initial commit

LICENSE.txt

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+The MIT License (MIT)
+
+Copyright (c) 2023 Alex "mcmonkey" Goodwin
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.MD

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+# ComfyUI-LoadImage-Advanced
+
+LoadImageとVae Encode、Upscaleを統合しただけのノードです
+
+![sample](./images/sample.png)

__init__.py

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+from .load_image_advanced import LoadImageUpscale, LoadImageUpscaleBy
+
+NODE_CLASS_MAPPINGS = {
+    "LoadImageUpscaleBy": LoadImageUpscaleBy,
+    "LoadImageUpscale": LoadImageUpscale,
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "LoadImageUpscaleBy": "Load Image Upscale By",
+    "LoadImageUpscale": "Load Image Upscale",
+}
+
+__all__ = ['NODE_CLASS_MAPPINGS', 'NODE_DISPLAY_NAME_MAPPINGS']

load_image_advanced.py

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+import folder_paths
+import os
+
+from nodes import MAX_RESOLUTION, ImageScale, ImageScaleBy, LatentUpscaleBy, LoadImage, VAEEncode
+
+
+class LoadImageUpscaleBy:
+    latent_upscale_methods = ["nearest-exact", "bilinear", "area", "bicubic", "bislerp"]
+    image_upscale_methods = ["nearest-exact", "bilinear", "area", "bicubic", "lanczos"]
+    
+    def __init__(self) -> None:
+        pass
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        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))]
+
+        return {
+            "required": {
+                "vae": ("VAE", ),
+                "image": (sorted(files), {"image_upload": True}),
+
+                "image_upscale_method": (cls.image_upscale_methods,),
+                "image_scale_by": ("FLOAT", {"default": 1.0, "min": 0.01, "max": 8.0, "step": 0.01}),
+
+                "latent_upscale_method": (cls.latent_upscale_methods,),
+                "latent_scale_by": ("FLOAT", {"default": 1.0, "min": 0.01, "max": 8.0, "step": 0.01}),
+            }
+        }
+        
+    CATEGORY = "image"
+    RETURN_TYPES = ("LATENT", "MASK")
+    FUNCTION = "load_image"
+    def load_image(self, image, vae, image_upscale_method, image_scale_by, latent_upscale_method, latent_scale_by):
+        (output_image, output_mask) = LoadImage().load_image(image)
+        (upscaled_image,) = ImageScaleBy().upscale(output_image, image_upscale_method, image_scale_by) if image_scale_by != 1.0 else (output_image, )
+        (latent,) = VAEEncode().encode(vae, upscaled_image)
+        (upscaled_latent,) = LatentUpscaleBy().upscale(latent, latent_upscale_method, latent_scale_by) if latent_scale_by != 1.0 else (latent, )
+        return (upscaled_latent, output_mask)
+    
+class LoadImageUpscale:
+    latent_upscale_methods = ["nearest-exact", "bilinear", "area", "bicubic", "bislerp"]
+    image_upscale_methods = ["nearest-exact", "bilinear", "area", "bicubic", "lanczos"]
+    crop_methods = ["disabled", "center"]
+    
+    def __init__(self) -> None:
+        pass
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        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))]
+
+        return {
+            "required": {
+                "vae": ("VAE", ),
+                "image": (sorted(files), {"image_upload": True}),
+
+                "image_upscale_method": (cls.image_upscale_methods,),
+                "width": ("INT", {"default": 512, "min": 0, "max": MAX_RESOLUTION, "step": 1}),
+                "height": ("INT", {"default": 512, "min": 0, "max": MAX_RESOLUTION, "step": 1}),
+                "crop": (cls.crop_methods,),
+            }
+        }
+        
+    CATEGORY = "image"
+    RETURN_TYPES = ("LATENT", "MASK")
+    FUNCTION = "load_image"
+    def load_image(self, image, vae, image_upscale_method, width, height, crop):
+        (output_image, output_mask) = LoadImage().load_image(image)
+        (upscaled_image,) = ImageScale().upscale(output_image, image_upscale_method, width, height, crop)
+
+        (latent,) = VAEEncode().encode(vae, upscaled_image)
+        return (latent, output_mask)