Add RandomXFromPresets nodes

README.jp.md

@@ -28,4 +28,9 @@ git clone https://github.com/nkchocoai/ComfyUI-SizeFromPresets.git
 ### Empty Latent Image From Presets (SDXL)
 - SDXL用の画像サイズのプリセットを選択できます。
 - 選択したサイズの空のLatent Imageと幅と高さを出力します。
-- プリセットは [presets/sdxl.csv](presets/sdxl.csv) から設定できます。
+- プリセットは [presets/sdxl.csv](presets/sdxl.csv) から設定できます。
+
+### Random ... From Presets (SD...)
+- CSVファイルに記載したプリセットからランダムに選びます。
+- Sizeの場合は、選ばれたサイズの幅と高さを出力します。
+- Latent Imageの場合は、選ばれたサイズの空のLatent Imageと幅と高さを出力します。

README.md

@@ -30,4 +30,9 @@ git clone https://github.com/nkchocoai/ComfyUI-SizeFromPresets.git
 ### Empty Latent Image From Presets (SDXL)
 - Select image size presets for SDXL.
 - Output empty latent image, width and height of the selected preset.
-- Presets can be set from [presets/sdxl.csv](presets/sdxl.csv).
+- Presets can be set from [presets/sdxl.csv](presets/sdxl.csv).
+
+### Random ... From Presets (SD...)
+- Select randomly from the presets listed in the CSV file.
+- In the case of Size, output width and height of the selected preset.
+- In the case of Empty Latent Image, output empty latent image, width and height of the selected preset.

__init__.py

@@ -1,10 +1,15 @@
 from .nodes.node import *
+from .nodes.random import *
 
 NODE_CLASS_MAPPINGS = {
     "SizeFromPresetsSD15": SizeFromPresetsSD15,
     "SizeFromPresetsSDXL": SizeFromPresetsSDXL,
     "EmptyLatentImageFromPresetsSD15": EmptyLatentImageFromPresetsSD15,
     "EmptyLatentImageFromPresetsSDXL": EmptyLatentImageFromPresetsSDXL,
+    "RandomSizeFromPresetsSD15": RandomSizeFromPresetsSD15,
+    "RandomSizeFromPresetsSDXL": RandomSizeFromPresetsSDXL,
+    "RandomEmptyLatentImageFromPresetsSD15": RandomEmptyLatentImageFromPresetsSD15,
+    "RandomEmptyLatentImageFromPresetsSDXL": RandomEmptyLatentImageFromPresetsSDXL,
 }
 
 NODE_DISPLAY_NAME_MAPPINGS = {
@@ -12,4 +17,8 @@ NODE_DISPLAY_NAME_MAPPINGS = {
     "SizeFromPresetsSDXL": "Size From Presets (SDXL)",
     "EmptyLatentImageFromPresetsSD15": "Empty Latent Image From Presets (SD1.5)",
     "EmptyLatentImageFromPresetsSDXL": "Empty Latent Image From Presets (SDXL)",
+    "RandomSizeFromPresetsSD15": "Random Size From Presets (SD1.5)",
+    "RandomSizeFromPresetsSDXL": "Random Size From Presets (SDXL)",
+    "RandomEmptyLatentImageFromPresetsSD15": "Random Empty Latent Image From Presets (SD1.5)",
+    "RandomEmptyLatentImageFromPresetsSDXL": "Random Empty Latent Image From Presets (SDXL)",
 }

nodes/random.py

@@ -0,0 +1,82 @@
+import csv
+import os
+
+import numpy as np
+import torch
+
+import folder_paths
+
+import comfy.model_management
+
+custom_nodes_dir = folder_paths.get_folder_paths("custom_nodes")[0]
+presets_dir = os.path.join(custom_nodes_dir, "ComfyUI-SizeFromPresets", "presets")
+
+def load_size_presets_input(file_name,digit):
+    with open(os.path.join(presets_dir, file_name),'r') as f:
+        reader = csv.reader(f)
+        data = [row for row in reader]
+        size_presets = [[int(v.strip()) for v in row] for row in data[1:]]
+
+    return [f'{w: >{digit}} x {h: >{digit}}' for w,h in size_presets]
+    
+class RandomSizeFromPresetsBase:
+    def __init__(self):
+        pass
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
+            }
+        }
+
+    RETURN_TYPES = ("INT","INT")
+    RETURN_NAMES = ("w","h")
+
+    FUNCTION = "get_size"
+
+    CATEGORY = "SizeFromPresets"
+
+    def get_size(self, seed):
+        random_gen = np.random.default_rng(seed)
+        preset = random_gen.choice(self.SIZE_PRESETS_INPUT)
+        w, h = [int(v.strip()) for v in preset.split('x')]
+        return (w, h)
+    
+class RandomSizeFromPresetsSD15(RandomSizeFromPresetsBase):
+    SIZE_PRESETS_INPUT = load_size_presets_input('sd15.csv',3)
+    
+class RandomSizeFromPresetsSDXL(RandomSizeFromPresetsBase):
+    SIZE_PRESETS_INPUT = load_size_presets_input('sdxl.csv',4)
+
+class RandomEmptyLatentImageFromPresetsBase:
+    def __init__(self):
+        self.device = comfy.model_management.intermediate_device()
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": { 
+                "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}),
+                "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
+            }
+        }
+    RETURN_TYPES = ("LATENT","INT","INT")
+    RETURN_NAMES = ("latent","w","h")
+    FUNCTION = "generate"
+
+    CATEGORY = "SizeFromPresets"
+
+    def generate(self, seed, batch_size=1):
+        random_gen = np.random.default_rng(seed)
+        preset = random_gen.choice(self.SIZE_PRESETS_INPUT)
+        w,h = [int(v.strip()) for v in preset.split('x')]
+        latent = torch.zeros([batch_size, 4, h // 8, w // 8], device=self.device)
+        return ({"samples":latent}, w, h)
+    
+class RandomEmptyLatentImageFromPresetsSD15(RandomEmptyLatentImageFromPresetsBase):
+    SIZE_PRESETS_INPUT = load_size_presets_input('sd15.csv',3)
+    
+class RandomEmptyLatentImageFromPresetsSDXL(RandomEmptyLatentImageFromPresetsBase):
+    SIZE_PRESETS_INPUT = load_size_presets_input('sdxl.csv',4)