152 lines
5.7 KiB
Markdown
152 lines
5.7 KiB
Markdown
# Documentation
|
|
- Class name: LoadPSD
|
|
- Category: 😺dzNodes/LayerUtility/SystemIO
|
|
- Output node: False
|
|
- Repo Ref: https://github.com/chflame163/ComfyUI_LayerStyle
|
|
|
|
加载PSD格式文件，并导出图层。 注意这个节点需要安装psd_tools依赖包，如果安装psd_tool中出现ModuleNotFoundError: No module named 'docopt'错误，请下载[docopt的whl](https://www.piwheels.org/project/docopt/)手动安装。
|
|
|
|
# Input types
|
|
|
|
## Required
|
|
|
|
- image
|
|
- 这里列出了ComfyUI/input下的*.psd文件，之前加载过的psd图片可以从这里选择。
|
|
- Comfy dtype: FILE
|
|
- 支持上传 PSD 文件。
|
|
|
|
- file_path
|
|
- psd文件的完整路径以及文件名。
|
|
- Comfy dtype: STRING
|
|
- Python dtype: str
|
|
|
|
- include_hidden_layer
|
|
- 是否包含隐藏图层。
|
|
- Comfy dtype: BOOLEAN
|
|
- Python dtype: bool
|
|
|
|
- find_layer_by
|
|
- 查找图层的方法，可选择按图层索引编号或者图层名称查找。图层组被作为一个图层对待。
|
|
- 可选值: ["layer_index", "layer_name"]
|
|
- Comfy dtype: STRING
|
|
- Python dtype: str
|
|
|
|
- layer_index
|
|
- 图层索引编号，0是最下面的图层，依次递增。如果include_hidden_layer设置为false，隐藏的图层不计入。设为-1则输出最上层的图层。
|
|
- Comfy dtype: INT
|
|
- Python dtype: int
|
|
|
|
- layer_name
|
|
- 图层名称。注意大小写和标点符号必须完全匹配。
|
|
- Comfy dtype: STRING
|
|
- Python dtype: str
|
|
|
|
# Output types
|
|
|
|
- flat_image
|
|
- 平面图像。
|
|
- Comfy dtype: IMAGE
|
|
- Python dtype: torch.Tensor
|
|
|
|
- layer_image
|
|
- 图层图像。
|
|
- Comfy dtype: IMAGE
|
|
- Python dtype: torch.Tensor
|
|
|
|
- all_layers
|
|
- 所有图层。
|
|
- Comfy dtype: IMAGE
|
|
- Python dtype: torch.Tensor
|
|
|
|
# Usage tips
|
|
- Infra type: CPU
|
|
|
|
# Source code
|
|
```python
|
|
class LoadPSD(LoadImage):
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
input_dir = folder_paths.get_input_directory()
|
|
files = [f for f in os.listdir(input_dir) if os.path.isfile(
|
|
os.path.join(input_dir, f)) and f.endswith(".psd")]
|
|
fine_layer_method = ["layer_index", "layer_name"]
|
|
return {"required":{
|
|
"image": (sorted(files), {"image_upload": True}),
|
|
"file_path": ("STRING", {"default": ""}),
|
|
"include_hidden_layer": ("BOOLEAN", {"default": False}),
|
|
"find_layer_by": (fine_layer_method,),
|
|
"layer_index": ("INT", {"default": 0, "min": -1, "max": 999, "step": 1}),
|
|
"layer_name": ("STRING", {"default": ""}),
|
|
},
|
|
"optional": {
|
|
}
|
|
}
|
|
|
|
|
|
RETURN_TYPES = ("IMAGE", "IMAGE", "IMAGE",)
|
|
RETURN_NAMES = ("flat_image", "layer_image", "all_layers",)
|
|
FUNCTION = "load_psd"
|
|
CATEGORY = '😺dzNodes/LayerUtility/SystemIO'
|
|
|
|
def load_psd(self, image, file_path, include_hidden_layer, layer_index, find_layer_by, layer_name,):
|
|
|
|
from psd_tools import PSDImage
|
|
from psd_tools.api.layers import Layer
|
|
|
|
NODE_NAME = 'LoadPSD'
|
|
number_of_layers = 1
|
|
layer_image = []
|
|
all_layers = []
|
|
if file_path == "":
|
|
psd_file_path = folder_paths.get_annotated_filepath(image)
|
|
else:
|
|
psd_file_path = folder_paths.get_annotated_filepath(file_path)
|
|
flat_image = Image.open(psd_file_path).convert("RGB")
|
|
width, height = flat_image.size
|
|
|
|
if image.endswith(".psd"):
|
|
from psd_tools import PSDImage
|
|
from psd_tools.api.layers import Layer
|
|
log(f"{NODE_NAME} -> Loading PSD file: {psd_file_path}")
|
|
psd_image = PSDImage.open(psd_file_path)
|
|
layers = []
|
|
for layer in psd_image:
|
|
if include_hidden_layer:
|
|
if not layer.is_visible():
|
|
layer.visible = True
|
|
layers.append(layer)
|
|
else:
|
|
if layer.is_visible():
|
|
layers.append(layer)
|
|
|
|
number_of_layers = len(layers)
|
|
for i in range(number_of_layers):
|
|
layer_canvas = Image.new("RGBA", (width, height), (0, 0, 0, 0))
|
|
layer_canvas.paste(layers[i].composite(), layers[i].bbox)
|
|
all_layers.append(pil2tensor(layer_canvas))
|
|
if find_layer_by == "layer_name":
|
|
if layers[i].name == layer_name:
|
|
layer_image.append(pil2tensor(layer_canvas))
|
|
log(f"{NODE_NAME} -> Layer {i} : {layer.name} found.")
|
|
elif find_layer_by == "layer_index":
|
|
if i == layer_index:
|
|
layer_image.append(pil2tensor(layer_canvas))
|
|
log(f"{NODE_NAME} -> Layer {i} : {layer.name} found.")
|
|
|
|
text = "Layer Not Found!"
|
|
font_file = list(FONT_DICT.values())[0]
|
|
empty_layer_image = generate_text_image(flat_image.width, flat_image.height, text, font_file, font_color="#F01000")
|
|
if layer_image == []:
|
|
if layer_index == -1:
|
|
layer_image.append(all_layers[-1])
|
|
elif find_layer_by == "layer_name":
|
|
log(f'{NODE_NAME} -> Layer "{layer_name}" not found, top layer will be output.', message_type="warning")
|
|
elif find_layer_by == "layer_index":
|
|
log(f'{NODE_NAME} -> Layer index {layer_index} not found, top layer will be output.', message_type="warning")
|
|
layer_image.append(pil2tensor(empty_layer_image))
|
|
else:
|
|
layer_image.append(pil2tensor(flat_image))
|
|
all_layers.append(pil2tensor(flat_image))
|
|
|
|
return (torch.cat([pil2tensor(flat_image),], dim=0), torch.cat(layer_image, dim=0), torch.cat(all_layers, dim=0),)
|
|
``` |