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Fix references to some official nodes

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This commit is contained in:
yolain
2025-10-15 21:28:01 +08:00
parent 737feef400
commit 755723ce64
4 changed files with 41 additions and 19 deletions
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py/nodes/adapter.py
+9 -2
View File
@@ -1,5 +1,6 @@
import re
import torch
import folder_paths
import comfy.utils, comfy.sample, comfy.samplers, comfy.controlnet, comfy.model_base, comfy.model_management, comfy.sampler_helpers, comfy.supported_models
from comfy_extras.nodes_compositing import JoinImageWithAlpha
from comfy.clip_vision import load as load_clip_vision
@@ -180,7 +181,10 @@ class icLightApply:
image = self.removebg(image)
else:
mask = torch.full((1, height, width), 1.0, dtype=torch.float32, device="cpu")
image, = JoinImageWithAlpha().join_image_with_alpha(image, mask)
try:
image, = JoinImageWithAlpha().execute(image, mask)
except:
image, = JoinImageWithAlpha().join_image_with_alpha(image, mask)
iclight = ICLight()
if mode == 'Foreground':
@@ -190,7 +194,10 @@ class icLightApply:
if source not in ['Use Background Image', 'Use Flipped Background Image']:
_, height, width, _ = lighting_image.shape
mask = torch.full((1, height, width), 1.0, dtype=torch.float32, device="cpu")
lighting_image, = JoinImageWithAlpha().join_image_with_alpha(lighting_image, mask)
try:
lighting_image, = JoinImageWithAlpha().execute(lighting_image, mask)
except:
lighting_image, = JoinImageWithAlpha().join_image_with_alpha(lighting_image, mask)
if batch_size < 2:
image = self.batch(image, lighting_image)
else:
py/nodes/image.py
+5 -1
View File
@@ -1396,7 +1396,11 @@ class humanSegmentation:
alpha = 1.0 - mask
output_image, = JoinImageWithAlpha().join_image_with_alpha(image, alpha)
try:
output_image, = JoinImageWithAlpha().execute(image, alpha)
except:
output_image, = JoinImageWithAlpha().join_image_with_alpha(image, alpha)
elif method == "human_parts (deeplabv3p)":
if method in cache:
py/nodes/inpaint.py
+4 -1
View File
@@ -331,7 +331,10 @@ class applyInpaint:
new_pipe = self.inpaint_model_conditioning(new_pipe, image, vae, mask, grow_mask_by, noise_mask=noise_mask)
cls = ALL_NODE_CLASS_MAPPINGS['DifferentialDiffusion']
if cls is not None:
model, = cls().apply(new_pipe['model'])
try:
model, = cls().execute(new_pipe['model'])
except Exception:
model, = cls().apply(new_pipe['model'])
new_pipe['model'] = model
else:
raise Exception("Differential Diffusion not found,please update comfyui")
py/nodes/samplers.py
+23 -15
View File
@@ -1,6 +1,7 @@
import sys, re, time
import torch
import comfy.utils, comfy.sample, comfy.samplers, comfy.controlnet, comfy.model_base, comfy.model_management, comfy.sampler_helpers, comfy.supported_models
import folder_paths
from comfy.model_patcher import ModelPatcher
from comfy_extras.nodes_mask import GrowMask
import comfy_extras.nodes_custom_sampler as custom_samplers
@@ -118,7 +119,14 @@ class samplerFull:
def get_custom_cls(self, sampler_name):
try:
cls = custom_samplers.__dict__[sampler_name]
return cls()
cls = cls()
if hasattr(cls, "get_sigmas"):
cls.execute = cls.get_sigmas
elif hasattr(cls, "get_guider"):
cls.execute = cls.get_guider
elif hasattr(cls, "get_sampler"):
cls.execute = cls.get_sampler
return cls
except:
raise Exception(f"Custom sampler {sampler_name} not found, Please updated your ComfyUI")
@@ -156,15 +164,15 @@ class samplerFull:
sigmas = optional_sigmas
else:
if scheduler == 'vp':
sigmas, = self.get_custom_cls('VPScheduler').get_sigmas(steps, beta_d, beta_min, eps_s)
sigmas, = self.get_custom_cls('VPScheduler').execute(steps, beta_d, beta_min, eps_s)
elif scheduler == 'karrasADV':
sigmas, = self.get_custom_cls('KarrasScheduler').get_sigmas(steps, sigma_max, sigma_min, rho)
sigmas, = self.get_custom_cls('KarrasScheduler').execute(steps, sigma_max, sigma_min, rho)
elif scheduler == 'exponentialADV':
sigmas, = self.get_custom_cls('ExponentialScheduler').get_sigmas(steps, sigma_max, sigma_min)
sigmas, = self.get_custom_cls('ExponentialScheduler').execute(steps, sigma_max, sigma_min)
elif scheduler == 'polyExponential':
sigmas, = self.get_custom_cls('PolyexponentialScheduler').get_sigmas(steps, sigma_max, sigma_min, rho)
sigmas, = self.get_custom_cls('PolyexponentialScheduler').execute(steps, sigma_max, sigma_min, rho)
elif scheduler == 'sdturbo':
sigmas, = self.get_custom_cls('SDTurboScheduler').get_sigmas(model, steps, denoise)
sigmas, = self.get_custom_cls('SDTurboScheduler').execute(model, steps, denoise)
elif scheduler == 'alignYourSteps':
model_type = get_sd_version(model)
if model_type == 'unknown':
@@ -173,11 +181,11 @@ class samplerFull:
elif scheduler == 'gits':
sigmas, = gitsScheduler().get_sigmas(coeff, steps, denoise)
else:
sigmas, = self.get_custom_cls('BasicScheduler').get_sigmas(model, scheduler, steps, denoise)
sigmas, = self.get_custom_cls('BasicScheduler').execute(model, scheduler, steps, denoise)
# filp_sigmas
if flip_sigmas:
sigmas, = self.get_custom_cls('FlipSigmas').get_sigmas(sigmas)
sigmas, = self.get_custom_cls('FlipSigmas').execute(sigmas)
#######################################################################################
# brushnet
@@ -209,12 +217,12 @@ class samplerFull:
positive = c
if guider in ['CFG', 'IP2P+CFG']:
_guider, = self.get_custom_cls('CFGGuider').get_guider(model, positive, negative, cfg)
_guider, = self.get_custom_cls('CFGGuider').execute(model, positive, negative, cfg)
elif guider in ['DualCFG', 'IP2P+DualCFG']:
_guider, = self.get_custom_cls('DualCFGGuider').get_guider(model, positive, middle,
_guider, = self.get_custom_cls('DualCFGGuider').execute(model, positive, middle,
negative, cfg, cfg_negative)
else:
_guider, = self.get_custom_cls('BasicGuider').get_guider(model, positive)
_guider, = self.get_custom_cls('BasicGuider').execute(model, positive)
# sampler
if optional_sampler:
@@ -223,7 +231,7 @@ class samplerFull:
if sampler_name == 'inversed_euler':
_sampler, = self.get_inversed_euler_sampler()
else:
_sampler, = self.get_custom_cls('KSamplerSelect').get_sampler(sampler_name)
_sampler, = self.get_custom_cls('KSamplerSelect').execute(sampler_name)
return (_guider, _sampler, sigmas)
@@ -277,18 +285,18 @@ class samplerFull:
if width_downscale_factor > 1.75:
log_node_warn("Patch model unet add downscale...")
log_node_warn("Downscale factor:" + str(width_downscale_factor))
(samp_model,) = cls().patch(samp_model, downscale_options['block_number'], width_downscale_factor, 0, 0.35, True, "bicubic",
(samp_model,) = cls().execute(samp_model, downscale_options['block_number'], width_downscale_factor, 0, 0.35, True, "bicubic",
"bicubic")
elif height_downscale_factor > 1.25:
log_node_warn("Patch model unet add downscale....")
log_node_warn("Downscale factor:" + str(height_downscale_factor))
(samp_model,) = cls().patch(samp_model, downscale_options['block_number'], height_downscale_factor, 0, 0.35, True, "bicubic",
(samp_model,) = cls().execute(samp_model, downscale_options['block_number'], height_downscale_factor, 0, 0.35, True, "bicubic",
"bicubic")
else:
cls = ALL_NODE_CLASS_MAPPINGS['PatchModelAddDownscale']
log_node_warn("Patch model unet add downscale....")
log_node_warn("Downscale factor:" + str(downscale_options['downscale_factor']))
(samp_model,) = cls().patch(samp_model, downscale_options['block_number'], downscale_options['downscale_factor'], downscale_options['start_percent'], downscale_options['end_percent'], downscale_options['downscale_after_skip'], downscale_options['downscale_method'], downscale_options['upscale_method'])
(samp_model,) = cls().execute(samp_model, downscale_options['block_number'], downscale_options['downscale_factor'], downscale_options['start_percent'], downscale_options['end_percent'], downscale_options['downscale_after_skip'], downscale_options['downscale_method'], downscale_options['upscale_method'])
return samp_model
def process_sample_state(pipe, samp_model, samp_clip, samp_samples, samp_vae, samp_seed, samp_positive,