Update nodes.py

nodes.py

@@ -34,7 +34,7 @@ class INTConstant:
     RETURN_NAMES = ("value",)
     FUNCTION = "get_value"
 
-    CATEGORY = "KJNodes"
+    CATEGORY = "KJNodes/constants"
 
     def get_value(self, value):
         return (value,)
@@ -51,16 +51,27 @@ class FloatConstant:
     RETURN_NAMES = ("value",)
     FUNCTION = "get_value"
 
-    CATEGORY = "KJNodes"
+    CATEGORY = "KJNodes/constants"
 
     def get_value(self, value):
         return (value,)
 
-def gaussian_kernel(kernel_size: int, sigma: float, device=None):
-        x, y = torch.meshgrid(torch.linspace(-1, 1, kernel_size, device=device), torch.linspace(-1, 1, kernel_size, device=device), indexing="ij")
-        d = torch.sqrt(x * x + y * y)
-        g = torch.exp(-(d * d) / (2.0 * sigma * sigma))
-        return g / g.sum()
+class StringConstant:
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "string": ("STRING", {"default": '', "multiline": False}),
+            }
+        }
+    RETURN_TYPES = ("STRING",)
+    FUNCTION = "passtring"
+
+    CATEGORY = "KJNodes/constants"
+
+    def passtring(self, string):
+        return (string, )
 
 class CreateFluidMask:
     
@@ -526,6 +537,38 @@ class GetImagesFromBatchIndexed:
         
         return (chosen_images,)
     
+class GetLatentsFromBatchIndexed:
+    
+    RETURN_TYPES = ("LATENT",)
+    FUNCTION = "indexedlatentsfrombatch"
+    CATEGORY = "KJNodes"
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                 "latents": ("LATENT",),
+                 "indexes": ("STRING", {"default": "0, 1, 2", "multiline": True}),
+        },
+    } 
+    
+    def indexedlatentsfrombatch(self, latents, indexes):
+        
+        samples = latents.copy()
+        latent_samples = samples["samples"] 
+
+        # Parse the indexes string into a list of integers
+        index_list = [int(index.strip()) for index in indexes.split(',')]
+        
+        # Convert list of indices to a PyTorch tensor
+        indices_tensor = torch.tensor(index_list, dtype=torch.long)
+        
+        # Select the latents at the specified indices
+        chosen_latents = latent_samples[indices_tensor]
+
+        samples["samples"] = chosen_latents
+        return (samples,)
+    
 class ReplaceImagesInBatch:
     
     RETURN_TYPES = ("IMAGE",)
@@ -3271,7 +3314,7 @@ class ImageTransformByNormalizedAmplitude:
         
         return (transformed_batch,)
     
-
+    
 NODE_CLASS_MAPPINGS = {
     "INTConstant": INTConstant,
     "FloatConstant": FloatConstant,
@@ -3331,7 +3374,9 @@ NODE_CLASS_MAPPINGS = {
     "ImageBatchRepeatInterleaving": ImageBatchRepeatInterleaving,
     "NormalizedAmplitudeToMask": NormalizedAmplitudeToMask,
     "OffsetMaskByNormalizedAmplitude": OffsetMaskByNormalizedAmplitude,
-    "ImageTransformByNormalizedAmplitude": ImageTransformByNormalizedAmplitude
+    "ImageTransformByNormalizedAmplitude": ImageTransformByNormalizedAmplitude,
+    "GetLatentsFromBatchIndexed": GetLatentsFromBatchIndexed,
+    "StringConstant": StringConstant
 }
 NODE_DISPLAY_NAME_MAPPINGS = {
     "INTConstant": "INT Constant",
@@ -3391,5 +3436,7 @@ NODE_DISPLAY_NAME_MAPPINGS = {
     "ImageBatchRepeatInterleaving": "ImageBatchRepeatInterleaving",
     "NormalizedAmplitudeToMask": "NormalizedAmplitudeToMask",
     "OffsetMaskByNormalizedAmplitude": "OffsetMaskByNormalizedAmplitude",
-    "ImageTransformByNormalizedAmplitude": "ImageTransformByNormalizedAmplitude"
+    "ImageTransformByNormalizedAmplitude": "ImageTransformByNormalizedAmplitude",
+    "GetLatentsFromBatchIndexed": "GetLatentsFromBatchIndexed",
+    "StringConstant": "StringConstant"
 }