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feat: support GITSScheduler

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Dr.Lt.Data
2024-06-20 23:53:56 +09:00
parent 0da935061b
commit e254cbed49
11 changed files with 150 additions and 84 deletions
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README.md
+1
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@@ -225,6 +225,7 @@ This custom node helps to conveniently enhance images through Detector, Detailer
* `KSampler (pipe)` - pipe version of KSampler
* `KSampler (advanced/pipe)` - pipe version of KSamplerAdvacned
* When converting the scheduler widget to input, refer to the `Impact Scheduler Adapter` node to resolve compatibility issues.
* `GITSScheduler Func Provider` - provider scheduler function for GITSScheduler
### Batch/List Util
__init__.py
+3 -1
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@@ -298,7 +298,8 @@ NODE_CLASS_MAPPINGS = {
"ImpactHFTransformersClassifierProvider": HF_TransformersClassifierProvider,
"ImpactSEGSClassify": SEGS_Classify,
"ImpactSchedulerAdapter": ImpactSchedulerAdapter
"ImpactSchedulerAdapter": ImpactSchedulerAdapter,
"GITSSchedulerFuncProvider": GITSSchedulerFuncProvider
}
@@ -431,6 +432,7 @@ NODE_DISPLAY_NAME_MAPPINGS = {
"SEGSPreviewCNet": "SEGSPreview (CNET Image)",
"ImpactSchedulerAdapter": "Impact Scheduler Adapter",
"GITSSchedulerFuncProvider": "GITSScheduler Func Provider",
}
if not impact.config.get_config()['mmdet_skip']:
modules/impact/animatediff_nodes.py
+8 -6
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@@ -26,6 +26,7 @@ class SEGSDetailerForAnimateDiff:
"optional": {
"refiner_basic_pipe_opt": ("BASIC_PIPE",),
"noise_mask_feather": ("INT", {"default": 20, "min": 0, "max": 100, "step": 1}),
"scheduler_func_opt": ("SCHEDULER_FUNC",),
}
}
@@ -39,7 +40,7 @@ class SEGSDetailerForAnimateDiff:
@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, noise_mask_feather=0):
denoise, basic_pipe, refiner_ratio=None, refiner_basic_pipe_opt=None, noise_mask_feather=0, scheduler_func_opt=None):
model, clip, vae, positive, negative = basic_pipe
if refiner_basic_pipe_opt is None:
@@ -89,7 +90,7 @@ class SEGSDetailerForAnimateDiff:
refiner_ratio=refiner_ratio, refiner_model=refiner_model,
refiner_clip=refiner_clip, refiner_positive=refiner_positive,
refiner_negative=refiner_negative, control_net_wrapper=seg.control_net_wrapper,
noise_mask_feather=noise_mask_feather)
noise_mask_feather=noise_mask_feather, scheduler_func=scheduler_func_opt)
if cnet_images is not None:
cnet_image_list.extend(cnet_images)
@@ -104,11 +105,11 @@ class SEGSDetailerForAnimateDiff:
return (segs[0], new_segs), cnet_image_list
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):
denoise, basic_pipe, refiner_ratio=None, refiner_basic_pipe_opt=None, inpaint_model=False, noise_mask_feather=0, scheduler_func_opt=None):
segs, cnet_images = 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,
noise_mask_feather=noise_mask_feather)
noise_mask_feather=noise_mask_feather, scheduler_func_opt=scheduler_func_opt)
if len(cnet_images) == 0:
cnet_images = [empty_pil_tensor()]
@@ -139,6 +140,7 @@ class DetailerForEachPipeForAnimateDiff:
"detailer_hook": ("DETAILER_HOOK",),
"refiner_basic_pipe_opt": ("BASIC_PIPE",),
"noise_mask_feather": ("INT", {"default": 20, "min": 0, "max": 100, "step": 1}),
"scheduler_func_opt": ("SCHEDULER_FUNC",),
}
}
@@ -152,7 +154,7 @@ class DetailerForEachPipeForAnimateDiff:
@staticmethod
def doit(image_frames, segs, guide_size, guide_size_for, max_size, seed, steps, cfg, sampler_name, scheduler,
denoise, feather, basic_pipe, refiner_ratio=None, detailer_hook=None, refiner_basic_pipe_opt=None,
noise_mask_feather=0):
noise_mask_feather=0, scheduler_func_opt=None):
enhanced_segs = []
cnet_image_list = []
@@ -160,7 +162,7 @@ class DetailerForEachPipeForAnimateDiff:
for sub_seg in segs[1]:
single_seg = segs[0], [sub_seg]
enhanced_seg, cnet_images = SEGSDetailerForAnimateDiff().do_detail(image_frames, single_seg, guide_size, guide_size_for, max_size, seed, steps, cfg, sampler_name, scheduler,
denoise, basic_pipe, refiner_ratio, refiner_basic_pipe_opt, noise_mask_feather)
denoise, basic_pipe, refiner_ratio, refiner_basic_pipe_opt, noise_mask_feather, scheduler_func_opt=scheduler_func_opt)
image_frames = SEGSPaste.doit(image_frames, enhanced_seg, feather, alpha=255)[0]
modules/impact/config.py
+1 -1
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@@ -1,7 +1,7 @@
import configparser
import os
version_code = [5, 14, 1]
version_code = [5, 15]
version = f"V{version_code[0]}.{version_code[1]}" + (f'.{version_code[2]}' if len(version_code) > 2 else '')
dependency_version = 21
modules/impact/core.py
+9 -7
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@@ -223,7 +223,7 @@ def enhance_detail(image, model, clip, vae, guide_size, guide_size_for_bbox, max
detailer_hook=None,
refiner_ratio=None, refiner_model=None, refiner_clip=None, refiner_positive=None,
refiner_negative=None, control_net_wrapper=None, cycle=1,
inpaint_model=False, noise_mask_feather=0):
inpaint_model=False, noise_mask_feather=0, scheduler_func=None):
if noise_mask is not None:
noise_mask = utils.tensor_gaussian_blur_mask(noise_mask, noise_mask_feather)
@@ -331,7 +331,8 @@ def enhance_detail(image, model, clip, vae, guide_size, guide_size_for_bbox, max
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)
refined_latent, denoise2, refiner_ratio, refiner_model, refiner_clip, refiner_positive, refiner_negative,
noise=noise, scheduler_func=scheduler_func)
if detailer_hook is not None:
refined_latent = detailer_hook.pre_decode(refined_latent)
@@ -364,7 +365,7 @@ def enhance_detail_for_animatediff(image_frames, model, clip, vae, guide_size, g
wildcard_opt=None, wildcard_opt_concat_mode=None,
detailer_hook=None,
refiner_ratio=None, refiner_model=None, refiner_clip=None, refiner_positive=None,
refiner_negative=None, control_net_wrapper=None, noise_mask_feather=0):
refiner_negative=None, control_net_wrapper=None, noise_mask_feather=0, scheduler_func=None):
if noise_mask is not None:
noise_mask = utils.tensor_gaussian_blur_mask(noise_mask, noise_mask_feather)
noise_mask = noise_mask.squeeze(3)
@@ -478,7 +479,7 @@ def enhance_detail_for_animatediff(image_frames, model, clip, vae, guide_size, g
latent = detailer_hook.post_encode(latent)
refined_latent = impact_sampling.ksampler_wrapper(model, seed, steps, cfg, sampler_name, scheduler, positive, negative,
latent, denoise, refiner_ratio, refiner_model, refiner_clip, refiner_positive, refiner_negative)
latent, denoise, refiner_ratio, refiner_model, refiner_clip, refiner_positive, refiner_negative, scheduler_func=scheduler_func)
if detailer_hook is not None:
refined_latent = detailer_hook.pre_decode(refined_latent)
@@ -1602,7 +1603,7 @@ class TwoSamplersForMaskUpscaler:
class PixelKSampleUpscaler:
def __init__(self, scale_method, model, vae, seed, steps, cfg, sampler_name, scheduler, positive, negative, denoise,
use_tiled_vae, upscale_model_opt=None, hook_opt=None, tile_size=512):
use_tiled_vae, upscale_model_opt=None, hook_opt=None, tile_size=512, scheduler_func=None):
self.params = scale_method, model, vae, seed, steps, cfg, sampler_name, scheduler, positive, negative, denoise
self.upscale_model = upscale_model_opt
self.hook = hook_opt
@@ -1610,6 +1611,7 @@ class PixelKSampleUpscaler:
self.tile_size = tile_size
self.is_tiled = False
self.vae = vae
self.scheduler_func = scheduler_func
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
@@ -1635,7 +1637,7 @@ class PixelKSampleUpscaler:
upscaled_latent, denoise)
refined_latent = impact_sampling.impact_sample(model, seed, steps, cfg, sampler_name, scheduler,
positive, negative, upscaled_latent, denoise)
positive, negative, upscaled_latent, denoise, scheduler_func_opt=self.scheduler_func)
return refined_latent
def upscale_shape(self, step_info, samples, w, h, save_temp_prefix=None):
@@ -1663,7 +1665,7 @@ class PixelKSampleUpscaler:
upscaled_latent, denoise)
refined_latent = impact_sampling.impact_sample(model, seed, steps, cfg, sampler_name, scheduler,
positive, negative, upscaled_latent, denoise)
positive, negative, upscaled_latent, denoise, scheduler_func_opt=self.scheduler_func)
return refined_latent
modules/impact/impact_pack.py
+36 -28
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@@ -201,6 +201,7 @@ class DetailerForEach:
"detailer_hook": ("DETAILER_HOOK",),
"inpaint_model": ("BOOLEAN", {"default": False, "label_on": "enabled", "label_off": "disabled"}),
"noise_mask_feather": ("INT", {"default": 20, "min": 0, "max": 100, "step": 1}),
"scheduler_func_opt": ("SCHEDULER_FUNC",),
}
}
@@ -213,7 +214,7 @@ class DetailerForEach:
def do_detail(image, segs, model, clip, vae, guide_size, guide_size_for_bbox, max_size, seed, steps, cfg, sampler_name, scheduler,
positive, negative, denoise, feather, noise_mask, force_inpaint, wildcard_opt=None, detailer_hook=None,
refiner_ratio=None, refiner_model=None, refiner_clip=None, refiner_positive=None, refiner_negative=None,
cycle=1, inpaint_model=False, noise_mask_feather=0):
cycle=1, inpaint_model=False, noise_mask_feather=0, scheduler_func_opt=None):
if len(image) > 1:
raise Exception('[Impact Pack] ERROR: DetailerForEach does not allow image batches.\nPlease refer to https://github.com/ltdrdata/ComfyUI-extension-tutorials/blob/Main/ComfyUI-Impact-Pack/tutorial/batching-detailer.md for more information.')
@@ -293,7 +294,8 @@ class DetailerForEach:
refiner_ratio=refiner_ratio, refiner_model=refiner_model,
refiner_clip=refiner_clip, refiner_positive=refiner_positive,
refiner_negative=refiner_negative, control_net_wrapper=seg.control_net_wrapper,
cycle=cycle, inpaint_model=inpaint_model, noise_mask_feather=noise_mask_feather)
cycle=cycle, inpaint_model=inpaint_model, noise_mask_feather=noise_mask_feather,
scheduler_func=scheduler_func_opt)
if cnet_pils is not None:
cnet_pil_list.extend(cnet_pils)
@@ -336,13 +338,13 @@ class DetailerForEach:
def doit(self, image, segs, model, clip, vae, guide_size, guide_size_for, max_size, seed, steps, cfg, sampler_name,
scheduler, positive, negative, denoise, feather, noise_mask, force_inpaint, wildcard, cycle=1,
detailer_hook=None, inpaint_model=False, noise_mask_feather=0):
detailer_hook=None, inpaint_model=False, noise_mask_feather=0, scheduler_func_opt=None):
enhanced_img, *_ = \
DetailerForEach.do_detail(image, segs, model, clip, vae, guide_size, guide_size_for, max_size, seed, steps,
cfg, sampler_name, scheduler, positive, negative, denoise, feather, noise_mask,
force_inpaint, wildcard, detailer_hook,
cycle=cycle, inpaint_model=inpaint_model, noise_mask_feather=noise_mask_feather)
cycle=cycle, inpaint_model=inpaint_model, noise_mask_feather=noise_mask_feather, scheduler_func_opt=scheduler_func_opt)
return (enhanced_img, )
@@ -372,11 +374,12 @@ class DetailerForEachPipe:
"cycle": ("INT", {"default": 1, "min": 1, "max": 10, "step": 1}),
},
"optional": {
"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": 20, "min": 0, "max": 100, "step": 1}),
}
"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": 20, "min": 0, "max": 100, "step": 1}),
"scheduler_func_opt": ("SCHEDULER_FUNC",),
}
}
RETURN_TYPES = ("IMAGE", "SEGS", "BASIC_PIPE", "IMAGE")
@@ -389,7 +392,7 @@ class DetailerForEachPipe:
def doit(self, image, segs, guide_size, guide_size_for, max_size, seed, steps, cfg, sampler_name, scheduler,
denoise, feather, noise_mask, force_inpaint, basic_pipe, wildcard,
refiner_ratio=None, detailer_hook=None, refiner_basic_pipe_opt=None,
cycle=1, inpaint_model=False, noise_mask_feather=0):
cycle=1, inpaint_model=False, noise_mask_feather=0, scheduler_func_opt=None):
if len(image) > 1:
raise Exception('[Impact Pack] ERROR: DetailerForEach does not allow image batches.\nPlease refer to https://github.com/ltdrdata/ComfyUI-extension-tutorials/blob/Main/ComfyUI-Impact-Pack/tutorial/batching-detailer.md for more information.')
@@ -407,13 +410,13 @@ class DetailerForEachPipe:
force_inpaint, wildcard, detailer_hook,
refiner_ratio=refiner_ratio, refiner_model=refiner_model,
refiner_clip=refiner_clip, refiner_positive=refiner_positive, refiner_negative=refiner_negative,
cycle=cycle, inpaint_model=inpaint_model, noise_mask_feather=noise_mask_feather)
cycle=cycle, inpaint_model=inpaint_model, noise_mask_feather=noise_mask_feather, scheduler_func_opt=scheduler_func_opt)
# set fallback image
if len(cnet_pil_list) == 0:
cnet_pil_list = [empty_pil_tensor()]
return (enhanced_img, new_segs, basic_pipe, cnet_pil_list)
return enhanced_img, new_segs, basic_pipe, cnet_pil_list
class FaceDetailer:
@@ -463,6 +466,7 @@ class FaceDetailer:
"detailer_hook": ("DETAILER_HOOK",),
"inpaint_model": ("BOOLEAN", {"default": False, "label_on": "enabled", "label_off": "disabled"}),
"noise_mask_feather": ("INT", {"default": 20, "min": 0, "max": 100, "step": 1}),
"scheduler_func_opt": ("SCHEDULER_FUNC",),
}}
RETURN_TYPES = ("IMAGE", "IMAGE", "IMAGE", "MASK", "DETAILER_PIPE", "IMAGE")
@@ -480,7 +484,7 @@ class FaceDetailer:
sam_mask_hint_use_negative, drop_size,
bbox_detector, segm_detector=None, sam_model_opt=None, wildcard_opt=None, detailer_hook=None,
refiner_ratio=None, refiner_model=None, refiner_clip=None, refiner_positive=None, refiner_negative=None, cycle=1,
inpaint_model=False, noise_mask_feather=0):
inpaint_model=False, noise_mask_feather=0, scheduler_func_opt=None):
# make default prompt as 'face' if empty prompt for CLIPSeg
bbox_detector.setAux('face')
@@ -512,7 +516,7 @@ class FaceDetailer:
refiner_ratio=refiner_ratio, refiner_model=refiner_model,
refiner_clip=refiner_clip, refiner_positive=refiner_positive,
refiner_negative=refiner_negative,
cycle=cycle, inpaint_model=inpaint_model, noise_mask_feather=noise_mask_feather)
cycle=cycle, inpaint_model=inpaint_model, noise_mask_feather=noise_mask_feather, scheduler_func_opt=scheduler_func_opt)
else:
enhanced_img = image
cropped_enhanced = []
@@ -538,7 +542,7 @@ class FaceDetailer:
bbox_threshold, bbox_dilation, bbox_crop_factor,
sam_detection_hint, sam_dilation, sam_threshold, sam_bbox_expansion, sam_mask_hint_threshold,
sam_mask_hint_use_negative, drop_size, bbox_detector, wildcard, cycle=1,
sam_model_opt=None, segm_detector_opt=None, detailer_hook=None, inpaint_model=False, noise_mask_feather=0):
sam_model_opt=None, segm_detector_opt=None, detailer_hook=None, inpaint_model=False, noise_mask_feather=0, scheduler_func_opt=None):
result_img = None
result_mask = None
@@ -556,7 +560,7 @@ class FaceDetailer:
bbox_threshold, bbox_dilation, bbox_crop_factor,
sam_detection_hint, sam_dilation, sam_threshold, sam_bbox_expansion, sam_mask_hint_threshold,
sam_mask_hint_use_negative, drop_size, bbox_detector, segm_detector_opt, sam_model_opt, wildcard, detailer_hook,
cycle=cycle, inpaint_model=inpaint_model, noise_mask_feather=noise_mask_feather)
cycle=cycle, inpaint_model=inpaint_model, noise_mask_feather=noise_mask_feather, scheduler_func_opt=scheduler_func_opt)
result_img = torch.cat((result_img, enhanced_img), dim=0) if result_img is not None else enhanced_img
result_mask = torch.cat((result_mask, mask), dim=0) if result_mask is not None else mask
@@ -1021,6 +1025,7 @@ class PixelKSampleUpscalerProvider:
"optional": {
"upscale_model_opt": ("UPSCALE_MODEL", ),
"pk_hook_opt": ("PK_HOOK", ),
"scheduler_func_opt": ("SCHEDULER_FUNC",),
}
}
@@ -1030,10 +1035,10 @@ class PixelKSampleUpscalerProvider:
CATEGORY = "ImpactPack/Upscale"
def doit(self, scale_method, model, vae, seed, steps, cfg, sampler_name, scheduler, positive, negative, denoise,
use_tiled_vae, upscale_model_opt=None, pk_hook_opt=None, tile_size=512):
use_tiled_vae, upscale_model_opt=None, pk_hook_opt=None, tile_size=512, scheduler_func_opt=None):
upscaler = core.PixelKSampleUpscaler(scale_method, model, vae, seed, steps, cfg, sampler_name, scheduler,
positive, negative, denoise, use_tiled_vae, upscale_model_opt, pk_hook_opt,
tile_size=tile_size)
tile_size=tile_size, scheduler_func=scheduler_func_opt)
return (upscaler, )
@@ -1057,6 +1062,7 @@ class PixelKSampleUpscalerProviderPipe(PixelKSampleUpscalerProvider):
"optional": {
"upscale_model_opt": ("UPSCALE_MODEL", ),
"pk_hook_opt": ("PK_HOOK", ),
"scheduler_func_opt": ("SCHEDULER_FUNC",),
}
}
@@ -1066,11 +1072,11 @@ class PixelKSampleUpscalerProviderPipe(PixelKSampleUpscalerProvider):
CATEGORY = "ImpactPack/Upscale"
def doit_pipe(self, scale_method, seed, steps, cfg, sampler_name, scheduler, denoise,
use_tiled_vae, basic_pipe, upscale_model_opt=None, pk_hook_opt=None, tile_size=512):
use_tiled_vae, basic_pipe, upscale_model_opt=None, pk_hook_opt=None, tile_size=512, scheduler_func_opt=None):
model, _, vae, positive, negative = basic_pipe
upscaler = core.PixelKSampleUpscaler(scale_method, model, vae, seed, steps, cfg, sampler_name, scheduler,
positive, negative, denoise, use_tiled_vae, upscale_model_opt, pk_hook_opt,
tile_size=tile_size)
tile_size=tile_size, scheduler_func=scheduler_func_opt)
return (upscaler, )
@@ -1305,6 +1311,7 @@ class FaceDetailerPipe:
"optional": {
"inpaint_model": ("BOOLEAN", {"default": False, "label_on": "enabled", "label_off": "disabled"}),
"noise_mask_feather": ("INT", {"default": 20, "min": 0, "max": 100, "step": 1}),
"scheduler_func_opt": ("SCHEDULER_FUNC",),
}
}
@@ -1319,7 +1326,7 @@ class FaceDetailerPipe:
denoise, feather, noise_mask, force_inpaint, bbox_threshold, bbox_dilation, bbox_crop_factor,
sam_detection_hint, sam_dilation, sam_threshold, sam_bbox_expansion,
sam_mask_hint_threshold, sam_mask_hint_use_negative, drop_size, refiner_ratio=None,
cycle=1, inpaint_model=False, noise_mask_feather=0):
cycle=1, inpaint_model=False, noise_mask_feather=0, scheduler_func_opt=None):
result_img = None
result_mask = None
@@ -1342,7 +1349,7 @@ class FaceDetailerPipe:
sam_mask_hint_use_negative, drop_size, bbox_detector, segm_detector, sam_model_opt, wildcard, detailer_hook,
refiner_ratio=refiner_ratio, refiner_model=refiner_model,
refiner_clip=refiner_clip, refiner_positive=refiner_positive, refiner_negative=refiner_negative,
cycle=cycle, inpaint_model=inpaint_model, noise_mask_feather=noise_mask_feather)
cycle=cycle, inpaint_model=inpaint_model, noise_mask_feather=noise_mask_feather, scheduler_func_opt=scheduler_func_opt)
result_img = torch.cat((result_img, enhanced_img), dim=0) if result_img is not None else enhanced_img
result_mask = torch.cat((result_mask, mask), dim=0) if result_mask is not None else mask
@@ -1395,6 +1402,7 @@ class MaskDetailerPipe:
"detailer_hook": ("DETAILER_HOOK",),
"inpaint_model": ("BOOLEAN", {"default": False, "label_on": "enabled", "label_off": "disabled"}),
"noise_mask_feather": ("INT", {"default": 20, "min": 0, "max": 100, "step": 1}),
"scheduler_func_opt": ("SCHEDULER_FUNC",),
}
}
@@ -1408,7 +1416,7 @@ class MaskDetailerPipe:
def doit(self, image, mask, basic_pipe, guide_size, guide_size_for, max_size, mask_mode,
seed, steps, cfg, sampler_name, scheduler, denoise,
feather, crop_factor, drop_size, refiner_ratio, batch_size, cycle=1,
refiner_basic_pipe_opt=None, detailer_hook=None, inpaint_model=False, noise_mask_feather=0):
refiner_basic_pipe_opt=None, detailer_hook=None, inpaint_model=False, noise_mask_feather=0, scheduler_func_opt=None):
if len(image) > 1:
raise Exception('[Impact Pack] ERROR: MaskDetailer does not allow image batches.\nPlease refer to https://github.com/ltdrdata/ComfyUI-extension-tutorials/blob/Main/ComfyUI-Impact-Pack/tutorial/batching-detailer.md for more information.')
@@ -1439,7 +1447,7 @@ class MaskDetailerPipe:
force_inpaint=True, wildcard_opt=None, detailer_hook=detailer_hook,
refiner_ratio=refiner_ratio, refiner_model=refiner_model, refiner_clip=refiner_clip,
refiner_positive=refiner_positive, refiner_negative=refiner_negative,
cycle=cycle, inpaint_model=inpaint_model, noise_mask_feather=noise_mask_feather)
cycle=cycle, inpaint_model=inpaint_model, noise_mask_feather=noise_mask_feather, scheduler_func_opt=scheduler_func_opt)
else:
enhanced_img, cropped_enhanced, cropped_enhanced_alpha = image, [], []
@@ -1472,7 +1480,7 @@ class DetailerForEachTest(DetailerForEach):
def doit(self, image, segs, model, clip, vae, guide_size, guide_size_for, max_size, seed, steps, cfg, sampler_name,
scheduler, positive, negative, denoise, feather, noise_mask, force_inpaint, wildcard, detailer_hook=None,
cycle=1, inpaint_model=False, noise_mask_feather=0):
cycle=1, inpaint_model=False, noise_mask_feather=0, scheduler_func_opt=None):
if len(image) > 1:
raise Exception('[Impact Pack] ERROR: DetailerForEach does not allow image batches.\nPlease refer to https://github.com/ltdrdata/ComfyUI-extension-tutorials/blob/Main/ComfyUI-Impact-Pack/tutorial/batching-detailer.md for more information.')
@@ -1481,7 +1489,7 @@ class DetailerForEachTest(DetailerForEach):
DetailerForEach.do_detail(image, segs, model, clip, vae, guide_size, guide_size_for, max_size, seed, steps,
cfg, sampler_name, scheduler, positive, negative, denoise, feather, noise_mask,
force_inpaint, wildcard, detailer_hook,
cycle=cycle, inpaint_model=inpaint_model, noise_mask_feather=noise_mask_feather)
cycle=cycle, inpaint_model=inpaint_model, noise_mask_feather=noise_mask_feather, scheduler_func_opt=scheduler_func_opt)
# set fallback image
if len(cropped) == 0:
@@ -1510,7 +1518,7 @@ class DetailerForEachTestPipe(DetailerForEachPipe):
def doit(self, image, segs, guide_size, guide_size_for, max_size, seed, steps, cfg, sampler_name, scheduler,
denoise, feather, noise_mask, force_inpaint, basic_pipe, wildcard, cycle=1,
refiner_ratio=None, detailer_hook=None, refiner_basic_pipe_opt=None, inpaint_model=False, noise_mask_feather=0):
refiner_ratio=None, detailer_hook=None, refiner_basic_pipe_opt=None, inpaint_model=False, noise_mask_feather=0, scheduler_func_opt=None):
if len(image) > 1:
raise Exception('[Impact Pack] ERROR: DetailerForEach does not allow image batches.\nPlease refer to https://github.com/ltdrdata/ComfyUI-extension-tutorials/blob/Main/ComfyUI-Impact-Pack/tutorial/batching-detailer.md for more information.')
@@ -1529,7 +1537,7 @@ class DetailerForEachTestPipe(DetailerForEachPipe):
refiner_ratio=refiner_ratio, refiner_model=refiner_model,
refiner_clip=refiner_clip, refiner_positive=refiner_positive,
refiner_negative=refiner_negative,
cycle=cycle, inpaint_model=inpaint_model, noise_mask_feather=noise_mask_feather)
cycle=cycle, inpaint_model=inpaint_model, noise_mask_feather=noise_mask_feather, scheduler_func_opt=scheduler_func_opt)
# set fallback image
if len(cropped) == 0:
modules/impact/impact_sampling.py
+27 -16
View File
@@ -151,11 +151,15 @@ def sample_with_custom_noise(model, add_noise, noise_seed, cfg, positive, negati
# When sampling one step at a time, it mitigates the problem. (especially for _sde series samplers)
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, sampler_opt=None, noise=None, callback=None):
if sampler_opt is None:
total_sigmas = calculate_sigmas(model, sampler_name, scheduler, steps)
latent_image, start_at_step, end_at_step, return_with_leftover_noise, sigma_ratio=1.0, sampler_opt=None, noise=None, callback=None, scheduler_func=None):
if scheduler_func is not None:
total_sigmas = scheduler_func(model, sampler_name, steps)
else:
total_sigmas = calculate_sigmas(model, "", scheduler, steps)
if sampler_opt is None:
total_sigmas = calculate_sigmas(model, sampler_name, scheduler, steps)
else:
total_sigmas = calculate_sigmas(model, "", scheduler, steps)
sigmas = total_sigmas
@@ -189,15 +193,15 @@ def separated_sample(model, add_noise, seed, steps, cfg, sampler_name, scheduler
return res[1]
def impact_sample(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=1.0, sigma_ratio=1.0, sampler_opt=None, noise=None):
def impact_sample(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=1.0, sigma_ratio=1.0, sampler_opt=None, noise=None, scheduler_func=None):
advanced_steps = math.floor(steps / denoise)
start_at_step = advanced_steps - steps
end_at_step = start_at_step + steps
return separated_sample(model, True, seed, advanced_steps, cfg, sampler_name, scheduler, positive, negative, latent_image, start_at_step, end_at_step, False)
return separated_sample(model, True, seed, advanced_steps, cfg, sampler_name, scheduler, positive, negative, latent_image, start_at_step, end_at_step, False, scheduler_func=scheduler_func)
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, sigma_factor=1.0, noise=None):
refiner_ratio=None, refiner_model=None, refiner_clip=None, refiner_positive=None, refiner_negative=None, sigma_factor=1.0, noise=None, scheduler_func=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:
# Use separated_sample instead of KSampler for `AYS scheduler`
@@ -207,7 +211,9 @@ def ksampler_wrapper(model, seed, steps, cfg, sampler_name, scheduler, positive,
start_at_step = advanced_steps - steps
end_at_step = start_at_step + steps
refined_latent = separated_sample(model, True, seed, advanced_steps, cfg, sampler_name, scheduler, positive, negative, latent_image, start_at_step, end_at_step, False, sigma_ratio=sigma_factor, noise=noise)
refined_latent = separated_sample(model, True, seed, advanced_steps, cfg, sampler_name, scheduler,
positive, negative, latent_image, start_at_step, end_at_step, False,
sigma_ratio=sigma_factor, noise=noise, scheduler_func=scheduler_func)
else:
advanced_steps = math.floor(steps / denoise)
start_at_step = advanced_steps - steps
@@ -215,7 +221,8 @@ def ksampler_wrapper(model, seed, steps, cfg, sampler_name, scheduler, positive,
# 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, sigma_ratio=sigma_factor, noise=noise)
positive, negative, latent_image, start_at_step, end_at_step, True,
sigma_ratio=sigma_factor, noise=noise, scheduler_func=scheduler_func)
if 'noise_mask' in latent_image:
# noise_latent = \
@@ -228,7 +235,8 @@ def ksampler_wrapper(model, seed, steps, cfg, sampler_name, scheduler, positive,
# 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, sigma_ratio=sigma_factor)
refiner_positive, refiner_negative, temp_latent, end_at_step, advanced_steps + 1, False,
sigma_ratio=sigma_factor, scheduler_func=scheduler_func)
return refined_latent
@@ -236,9 +244,10 @@ def ksampler_wrapper(model, seed, steps, cfg, sampler_name, scheduler, positive,
class KSamplerAdvancedWrapper:
params = None
def __init__(self, model, cfg, sampler_name, scheduler, positive, negative, sampler_opt=None, sigma_factor=1.0):
def __init__(self, model, cfg, sampler_name, scheduler, positive, negative, sampler_opt=None, sigma_factor=1.0, scheduler_func=None):
self.params = model, cfg, sampler_name, scheduler, positive, negative, sigma_factor
self.sampler_opt = sampler_opt
self.scheduler_func = scheduler_func
def clone_with_conditionings(self, positive, negative):
model, cfg, sampler_name, scheduler, _, _, _ = self.params
@@ -269,7 +278,8 @@ class KSamplerAdvancedWrapper:
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 * sigma_factor, sampler_opt=self.sampler_opt, noise=noise)
return_with_leftover_noise, sigma_ratio=sigma_ratio * sigma_factor,
sampler_opt=self.sampler_opt, noise=noise, scheduler_func=self.scheduler_func)
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")
@@ -292,8 +302,8 @@ class KSamplerAdvancedWrapper:
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 * sigma_factor, sampler_opt=self.sampler_opt)
positive, negative, latent_image, start_at_step-compensate, end_at_step, return_with_leftover_noise,
sigma_ratio=recovery_sigma_ratio * sigma_factor, sampler_opt=self.sampler_opt, scheduler_func=self.scheduler_func)
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")
@@ -304,8 +314,9 @@ class KSamplerAdvancedWrapper:
class KSamplerWrapper:
params = None
def __init__(self, model, seed, steps, cfg, sampler_name, scheduler, positive, negative, denoise):
def __init__(self, model, seed, steps, cfg, sampler_name, scheduler, positive, negative, denoise, scheduler_func=None):
self.params = model, seed, steps, cfg, sampler_name, scheduler, positive, negative, denoise
self.scheduler_func = scheduler_func
def sample(self, latent_image, hook=None):
model, seed, steps, cfg, sampler_name, scheduler, positive, negative, denoise = self.params
@@ -314,4 +325,4 @@ class KSamplerWrapper:
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]
return impact_sample(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise, scheduler_func=self.scheduler_func)
modules/impact/segs_nodes.py
+12 -9
View File
@@ -41,6 +41,7 @@ class SEGSDetailer:
"refiner_basic_pipe_opt": ("BASIC_PIPE",),
"inpaint_model": ("BOOLEAN", {"default": False, "label_on": "enabled", "label_off": "disabled"}),
"noise_mask_feather": ("INT", {"default": 20, "min": 0, "max": 100, "step": 1}),
"scheduler_func_opt": ("SCHEDULER_FUNC",),
}
}
@@ -55,7 +56,7 @@ class SEGSDetailer:
@staticmethod
def do_detail(image, segs, guide_size, guide_size_for, max_size, seed, steps, cfg, sampler_name, scheduler,
denoise, noise_mask, force_inpaint, basic_pipe, refiner_ratio=None, batch_size=1, cycle=1,
refiner_basic_pipe_opt=None, inpaint_model=False, noise_mask_feather=0):
refiner_basic_pipe_opt=None, inpaint_model=False, noise_mask_feather=0, scheduler_func_opt=None):
model, clip, vae, positive, negative = basic_pipe
if refiner_basic_pipe_opt is None:
@@ -108,7 +109,7 @@ class SEGSDetailer:
refiner_ratio=refiner_ratio, refiner_model=refiner_model,
refiner_clip=refiner_clip, refiner_positive=refiner_positive, refiner_negative=refiner_negative,
control_net_wrapper=seg.control_net_wrapper, cycle=cycle,
inpaint_model=inpaint_model, noise_mask_feather=noise_mask_feather)
inpaint_model=inpaint_model, noise_mask_feather=noise_mask_feather, scheduler_func_opt=scheduler_func_opt)
if cnet_pils is not None:
cnet_pil_list.extend(cnet_pils)
@@ -125,7 +126,7 @@ class SEGSDetailer:
def doit(self, image, segs, guide_size, guide_size_for, max_size, seed, steps, cfg, sampler_name, scheduler,
denoise, noise_mask, force_inpaint, basic_pipe, refiner_ratio=None, batch_size=1, cycle=1,
refiner_basic_pipe_opt=None, inpaint_model=False, noise_mask_feather=0):
refiner_basic_pipe_opt=None, inpaint_model=False, noise_mask_feather=0, scheduler_func_opt=None):
if len(image) > 1:
raise Exception('[Impact Pack] ERROR: SEGSDetailer does not allow image batches.\nPlease refer to https://github.com/ltdrdata/ComfyUI-extension-tutorials/blob/Main/ComfyUI-Impact-Pack/tutorial/batching-detailer.md for more information.')
@@ -133,13 +134,13 @@ class SEGSDetailer:
segs, cnet_pil_list = SEGSDetailer.do_detail(image, segs, guide_size, guide_size_for, max_size, seed, steps, cfg, sampler_name,
scheduler, denoise, noise_mask, force_inpaint, basic_pipe, refiner_ratio, batch_size, cycle=cycle,
refiner_basic_pipe_opt=refiner_basic_pipe_opt,
inpaint_model=inpaint_model, noise_mask_feather=noise_mask_feather)
inpaint_model=inpaint_model, noise_mask_feather=noise_mask_feather, scheduler_func_opt=scheduler_func_opt)
# set fallback image
if len(cnet_pil_list) == 0:
cnet_pil_list = [empty_pil_tensor()]
return (segs, cnet_pil_list)
return segs, cnet_pil_list
class SEGSPaste:
@@ -1743,6 +1744,7 @@ class SEGSUpscaler:
"optional": {
"upscale_model_opt": ("UPSCALE_MODEL",),
"upscaler_hook_opt": ("UPSCALER_HOOK",),
"scheduler_func_opt": ("SCHEDULER_FUNC",),
}
}
@@ -1754,7 +1756,7 @@ class SEGSUpscaler:
@staticmethod
def doit(image, segs, model, clip, vae, rescale_factor, resampling_method, supersample, rounding_modulus,
seed, steps, cfg, sampler_name, scheduler, positive, negative, denoise, feather, inpaint_model, noise_mask_feather,
upscale_model_opt=None, upscaler_hook_opt=None):
upscale_model_opt=None, upscaler_hook_opt=None, scheduler_func_opt=None):
new_image = segs_upscaler.upscaler(image, upscale_model_opt, rescale_factor, resampling_method, supersample, rounding_modulus)
@@ -1780,7 +1782,7 @@ class SEGSUpscaler:
enhanced_image = segs_upscaler.img2img_segs(cropped_image, model, clip, vae, seg_seed, steps, cfg, sampler_name, scheduler,
positive, negative, denoise,
noise_mask=cropped_mask, control_net_wrapper=seg.control_net_wrapper,
inpaint_model=inpaint_model, noise_mask_feather=noise_mask_feather)
inpaint_model=inpaint_model, noise_mask_feather=noise_mask_feather, scheduler_func_opt=scheduler_func_opt)
if not (enhanced_image is None):
new_image = new_image.cpu()
enhanced_image = enhanced_image.cpu()
@@ -1822,6 +1824,7 @@ class SEGSUpscalerPipe:
"optional": {
"upscale_model_opt": ("UPSCALE_MODEL",),
"upscaler_hook_opt": ("UPSCALER_HOOK",),
"scheduler_func_opt": ("SCHEDULER_FUNC",),
}
}
@@ -1833,10 +1836,10 @@ class SEGSUpscalerPipe:
@staticmethod
def doit(image, segs, basic_pipe, rescale_factor, resampling_method, supersample, rounding_modulus,
seed, steps, cfg, sampler_name, scheduler, denoise, feather, inpaint_model, noise_mask_feather,
upscale_model_opt=None, upscaler_hook_opt=None):
upscale_model_opt=None, upscaler_hook_opt=None, scheduler_func_opt=None):
model, clip, vae, positive, negative = basic_pipe
return SEGSUpscaler.doit(image, segs, model, clip, vae, rescale_factor, resampling_method, supersample, rounding_modulus,
seed, steps, cfg, sampler_name, scheduler, positive, negative, denoise, feather, inpaint_model, noise_mask_feather,
upscale_model_opt=upscale_model_opt, upscaler_hook_opt=upscaler_hook_opt)
upscale_model_opt=upscale_model_opt, upscaler_hook_opt=upscaler_hook_opt, scheduler_func_opt=scheduler_func_opt)
modules/impact/segs_upscaler.py
+2 -2
View File
@@ -83,7 +83,7 @@ def upscaler(image, upscale_model, rescale_factor, resampling_method, supersampl
def img2img_segs(image, model, clip, vae, seed, steps, cfg, sampler_name, scheduler,
positive, negative, denoise, noise_mask, control_net_wrapper=None,
inpaint_model=False, noise_mask_feather=0):
inpaint_model=False, noise_mask_feather=0, scheduler_func_opt=None):
original_image_size = image.shape[1:3]
@@ -115,7 +115,7 @@ def img2img_segs(image, model, clip, vae, seed, steps, cfg, sampler_name, schedu
refined_latent = latent_image
# ksampler
refined_latent = impact_sampling.ksampler_wrapper(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, refined_latent, denoise)
refined_latent = impact_sampling.ksampler_wrapper(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, refined_latent, denoise, scheduler_func=scheduler_func_opt)
# non-latent downscale - latent downscale cause bad quality
refined_image = vae.decode(refined_latent['samples'])
modules/impact/special_samplers.py
+50 -13
View File
@@ -49,6 +49,9 @@ class KSamplerProvider:
"denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
"basic_pipe": ("BASIC_PIPE", )
},
"optional": {
"scheduler_func_opt": ("SCHEDULER_FUNC",),
}
}
RETURN_TYPES = ("KSAMPLER",)
@@ -57,9 +60,9 @@ class KSamplerProvider:
CATEGORY = "ImpactPack/Sampler"
@staticmethod
def doit(seed, steps, cfg, sampler_name, scheduler, denoise, basic_pipe):
def doit(seed, steps, cfg, sampler_name, scheduler, denoise, basic_pipe, scheduler_func_opt=None):
model, _, _, positive, negative = basic_pipe
sampler = KSamplerWrapper(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, denoise)
sampler = KSamplerWrapper(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, denoise, scheduler_func=scheduler_func_opt)
return (sampler, )
@@ -74,7 +77,8 @@ class KSamplerAdvancedProvider:
"basic_pipe": ("BASIC_PIPE", )
},
"optional": {
"sampler_opt": ("SAMPLER", )
"sampler_opt": ("SAMPLER", ),
"scheduler_func_opt": ("SCHEDULER_FUNC",),
}
}
@@ -84,9 +88,9 @@ class KSamplerAdvancedProvider:
CATEGORY = "ImpactPack/Sampler"
@staticmethod
def doit(cfg, sampler_name, scheduler, basic_pipe, sigma_factor=1.0, sampler_opt=None):
def doit(cfg, sampler_name, scheduler, basic_pipe, sigma_factor=1.0, sampler_opt=None, scheduler_func_opt=None):
model, _, _, positive, negative = basic_pipe
sampler = KSamplerAdvancedWrapper(model, cfg, sampler_name, scheduler, positive, negative, sampler_opt=sampler_opt, sigma_factor=sigma_factor)
sampler = KSamplerAdvancedWrapper(model, cfg, sampler_name, scheduler, positive, negative, sampler_opt=sampler_opt, sigma_factor=sigma_factor, scheduler_func=scheduler_func_opt)
return (sampler, )
@@ -581,18 +585,22 @@ class KSamplerBasicPipe:
"scheduler": (core.SCHEDULERS, ),
"latent_image": ("LATENT", ),
"denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
}
},
"optional":
{
"scheduler_func_opt": ("SCHEDULER_FUNC", ),
}
}
RETURN_TYPES = ("BASIC_PIPE", "LATENT", "VAE")
FUNCTION = "sample"
CATEGORY = "sampling"
CATEGORY = "ImpactPack/sampling"
@staticmethod
def sample(basic_pipe, seed, steps, cfg, sampler_name, scheduler, latent_image, denoise=1.0):
def sample(basic_pipe, seed, steps, cfg, sampler_name, scheduler, latent_image, denoise=1.0, scheduler_func_opt=None):
model, clip, vae, positive, negative = basic_pipe
latent = impact_sample(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise)
latent = impact_sample(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise, scheduler_func=scheduler_func_opt)
return basic_pipe, latent, vae
@@ -611,18 +619,47 @@ class KSamplerAdvancedBasicPipe:
"start_at_step": ("INT", {"default": 0, "min": 0, "max": 10000}),
"end_at_step": ("INT", {"default": 10000, "min": 0, "max": 10000}),
"return_with_leftover_noise": ("BOOLEAN", {"default": False, "label_on": "enable", "label_off": "disable"}),
}
},
"optional":
{
"scheduler_func_opt": ("SCHEDULER_FUNC", ),
}
}
RETURN_TYPES = ("BASIC_PIPE", "LATENT", "VAE")
FUNCTION = "sample"
CATEGORY = "sampling"
CATEGORY = "ImpactPack/sampling"
@staticmethod
def sample(basic_pipe, add_noise, noise_seed, steps, cfg, sampler_name, scheduler, latent_image, start_at_step, end_at_step, return_with_leftover_noise, denoise=1.0):
def sample(basic_pipe, add_noise, noise_seed, steps, cfg, sampler_name, scheduler, latent_image, start_at_step, end_at_step, return_with_leftover_noise, denoise=1.0, scheduler_func_opt=None):
model, clip, vae, positive, negative = basic_pipe
latent = separated_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)
latent = separated_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, scheduler_func=scheduler_func_opt)
return basic_pipe, latent, vae
class GITSSchedulerFuncProvider:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"coeff": ("FLOAT", {"default": 1.20, "min": 0.80, "max": 1.50, "step": 0.05}),
"denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
}
}
RETURN_TYPES = ("SCHEDULER_FUNC",)
CATEGORY = "ImpactPack/sampling"
FUNCTION = "doit"
def doit(self, coeff, denoise):
try:
import comfy_extras.nodes_gits as node_gits
except Exception:
raise Exception("[Impact Pack] ComfyUI is an outdated version.")
def f(model, sampler, steps):
return node_gits.GITSScheduler().get_sigmas(coeff, steps, denoise)[0]
return (f, )
pyproject.toml
+1 -1
View File
@@ -1,7 +1,7 @@
[project]
name = "comfyui-impact-pack"
description = "This extension offers various detector nodes and detailer nodes that allow you to configure a workflow that automatically enhances facial details. And provide iterative upscaler."
version = "5.14.1"
version = "5.15"
license = "LICENSE"
dependencies = ["segment-anything", "scikit-image", "piexif", "transformers", "opencv-python-headless", "GitPython", "scipy>=1.11.4"]