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add:alignYourSteps of scheduler in easy preSamplingCustom #146

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yolain
2024-04-26 15:35:06 +08:00
parent f6b5f5c99c
commit 56de6f0bc9
6 changed files with 45 additions and 29 deletions
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README.en.md
+2 -2
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@@ -35,14 +35,14 @@
**v1.1.6 (2024/4/26)**
- `easy preSamplingCustom` added **alignYourSteps** to **schedulder** widget
- `easy kSampler` & `easy fullkSampler` added **Preview&Choose** to **image_output** widget
- Added `easy styleAlignedBatchAlign` - Credit of [style_aligned_comfy](https://github.com/brianfitzgerald/style_aligned_comfy)
- Added `easy ckptNames`
- Added `easy controlnetNames`
- Added `easy imagesSplitimage` - Batch images split into single images
- Added `easy imageCount` - Get Image Count
- Added `easy textSwitch` - Text Switch
- `easy kSampler` & `easy fullkSampler` added **Preview&Choose** to **image_output** widget
**v1.1.5 (2024/4/24)**
README.md
+2 -1
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@@ -39,13 +39,14 @@
**v1.1.6 (2024/4/26)**
- `easy preSamplingCustom` schedulder 增加 **alignYourSteps** 选项
- `easy kSampler` 和 `easy fullkSampler` 的 **image_output** 增加 **Preview&Choose**选项
- 增加 `easy styleAlignedBatchAlign` - 风格对齐 [style_aligned_comfy](https://github.com/brianfitzgerald/style_aligned_comfy)
- 增加 `easy ckptNames`
- 增加 `easy controlnetNames`
- 增加 `easy imagesSplitimage` - 批次图像拆分单张
- 增加 `easy imageCount` - 图像数量
- 增加 `easy textSwitch` - 文字切换
- `easy kSampler` 和 `easy fullkSampler` 的 **image_output** 增加 **Preview&Choose**选项
**v1.1.5**
py/easyNodes.py
+31 -17
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@@ -3275,7 +3275,7 @@ class samplerSettingsNoiseIn:
return (new_pipe,)
# 预采样设置（自定义）
from comfy_extras.nodes_custom_sampler import BasicGuider, DualCFGGuider, CFGGuider, KSamplerSelect, DisableNoise, RandomNoise, BasicScheduler, KarrasScheduler, ExponentialScheduler, PolyexponentialScheduler, SDTurboScheduler, VPScheduler, FlipSigmas
import comfy_extras.nodes_custom_sampler as custom_samplers
from tqdm import trange
class samplerCustomSettings:
@@ -3290,7 +3290,7 @@ class samplerCustomSettings:
"cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0}),
"cfg_negative": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0}),
"sampler_name": (comfy.samplers.KSampler.SAMPLERS + ['inversed_euler'],),
"scheduler": (comfy.samplers.KSampler.SCHEDULERS + ['karrasADV','exponentialADV','polyExponential', 'sdturbo', 'vp'],),
"scheduler": (comfy.samplers.KSampler.SCHEDULERS + ['karrasADV','exponentialADV','polyExponential', 'sdturbo', 'vp', 'alignYourSteps'],),
"steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
"sigma_max": ("FLOAT", {"default": 14.614642, "min": 0.0, "max": 1000.0, "step": 0.01, "round": False}),
"sigma_min": ("FLOAT", {"default": 0.0291675, "min": 0.0, "max": 1000.0, "step": 0.01, "round": False}),
@@ -3380,6 +3380,14 @@ class samplerCustomSettings:
ksampler = comfy.samplers.KSAMPLER(sample_inversed_euler)
return (ksampler,)
def get_custom_cls(self, sampler_name):
try:
cls = custom_samplers.__dict__[sampler_name]
print(cls)
return cls()
except:
raise Exception(f"Custom sampler {sampler_name} not found, Please updated your ComfyUI")
def settings(self, pipe, guider, cfg, cfg_negative, sampler_name, scheduler, steps, sigma_max, sigma_min, rho, beta_d, beta_min, eps_s, flip_sigmas, denoise, add_noise, seed, image_to_latent=None, latent=None, optional_sampler=None, optional_sigmas=None, prompt=None, extra_pnginfo=None, my_unique_id=None):
# 图生图转换
@@ -3389,7 +3397,6 @@ class samplerCustomSettings:
negative = pipe['negative']
batch_size = pipe["loader_settings"]["batch_size"] if "batch_size" in pipe["loader_settings"] else 1
_guider, sigmas = None, None
print(get_sd_version(model))
if image_to_latent is not None:
if guider == "IP2P+DualCFG":
positive, negative, latent = self.ip2p(pipe['positive'], pipe['negative'], vae, image_to_latent)
@@ -3411,11 +3418,11 @@ class samplerCustomSettings:
# guider
if guider == 'CFG':
_guider, = CFGGuider().get_guider(model, positive, negative, cfg)
_guider, = self.get_custom_cls('CFGGuider').get_guider(model, positive, negative, cfg)
elif guider in ['DualCFG', 'IP2P+DualCFG']:
_guider, = DualCFGGuider().get_guider(model, positive, negative, pipe['negative'], cfg, cfg_negative)
_guider, = self.get_custom_cls('DualCFGGuider').get_guider(model, positive, negative, pipe['negative'], cfg, cfg_negative)
else:
_guider, = BasicGuider().get_guider(model, positive)
_guider, = self.get_custom_cls('BasicGuider').get_guider(model, positive)
# sampler
if optional_sampler:
@@ -3424,35 +3431,42 @@ class samplerCustomSettings:
if sampler_name == 'inversed_euler':
sampler, = self.get_inversed_euler_sampler()
else:
sampler, = KSamplerSelect().get_sampler(sampler_name)
sampler, = self.get_custom_cls('KSamplerSelect').get_sampler(sampler_name)
# sigmas
if optional_sigmas:
sigmas = optional_sigmas
else:
if scheduler == 'vp':
sigmas, = VPScheduler().get_sigmas(steps, beta_d, beta_min, eps_s)
sigmas, = self.get_custom_cls('VPScheduler').get_sigmas(steps, beta_d, beta_min, eps_s)
elif scheduler == 'karrasADV':
sigmas, = KarrasScheduler().get_sigmas(steps, sigma_max, sigma_min, rho)
sigmas, = self.get_custom_cls('KarrasScheduler').get_sigmas(steps, sigma_max, sigma_min, rho)
elif scheduler == 'exponentialADV':
sigmas, = ExponentialScheduler().get_sigmas(steps, sigma_max, sigma_min)
sigmas, = self.get_custom_cls('ExponentialScheduler').get_sigmas(steps, sigma_max, sigma_min)
elif scheduler == 'polyExponential':
sigmas, = PolyexponentialScheduler().get_sigmas(steps, sigma_max, sigma_min, rho)
sigmas, = self.get_custom_cls('PolyexponentialScheduler').get_sigmas(steps, sigma_max, sigma_min, rho)
elif scheduler == 'sdturbo':
sigmas, = SDTurboScheduler().get_sigmas(model, steps, denoise)
sigmas, = self.get_custom_cls('SDTurboScheduler').get_sigmas(model, steps, denoise)
elif scheduler == 'alignYourSteps':
pass
try:
from comfy_extras.nodes_align_your_steps import AlignYourStepsScheduler
model_type = get_sd_version(model)
if model_type == 'unknown':
raise Exception("This Model not supported")
sigmas, = AlignYourStepsScheduler().get_sigmas(model_type.upper(), steps)
except:
raise Exception("Please update your ComfyUI")
else:
sigmas, = BasicScheduler().get_sigmas(model, scheduler, steps, denoise)
sigmas, = self.get_custom_cls('BasicScheduler').get_sigmas(model, scheduler, steps, denoise)
# filp_sigmas
if flip_sigmas:
sigmas, = FlipSigmas().get_sigmas(sigmas)
sigmas, = self.get_custom_cls('FlipSigmas').get_sigmas(sigmas)
# noise
if add_noise == 'disable':
noise, = DisableNoise().get_noise()
noise, = self.get_custom_cls('DisableNoise').get_noise()
else:
noise, = RandomNoise().get_noise(seed)
noise, = self.get_custom_cls('RandomNoise').get_noise(seed)
new_pipe = {
"model": pipe['model'],
py/layer_diffuse/func.py
+6 -6
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@@ -58,15 +58,15 @@ class LayerDiffuse:
except:
pass
if method in [LayerMethod.FG_ONLY_CONV, LayerMethod.FG_ONLY_ATTN] and sd_version == 'sd15':
if method in [LayerMethod.FG_ONLY_CONV, LayerMethod.FG_ONLY_ATTN] and sd_version == 'sd1':
self.frames = 1
elif method in [LayerMethod.BG_TO_BLEND, LayerMethod.FG_TO_BLEND, LayerMethod.BG_BLEND_TO_FG, LayerMethod.FG_BLEND_TO_BG] and sd_version == 'sd15':
elif method in [LayerMethod.BG_TO_BLEND, LayerMethod.FG_TO_BLEND, LayerMethod.BG_BLEND_TO_FG, LayerMethod.FG_BLEND_TO_BG] and sd_version == 'sd1':
self.frames = 2
batch_size, _, height, width = samples['samples'].shape
if batch_size % 2 != 0:
raise Exception(f"The batch size should be a multiple of 2. 批次大小需为2的倍数")
control_img = image
elif method == LayerMethod.EVERYTHING and sd_version == 'sd15':
elif method == LayerMethod.EVERYTHING and sd_version == 'sd1':
batch_size, _, height, width = samples['samples'].shape
self.frames = 3
if batch_size % 3 != 0:
@@ -77,7 +77,7 @@ class LayerDiffuse:
model_path = get_local_filepath(model_url, LAYER_DIFFUSION_DIR)
layer_lora_state_dict = load_layer_model_state_dict(model_path)
work_model = model.clone()
if sd_version == 'sd15':
if sd_version == 'sd1':
patcher = AttentionSharingPatcher(
work_model, self.frames, use_control=control_img is not None
)
@@ -97,7 +97,7 @@ class LayerDiffuse:
else:
c_concat = model.model.latent_format.process_in(torch.cat([samples["samples"], blend_samples["samples"]], dim=1))
samp_model, positive, negative = (work_model,) + self.apply_layer_c_concat(positive, negative, c_concat)
elif sd_version == 'sd15':
elif sd_version == 'sd1':
if method in [LayerMethod.BG_TO_BLEND, LayerMethod.BG_BLEND_TO_FG]:
additional_cond = (additional_cond[0], None)
elif method in [LayerMethod.FG_TO_BLEND, LayerMethod.FG_BLEND_TO_BG]:
@@ -169,7 +169,7 @@ class LayerDiffuse:
if sd_version not in ['sdxl', 'sd15']:
raise Exception(f"Only SDXL and SD1.5 model supported for Layer Diffusion")
method = self.get_layer_diffusion_method(layer_diffusion_method, blend_samples is not None)
sd15_allow = True if sd_version == 'sd15' and method in [LayerMethod.FG_ONLY_ATTN, LayerMethod.EVERYTHING, LayerMethod.BG_TO_BLEND, LayerMethod.BG_BLEND_TO_FG] else False
sd15_allow = True if sd_version == 'sd1' and method in [LayerMethod.FG_ONLY_ATTN, LayerMethod.EVERYTHING, LayerMethod.BG_TO_BLEND, LayerMethod.BG_BLEND_TO_FG] else False
sdxl_allow = True if sd_version == 'sdxl' and method in [LayerMethod.FG_ONLY_CONV, LayerMethod.FG_ONLY_ATTN, LayerMethod.BG_BLEND_TO_FG] else False
if sdxl_allow or sd15_allow:
if self.vae_transparent_decoder is None:
py/libs/utils.py
+1 -2
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@@ -98,13 +98,12 @@ import comfy.supported_models_base
def get_sd_version(model):
base: BaseModel = model.model
model_config: comfy.supported_models.supported_models_base.BASE = base.model_config
print(model_config)
if isinstance(model_config, comfy.supported_models.SDXL):
return 'sdxl'
elif isinstance(
model_config, (comfy.supported_models.SD15, comfy.supported_models.SD20)
):
return 'sd15'
return 'sd1'
elif isinstance(
model_config, (comfy.supported_models.SVD_img2vid)
):
web/js/easy/easyDynamicWidgets.js
+3 -1
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@@ -297,7 +297,9 @@ function widgetLogic(node, widget) {
toggleWidget(node, findWidgetByName(node, 'beta_min'),true)
toggleWidget(node, findWidgetByName(node, 'eps_s'),true)
}else{
toggleWidget(node, findWidgetByName(node, 'denoise'),true)
if(widget.value == 'alignYourSteps'){
toggleWidget(node, findWidgetByName(node, 'denoise'))
}else toggleWidget(node, findWidgetByName(node, 'denoise'),true)
toggleWidget(node, findWidgetByName(node, 'sigma_max'))
toggleWidget(node, findWidgetByName(node, 'sigma_min'))
toggleWidget(node, findWidgetByName(node, 'beta_d'))