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api_nodes.py
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api_nodes.py
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# from ..comfyui_controlnet_aux.node_wrappers.openpose import OpenPose_Preprocessor
import folder_paths
import json
import comfy.samplers
import comfy.sample
import nodes
from comfy_extras.nodes_mask import (
ImageToMask,
ImageCompositeMasked,
LatentCompositeMasked,
)
import torch
import numpy as np
from PIL import Image, ImageOps
import hashlib
import os
from PIL.PngImagePlugin import PngImageFile, PngInfo
class LoadImageWithMetaData:
@classmethod
def INPUT_TYPES(s):
# input_dir = folder_paths.get_input_directory()
# files = [f for f in os.listdir(input_dir) if os.path.isfile(os.path.join(input_dir, f))]
# print("***files: ",files)
return {
"required": {
"image_path": (
"STRING",
{
"multiline": False, # True if you want the field to look like the one on the ClipTextEncode node
"default": "Hello World!",
},
),
},
# {"image": (sorted(files), )},
# "hidden": {"image_path": "PROMPT",}
}
CATEGORY = "Auto-Photoshop-SD"
OUTPUT_NODE = True
# RETURN_TYPES = ("IMAGE", "MASK")
RETURN_TYPES = ()
FUNCTION = "load_image"
def load_image(self, image_path):
# image_path = folder_paths.get_annotated_filepath(image)
# image_path = image
print("***image_path: ", image_path)
# Open the image file
image_temp = Image.open(image_path)
# Check if the image is a PNG file
if isinstance(image_temp, PngImageFile):
# Get the metadata from the image
metadata = image_temp.info
print("metadata:", metadata)
# Print the metadata
for key, value in metadata.items():
print(f"{key}: {value}")
i = Image.open(image_path)
i = ImageOps.exif_transpose(i)
image = i.convert("RGB")
image = np.array(image).astype(np.float32) / 255.0
image = torch.from_numpy(image)[None,]
if "A" in i.getbands():
mask = np.array(i.getchannel("A")).astype(np.float32) / 255.0
mask = 1.0 - torch.from_numpy(mask)
else:
mask = torch.zeros((64, 64), dtype=torch.float32, device="cpu")
# return (image, mask)
print("type of metadata: ", type(metadata))
print("type of prompt: ", type(metadata["prompt"]))
print("type of workflow: ", type(metadata["workflow"]))
return {"ui": {"prompt": metadata["prompt"], "workflow": metadata["workflow"]}}
# return { "prompt":metadata['prompt'],"workflow":metadata['workflow'] }
class GetConfig:
@classmethod
def INPUT_TYPES(s):
input_dir = folder_paths.get_input_directory()
files = [
f
for f in os.listdir(input_dir)
if os.path.isfile(os.path.join(input_dir, f))
]
return {
"required": {
"embeddings": (folder_paths.get_folder_paths("embeddings"),),
},
"optional": {
"controlnet_config": (controlnet_config.copy()),
}
# {"image": (sorted(files), )},
}
CATEGORY = "Auto-Photoshop-SD"
OUTPUT_NODE = True
RETURN_TYPES = ()
FUNCTION = "get_config"
def get_config(self):
checkpoints = folder_paths.get_filename_list("checkpoints")
samplers = comfy.samplers.KSampler.SAMPLERS
schedulers = comfy.samplers.KSampler.SCHEDULERS
loras = folder_paths.get_filename_list("loras")
latent_upscale_methods = [
"nearest-exact",
"bilinear",
"area",
"bicubic",
"bislerp",
]
latent_upscale_crop_methods = ["disabled", "center"]
# print("checkpoints: ", checkpoints)
return {
"ui": {
"checkpoints": checkpoints,
"samplers": samplers,
"schedulers": schedulers,
"latent_upscale_methods": latent_upscale_methods,
"latent_upscale_crop_methods": latent_upscale_crop_methods,
"loras": loras,
}
}
import base64
from io import BytesIO
class LoadImageBase64:
@classmethod
def INPUT_TYPES(s):
# input_dir = folder_paths.get_input_directory()
# files = [f for f in os.listdir(input_dir) if os.path.isfile(os.path.join(input_dir, f))]
return {
"required": {
"image_base64": (
"STRING",
{
"multiline": False, # True if you want the field to look like the one on the ClipTextEncode node
"default": "",
},
),
}
}
CATEGORY = "Auto-Photoshop-SD"
RETURN_TYPES = ("IMAGE", "MASK")
FUNCTION = "load_image_from_base64"
def load_image_from_base64(self, image_base64):
# Decode the base64 string
imgdata = base64.b64decode(image_base64)
# Open the image from memory
i = Image.open(BytesIO(imgdata))
i = ImageOps.exif_transpose(i)
image = i.convert("RGB")
image = np.array(image).astype(np.float32) / 255.0
image = torch.from_numpy(image)[None,]
if "A" in i.getbands():
mask = np.array(i.getchannel("A")).astype(np.float32) / 255.0
mask = 1.0 - torch.from_numpy(mask)
else:
mask = torch.zeros((64, 64), dtype=torch.float32, device="cpu")
return (image, mask)
from nodes import LoraLoader # Adjust this import statement to your project structure
import re
class LoadLorasFromPrompt:
def __init__(self):
self.lora_loaders = []
self.lora_list = folder_paths.get_filename_list("loras")
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"model": ("MODEL",),
"clip": ("CLIP",),
"prompt": ("STRING", {"multiline": True, "default": ""}),
}
}
CATEGORY = "Auto-Photoshop-SD"
RETURN_TYPES = ("MODEL", "CLIP", "STRING")
FUNCTION = "load_loras_from_prompt"
def extract_lora_info(self, prompt):
# Extract LoRA info
lora_info_list = re.findall(r"<lora:(.*?):(.*?)>", prompt)
# Remove LoRA symbols from the prompt
prompt_without_lora = re.sub(r"<lora:(.*?):(.*?)>", "", prompt)
return prompt_without_lora, lora_info_list
def load_loras_from_prompt(self, model, clip, prompt):
# Parse the loras_prompt string
prompt_without_lora, lora_info_list = self.extract_lora_info(prompt)
# print("prompt:", prompt)
# print("prompt_without_lora:", prompt_without_lora)
# print("lora_info_list:", lora_info_list)
out_model = model
out_clip = clip
# Create a LoraLoader for each lora and load it
for lora_name, strength in lora_info_list:
lora_name += (
".safetensors" # Add the .safetensors extension to the lora_name
)
strength = float(strength)
# print("lora_name:", lora_name)
# print("type(strength):", type(strength))
if lora_name in self.lora_list:
lora_loader = LoraLoader()
out_model, out_clip = lora_loader.load_lora(
out_model, out_clip, lora_name, strength, strength
)
self.lora_loaders.append((out_model, out_clip))
else:
print(
f"WARNING: The specified LoRa '{lora_name}' does not exist and will be skipped. Please ensure the LoRa name is correct and that the corresponding .safetensors file is available."
)
# return self.lora_loaders[-1]
# return (out_model,out_clip)
return (out_model, out_clip, prompt_without_lora)
import numpy as np
class GaussianLatentImage:
def __init__(self, device="cpu"):
self.device = device
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"width": (
"INT",
{"default": 512, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8},
),
"height": (
"INT",
{"default": 512, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8},
),
"batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}),
"seed": ("INT", {"default": 0, "min": 0, "max": 0xFFFFFFFFFFFFFFFF}),
}
}
RETURN_TYPES = ("LATENT",)
FUNCTION = "generate"
CATEGORY = "Auto-Photoshop-SD"
def generate(self, width, height, batch_size=1, seed=0):
# Set the seed for reproducibility
torch.manual_seed(seed)
# Define the mean and standard deviation
mean = 0
var = 10
sigma = var**0.5
# Generate Gaussian noise
gaussian = torch.randn((batch_size, 4, height // 8, width // 8)) * sigma + mean
# Move the tensor to the specified device
latent = gaussian.float().to(self.device)
return ({"samples": latent},)
class APS_LatentBatch:
@classmethod
def INPUT_TYPES(s):
return {"required": {"latent1": ("LATENT",), "latent2": ("LATENT",)}}
RETURN_TYPES = ("LATENT",)
FUNCTION = "batch"
CATEGORY = "Auto-Photoshop-SD"
def batch(self, latent1, latent2):
latent1_samples = latent1["samples"]
latent2_samples = latent2["samples"]
if latent1_samples.shape[1:] != latent2_samples.shape[1:]:
latent2_samples = comfy.utils.common_upscale(
latent2_samples.movedim(-1, 1),
latent1_samples.shape[2],
latent1_samples.shape[1],
"bilinear",
"center",
).movedim(1, -1)
s = torch.cat((latent1_samples, latent2_samples), dim=0)
return ({"samples": s},)
import io
import base64
from PIL import Image, ImageFilter
from torchvision import transforms
class MaskExpansion:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"mask": ("IMAGE",),
"expansion": ("INT", {"default": 0, "min": 0, "max": 256, "step": 1}),
"blur": ("INT", {"default": 0, "min": 0, "max": 64, "step": 1}),
}
}
RETURN_TYPES = ("IMAGE",)
FUNCTION = "expandAndBlur"
CATEGORY = "Auto-Photoshop-SD"
def expandAndBlur(self, **kwarg):
mask = kwarg.get("mask")
expansion = kwarg.get("expansion")
blur = kwarg.get("blur")
# print("type: mask: ", type(mask))
expanded_mask = self.maskExpansionHandler(mask, expansion, blur)
# print("expanded_mask:",expanded_mask)
# print("type: expanded_mask: ", type(expanded_mask))
return (expanded_mask,)
def b64_2_img(self, base64_image):
image = Image.open(io.BytesIO(base64.b64decode(base64_image.split(",", 1)[0])))
return image
def reserveBorderPixels(self, img, dilation_img):
pixels = img.load()
width, height = img.size
dilation_pixels = dilation_img.load()
depth = 1
for x in range(width):
for d in range(depth):
dilation_pixels[x, d] = pixels[x, d]
dilation_pixels[x, height - (d + 1)] = pixels[x, height - (d + 1)]
for y in range(height):
for d in range(depth):
dilation_pixels[d, y] = pixels[d, y]
dilation_pixels[width - (d + 1), y] = pixels[width - (d + 1), y]
return dilation_img
def maskExpansion(self, mask_img, mask_expansion, blur=10):
iteration = mask_expansion
dilated_img = self.applyDilation(mask_img, iteration)
blurred_image = dilated_img.filter(ImageFilter.GaussianBlur(radius=blur))
mask_with_border = self.reserveBorderPixels(mask_img, blurred_image)
return mask_with_border
async def base64ToPng(self, base64_image, image_path):
base64_img_bytes = base64_image.encode("utf-8")
with open(image_path, "wb") as file_to_save:
decoded_image_data = base64.decodebytes(base64_img_bytes)
file_to_save.write(decoded_image_data)
def applyDilation(self, img, iteration=20, max_filter=3):
dilation_img = img.copy()
for i in range(iteration):
dilation_img = dilation_img.filter(ImageFilter.MaxFilter(max_filter))
return dilation_img
def maskExpansionHandler(self, input_mask, mask_expansion, blur):
try:
# Check if input is a string or a tensor
if isinstance(input_mask, str):
self.base64ToPng(input_mask, "original_mask.png")
mask_image = self.b64_2_img(input_mask)
elif torch.is_tensor(input_mask):
# Ensure the tensor is 3-dimensional
# print("Shape of tensor: ", input_mask.size())
# print("Number of dimensions: ", input_mask.dim())
tensor = input_mask.squeeze(0).permute(
2, 0, 1
) # Remove batch dimension and rearrange dimensions
transform = transforms.ToPILImage()
mask_image = transform(tensor)
else:
raise ValueError(
"Input mask must be a base64 string or a PyTorch tensor"
)
expanded_mask_img = self.maskExpansion(mask_image, mask_expansion, blur)
# Convert PIL Image to PyTorch tensor
transform = transforms.ToTensor()
expanded_mask_tensor = transform(expanded_mask_img)
expanded_mask_tensor = expanded_mask_tensor.unsqueeze(0).permute(0, 2, 3, 1)
return expanded_mask_tensor
except:
raise Exception(f"couldn't perform mask expansion")
preprocessor_list = [
"None",
"CannyEdgePreprocessor",
"OpenposePreprocessor",
"HEDPreprocessor",
"FakeScribblePreprocessor",
"InpaintPreprocessor",
"LeReS-DepthMapPreprocessor",
"AnimeLineArtPreprocessor",
"LineArtPreprocessor",
"Manga2Anime_LineArt_Preprocessor",
"MediaPipe-FaceMeshPreprocessor",
"MiDaS-NormalMapPreprocessor",
"MiDaS-DepthMapPreprocessor",
"M-LSDPreprocessor",
"BAE-NormalMapPreprocessor",
"OneFormer-COCO-SemSegPreprocessor",
"OneFormer-ADE20K-SemSegPreprocessor",
"PiDiNetPreprocessor",
"ScribblePreprocessor",
"Scribble_XDoG_Preprocessor",
"SAMPreprocessor",
"ShufflePreprocessor",
"TilePreprocessor",
"UniFormer-SemSegPreprocessor",
"SemSegPreprocessor",
"Zoe-DepthMapPreprocessor",
]
controlnet_config = {
"CannyEdgePreprocessor": {
"low_threshold": 100,
"high_threshold": 200,
"resolution": 512,
"threshold_mapping": {
"threshold_a": "low_threshold",
"threshold_b": "high_threshold",
},
"param_config": {
"low_threshold": {
"type": "INT",
"default": 100,
"min": 0,
"max": 255,
"step": 1,
},
"high_threshold": {
"type": "INT",
"default": 200,
"min": 0,
"max": 255,
"step": 1,
},
},
},
"OpenposePreprocessor": {
"detect_hand": "enable",
"detect_body": "enable",
"detect_face": "enable",
"resolution": 512,
},
"HEDPreprocessor": {"safe": "enable"},
"FakeScribblePreprocessor": {"safe": "enable"},
"InpaintPreprocessor": {"mask": ""},
"LeReS-DepthMapPreprocessor": {"boost": "enable"},
"AnimeLineArtPreprocessor": {"resolution": 512},
"LineArtPreprocessor": {"resolution": 512, "coarse": "enable"},
"Manga2Anime_LineArt_Preprocessor": {
"resolution": 512,
},
"MediaPipe-FaceMeshPreprocessor": {
"max_faces": 10,
"min_confidence": 0.5,
"resolution": 512,
"threshold_mapping": {
"threshold_a": "max_faces",
"threshold_b": "min_confidence",
},
"param_config": {
"max_faces": {
"type": "INT",
"default": 10,
"min": 1,
"max": 50,
"step": 1,
},
"min_confidence": {
"type": "FLOAT",
"default": 0.5,
"min": 0.01,
"max": 1.0,
"step": 0.01,
},
},
},
"MiDaS-NormalMapPreprocessor": {
"a": np.pi * 2.0,
"bg_threshold": 0.1,
"resolution": 512,
"threshold_mapping": {
"threshold_a": "a",
"threshold_b": "bg_threshold",
},
"param_config": {
"a": {
"type": "FLOAT",
"default": np.pi * 2.0,
"min": 0.0,
"max": np.pi * 5.0,
"step": 0.05,
},
"bg_threshold": {
"type": "FLOAT",
"default": 0.1,
"min": 0,
"max": 1,
"step": 0.05,
},
},
},
"MiDaS-DepthMapPreprocessor": {
"a": np.pi * 2.0,
"bg_threshold": 0.1,
"resolution": 512,
"threshold_mapping": {
"threshold_a": "a",
"threshold_b": "bg_threshold",
},
"param_config": {
"a": {
"type": "FLOAT",
"default": np.pi * 2.0,
"min": 0.0,
"max": np.pi * 5.0,
"step": 0.05,
},
"bg_threshold": {
"type": "FLOAT",
"default": 0.1,
"min": 0,
"max": 1,
"step": 0.05,
},
},
},
"M-LSDPreprocessor": {
"score_threshold": 0.1,
"dist_threshold": 0.1,
"resolution": 512,
"threshold_mapping": {
"threshold_a": "score_threshold",
"threshold_b": "dist_threshold",
},
"param_config": {
"score_threshold": {
"type": "FLOAT",
"default": 0.1,
"min": 0.01,
"max": 2.0,
"step": 0.01,
},
"dist_threshold": {
"type": "FLOAT",
"default": 0.1,
"min": 0.01,
"max": 20.0,
"step": 0.01,
},
},
},
"BAE-NormalMapPreprocessor": {"resolution": 512},
"OneFormer-COCO-SemSegPreprocessor": {"resolution": 512},
"OneFormer-ADE20K-SemSegPreprocessor": {"resolution": 512},
"PiDiNetPreprocessor": {"safe": "enable", "resolution": 512},
"ScribblePreprocessor": {"resolution": 512},
"Scribble_XDoG_Preprocessor": {
"threshold": 32,
"resolution": 512,
"threshold_mapping": {
"threshold_a": "threshold",
},
"param_config": {
"threshold": {
"type": "INT",
"default": 32,
"min": 1,
"max": 64,
"step": 64,
},
},
},
# "SAMPreprocessor": {"resolution": 512},
"ShufflePreprocessor": {"resolution": 512},
"TilePreprocessor": {
"pyrUp_iters": 3,
"resolution": 512,
"threshold_mapping": {
"threshold_a": "pyrUp_iters",
},
"param_config": {
"pyrUp_iters": {
"type": "INT",
"default": 3,
"min": 1,
"max": 10,
"step": 1,
}
},
},
"UniFormer-SemSegPreprocessor": {"resolution": 512},
"SemSegPreprocessor": {"resolution": 512},
"Zoe-DepthMapPreprocessor": {"resolution": 512},
}
def convert_number(num, num_type):
if num_type == "INT":
return int(num)
elif num_type == "FLOAT":
return float(num)
else:
return "Invalid number type"
class ControlnetUnit:
def __init__(
self,
):
self.map = nodes.NODE_CLASS_MAPPINGS
self.map_param = controlnet_config.copy()
# @classmethod
# def INPUT_TYPES(s):
# return {"required": { "image": ("IMAGE",),
# "preprocessor_name": (s.preprocessor_list,)},
# "resolution": ("INT", {"default": 512, "min": 64, "max": 2048, "step": 64}),
# "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}),
# }
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"positive": ("CONDITIONING",),
"negative": ("CONDITIONING",),
"preprocessor_name": (preprocessor_list,),
"control_net_name": (folder_paths.get_filename_list("controlnet"),),
"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},
),
"resolution": (
"INT",
{"default": 512, "min": 64, "max": 2048, "step": 64},
),
},
"optional": {
"image": ("IMAGE",),
"mask": ("MASK",),
"threshold_a": (
"FLOAT",
{
"default": 0.0,
},
),
"threshold_b": (
"FLOAT",
{
"default": 0.0,
},
),
},
}
RETURN_TYPES = ("IMAGE", "CONDITIONING", "CONDITIONING")
RETURN_NAMES = ("preprocessed_image", "positive", "negative")
FUNCTION = "preprocessAndApply"
CATEGORY = "Auto-Photoshop-SD"
def preprocessAndApply(
self,
**kwargs,
):
instance = self.map[kwargs["preprocessor_name"]]
self.preprocessor = instance()
self.method = getattr(self.preprocessor, self.preprocessor.FUNCTION)
self.param = self.map_param.get(kwargs["preprocessor_name"], {}).copy()
if "mask" in self.param:
# print("mask:", kwargs["mask"])
self.param["mask"] = kwargs["mask"]
if "resolution" in self.param:
# print("resolution:", kwargs["resolution"])
self.param["resolution"] = kwargs["resolution"]
threshold_mapping = self.param.pop("threshold_mapping", None)
param_config = self.param.pop(
"param_config", None
) # don't pass param_config to method(), delete param_config
if threshold_mapping:
threshold_a_param_name = threshold_mapping.get("threshold_a")
threshold_b_param_name = threshold_mapping.get("threshold_b")
if threshold_a_param_name and "threshold_a" in kwargs:
value = kwargs["threshold_a"]
var_type = param_config[threshold_a_param_name]["type"]
converted_value = convert_number(value, var_type)
self.param.update({threshold_a_param_name: converted_value})
if threshold_b_param_name and "threshold_b" in kwargs:
value = kwargs["threshold_b"]
var_type = param_config[threshold_b_param_name]["type"]
converted_value = convert_number(value, var_type)
self.param.update({threshold_b_param_name: converted_value})
res = self.method(kwargs["image"], **self.param)
preprocessed_image = res
if "result" in res:
# print("res:", res)
(preprocessed_image,) = res["result"]
# print("type(res['result']):", type(res["result"]))
# print("type(preprocessed_image): ", type(preprocessed_image))
elif isinstance(res, tuple):
(preprocessed_image,) = res
(controlnet,) = nodes.ControlNetLoader().load_controlnet(
kwargs["control_net_name"]
)
(
new_positive,
new_negative,
) = nodes.ControlNetApplyAdvanced().apply_controlnet(
kwargs["positive"],
kwargs["negative"],
controlnet,
preprocessed_image,
kwargs["strength"],
kwargs["start_percent"],
kwargs["end_percent"],
)
return (preprocessed_image, new_positive, new_negative)
class ControlNetScript:
@classmethod
def INPUT_TYPES(s):
# model_list = folder_paths.get_filename_list("controlnet")
model_list = ["None"] + folder_paths.get_filename_list("controlnet")
# print("type model_list: ",type (model_list))
return {
"required": {
"positive": ("CONDITIONING",),
"negative": ("CONDITIONING",),
"is_enabled_1": (["disable", "enable"], {"default": "disable"}),
"preprocessor_name_1": (preprocessor_list,),
"control_net_name_1": (model_list,),
"strength_1": (
"FLOAT",
{"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01},
),
"threshold_a_1": (
"FLOAT",
{
"default": 0.0,
},
),
"threshold_b_1": (
"FLOAT",
{
"default": 0.0,
},
),
"start_percent_1": (
"FLOAT",
{"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001},
),
"end_percent_1": (
"FLOAT",
{"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001},
),
"resolution_1": (
"INT",
{"default": 512, "min": 64, "max": 2048, "step": 64},
),
"is_enabled_2": (["disable", "enable"], {"default": "disable"}),
"preprocessor_name_2": (preprocessor_list,),
"control_net_name_2": (model_list,),
"strength_2": (
"FLOAT",
{"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01},
),
"threshold_a_2": (
"FLOAT",
{
"default": 0.0,
},
),
"threshold_b_2": (
"FLOAT",
{
"default": 0.0,
},
),
"start_percent_2": (
"FLOAT",
{"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001},
),
"end_percent_2": (
"FLOAT",
{"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001},
),
"resolution_2": (
"INT",
{"default": 512, "min": 64, "max": 2048, "step": 64},
),
"is_enabled_3": (["disable", "enable"], {"default": "disable"}),
"preprocessor_name_3": (preprocessor_list,),
"control_net_name_3": (model_list,),
"strength_3": (
"FLOAT",
{"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01},
),
"threshold_a_3": (
"FLOAT",
{
"default": 0.0,
},
),
"threshold_b_3": (
"FLOAT",
{
"default": 0.0,
},
),
"start_percent_3": (
"FLOAT",
{"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001},
),
"end_percent_3": (
"FLOAT",
{"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001},
),
"resolution_3": (
"INT",
{"default": 512, "min": 64, "max": 2048, "step": 64},
),
},
"optional": {
"image_1": ("IMAGE",),
"mask_1": ("IMAGE",),
"image_2": ("IMAGE",),
"mask_2": ("IMAGE",),
"image_3": ("IMAGE",),
"mask_3": ("IMAGE",),
},
}
RETURN_TYPES = ("IMAGE", "IMAGE", "IMAGE", "CONDITIONING", "CONDITIONING")
RETURN_NAMES = (
"preprocessed_image_1",
"preprocessed_image_2",
"preprocessed_image_3",
"positive",
"negative",
)
FUNCTION = "preprocessAndApply"
CATEGORY = "Auto-Photoshop-SD"
def preprocessAndApply(self, **kwargs):
preprocessed_images = [kwargs.get(f"image_{i+1}", "") for i in range(3)]
last_positive = kwargs["positive"]
last_negative = kwargs["negative"]
for i in range(3):
args = {
"image": kwargs.get(f"image_{i+1}", ""),
"mask": kwargs.get(f"mask_{i+1}", ""),
"preprocessor_name": kwargs.get(f"preprocessor_name_{i+1}", ""),
"control_net_name": kwargs.get(f"control_net_name_{i+1}", ""),
"strength": kwargs.get(f"strength_{i+1}", ""),
"start_percent": kwargs.get(f"start_percent_{i+1}", ""),
"end_percent": kwargs.get(f"end_percent_{i+1}", ""),
"resolution": kwargs.get(f"resolution_{i+1}", ""),
"threshold_a": kwargs.get(f"threshold_a_{i+1}", 0),
"threshold_b": kwargs.get(f"threshold_b_{i+1}", 0),
"positive": last_positive,
"negative": last_negative,
}
if (
kwargs[f"is_enabled_{i+1}"] == "enable"
and args["preprocessor_name"] != "None"
and args["control_net_name"] != "None"
):
# load image and mask if they are file name
if isinstance(args["image"], str) and args["image"] != "":
(
args["image"],
_mask,
) = nodes.LoadImage().load_image(args["image"])
if (
isinstance(args["mask"], str) and args["mask"] != ""
): # mask is string file name
(
args["mask"],
_mask,
) = nodes.LoadImage().load_image(args["mask"])
(args["mask"],) = ImageToMask().image_to_mask(args["mask"], "red")
elif args["mask"] != "":
(args["mask"],) = ImageToMask().image_to_mask(args["mask"], "red")
(
preprocessed_images[i],
last_positive,
last_negative,
) = ControlnetUnit().preprocessAndApply(**args)
return (
preprocessed_images[0],
preprocessed_images[1],
preprocessed_images[2],
last_positive,
last_negative,
)
class ContentMaskLatent:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"content_mask": (
["original", "latent_noise", "latent_nothing"],
{"default": "original"},
),
"init_image": ("IMAGE",),
"mask": ("IMAGE",),
"width": (
"INT",
{"default": 512, "min": 0, "max": nodes.MAX_RESOLUTION, "step": 1},
),
"height": (
"INT",
{"default": 512, "min": 0, "max": nodes.MAX_RESOLUTION, "step": 1},
),
"vae": ("VAE",),
"seed": ("INT", {"default": 0, "min": 0, "max": 0xFFFFFFFFFFFFFFFF}),
}
}
RETURN_TYPES = ("LATENT", "IMAGE", "IMAGE", "IMAGE")
RETURN_NAMES = (
"latents",
"original_preview",
"latent_noise_preview",
"latent_nothing_preview",
)
FUNCTION = "generateContentMaskLatent"
CATEGORY = "Auto-Photoshop-SD"
def generateContentMaskLatent(self, **kwargs):
content_mask = kwargs.get("content_mask")
init_image = kwargs.get("init_image", "")
mask = kwargs.get("mask", "")