requiresments

__init__.py

@@ -31,6 +31,7 @@ NODE_CLASS_MAPPINGS = {
     "abyz22_Frompipe":abyz22_Frompipe,
     "abyz22_Editpipe":abyz22_Editpipe,
     "abyz22_Convertpipe":abyz22_Convertpipe,
+    "abyz22_RemoveControlnet":abyz22_RemoveControlnet,
 }
 
 # A dictionary that contains the friendly/humanly readable titles for the nodes
@@ -57,4 +58,5 @@ NODE_DISPLAY_NAME_MAPPINGS = {
     "abyz22_Frompipe":"abyz22_Frompipe",
     "abyz22_Editpipe":"abyz22_Editpipe",
     "abyz22_Convertpipe":"abyz22_Convertpipe",
+    "abyz22_RemoveControlnet":"abyz22_RemoveControlnet",
 }

outpainting_lama.py

@@ -16,6 +16,27 @@ from scipy import ndimage
 from torch import device
 import nodes
 
+# class name:
+#     def __init__(self):
+#         pass
+
+#     @classmethod
+#     def INPUT_TYPES(s):
+#         return {
+#         "required": {},
+#         "optional": {},
+#         }
+#     RETURN_TYPES = ()
+#     RETURN_NAMES = ()
+
+#     FUNCTION = "run"
+
+#     CATEGORY = "abyz22"
+
+#     def run(self,*args,**kwargs):
+#         return None
+
+
 # Get the absolute path of various directories
 my_dir = os.path.dirname(os.path.abspath(__file__))
 custom_nodes_dir = os.path.abspath(os.path.join(my_dir, ".."))
@@ -365,7 +386,7 @@ class abyz22_lamaPreprocessor:
             encoded_image_dict = {"samples": encoded_image.cpu()}
             mask_with_1 = torch.ones_like(pixels[:, :, :, 0])
             encoded_image_dict = SetLatentNoiseMask().set_mask(encoded_image_dict, mask_with_1)[0]
-            return (pixels.to("cuda" if torch.cuda.is_available() else "cpu"), encoded_image_dict)
+            return (pixels.to("cuda" if torch.cuda.is_available() else "cpu"), encoded_image_dict,pixels.to("cuda" if torch.cuda.is_available() else "cpu"))
 
         np.random.seed(seed)
         random.seed(seed)
@@ -468,16 +489,18 @@ class abyz22_lamaPreprocessor:
             .permute(0, 2, 3, 1)
             .to("cuda" if torch.cuda.is_available() else "cpu")
         )
-        return (img, encoded_image_dict)  # image= 1,h,w,c  0.0~1.0
+        return (img, encoded_image_dict,image[:,:,:,0:3].to("cuda" if torch.cuda.is_available() else "cpu"))  # image= 1,h,w,c  0.0~1.0
 
     # masks.to("cuda" if torch.cuda.is_available() else "cpu")
     RETURN_TYPES = (
         "IMAGE",
         "LATENT",
+        "IMAGE",
     )
     RETURN_NAMES = (
         "LaMa Preprocessed Image",
         "LaMa Preprocessed Latent",
+        "LaMa Image"
     )
     FUNCTION = "preprocess"
     CATEGORY = "abyz22"
@@ -566,3 +589,31 @@ class abyz22_lamaInpaint:
         print(final_img.shape, final_img.min(), final_img.max())
 
         return (final_img.to("cuda" if torch.cuda.is_available() else "cpu"), latent,lama_image)
+
+class abyz22_RemoveControlnet:
+    def __init__(self):
+        pass
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "Conditioning": ("CONDITIONING",),  
+                "ControlNet": ("CONDITIONING",),
+            },
+        }
+
+    RETURN_TYPES = ("CONDITIONING",)
+    RETURN_NAMES = ('Conditioning',)
+
+    FUNCTION = "run"
+
+    CATEGORY = "abyz22"
+
+    def run(self,*args,**kwargs):
+        print(kwargs['Conditioning'][0][1])
+        print(len(kwargs['Conditioning'][0][1]))
+        print('☆★ '*20)
+        print(kwargs['ControlNet'][0][1])
+        print(len(kwargs['ControlNet'][0][1]))
+        return None

outpainting_node.py

@@ -2,8 +2,27 @@ import math, cv2, random, torch, torchvision
 import numpy as np
 import nodes, folder_paths  # 기본노드, 파일로드
 
-# padding1 conditioning
-# padding2 conditioning's'  (와카 여러개 된것 (프롬프트 여러개))
+
+# class name:
+#     def __init__(self):
+#         pass
+
+#     @classmethod
+#     def INPUT_TYPES(s):
+#         return {
+#         "required": {},
+#         "optional": {},
+#         }
+#     RETURN_TYPES = ()
+#     RETURN_NAMES = ()
+
+#     FUNCTION = "run"
+
+#     CATEGORY = "abyz22"
+
+#     def run(self,*args,**kwargs):
+#         return None
+
 
 
 def normalize_size_base_64(w, h):
@@ -168,6 +187,17 @@ class abyz22_Pad_Image:
         latent = nodes.VAEEncode().encode(vae, final_image)[0]
         ctrl_net_load = nodes.ControlNetLoader().load_controlnet(control_net_name)[0]
         conditioning1 = nodes.ControlNetApply().apply_controlnet(conditioning, ctrl_net_load, final_pose_image, pose_strength)[0]
+        # print(len(conditioning))
+        # print(len(conditioning[0]))
+        # print(len(conditioning[0][0]))
+        # print(len(conditioning[0][1]))
+        # print(conditioning[0][1])
+        # print(' ☆★'*20)
+        # print(len(conditioning1))
+        # print(len(conditioning1[0]))
+        # print(len(conditioning1[0][0]))
+        # print(len(conditioning1[0][1]))
+        # print(conditioning1[0][1])
         return (
             final_image,
             final_pose_image,

requirements.txt

@@ -0,0 +1 @@
+openpyxl

set_queue.py

@@ -121,7 +121,7 @@ class abyz22_SetQueue:
                 "Num_of_Prompts": ("INT", {"default": 1, "min": 0, "max": 30, "step": 1}),
                 "image_per_Prompt": ("INT", {"default": 24, "min": 1, "max": 30, "step": 1}),
                 "batch_size": ("INT", {"default": 1, "min": 1, "max": 30, "step": 1}),
-                "temp": ("INT", {"default": 1, "min": 1, "max": 0xFFFFFFFFFFFFFFFF}),
+                "seed": ("INT", {"default": 1, "min": 1, "max": 0xFFFFFFFFFFFFFFFF}),
                 "prompt": ("STRING", {"multiline": True}),
             },
         }
@@ -155,7 +155,7 @@ class abyz22_SetQueue:
                 if (kwargs["Num_of_Prompts"] == 1) and (kwargs["prompt"] != ""):  # 폴더1개만이고 프롬프트 있으면,
                     self.prompt = kwargs["prompt"]
                 else:
-                    self.prompt = prompts_maker.make_prompt(mode=kwargs["mode_type"], etc=self.highest_num+1+self.cur_prompts)
+                    self.prompt = prompts_maker.make_prompt(mode=kwargs["mode_type"], etc=self.highest_num+1+self.cur_prompts, seed=kwargs["seed"])
 
                 epath = "E:/stable-diffusion-webui/outputs/history.xlsx"
                 T_workbook = load_workbook(epath, data_only=True)

small_head.py

@@ -19,7 +19,7 @@ import nodes, folder_paths  # 기본노드, 파일로드
 
 #     CATEGORY = "abyz22"
 
-#     def run(sefl,*args,**kwargs):
+#     def run(self,*args,**kwargs):
 #         return None