started for dynamic inputs
This commit is contained in:
Hamilton
+19
-10
@@ -1,19 +1,28 @@
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from .nodes import FitSize, FitSizeFromImage, FitResizeImage, FitResizeLatent, LoadToFitResizeLatent
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from .nodes import *
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from .startup_utils import symlink_web_dir
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# A dictionary that contains all nodes you want to export with their names
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# NOTE: names should be globally unique
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NODE_CLASS_MAPPINGS = {
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"Fit Size From Int": FitSize,
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"Fit Size From Image": FitSizeFromImage,
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"Fit Image And Resize": FitResizeLatent,
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"Load Image And Resize To Fit": LoadToFitResizeLatent,
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"FS: Fit Size From Int": FitSize,
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"FS: Fit Size From Image": FitSizeFromImage,
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"FS: Fit Image And Resize": FitResizeLatent,
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"FS: Load Image And Resize To Fit": LoadToFitResizeLatent,
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"FS: Pick Image From Batch": RandomImageFromBatch,
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"FS: Crop Image Into Even Pieces": CropImageIntoEvenPieces,
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"Fit Size From Int": "Fit Size From Int",
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"Fit Size From Image": "Fit Size From Image",
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"Fit Image And Resize": "Fit Image And Resize",
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"Load Image And Resize To Fit": "Load Image And Resize To Fit",
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"FS: Fit Size From Int": "Fit Size From Int",
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"FS: Fit Size From Image": "Fit Size From Image",
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"FS: Fit Image And Resize": "Fit Image And Resize",
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"FS: Load Image And Resize To Fit": "Load Image And Resize To Fit",
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"FS: Pick Image From Batch": "Pick Image From Batch",
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"FS: Crop Image Into Even Pieces": "Crop Image Into Even Pieces",
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}
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__all__ = ['NODE_CLASS_MAPPINGS', 'NODE_DISPLAY_NAME_MAPPINGS']
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__all__ = ['NODE_CLASS_MAPPINGS', 'NODE_DISPLAY_NAME_MAPPINGS']
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EXTENSION_NAME = "Fitsize"
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symlink_web_dir("js", EXTENSION_NAME)
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@@ -110,7 +110,7 @@ class FitSize:
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RETURN_NAMES = ("Fit Width", "Fit Height", "Aspect Ratio")
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FUNCTION = "fit_to_size"
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CATEGORY = "Fitsize"
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CATEGORY = "Fitsize/Numbers"
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def fit_to_size (self, original_width, original_height, max_size, upscale="false"):
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values = get_max_size(original_width, original_height, max_size, upscale)
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@@ -134,7 +134,7 @@ class FitSizeFromImage:
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RETURN_NAMES = ("Fit Width", "Fit Height", "Aspect Ratio")
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FUNCTION = "fit_to_size_from_image"
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CATEGORY = "Fitsize"
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CATEGORY = "Fitsize/Numbers"
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def fit_to_size_from_image (self, image, max_size, upscale="false"):
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size = get_image_size(image)
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@@ -160,7 +160,7 @@ class FitResizeImage:
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RETURN_NAMES = ("Image","Fit Width", "Fit Height", "Aspect Ratio")
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FUNCTION = "fit_resize_image"
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CATEGORY = "Fitsize"
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CATEGORY = "Fitsize/Image"
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def fit_resize_image (self, image, max_size=768, resampling="bicubic", upscale="false", latent=False):
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size = get_image_size(image)
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@@ -208,7 +208,7 @@ class FitResizeLatent():
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)
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FUNCTION = "fit_resize_latent"
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CATEGORY = "Fitsize"
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CATEGORY = "Fitsize/Image"
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def fit_resize_latent (self, image, vae, max_size=768, resampling="bicubic", upscale="false", batch_size=1, add_noise=0.0):
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@@ -252,7 +252,7 @@ class LoadToFitResizeLatent():
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)
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FUNCTION = "fit_resize_latent"
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CATEGORY = "Fitsize"
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CATEGORY = "Fitsize/Image"
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@staticmethod
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def load_image(image):
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@@ -299,4 +299,103 @@ class LoadToFitResizeLatent():
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new_height,
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aspect_ratio,
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mask,
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)
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)
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class CropImageIntoEvenPieces:
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def __init__(self):
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pass
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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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"image": ("IMAGE",),
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"rows": ("INT", {"default": 3, "min": 1, "max": 32, "step": 1,}),
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"columns": ("INT", {"default": 1, "min": 1, "max": 32, "step": 1,}),
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},
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}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "run"
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CATEGORY = "Fitsize/Image"
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def run(self, image, rows, columns):
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if rows < 1:
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rows = 1
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if columns < 1:
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columns = 1
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w = image.shape[2] # width
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h = image.shape[1] # height
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crop_width = int(w / columns)
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crop_height = int(h / rows)
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image = image.numpy()
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pieces = []
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for i in range(rows):
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for j in range(columns):
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y = i * crop_height
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x = j * crop_width
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crop = image[: , y : y + crop_height , x : x + crop_width , :]
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pieces.append(torch.from_numpy(crop))
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# image[:, y : y + height, x : x + width, :]
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return (torch.cat(pieces, dim=0), )
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class RandomImageFromBatch:
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def __init__(self):
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pass
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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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"images": ("IMAGE", ),
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"seed": ("INT", {"default": 0}),
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"start_index": ("INT", {"default": -1, "min": -1, "max": 32, "step": 1,}),
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"select_amount": ("INT", {"default": 1, "min": 1, "max": 32, "step": 1,}),
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},
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}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "run"
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CATEGORY = "Fitsize/Image"
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def run(self, images, seed, start_index, select_amount):
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# if type(images) == torch.Tensor:
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# images = images.numpy()
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if start_index == -1:
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start_index = np.random.randint(0, images.shape[0])
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if start_index >= images.shape[0]:
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start_index = images.shape[0]-1
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if select_amount > images.shape[0]:
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select_amount = images.shape[0]
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if select_amount < 1:
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select_amount = 1
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print(
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f"RandomImageFromBatch: start_index {start_index},",
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f"select_amount {select_amount}",
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f"total_images {images.shape[0]}",)
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selected = images[start_index:start_index + select_amount]
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print(f"RandomImageFromBatch: selected {selected.shape[0]} images")
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return (selected, )
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@@ -0,0 +1,31 @@
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import os
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from pathlib import Path
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import folder_paths
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# from: https://github.com/M1kep/ComfyLiterals
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def symlink_web_dir(local_path, extension_name):
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comfy_web_ext_root = Path(os.path.join(folder_paths.base_path, "web", "extensions"))
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target_dir = Path(os.path.join(comfy_web_ext_root, extension_name))
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extension_path = Path(__file__).parent.resolve()
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if target_dir.exists():
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print(f"Web extensions folder found at {target_dir}")
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elif comfy_web_ext_root.exists():
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try:
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os.symlink((os.path.join(extension_path, local_path)), target_dir)
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except OSError as e: # OSError
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print(
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f"Error:\n{e}\n"
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f"Failed to create symlink to {target_dir}. Please copy the folder manually.\n"
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f"Source: {os.path.join(extension_path, local_path)}\n"
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f"Target: {target_dir}"
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)
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except Exception as e:
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print(f"Unexpected error:\n{e}")
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else:
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print(
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f"Failed to find comfy root automatically, please copy the folder {os.path.join(extension_path, 'web')} manually in the web/extensions folder of ComfyUI"
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)
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