new node, minor fixes

Added a image manipulation tool node, uses PIL. Also made the loader be able to use baked in Vae

chibi_nodes.py

@@ -1,9 +1,12 @@
 import torch
 import folder_paths
 import comfy.sd
+import comfy.utils
+from PIL import Image, ImageOps
+import numpy as np
 
 ### GLOBALS ###
-MAX_RESOLUTION=8192
+MAX_RESOLUTION=32768
 
 
 class Loader:
@@ -14,7 +17,7 @@ class Loader:
     def INPUT_TYPES(s):
         return {"required":{
             "Checkpoint": (folder_paths.get_filename_list("checkpoints"), ),
-            "Vae": (folder_paths.get_filename_list("vae"), ),
+            "Vae": (["Included"] + folder_paths.get_filename_list("vae"), ),
             "stop_at_clip_layer": ("INT", {"default": -1, "min": -24, "max": -1, "step": 1}),
             "width": ("INT", {"default": 512, "min": 16, "max": MAX_RESOLUTION, "step": 8}),
             "height": ("INT", {"default": 512, "min": 16, "max": MAX_RESOLUTION, "step": 8}),
@@ -27,10 +30,16 @@ class Loader:
 
     def loader(self, Checkpoint,Vae,stop_at_clip_layer,width,height,batch_size):
         ckpt_path = folder_paths.get_full_path("checkpoints", Checkpoint)
-        ckpt = comfy.sd.load_checkpoint_guess_config(ckpt_path, output_vae=False, output_clip=True, embedding_directory=folder_paths.get_folder_paths("embeddings"))
+        output_vae = False
+        if Vae == "Included":
+            output_vae = True
+        ckpt = comfy.sd.load_checkpoint_guess_config(ckpt_path, output_vae=output_vae, output_clip=True, embedding_directory=folder_paths.get_folder_paths("embeddings"))
 
-        vae_path = folder_paths.get_full_path("vae", Vae)
-        vae = comfy.sd.VAE(ckpt_path=vae_path)
+        if Vae == "Included":
+            vae = ckpt[:3][2]
+        else:
+            vae_path = folder_paths.get_full_path("vae", Vae)
+            vae = comfy.sd.VAE(ckpt_path=vae_path)
 
         clip = ckpt[:3][1].clone()
         clip.clip_layer(stop_at_clip_layer)
@@ -69,9 +78,60 @@ class Prompts:
         
         return ([[pos_cond, {"pooled_output": pos_pooled}]],[[neg_cond, {"pooled_output": neg_pooled}]],)
 
-    
+class ImageTool:
+    def __init__(self):
+        pass
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+            "image": ("IMAGE",),
+            "width": ("INT", {"default": 1920, "min": 16, "max": MAX_RESOLUTION, "step": 1}),
+            "height": ("INT", {"default": 1080, "min": 16, "max": MAX_RESOLUTION, "step": 1}),
+            "crop": ([False,True],),
+            "rotate":  ("INT", {"default": 0, "min": 0, "max": 360, "step": 1}),
+            "mirror": ([False,True],),
+            "flip":([False,True],),
+            "bgcolor": (["black","white"],),
+               },
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "imagetools"
+    CATEGORY = "Chibi-Nodes"
+
+    def imagetools(self, image, height, width, crop, rotate, mirror, flip, bgcolor):
+        image = Image.fromarray(np.clip(255. * image[0].cpu().numpy(),0,255).astype(np.uint8))
+        image = image.rotate(rotate,fillcolor=bgcolor)
+        if mirror:
+            image = ImageOps.mirror(image)
+        
+        if flip:
+            image = ImageOps.flip(image)
+
+        if crop:
+            im_width, im_height = image.size   
+            left = (im_width - width)/2
+            top = (im_height - height)/2
+            right = (im_width + width)/2
+            bottom = (im_height + height)/2
+            image = image.crop((left, top, right, bottom))            
+            
+        else:
+            image = image.resize((width,height), Image.Resampling.LANCZOS)
+
+        image = ImageOps.exif_transpose(image)
+        image = image.convert("RGB")
+        image = np.array(image).astype(np.float32) / 255.0
+        image = torch.from_numpy(image)[None,]
+        
+        return(image,)
+
 NODE_CLASS_MAPPINGS = {
     "Loader":Loader,
-    "Prompts": Prompts
+    "Prompts": Prompts,
+    "ImageTool": ImageTool,
+
     
 }