initial upload

__init__.py

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+from .chibi_nodes import NODE_CLASS_MAPPINGS
+
+__all__ = ["NODE_CLASS_MAPPINGS"]

chibi_nodes.py

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+import torch
+import folder_paths
+import comfy.sd
+
+### GLOBALS ###
+MAX_RESOLUTION=8192
+
+
+class Loader:
+    def __init__(self):
+        pass
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required":{
+            "Checkpoint": (folder_paths.get_filename_list("checkpoints"), ),
+            "Vae": (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}),
+            "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}),
+        }}
+
+    RETURN_TYPES = ("MODEL","VAE","CLIP","LATENT",)
+    FUNCTION = "loader"
+    CATEGORY = "Chibi-Nodes"
+
+    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"))
+
+        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)
+
+        latent = torch.zeros([batch_size, 4, height // 8, width // 8])
+
+        return(ckpt[:3][0],vae,clip,{"samples":latent})
+
+
+
+class Prompts:
+    def __init__(self):
+        pass
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "clip": ("CLIP",),
+                "Positive": ("STRING", {"default": "Positive Prompt","multiline": True}),
+                "Negative": ("STRING", {"default": "Negative Prompt","multiline": True}),
+
+               },
+        }
+
+    RETURN_TYPES = ("CONDITIONING","CONDITIONING",)
+    RETURN_NAMES = ("Positive Conditioning", "Negative Conditioning")
+    FUNCTION = "prompts"
+    CATEGORY = "Chibi-Nodes"
+
+    def prompts(self, clip, Positive, Negative):
+        pos_cond_raw = clip.tokenize(Positive)
+        neg_cond_raw = clip.tokenize(Negative)
+        pos_cond, pos_pooled = clip.encode_from_tokens(pos_cond_raw, return_pooled=True)
+        neg_cond, neg_pooled = clip.encode_from_tokens(neg_cond_raw, return_pooled=True)
+        
+        return ([[pos_cond, {"pooled_output": pos_pooled}]],[[neg_cond, {"pooled_output": neg_pooled}]],)
+
+    
+NODE_CLASS_MAPPINGS = {
+    "Loader":Loader,
+    "Prompts": Prompts
+    
+}