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added: TwoAdvancedSamplersForMask, KSamplerAdvancedProvider

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Dr.Lt.Data
2023-06-28 11:36:07 +09:00
parent 1666026ed8
commit a5ca503dc0
6 changed files with 1091 additions and 1 deletions
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README.md
+4
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@@ -63,6 +63,9 @@ This takes latent as input and outputs latent as the result.
* TiledKSamplerProvider - ComfyUI_TiledKSampler is a wrapper that provides KSAMPLER.
* You need to install the [ComfyUI_TiledKSampler](https://github.com/BlenderNeko/ComfyUI_TiledKSampler) node extension.
* TwoAdvancedSamplersForMask - TwoSamplersForMask is similar to TwoAdvancedSamplersForMask, but they differ in their operation. TwoSamplersForMask performs sampling in the mask area only after all the samples in the base area are finished. On the other hand, TwoAdvancedSamplersForMask performs sampling in both the base area and the mask area sequentially at each step.
* KSamplerAdvancedProvider - This is a wrapper that enables KSampler to be used in TwoAdvancedSamplersForMask.
* TwoSamplersForMaskUpscalerProvider - This is an Upscaler that extends TwoSamplersForMask to be used in Iterative Upscale.
* TwoSamplersForMaskUpscalerProviderPipe - pipe version of TwoSamplersForMaskUpscalerProvider.
@@ -194,6 +197,7 @@ This takes latent as input and outputs latent as the result.
* [CLIPSeg Tutorial](https://github.com/ltdrdata/ComfyUI-extension-tutorials/blob/Main/ComfyUI-Impact-Pack/tutorial/clipseg.md)
* [Extreme Highresolution Upscale](https://github.com/ltdrdata/ComfyUI-extension-tutorials/blob/Main/ComfyUI-Impact-Pack/tutorial/extreme-upscale.md)
* [TwoSamplersForMask](https://github.com/ltdrdata/ComfyUI-extension-tutorials/blob/Main/ComfyUI-Impact-Pack/tutorial/TwoSamplers.md)
* [TwoAdvancedSamplersForMask](https://github.com/ltdrdata/ComfyUI-extension-tutorials/blob/Main/ComfyUI-Impact-Pack/tutorial/TwoAdvancedSamplers.md)
* [Advanced Iterative Upscale: PK_HOOK](https://github.com/ltdrdata/ComfyUI-extension-tutorials/blob/Main/ComfyUI-Impact-Pack/tutorial/pk_hook.md)
* [Advanced Iterative Upscale: TwoSamplersForMask Upscale Provider](https://github.com/ltdrdata/ComfyUI-extension-tutorials/blob/Main/ComfyUI-Impact-Pack/tutorial/TwoSamplersUpscale.md)
__init__.py
+3
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@@ -135,6 +135,9 @@ NODE_CLASS_MAPPINGS = {
"TwoSamplersForMask": TwoSamplersForMask,
"TiledKSamplerProvider": TiledKSamplerProvider,
"KSamplerAdvancedProvider": KSamplerAdvancedProvider,
"TwoAdvancedSamplersForMask": TwoAdvancedSamplersForMask,
"PreviewBridge": PreviewBridge,
"ImageSender": ImageSender,
"ImageReceiver": ImageReceiver,
modules/impact/config.py
+1 -1
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@@ -1,7 +1,7 @@
import configparser
import os
version = "V2.15.3"
version = "V2.16"
dependency_version = 1
modules/impact/core.py
+20
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@@ -674,6 +674,26 @@ class KSamplerWrapper:
return nodes.common_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=denoise)[0]
class KSamplerAdvancedWrapper:
params = None
def __init__(self, model, cfg, sampler_name, scheduler, positive, negative):
self.params = model, cfg, sampler_name, scheduler, positive, negative
def sample_advanced(self, add_noise, seed, steps, latent_image, start_at_step, end_at_step, return_with_leftover_noise, hook=None):
model, cfg, sampler_name, scheduler, positive, negative = self.params
if hook is not None:
model, seed, steps, cfg, sampler_name, scheduler, positive, negative, upscaled_latent = \
hook.pre_ksample_advanced(model, add_noise, seed, steps, cfg, sampler_name, scheduler,
positive, negative, latent_image, start_at_step, end_at_step,
return_with_leftover_noise)
return nodes.KSamplerAdvanced().sample(model, add_noise, seed, steps, cfg, sampler_name, scheduler,
positive, negative, latent_image, start_at_step, end_at_step,
return_with_leftover_noise)[0]
class PixelKSampleHook:
cur_step = 0
total_step = 0
modules/impact/impact_pack.py
+85
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@@ -7,6 +7,7 @@ import warnings
from segment_anything import sam_model_registry
from io import BytesIO
import piexif
import math
import zipfile
import re
from server import PromptServer
@@ -432,6 +433,28 @@ class KSamplerProvider:
return (sampler, )
class KSamplerAdvancedProvider:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0}),
"sampler_name": (comfy.samplers.KSampler.SAMPLERS, ),
"scheduler": (comfy.samplers.KSampler.SCHEDULERS, ),
"basic_pipe": ("BASIC_PIPE", )
},
}
RETURN_TYPES = ("KSAMPLER_ADVANCED",)
FUNCTION = "doit"
CATEGORY = "ImpactPack/Sampler"
def doit(self, cfg, sampler_name, scheduler, basic_pipe):
model, _, _, positive, negative = basic_pipe
sampler = core.KSamplerAdvancedWrapper(model, cfg, sampler_name, scheduler, positive, negative)
return (sampler, )
class TwoSamplersForMask:
@classmethod
def INPUT_TYPES(s):
@@ -462,6 +485,68 @@ class TwoSamplersForMask:
return (new_latent_image, )
class TwoAdvancedSamplersForMask:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
"steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
"denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
"latent_image": ("LATENT", ),
"base_sampler": ("KSAMPLER_ADVANCED", ),
"mask_sampler": ("KSAMPLER_ADVANCED", ),
"mask": ("MASK", ),
"overlap_factor": ("INT", {"default": 10, "min": 0, "max": 10000})
},
}
RETURN_TYPES = ("LATENT", )
FUNCTION = "doit"
CATEGORY = "ImpactPack/Sampler"
@staticmethod
def mask_erosion(latent_image, mask, grow_mask_by):
w = latent_image['samples'].shape[3]
h = latent_image['samples'].shape[2]
mask2 = torch.nn.functional.interpolate(mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])), size=(w, h), mode="bilinear")
if grow_mask_by == 0:
mask_erosion = mask2
else:
kernel_tensor = torch.ones((1, 1, grow_mask_by, grow_mask_by))
padding = math.ceil((grow_mask_by - 1) / 2)
mask_erosion = torch.clamp(torch.nn.functional.conv2d(mask2.round(), kernel_tensor, padding=padding), 0, 1)
return mask_erosion[:, :, :w, :h].round()
def doit(self, seed, steps, denoise, latent_image, base_sampler, mask_sampler, mask, overlap_factor):
inv_mask = torch.where(mask != 1.0, torch.tensor(1.0), torch.tensor(0.0))
adv_steps = int(steps / denoise)
start_at_step = adv_steps - steps
new_latent_image = {'samples': latent_image['samples']}
mask_erosion = TwoAdvancedSamplersForMask.mask_erosion(latent_image, mask, overlap_factor)
for i in range(start_at_step, adv_steps):
add_noise = "enable" if i == start_at_step else "disable"
return_with_leftover_noise = "enable" if i+1 != adv_steps else "disable"
new_latent_image['noise_mask'] = inv_mask
new_latent_image = base_sampler.sample_advanced(add_noise, seed, adv_steps, new_latent_image, i, i + 1, "enable")
new_latent_image['noise_mask'] = mask_erosion
new_latent_image = mask_sampler.sample_advanced("disable", seed, adv_steps, new_latent_image, i, i + 1, return_with_leftover_noise)
del new_latent_image['noise_mask']
return (new_latent_image, )
class FaceDetailer:
@classmethod
def INPUT_TYPES(s):
temp/advanced-sampler.json
+978
View File
@@ -0,0 +1,978 @@
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