nodes/TashaSkyUp-EternalKernelPytorchNodes
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

restructure and rename to be more generic

Browse Source
This commit is contained in:
Tasha Upchurch
2023-05-25 14:41:25 -04:00
parent 9807e12198
commit d70859a5da
4 changed files with 120 additions and 92 deletions
Download Patch File Download Diff File Expand all files Collapse all files
__init__.py
+58 -27
View File
@@ -1,12 +1,21 @@
NODE_CLASS_MAPPINGS = {}
try:
import comfy.samplers
from custom_nodes.EternalKernelLiteGraphNodes.image import PreviewImageTest
from custom_nodes.EternalKernelLiteGraphNodes.image import TinyTxtToImg
use_generic_nodes = False
except:
use_generic_nodes = True
print("Failed to import comfy.samplers, assuming no comfyui skipping SD related nodes")
from custom_nodes.EternalKernelLiteGraphNodes.latent import LatentInterpolation
from custom_nodes.EternalKernelLiteGraphNodes.image import PadToMatch
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
@@ -15,34 +24,56 @@ from custom_nodes.EternalKernelLiteGraphNodes.image import SelectFromRGBSimilari
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.image import ImageDistanceMask
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
if use_generic_nodes:
NODE_CLASS_MAPPINGS_GENERIC = {
"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,
"Eval Widget": ExecWidget,
"Image Distance Mask": ImageDistanceMask,
}
NODE_CLASS_MAPPINGS.update(NODE_CLASS_MAPPINGS_GENERIC)
else:
NODE_CLASS_MAPPINGS_COMFY = {
"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,
}
NODE_CLASS_MAPPINGS.update(NODE_CLASS_MAPPINGS_COMFY)
for k, v in NLP_NODE_CLASS_MAPPINGS.items():
NODE_CLASS_MAPPINGS[k] = v
image.py
+58 -61
View File
@@ -1,12 +1,17 @@
import comfy.samplers
import custom_nodes.Derfuu_ComfyUI_ModdedNodes.components.fields as field
import PIL.Image
try:
import comfy.samplers
import deactivated_custom_nodes.Derfuu_ComfyUI_ModdedNodes.components.fields as field
from nodes import CLIPTextEncode, VAEEncode, VAEDecode, KSampler, CheckpointLoaderSimple, EmptyLatentImage, \
SaveImage
from custom_nodes.ComfyUI_ADV_CLIP_emb.nodes import AdvancedCLIPTextEncode as CLIPTextEncodeAdvanced
except ImportError as e:
print("comfy.samplers not found, skipping comfyui")
print(e)
SaveImage = None
import torch
import torchvision
import PIL.Image as Image
from nodes import CLIPTextEncode, VAEEncode, VAEDecode, KSampler, CheckpointLoaderSimple, EmptyLatentImage, SaveImage
from custom_nodes.ComfyUI_ADV_CLIP_emb.nodes import AdvancedCLIPTextEncode as CLIPTextEncodeAdvanced
import doctest
try:
import folder_paths
@@ -107,7 +112,7 @@ class TinyTxtToImg:
}
}
CATEGORY = "EternalKernelNodes"
CATEGORY = "ETK"
RETURN_TYPES = ("IMAGE",)
FUNCTION = "tinytxt2img"
@@ -186,36 +191,37 @@ class TinyTxtToImg:
return (image,)
class PreviewImageTest(SaveImage):
def __init__(self):
self.output_dir = folder_paths.get_temp_directory()
self.type = "temp"
if SaveImage != None:
class PreviewImageTest(SaveImage):
def __init__(self):
self.output_dir = folder_paths.get_temp_directory()
self.type = "temp"
@classmethod
def INPUT_TYPES(s):
return {
"hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},
"required": {
"images": ("IMAGE",),
},
"optional": {
"prompt": ("STRING", {"multiline": True}),
"neg_prompt": ("STRING", {"multiline": True}),
"clip_encoder": (["comfy -ignore below", "advanced"],),
"token_normalization": (["none", "mean", "length", "length+mean"],),
"weight_interpretation": (["comfy", "A1111", "compel", "comfy++"],)
},
}
@classmethod
def INPUT_TYPES(s):
return {
"hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},
"required": {
"images": ("IMAGE",),
},
"optional": {
"prompt": ("STRING", {"multiline": True}),
"neg_prompt": ("STRING", {"multiline": True}),
"clip_encoder": (["comfy -ignore below", "advanced"],),
"token_normalization": (["none", "mean", "length", "length+mean"],),
"weight_interpretation": (["comfy", "A1111", "compel", "comfy++"],)
},
}
def myfunc(self, images, prompt=None, extra_pnginfo=None, ret=None):
return ret
def myfunc(self, images, prompt=None, extra_pnginfo=None, ret=None):
return ret
CATEGORY = "image"
CATEGORY = "ETK/image"
def save_images(self, images, filename_prefix="ComfyUI", prompt=None, extra_pnginfo=None, **kwargs):
ret = super().save_images(images, filename_prefix, prompt, extra_pnginfo)
my_ret = self.myfunc(images, prompt, extra_pnginfo, ret)
return my_ret
def save_images(self, images, filename_prefix="ComfyUI", prompt=None, extra_pnginfo=None, **kwargs):
ret = super().save_images(images, filename_prefix, prompt, extra_pnginfo)
my_ret = self.myfunc(images, prompt, extra_pnginfo, ret)
return my_ret
class ExecWidget:
@@ -296,7 +302,7 @@ class LoadImage:
{"image": (sorted(os.listdir(input_dir)),)},
}
CATEGORY = "image"
CATEGORY = "ETK/image"
RETURN_TYPES = ("IMAGE", "MASK", "IMAGE")
FUNCTION = "load_image_handler"
@@ -427,7 +433,7 @@ class SelectFromRGBSimilarity:
}
}
CATEGORY = "image"
CATEGORY = "ETK/image"
RETURN_TYPES = ("IMAGE",)
@@ -511,7 +517,7 @@ class Quantize:
RETURN_TYPES = ("IMAGE",)
FUNCTION = "quantize"
CATEGORY = "image"
CATEGORY = "ETK/image"
def quantize(self, image: torch.Tensor, colors: int = 256):
"""
@@ -576,8 +582,7 @@ class select_from_batch:
"min": 0,
"max": 100,
"step": 1
}),}
}), }
,
"optional": {
@@ -586,14 +591,13 @@ class select_from_batch:
},
}
CATEGORY = "ETK/image"
CATEGORY = "image"
RETURN_TYPES = ("IMAGE","LATENT",)
RETURN_TYPES = ("IMAGE", "LATENT",)
FUNCTION = "select_from_batch"
def select_from_batch(self, index=0, image=None,latent=None):
def select_from_batch(self, index=0, image=None, latent=None):
"""
>>> image = torch.rand((1, 512, 512,3))
>>> index = 0
@@ -611,16 +615,15 @@ class select_from_batch:
o_lat_dict = {}
if image is None:
print(latent["samples"].shape)
smpls=latent["samples"][index:index+1,:,:,:].expand(1, -1, -1, -1).clone()
smpls = latent["samples"][index:index + 1, :, :, :].expand(1, -1, -1, -1).clone()
o_lat_dict["samples"] = smpls
olat = o_lat_dict
print(olat["samples"].shape)
else:
oimg = image[index:index+1,:,:,:].clone()
oimg = image[index:index + 1, :, :, :].clone()
print(oimg.shape)
return (oimg,olat,)
return (oimg, olat,)
class ImageDistanceMask:
@@ -663,14 +666,13 @@ class ImageDistanceMask:
}
}
CATEGORY = "image"
CATEGORY = "ETK/image"
RETURN_TYPES = ("IMAGE",)
FUNCTION = "distance_mask_image"
def distance_mask_image(self, image, x, y, falloff_method=None, Z=1, aspect_ratio=1.0):
import torch.nn.functional as F
B, H, W, C = image.shape
# Create a meshgrid for the coordinates
@@ -760,7 +762,7 @@ class rgba_lower_clip:
RETURN_TYPES = ("IMAGE",)
FUNCTION = "rgba_lower_clip"
CATEGORY = "EternalKernelNodes/postprocessing"
CATEGORY = "ETK/image"
def rgba_lower_clip(self, image: torch.Tensor, nrm_after, r, g, b, a):
# image is (B,W,H,C)
@@ -831,7 +833,7 @@ class rgba_upper_clip:
RETURN_TYPES = ("IMAGE",)
FUNCTION = "rgba_upper_clip"
CATEGORY = "EternalKernelNodes/postprocessing"
CATEGORY = "ETK/image"
def rgba_upper_clip(self, image: torch.Tensor, nrm_after, r, g, b, a):
# image is (B,W,H,C)
@@ -894,7 +896,7 @@ class RGBA_MOD:
RETURN_TYPES = ("IMAGE",)
FUNCTION = "rgba_mod"
CATEGORY = "EternalKernelNodes/postprocessing"
CATEGORY = "ETK/image"
def rgba_mod(self, image: torch.Tensor, r, g, b, a):
"""simply multiply the RGBA values by the sliders
@@ -942,12 +944,7 @@ class ImageBC:
RETURN_TYPES = ("IMAGE",)
FUNCTION = "image_b_c"
CATEGORY = "EternalKernelNodes/postprocessing"
import torch
import torch
import torchvision
CATEGORY = "ETK/image"
def image_b_c(self, image, contrast, brightness):
"""
@@ -1019,7 +1016,7 @@ class PadToMatch:
RETURN_TYPES = ("IMAGE", "IMAGE")
FUNCTION = "padme"
CATEGORY = "EternalKernelNodes/postprocessing"
CATEGORY = "ETK/image"
def padme(self, image1, image2, im2_x_center, im2_y_center):
"""
@@ -1087,7 +1084,7 @@ class StackImages:
RETURN_TYPES = ("IMAGE",)
FUNCTION = "stackme"
CATEGORY = "EternalKernelNodes/postprocessing"
CATEGORY = "ETK/image"
def stackme(self, image1, image2=None, number_of_images=2):
"""
@@ -1128,7 +1125,7 @@ class rgba_merge:
}
}
CATEGORY = "image"
CATEGORY = "ETK/image"
RETURN_TYPES = ("IMAGE",)
@@ -1149,7 +1146,7 @@ class rgba_split:
}
}
CATEGORY = "image"
CATEGORY = "ETK/image"
RETURN_TYPES = ("IMAGE", "IMAGE", "IMAGE", "IMAGE")
latent.py
+1 -1
View File
@@ -20,7 +20,7 @@ class LatentInterpolation:
RETURN_TYPES = ("LATENT",)
FUNCTION = "interpolate"
CATEGORY = "EternalKernelNodes"
CATEGORY = "ETK/Latent"
def interpolate(self, latent1, latent2, t):
# Ensure that the latents are dictionaries
nlp.py
+3 -3
View File
@@ -1,10 +1,10 @@
import comfy.samplers
import custom_nodes.Derfuu_ComfyUI_ModdedNodes.components.fields as field
#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