# Documentation - Class name: `SaltInput` - Category: `SALT/IO` - Output node: `True` SaltInput节点旨在为Salt AI平台的工作流创建和配置输入。它允许用户定义输入的各个方面，如名称、描述、类型和默认值，以及其他属性，如允许的值、是否需要用户覆盖，以及路径是否应被视为相对路径。该节点在为不同类型的数据定制和简化输入过程中发挥着关键作用，确保工作流设计的灵活性和适应性。 # Input types ## Required - **`input_name`** - 指定输入的名称，作为工作流中输入字段的唯一标识符和标签。 - Comfy dtype: `STRING` - Python dtype: `str` - **`input_desc`** - 为输入提供描述，为用户提供关于如何提供输入值的上下文或说明。 - Comfy dtype: `STRING` - Python dtype: `str` - **`input_type`** - 定义输入的类型，如STRING、FLOAT、INT等，决定了用户需要提供的数据的预期格式和性质。 - Comfy dtype: `COMBO[STRING]` - Python dtype: `list[str]` - **`input_value`** - 输入的默认值，可以预先填写但用户可以更改。支持多行字符串，设计用于动态输入场景。 - Comfy dtype: `STRING` - Python dtype: `str` ## Optional - **`input_image`** - 可选参数，用于提供图像作为输入，增强节点处理视觉数据类型的能力。 - Comfy dtype: `IMAGE` - Python dtype: `Image` - **`input_mask`** - 可选参数，用于提供蒙版作为输入，在需要识别或处理图像特定区域的场景中很有用。 - Comfy dtype: `MASK` - Python dtype: `Mask` - **`input_allowed_values`** - 指定输入允许值的字符串，指导用户提供有效数据并确保输入完整性。 - Comfy dtype: `STRING` - Python dtype: `str` - **`user_override_required`** - 确定用户是否必须为默认输入值提供覆盖，确保关键输入的用户交互和验证。 - Comfy dtype: `BOOLEAN` - Python dtype: `bool` - **`relative_path`** - 指示提供的输入路径是否应被视为相对路径，影响输入数据的访问和管理方式。 - Comfy dtype: `BOOLEAN` - Python dtype: `bool` # Output types - **`value`** - 输出配置的输入值。 - Comfy dtype: `*` - Python dtype: `unknown` - **`ui`** - 生成输入配置的用户界面表示，包括元数据和输出结果，便于交互和视觉反馈。 - Comfy dtype: `*` - Python dtype: `unknown` ## Usage tips - Infra type: `CPU` - Common nodes: unknown ## Source code ```python class SaltInput: @classmethod def INPUT_TYPES(cls): return { "required": { "input_name": ("STRING", {}), "input_desc": ("STRING", {}), "input_type": (["STRING", "FLOAT", "INT", "BOOLEAN", "IMAGE", "MASK", "SEED", "FILE"],), "input_value": ("STRING", {"multiline": True, "dynamicPrompts": False}) }, "optional": { "input_image": ("IMAGE",), "input_mask": ("MASK",), "input_allowed_values": ("STRING", {"default": ""}), "user_override_required": ("BOOLEAN", {"default": False}), "relative_path": ("BOOLEAN", {"default": False}) }, "hidden": {"unique_id": "UNIQUE_ID"}, } OUTPUT_NODE = True RETURN_TYPES = (WILDCARD,) RETURN_NAMES = ("value",) FUNCTION = "input" CATEGORY = "SALT/IO" def input( self, input_name, input_desc, input_value, input_type, input_image=None, input_mask=None, input_allowed_values="", user_override_required=False, relative_path=False, unique_id=0, ): src_image = None src_file = None is_asset = False # Is an asset type if input_type in ["IMAGE", "MASK", "FILE"]: is_asset = True # UI Output ui = { "ui": { "salt_input": [{ "id": unique_id, "name": input_name or "input_" + str(unique_id), "description": input_desc or "", "asset": is_asset or False, "type": input_type or "string", "value": input_value or "", }] } } out = "" if is_asset: # Input value must be evaluated first to override input images/masks if input_value.strip(): input_value = input_value.strip() # Load image from path from input_value if input_value.endswith(('.png', '.jpeg', '.jpg', '.gif', '.webp', '.tiff')): try: src_image = Image.open(input_value).convert("RGBA") except Exception as e: print(f"Error loading image from specified path {input_value}: {e}") # Passthrough input_value (which should be a path from Salt Backend) elif input_type == "FILE": src_file = input_value # if os.path.exists(input_value) else "None" if relative_path: src_file = get_relative_path(src_file) # Log value to console log_values(unique_id, input_name, input_type, src_file) return {"ui": ui, "result": (src_file,)} else: # Zoinks; how'd we get here? raise AttributeError("Invalid node configuration! Do you mean to use `IMAGE`, `MASK`, or `FILE` input_types?") elif isinstance(input_image, torch.Tensor): # Input `IMAGE` is provided, so we act like a passthrough return (input_image, ui) elif isinstance(input_mask, torch.Tensor): # Input `MASK` is provided, so we act like a passthrough return (input_mask, ui) if src_image: if input_type == "MASK": # If it's a mask and the image has an alpha channel, extract it if src_image.mode == "RGBA": alpha_channel = src_image.split()[-1] src_image = pil2mask(alpha_channel.convert("L")) # If no alpha channel, convert the whole image to grayscale as a mask (could be bitwise representation) else: src_image = pil2mask(src_image.convert("L")) elif input_type == "IMAGE": # Ensure image is in RGB for `IMAGE`` data type src_image = pil2tensor(src_image.convert("RGB")) else: # Gracefully allow execution to continue, provided a black image (to hopefully signal issue?) print("[WARNING] Unable to determine IMAGE or MASK to load!") print("[WARNING] Returning image blank") src_blank = Image.new("RGB", (512, 512), (0, 0, 0)) if input_type == "IMAGE": src_image = pil2tensor(src_blank) else: src_image = pil2mask(src_blank) # Log value to console log_values(unique_id, input_name, input_type, src_image) return (src_image, ui) # We're still here? We must be dealing with a primitive value if input_allowed_values != "" and input_value.strip() not in [o.strip() for o in input_allowed_values.split(',')]: raise ValueError('The provided input is not a supported value') match input_type: case "STRING": out = str(input_value) case "INT": out = int(input_value) case "SEED": out = int(input_value) case "FLOAT": out = float(input_value) case "BOOLEAN": out = bool_str(input_value) case _: out = input_value # Log value to console log_values(unique_id, input_name, input_type, out) return (out, ui) ```