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braintacles
2023-09-05 20:16:03 +09:00
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.gitconfig
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[user]
name = braintacles
email = braintacles@gmail.com
.vscode/settings.json
Vendored
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{
"[python]": {
"editor.defaultFormatter": "ms-python.autopep8"
},
"python.formatting.provider": "none"
}
README.md
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# braintacles-nodes
ComfyUI Nodes
- CLIPTextEncodeSDXL-Multi-IO
- Encode your prompt with different CLIPs, useful for merging or testing different LoRAs etc...
- CLIPTextEncodeSDXL-Pipe
- If you are using lots of conditioning combine/concat/average etc. this will help keeping your workspace clean
- Empty Latent Image from Aspect-Ratio
- Write an arbitrary aspect ratio like `2.35:1` or `16:9` etc, choose your orientation and get a latent, width and height outputs as well as the aspect ratio float
- Random Find and Replace
- Useful for those of us with prompt generator setups.
__init__.py
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from .braintacles_nodes import NODE_CLASS_MAPPINGS
__all__ = ["NODE_CLASS_MAPPINGS"]
braintacles_nodes.py
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import torch
from .softmaxsplatting import run
from PIL import Image
import numpy as np
import random
class CLIPTextEncodeSDXL_Multi_IO:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"clip": ("CLIP", ),
"text_g": ("STRING", {"multiline": True, "default": "CLIP_G"}),
"text_l": ("STRING", {"multiline": True, "default": "CLIP_L"}),
},
"optional": {
"clip2": ("CLIP", ),
"clip3": ("CLIP", ),
"clip4": ("CLIP", ),
"latent": ("LATENT", ),
}}
RETURN_TYPES = ("CONDITIONING","CONDITIONING","CONDITIONING","CONDITIONING","LATENT")
FUNCTION = "encode"
CATEGORY = "braintacles/conditioning"
@staticmethod
def encode_with_clip(clip, text_g, text_l):
tokens = clip.tokenize(text_g)
tokens["l"] = clip.tokenize(text_l)["l"]
if len(tokens["l"]) != len(tokens["g"]):
empty = clip.tokenize("")
while len(tokens["l"]) < len(tokens["g"]):
tokens["l"] += empty["l"]
while len(tokens["l"]) > len(tokens["g"]):
tokens["g"] += empty["g"]
cond, pooled = clip.encode_from_tokens(tokens, return_pooled=True)
return cond, pooled
def encode(self, clip, text_g, text_l,clip2=None,clip3=None,clip4=None,latent=None,**kwargs):
width = 1024
height = 1024
if latent is not None:
width = latent["samples"].shape[-1] * 8
height = latent["samples"].shape[-2] * 8
print("Latent is not none, width and height are ", width, height)
cond, pooled = self.encode_with_clip(clip, text_g, text_l)
return_list = [
[[cond, {"pooled_output": pooled, "width": width, "height": height, "crop_w": 0, "crop_h": 0, "target_width": width, "target_height": height}]],
[[cond, {"pooled_output": pooled, "width": width, "height": height, "crop_w": 0, "crop_h": 0, "target_width": width, "target_height": height}]],
[[cond, {"pooled_output": pooled, "width": width, "height": height, "crop_w": 0, "crop_h": 0, "target_width": width, "target_height": height}]],
[[cond, {"pooled_output": pooled, "width": width, "height": height, "crop_w": 0, "crop_h": 0, "target_width": width, "target_height": height}]],
]
if clip2 is not None:
cond2, pooled2 = self.encode_with_clip(clip2, text_g, text_l)
return_list[1] = [[cond2, {"pooled_output": pooled2, "width": width, "height": height, "crop_w": 0, "crop_h": 0, "target_width": width, "target_height": height}]]
if clip3 is not None:
cond3, pooled3 = self.encode_with_clip(clip3, text_g, text_l)
return_list[2] = [[cond3, {"pooled_output": pooled3, "width": width, "height": height, "crop_w": 0, "crop_h": 0, "target_width": width, "target_height": height}]]
if clip4 is not None:
cond4, pooled4 = self.encode_with_clip(clip4, text_g, text_l)
return_list[3] = [[cond4, {"pooled_output": pooled4, "width": width, "height": height, "crop_w": 0, "crop_h": 0, "target_width": width, "target_height": height}]]
return (return_list[0],return_list[1],return_list[2],return_list[3],latent, )
class CLIPTextEncodeSDXL_Pipe:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"clip": ("CLIP", ),
"text_g": ("STRING", {"multiline": True, "default": "CLIP_G"}),
"text_l": ("STRING", {"multiline": True, "default": "CLIP_L"}),
},
"optional": {
"refiner_clip": ("CLIP", ),
"latent": ("LATENT", ),
}}
RETURN_TYPES = ("CONDITIONING","CONDITIONING","CLIP","CLIP","LATENT")
RETURN_NAMES = ("conditioning","refiner conditioning","CLIP","refiner CLIP","latent")
FUNCTION = "encode"
CATEGORY = "braintacles/conditioning"
@staticmethod
def encode_with_clip(clip, text_g, text_l):
tokens = clip.tokenize(text_g)
tokens["l"] = clip.tokenize(text_l)["l"]
if len(tokens["l"]) != len(tokens["g"]):
empty = clip.tokenize("")
while len(tokens["l"]) < len(tokens["g"]):
tokens["l"] += empty["l"]
while len(tokens["l"]) > len(tokens["g"]):
tokens["g"] += empty["g"]
cond, pooled = clip.encode_from_tokens(tokens, return_pooled=True)
return cond, pooled
def encode(self, clip, text_g, text_l,refiner_clip=None,latent=None,**kwargs):
width = 1024
height = 1024
if latent is not None:
width = latent["samples"].shape[-1] * 8
height = latent["samples"].shape[-2] * 8
print("Latent is not none, width and height are ", width, height)
cond, pooled = self.encode_with_clip(clip, text_g, text_l)
return_list = [
[[cond, {"pooled_output": pooled, "width": width, "height": height, "crop_w": 0, "crop_h": 0, "target_width": width, "target_height": height}]],
[[cond, {"pooled_output": pooled, "width": width, "height": height, "crop_w": 0, "crop_h": 0, "target_width": width, "target_height": height}]],
]
if refiner_clip is not None:
cond2, pooled2 = self.encode_with_clip(refiner_clip, text_g, text_l)
return_list[1] = [[cond2, {"pooled_output": pooled2, "width": width, "height": height, "crop_w": 0, "crop_h": 0, "target_width": width, "target_height": height}]]
return (return_list[0],return_list[1],clip,refiner_clip,latent, )
class EmptyLatentImageFromAspectRatio:
def __init__(self, device="cpu"):
self.device = device
@classmethod
def INPUT_TYPES(s):
return {"required": { "short_side": ("INT", {"default": 1024, "min": 64, "max": 4096, "step": 8}),
"orientation": (["square","landscape","portrait","random"],),
"aspect_ratio": ("STRING", {"default":"1:1"},),
"batch_size": ("INT", {"default": 1, "min": 1, "max": 64}),
"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
}
}
RETURN_TYPES = ("LATENT","INT","INT","FLOAT")
RETURN_NAMES = ("samples","width","height","aspect ratio")
FUNCTION = "generate"
CATEGORY = "braintacles/latent"
def generate(self, short_side, orientation, aspect_ratio, batch_size=1, seed=0):
if orientation == "random":
random.seed(seed)
orientation = random.choice(["square","landscape","portrait"])
if orientation == "square" or aspect_ratio == "1:1":
width = height = short_side
elif orientation == "landscape":
#short side is height
height = short_side
width = int(height * float(aspect_ratio.split(":")[0]) / float(aspect_ratio.split(":")[1]))
elif orientation == "portrait":
#short side is width
width = short_side
height = int(width * float(aspect_ratio.split(":")[0]) / float(aspect_ratio.split(":")[1]))
latent = torch.zeros([batch_size, 4, height // 8, width // 8])
aspect_ratio_float = float(aspect_ratio.split(":")[0]) / float(aspect_ratio.split(":")[1]) if orientation == "landscape" else float(aspect_ratio.split(":")[1]) / float(aspect_ratio.split(":")[0])
return ({"samples":latent}, width, height, aspect_ratio_float,)
class RandomFindAndReplace:
def __init__(self):
pass
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"prompt": ("STRING", {"multiline": True}),
"find": ("STRING", {"default": "String to Find & Replace","multiline": False}),
"choices": ("STRING", {"default": "Choices","multiline": True}),
"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
}
}
RETURN_TYPES = ("STRING","STRING","INT",)
RETURN_NAMES = ("prompt","random_choice","seed",)
FUNCTION = "replace_prompt_with_random_line"
CATEGORY = "braintacles/Prompt"
def replace_prompt_with_random_line(self, prompt, find, choices, seed):
lines = choices.split("\n")
random.seed(seed)
choice = random.choice(lines)
prompt = prompt.replace(find, choice)
return (prompt,choice,seed,)
NODE_CLASS_MAPPINGS = {
"CLIPTextEncodeSDXL-Multi-IO": CLIPTextEncodeSDXL_Multi_IO,
"CLIPTextEncodeSDXL-Pipe": CLIPTextEncodeSDXL_Pipe,
"Empty Latent Image from Aspect-Ratio": EmptyLatentImageFromAspectRatio,
"Random Find and Replace": RandomFindAndReplace,
}