diff --git a/README.MD b/README.MD index 55a4c88..fe42b63 100644 --- a/README.MD +++ b/README.MD @@ -136,6 +136,7 @@ When this error has occurred, please check the network environment. **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.
+* 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. +### RandomGeneratorV2 +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. + + ### NumberCalculator ![image](image/number_calculator_node.jpg) diff --git a/README_CN.MD b/README_CN.MD index 3d6f832..b3876d7 100644 --- a/README_CN.MD +++ b/README_CN.MD @@ -117,6 +117,7 @@ os.environ['HF_ENDPOINT'] = 'https://hf-mirror.com' ## 更新说明 **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。 +### RandomGeneratorV2 +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。 + ### NumberCalculator ![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*。支持的运算包括```+```、```-```、```*```、```/```、```**```、```//```、```%```。 diff --git a/py/florence2_ultra.py b/py/florence2_ultra.py index 8490f2a..0833e89 100644 --- a/py/florence2_ultra.py +++ b/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) diff --git a/py/random_generator.py b/py/random_generator.py index f72be66..eeb54a3 100644 --- a/py/random_generator.py +++ b/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 @@ class LSRandomGenerator: 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 @@ class LSRandomGenerator: 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" } \ No newline at end of file diff --git a/pyproject.toml b/pyproject.toml index 458aa46..16edb9d 100644 --- a/pyproject.toml +++ b/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"]