nodes/ltdrdata-ComfyUI-Impact-Pack
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improve: KSamplerAdvancedProvider - add 'sigma_factor'

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Dr.Lt.Data
2024-02-13 00:04:29 +09:00
parent dd6ecaf9c2
commit 8a4050ed2a
4 changed files with 17 additions and 14 deletions
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README.md
+2 -1
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@@ -158,7 +158,8 @@ This takes latent as input and outputs latent as the result.
* You need to install the [BlenderNeko/ComfyUI_TiledKSampler](https://github.com/BlenderNeko/ComfyUI_TiledKSampler) node extension.
* TwoAdvancedSamplersForMask - TwoSamplersForMask is similar to TwoAdvancedSamplersForMask, but they differ in their operation. TwoSamplersForMask performs sampling in the mask area only after all the samples in the base area are finished. On the other hand, TwoAdvancedSamplersForMask performs sampling in both the base area and the mask area sequentially at each step.
* KSamplerAdvancedProvider - This is a wrapper that enables KSampler to be used in TwoAdvancedSamplersForMask.
* KSamplerAdvancedProvider - This is a wrapper that enables KSampler to be used in TwoAdvancedSamplersForMask, RegionalSampler.
* sigma_factor: By multiplying the noise schedule by the sigma_factor, you can adjust the amount of denoising based on the configured denoise.
* TwoSamplersForMaskUpscalerProvider - This is an Upscaler that extends TwoSamplersForMask to be used in Iterative Upscale.
* TwoSamplersForMaskUpscalerProviderPipe - pipe version of TwoSamplersForMaskUpscalerProvider.
modules/impact/config.py
+1 -1
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@@ -2,7 +2,7 @@ import configparser
import os
version_code = [4, 75, 1]
version_code = [4, 76]
version = f"V{version_code[0]}.{version_code[1]}" + (f'.{version_code[2]}' if len(version_code) > 2 else '')
dependency_version = 20
modules/impact/impact_sampling.py
+11 -10
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@@ -119,10 +119,10 @@ def separated_sample(model, add_noise, seed, steps, cfg, sampler_name, scheduler
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):
refiner_ratio=None, refiner_model=None, refiner_clip=None, refiner_positive=None, refiner_negative=None, sigma_factor=1.0):
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]
refined_latent = nodes.KSampler().sample(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise * sigma_factor)[0]
else:
advanced_steps = math.floor(steps / denoise)
start_at_step = advanced_steps - steps
@@ -130,7 +130,7 @@ 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)
positive, negative, latent_image, start_at_step, end_at_step, True, sigma_ratio=sigma_factor)
if 'noise_mask' in latent_image:
# noise_latent = \
@@ -143,7 +143,7 @@ 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)
refiner_positive, refiner_negative, temp_latent, end_at_step, advanced_steps + 1, False, sigma_ratio=sigma_factor)
return refined_latent
@@ -151,18 +151,19 @@ 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):
self.params = model, cfg, sampler_name, scheduler, positive, negative
def __init__(self, model, cfg, sampler_name, scheduler, positive, negative, sampler_opt=None, sigma_factor=1.0):
self.params = model, cfg, sampler_name, scheduler, positive, negative, sigma_factor
self.sampler_opt = sampler_opt
def clone_with_conditionings(self, positive, negative):
model, cfg, sampler_name, scheduler, _, _ = self.params
model, cfg, sampler_name, scheduler, _, _, _ = self.params
return KSamplerAdvancedWrapper(model, cfg, sampler_name, scheduler, positive, negative, self.sampler_opt)
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
model, cfg, sampler_name, scheduler, positive, negative, sigma_factor = self.params
# steps, start_at_step, end_at_step = self.compensate_denoise(steps, start_at_step, end_at_step)
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,
@@ -183,7 +184,7 @@ 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, sampler_opt=self.sampler_opt)
return_with_leftover_noise, sigma_ratio=sigma_ratio * sigma_factor, sampler_opt=self.sampler_opt)
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")
@@ -207,7 +208,7 @@ 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, sampler_opt=self.sampler_opt)
return_with_leftover_noise, sigma_ratio=recovery_sigma_ratio * sigma_factor, sampler_opt=self.sampler_opt)
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")
modules/impact/special_samplers.py
+3 -2
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@@ -67,6 +67,7 @@ class KSamplerAdvancedProvider:
"cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0}),
"sampler_name": (comfy.samplers.KSampler.SAMPLERS, ),
"scheduler": (comfy.samplers.KSampler.SCHEDULERS, ),
"sigma_factor": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
"basic_pipe": ("BASIC_PIPE", )
},
"optional": {
@@ -79,9 +80,9 @@ class KSamplerAdvancedProvider:
CATEGORY = "ImpactPack/Sampler"
def doit(self, cfg, sampler_name, scheduler, basic_pipe, sampler_opt=None):
def doit(self, cfg, sampler_name, scheduler, basic_pipe, sigma_factor=1.0, sampler_opt=None):
model, _, _, positive, negative = basic_pipe
sampler = KSamplerAdvancedWrapper(model, cfg, sampler_name, scheduler, positive, negative, sampler_opt=sampler_opt)
sampler = KSamplerAdvancedWrapper(model, cfg, sampler_name, scheduler, positive, negative, sampler_opt=sampler_opt, sigma_factor=sigma_factor)
return (sampler, )