initial commit

variety of nodes to support many things including a start on alpha channel support for comfyui
2023-05-25 12:55:40 -04:00
parent 5aefcd5a4b
commit 9807e12198
8 changed files with 1479 additions and 0 deletions
@@ -0,0 +1,48 @@
+from custom_nodes.EternalKernelLiteGraphNodes.latent import LatentInterpolation
+from custom_nodes.EternalKernelLiteGraphNodes.image import PadToMatch
+from custom_nodes.EternalKernelLiteGraphNodes.image import StackImages
+from custom_nodes.EternalKernelLiteGraphNodes.image import ImageBC
+from custom_nodes.EternalKernelLiteGraphNodes.image import RGBA_MOD
+from custom_nodes.EternalKernelLiteGraphNodes.image import rgba_lower_clip
+from custom_nodes.EternalKernelLiteGraphNodes.image import rgba_upper_clip
+
+
+from custom_nodes.EternalKernelLiteGraphNodes.image import rgba_merge
+from custom_nodes.EternalKernelLiteGraphNodes.image import rgba_split
+from custom_nodes.EternalKernelLiteGraphNodes.image import select_from_batch
+from custom_nodes.EternalKernelLiteGraphNodes.image import Quantize
+from custom_nodes.EternalKernelLiteGraphNodes.image import SelectFromRGBSimilarity
+from custom_nodes.EternalKernelLiteGraphNodes.image import LoadImage
+from custom_nodes.EternalKernelLiteGraphNodes.image import PromptTemplate
+from custom_nodes.EternalKernelLiteGraphNodes.image import ExecWidget
+from custom_nodes.EternalKernelLiteGraphNodes.image import TinyTxtToImg
+from custom_nodes.EternalKernelLiteGraphNodes.image import ImageDistanceMask
+from custom_nodes.EternalKernelLiteGraphNodes.image import PreviewImageTest
+
+
+from custom_nodes.EternalKernelLiteGraphNodes.nlp import NODE_CLASS_MAPPINGS as NLP_NODE_CLASS_MAPPINGS
+
+
+NODE_CLASS_MAPPINGS = {
+    "Interpolation": LatentInterpolation,
+    "Image Pad To Match": PadToMatch,
+    "Image Stack": StackImages,
+    "Image B/C": ImageBC,
+    "Image RGBA Mod": RGBA_MOD,
+    "Image RGBA Lower Clip": rgba_lower_clip,
+    "Image RGBA Upper Clip": rgba_upper_clip,
+    "Image RGBA Merge": rgba_merge,
+    "Image RGBA Split": rgba_split,
+    "Select From Batch": select_from_batch,
+    "Quantize": Quantize,
+    "Select From RGB Similarity": SelectFromRGBSimilarity,
+    "Load Layered Image": LoadImage,
+    "Prompt Template": PromptTemplate,
+    "Eval Widget": ExecWidget,
+    "Tiny Txt 2 Img":TinyTxtToImg,
+    "Image Distance Mask":ImageDistanceMask,
+    "Preview Image Test":PreviewImageTest,
+}
+
+for k,v in NLP_NODE_CLASS_MAPPINGS.items():
+    NODE_CLASS_MAPPINGS[k] = v
@@ -0,0 +1,13 @@
+import sys
+
+FLOAT = ("FLOAT", {"default": 1,
+                   "min": -sys.float_info.max,
+                   "max": sys.float_info.max,
+                   "step": 0.01})
+BOOL = ("BOOL", {"default": False})
+INT = ("INT", {"default": 1,
+               "min": -sys.maxsize,
+               "max": sys.maxsize,
+               "step": 1})
+STRING = ("STRING", {"default": ""})
+STRING_ML = ("STRING", {"multiline": True})
@@ -0,0 +1,22 @@
+def get_latent_size(LATENT, ORIGINAL_VALUES=False):
+    lc = LATENT.copy()
+    size = lc["samples"].shape[3], lc["samples"].shape[2]
+    if ORIGINAL_VALUES == False:
+        size = size[0] * 8, size[1] * 8
+    return size
+
+
+def get_image_size(IMAGE):
+    samples = IMAGE.movedim(-1, 1)
+    size = samples.shape[3], samples.shape[2]
+    # size = size.movedim(1, -1)
+    return size
+
+
+def get_conditioning_size(CONDITIONING):
+    size = CONDITIONING["area"]
+    width = size[1]
+    height = size[0]
+    x_offs = size[3]
+    y_offs = size[2]
+    return ({"w": width, "h": height}, {"x": x_offs, "y": y_offs})
@@ -0,0 +1,22 @@
+TREE_MAIN = "EternalKernelNodes"
+
+TREE_VARIABLE = TREE_MAIN + "/Variables"
+TREE_TUPLES = TREE_MAIN + "/Tuples"
+TREE_MATH = TREE_MAIN + "/Math"
+TREE_FUNCTIONS = TREE_MAIN + "/Functions"
+TREE_MODDED = TREE_MAIN + "/Modded nodes"
+
+TREE_CONVERTERS = TREE_FUNCTIONS + "/Converters"
+
+TREE_TRIGONOMETRY = TREE_MATH + "/Trigonometry"
+
+TREE_TUPLE_MODDED = TREE_TUPLES + "/Modded nodes"
+
+TREE_TUPLE_LATENTS = TREE_TUPLE_MODDED + "/Latents"
+TREE_TUPLE_CONDITIONING = TREE_TUPLE_MODDED + "/Conditioning"
+
+TREE_COND = TREE_MODDED + "/Conditions"
+TREE_IMAGES = TREE_MODDED + "/Image"
+TREE_LATENTS = TREE_MODDED + "/Latent"
+
+TREE_DEBUG = TREE_MAIN + "/Debug"
@@ -0,0 +1,77 @@
+#from nodes import MAX_RESOLUTION
+import torch
+
+from custom_nodes.EternalKernelLiteGraphNodes.components import fields as field
+
+
+class LatentInterpolation:
+    def __init__(self) -> None:
+        pass
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "latent1": ("LATENT",),
+                "latent2": ("LATENT",),
+                "t": field.FLOAT,
+            }
+        }
+
+    RETURN_TYPES = ("LATENT",)
+    FUNCTION = "interpolate"
+    CATEGORY = "EternalKernelNodes"
+
+    def interpolate(self, latent1, latent2, t):
+        # Ensure that the latents are dictionaries
+        if not (isinstance(latent1, dict) and isinstance(latent2, dict)):
+            raise ValueError("Latent values must be dictionaries.")
+
+        # Ensure that both latents have the same keys
+        if set(latent1.keys()) != set(latent2.keys()):
+            raise ValueError("Latent dictionaries must have the same keys.")
+
+        inter_latent = {}
+        for key in latent1:
+            if isinstance(latent1[key], (list, tuple)):
+                inter_latent[key] = [(1 - t) * l1_val + t * l2_val for l1_val, l2_val in
+                                     zip(latent1[key], latent2[key])]
+            elif isinstance(latent1[key], torch.Tensor):
+                inter_latent[key] = (1 - t) * latent1[key] + t * latent2[key]
+            else:
+                raise ValueError(f"Unsupported value type for key '{key}' in latent dictionaries.")
+
+        return (inter_latent,)
+
+
+def test_latent_interpolation():
+    import torch
+    # Create an instance of the LatentInterpolation class
+    li = LatentInterpolation()
+
+    # Define test cases for the interpolate method
+    # latents are at least 4 diminsional
+    # latents are of the same length
+    test_cases = [
+        {
+            "input": {
+                "latent1": torch.tensor([[[[1.0, 2.0], [3.0, 4.0]]]]),
+                "latent2": torch.tensor([[[[5.0, 6.0], [7.0, 8.0]]]]),
+                "t": 0.5,
+
+            },
+            "expected": torch.tensor([[[[3.0, 4.0], [5.0, 6.0]]]]),
+        },
+    ]
+
+    # Run the test cases
+    for test_case in test_cases:
+        result = li.interpolate(**test_case["input"])
+        if not (result[0][0] == test_case["expected"]).all().item():
+            print(f"Expected {test_case['expected']} but got {result}")
+
+
+
+
+if __name__ == "__main__":
+    test_latent_interpolation()
@@ -0,0 +1,105 @@
+import comfy.samplers
+import custom_nodes.Derfuu_ComfyUI_ModdedNodes.components.fields as field
+import PIL.Image
+import torch
+import torchvision
+import PIL.Image as Image
+from nodes import CLIPTextEncode, VAEEncode, VAEDecode, KSampler, CheckpointLoaderSimple, EmptyLatentImage
+import doctest
+# make sure to import ABC
+from abc import ABC, abstractmethod
+
+import os
+import openai
+
+
+class Config:
+    api_key = os.environ.get('OPENAI_API_KEY')
+    organization = os.environ.get('OPENAI_ORG')
+
+
+NODE_CLASS_MAPPINGS = {}
+
+openai.organization = Config.organization
+openai.api_key = Config.api_key
+
+
+class SimpleTextWidget:
+    """base class for simple construction"""
+
+    def __init__(self, func):
+        self.func = func
+
+
+    @classmethod
+    def register(self, name):
+        global NODE_CLASS_MAPPINGS
+
+        # register the widget
+        NODE_CLASS_MAPPINGS[name] = self
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {"required":
+            {
+                "text": ("STRING", {"multiline": True}),
+                "Display": ("SERVER_STRING", {"multiline": True}),
+            },
+
+        }
+
+    CATEGORY = "text"
+    RETURN_TYPES = ("STRING",)
+    FUNCTION = "handler"
+
+    def handler(self, text,Display):
+        ret = self.func(text)
+        return (ret,)
+
+
+try:
+    import folder_paths
+except:
+    pass
+
+import os
+import numpy as np
+import hashlib
+
+### info for code completion AI ###
+"""
+all of these classes are plugins for comfyui and follow the same pattern
+all of the images are torch tensors and it is unknown and unimportant if they are on the cpu or gpu
+all image inputs are (B,W,H,C)
+
+avoid numpy and PIL as much as possible
+"""
+
+
+class LLMCompletion(SimpleTextWidget):
+    """uses the input text to call the specified LLM model and returns the output string"""
+
+    def OAI_completion(self, text):
+        out_message = [{"role": "user", "content": text}]
+
+        response = openai.ChatCompletion.create(
+            model="gpt-3.5-turbo",
+            messages=out_message
+        )
+        gpt_message = response["choices"][0]["message"]["content"].strip()
+        self.server_strings[self.SSID] = gpt_message
+        return gpt_message
+
+    def __init__(self):
+        self.func = self.OAI_completion
+        super().__init__(self.func)
+        import uuid
+        self.SSID = str(uuid.uuid4())
+        from main import server_obj_holder
+        self.server_strings = server_obj_holder[0].server_strings
+        self.server_strings["self.SSID"] = "empty"
+
+
+
+
+LLMCompletion.register("LLM_Completion")