added Dictionary Convert, Fictionary Get, Image Filters nodes
Signed-off-by: bigcat88 <bigcat88@icloud.com>
This commit is contained in:
bigcat88
@@ -0,0 +1,15 @@
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# Declare files that always have LF line endings on checkout
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* text eol=lf
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# Denote all files that are truly binary and should not be modified
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*.bin binary
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*.heif binary
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*.heic binary
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*.hif binary
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*.avif binary
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*.png binary
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*.gif binary
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*.webp binary
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*.tiff binary
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*.jpeg binary
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*.jpg binary
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@@ -0,0 +1,32 @@
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exclude: ^(screenshots)/
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repos:
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- repo: https://github.com/pre-commit/pre-commit-hooks
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rev: v5.0.0
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hooks:
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- id: check-yaml
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- id: check-toml
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- id: end-of-file-fixer
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- id: trailing-whitespace
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- id: mixed-line-ending
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- repo: https://github.com/PyCQA/isort
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rev: 5.13.2
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hooks:
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- id: isort
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files: .
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- repo: https://github.com/psf/black
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rev: 24.10.0
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hooks:
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- id: black
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files: .
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- repo: https://github.com/tox-dev/pyproject-fmt
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rev: 2.3.1
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hooks:
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- id: pyproject-fmt
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- repo: https://github.com/astral-sh/ruff-pre-commit
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rev: v0.6.9
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hooks:
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- id: ruff
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@@ -23,6 +23,13 @@ Current `Visionatrix/Text` nodes list:
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- **VixMultilineText** - node to just hold text(code compatible).
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- **VixTextConcatenate** - node to concatenate up to 4 different strings with optional delimiter.
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- **VixTextReplace** - find and replace substring in text.
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- **VixDictionaryConvert** - node to create dictionary from text.
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- **VixDictionaryGet** - node to get value by key from dictionary.
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Current `Visionatrix/Image` nodes list:
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- **VixImageFilters** - applies brightness, saturation, sharpness and other simple Pillow filters to an image.
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### Incorporated nodes
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+2
-504
@@ -1,505 +1,3 @@
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import json
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from .nodes import NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS
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from .style_aligned import StyleAlignedBatchAlign
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class AnyType(str):
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def __ne__(self, __value: object) -> bool:
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return False
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any_typ = AnyType("*")
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class VixUiAspectRatioSelector:
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"aspect_ratio": (
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[
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"1:1 (1024x1024)",
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"2:3 (832x1216)",
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"3:4 (896x1152)",
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"5:8 (768x1216)",
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"9:16 (768x1344)",
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"9:19 (704x1472)",
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"9:21 (640x1536)",
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"3:2 (1216x832)",
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"4:3 (1152x896)",
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"8:5 (1216x768)",
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"16:9 (1344x768)",
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"19:9 (1472x704)",
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"21:9 (1536x640)",
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],
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),
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"display_name": ("STRING", {"default": "Aspect Ratio"}),
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"optional": ("BOOLEAN", {"default": True}),
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"advanced": ("BOOLEAN", {"default": True}),
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"order": ("INT", {"default": 20}),
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"custom_id": ("STRING", {"default": "aspect_ratio"}),
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},
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"optional": {
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"hidden": ("BOOLEAN", {"default": False}),
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},
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}
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RETURN_TYPES = ("STRING", "INT", "INT")
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RETURN_NAMES = ("ratio", "width", "height")
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FUNCTION = "do_it"
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CATEGORY = "Visionatrix/UI"
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def do_it(self, aspect_ratio, **kwargs):
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ratio, dims = aspect_ratio.split(" (")
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dims = dims[:-1] # Remove the closing parenthesis
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width, height = map(int, dims.split("x"))
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return ratio, width, height
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class VixUiCheckbox:
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"state": ("BOOLEAN", {"default": False}),
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"display_name": ("STRING", {"default": "Display Name"}),
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"optional": ("BOOLEAN", {"default": True}),
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"advanced": ("BOOLEAN", {"default": True}),
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"order": ("INT", {"default": 99}),
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"custom_id": ("STRING", {"default": ""}),
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},
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"optional": {
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"hidden": ("BOOLEAN", {"default": False}),
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},
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}
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RETURN_TYPES = ("BOOLEAN", "INT")
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RETURN_NAMES = ("bool", "int")
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CATEGORY = "Visionatrix/UI"
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FUNCTION = "do_it"
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@classmethod
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def do_it(cls, state, **kwargs) -> tuple:
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return state, int(state)
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class VixUiRangeFloat:
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"value": ("FLOAT", {"default": 4.0, "step": 0.01, "round": False}),
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"display_name": ("STRING", {"default": "Display Range"}),
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"optional": ("BOOLEAN", {"default": True}),
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"advanced": ("BOOLEAN", {"default": True}),
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"min": ("FLOAT", {"default": 1.0, "step": 0.01, "round": False}),
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"max": ("FLOAT", {"default": 9.0, "step": 0.01, "round": False}),
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"step": ("FLOAT", {"default": 0.1, "step": 0.01, "round": False}),
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"order": ("INT", {"default": 99}),
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"custom_id": ("STRING", {"default": ""}),
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},
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"optional": {
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"hidden": ("BOOLEAN", {"default": False}),
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},
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}
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FUNCTION = "do_it"
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CATEGORY = "Visionatrix/UI"
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RETURN_TYPES = ("FLOAT",)
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@classmethod
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def do_it(cls, value, **kwargs) -> tuple:
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return (value,)
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class VixUiRangeScaleFloat:
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"value": ("FLOAT", {"default": 4.0, "step": 0.01, "round": False}),
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"display_name": ("STRING", {"default": "Image Size Factor"}),
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"optional": ("BOOLEAN", {"default": True}),
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"advanced": ("BOOLEAN", {"default": True}),
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"source_input_name": ("STRING", {"default": ""}),
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"min": ("FLOAT", {"default": 1.0, "step": 0.01, "round": False}),
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"max": ("FLOAT", {"default": 9.0, "step": 0.01, "round": False}),
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"step": ("FLOAT", {"default": 0.1, "step": 0.01, "round": False}),
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"order": ("INT", {"default": 99}),
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"custom_id": ("STRING", {"default": ""}),
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},
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"optional": {
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"hidden": ("BOOLEAN", {"default": False}),
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},
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}
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FUNCTION = "do_it"
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CATEGORY = "Visionatrix/UI"
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RETURN_TYPES = ("FLOAT",)
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@classmethod
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def do_it(cls, value, **kwargs) -> tuple:
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return (value,)
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class VixUiRangeInt:
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"value": ("INT", {"default": 10}),
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"display_name": ("STRING", {"default": "Display Range"}),
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"optional": ("BOOLEAN", {"default": True}),
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"advanced": ("BOOLEAN", {"default": True}),
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"min": ("INT", {"default": 1}),
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"max": ("INT", {"default": 20}),
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"step": ("INT", {"default": 1}),
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"order": ("INT", {"default": 99}),
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"custom_id": ("STRING", {"default": ""}),
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},
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"optional": {
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"hidden": ("BOOLEAN", {"default": False}),
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},
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}
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FUNCTION = "do_it"
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CATEGORY = "Visionatrix/UI"
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RETURN_TYPES = ("INT",)
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@classmethod
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def do_it(cls, value, **kwargs) -> tuple:
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return (value,)
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class VixUiList:
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"default_value": ("STRING", {}),
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"possible_values": ("STRING", {"default": "[]", "multiline": True}),
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"display_name": ("STRING", {"default": "Dropdown list"}),
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"optional": ("BOOLEAN", {"default": True}),
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"advanced": ("BOOLEAN", {"default": True}),
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"order": ("INT", {"default": 99}),
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"custom_id": ("STRING", {"default": ""}),
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},
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"optional": {
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"hidden": ("BOOLEAN", {"default": False}),
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},
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}
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RETURN_TYPES = (any_typ,)
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FUNCTION = "do_it"
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CATEGORY = "Visionatrix/UI"
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@classmethod
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def do_it(cls, default_value, **kwargs) -> tuple:
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possible_values = json.loads(kwargs["possible_values"])
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if isinstance(possible_values, dict) and default_value in possible_values:
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return (possible_values[default_value],)
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return (default_value,)
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class VixUiPrompt:
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"text": ("STRING", {"default": "", "multiline": True, "dynamicPrompts": True}),
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"display_name": ("STRING", {"default": "Prompt"}),
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"optional": ("BOOLEAN", {"default": False}),
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"advanced": ("BOOLEAN", {"default": False}),
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"order": ("INT", {"default": 10}),
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"custom_id": ("STRING", {"default": ""}),
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},
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"optional": {
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"hidden": ("BOOLEAN", {"default": False}),
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"translatable": ("BOOLEAN", {"default": False}),
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},
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}
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RETURN_TYPES = ("STRING",)
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FUNCTION = "do_it"
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CATEGORY = "Visionatrix/UI"
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@classmethod
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def do_it(cls, text, **kwargs) -> tuple:
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return (text,)
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class VixUiCheckboxLogic:
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"state": ("BOOLEAN", {"default": False}),
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"display_name": ("STRING", {"default": "Display Name"}),
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"optional": ("BOOLEAN", {"default": True}),
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"advanced": ("BOOLEAN", {"default": True}),
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"order": ("INT", {"default": 99}),
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"custom_id": ("STRING", {"default": ""}),
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},
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"optional": {
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"input_off_state": (any_typ, {"lazy": True}),
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"input_on_state": (any_typ, {"lazy": True}),
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"hidden": ("BOOLEAN", {"default": False}),
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},
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}
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RETURN_TYPES = (any_typ,)
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RETURN_NAMES = ("output_to",)
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CATEGORY = "Visionatrix/UI"
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FUNCTION = "do_it"
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@classmethod
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def do_it(cls, state, **kwargs) -> tuple:
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if state is False:
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return (kwargs.get("input_off_state", None),)
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return (kwargs.get("input_on_state", None),)
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@staticmethod
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def check_lazy_status(state, **kwargs):
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if state is False:
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return ["input_off_state"]
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return ["input_on_state"]
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class VixUiListLogic:
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"default_value": ("STRING", {}),
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"possible_values": ("STRING", {"default": "[]", "multiline": True}),
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"display_name": ("STRING", {"default": "Display Name"}),
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"optional": ("BOOLEAN", {"default": True}),
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"advanced": ("BOOLEAN", {"default": True}),
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"order": ("INT", {"default": 99}),
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"custom_id": ("STRING", {"default": ""}),
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},
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"optional": {
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"input_first": (any_typ, {"lazy": True}),
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"input_second": (any_typ, {"lazy": True}),
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"input_third": (any_typ, {"lazy": True}),
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"input_fourth": (any_typ, {"lazy": True}),
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"input_fifth": (any_typ, {"lazy": True}),
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"input_sixth": (any_typ, {"lazy": True}),
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"hidden": ("BOOLEAN", {"default": False}),
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},
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}
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RETURN_TYPES = (any_typ,)
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RETURN_NAMES = ("output_to",)
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CATEGORY = "Visionatrix/UI"
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FUNCTION = "do_it"
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@classmethod
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def do_it(cls, default_value, **kwargs) -> tuple:
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list_with_values: list = json.loads(kwargs["possible_values"])
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index_to_return = list_with_values.index(default_value)
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if index_to_return == 0:
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return (kwargs["input_first"],)
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if index_to_return == 1:
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return (kwargs["input_second"],)
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if index_to_return == 2:
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return (kwargs["input_third"],)
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if index_to_return == 3:
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return (kwargs["input_fourth"],)
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if index_to_return == 4:
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return (kwargs["input_fifth"],)
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if index_to_return == 5:
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return (kwargs["input_sixth"],)
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raise RuntimeError("Workflow logic error")
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@staticmethod
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def check_lazy_status(default_value, **kwargs):
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list_with_values: list = json.loads(kwargs["possible_values"])
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index_to_return = list_with_values.index(default_value)
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if index_to_return == 0:
|
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return ["input_first"]
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if index_to_return == 1:
|
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return ["input_second"]
|
||||
if index_to_return == 2:
|
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return ["input_third"]
|
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if index_to_return == 3:
|
||||
return ["input_fourth"]
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if index_to_return == 4:
|
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return ["input_fifth"]
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||||
if index_to_return == 5:
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return ["input_sixth"]
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raise RuntimeError("Workflow logic error")
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|
||||
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class VixUiWorkflowMetadata:
|
||||
@classmethod
|
||||
def INPUT_TYPES(cls):
|
||||
return {
|
||||
"required": {
|
||||
"name": ("STRING", {}),
|
||||
"display_name": ("STRING", {}),
|
||||
"description": ("STRING", {"default": ""}),
|
||||
"author": ("STRING", {}),
|
||||
"homepage": ("STRING", {"default": ""}),
|
||||
"documentation": ("STRING", {"default": ""}),
|
||||
"license": ("STRING", {"default": ""}),
|
||||
"tags": ("STRING", {"default": "[\"general\"]", "multiline": True}),
|
||||
"version": ("STRING", {"default": "1.0.0"})
|
||||
},
|
||||
"optional": {
|
||||
"requires": ("STRING", {"default": "[]", "multiline": True}),
|
||||
"is_seed_supported": ("BOOLEAN", {"default": True}),
|
||||
"is_count_supported": ("BOOLEAN", {"default": True}),
|
||||
"is_translations_supported": ("BOOLEAN", {"default": False}),
|
||||
"is_macos_supported": ("BOOLEAN", {"default": True}),
|
||||
"required_memory_gb": ("FLOAT", {"default": 0.0, "step": 0.1, "round": False}),
|
||||
"hidden": ("BOOLEAN", {"default": False}),
|
||||
"remote_vae": ("BOOLEAN", {"default": False}),
|
||||
}
|
||||
}
|
||||
RETURN_TYPES = ("STRING",)
|
||||
FUNCTION = "do_it"
|
||||
CATEGORY = "Visionatrix/UI"
|
||||
|
||||
@classmethod
|
||||
def do_it(cls, text, **kwargs) -> tuple:
|
||||
return (text,)
|
||||
|
||||
|
||||
class VixDynamicLoraDefinition:
|
||||
@classmethod
|
||||
def INPUT_TYPES(cls):
|
||||
return {
|
||||
"required": {
|
||||
"model": ("MODEL", {"tooltip": "The diffusion model the LoRA will be applied to."}),
|
||||
"clip": ("CLIP", {"tooltip": "The CLIP model the LoRA will be applied to."}),
|
||||
"base_model_type": ("STRING", {"tooltip": "The base type of model in CivitAI format."}),
|
||||
"description": ("STRING", {"tooltip": "Brief explanation of LoRA functionality at the added place."}),
|
||||
},
|
||||
}
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||||
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||||
RETURN_TYPES = ("MODEL", "CLIP")
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||||
OUTPUT_TOOLTIPS = ("The modified diffusion model.", "The modified CLIP model.")
|
||||
CATEGORY = "Visionatrix/UI"
|
||||
FUNCTION = "do_it"
|
||||
DESCRIPTION = "Node that allows dynamic selection of any supported LoRAs from CivitAI in the Visionatrix UI."
|
||||
|
||||
@classmethod
|
||||
def do_it(cls, model, clip, **kwargs) -> tuple:
|
||||
return model, clip
|
||||
|
||||
|
||||
class VixCheckboxLogic:
|
||||
@classmethod
|
||||
def INPUT_TYPES(cls):
|
||||
return {
|
||||
"required": {
|
||||
"state": ("BOOLEAN", {"default": False}),
|
||||
"input_off_state": (any_typ, {"lazy": True}),
|
||||
"input_on_state": (any_typ, {"lazy": True}),
|
||||
},
|
||||
"optional": {},
|
||||
}
|
||||
|
||||
RETURN_TYPES = (any_typ,)
|
||||
RETURN_NAMES = ("output_to",)
|
||||
CATEGORY = "Visionatrix/Logic"
|
||||
FUNCTION = "do_it"
|
||||
|
||||
@classmethod
|
||||
def do_it(cls, state, **kwargs) -> tuple:
|
||||
if state is False:
|
||||
return (kwargs.get("input_off_state", None),)
|
||||
return (kwargs.get("input_on_state", None),)
|
||||
|
||||
@staticmethod
|
||||
def check_lazy_status(state, **kwargs):
|
||||
if state is False:
|
||||
return ["input_off_state"]
|
||||
return ["input_on_state"]
|
||||
|
||||
|
||||
class VixMultilineText:
|
||||
@classmethod
|
||||
def INPUT_TYPES(cls):
|
||||
return {
|
||||
"required": {
|
||||
"text": ("STRING", {"default": "", "multiline": True, "dynamicPrompts": False}),
|
||||
},
|
||||
"optional": {},
|
||||
}
|
||||
RETURN_TYPES = ("STRING",)
|
||||
FUNCTION = "do_it"
|
||||
CATEGORY = "Visionatrix/Text"
|
||||
|
||||
@classmethod
|
||||
def do_it(cls, text, **kwargs) -> tuple:
|
||||
return (text,)
|
||||
|
||||
|
||||
class VixTextConcatenate:
|
||||
@classmethod
|
||||
def INPUT_TYPES(cls):
|
||||
return {
|
||||
"required": {
|
||||
"delimiter": ("STRING", {"default": ", "}),
|
||||
"clean_whitespace": (["true", "false"],),
|
||||
},
|
||||
"optional": {
|
||||
"text_a": ("STRING", {"forceInput": True}),
|
||||
"text_b": ("STRING", {"forceInput": True}),
|
||||
"text_c": ("STRING", {"forceInput": True}),
|
||||
"text_d": ("STRING", {"forceInput": True}),
|
||||
}
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("STRING",)
|
||||
FUNCTION = "do_it"
|
||||
CATEGORY = "Visionatrix/Text"
|
||||
|
||||
@classmethod
|
||||
def do_it(cls, delimiter: str, clean_whitespace: str, **kwargs):
|
||||
delim = "\n" if delimiter == "\\n" else delimiter
|
||||
strip = clean_whitespace.lower() == "true"
|
||||
parts = (
|
||||
(val.strip() if strip else val)
|
||||
for _, val in sorted(kwargs.items())
|
||||
if isinstance(val, str)
|
||||
)
|
||||
return (delim.join(filter(None, parts)),)
|
||||
|
||||
|
||||
|
||||
NODE_CLASS_MAPPINGS = {
|
||||
"VixUiAspectRatioSelector": VixUiAspectRatioSelector,
|
||||
"VixUiCheckbox": VixUiCheckbox,
|
||||
"VixUiRangeFloat": VixUiRangeFloat,
|
||||
"VixUiRangeScaleFloat": VixUiRangeScaleFloat,
|
||||
"VixUiRangeInt": VixUiRangeInt,
|
||||
"VixUiList": VixUiList,
|
||||
"VixUiPrompt": VixUiPrompt,
|
||||
"VixUiCheckboxLogic": VixUiCheckboxLogic,
|
||||
"VixUiListLogic": VixUiListLogic,
|
||||
"VixUiWorkflowMetadata": VixUiWorkflowMetadata,
|
||||
"VixDynamicLoraDefinition": VixDynamicLoraDefinition,
|
||||
"VixCheckboxLogic": VixCheckboxLogic,
|
||||
"StyleAlignedBatchAlign": StyleAlignedBatchAlign,
|
||||
"VixMultilineText": VixMultilineText,
|
||||
"VixTextConcatenate": VixTextConcatenate,
|
||||
}
|
||||
|
||||
NODE_DISPLAY_NAME_MAPPINGS = {
|
||||
"VixUiAspectRatioSelector": "VixUI-Aspect Ratio",
|
||||
"VixUiCheckbox": "VixUI-Checkbox",
|
||||
"VixUiRangeFloat": "VixUI-RangeFloat",
|
||||
"VixUiRangeScaleFloat": "VixUI-RangeScaleFloat",
|
||||
"VixUiRangeInt": "VixUI-RangeInt",
|
||||
"VixUiList": "VixUI-List",
|
||||
"VixUiPrompt": "VixUI-Prompt",
|
||||
"VixUiCheckboxLogic": "VixUI-CheckboxLogic",
|
||||
"VixUiListLogic": "VixUI-ListLogic",
|
||||
"VixUiWorkflowMetadata": "VixUI-WorkflowMetadata",
|
||||
"VixDynamicLoraDefinition": "Vix-DynamicLoraDefinition",
|
||||
"VixCheckboxLogic": "Vix-CheckboxLogic",
|
||||
"StyleAlignedBatchAlign": "StyleAligned Batch Align",
|
||||
"VixMultilineText": "Text Multiline",
|
||||
"VixTextConcatenate": "Text Concatenate",
|
||||
}
|
||||
__all__ = ["NODE_CLASS_MAPPINGS", "NODE_DISPLAY_NAME_MAPPINGS"]
|
||||
|
||||
@@ -0,0 +1,125 @@
|
||||
import numpy as np
|
||||
import torch
|
||||
from PIL import Image, ImageEnhance, ImageFilter
|
||||
|
||||
from .utils import image_to_pillow, pillow_to_image
|
||||
|
||||
|
||||
class VixImageFilters:
|
||||
@classmethod
|
||||
def INPUT_TYPES(cls):
|
||||
return {
|
||||
"required": {
|
||||
"image": ("IMAGE",),
|
||||
"brightness": (
|
||||
"FLOAT",
|
||||
{"default": 0.0, "min": -1.0, "max": 1.0, "step": 0.01},
|
||||
),
|
||||
"contrast": (
|
||||
"FLOAT",
|
||||
{"default": 1.0, "min": -1.0, "max": 2.0, "step": 0.01},
|
||||
),
|
||||
"saturation": (
|
||||
"FLOAT",
|
||||
{"default": 1.0, "min": 0.0, "max": 5.0, "step": 0.01},
|
||||
),
|
||||
"sharpness": (
|
||||
"FLOAT",
|
||||
{"default": 1.0, "min": -5.0, "max": 5.0, "step": 0.01},
|
||||
),
|
||||
"blur": ("INT", {"default": 0, "min": 0, "max": 16, "step": 1}),
|
||||
"gaussian_blur": (
|
||||
"FLOAT",
|
||||
{"default": 0.0, "min": 0.0, "max": 1024.0, "step": 0.1},
|
||||
),
|
||||
"edge_enhance": (
|
||||
"FLOAT",
|
||||
{"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01},
|
||||
),
|
||||
},
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("IMAGE",)
|
||||
FUNCTION = "do_it"
|
||||
CATEGORY = "Visionatrix/Image"
|
||||
|
||||
@classmethod
|
||||
def process_image(
|
||||
cls,
|
||||
img: torch.Tensor,
|
||||
brightness: float,
|
||||
contrast: float,
|
||||
saturation: float,
|
||||
sharpness: float,
|
||||
blur: int,
|
||||
gaussian_blur: float,
|
||||
edge_enhance: float,
|
||||
processing_list: bool = False,
|
||||
) -> torch.Tensor:
|
||||
img = np.clip(img + brightness, 0.0, 1.0) if brightness != 0.0 else img
|
||||
img = np.clip(img * contrast, 0.0, 1.0) if contrast != 1.0 else img
|
||||
|
||||
pil_image = None
|
||||
|
||||
if saturation != 1.0:
|
||||
pil_image = ImageEnhance.Color(image_to_pillow(img)).enhance(saturation)
|
||||
|
||||
if sharpness != 1.0:
|
||||
pil_image = ImageEnhance.Sharpness(pil_image or image_to_pillow(img)).enhance(sharpness)
|
||||
|
||||
if blur > 0:
|
||||
pil_image = pil_image or image_to_pillow(img)
|
||||
for _ in range(blur):
|
||||
pil_image = pil_image.filter(ImageFilter.BLUR)
|
||||
|
||||
if gaussian_blur > 0.0:
|
||||
pil_image = pil_image or image_to_pillow(img)
|
||||
pil_image = pil_image.filter(ImageFilter.GaussianBlur(radius=gaussian_blur))
|
||||
|
||||
if edge_enhance > 0.0:
|
||||
pil_image = pil_image or image_to_pillow(img)
|
||||
edge_enhanced = pil_image.filter(ImageFilter.EDGE_ENHANCE_MORE)
|
||||
mask = Image.new("L", pil_image.size, color=round(edge_enhance * 255))
|
||||
pil_image = Image.composite(edge_enhanced, pil_image, mask)
|
||||
|
||||
return pillow_to_image(pil_image) if pil_image else (img.unsqueeze(0) if processing_list else img)
|
||||
|
||||
def do_it(
|
||||
self,
|
||||
image: torch.Tensor | list[torch.Tensor],
|
||||
brightness: float,
|
||||
contrast: float,
|
||||
saturation: float,
|
||||
sharpness: float,
|
||||
blur: int,
|
||||
gaussian_blur: float,
|
||||
edge_enhance: float,
|
||||
):
|
||||
if len(image) > 1:
|
||||
result = [
|
||||
self.process_image(
|
||||
img,
|
||||
brightness,
|
||||
contrast,
|
||||
saturation,
|
||||
sharpness,
|
||||
blur,
|
||||
gaussian_blur,
|
||||
edge_enhance,
|
||||
processing_list=True,
|
||||
)
|
||||
for img in image
|
||||
]
|
||||
return (torch.cat(result, dim=0),)
|
||||
return (
|
||||
self.process_image(
|
||||
image,
|
||||
brightness,
|
||||
contrast,
|
||||
saturation,
|
||||
sharpness,
|
||||
blur,
|
||||
gaussian_blur,
|
||||
edge_enhance,
|
||||
),
|
||||
)
|
||||
@@ -0,0 +1,484 @@
|
||||
import json
|
||||
|
||||
from . import image, text
|
||||
from .style_aligned import StyleAlignedBatchAlign
|
||||
|
||||
|
||||
class AnyType(str):
|
||||
def __ne__(self, __value: object) -> bool:
|
||||
return False
|
||||
|
||||
|
||||
any_typ = AnyType("*")
|
||||
|
||||
|
||||
class VixUiAspectRatioSelector:
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {
|
||||
"required": {
|
||||
"aspect_ratio": (
|
||||
[
|
||||
"1:1 (1024x1024)",
|
||||
"2:3 (832x1216)",
|
||||
"3:4 (896x1152)",
|
||||
"5:8 (768x1216)",
|
||||
"9:16 (768x1344)",
|
||||
"9:19 (704x1472)",
|
||||
"9:21 (640x1536)",
|
||||
"3:2 (1216x832)",
|
||||
"4:3 (1152x896)",
|
||||
"8:5 (1216x768)",
|
||||
"16:9 (1344x768)",
|
||||
"19:9 (1472x704)",
|
||||
"21:9 (1536x640)",
|
||||
],
|
||||
),
|
||||
"display_name": ("STRING", {"default": "Aspect Ratio"}),
|
||||
"optional": ("BOOLEAN", {"default": True}),
|
||||
"advanced": ("BOOLEAN", {"default": True}),
|
||||
"order": ("INT", {"default": 20}),
|
||||
"custom_id": ("STRING", {"default": "aspect_ratio"}),
|
||||
},
|
||||
"optional": {
|
||||
"hidden": ("BOOLEAN", {"default": False}),
|
||||
},
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("STRING", "INT", "INT")
|
||||
RETURN_NAMES = ("ratio", "width", "height")
|
||||
FUNCTION = "do_it"
|
||||
CATEGORY = "Visionatrix/UI"
|
||||
|
||||
def do_it(self, aspect_ratio, **kwargs):
|
||||
ratio, dims = aspect_ratio.split(" (")
|
||||
dims = dims[:-1] # Remove the closing parenthesis
|
||||
width, height = map(int, dims.split("x"))
|
||||
return ratio, width, height
|
||||
|
||||
|
||||
class VixUiCheckbox:
|
||||
@classmethod
|
||||
def INPUT_TYPES(cls):
|
||||
return {
|
||||
"required": {
|
||||
"state": ("BOOLEAN", {"default": False}),
|
||||
"display_name": ("STRING", {"default": "Display Name"}),
|
||||
"optional": ("BOOLEAN", {"default": True}),
|
||||
"advanced": ("BOOLEAN", {"default": True}),
|
||||
"order": ("INT", {"default": 99}),
|
||||
"custom_id": ("STRING", {"default": ""}),
|
||||
},
|
||||
"optional": {
|
||||
"hidden": ("BOOLEAN", {"default": False}),
|
||||
},
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("BOOLEAN", "INT")
|
||||
RETURN_NAMES = ("bool", "int")
|
||||
CATEGORY = "Visionatrix/UI"
|
||||
FUNCTION = "do_it"
|
||||
|
||||
@classmethod
|
||||
def do_it(cls, state, **kwargs) -> tuple:
|
||||
return state, int(state)
|
||||
|
||||
|
||||
class VixUiRangeFloat:
|
||||
@classmethod
|
||||
def INPUT_TYPES(cls):
|
||||
return {
|
||||
"required": {
|
||||
"value": ("FLOAT", {"default": 4.0, "step": 0.01, "round": False}),
|
||||
"display_name": ("STRING", {"default": "Display Range"}),
|
||||
"optional": ("BOOLEAN", {"default": True}),
|
||||
"advanced": ("BOOLEAN", {"default": True}),
|
||||
"min": ("FLOAT", {"default": 1.0, "step": 0.01, "round": False}),
|
||||
"max": ("FLOAT", {"default": 9.0, "step": 0.01, "round": False}),
|
||||
"step": ("FLOAT", {"default": 0.1, "step": 0.01, "round": False}),
|
||||
"order": ("INT", {"default": 99}),
|
||||
"custom_id": ("STRING", {"default": ""}),
|
||||
},
|
||||
"optional": {
|
||||
"hidden": ("BOOLEAN", {"default": False}),
|
||||
},
|
||||
}
|
||||
|
||||
FUNCTION = "do_it"
|
||||
CATEGORY = "Visionatrix/UI"
|
||||
RETURN_TYPES = ("FLOAT",)
|
||||
|
||||
@classmethod
|
||||
def do_it(cls, value, **kwargs) -> tuple:
|
||||
return (value,)
|
||||
|
||||
|
||||
class VixUiRangeScaleFloat:
|
||||
@classmethod
|
||||
def INPUT_TYPES(cls):
|
||||
return {
|
||||
"required": {
|
||||
"value": ("FLOAT", {"default": 4.0, "step": 0.01, "round": False}),
|
||||
"display_name": ("STRING", {"default": "Image Size Factor"}),
|
||||
"optional": ("BOOLEAN", {"default": True}),
|
||||
"advanced": ("BOOLEAN", {"default": True}),
|
||||
"source_input_name": ("STRING", {"default": ""}),
|
||||
"min": ("FLOAT", {"default": 1.0, "step": 0.01, "round": False}),
|
||||
"max": ("FLOAT", {"default": 9.0, "step": 0.01, "round": False}),
|
||||
"step": ("FLOAT", {"default": 0.1, "step": 0.01, "round": False}),
|
||||
"order": ("INT", {"default": 99}),
|
||||
"custom_id": ("STRING", {"default": ""}),
|
||||
},
|
||||
"optional": {
|
||||
"hidden": ("BOOLEAN", {"default": False}),
|
||||
},
|
||||
}
|
||||
|
||||
FUNCTION = "do_it"
|
||||
CATEGORY = "Visionatrix/UI"
|
||||
RETURN_TYPES = ("FLOAT",)
|
||||
|
||||
@classmethod
|
||||
def do_it(cls, value, **kwargs) -> tuple:
|
||||
return (value,)
|
||||
|
||||
|
||||
class VixUiRangeInt:
|
||||
@classmethod
|
||||
def INPUT_TYPES(cls):
|
||||
return {
|
||||
"required": {
|
||||
"value": ("INT", {"default": 10}),
|
||||
"display_name": ("STRING", {"default": "Display Range"}),
|
||||
"optional": ("BOOLEAN", {"default": True}),
|
||||
"advanced": ("BOOLEAN", {"default": True}),
|
||||
"min": ("INT", {"default": 1}),
|
||||
"max": ("INT", {"default": 20}),
|
||||
"step": ("INT", {"default": 1}),
|
||||
"order": ("INT", {"default": 99}),
|
||||
"custom_id": ("STRING", {"default": ""}),
|
||||
},
|
||||
"optional": {
|
||||
"hidden": ("BOOLEAN", {"default": False}),
|
||||
},
|
||||
}
|
||||
|
||||
FUNCTION = "do_it"
|
||||
CATEGORY = "Visionatrix/UI"
|
||||
RETURN_TYPES = ("INT",)
|
||||
|
||||
@classmethod
|
||||
def do_it(cls, value, **kwargs) -> tuple:
|
||||
return (value,)
|
||||
|
||||
|
||||
class VixUiList:
|
||||
@classmethod
|
||||
def INPUT_TYPES(cls):
|
||||
return {
|
||||
"required": {
|
||||
"default_value": ("STRING", {}),
|
||||
"possible_values": ("STRING", {"default": "[]", "multiline": True}),
|
||||
"display_name": ("STRING", {"default": "Dropdown list"}),
|
||||
"optional": ("BOOLEAN", {"default": True}),
|
||||
"advanced": ("BOOLEAN", {"default": True}),
|
||||
"order": ("INT", {"default": 99}),
|
||||
"custom_id": ("STRING", {"default": ""}),
|
||||
},
|
||||
"optional": {
|
||||
"hidden": ("BOOLEAN", {"default": False}),
|
||||
},
|
||||
}
|
||||
|
||||
RETURN_TYPES = (any_typ,)
|
||||
FUNCTION = "do_it"
|
||||
CATEGORY = "Visionatrix/UI"
|
||||
|
||||
@classmethod
|
||||
def do_it(cls, default_value, **kwargs) -> tuple:
|
||||
possible_values = json.loads(kwargs["possible_values"])
|
||||
if isinstance(possible_values, dict) and default_value in possible_values:
|
||||
return (possible_values[default_value],)
|
||||
return (default_value,)
|
||||
|
||||
|
||||
class VixUiPrompt:
|
||||
@classmethod
|
||||
def INPUT_TYPES(cls):
|
||||
return {
|
||||
"required": {
|
||||
"text": (
|
||||
"STRING",
|
||||
{"default": "", "multiline": True, "dynamicPrompts": True},
|
||||
),
|
||||
"display_name": ("STRING", {"default": "Prompt"}),
|
||||
"optional": ("BOOLEAN", {"default": False}),
|
||||
"advanced": ("BOOLEAN", {"default": False}),
|
||||
"order": ("INT", {"default": 10}),
|
||||
"custom_id": ("STRING", {"default": ""}),
|
||||
},
|
||||
"optional": {
|
||||
"hidden": ("BOOLEAN", {"default": False}),
|
||||
"translatable": ("BOOLEAN", {"default": False}),
|
||||
},
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("STRING",)
|
||||
FUNCTION = "do_it"
|
||||
CATEGORY = "Visionatrix/UI"
|
||||
|
||||
@classmethod
|
||||
def do_it(cls, text, **kwargs) -> tuple:
|
||||
return (text,)
|
||||
|
||||
|
||||
class VixUiCheckboxLogic:
|
||||
@classmethod
|
||||
def INPUT_TYPES(cls):
|
||||
return {
|
||||
"required": {
|
||||
"state": ("BOOLEAN", {"default": False}),
|
||||
"display_name": ("STRING", {"default": "Display Name"}),
|
||||
"optional": ("BOOLEAN", {"default": True}),
|
||||
"advanced": ("BOOLEAN", {"default": True}),
|
||||
"order": ("INT", {"default": 99}),
|
||||
"custom_id": ("STRING", {"default": ""}),
|
||||
},
|
||||
"optional": {
|
||||
"input_off_state": (any_typ, {"lazy": True}),
|
||||
"input_on_state": (any_typ, {"lazy": True}),
|
||||
"hidden": ("BOOLEAN", {"default": False}),
|
||||
},
|
||||
}
|
||||
|
||||
RETURN_TYPES = (any_typ,)
|
||||
RETURN_NAMES = ("output_to",)
|
||||
CATEGORY = "Visionatrix/UI"
|
||||
FUNCTION = "do_it"
|
||||
|
||||
@classmethod
|
||||
def do_it(cls, state, **kwargs) -> tuple:
|
||||
if state is False:
|
||||
return (kwargs.get("input_off_state"),)
|
||||
return (kwargs.get("input_on_state"),)
|
||||
|
||||
@staticmethod
|
||||
def check_lazy_status(state, **kwargs):
|
||||
if state is False:
|
||||
return ["input_off_state"]
|
||||
return ["input_on_state"]
|
||||
|
||||
|
||||
class VixUiListLogic:
|
||||
@classmethod
|
||||
def INPUT_TYPES(cls):
|
||||
return {
|
||||
"required": {
|
||||
"default_value": ("STRING", {}),
|
||||
"possible_values": ("STRING", {"default": "[]", "multiline": True}),
|
||||
"display_name": ("STRING", {"default": "Display Name"}),
|
||||
"optional": ("BOOLEAN", {"default": True}),
|
||||
"advanced": ("BOOLEAN", {"default": True}),
|
||||
"order": ("INT", {"default": 99}),
|
||||
"custom_id": ("STRING", {"default": ""}),
|
||||
},
|
||||
"optional": {
|
||||
"input_first": (any_typ, {"lazy": True}),
|
||||
"input_second": (any_typ, {"lazy": True}),
|
||||
"input_third": (any_typ, {"lazy": True}),
|
||||
"input_fourth": (any_typ, {"lazy": True}),
|
||||
"input_fifth": (any_typ, {"lazy": True}),
|
||||
"input_sixth": (any_typ, {"lazy": True}),
|
||||
"hidden": ("BOOLEAN", {"default": False}),
|
||||
},
|
||||
}
|
||||
|
||||
RETURN_TYPES = (any_typ,)
|
||||
RETURN_NAMES = ("output_to",)
|
||||
CATEGORY = "Visionatrix/UI"
|
||||
FUNCTION = "do_it"
|
||||
|
||||
@classmethod
|
||||
def do_it(cls, default_value, **kwargs) -> tuple:
|
||||
list_with_values: list = json.loads(kwargs["possible_values"])
|
||||
index_to_return = list_with_values.index(default_value)
|
||||
if index_to_return == 0:
|
||||
return (kwargs["input_first"],)
|
||||
if index_to_return == 1:
|
||||
return (kwargs["input_second"],)
|
||||
if index_to_return == 2:
|
||||
return (kwargs["input_third"],)
|
||||
if index_to_return == 3:
|
||||
return (kwargs["input_fourth"],)
|
||||
if index_to_return == 4:
|
||||
return (kwargs["input_fifth"],)
|
||||
if index_to_return == 5:
|
||||
return (kwargs["input_sixth"],)
|
||||
raise RuntimeError("Workflow logic error")
|
||||
|
||||
@staticmethod
|
||||
def check_lazy_status(default_value, **kwargs):
|
||||
list_with_values: list = json.loads(kwargs["possible_values"])
|
||||
index_to_return = list_with_values.index(default_value)
|
||||
if index_to_return == 0:
|
||||
return ["input_first"]
|
||||
if index_to_return == 1:
|
||||
return ["input_second"]
|
||||
if index_to_return == 2:
|
||||
return ["input_third"]
|
||||
if index_to_return == 3:
|
||||
return ["input_fourth"]
|
||||
if index_to_return == 4:
|
||||
return ["input_fifth"]
|
||||
if index_to_return == 5:
|
||||
return ["input_sixth"]
|
||||
raise RuntimeError("Workflow logic error")
|
||||
|
||||
|
||||
class VixUiWorkflowMetadata:
|
||||
@classmethod
|
||||
def INPUT_TYPES(cls):
|
||||
return {
|
||||
"required": {
|
||||
"name": ("STRING", {}),
|
||||
"display_name": ("STRING", {}),
|
||||
"description": ("STRING", {"default": ""}),
|
||||
"author": ("STRING", {}),
|
||||
"homepage": ("STRING", {"default": ""}),
|
||||
"documentation": ("STRING", {"default": ""}),
|
||||
"license": ("STRING", {"default": ""}),
|
||||
"tags": ("STRING", {"default": '["general"]', "multiline": True}),
|
||||
"version": ("STRING", {"default": "1.0.0"}),
|
||||
},
|
||||
"optional": {
|
||||
"requires": ("STRING", {"default": "[]", "multiline": True}),
|
||||
"is_seed_supported": ("BOOLEAN", {"default": True}),
|
||||
"is_count_supported": ("BOOLEAN", {"default": True}),
|
||||
"is_translations_supported": ("BOOLEAN", {"default": False}),
|
||||
"is_macos_supported": ("BOOLEAN", {"default": True}),
|
||||
"required_memory_gb": (
|
||||
"FLOAT",
|
||||
{"default": 0.0, "step": 0.1, "round": False},
|
||||
),
|
||||
"hidden": ("BOOLEAN", {"default": False}),
|
||||
"remote_vae": ("BOOLEAN", {"default": False}),
|
||||
},
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("STRING",)
|
||||
FUNCTION = "do_it"
|
||||
CATEGORY = "Visionatrix/UI"
|
||||
|
||||
@classmethod
|
||||
def do_it(cls, text, **kwargs) -> tuple:
|
||||
return (text,)
|
||||
|
||||
|
||||
class VixDynamicLoraDefinition:
|
||||
@classmethod
|
||||
def INPUT_TYPES(cls):
|
||||
return {
|
||||
"required": {
|
||||
"model": (
|
||||
"MODEL",
|
||||
{"tooltip": "The diffusion model the LoRA will be applied to."},
|
||||
),
|
||||
"clip": (
|
||||
"CLIP",
|
||||
{"tooltip": "The CLIP model the LoRA will be applied to."},
|
||||
),
|
||||
"base_model_type": (
|
||||
"STRING",
|
||||
{"tooltip": "The base type of model in CivitAI format."},
|
||||
),
|
||||
"description": (
|
||||
"STRING",
|
||||
{"tooltip": "Brief explanation of LoRA functionality at the added place."},
|
||||
),
|
||||
},
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("MODEL", "CLIP")
|
||||
OUTPUT_TOOLTIPS = ("The modified diffusion model.", "The modified CLIP model.")
|
||||
CATEGORY = "Visionatrix/UI"
|
||||
FUNCTION = "do_it"
|
||||
DESCRIPTION = "Node that allows dynamic selection of any supported LoRAs from CivitAI in the Visionatrix UI."
|
||||
|
||||
@classmethod
|
||||
def do_it(cls, model, clip, **kwargs) -> tuple:
|
||||
return model, clip
|
||||
|
||||
|
||||
class VixCheckboxLogic:
|
||||
@classmethod
|
||||
def INPUT_TYPES(cls):
|
||||
return {
|
||||
"required": {
|
||||
"state": ("BOOLEAN", {"default": False}),
|
||||
"input_off_state": (any_typ, {"lazy": True}),
|
||||
"input_on_state": (any_typ, {"lazy": True}),
|
||||
},
|
||||
"optional": {},
|
||||
}
|
||||
|
||||
RETURN_TYPES = (any_typ,)
|
||||
RETURN_NAMES = ("output_to",)
|
||||
CATEGORY = "Visionatrix/Logic"
|
||||
FUNCTION = "do_it"
|
||||
|
||||
@classmethod
|
||||
def do_it(cls, state, **kwargs) -> tuple:
|
||||
if state is False:
|
||||
return (kwargs.get("input_off_state"),)
|
||||
return (kwargs.get("input_on_state"),)
|
||||
|
||||
@staticmethod
|
||||
def check_lazy_status(state, **kwargs):
|
||||
if state is False:
|
||||
return ["input_off_state"]
|
||||
return ["input_on_state"]
|
||||
|
||||
|
||||
NODE_CLASS_MAPPINGS = {
|
||||
"VixUiAspectRatioSelector": VixUiAspectRatioSelector,
|
||||
"VixUiCheckbox": VixUiCheckbox,
|
||||
"VixUiRangeFloat": VixUiRangeFloat,
|
||||
"VixUiRangeScaleFloat": VixUiRangeScaleFloat,
|
||||
"VixUiRangeInt": VixUiRangeInt,
|
||||
"VixUiList": VixUiList,
|
||||
"VixUiPrompt": VixUiPrompt,
|
||||
"VixUiCheckboxLogic": VixUiCheckboxLogic,
|
||||
"VixUiListLogic": VixUiListLogic,
|
||||
"VixUiWorkflowMetadata": VixUiWorkflowMetadata,
|
||||
"VixDynamicLoraDefinition": VixDynamicLoraDefinition,
|
||||
"VixCheckboxLogic": VixCheckboxLogic,
|
||||
"StyleAlignedBatchAlign": StyleAlignedBatchAlign,
|
||||
"VixMultilineText": text.VixMultilineText,
|
||||
"VixTextConcatenate": text.VixTextConcatenate,
|
||||
"VixTextReplace": text.VixTextReplace,
|
||||
"VixDictionaryConvert": text.VixDictionaryConvert,
|
||||
"VixDictionaryGet": text.VixDictionaryGet,
|
||||
"VixImageFilters": image.VixImageFilters,
|
||||
}
|
||||
|
||||
NODE_DISPLAY_NAME_MAPPINGS = {
|
||||
"VixUiAspectRatioSelector": "Aspect Ratio (VixUI)",
|
||||
"VixUiCheckbox": "Checkbox (VixUI)",
|
||||
"VixUiRangeFloat": "Range Float (VixUI)",
|
||||
"VixUiRangeScaleFloat": "Range Scale Float (VixUI)",
|
||||
"VixUiRangeInt": "Range Int (VixUI)",
|
||||
"VixUiList": "List (VixUI)",
|
||||
"VixUiPrompt": "Prompt (VixUI)",
|
||||
"VixUiCheckboxLogic": "Checkbox Logic (VixUI)",
|
||||
"VixUiListLogic": "List Logic (VixUI)",
|
||||
"VixUiWorkflowMetadata": "Workflow Metadata (VixUI)",
|
||||
"VixDynamicLoraDefinition": "Vix Dynamic Lora Definition",
|
||||
"VixCheckboxLogic": "Vix Checkbox Logic",
|
||||
"StyleAlignedBatchAlign": "StyleAligned Batch Align",
|
||||
"VixMultilineText": "Text Multiline",
|
||||
"VixTextConcatenate": "Text Concatenate",
|
||||
"VixTextReplace": "Text Replace",
|
||||
"VixDictionaryConvert": "Convert to Dictionary",
|
||||
"VixDictionaryGet": "Dictionary Get",
|
||||
"VixImageFilters": "Image Filters",
|
||||
}
|
||||
+45
-2
@@ -1,10 +1,53 @@
|
||||
[project]
|
||||
name = "comfyui-visionatrix"
|
||||
version = "1.2.0"
|
||||
description = "The ComfyUI-Visionatrix nodes are designed for convenient ComfyUI to [a/Visionatrix](https://github.com/Visionatrix/Visionatrix) workflow support migration, in particular to extract prompt input params (input, textarea, checkbox, select, range, file) to be used in simplified Visionatrix UI."
|
||||
version = "1.1.0"
|
||||
license = {file = "LICENSE"}
|
||||
license = { file = "LICENSE" }
|
||||
classifiers = [
|
||||
"Programming Language :: Python :: 3 :: Only",
|
||||
"Programming Language :: Python :: 3.9",
|
||||
"Programming Language :: Python :: 3.10",
|
||||
"Programming Language :: Python :: 3.11",
|
||||
"Programming Language :: Python :: 3.12",
|
||||
"Programming Language :: Python :: 3.13",
|
||||
]
|
||||
urls.Repository = "https://github.com/Visionatrix/ComfyUI-Visionatrix"
|
||||
|
||||
[tool.black]
|
||||
line-length = 120
|
||||
preview = true
|
||||
|
||||
[tool.ruff]
|
||||
target-version = "py310"
|
||||
line-length = 120
|
||||
lint.select = [
|
||||
"A",
|
||||
"B",
|
||||
"C",
|
||||
"E",
|
||||
"F",
|
||||
"G",
|
||||
"I",
|
||||
"PIE",
|
||||
"Q",
|
||||
"RET",
|
||||
"RUF",
|
||||
"S",
|
||||
"SIM",
|
||||
"UP",
|
||||
"W",
|
||||
]
|
||||
lint.extend-ignore = [
|
||||
"I001",
|
||||
"RUF100",
|
||||
"S311",
|
||||
"S603",
|
||||
]
|
||||
lint.mccabe.max-complexity = 20
|
||||
|
||||
[tool.isort]
|
||||
profile = "black"
|
||||
|
||||
[tool.comfy]
|
||||
PublisherId = "visionatrix"
|
||||
DisplayName = "ComfyUI-Visionatrix"
|
||||
|
||||
@@ -0,0 +1,3 @@
|
||||
torch
|
||||
pillow
|
||||
numpy
|
||||
+5
-12
@@ -3,12 +3,10 @@
|
||||
# Copyright (c) 2023 Brian Fitzgerald
|
||||
# original repository: https://github.com/brianfitzgerald/style_aligned_comfy
|
||||
|
||||
import torch.nn as nn
|
||||
import torch
|
||||
|
||||
import torch.nn as nn
|
||||
from comfy.model_patcher import ModelPatcher
|
||||
|
||||
|
||||
T = torch.Tensor
|
||||
|
||||
|
||||
@@ -54,8 +52,7 @@ def adain(feat: T) -> T:
|
||||
feat_style_mean = expand_first(feat_mean)
|
||||
feat_style_std = expand_first(feat_std)
|
||||
feat = (feat - feat_mean) / feat_std
|
||||
feat = feat * feat_style_std + feat_style_mean
|
||||
return feat
|
||||
return feat * feat_style_std + feat_style_mean
|
||||
|
||||
|
||||
class SharedAttentionProcessor:
|
||||
@@ -89,16 +86,14 @@ def get_norm_layers(
|
||||
norm_layers_["group"].append(layer)
|
||||
else:
|
||||
for child_layer in layer.children():
|
||||
get_norm_layers(
|
||||
child_layer, norm_layers_, share_layer_norm, share_group_norm
|
||||
)
|
||||
get_norm_layers(child_layer, norm_layers_, share_layer_norm, share_group_norm)
|
||||
|
||||
|
||||
def register_norm_forward(
|
||||
norm_layer: nn.GroupNorm | nn.LayerNorm,
|
||||
) -> nn.GroupNorm | nn.LayerNorm:
|
||||
if not hasattr(norm_layer, "orig_forward"):
|
||||
setattr(norm_layer, "orig_forward", norm_layer.forward)
|
||||
setattr(norm_layer, "orig_forward", norm_layer.forward) # noqa
|
||||
orig_forward = norm_layer.orig_forward
|
||||
|
||||
def forward_(hidden_states: T) -> T:
|
||||
@@ -118,9 +113,7 @@ def register_shared_norm(
|
||||
):
|
||||
norm_layers = {"group": [], "layer": []}
|
||||
get_norm_layers(model.model, norm_layers, share_layer_norm, share_group_norm)
|
||||
print(
|
||||
f"Patching {len(norm_layers['group'])} group norms, {len(norm_layers['layer'])} layer norms."
|
||||
)
|
||||
print(f"Patching {len(norm_layers['group'])} group norms, {len(norm_layers['layer'])} layer norms.")
|
||||
return [register_norm_forward(layer) for layer in norm_layers["group"]] + [
|
||||
register_norm_forward(layer) for layer in norm_layers["layer"]
|
||||
]
|
||||
|
||||
@@ -0,0 +1,119 @@
|
||||
import ast
|
||||
import re
|
||||
|
||||
|
||||
class VixMultilineText:
|
||||
@classmethod
|
||||
def INPUT_TYPES(cls):
|
||||
return {
|
||||
"required": {
|
||||
"text": (
|
||||
"STRING",
|
||||
{"default": "", "multiline": True, "dynamicPrompts": False},
|
||||
),
|
||||
},
|
||||
"optional": {},
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("STRING",)
|
||||
FUNCTION = "do_it"
|
||||
CATEGORY = "Visionatrix/Text"
|
||||
|
||||
@classmethod
|
||||
def do_it(cls, text, **kwargs) -> tuple:
|
||||
return (text,)
|
||||
|
||||
|
||||
class VixTextConcatenate:
|
||||
@classmethod
|
||||
def INPUT_TYPES(cls):
|
||||
return {
|
||||
"required": {
|
||||
"delimiter": ("STRING", {"default": ", "}),
|
||||
"clean_whitespace": (["true", "false"],),
|
||||
},
|
||||
"optional": {
|
||||
"text_a": ("STRING", {"forceInput": True}),
|
||||
"text_b": ("STRING", {"forceInput": True}),
|
||||
"text_c": ("STRING", {"forceInput": True}),
|
||||
"text_d": ("STRING", {"forceInput": True}),
|
||||
},
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("STRING",)
|
||||
FUNCTION = "do_it"
|
||||
CATEGORY = "Visionatrix/Text"
|
||||
|
||||
@classmethod
|
||||
def do_it(cls, delimiter: str, clean_whitespace: str, **kwargs):
|
||||
delim = "\n" if delimiter == "\\n" else delimiter
|
||||
strip = clean_whitespace.lower() == "true"
|
||||
parts = ((val.strip() if strip else val) for _, val in sorted(kwargs.items()) if isinstance(val, str))
|
||||
return (delim.join(filter(None, parts)),)
|
||||
|
||||
|
||||
class VixTextReplace:
|
||||
@classmethod
|
||||
def INPUT_TYPES(cls):
|
||||
return {
|
||||
"required": {
|
||||
"text": ("STRING", {"forceInput": True}),
|
||||
"find": ("STRING", {"default": "", "multiline": False}),
|
||||
"replace": ("STRING", {"default": "", "multiline": False}),
|
||||
},
|
||||
"optional": {},
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("STRING", "NUMBER", "FLOAT", "INT")
|
||||
RETURN_NAMES = (
|
||||
"result_text",
|
||||
"replacement_count_number",
|
||||
"replacement_count_float",
|
||||
"replacement_count_int",
|
||||
)
|
||||
FUNCTION = "do_it"
|
||||
CATEGORY = "Visionatrix/Text"
|
||||
|
||||
@classmethod
|
||||
def do_it(cls, text: str, find: str, replace: str):
|
||||
modified_text, count = re.subn(find, replace, text)
|
||||
return modified_text, count, float(count), count
|
||||
|
||||
|
||||
class VixDictionaryConvert:
|
||||
@classmethod
|
||||
def INPUT_TYPES(cls):
|
||||
return {
|
||||
"required": {"dictionary_text": ("STRING", {"forceInput": True})},
|
||||
"optional": {},
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("DICT",)
|
||||
FUNCTION = "do_it"
|
||||
CATEGORY = "Visionatrix/Text"
|
||||
|
||||
@classmethod
|
||||
def do_it(cls, dictionary_text: str):
|
||||
return (ast.literal_eval(dictionary_text),)
|
||||
|
||||
|
||||
class VixDictionaryGet:
|
||||
@classmethod
|
||||
def INPUT_TYPES(cls):
|
||||
return {
|
||||
"required": {
|
||||
"dictionary": ("DICT",),
|
||||
"key": ("STRING", {"default": "", "multiline": False}),
|
||||
},
|
||||
"optional": {
|
||||
"default_value": ("STRING", {"default": "", "multiline": False}),
|
||||
},
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("STRING",)
|
||||
FUNCTION = "do_it"
|
||||
CATEGORY = "Visionatrix/Text"
|
||||
|
||||
@classmethod
|
||||
def do_it(cls, dictionary: dict, key: str, default_value=""):
|
||||
return (str(dictionary.get(key, default_value)),)
|
||||
@@ -0,0 +1,18 @@
|
||||
import numpy as np
|
||||
from PIL import Image
|
||||
from torch import Tensor, from_numpy
|
||||
|
||||
|
||||
def image_to_pillow(image: Tensor) -> Image.Image:
|
||||
return Image.fromarray(np.clip(255.0 * image.cpu().numpy().squeeze(), 0, 255).astype(np.uint8))
|
||||
|
||||
|
||||
def images_to_pillow(images: Tensor | list[Tensor]) -> list[Image.Image]:
|
||||
pillow_images = []
|
||||
for _bn, image in enumerate(images):
|
||||
pillow_images.append(image_to_pillow(image))
|
||||
return pillow_images
|
||||
|
||||
|
||||
def pillow_to_image(image: Image.Image) -> Tensor:
|
||||
return from_numpy(np.array(image).astype(np.float32) / 255.0).unsqueeze(0)
|
||||
Reference in New Issue
Block a user