nodes/youyegit-tdxh_node_comfyui
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add some efficient nodes

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youyegit
2023-09-17 21:41:26 +08:00
parent 70fe0d600d
commit 355a6ace6f
2 changed files with 248 additions and 50 deletions
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tdxh_lib.py
+12 -12
View File
@@ -1,13 +1,4 @@
# Define a function that takes a tuple representing the image width and height as a parameter
def get_SDXL_best_size(image_size):
# Assign the image width and height to w and h respectively
w, h = image_size
# Calculate the image aspect ratio
ratio = w / h
# Define a list to store the target sizes
SD_XL_BASE_RATIOS = {
SD_XL_BASE_RATIOS = {
"0.33": (576, 1728), # guess and add
"0.35": (576, 1664), # guess and add
"0.42": (640, 1536), # add
@@ -39,8 +30,17 @@ def get_SDXL_best_size(image_size):
"3.0": (1728, 576),
}
target_sizes_show = [f"{k}:{v}" for k, v in SD_XL_BASE_RATIOS.items()]
# Define a function that takes a tuple representing the image width and height as a parameter
def get_SDXL_best_size(image_size):
# Assign the image width and height to w and h respectively
w, h = image_size
# Calculate the image aspect ratio
ratio = w / h
# Define a list to store the target sizes
target_sizes_more = [v for _, v in SD_XL_BASE_RATIOS.items()]
# print(target_sizes_more)
target_sizes = target_sizes_more
# target_sizes = [
@@ -55,7 +55,6 @@ def get_SDXL_best_size(image_size):
# (640, 1536) # 0.4166 # 5/12
# ]
# Define a variable to store the minimum difference
min_diff = float('inf')
# Define a variable to store the closest target size
@@ -84,3 +83,4 @@ def test1():
print(get_SDXL_best_size((1200, 500))) # Output (1536, 640)
# test1()
# print(target_sizes_show)
tdxh_node_comfyui.py
+236 -38
View File
@@ -10,7 +10,7 @@ import comfy.sd
import folder_paths
from .tdxh_lib import get_SDXL_best_size
from .tdxh_lib import get_SDXL_best_size, target_sizes_show
# Get the absolute path of various directories
my_dir = os.path.dirname(os.path.abspath(__file__))
@@ -70,6 +70,7 @@ class TdxhImageToSizeAdvanced:
"max": 8192,
"step": 8
}),
"width_multiply_by_height": (target_sizes_show,{"default": '1.0: (1024, 1024)'}),
"ratio": ("FLOAT", {
"default": 1.0,
"min": 0.0,
@@ -77,7 +78,7 @@ class TdxhImageToSizeAdvanced:
"step": 0.1
}),
"what_to_follow": ([
"only_width", "only_height", "both_width_and_height", "only_ratio",
"only_width", "only_height", "both_width_and_height","width * height", "only_ratio",
"only_image","get_SDXL_best_size"
],),
}
@@ -89,7 +90,7 @@ class TdxhImageToSizeAdvanced:
#OUTPUT_NODE = False
CATEGORY = "TDXH/tdxh_image"
def tdxh_image_to_size_advanced(self, image, width, height, ratio,what_to_follow):
def tdxh_image_to_size_advanced(self, image, width, height, width_multiply_by_height,ratio,what_to_follow):
image_size = self.tdxh_image_to_size(image)
# width = self.tdxh_nearest_divisible_by_8(width)
# height = self.tdxh_nearest_divisible_by_8(height)
@@ -102,6 +103,9 @@ class TdxhImageToSizeAdvanced:
w, h = self.tdxh_nearest_divisible_by_8(w), self.tdxh_nearest_divisible_by_8(h)
elif what_to_follow == "both_width_and_height":
w, h = width, height
elif what_to_follow == "width * height":
w_h_str = width_multiply_by_height.split(':')[-1].strip('()') # '3.0: (1728, 576)'
w, h = map(int, w_h_str.split(','))
elif what_to_follow == "only_width":
new_height = self.tdxh_nearest_divisible_by_8(image_size[1] * width / image_size[0])
w, h = width, new_height
@@ -138,19 +142,11 @@ class TdxhLoraLoader:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"bool_int": ("INT", {"default": 1, "min": 0, "max": 1, "step": 1}),
"model": ("MODEL",),
"clip": ("CLIP", ),
"bool_int": ("INT", {
"default": 1,
"min": 0,
"max": 1,
"step": 1
}),
"lora_name": (folder_paths.get_filename_list("loras"), ),
"strength_both": ("FLOAT", {
"default": 0.5, "min": -10.0,
"max": 10.0, "step": 0.05
}),
"strength_model": ("FLOAT", {
"default": 0.5, "min": -10.0,
"max": 10.0, "step": 0.05
@@ -159,6 +155,11 @@ class TdxhLoraLoader:
"default": 0.5, "min": -10.0,
"max": 10.0, "step": 0.05
}),
"strength_both": ("FLOAT", {
"default": 0.5, "min": -10.0,
"max": 10.0, "step": 0.05
}),
"what_to_follow": ([
"only_strength_both",
"strength_model_and_strength_clip"
@@ -171,29 +172,12 @@ class TdxhLoraLoader:
CATEGORY = "TDXH/tdxh_model"
def load_lora(self, model, clip, bool_int, lora_name, strength_both,strength_model, strength_clip, what_to_follow):
from nodes import LoraLoader
if bool_int == 0:
return (model, clip)
if what_to_follow == "only_strength_both":
strength_model, strength_clip = strength_both, strength_both
if strength_model == 0 and strength_clip == 0:
return (model, clip)
lora_path = folder_paths.get_full_path("loras", lora_name)
lora = None
if self.loaded_lora is not None:
if self.loaded_lora[0] == lora_path:
lora = self.loaded_lora[1]
else:
temp = self.loaded_lora
self.loaded_lora = None
del temp
if lora is None:
lora = comfy.utils.load_torch_file(lora_path, safe_load=True)
self.loaded_lora = (lora_path, lora)
model_lora, clip_lora = comfy.sd.load_lora_for_models(model, clip, lora, strength_model, strength_clip)
return (model_lora, clip_lora)
return LoraLoader().load_lora( model, clip, lora_name, strength_model, strength_clip)
class TdxhIntInput:
@classmethod
@@ -363,12 +347,7 @@ class TdxhOnOrOff:
def INPUT_TYPES(cls):
return {
"required": {
"ON_or_OFF": (
[
"ON",
"OFF"
],
),
"ON_or_OFF": (["ON", "OFF"],),
}
}
@@ -381,6 +360,209 @@ class TdxhOnOrOff:
def tdxh_value_output(self, ON_or_OFF):
bool_num = 1 if ON_or_OFF == "ON" else 0
return (bool_num, bool_num)
class TdxhBoolNumber:
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"bool_int": ("INT", {"default": 1, "min": 0, "max": 1, "step": 1}),
"bool_int_from_master": ("INT", {"default": 1, "min": 0, "max": 1, "step": 1}),
"control_by_master": (["ON", "OFF"],{"default":"OFF"}),
}
}
RETURN_TYPES = ("NUMBER","INT")
RETURN_NAMES = ("NUMBER","INT")
FUNCTION = "tdxh_value_output"
#OUTPUT_NODE = False
CATEGORY = "TDXH/tdxh_bool"
def tdxh_value_output(self, bool_int, bool_int_from_master, control_by_master):
if control_by_master == "OFF":
bool_num = bool_int
else:
if bool_int_from_master==1:
bool_num =bool_int
else:
bool_num = 0
return (bool_num, bool_num)
class TdxhClipVison:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"bool_int": ("INT", {"default": 1, "min": 0, "max": 1, "step": 1}),
"clip_name": (folder_paths.get_filename_list("clip_vision"), ), # CLIPVisionLoader
# "clip_vision": ("CLIP_VISION",),
"image": ("IMAGE",), # CLIPVisionEncode
"conditioning": ("CONDITIONING", ),
# "clip_vision_output": ("CLIP_VISION_OUTPUT", ),
"strength": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
"noise_augmentation": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01}),
}}
RETURN_TYPES = ("CONDITIONING",)
FUNCTION = "apply_adm"
CATEGORY = "TDXH/tdxh_efficiency"
def apply_adm(self,bool_int, clip_name, image, conditioning, strength, noise_augmentation):
from nodes import CLIPVisionLoader, CLIPVisionEncode, unCLIPConditioning
if bool_int == 0 or strength == 0:
return (conditioning,)
clip_vision = CLIPVisionLoader().load_clip(clip_name)[0]
clip_vision_output = CLIPVisionEncode().encode(clip_vision,image)[0]
return unCLIPConditioning().apply_adm(conditioning, clip_vision_output, strength, noise_augmentation)
from custom_nodes.comfyui_controlnet_aux import AUX_NODE_MAPPINGS,AIO_NOT_SUPPORTED
from nodes import MAX_RESOLUTION
class TdxhControlNetProcessor:
from nodes import ImageScale
upscale_methods = ImageScale.upscale_methods
crop_methods = ImageScale.crop_methods
@classmethod
def INPUT_TYPES(s):
auxs = list(AUX_NODE_MAPPINGS.keys())
for name in AIO_NOT_SUPPORTED:
if name in auxs: auxs.remove(name)
auxs.append("Invert")
auxs.append("None")
return {
"required": {
"bool_int": ("INT", {"default": 1, "min": 0, "max": 1, "step": 1}),
"image": ("IMAGE",),
"upscale_method": (s.upscale_methods,),
"width": ("INT", {"default": 512, "min": 1, "max": MAX_RESOLUTION, "step": 1}),
"height": ("INT", {"default": 512, "min": 1, "max": MAX_RESOLUTION, "step": 1}),
"crop": (s.crop_methods,),
# "image": ("IMAGE",),
"preprocessor": (auxs, {"default": "CannyEdgePreprocessor"})
}
}
RETURN_TYPES = ("IMAGE",)
FUNCTION = "execute"
CATEGORY = "TDXH/tdxh_efficiency"
def execute(self, bool_int,
image, upscale_method, width, height, crop,
preprocessor):
from nodes import ImageScale, ImageInvert
from custom_nodes.comfyui_controlnet_aux import AIO_Preprocessor
if bool_int == 0:
return (image,)
image = ImageScale().upscale(image, upscale_method, width, height, crop)[0]
if preprocessor == "None":
return (image,)
if preprocessor == "Invert":
return ImageInvert().invert(image)
return AIO_Preprocessor().execute( preprocessor, image)
class TdxhControlNetApply:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"bool_int": ("INT", {"default": 1, "min": 0, "max": 1, "step": 1}),
"control_net_name": (folder_paths.get_filename_list("controlnet"), ),
"positive": ("CONDITIONING", ),
"negative": ("CONDITIONING", ),
# "control_net": ("CONTROL_NET", ),
"image": ("IMAGE", ),
"strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
"start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}),
"end_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001})
}}
RETURN_TYPES = ("CONDITIONING","CONDITIONING")
RETURN_NAMES = ("positive", "negative")
FUNCTION = "apply_controlnet"
CATEGORY = "TDXH/tdxh_efficiency"
def apply_controlnet(self, bool_int,
control_net_name,
positive, negative, image, strength, start_percent, end_percent):
from nodes import ControlNetLoader,ControlNetApplyAdvanced
if bool_int == 0 or strength == 0:
return (positive, negative)
control_net=ControlNetLoader().load_controlnet(control_net_name)[0]
return ControlNetApplyAdvanced().apply_controlnet(positive, negative, control_net, image, strength, start_percent, end_percent)
class TdxhReference:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"bool_int": ("INT", {"default": 1, "min": 0, "max": 1, "step": 1}),
"main_latent":("LATENT",),
"pixels": ("IMAGE", ),
"vae": ("VAE", ),
"model": ("MODEL",),
# "reference": ("LATENT",),
"batch_size": ("INT", {"default": 1, "min": 1, "max": 64})
}}
RETURN_TYPES = ("MODEL", "LATENT")
FUNCTION = "reference_only"
CATEGORY = "TDXH/tdxh_efficiency"
def reference_only(self, bool_int, main_latent, pixels, vae, model, batch_size):
if bool_int == 0:
return (model,main_latent)
from nodes import VAEEncode
from custom_nodes.reference_only import ReferenceOnlySimple
reference=VAEEncode().encode(vae, pixels)[0]
return ReferenceOnlySimple().reference_only(model, reference, batch_size)
class TdxhImg2ImgLatent:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"bool_int": ("INT", {"default": 1, "min": 0, "max": 1, "step": 1}),
"main_latent":("LATENT",),
"main_width": ("INT", {"default": 1024, "min": 64, "max": MAX_RESOLUTION, "step": 8}),
"main_height": ("INT", {"default": 1024, "min": 64, "max": MAX_RESOLUTION, "step": 8}),
"pixels": ("IMAGE", ),
"vae": ("VAE", ),
# "samples": ("LATENT",),
"amount": ("INT", {"default": 1, "min": 1, "max": 64}),
"pixels_width": ("INT", {"default": 1024, "min": 64, "max": MAX_RESOLUTION, "step": 8}),
"pixels_height": ("INT", {"default": 1024, "min": 64, "max": MAX_RESOLUTION, "step": 8}),
"denoise_img2img":("FLOAT", {"default": 0.5, "min": 0, "max": 1.0, "step": 0.05}),
}}
RETURN_TYPES = ("LATENT","INT","INT","FLOAT")
RETURN_NAMES = ("LATENT","width_INT","height_INT","denoise")
FUNCTION = "repeat"
CATEGORY = "TDXH/tdxh_efficiency"
def repeat(self, bool_int, main_latent, main_width,main_height, pixels, vae, amount,pixels_width,pixels_height, denoise_img2img):
if bool_int == 0:
return (main_latent,main_width,main_height,1.0)
from nodes import VAEEncode,RepeatLatentBatch
samples = VAEEncode().encode(vae, pixels)[0]
return (RepeatLatentBatch().repeat(samples,amount)[0],pixels_width,pixels_height, denoise_img2img)
NODE_CLASS_MAPPINGS = {
# tdxh_image
@@ -395,6 +577,14 @@ NODE_CLASS_MAPPINGS = {
"TdxhStringInputTranslator":TdxhStringInputTranslator,
# tdxh_bool
"TdxhOnOrOff":TdxhOnOrOff,
"TdxhBoolNumber":TdxhBoolNumber,
# tdxh_efficiency
"TdxhClipVison" : TdxhClipVison,
"TdxhControlNetProcessor":TdxhControlNetProcessor,
"TdxhControlNetApply":TdxhControlNetApply,
"TdxhReference":TdxhReference,
"TdxhImg2ImgLatent":TdxhImg2ImgLatent,
}
NODE_DISPLAY_NAME_MAPPINGS = {
@@ -410,6 +600,14 @@ NODE_DISPLAY_NAME_MAPPINGS = {
"TdxhStringInputTranslator":"TdxhStringInputTranslator",
# tdxh_bool
"TdxhOnOrOff":"TdxhOnOrOff",
"TdxhBoolNumber":"TdxhBoolNumber",
# tdxh_efficiency
"TdxhClipVison" : "TdxhClipVison",
"TdxhControlNetProcessor":"TdxhControlNetProcessor",
"TdxhControlNetApply":"TdxhControlNetApply",
"TdxhReference":"TdxhReference",
"TdxhImg2ImgLatent":"TdxhImg2ImgLatent",
}