commit RandomGeneratorV2 node, fix LoadFlorence2Model to compatible t…

…ransformers 4.45.x

README.MD

@@ -136,6 +136,7 @@ When this error has occurred, please check the network environment.
 
 <font size="4">**If the dependency package error after updating,  please double clicking ```repair_dependency.bat``` (for Official ComfyUI Protable) or  ```repair_dependency_aki.bat``` (for ComfyUI-aki-v1.x) in the plugin folder to reinstall the dependency packages. </font><br />    
 
+* Commit [RandomGeneratorV2](#RandomGeneratorV2) node, add least random range and seed options.
 * Commit [TextJoinV2](#TextJoinV2) node, add delimiter options on top of TextJion.
 * Commit [GaussianBlurV2](#GaussianBlurV2) node, The parameter accuracy has been improved to 0.01.
 * Commit [UserPromptGeneratorTxtImgWithReference](#UserPromptGeneratorTxtImgWithReference) node.
@@ -1634,6 +1635,25 @@ int: Integer random number.
 float: Float random number.
 bool: Boolean random number.
 
+### <a id="table1">RandomGeneratorV2</a>
+On the based of [RandomGenerator](#RandomGenerator), add  the least random range and seed options.
+
+Node Options:
+![image](image/random_generator_v2_node.jpg)  
+* image: Optional input, generate a list of random numbers that match the quantity in batches according to the image.
+* min_value: Minimum value. Random numbers will randomly take values from the minimum to the maximum.
+* max_value: Maximum value. Random numbers will randomly take values from the minimum to the maximum.
+* least: Minimum random range. Random numbers will randomly at least take this value.
+* float_decimal_places: Precision of float value.
+* seed: The seed of random number.
+* control_after_generate: Seed change options. If this option is fixed, the generated random number will always be the same.
+
+Outputs:
+int: Integer random number.
+float: Float random number.
+bool: Boolean random number.
+
+
 ### <a id="table1">NumberCalculator</a>
 
 ![image](image/number_calculator_node.jpg)    

README_CN.MD

@@ -117,6 +117,7 @@ os.environ['HF_ENDPOINT'] = 'https://hf-mirror.com'
 ## 更新说明
 <font size="4">**如果本插件更新后出现依赖包错误，请双击运行插件目录下的```install_requirements.bat```(官方便携包)，或 ```install_requirements_aki.bat```(秋叶整合包) 重新安装依赖包。
 
+* 添加 [RandomGeneratorV2](#RandomGeneratorV2) 节点，增加最小随机范围和种子选项。 
 * 添加 [TextJoinV2](#TextJoinV2) 节点，在TextJion基础上增加分隔符选项。
 * 添加 [GaussianBlurV2](#GaussianBlurV2) 节点，参数精度提升到0.01。
 * 添加 [UserPromptGeneratorTxtImgWithReference](#UserPromptGeneratorTxtImgWithReference) 节点。 
@@ -1455,6 +1456,24 @@ int: 整数随机数。
 float: 浮点数随机数。
 bool: 布尔随机数。
 
+### <a id="table1">RandomGeneratorV2</a>
+在[RandomGenerator](#RandomGenerator) 的基础上，增加了最小随机范围选项，以及seed选项。
+
+节点选项说明:
+![image](image/random_generator_v2_node.jpg)  
+* image: 可选输入，按照图片批量生成数量相符的随机数列表。
+* min_value：最小值。随机数将从最小值到最大值之间随机取值。
+* max_value：最大值。随机数将从最小值到最大值之间随机取值。
+* least: 最小随机范围。随机数将至少取到该值。
+* float_decimal_places：浮点数精度。
+* seed: 随机数种子。
+* control_after_generate: 种子值变化选项。如果此选项固定，生成的随机数将始终相同。
+
+输出:
+int: 整数随机数。
+float: 浮点数随机数。
+bool: 布尔随机数。
+
 ### <a id="table1">NumberCalculator</a>
 ![image](image/number_calculator_node.jpg)    
 对两个数值进行数学运算并输出整数和浮点数结果<sup>*</sup>。支持的运算包括```+```、```-```、```*```、```/```、```**```、```//```、```%```。

py/florence2_ultra.py

@@ -31,15 +31,18 @@ def fixed_get_imports(filename) -> list[str]:
     if os.path.basename(filename) != "modeling_florence2.py":
         return get_imports(filename)
     imports = get_imports(filename)
-    imports.remove("flash_attn")
+    try:
+        imports.remove("flash_attn")
+    except:
+        pass
     return imports
 
 def load_model(version):
     florence_path = os.path.join(folder_paths.models_dir, "florence2")
     os.makedirs(florence_path, exist_ok=True)
 
-
     model_path = os.path.join(florence_path, version)
+    attention = 'sdpa'
 
     if not os.path.exists(model_path):
         log(f"Downloading Florence2 {version} model...")
@@ -49,11 +52,14 @@ def load_model(version):
 
     try:
         with patch("transformers.dynamic_module_utils.get_imports", fixed_get_imports):
-            model = AutoModelForCausalLM.from_pretrained(model_path, trust_remote_code=True)
+            # model = AutoModelForCausalLM.from_pretrained(model_path, trust_remote_code=True)
+            model = AutoModelForCausalLM.from_pretrained(model_path, attn_implementation=attention, device_map=device,
+                                                         torch_dtype=torch.float32, trust_remote_code=True)
             processor = AutoProcessor.from_pretrained(model_path, trust_remote_code=True)
     except Exception as e:
         try:
-            model = AutoModelForCausalLM.from_pretrained(model_path, trust_remote_code=True)
+            model = AutoModelForCausalLM.from_pretrained(model_path, attn_implementation=attention, device_map=device,
+                                                         torch_dtype=torch.float32, trust_remote_code=True)
             processor = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
         except Exception as e:
             sys.path.append(model_path)
@@ -68,7 +74,6 @@ def load_model(version):
                 log(f"Error loading model or tokenizer: {str(e)}", message_type='error')
                 return (None, None)
 
-            attention = 'sdpa'
             # Load the model configuration
             model_config = Florence2Config.from_pretrained(model_path)
             # Load the model
@@ -82,10 +87,8 @@ def load_model(version):
 
             processor = AutoProcessor.from_pretrained(model_path, trust_remote_code=True)
 
-
     return (model.to(device), processor)
 
-
 def fig_to_pil(fig):
     buf = io.BytesIO()
     fig.savefig(buf, format='png', dpi=100, bbox_inches='tight', pad_inches=0)

py/random_generator.py

@@ -1,12 +1,17 @@
 from .imagefunc import AnyType
 import random
 
-NODE_NAME = 'RandomGenerator'
-
+def generate_unique_seed() -> int:
+    while True:
+        new_number = random.randint(0, 1e14)
+        if new_number not in self.previous_seeds:
+            self.previous_seeds.add(new_number)
+            return new_number
 
 class LSRandomGenerator:
 
     def __init__(self):
+        self.NODE_NAME = 'RandomGenerator'
         self.previous_seeds= set({})
         self.fixed_seed = 0
         pass
@@ -37,7 +42,7 @@ def random_generator(self, min_value, max_value, float_decimal_places, fix_seed,
             batch_size = image.shape[0]
         ret_nunbers = []
         for i in range(batch_size):
-            new_seed = self.generate_unique_seed()
+            new_seed = generate_unique_seed()
             if fix_seed:
                 if self.fixed_seed == 0:
                     self.fixed_seed = new_seed
@@ -60,18 +65,76 @@ def random_generator(self, min_value, max_value, float_decimal_places, fix_seed,
         else:
             return (ret_nunbers[0][0], ret_nunbers[0][1], ret_nunbers[0][2])
 
-    def generate_unique_seed(self) -> int:
-        while True:
-            new_number = random.randint(0, 1e14)
-            if new_number not in self.previous_seeds:
-                self.previous_seeds.add(new_number)
-                return new_number
+class LS_RandomGeneratorV2:
+
+    def __init__(self):
+        self.NODE_NAME = 'RandomGeneratorV2'
+        self.previous_seeds= set({})
+        self.fixed_seed = 0
+        pass
+
+    @classmethod
+    def INPUT_TYPES(self):
+
+        return {
+            "required": {
+                "min_value": ("FLOAT", {"default": 0, "min": -1.0e14, "max": 1.0e14, "step": 0.01}),
+                "max_value": ("FLOAT", {"default": 10, "min": -1.0e14, "max": 1.0e14, "step": 0.01}),
+                "least": ("FLOAT", {"default": 0, "min": 0, "max": 1.0e14, "step": 0.01}),
+                "float_decimal_places": ("INT", {"default": 1, "min": 1, "max": 14, "step": 1}),
+                "seed":("INT", {"default": 0, "min": 0, "max": 1e14, "step": 1}),
+
+            },
+            "optional": {
+                "image": ("IMAGE",),
+            }
+        }
+
+    RETURN_TYPES = ("INT", "FLOAT", "BOOLEAN",)
+    RETURN_NAMES = ("int", "float", "bool",)
+    # OUTPUT_IS_LIST = (True, True, True,)
+    FUNCTION = 'random_generator_v2'
+    CATEGORY = '😺dzNodes/LayerUtility/Data'
+
+    def random_generator_v2(self, min_value, max_value, least, float_decimal_places, seed, image=None):
+        batch_size = 1
+        if image is not None:
+            batch_size = image.shape[0]
+        ret_nunbers = []
+        for i in range(batch_size):
+
+            random.seed(seed)
+            max_loop = 500
+            i = 0
+            while i < max_loop:
+                new_number = random.uniform(min_value, max_value)
+                if abs(new_number) - least >= 0 or least > max_value:
+                    break
+                i += 1
+
+            # 转浮点
+            factor = random.uniform(3, 9)
+            random_float = new_number / factor
+            random_float = round(random_float * factor, float_decimal_places)
+            random_int = int(random_float)
+            random_bool = random_int %2 == 0
+            ret_nunbers.append((random_int, random_float, random_bool))
+
+        if len(ret_nunbers) > 1:
+            ret_ints = [item[0] for item in ret_nunbers]
+            ret_floats = [item[1] for item in ret_nunbers]
+            ret_bools = [item[2] for item in ret_nunbers]
+            return (ret_ints, ret_floats, ret_bools)
+        else:
+            return (ret_nunbers[0][0], ret_nunbers[0][1], ret_nunbers[0][2])
 
 
 NODE_CLASS_MAPPINGS = {
-    "LayerUtility: RandomGenerator": LSRandomGenerator
+    "LayerUtility: RandomGenerator": LSRandomGenerator,
+    "LayerUtility: RandomGeneratorV2": LS_RandomGeneratorV2
 }
 
 NODE_DISPLAY_NAME_MAPPINGS = {
-    "LayerUtility: RandomGenerator": "LayerUtility: Random Generator"
+    "LayerUtility: RandomGenerator": "LayerUtility: Random Generator",
+    "LayerUtility: RandomGeneratorV2": "LayerUtility: Random Generator V2"
 }

pyproject.toml

@@ -1,7 +1,7 @@
 [project]
 name = "comfyui_layerstyle"
 description = "A set of nodes for ComfyUI it generate image like Adobe Photoshop's Layer Style. the Drop Shadow is first completed node, and follow-up work is in progress."
-version = "1.0.71"
+version = "1.0.72"
 license = "MIT"
 dependencies = ["numpy", "pillow", "torch", "matplotlib", "Scipy", "scikit_image", "scikit_learn", "opencv-contrib-python", "pymatting", "segment_anything", "timm", "addict", "yapf", "colour-science", "wget", "mediapipe", "loguru", "typer_config", "fastapi", "rich", "google-generativeai", "diffusers", "omegaconf", "tqdm", "transformers", "kornia", "image-reward", "ultralytics", "blend_modes", "blind-watermark", "qrcode", "pyzbar", "transparent-background", "huggingface_hub", "accelerate", "bitsandbytes", "torchscale", "wandb", "hydra-core", "psd-tools", "inference-cli[yolo-world]", "inference-gpu[yolo-world]", "onnxruntime"]