improve: Regional Sampler - use impact_sampling instead of KSamplerAdvanced.
- Handle the _sde sampler in a more appropriate manner.
This commit is contained in:
Dr.Lt.Data
+5
-2
@@ -106,6 +106,7 @@ from impact.special_samplers import *
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from impact.hf_nodes import *
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from impact.bridge_nodes import *
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from impact.hook_nodes import *
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from impact.animatediff_nodes import *
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import threading
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@@ -139,6 +140,7 @@ NODE_CLASS_MAPPINGS = {
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"DetailerForEachDebug": DetailerForEachTest,
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"DetailerForEachPipe": DetailerForEachPipe,
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"DetailerForEachDebugPipe": DetailerForEachTestPipe,
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"DetailerForEachPipeForAnimateDiff": DetailerForEachPipeForAnimateDiff,
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"SAMDetectorCombined": SAMDetectorCombined,
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"SAMDetectorSegmented": SAMDetectorSegmented,
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@@ -349,12 +351,13 @@ NODE_DISPLAY_NAME_MAPPINGS = {
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"DetailerForEachPipe": "Detailer (SEGS/pipe)",
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"DetailerForEachDebug": "DetailerDebug (SEGS)",
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"DetailerForEachDebugPipe": "DetailerDebug (SEGS/pipe)",
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"SEGSDetailerForAnimateDiff": "Detailer For AnimateDiff (SEGS/pipe)",
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"SEGSDetailerForAnimateDiff": "SEGSDetailer For AnimateDiff (SEGS/pipe)",
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"DetailerForEachPipeForAnimateDiff": "Detailer For AnimateDiff (SEGS/pipe)",
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"SAMDetectorCombined": "SAMDetector (combined)",
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"SAMDetectorSegmented": "SAMDetector (segmented)",
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"FaceDetailerPipe": "FaceDetailer (pipe)",
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"MaskDetailerPipe": "MaskDetailer (Pipe)",
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"MaskDetailerPipe": "MaskDetailer (pipe)",
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"FromDetailerPipeSDXL": "FromDetailer (SDXL/pipe)",
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"BasicPipeToDetailerPipeSDXL": "BasicPipe -> DetailerPipe (SDXL)",
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@@ -0,0 +1,145 @@
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from nodes import MAX_RESOLUTION
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from impact.utils import *
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import impact.core as core
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from impact.core import SEG
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from impact.segs_nodes import SEGSPaste
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class SEGSDetailerForAnimateDiff:
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@classmethod
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def INPUT_TYPES(cls):
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return {"required": {
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"image_frames": ("IMAGE", ),
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"segs": ("SEGS", ),
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"guide_size": ("FLOAT", {"default": 256, "min": 64, "max": MAX_RESOLUTION, "step": 8}),
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"guide_size_for": ("BOOLEAN", {"default": True, "label_on": "bbox", "label_off": "crop_region"}),
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"max_size": ("FLOAT", {"default": 768, "min": 64, "max": MAX_RESOLUTION, "step": 8}),
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"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
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"steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
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"cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0}),
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"sampler_name": (comfy.samplers.KSampler.SAMPLERS,),
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"scheduler": (comfy.samplers.KSampler.SCHEDULERS,),
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"denoise": ("FLOAT", {"default": 0.5, "min": 0.0001, "max": 1.0, "step": 0.01}),
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"basic_pipe": ("BASIC_PIPE",),
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"refiner_ratio": ("FLOAT", {"default": 0.2, "min": 0.0, "max": 1.0})
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},
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"optional": {
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"refiner_basic_pipe_opt": ("BASIC_PIPE",),
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# TODO: "inpaint_model": ("BOOLEAN", {"default": False, "label_on": "enabled", "label_off": "disabled"}),
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# TODO: "noise_mask_feather": ("INT", {"default": 0, "min": 0, "max": 100, "step": 1}),
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}
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}
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RETURN_TYPES = ("SEGS",)
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RETURN_NAMES = ("segs",)
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OUTPUT_IS_LIST = (False,)
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FUNCTION = "doit"
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CATEGORY = "ImpactPack/Detailer"
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@staticmethod
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def do_detail(image_frames, segs, guide_size, guide_size_for, max_size, seed, steps, cfg, sampler_name, scheduler,
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denoise, basic_pipe, refiner_ratio=None, refiner_basic_pipe_opt=None, inpaint_model=False, noise_mask_feather=0):
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model, clip, vae, positive, negative = basic_pipe
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if refiner_basic_pipe_opt is None:
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refiner_model, refiner_clip, refiner_positive, refiner_negative = None, None, None, None
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else:
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refiner_model, refiner_clip, _, refiner_positive, refiner_negative = refiner_basic_pipe_opt
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segs = core.segs_scale_match(segs, image_frames.shape)
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new_segs = []
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for seg in segs[1]:
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cropped_image_frames = None
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for image in image_frames:
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image = image.unsqueeze(0)
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cropped_image = seg.cropped_image if seg.cropped_image is not None else crop_tensor4(image, seg.crop_region)
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cropped_image = to_tensor(cropped_image)
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if cropped_image_frames is None:
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cropped_image_frames = cropped_image
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else:
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cropped_image_frames = torch.concat((cropped_image_frames, cropped_image), dim=0)
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cropped_image_frames = cropped_image_frames.numpy()
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enhanced_image_tensor = core.enhance_detail_for_animatediff(cropped_image_frames, model, clip, vae, guide_size, guide_size_for, max_size,
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seg.bbox, seed, steps, cfg, sampler_name, scheduler,
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positive, negative, denoise, seg.cropped_mask,
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refiner_ratio=refiner_ratio, refiner_model=refiner_model,
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refiner_clip=refiner_clip, refiner_positive=refiner_positive,
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refiner_negative=refiner_negative,
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inpaint_model=inpaint_model, noise_mask_feather=noise_mask_feather)
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if enhanced_image_tensor is None:
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new_cropped_image = cropped_image_frames
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else:
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new_cropped_image = enhanced_image_tensor.numpy()
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new_seg = SEG(new_cropped_image, seg.cropped_mask, seg.confidence, seg.crop_region, seg.bbox, seg.label, None)
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new_segs.append(new_seg)
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return (segs[0], new_segs)
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def doit(self, image_frames, segs, guide_size, guide_size_for, max_size, seed, steps, cfg, sampler_name, scheduler,
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denoise, basic_pipe, refiner_ratio=None, refiner_basic_pipe_opt=None, inpaint_model=False, noise_mask_feather=0):
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segs = SEGSDetailerForAnimateDiff.do_detail(image_frames, segs, guide_size, guide_size_for, max_size, seed, steps, cfg, sampler_name,
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scheduler, denoise, basic_pipe, refiner_ratio, refiner_basic_pipe_opt,
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inpaint_model=inpaint_model, noise_mask_feather=noise_mask_feather)
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return (segs,)
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class DetailerForEachPipeForAnimateDiff:
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@classmethod
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def INPUT_TYPES(cls):
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return {"required": {
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"image_frames": ("IMAGE", ),
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"segs": ("SEGS", ),
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"guide_size": ("FLOAT", {"default": 384, "min": 64, "max": nodes.MAX_RESOLUTION, "step": 8}),
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"guide_size_for": ("BOOLEAN", {"default": True, "label_on": "bbox", "label_off": "crop_region"}),
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"max_size": ("FLOAT", {"default": 1024, "min": 64, "max": nodes.MAX_RESOLUTION, "step": 8}),
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"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
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"steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
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"cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0}),
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"sampler_name": (comfy.samplers.KSampler.SAMPLERS,),
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"scheduler": (comfy.samplers.KSampler.SCHEDULERS,),
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"denoise": ("FLOAT", {"default": 0.5, "min": 0.0001, "max": 1.0, "step": 0.01}),
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"feather": ("INT", {"default": 5, "min": 0, "max": 100, "step": 1}),
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"basic_pipe": ("BASIC_PIPE", ),
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"refiner_ratio": ("FLOAT", {"default": 0.2, "min": 0.0, "max": 1.0}),
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},
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"optional": {
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"detailer_hook": ("DETAILER_HOOK",),
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"refiner_basic_pipe_opt": ("BASIC_PIPE",),
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# "inpaint_model": ("BOOLEAN", {"default": False, "label_on": "enabled", "label_off": "disabled"}),
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# "noise_mask_feather": ("INT", {"default": 0, "min": 0, "max": 100, "step": 1}),
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}
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}
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RETURN_TYPES = ("IMAGE", "SEGS", "BASIC_PIPE")
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RETURN_NAMES = ("image", "segs", "basic_pipe")
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OUTPUT_IS_LIST = (False, False, False, True)
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FUNCTION = "doit"
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CATEGORY = "ImpactPack/Detailer"
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@staticmethod
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def doit(image_frames, segs, guide_size, guide_size_for, max_size, seed, steps, cfg, sampler_name, scheduler,
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denoise, feather, basic_pipe, refiner_ratio=None, detailer_hook=None, refiner_basic_pipe_opt=None,
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inpaint_model=False, noise_mask_feather=0):
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enhanced_segs = []
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for sub_seg in segs[1]:
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single_seg = segs[0], [sub_seg]
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enhanced_seg = SEGSDetailerForAnimateDiff().do_detail(image_frames, single_seg, guide_size, guide_size_for, max_size, seed, steps, cfg, sampler_name, scheduler,
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denoise, basic_pipe, refiner_ratio, refiner_basic_pipe_opt, inpaint_model, noise_mask_feather)
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image_frames = SEGSPaste.doit(image_frames, enhanced_seg, feather, alpha=255)[0]
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enhanced_segs += enhanced_seg[1]
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new_segs = segs[0], enhanced_segs
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return image_frames, new_segs, basic_pipe
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@@ -2,7 +2,7 @@ import configparser
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import os
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version_code = [4, 68]
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version_code = [4, 69]
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version = f"V{version_code[0]}.{version_code[1]}" + (f'.{version_code[2]}' if len(version_code) > 2 else '')
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dependency_version = 20
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+18
-140
@@ -1,15 +1,9 @@
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import copy
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import os
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import numpy
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import torch
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from segment_anything import SamPredictor
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import torch.nn.functional as F
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from impact.utils import *
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from collections import namedtuple
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import numpy as np
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from skimage.measure import label, regionprops
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from skimage.measure import label
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import nodes
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import comfy_extras.nodes_upscale_model as model_upscale
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@@ -21,7 +15,9 @@ import cv2
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import time
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from comfy import model_management
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from impact import utils
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from scipy.ndimage import distance_transform_edt
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from impact import impact_sampling
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from concurrent.futures import ThreadPoolExecutor
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SEG = namedtuple("SEG",
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['cropped_image', 'cropped_mask', 'confidence', 'crop_region', 'bbox', 'label', 'control_net_wrapper'],
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@@ -69,44 +65,6 @@ def erosion_mask(mask, grow_mask_by):
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return mask_erosion[:, :, :w, :h].round().cpu()
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def ksampler_wrapper(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise,
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refiner_ratio=None, refiner_model=None, refiner_clip=None, refiner_positive=None,
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refiner_negative=None):
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if refiner_ratio is None or refiner_model is None or refiner_clip is None or refiner_positive is None or refiner_negative is None:
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refined_latent = \
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nodes.KSampler().sample(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image,
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denoise)[0]
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else:
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advanced_steps = math.floor(steps / denoise)
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start_at_step = advanced_steps - steps
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end_at_step = start_at_step + math.floor(steps * (1.0 - refiner_ratio))
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print(f"pre: {start_at_step} .. {end_at_step} / {advanced_steps}")
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temp_latent = \
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nodes.KSamplerAdvanced().sample(model, "enable", seed, advanced_steps, cfg, sampler_name, scheduler,
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positive, negative, latent_image, start_at_step, end_at_step,
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"enable")[0]
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if 'noise_mask' in latent_image:
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# noise_latent = \
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# nodes.KSamplerAdvanced().sample(refiner_model, "enable", seed, advanced_steps, cfg, sampler_name,
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# scheduler, refiner_positive, refiner_negative, latent_image, end_at_step,
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# end_at_step, "enable")[0]
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latent_compositor = nodes.NODE_CLASS_MAPPINGS['LatentCompositeMasked']()
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temp_latent = \
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latent_compositor.composite(latent_image, temp_latent, 0, 0, False, latent_image['noise_mask'])[0]
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print(f"post: {end_at_step} .. {advanced_steps + 1} / {advanced_steps}")
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refined_latent = \
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nodes.KSamplerAdvanced().sample(refiner_model, "disable", seed, advanced_steps, cfg, sampler_name, scheduler,
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refiner_positive, refiner_negative, temp_latent, end_at_step,
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advanced_steps + 1,
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"disable")[0]
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return refined_latent
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class REGIONAL_PROMPT:
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def __init__(self, mask, sampler):
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mask = make_2d_mask(mask)
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@@ -288,9 +246,8 @@ def enhance_detail(image, model, clip, vae, guide_size, guide_size_for_bbox, max
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model2, seed2, steps2, cfg2, sampler_name2, scheduler2, positive2, negative2, upscaled_latent2, denoise2 = \
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model, seed + i, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise
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refined_latent = ksampler_wrapper(model2, seed2, steps2, cfg2, sampler_name2, scheduler2, positive2, negative2,
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refined_latent, denoise2,
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refiner_ratio, refiner_model, refiner_clip, refiner_positive, refiner_negative)
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refined_latent = impact_sampling.ksampler_wrapper(model2, seed2, steps2, cfg2, sampler_name2, scheduler2, positive2, negative2,
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refined_latent, denoise2, refiner_ratio, refiner_model, refiner_clip, refiner_positive, refiner_negative)
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if detailer_hook is not None:
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refined_latent = detailer_hook.pre_decode(refined_latent)
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@@ -369,7 +326,7 @@ def enhance_detail_for_animatediff(image_frames, model, clip, vae, guide_size, g
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print(f"Detailer: segment upscale for ({bbox_w, bbox_h}) | crop region {w, h} x {upscale} -> {new_w, new_h}")
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# upscale the mask tensor by a factor of 2 using bilinear interpolation
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if isinstance(noise_mask, numpy.ndarray):
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if isinstance(noise_mask, np.ndarray):
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noise_mask = torch.from_numpy(noise_mask)
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if len(noise_mask.shape) == 2:
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@@ -424,9 +381,8 @@ def enhance_detail_for_animatediff(image_frames, model, clip, vae, guide_size, g
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if detailer_hook is not None:
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latent = detailer_hook.post_encode(latent)
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refined_latent = ksampler_wrapper(model, seed, steps, cfg, sampler_name, scheduler, positive, negative,
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latent, denoise,
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refiner_ratio, refiner_model, refiner_clip, refiner_positive, refiner_negative)
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refined_latent = impact_sampling.ksampler_wrapper(model, seed, steps, cfg, sampler_name, scheduler, positive, negative,
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latent, denoise, refiner_ratio, refiner_model, refiner_clip, refiner_positive, refiner_negative)
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if detailer_hook is not None:
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refined_latent = detailer_hook.pre_decode(refined_latent)
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@@ -474,6 +430,7 @@ def sam_predict(predictor, points, plabs, bbox, threshold):
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selected = False
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max_score = 0
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max_mask = None
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for idx in range(len(scores)):
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if scores[idx] > max_score:
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max_score = scores[idx]
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@@ -485,7 +442,7 @@ def sam_predict(predictor, points, plabs, bbox, threshold):
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else:
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pass
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if not selected:
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if not selected and max_mask is not None:
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total_masks.append(max_mask)
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return total_masks
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@@ -745,7 +702,7 @@ def segs_scale_match(segs, target_shape):
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new_seg = SEG(cropped_image, cropped_mask, seg.confidence, crop_region, bbox, seg.label, seg.control_net_wrapper)
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new_segs.append(new_seg)
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return ((th, tw), new_segs)
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return (th, tw), new_segs
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# Used Python's slicing feature. stacked_masks[2::3] means starting from index 2, selecting every third tensor with a step size of 3.
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@@ -1168,7 +1125,7 @@ def segs_to_masklist(segs):
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masks = []
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for seg in segs[1]:
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if isinstance(seg.cropped_mask, numpy.ndarray):
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if isinstance(seg.cropped_mask, np.ndarray):
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cropped_mask = torch.from_numpy(seg.cropped_mask)
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else:
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cropped_mask = seg.cropped_mask
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@@ -1217,78 +1174,6 @@ def vae_encode(vae, pixels, use_tile, hook, tile_size=512):
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return samples
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class KSamplerWrapper:
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params = None
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def __init__(self, model, seed, steps, cfg, sampler_name, scheduler, positive, negative, denoise):
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self.params = model, seed, steps, cfg, sampler_name, scheduler, positive, negative, denoise
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def sample(self, latent_image, hook=None):
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model, seed, steps, cfg, sampler_name, scheduler, positive, negative, denoise = self.params
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if hook is not None:
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model, seed, steps, cfg, sampler_name, scheduler, positive, negative, upscaled_latent, denoise = \
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hook.pre_ksample(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image,
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denoise)
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return nodes.common_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image,
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denoise=denoise)[0]
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class KSamplerAdvancedWrapper:
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params = None
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def __init__(self, model, cfg, sampler_name, scheduler, positive, negative):
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self.params = model, cfg, sampler_name, scheduler, positive, negative
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def sample_advanced(self, add_noise, seed, steps, latent_image, start_at_step, end_at_step,
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return_with_leftover_noise, hook=None, recover_special_sampler=False):
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model, cfg, sampler_name, scheduler, positive, negative = self.params
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if hook is not None:
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model, seed, steps, cfg, sampler_name, scheduler, positive, negative, upscaled_latent = \
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hook.pre_ksample_advanced(model, add_noise, seed, steps, cfg, sampler_name, scheduler,
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||||
positive, negative, latent_image, start_at_step, end_at_step,
|
||||
return_with_leftover_noise)
|
||||
|
||||
if recover_special_sampler and sampler_name in ['uni_pc', 'uni_pc_bh2', 'dpmpp_sde', 'dpmpp_sde_gpu', 'dpmpp_2m_sde', 'dpmpp_2m_sde_gpu', 'dpmpp_3m_sde', 'dpmpp_3m_sde_gpu']:
|
||||
base_image = latent_image.copy()
|
||||
else:
|
||||
base_image = None
|
||||
|
||||
try:
|
||||
latent_image = 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]
|
||||
except ValueError as e:
|
||||
if str(e) == 'sigma_min and sigma_max must not be 0':
|
||||
print(f"\nWARN: sampling skipped - sigma_min and sigma_max are 0")
|
||||
return latent_image
|
||||
|
||||
if recover_special_sampler and sampler_name in ['uni_pc', 'uni_pc_bh2', 'dpmpp_sde', 'dpmpp_sde_gpu', 'dpmpp_2m_sde', 'dpmpp_2m_sde_gpu', 'dpmpp_3m_sde', 'dpmpp_3m_sde_gpu']:
|
||||
compensate = 0 if sampler_name in ['uni_pc', 'uni_pc_bh2'] else 2
|
||||
sampler_name = 'dpmpp_fast' if sampler_name in ['uni_pc', 'uni_pc_bh2', 'dpmpp_sde', 'dpmpp_sde_gpu'] else 'dpmpp_2m'
|
||||
latent_compositor = nodes.NODE_CLASS_MAPPINGS['LatentCompositeMasked']()
|
||||
|
||||
noise_mask = latent_image['noise_mask']
|
||||
|
||||
if len(noise_mask.shape) == 4:
|
||||
noise_mask = noise_mask.squeeze(0).squeeze(0)
|
||||
|
||||
latent_image = \
|
||||
latent_compositor.composite(base_image, latent_image, 0, 0, False, noise_mask)[0]
|
||||
|
||||
try:
|
||||
latent_image = nodes.KSamplerAdvanced().sample(model, add_noise, seed, steps, cfg, sampler_name, scheduler,
|
||||
positive, negative, latent_image, start_at_step-compensate, end_at_step,
|
||||
return_with_leftover_noise)[0]
|
||||
except ValueError as e:
|
||||
if str(e) == 'sigma_min and sigma_max must not be 0':
|
||||
print(f"\nWARN: sampling skipped - sigma_min and sigma_max are 0")
|
||||
|
||||
return latent_image
|
||||
|
||||
|
||||
def latent_upscale_on_pixel_space_shape(samples, scale_method, w, h, vae, use_tile=False, tile_size=512,
|
||||
save_temp_prefix=None, hook=None):
|
||||
pixels = vae_decode(vae, samples, use_tile, hook, tile_size=tile_size)
|
||||
@@ -1323,7 +1208,7 @@ def latent_upscale_on_pixel_space2(samples, scale_method, scale_factor, vae, use
|
||||
|
||||
def latent_upscale_on_pixel_space(samples, scale_method, scale_factor, vae, use_tile=False, tile_size=512,
|
||||
save_temp_prefix=None, hook=None):
|
||||
return latent_upscale_on_pixel_space2(samples, scale_method, scale_factor, vae, use_tile, tile_size, save_temp_prefix, hook)[0]
|
||||
return latent_upscale_on_pixel_space2(samples, scale_method, scale_factor, vae, use_tile, tile_size, save_temp_prefix, hook)[0]
|
||||
|
||||
|
||||
def latent_upscale_on_pixel_space_with_model_shape(samples, scale_method, upscale_model, new_w, new_h, vae,
|
||||
@@ -1692,10 +1577,8 @@ class TiledKSamplerWrapper:
|
||||
hook.pre_ksample(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image,
|
||||
denoise)
|
||||
|
||||
return \
|
||||
TiledKSampler().sample(model, seed, tile_width, tile_height, tiling_strategy, steps, cfg, sampler_name,
|
||||
scheduler,
|
||||
positive, negative, latent_image, denoise)[0]
|
||||
return TiledKSampler().sample(model, seed, tile_width, tile_height, tiling_strategy, steps, cfg, sampler_name,
|
||||
scheduler, positive, negative, latent_image, denoise)[0]
|
||||
|
||||
|
||||
class PixelTiledKSampleUpscaler:
|
||||
@@ -1722,10 +1605,8 @@ class PixelTiledKSampleUpscaler:
|
||||
scale_method, model, vae, seed, steps, cfg, sampler_name, scheduler, positive, negative, denoise = self.params
|
||||
tile_width, tile_height, tiling_strategy = self.tile_params
|
||||
|
||||
return \
|
||||
TiledKSampler().sample(model, seed, tile_width, tile_height, tiling_strategy, steps, cfg, sampler_name,
|
||||
scheduler,
|
||||
positive, negative, latent, denoise)[0]
|
||||
return TiledKSampler().sample(model, seed, tile_width, tile_height, tiling_strategy, steps, cfg, sampler_name,
|
||||
scheduler, positive, negative, latent, denoise)[0]
|
||||
|
||||
def upscale(self, step_info, samples, upscale_factor, save_temp_prefix=None):
|
||||
scale_method, model, vae, seed, steps, cfg, sampler_name, scheduler, positive, negative, denoise = self.params
|
||||
@@ -1838,9 +1719,6 @@ def update_node_status(node, text, progress=None):
|
||||
}, PromptServer.instance.client_id)
|
||||
|
||||
|
||||
from concurrent.futures import ThreadPoolExecutor
|
||||
|
||||
|
||||
def random_mask_raw(mask, bbox, factor):
|
||||
x1, y1, x2, y2 = bbox
|
||||
w = x2 - x1
|
||||
|
||||
@@ -176,7 +176,7 @@ class DetailerForEach:
|
||||
"optional": {
|
||||
"detailer_hook": ("DETAILER_HOOK",),
|
||||
"inpaint_model": ("BOOLEAN", {"default": False, "label_on": "enabled", "label_off": "disabled"}),
|
||||
"noise_mask_feather": ("INT", {"default": 10, "min": 0, "max": 100, "step": 1}),
|
||||
"noise_mask_feather": ("INT", {"default": 0, "min": 0, "max": 100, "step": 1}),
|
||||
}
|
||||
}
|
||||
|
||||
@@ -331,7 +331,7 @@ class DetailerForEachPipe:
|
||||
"detailer_hook": ("DETAILER_HOOK",),
|
||||
"refiner_basic_pipe_opt": ("BASIC_PIPE",),
|
||||
"inpaint_model": ("BOOLEAN", {"default": False, "label_on": "enabled", "label_off": "disabled"}),
|
||||
"noise_mask_feather": ("INT", {"default": 10, "min": 0, "max": 100, "step": 1}),
|
||||
"noise_mask_feather": ("INT", {"default": 0, "min": 0, "max": 100, "step": 1}),
|
||||
}
|
||||
}
|
||||
|
||||
@@ -418,7 +418,7 @@ class FaceDetailer:
|
||||
"segm_detector_opt": ("SEGM_DETECTOR", ),
|
||||
"detailer_hook": ("DETAILER_HOOK",),
|
||||
"inpaint_model": ("BOOLEAN", {"default": False, "label_on": "enabled", "label_off": "disabled"}),
|
||||
"noise_mask_feather": ("INT", {"default": 10, "min": 0, "max": 100, "step": 1}),
|
||||
"noise_mask_feather": ("INT", {"default": 0, "min": 0, "max": 100, "step": 1}),
|
||||
}}
|
||||
|
||||
RETURN_TYPES = ("IMAGE", "IMAGE", "IMAGE", "MASK", "DETAILER_PIPE", "IMAGE")
|
||||
@@ -1189,7 +1189,7 @@ class FaceDetailerPipe:
|
||||
},
|
||||
"optional": {
|
||||
"inpaint_model": ("BOOLEAN", {"default": False, "label_on": "enabled", "label_off": "disabled"}),
|
||||
"noise_mask_feather": ("INT", {"default": 10, "min": 0, "max": 100, "step": 1}),
|
||||
"noise_mask_feather": ("INT", {"default": 0, "min": 0, "max": 100, "step": 1}),
|
||||
}
|
||||
}
|
||||
|
||||
@@ -1279,7 +1279,7 @@ class MaskDetailerPipe:
|
||||
"refiner_basic_pipe_opt": ("BASIC_PIPE", ),
|
||||
"detailer_hook": ("DETAILER_HOOK",),
|
||||
"inpaint_model": ("BOOLEAN", {"default": False, "label_on": "enabled", "label_off": "disabled"}),
|
||||
"noise_mask_feather": ("INT", {"default": 10, "min": 0, "max": 100, "step": 1}),
|
||||
"noise_mask_feather": ("INT", {"default": 0, "min": 0, "max": 100, "step": 1}),
|
||||
}
|
||||
}
|
||||
|
||||
@@ -1288,7 +1288,7 @@ class MaskDetailerPipe:
|
||||
OUTPUT_IS_LIST = (False, True, True, False, False)
|
||||
FUNCTION = "doit"
|
||||
|
||||
CATEGORY = "ImpactPack/__for_test"
|
||||
CATEGORY = "ImpactPack/Detailer"
|
||||
|
||||
def doit(self, image, mask, basic_pipe, guide_size, guide_size_for, max_size, mask_mode,
|
||||
seed, steps, cfg, sampler_name, scheduler, denoise,
|
||||
|
||||
@@ -0,0 +1,222 @@
|
||||
import nodes
|
||||
from comfy.k_diffusion import sampling as k_diffusion_sampling
|
||||
from comfy import samplers
|
||||
from comfy_extras import nodes_custom_sampler
|
||||
|
||||
import torch
|
||||
import math
|
||||
|
||||
|
||||
def calculate_sigmas(model, sampler, scheduler, steps):
|
||||
discard_penultimate_sigma = False
|
||||
if sampler in ['dpm_2', 'dpm_2_ancestral', 'uni_pc', 'uni_pc_bh2']:
|
||||
steps += 1
|
||||
discard_penultimate_sigma = True
|
||||
|
||||
sigmas = samplers.calculate_sigmas_scheduler(model.model, scheduler, steps)
|
||||
|
||||
if discard_penultimate_sigma:
|
||||
sigmas = torch.cat([sigmas[:-2], sigmas[-1:]])
|
||||
return sigmas
|
||||
|
||||
|
||||
def get_noise_sampler(x, cpu, total_sigmas, **kwargs):
|
||||
if 'extra_args' in kwargs and 'seed' in kwargs['extra_args']:
|
||||
sigma_min, sigma_max = total_sigmas[total_sigmas > 0].min(), total_sigmas.max()
|
||||
seed = kwargs['extra_args'].get("seed", None)
|
||||
return k_diffusion_sampling.BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=seed, cpu=cpu)
|
||||
return None
|
||||
|
||||
|
||||
def ksampler(sampler_name, total_sigmas, extra_options={}, inpaint_options={}):
|
||||
if sampler_name == "dpmpp_sde":
|
||||
def sample_dpmpp_sde(model, x, sigmas, **kwargs):
|
||||
noise_sampler = get_noise_sampler(x, True, total_sigmas, **kwargs)
|
||||
if noise_sampler is not None:
|
||||
kwargs['noise_sampler'] = noise_sampler
|
||||
|
||||
return k_diffusion_sampling.sample_dpmpp_sde(model, x, sigmas, **kwargs)
|
||||
|
||||
sampler_function = sample_dpmpp_sde
|
||||
|
||||
elif sampler_name == "dpmpp_sde_gpu":
|
||||
def sample_dpmpp_sde(model, x, sigmas, **kwargs):
|
||||
noise_sampler = get_noise_sampler(x, False, total_sigmas, **kwargs)
|
||||
if noise_sampler is not None:
|
||||
kwargs['noise_sampler'] = noise_sampler
|
||||
|
||||
return k_diffusion_sampling.sample_dpmpp_sde_gpu(model, x, sigmas, **kwargs)
|
||||
|
||||
sampler_function = sample_dpmpp_sde
|
||||
|
||||
elif sampler_name == "dpmpp_2m_sde":
|
||||
def sample_dpmpp_sde(model, x, sigmas, **kwargs):
|
||||
noise_sampler = get_noise_sampler(x, True, total_sigmas, **kwargs)
|
||||
if noise_sampler is not None:
|
||||
kwargs['noise_sampler'] = noise_sampler
|
||||
|
||||
return k_diffusion_sampling.sample_dpmpp_2m_sde(model, x, sigmas, **kwargs)
|
||||
|
||||
sampler_function = sample_dpmpp_sde
|
||||
|
||||
elif sampler_name == "dpmpp_2m_sde_gpu":
|
||||
def sample_dpmpp_sde(model, x, sigmas, **kwargs):
|
||||
noise_sampler = get_noise_sampler(x, False, total_sigmas, **kwargs)
|
||||
if noise_sampler is not None:
|
||||
kwargs['noise_sampler'] = noise_sampler
|
||||
|
||||
return k_diffusion_sampling.sample_dpmpp_2m_sde_gpu(model, x, sigmas, **kwargs)
|
||||
|
||||
sampler_function = sample_dpmpp_sde
|
||||
|
||||
elif sampler_name == "dpmpp_3m_sde":
|
||||
def sample_dpmpp_sde(model, x, sigmas, **kwargs):
|
||||
noise_sampler = get_noise_sampler(x, True, total_sigmas, **kwargs)
|
||||
if noise_sampler is not None:
|
||||
kwargs['noise_sampler'] = noise_sampler
|
||||
|
||||
return k_diffusion_sampling.sample_dpmpp_2m_sde(model, x, sigmas, **kwargs)
|
||||
|
||||
sampler_function = sample_dpmpp_sde
|
||||
|
||||
elif sampler_name == "dpmpp_3m_sde_gpu":
|
||||
def sample_dpmpp_sde(model, x, sigmas, **kwargs):
|
||||
noise_sampler = get_noise_sampler(x, False, total_sigmas, **kwargs)
|
||||
if noise_sampler is not None:
|
||||
kwargs['noise_sampler'] = noise_sampler
|
||||
|
||||
return k_diffusion_sampling.sample_dpmpp_2m_sde_gpu(model, x, sigmas, **kwargs)
|
||||
|
||||
sampler_function = sample_dpmpp_sde
|
||||
else:
|
||||
return samplers.ksampler(sampler_name, extra_options, inpaint_options)
|
||||
|
||||
return samplers.KSAMPLER(sampler_function, extra_options, inpaint_options)
|
||||
|
||||
|
||||
def separated_sample(model, add_noise, seed, steps, cfg, sampler_name, scheduler, positive, negative,
|
||||
latent_image, start_at_step, end_at_step, return_with_leftover_noise, sigma_ratio=1.0):
|
||||
total_sigmas = calculate_sigmas(model, sampler_name, scheduler, steps)
|
||||
|
||||
sigmas = total_sigmas[start_at_step:end_at_step+1] * sigma_ratio
|
||||
impact_sampler = ksampler(sampler_name, total_sigmas)
|
||||
|
||||
if len(sigmas) == 0 or (len(sigmas) == 1 and sigmas[0] == 0):
|
||||
return latent_image
|
||||
|
||||
res = nodes_custom_sampler.SamplerCustom().sample(model, add_noise, seed, cfg, positive, negative, impact_sampler, sigmas, latent_image)
|
||||
|
||||
if return_with_leftover_noise:
|
||||
return res[0]
|
||||
else:
|
||||
return res[1]
|
||||
|
||||
|
||||
def ksampler_wrapper(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise,
|
||||
refiner_ratio=None, refiner_model=None, refiner_clip=None, refiner_positive=None, refiner_negative=None):
|
||||
|
||||
if refiner_ratio is None or refiner_model is None or refiner_clip is None or refiner_positive is None or refiner_negative is None:
|
||||
refined_latent = nodes.KSampler().sample(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise)[0]
|
||||
else:
|
||||
advanced_steps = math.floor(steps / denoise)
|
||||
start_at_step = advanced_steps - steps
|
||||
end_at_step = start_at_step + math.floor(steps * (1.0 - refiner_ratio))
|
||||
|
||||
print(f"pre: {start_at_step} .. {end_at_step} / {advanced_steps}")
|
||||
temp_latent = separated_sample(model, True, seed, advanced_steps, cfg, sampler_name, scheduler,
|
||||
positive, negative, latent_image, start_at_step, end_at_step, True)
|
||||
|
||||
if 'noise_mask' in latent_image:
|
||||
# noise_latent = \
|
||||
# impact_sampling.separated_sample(refiner_model, "enable", seed, advanced_steps, cfg, sampler_name,
|
||||
# scheduler, refiner_positive, refiner_negative, latent_image, end_at_step,
|
||||
# end_at_step, "enable")
|
||||
|
||||
latent_compositor = nodes.NODE_CLASS_MAPPINGS['LatentCompositeMasked']()
|
||||
temp_latent = latent_compositor.composite(latent_image, temp_latent, 0, 0, False, latent_image['noise_mask'])[0]
|
||||
|
||||
print(f"post: {end_at_step} .. {advanced_steps + 1} / {advanced_steps}")
|
||||
refined_latent = separated_sample(refiner_model, False, seed, advanced_steps, cfg, sampler_name, scheduler,
|
||||
refiner_positive, refiner_negative, temp_latent, end_at_step, advanced_steps + 1, False)
|
||||
|
||||
return refined_latent
|
||||
|
||||
|
||||
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,
|
||||
recovery_mode="ratio additional", recovery_sampler="AUTO", recovery_sigma_ratio=1.0):
|
||||
|
||||
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)
|
||||
|
||||
if recovery_mode != 'DISABLE' and sampler_name in ['uni_pc', 'uni_pc_bh2', 'dpmpp_sde', 'dpmpp_sde_gpu', 'dpmpp_2m_sde', 'dpmpp_2m_sde_gpu', 'dpmpp_3m_sde', 'dpmpp_3m_sde_gpu']:
|
||||
base_image = latent_image.copy()
|
||||
if recovery_mode == "ratio between":
|
||||
sigma_ratio = 1.0 - recovery_sigma_ratio
|
||||
else:
|
||||
sigma_ratio = 1.0
|
||||
else:
|
||||
base_image = None
|
||||
sigma_ratio = 1.0
|
||||
|
||||
try:
|
||||
if sigma_ratio > 0:
|
||||
latent_image = separated_sample(model, add_noise, seed, steps, cfg, sampler_name, scheduler,
|
||||
positive, negative, latent_image, start_at_step, end_at_step,
|
||||
return_with_leftover_noise, sigma_ratio=sigma_ratio)
|
||||
except ValueError as e:
|
||||
if str(e) == 'sigma_min and sigma_max must not be 0':
|
||||
print(f"\nWARN: sampling skipped - sigma_min and sigma_max are 0")
|
||||
return latent_image
|
||||
|
||||
if (recovery_sigma_ratio > 0 and recovery_mode != 'DISABLE' and
|
||||
sampler_name in ['uni_pc', 'uni_pc_bh2', 'dpmpp_sde', 'dpmpp_sde_gpu', 'dpmpp_2m_sde', 'dpmpp_2m_sde_gpu', 'dpmpp_3m_sde', 'dpmpp_3m_sde_gpu']):
|
||||
compensate = 0 if sampler_name in ['uni_pc', 'uni_pc_bh2', 'dpmpp_sde', 'dpmpp_sde_gpu', 'dpmpp_2m_sde', 'dpmpp_2m_sde_gpu', 'dpmpp_3m_sde', 'dpmpp_3m_sde_gpu'] else 2
|
||||
if recovery_sampler == "AUTO":
|
||||
recovery_sampler = 'dpm_fast' if sampler_name in ['uni_pc', 'uni_pc_bh2', 'dpmpp_sde', 'dpmpp_sde_gpu'] else 'dpmpp_2m'
|
||||
|
||||
latent_compositor = nodes.NODE_CLASS_MAPPINGS['LatentCompositeMasked']()
|
||||
|
||||
noise_mask = latent_image['noise_mask']
|
||||
|
||||
if len(noise_mask.shape) == 4:
|
||||
noise_mask = noise_mask.squeeze(0).squeeze(0)
|
||||
|
||||
latent_image = latent_compositor.composite(base_image, latent_image, 0, 0, False, noise_mask)[0]
|
||||
|
||||
try:
|
||||
latent_image = separated_sample(model, add_noise, seed, steps, cfg, recovery_sampler, scheduler,
|
||||
positive, negative, latent_image, start_at_step-compensate, end_at_step,
|
||||
return_with_leftover_noise, sigma_ratio=recovery_sigma_ratio)
|
||||
except ValueError as e:
|
||||
if str(e) == 'sigma_min and sigma_max must not be 0':
|
||||
print(f"\nWARN: sampling skipped - sigma_min and sigma_max are 0")
|
||||
|
||||
return latent_image
|
||||
|
||||
|
||||
class KSamplerWrapper:
|
||||
params = None
|
||||
|
||||
def __init__(self, model, seed, steps, cfg, sampler_name, scheduler, positive, negative, denoise):
|
||||
self.params = model, seed, steps, cfg, sampler_name, scheduler, positive, negative, denoise
|
||||
|
||||
def sample(self, latent_image, hook=None):
|
||||
model, seed, steps, cfg, sampler_name, scheduler, positive, negative, denoise = self.params
|
||||
|
||||
if hook is not None:
|
||||
model, seed, steps, cfg, sampler_name, scheduler, positive, negative, upscaled_latent, denoise = \
|
||||
hook.pre_ksample(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image,
|
||||
denoise)
|
||||
|
||||
return nodes.common_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image,
|
||||
denoise=denoise)[0]
|
||||
@@ -1,8 +1,6 @@
|
||||
import os
|
||||
import sys
|
||||
|
||||
import torch
|
||||
|
||||
import impact.impact_server
|
||||
from nodes import MAX_RESOLUTION
|
||||
|
||||
@@ -39,7 +37,7 @@ class SEGSDetailer:
|
||||
"optional": {
|
||||
"refiner_basic_pipe_opt": ("BASIC_PIPE",),
|
||||
"inpaint_model": ("BOOLEAN", {"default": False, "label_on": "enabled", "label_off": "disabled"}),
|
||||
"noise_mask_feather": ("INT", {"default": 10, "min": 0, "max": 100, "step": 1}),
|
||||
"noise_mask_feather": ("INT", {"default": 0, "min": 0, "max": 100, "step": 1}),
|
||||
}
|
||||
}
|
||||
|
||||
@@ -125,94 +123,6 @@ class SEGSDetailer:
|
||||
return (segs, cnet_pil_list)
|
||||
|
||||
|
||||
class SEGSDetailerForAnimateDiff:
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {"required": {
|
||||
"image_frames": ("IMAGE", ),
|
||||
"segs": ("SEGS", ),
|
||||
"guide_size": ("FLOAT", {"default": 256, "min": 64, "max": MAX_RESOLUTION, "step": 8}),
|
||||
"guide_size_for": ("BOOLEAN", {"default": True, "label_on": "bbox", "label_off": "crop_region"}),
|
||||
"max_size": ("FLOAT", {"default": 768, "min": 64, "max": MAX_RESOLUTION, "step": 8}),
|
||||
"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
|
||||
"steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
|
||||
"cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0}),
|
||||
"sampler_name": (comfy.samplers.KSampler.SAMPLERS,),
|
||||
"scheduler": (comfy.samplers.KSampler.SCHEDULERS,),
|
||||
"denoise": ("FLOAT", {"default": 0.5, "min": 0.0001, "max": 1.0, "step": 0.01}),
|
||||
"basic_pipe": ("BASIC_PIPE",),
|
||||
"refiner_ratio": ("FLOAT", {"default": 0.2, "min": 0.0, "max": 1.0})
|
||||
},
|
||||
"optional": {
|
||||
"refiner_basic_pipe_opt": ("BASIC_PIPE",),
|
||||
# TODO: "inpaint_model": ("BOOLEAN", {"default": False, "label_on": "enabled", "label_off": "disabled"}),
|
||||
# TODO: "noise_mask_feather": ("INT", {"default": 10, "min": 0, "max": 100, "step": 1}),
|
||||
}
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("SEGS",)
|
||||
RETURN_NAMES = ("segs",)
|
||||
OUTPUT_IS_LIST = (False,)
|
||||
|
||||
FUNCTION = "doit"
|
||||
|
||||
CATEGORY = "ImpactPack/Detailer"
|
||||
|
||||
@staticmethod
|
||||
def do_detail(image_frames, segs, guide_size, guide_size_for, max_size, seed, steps, cfg, sampler_name, scheduler,
|
||||
denoise, basic_pipe, refiner_ratio=None, refiner_basic_pipe_opt=None, inpaint_model=False, noise_mask_feather=0):
|
||||
|
||||
model, clip, vae, positive, negative = basic_pipe
|
||||
if refiner_basic_pipe_opt is None:
|
||||
refiner_model, refiner_clip, refiner_positive, refiner_negative = None, None, None, None
|
||||
else:
|
||||
refiner_model, refiner_clip, _, refiner_positive, refiner_negative = refiner_basic_pipe_opt
|
||||
|
||||
segs = core.segs_scale_match(segs, image_frames.shape)
|
||||
|
||||
new_segs = []
|
||||
|
||||
for seg in segs[1]:
|
||||
cropped_image_frames = None
|
||||
|
||||
for image in image_frames:
|
||||
image = image.unsqueeze(0)
|
||||
cropped_image = seg.cropped_image if seg.cropped_image is not None else crop_tensor4(image, seg.crop_region)
|
||||
cropped_image = to_tensor(cropped_image)
|
||||
if cropped_image_frames is None:
|
||||
cropped_image_frames = cropped_image
|
||||
else:
|
||||
cropped_image_frames = torch.concat((cropped_image_frames, cropped_image), dim=0)
|
||||
|
||||
cropped_image_frames = cropped_image_frames.numpy()
|
||||
enhanced_image_tensor = core.enhance_detail_for_animatediff(cropped_image_frames, model, clip, vae, guide_size, guide_size_for, max_size,
|
||||
seg.bbox, seed, steps, cfg, sampler_name, scheduler,
|
||||
positive, negative, denoise, seg.cropped_mask,
|
||||
refiner_ratio=refiner_ratio, refiner_model=refiner_model,
|
||||
refiner_clip=refiner_clip, refiner_positive=refiner_positive,
|
||||
refiner_negative=refiner_negative,
|
||||
inpaint_model=inpaint_model, noise_mask_feather=noise_mask_feather)
|
||||
|
||||
if enhanced_image_tensor is None:
|
||||
new_cropped_image = cropped_image_frames
|
||||
else:
|
||||
new_cropped_image = enhanced_image_tensor.numpy()
|
||||
|
||||
new_seg = SEG(new_cropped_image, seg.cropped_mask, seg.confidence, seg.crop_region, seg.bbox, seg.label, None)
|
||||
new_segs.append(new_seg)
|
||||
|
||||
return (segs[0], new_segs)
|
||||
|
||||
def doit(self, image_frames, segs, guide_size, guide_size_for, max_size, seed, steps, cfg, sampler_name, scheduler,
|
||||
denoise, basic_pipe, refiner_ratio=None, refiner_basic_pipe_opt=None, inpaint_model=False, noise_mask_feather=0):
|
||||
|
||||
segs = SEGSDetailerForAnimateDiff.do_detail(image_frames, segs, guide_size, guide_size_for, max_size, seed, steps, cfg, sampler_name,
|
||||
scheduler, denoise, basic_pipe, refiner_ratio, refiner_basic_pipe_opt,
|
||||
inpaint_model=inpaint_model, noise_mask_feather=noise_mask_feather)
|
||||
|
||||
return (segs,)
|
||||
|
||||
|
||||
class SEGSPaste:
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
|
||||
@@ -1,11 +1,10 @@
|
||||
import time
|
||||
|
||||
import comfy
|
||||
import math
|
||||
import impact.core as core
|
||||
from impact.utils import *
|
||||
from nodes import MAX_RESOLUTION
|
||||
import nodes
|
||||
from impact.impact_sampling import KSamplerWrapper, KSamplerAdvancedWrapper
|
||||
|
||||
|
||||
class TiledKSamplerProvider:
|
||||
@classmethod
|
||||
@@ -57,7 +56,7 @@ class KSamplerProvider:
|
||||
|
||||
def doit(self, seed, steps, cfg, sampler_name, scheduler, denoise, basic_pipe):
|
||||
model, _, _, positive, negative = basic_pipe
|
||||
sampler = core.KSamplerWrapper(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, denoise)
|
||||
sampler = KSamplerWrapper(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, denoise)
|
||||
return (sampler, )
|
||||
|
||||
|
||||
@@ -79,7 +78,7 @@ class KSamplerAdvancedProvider:
|
||||
|
||||
def doit(self, cfg, sampler_name, scheduler, basic_pipe):
|
||||
model, _, _, positive, negative = basic_pipe
|
||||
sampler = core.KSamplerAdvancedWrapper(model, cfg, sampler_name, scheduler, positive, negative)
|
||||
sampler = KSamplerAdvancedWrapper(model, cfg, sampler_name, scheduler, positive, negative)
|
||||
return (sampler, )
|
||||
|
||||
|
||||
@@ -167,10 +166,10 @@ class TwoAdvancedSamplersForMask:
|
||||
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", recover_special_sampler=True)
|
||||
new_latent_image = base_sampler.sample_advanced(add_noise, seed, adv_steps, new_latent_image, i, i + 1, "enable", recovery_mode="ratio additional")
|
||||
|
||||
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, recover_special_sampler=True)
|
||||
new_latent_image = mask_sampler.sample_advanced("disable", seed, adv_steps, new_latent_image, i, i + 1, return_with_leftover_noise, recovery_mode="ratio additional")
|
||||
|
||||
del new_latent_image['noise_mask']
|
||||
|
||||
@@ -287,6 +286,9 @@ class RegionalSampler:
|
||||
"regional_prompts": ("REGIONAL_PROMPTS", ),
|
||||
"overlap_factor": ("INT", {"default": 10, "min": 0, "max": 10000}),
|
||||
"restore_latent": ("BOOLEAN", {"default": True, "label_on": "enabled", "label_off": "disabled"}),
|
||||
"additional_mode": (["DISABLE", "ratio additional", "ratio between"], {"default": "ratio between"}),
|
||||
"additional_sampler": (["AUTO", "euler", "heun", "heunpp2", "dpm_2", "dpm_fast", "dpmpp_2m", "ddpm"],),
|
||||
"additional_sigma_ratio": ("FLOAT", {"default": 0.3, "min": 0.0, "max": 1.0, "step": 0.01}),
|
||||
},
|
||||
"hidden": {"unique_id": "UNIQUE_ID"},
|
||||
}
|
||||
@@ -314,7 +316,8 @@ class RegionalSampler:
|
||||
|
||||
return mask_erosion[:, :, :w, :h].round()
|
||||
|
||||
def doit(self, seed, seed_2nd, seed_2nd_mode, steps, base_only_steps, denoise, samples, base_sampler, regional_prompts, overlap_factor, restore_latent, unique_id=None):
|
||||
def doit(self, seed, seed_2nd, seed_2nd_mode, steps, base_only_steps, denoise, samples, base_sampler, regional_prompts, overlap_factor, restore_latent,
|
||||
additional_mode, additional_sampler, additional_sigma_ratio, unique_id=None):
|
||||
if restore_latent:
|
||||
latent_compositor = nodes.NODE_CLASS_MAPPINGS['LatentCompositeMasked']()
|
||||
else:
|
||||
@@ -332,12 +335,12 @@ class RegionalSampler:
|
||||
region_len = len(regional_prompts)
|
||||
total = steps*region_len
|
||||
|
||||
leftover_noise = 'disable'
|
||||
leftover_noise = False
|
||||
if base_only_steps > 0:
|
||||
if seed_2nd_mode == 'ignore':
|
||||
leftover_noise = 'enable'
|
||||
leftover_noise = True
|
||||
|
||||
samples = base_sampler.sample_advanced("enable", seed, adv_steps, samples, start_at_step, start_at_step + base_only_steps, leftover_noise, recover_special_sampler=False)
|
||||
samples = base_sampler.sample_advanced(True, seed, adv_steps, samples, start_at_step, start_at_step + base_only_steps, leftover_noise, recovery_mode="DISABLE")
|
||||
|
||||
if seed_2nd_mode == "seed+seed_2nd":
|
||||
seed += seed_2nd
|
||||
@@ -353,16 +356,17 @@ class RegionalSampler:
|
||||
new_latent_image = samples.copy()
|
||||
base_latent_image = None
|
||||
|
||||
if leftover_noise != 'enable':
|
||||
add_noise = "enable"
|
||||
if not leftover_noise:
|
||||
add_noise = True
|
||||
else:
|
||||
add_noise = "disable"
|
||||
add_noise = False
|
||||
|
||||
for i in range(start_at_step+base_only_steps, adv_steps):
|
||||
core.update_node_status(unique_id, f"{i}/{steps} steps | ", ((i-start_at_step)*region_len)/total)
|
||||
|
||||
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", recover_special_sampler=True)
|
||||
new_latent_image = base_sampler.sample_advanced(add_noise, seed, adv_steps, new_latent_image, i, i + 1, True,
|
||||
recovery_mode=additional_mode, recovery_sampler=additional_sampler, recovery_sigma_ratio=additional_sigma_ratio)
|
||||
|
||||
if restore_latent:
|
||||
if 'noise_mask' in new_latent_image:
|
||||
@@ -379,8 +383,8 @@ class RegionalSampler:
|
||||
region_mask = regional_prompt.get_mask_erosion(overlap_factor).squeeze(0).squeeze(0)
|
||||
|
||||
new_latent_image['noise_mask'] = region_mask
|
||||
new_latent_image = regional_prompt.sampler.sample_advanced("disable", seed, adv_steps, new_latent_image,
|
||||
i, i + 1, "enable", recover_special_sampler=True)
|
||||
new_latent_image = regional_prompt.sampler.sample_advanced(False, seed, adv_steps, new_latent_image, i, i + 1, True,
|
||||
recovery_mode=additional_mode, recovery_sampler=additional_sampler, recovery_sigma_ratio=additional_sigma_ratio)
|
||||
|
||||
if restore_latent:
|
||||
del new_latent_image['noise_mask']
|
||||
@@ -389,7 +393,7 @@ class RegionalSampler:
|
||||
|
||||
j += 1
|
||||
|
||||
add_noise = 'disable'
|
||||
add_noise = False
|
||||
|
||||
# finalize
|
||||
core.update_node_status(unique_id, f"finalize")
|
||||
@@ -399,7 +403,8 @@ class RegionalSampler:
|
||||
base_latent_image = new_latent_image
|
||||
|
||||
new_latent_image['noise_mask'] = inv_mask
|
||||
new_latent_image = base_sampler.sample_advanced("disable", seed, adv_steps, new_latent_image, adv_steps, adv_steps+1, "disable", recover_special_sampler=False)
|
||||
new_latent_image = base_sampler.sample_advanced(False, seed, adv_steps, new_latent_image, adv_steps, adv_steps+1, False,
|
||||
recovery_mode=additional_mode, recovery_sampler=additional_sampler, recovery_sigma_ratio=additional_sigma_ratio)
|
||||
|
||||
core.update_node_status(unique_id, f"{steps}/{steps} steps", total)
|
||||
core.update_node_status(unique_id, "", None)
|
||||
@@ -428,6 +433,9 @@ class RegionalSamplerAdvanced:
|
||||
"latent_image": ("LATENT", ),
|
||||
"base_sampler": ("KSAMPLER_ADVANCED", ),
|
||||
"regional_prompts": ("REGIONAL_PROMPTS", ),
|
||||
"additional_mode": (["DISABLE", "ratio additional", "ratio between"], {"default": "ratio between"}),
|
||||
"additional_sampler": (["AUTO", "euler", "heun", "heunpp2", "dpm_2", "dpm_fast", "dpmpp_2m", "ddpm"],),
|
||||
"additional_sigma_ratio": ("FLOAT", {"default": 0.3, "min": 0.0, "max": 1.0, "step": 0.01}),
|
||||
},
|
||||
"hidden": {"unique_id": "UNIQUE_ID"},
|
||||
}
|
||||
@@ -437,8 +445,9 @@ class RegionalSamplerAdvanced:
|
||||
|
||||
CATEGORY = "ImpactPack/Regional"
|
||||
|
||||
def doit(self, add_noise, noise_seed, steps, start_at_step, end_at_step, overlap_factor, restore_latent,
|
||||
return_with_leftover_noise, latent_image, base_sampler, regional_prompts, unique_id):
|
||||
def doit(self, add_noise, noise_seed, steps, start_at_step, end_at_step, overlap_factor, restore_latent, return_with_leftover_noise, latent_image, base_sampler, regional_prompts,
|
||||
additional_mode, additional_sampler, additional_sigma_ratio, unique_id):
|
||||
|
||||
if restore_latent:
|
||||
latent_compositor = nodes.NODE_CLASS_MAPPINGS['LatentCompositeMasked']()
|
||||
else:
|
||||
@@ -458,13 +467,14 @@ class RegionalSamplerAdvanced:
|
||||
base_latent_image = None
|
||||
region_masks = {}
|
||||
|
||||
for i in range(start_at_step, end_at_step):
|
||||
for i in range(start_at_step, end_at_step-1):
|
||||
core.update_node_status(unique_id, f"{start_at_step+i}/{end_at_step} steps | ", ((i-start_at_step)*region_len)/total)
|
||||
|
||||
cur_add_noise = "enable" if i == start_at_step and add_noise else "disable"
|
||||
cur_add_noise = True if i == start_at_step and add_noise else False
|
||||
|
||||
new_latent_image['noise_mask'] = inv_mask
|
||||
new_latent_image = base_sampler.sample_advanced(cur_add_noise, noise_seed, steps, new_latent_image, i, i + 1, "enable", recover_special_sampler=True)
|
||||
new_latent_image = base_sampler.sample_advanced(cur_add_noise, noise_seed, steps, new_latent_image, i, i + 1, True,
|
||||
recovery_mode=additional_mode, recovery_sampler=additional_sampler, recovery_sigma_ratio=additional_sigma_ratio)
|
||||
|
||||
if restore_latent:
|
||||
del new_latent_image['noise_mask']
|
||||
@@ -484,8 +494,8 @@ class RegionalSamplerAdvanced:
|
||||
region_mask = region_masks[j]
|
||||
|
||||
new_latent_image['noise_mask'] = region_mask
|
||||
new_latent_image = regional_prompt.sampler.sample_advanced("disable", noise_seed, steps, new_latent_image,
|
||||
i, i + 1, "enable", recover_special_sampler=True)
|
||||
new_latent_image = regional_prompt.sampler.sample_advanced(False, noise_seed, steps, new_latent_image, i, i + 1, True,
|
||||
recovery_mode=additional_mode, recovery_sampler=additional_sampler, recovery_sigma_ratio=additional_sigma_ratio)
|
||||
|
||||
if restore_latent:
|
||||
del new_latent_image['noise_mask']
|
||||
@@ -502,7 +512,8 @@ class RegionalSamplerAdvanced:
|
||||
base_latent_image = new_latent_image
|
||||
|
||||
new_latent_image['noise_mask'] = inv_mask
|
||||
new_latent_image = base_sampler.sample_advanced("disable", noise_seed, steps, new_latent_image, end_at_step, end_at_step+1, return_with_leftover_noise, recover_special_sampler=False)
|
||||
new_latent_image = base_sampler.sample_advanced(False, noise_seed, steps, new_latent_image, end_at_step-1, end_at_step, return_with_leftover_noise,
|
||||
recovery_mode=additional_mode, recovery_sampler=additional_sampler, recovery_sigma_ratio=additional_sigma_ratio)
|
||||
|
||||
core.update_node_status(unique_id, f"{end_at_step}/{end_at_step} steps", total)
|
||||
core.update_node_status(unique_id, "", None)
|
||||
@@ -539,7 +550,7 @@ class KSamplerBasicPipe:
|
||||
def sample(self, basic_pipe, seed, steps, cfg, sampler_name, scheduler, latent_image, denoise=1.0):
|
||||
model, clip, vae, positive, negative = basic_pipe
|
||||
latent = nodes.KSampler().sample(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise)[0]
|
||||
return (basic_pipe, latent, vae)
|
||||
return basic_pipe, latent, vae
|
||||
|
||||
|
||||
class KSamplerAdvancedBasicPipe:
|
||||
@@ -579,4 +590,5 @@ class KSamplerAdvancedBasicPipe:
|
||||
return_with_leftover_noise = "disable"
|
||||
|
||||
latent = nodes.KSamplerAdvanced().sample(model, add_noise, noise_seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, start_at_step, end_at_step, return_with_leftover_noise, denoise)[0]
|
||||
return (basic_pipe, latent, vae)
|
||||
return basic_pipe, latent, vae
|
||||
|
||||
|
||||
Reference in New Issue
Block a user