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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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modules/impact/config.py
+1 -1
View File
@@ -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
View File
@@ -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, )