nodes/ltdrdata-ComfyUI-Impact-Pack
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RegionalSampler, CombineRegionalPrompts, RegionalPrompt added

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Dr.Lt.Data
2023-07-04 00:41:18 +09:00
parent 696bfe46ed
commit eef38128d6
6 changed files with 1121 additions and 1 deletions
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README.md
+3
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@@ -88,6 +88,9 @@ This takes latent as input and outputs latent as the result.
* When an image is generated with the "fixed" mode, the prompt used for that particular generation is stored in the metadata.
* Known Issue: The presetText.js script from **pythongosssss's [ComfyUI-Custom-Scripts](https://github.com/pythongosssss/ComfyUI-Custom-Scripts)** is causing a conflict, preventing it from being used together.
* RegionalSampler, CombineRegionalPrompts, RegionalPrompt - experimental feature
- multiple region version of TwoAdvancedSamplersForMask
## Feature
* Interactive SAM Detector (Clipspace) - When you right-click on a node that has 'MASK' and 'IMAGE' outputs, a context menu will open. From this menu, you can either open a dialog to create a SAM Mask using 'Open in SAM Detector', or copy the content (likely mask data) using 'Copy (Clipspace)' and generate a mask using 'Impact SAM Detector' from the clipspace menu, and then paste it using 'Paste (Clipspace)'.
__init__.py
+4
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@@ -163,6 +163,10 @@ NODE_CLASS_MAPPINGS = {
# "SEGPick": SEGPick,
# "SEGEdit": SEGEdit,
"RegionalSampler": RegionalSampler,
"CombineRegionalPrompts": CombineRegionalPrompts,
"RegionalPrompt": RegionalPrompt,
"MaskPainter": impact.legacy_nodes.MaskPainter,
"MMDetLoader": impact.legacy_nodes.MMDetLoader,
"SegsMaskCombine": impact.legacy_nodes.SegsMaskCombine,
modules/impact/config.py
+1 -1
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@@ -1,7 +1,7 @@
import configparser
import os
version = "V2.20.2"
version = "V2.21"
dependency_version = 1
modules/impact/core.py
+34
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@@ -15,11 +15,45 @@ import comfy_extras.nodes_upscale_model as model_upscale
from server import PromptServer
import comfy
import impact.wildcards as wildcards
import math
SEG = namedtuple("SEG", ['cropped_image', 'cropped_mask', 'confidence', 'crop_region', 'bbox', 'label'],
defaults=[None])
def erosion_mask(mask, grow_mask_by):
w = mask.shape[1]
h = mask.shape[0]
mask = mask.clone()
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()
class REGIONAL_PROMPT:
def __init__(self, mask, sampler):
self.mask = mask
self.sampler = sampler
self.mask_erosion = None
self.erosion_factor = None
def get_mask_erosion(self, factor):
if self.mask_erosion is None or self.erosion_factor != factor:
self.mask_erosion = erosion_mask(self.mask, factor)
return self.mask_erosion
class NO_BBOX_DETECTOR:
pass
modules/impact/impact_pack.py
+104
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@@ -608,6 +608,110 @@ class TwoAdvancedSamplersForMask:
return (new_latent_image, )
class RegionalPrompt:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"mask": ("MASK", ),
"advanced_sampler": ("KSAMPLER_ADVANCED", ),
},
}
RETURN_TYPES = ("REGIONAL_PROMPTS", )
FUNCTION = "doit"
CATEGORY = "ImpactPack/experimental"
def doit(self, mask, advanced_sampler):
regional_prompt = core.REGIONAL_PROMPT(mask, advanced_sampler)
return ([regional_prompt], )
class CombineRegionalPrompts:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"regional_prompts1": ("REGIONAL_PROMPTS", ),
"regional_prompts2": ("REGIONAL_PROMPTS", ),
},
}
RETURN_TYPES = ("REGIONAL_PROMPTS", )
FUNCTION = "doit"
CATEGORY = "ImpactPack/experimental"
def doit(self, regional_prompts1, regional_prompts2):
return (regional_prompts1 + regional_prompts2, )
class RegionalSampler:
@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}),
"samples": ("LATENT", ),
"base_sampler": ("KSAMPLER_ADVANCED", ),
"regional_prompts": ("REGIONAL_PROMPTS", ),
"overlap_factor": ("INT", {"default": 10, "min": 0, "max": 10000})
},
}
RETURN_TYPES = ("LATENT", )
FUNCTION = "doit"
CATEGORY = "ImpactPack/experimental"
@staticmethod
def mask_erosion(samples, mask, grow_mask_by):
mask = mask.clone()
w = samples['samples'].shape[3]
h = samples['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, samples, base_sampler, regional_prompts, overlap_factor):
masks = [regional_prompt.mask.numpy() for regional_prompt in regional_prompts]
masks = [np.ceil(mask).astype(np.int32) for mask in masks]
combined_mask = torch.from_numpy(np.bitwise_or.reduce(masks))
inv_mask = torch.where(combined_mask == 0, torch.tensor(1.0), torch.tensor(0.0))
adv_steps = int(steps / denoise)
start_at_step = adv_steps - steps
new_latent_image = samples.copy()
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")
for regional_prompt in regional_prompts:
new_latent_image['noise_mask'] = regional_prompt.get_mask_erosion(overlap_factor)
new_latent_image = regional_prompt.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):
test/regional_prompt.json
+975
View File
@@ -0,0 +1,975 @@
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