampler_name2, scheduler2, positive2, negative2, upscaled_latent2, denoise2 = \
model, seed + i, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise
noise = None
refined_latent = impact_sampling.ksampler_wrapper(model2, seed2, steps2, cfg2, sampler_name2, scheduler2, positive2, negative2,
refined_latent, denoise2, refiner_ratio, refiner_model, refiner_clip, refiner_positive, refiner_negative,
noise=noise, scheduler_func=scheduler_func, sampler_opt=sampler_opt)
if detailer_hook is not None:
refined_latent = detailer_hook.pre_decode(refined_latent)
# non-latent downscale - latent downscale cause bad quality
start = time.time()
if vae_tiled_decode:
(refined_image,) = nodes.VAEDecodeTiled().decode(vae, refined_latent, 512) # using default settings
print(f"[Impact Pack] vae decoded (tiled) in {time.time() - start:.1f}s")
try:
refined_image = vae.decode(refined_latent['samples'])
except Exception as e:
# usually an out-of-memory exception from the decode, so try a tiled approach
print(f"[Impact Pack] failed after {time.time() - start:.1f}s, doing vae.decode_tiled 64...")
refined_image = vae.decode_tiled(refined_latent["samples"], tile_x=64, tile_y=64, )
print(f"[Impact Pack] vae decoded in {time.time() - start:.1f}s")
else:
try:
refined_image = vae.decode(refined_latent['samples'])
except Exception as e:
# usually an out-of-memory exception from the decode, so try a tiled approach
print(f"[Impact Pack] failed after {time.time() - start:.1f}s, doing vae.decode_tiled 64...")
refined_image = vae.decode_tiled(refined_latent["samples"], tile_x=64, tile_y=64, )
print(f"[Impact Pack] vae decoded in {time.time() - start:.1f}s")
# skipped
refined_image = upscaled_image
if detailer_hook is not None:
refined_image = detailer_hook.post_decode(refined_image)
modules/impact/hook_nodes.py
+23
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@@ -1,6 +1,8 @@
import sys
from . import hooks
from . import defs
from . import utils
import nodes
class SEGSOrderedFilterDetailerHookProvider:
@@ -83,3 +85,24 @@ class PreviewDetailerHookProvider:
def doit(self, quality, unique_id):
hook = hooks.PreviewDetailerHook(unique_id, quality)
return hook, hook
class LamaRemoverDetailerHookProvider:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"mask_threshold":("INT", {"default": 250, "min": 0, "max": 255, "step": 1, "display": "slider"}),
"gaussblur_radius": ("INT", {"default": 8, "min": 0, "max": 20, "step": 1, "display": "slider"}),
"skip_sampling": ("BOOLEAN", {"default": True}),
}
}
RETURN_TYPES = ("DETAILER_HOOK", )
FUNCTION = "doit"
CATEGORY = "ImpactPack/Util"
def doit(self, mask_threshold, gaussblur_radius, skip_sampling):
hook = hooks.LamaRemoverDetailerHook(mask_threshold, gaussblur_radius, skip_sampling)
return (hook, )
modules/impact/hooks.py
+31 -4
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@@ -25,7 +25,7 @@ class PixelKSampleHook:
def post_decode(self, pixels):
return pixels
def post_upscale(self, pixels):
def post_upscale(self, pixels, mask=None):
return pixels
def post_encode(self, samples):
@@ -64,8 +64,8 @@ class PixelKSampleHookCombine(PixelKSampleHook):
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_upscale(self, pixels, mask=None):
return self.hook2.post_upscale(self.hook1.post_upscale(pixels, mask), mask)
def post_encode(self, samples):
return self.hook2.post_encode(self.hook1.post_encode(samples))
@@ -109,12 +109,15 @@ class DetailerHookCombine(PixelKSampleHookCombine):
noise_2nd, is_touched = self.hook2.get_custom_noise(seed, noise, is_touched)
return noise, is_touched
def get_custom_sampler():
def get_custom_sampler(self):
if self.hook1.get_custom_sampler() is not None:
return self.hook1.get_custom_sampler()
else:
return self.hook2.get_custom_sampler()
def get_skip_sampling(self):
return self.hook1.get_skip_sampling() and self.hook2.get_skip_sampling()
class SimpleCfgScheduleHook(PixelKSampleHook):
target_cfg = 0
@@ -182,6 +185,9 @@ class DetailerHook(PixelKSampleHook):
def get_custom_sampler(self):
return None
def get_skip_sampling(self):
return False
class CustomSamplerDetailerHookProvider(DetailerHook):
def __init__(self, sampler):
@@ -504,6 +510,27 @@ class SEGSLabelFilterDetailerHook(DetailerHook):
return segs_nodes.SEGSLabelFilter().doit(segs, "", self.labels)[0]
class LamaRemoverDetailerHook(DetailerHook):
def __init__(self, mask_threshold, gaussblur_radius, skip_sampling):
super().__init__()
self.mask_threshold = mask_threshold
self.gaussblur_radius = gaussblur_radius
self.skip_sampling = skip_sampling
def post_upscale(self, img, mask=None):
if "LamaRemover" in nodes.NODE_CLASS_MAPPINGS:
lama_remover_obj = nodes.NODE_CLASS_MAPPINGS['LamaRemover']()
else:
utils.try_install_custom_node('https://github.com/Layer-norm/comfyui-lama-remover',
"To use 'LAMARemoverDetailerHookProvider', 'comfyui-lama-remover' nodepack is required.")
raise Exception("'LamaRemover' node is not installed.")
return lama_remover_obj.lama_remover(img, masks=mask, mask_threshold=self.mask_threshold, gaussblur_radius=self.gaussblur_radius, invert_mask=False)[0]
def get_skip_sampling(self):
return self.skip_sampling
class PreviewDetailerHook(DetailerHook):
def __init__(self, node_id, quality):
super().__init__()
pyproject.toml
+1 -1
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@@ -1,7 +1,7 @@
[project]
name = "comfyui-impact-pack"
description = "This node pack offers various detector nodes and detailer nodes that allow you to configure a workflow that automatically enhances facial details. And provide iterative upscaler."
version = "8.16.1"
version = "8.17"
license = { file = "LICENSE.txt" }
dependencies = ["segment-anything", "scikit-image", "piexif", "transformers", "opencv-python-headless", "GitPython", "scipy>=1.11.4"]