op load model load

comfy-nodes/external_image_batch.py

@@ -39,38 +39,18 @@ class ComfyUIDeployExternalImageBatch:
 
     CATEGORY = "image"
     
-    def process_image(self, image):
-        image = ImageOps.exif_transpose(image)
-        image = image.convert("RGB")
-        image = np.array(image).astype(np.float32) / 255.0
-        image_tensor = torch.from_numpy(image)[None,]
-        return image_tensor
-    
     def run(self, input_id, images=None, default_value=None, display_name=None, description=None):
-        import requests
-        import zipfile
-        import io
-        
         processed_images = []
         try:
             images_list = json.loads(images)  # Assuming images is a JSON array string
             print(images_list)
             for img_input in images_list:
                 if img_input.startswith('http'):
+                    import requests
                     from io import BytesIO
                     print("Fetching image from url: ", img_input)
                     response = requests.get(img_input)
                     image = Image.open(BytesIO(response.content))
-                elif img_input.startswith('http') and img_input.endswith('.zip'):
-                    print("Fetching zip file from url: ", img_input)
-                    response = requests.get(img_input)
-                    zip_file = zipfile.ZipFile(io.BytesIO(response.content))
-                    for file_name in zip_file.namelist():
-                        if file_name.lower().endswith(('.png', '.jpg', '.jpeg')):
-                            with zip_file.open(file_name) as file:
-                                image = Image.open(file)
-                                image = self.process_image(image)
-                                processed_images.append(image)
                 elif img_input.startswith('data:image/png;base64,') or img_input.startswith('data:image/jpeg;base64,') or img_input.startswith('data:image/jpg;base64,'):
                     import base64
                     from io import BytesIO

comfy-nodes/outerport_load_model.py

@@ -0,0 +1,39 @@
+import folder_paths
+from PIL import Image, ImageOps
+import numpy as np
+import torch
+import folder_paths
+from tqdm import tqdm
+
+class AnyType(str):
+    def __ne__(self, __value: object) -> bool:
+        return False
+
+
+WILDCARD = AnyType("*")
+
+
+class OuterPortLoadModel:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": { 
+                "ckpt_name": (folder_paths.get_filename_list("checkpoints"), {"tooltip": "The name of the checkpoint (model) to load."}),
+            }
+        }
+    RETURN_TYPES = ("MODEL", "CLIP", "VAE")
+    OUTPUT_TOOLTIPS = ("The model used for denoising latents.", 
+                       "The CLIP model used for encoding text prompts.", 
+                       "The VAE model used for encoding and decoding images to and from latent space.")
+    FUNCTION = "load_checkpoint"
+
+    CATEGORY = "loaders"
+    DESCRIPTION = "Loads a diffusion model checkpoint, diffusion models are used to denoise latents."
+
+    def load_checkpoint(self, ckpt_name):
+        ckpt_path = folder_paths.get_full_path("checkpoints", ckpt_name)
+        out = comfy.sd.load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, embedding_directory=folder_paths.get_folder_paths("embeddings"))
+        return out[:3]
+
+NODE_CLASS_MAPPINGS = {"OuterPortLoadModel": OuterPortLoadModel}
+NODE_DISPLAY_NAME_MAPPINGS = {"OuterPortLoadModel": "Outer Port Load Model"}