Commit
•
64bff7e
1
Parent(s):
189f51c
Upload 8 files
Browse files- ComfyUI_Comfyroll_CustomNodes/Comfyroll_Nodes.py +1160 -0
- ComfyUI_Comfyroll_CustomNodes/Comfyroll_Pipe_Nodes.py +271 -0
- ComfyUI_Comfyroll_CustomNodes/README.md +89 -0
- ComfyUI_Comfyroll_CustomNodes/__init__.py +48 -0
- ComfyUI_Comfyroll_CustomNodes/images/custom_nodes_image1.png +3 -0
- ComfyUI_Comfyroll_CustomNodes/images/custom_nodes_image2.jpg +3 -0
- ComfyUI_Comfyroll_CustomNodes/images/custom_nodes_image3.JPG +3 -0
- ComfyUI_Comfyroll_CustomNodes/images/custom_nodes_image4.JPG +3 -0
ComfyUI_Comfyroll_CustomNodes/Comfyroll_Nodes.py
ADDED
@@ -0,0 +1,1160 @@
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1 |
+
#---------------------------------------------------------------------------------------------------------------------------------------------------#
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2 |
+
# Comfyroll Custom Nodes by RockOfFire and Akatsuzi https://github.com/RockOfFire/ComfyUI_Comfyroll_CustomNodes #
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3 |
+
# for ComfyUI https://github.com/comfyanonymous/ComfyUI #
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4 |
+
#---------------------------------------------------------------------------------------------------------------------------------------------------#
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5 |
+
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6 |
+
import torch
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7 |
+
import numpy as np
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8 |
+
from PIL import Image, ImageEnhance
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9 |
+
from PIL.PngImagePlugin import PngInfo
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10 |
+
import os
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11 |
+
import sys
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12 |
+
import io
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13 |
+
import matplotlib.pyplot as plt
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14 |
+
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15 |
+
sys.path.insert(0, os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy"))
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16 |
+
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17 |
+
import comfy.sd
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18 |
+
import comfy.utils
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19 |
+
import comfy.model_management
|
20 |
+
|
21 |
+
import folder_paths
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22 |
+
import json
|
23 |
+
from nodes import MAX_RESOLUTION
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24 |
+
import typing as tg
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25 |
+
|
26 |
+
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27 |
+
#---------------------------------------------------------------------------------------------------------------------------------------------------#
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28 |
+
|
29 |
+
def tensor2pil(image):
|
30 |
+
return Image.fromarray(np.clip(255. * image.cpu().numpy().squeeze(), 0, 255).astype(np.uint8))
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31 |
+
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32 |
+
def pil2tensor(image):
|
33 |
+
return torch.from_numpy(np.array(image).astype(np.float32) / 255.0).unsqueeze(0)
|
34 |
+
|
35 |
+
|
36 |
+
#---------------------------------------------------------------------------------------------------------------------------------------------------#
|
37 |
+
|
38 |
+
class ComfyRoll_InputImages:
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39 |
+
def __init__(self):
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40 |
+
pass
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41 |
+
|
42 |
+
@classmethod
|
43 |
+
def INPUT_TYPES(cls):
|
44 |
+
return {
|
45 |
+
"required": {
|
46 |
+
"Input": ("INT", {"default": 1, "min": 1, "max": 2}),
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47 |
+
"image1": ("IMAGE",),
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48 |
+
"image2": ("IMAGE",)
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49 |
+
}
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50 |
+
}
|
51 |
+
|
52 |
+
RETURN_TYPES = ("IMAGE",)
|
53 |
+
OUTPUT_NODE = True
|
54 |
+
FUNCTION = "InputImages"
|
55 |
+
|
56 |
+
CATEGORY = "Comfyroll/Logic"
|
57 |
+
|
58 |
+
def InputImages(self, Input, image1, image2):
|
59 |
+
if Input == 1:
|
60 |
+
return (image1, )
|
61 |
+
else:
|
62 |
+
return (image2, )
|
63 |
+
|
64 |
+
#---------------------------------------------------------------------------------------------------------------------------------------------------#
|
65 |
+
|
66 |
+
class ComfyRoll_InputImages_4way:
|
67 |
+
def __init__(self):
|
68 |
+
pass
|
69 |
+
|
70 |
+
@classmethod
|
71 |
+
def INPUT_TYPES(cls):
|
72 |
+
return {
|
73 |
+
"required": {
|
74 |
+
"Input": ("INT", {"default": 1, "min": 1, "max": 4}),
|
75 |
+
"image1": ("IMAGE",),
|
76 |
+
},
|
77 |
+
"optional": {
|
78 |
+
"image2": ("IMAGE",),
|
79 |
+
"image3": ("IMAGE",),
|
80 |
+
"image4": ("IMAGE",),
|
81 |
+
}
|
82 |
+
}
|
83 |
+
|
84 |
+
RETURN_TYPES = ("IMAGE",)
|
85 |
+
OUTPUT_NODE = True
|
86 |
+
FUNCTION = "InputImages_4"
|
87 |
+
|
88 |
+
CATEGORY = "Comfyroll/Logic"
|
89 |
+
|
90 |
+
def InputImages_4(self, Input, image1, image2=None, image3=None, image4=None):
|
91 |
+
if Input == 1:
|
92 |
+
return (image1, )
|
93 |
+
elif Input == 2:
|
94 |
+
return (image2, )
|
95 |
+
elif Input == 3:
|
96 |
+
return (image3, )
|
97 |
+
else:
|
98 |
+
return (image4, )
|
99 |
+
|
100 |
+
#---------------------------------------------------------------------------------------------------------------------------------------------------#
|
101 |
+
|
102 |
+
class ComfyRoll_InputLatents:
|
103 |
+
def __init__(self):
|
104 |
+
pass
|
105 |
+
|
106 |
+
@classmethod
|
107 |
+
def INPUT_TYPES(cls):
|
108 |
+
return {
|
109 |
+
"required": {
|
110 |
+
"Input": ("INT", {"default": 1, "min": 1, "max": 2}),
|
111 |
+
"latent1": ("LATENT",),
|
112 |
+
"latent2": ("LATENT",)
|
113 |
+
}
|
114 |
+
}
|
115 |
+
|
116 |
+
RETURN_TYPES = ("LATENT",)
|
117 |
+
OUTPUT_NODE = True
|
118 |
+
FUNCTION = "InputLatents"
|
119 |
+
|
120 |
+
CATEGORY = "Comfyroll/Logic"
|
121 |
+
|
122 |
+
def InputLatents(self, Input, latent1, latent2):
|
123 |
+
if Input == 1:
|
124 |
+
return (latent1, )
|
125 |
+
else:
|
126 |
+
return (latent2, )
|
127 |
+
|
128 |
+
|
129 |
+
|
130 |
+
#---------------------------------------------------------------------------------------------------------------------------------------------------#
|
131 |
+
|
132 |
+
class ComfyRoll_InputConditioning:
|
133 |
+
def __init__(self):
|
134 |
+
pass
|
135 |
+
|
136 |
+
@classmethod
|
137 |
+
def INPUT_TYPES(cls):
|
138 |
+
return {
|
139 |
+
"required": {
|
140 |
+
"Input": ("INT", {"default": 1, "min": 1, "max": 2}),
|
141 |
+
"conditioning1": ("CONDITIONING",),
|
142 |
+
"conditioning2": ("CONDITIONING",)
|
143 |
+
}
|
144 |
+
}
|
145 |
+
|
146 |
+
RETURN_TYPES = ("CONDITIONING",)
|
147 |
+
OUTPUT_NODE = True
|
148 |
+
FUNCTION = "InputConditioning"
|
149 |
+
|
150 |
+
CATEGORY = "Comfyroll/Logic"
|
151 |
+
|
152 |
+
def InputConditioning(self, Input, conditioning1, conditioning2):
|
153 |
+
if Input == 1:
|
154 |
+
return (conditioning1, )
|
155 |
+
else:
|
156 |
+
return (conditioning2, )
|
157 |
+
|
158 |
+
#---------------------------------------------------------------------------------------------------------------------------------------------------#
|
159 |
+
|
160 |
+
class ComfyRoll_InputClip:
|
161 |
+
def __init__(self):
|
162 |
+
pass
|
163 |
+
|
164 |
+
@classmethod
|
165 |
+
def INPUT_TYPES(cls):
|
166 |
+
return {
|
167 |
+
"required": {
|
168 |
+
"Input": ("INT", {"default": 1, "min": 1, "max": 2}),
|
169 |
+
"clip1": ("CLIP",),
|
170 |
+
"clip2": ("CLIP",)
|
171 |
+
}
|
172 |
+
}
|
173 |
+
|
174 |
+
RETURN_TYPES = ("CLIP",)
|
175 |
+
OUTPUT_NODE = True
|
176 |
+
FUNCTION = "InputClip"
|
177 |
+
|
178 |
+
CATEGORY = "Comfyroll/Logic"
|
179 |
+
|
180 |
+
def InputClip(self, Input, clip1, clip2):
|
181 |
+
if Input == 1:
|
182 |
+
return (clip1, )
|
183 |
+
else:
|
184 |
+
return (clip2, )
|
185 |
+
|
186 |
+
#---------------------------------------------------------------------------------------------------------------------------------------------------#
|
187 |
+
|
188 |
+
class ComfyRoll_InputModel:
|
189 |
+
def __init__(self):
|
190 |
+
pass
|
191 |
+
|
192 |
+
@classmethod
|
193 |
+
def INPUT_TYPES(cls):
|
194 |
+
return {
|
195 |
+
"required": {
|
196 |
+
"Input": ("INT", {"default": 1, "min": 1, "max": 2}),
|
197 |
+
"model1": ("MODEL",),
|
198 |
+
"model2": ("MODEL",)
|
199 |
+
}
|
200 |
+
}
|
201 |
+
|
202 |
+
RETURN_TYPES = ("MODEL",)
|
203 |
+
OUTPUT_NODE = True
|
204 |
+
FUNCTION = "InputModel"
|
205 |
+
|
206 |
+
CATEGORY = "Comfyroll/Logic"
|
207 |
+
|
208 |
+
def InputModel(self, Input, model1, model2):
|
209 |
+
if Input == 1:
|
210 |
+
return (model1, )
|
211 |
+
else:
|
212 |
+
return (model2, )
|
213 |
+
|
214 |
+
#---------------------------------------------------------------------------------------------------------------------------------------------------#
|
215 |
+
|
216 |
+
class ComfyRoll_InputControlNet:
|
217 |
+
def __init__(self):
|
218 |
+
pass
|
219 |
+
|
220 |
+
@classmethod
|
221 |
+
def INPUT_TYPES(cls):
|
222 |
+
return {
|
223 |
+
"required": {
|
224 |
+
"Input": ("INT", {"default": 1, "min": 1, "max": 2}),
|
225 |
+
"control_net1": ("CONTROL_NET",),
|
226 |
+
"control_net2": ("CONTROL_NET",)
|
227 |
+
}
|
228 |
+
}
|
229 |
+
|
230 |
+
RETURN_TYPES = ("CONTROL_NET",)
|
231 |
+
OUTPUT_NODE = True
|
232 |
+
FUNCTION = "InputControlNet"
|
233 |
+
|
234 |
+
CATEGORY = "Comfyroll/Logic"
|
235 |
+
|
236 |
+
def InputControlNet(self, Input, control_net1, control_net2):
|
237 |
+
if Input == 1:
|
238 |
+
return (control_net1, )
|
239 |
+
else:
|
240 |
+
return (control_net2, )
|
241 |
+
#---------------------------------------------------------------------------------------------------------------------------------------------------#
|
242 |
+
|
243 |
+
class ComfyRoll_InputLatentsText:
|
244 |
+
def __init__(self):
|
245 |
+
pass
|
246 |
+
|
247 |
+
@classmethod
|
248 |
+
def INPUT_TYPES(cls):
|
249 |
+
return {
|
250 |
+
"required": {
|
251 |
+
"Input": (["txt2img", "img2img"],),
|
252 |
+
"txt2img": ("LATENT",),
|
253 |
+
"img2img": ("LATENT",)
|
254 |
+
}
|
255 |
+
}
|
256 |
+
|
257 |
+
RETURN_TYPES = ("LATENT",)
|
258 |
+
OUTPUT_NODE = True
|
259 |
+
FUNCTION = "InputLatentsText"
|
260 |
+
|
261 |
+
CATEGORY = "Comfyroll/Process"
|
262 |
+
|
263 |
+
def InputLatentsText(self, Input, txt2img, img2img):
|
264 |
+
if Input == "txt2img":
|
265 |
+
return (txt2img, )
|
266 |
+
else:
|
267 |
+
return (img2img, )
|
268 |
+
|
269 |
+
|
270 |
+
#---------------------------------------------------------------------------------------------------------------------------------------------------#
|
271 |
+
|
272 |
+
class ComfyRoll_HiResFixSwitch:
|
273 |
+
def __init__(self):
|
274 |
+
pass
|
275 |
+
|
276 |
+
@classmethod
|
277 |
+
def INPUT_TYPES(cls):
|
278 |
+
return {
|
279 |
+
"required": {
|
280 |
+
"Input": (["latent_upscale", "image_upscale"],),
|
281 |
+
"latent_upscale": ("LATENT",),
|
282 |
+
"image_upscale": ("LATENT",)
|
283 |
+
}
|
284 |
+
}
|
285 |
+
|
286 |
+
RETURN_TYPES = ("LATENT",)
|
287 |
+
OUTPUT_NODE = True
|
288 |
+
FUNCTION = "InputHiResText"
|
289 |
+
|
290 |
+
CATEGORY = "Comfyroll/Process"
|
291 |
+
|
292 |
+
def InputHiResText(self, Input, latent_upscale, image_upscale):
|
293 |
+
if Input == "latent_upscale":
|
294 |
+
return (latent_upscale, )
|
295 |
+
else:
|
296 |
+
return (image_upscale, )
|
297 |
+
|
298 |
+
|
299 |
+
#---------------------------------------------------------------------------------------------------------------------------------------------------#
|
300 |
+
|
301 |
+
class ComfyRoll_LoraLoader:
|
302 |
+
def __init__(self):
|
303 |
+
self.loaded_lora = None
|
304 |
+
|
305 |
+
@classmethod
|
306 |
+
def INPUT_TYPES(s):
|
307 |
+
file_list = folder_paths.get_filename_list("loras")
|
308 |
+
file_list.insert(0, "None")
|
309 |
+
return {"required": { "model": ("MODEL",),
|
310 |
+
"clip": ("CLIP", ),
|
311 |
+
"switch": ([
|
312 |
+
"On",
|
313 |
+
"Off"],),
|
314 |
+
"lora_name": (file_list, ),
|
315 |
+
"strength_model": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
|
316 |
+
"strength_clip": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
|
317 |
+
}}
|
318 |
+
RETURN_TYPES = ("MODEL", "CLIP")
|
319 |
+
FUNCTION = "load_lora"
|
320 |
+
|
321 |
+
CATEGORY = "Comfyroll/IO"
|
322 |
+
|
323 |
+
def load_lora(self, model, clip, switch, lora_name, strength_model, strength_clip):
|
324 |
+
if strength_model == 0 and strength_clip == 0:
|
325 |
+
return (model, clip)
|
326 |
+
|
327 |
+
if switch == "Off" or lora_name == "None":
|
328 |
+
return (model, clip)
|
329 |
+
|
330 |
+
lora_path = folder_paths.get_full_path("loras", lora_name)
|
331 |
+
lora = None
|
332 |
+
if self.loaded_lora is not None:
|
333 |
+
if self.loaded_lora[0] == lora_path:
|
334 |
+
lora = self.loaded_lora[1]
|
335 |
+
else:
|
336 |
+
del self.loaded_lora
|
337 |
+
|
338 |
+
if lora is None:
|
339 |
+
lora = comfy.utils.load_torch_file(lora_path, safe_load=True)
|
340 |
+
self.loaded_lora = (lora_path, lora)
|
341 |
+
|
342 |
+
model_lora, clip_lora = comfy.sd.load_lora_for_models(model, clip, lora, strength_model, strength_clip)
|
343 |
+
return (model_lora, clip_lora)
|
344 |
+
|
345 |
+
#---------------------------------------------------------------------------------------------------------------------------------------------------#
|
346 |
+
|
347 |
+
class ComfyRoll_ApplyControlNet:
|
348 |
+
@classmethod
|
349 |
+
def INPUT_TYPES(s):
|
350 |
+
return {"required": {"conditioning": ("CONDITIONING", ),
|
351 |
+
"control_net": ("CONTROL_NET", ),
|
352 |
+
"image": ("IMAGE", ),
|
353 |
+
"switch": ([
|
354 |
+
"On",
|
355 |
+
"Off"],),
|
356 |
+
"strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01})
|
357 |
+
}}
|
358 |
+
RETURN_TYPES = ("CONDITIONING",)
|
359 |
+
FUNCTION = "apply_controlnet"
|
360 |
+
|
361 |
+
CATEGORY = "Comfyroll/Conditioning"
|
362 |
+
|
363 |
+
def apply_controlnet(self, conditioning, control_net, image, switch, strength):
|
364 |
+
if strength == 0 or switch == "Off":
|
365 |
+
return (conditioning, )
|
366 |
+
|
367 |
+
c = []
|
368 |
+
control_hint = image.movedim(-1,1)
|
369 |
+
for t in conditioning:
|
370 |
+
n = [t[0], t[1].copy()]
|
371 |
+
c_net = control_net.copy().set_cond_hint(control_hint, strength)
|
372 |
+
if 'control' in t[1]:
|
373 |
+
c_net.set_previous_controlnet(t[1]['control'])
|
374 |
+
n[1]['control'] = c_net
|
375 |
+
c.append(n)
|
376 |
+
return (c, )
|
377 |
+
|
378 |
+
#---------------------------------------------------------------------------------------------------------------------------------------------------#
|
379 |
+
|
380 |
+
class ComfyRoll_ImageSize_Float:
|
381 |
+
def __init__(self):
|
382 |
+
pass
|
383 |
+
|
384 |
+
@classmethod
|
385 |
+
def INPUT_TYPES(s):
|
386 |
+
return {
|
387 |
+
"required": {
|
388 |
+
"width": ("INT", {"default": 512, "min": 64, "max": 2048}),
|
389 |
+
"height": ("INT", {"default": 512, "min": 64, "max": 2048}),
|
390 |
+
"upscale_factor": ("FLOAT", {"default": 1, "min": 1, "max": 2000})
|
391 |
+
}
|
392 |
+
}
|
393 |
+
RETURN_TYPES = ("INT", "INT", "FLOAT")
|
394 |
+
#RETURN_NAMES = ("Width", "Height")
|
395 |
+
FUNCTION = "ImageSize_Float"
|
396 |
+
|
397 |
+
CATEGORY = "Comfyroll/Image"
|
398 |
+
|
399 |
+
def ImageSize_Float(self, width, height, upscale_factor):
|
400 |
+
return(width, height, upscale_factor)
|
401 |
+
|
402 |
+
#---------------------------------------------------------------------------------------------------------------------------------------------------#
|
403 |
+
|
404 |
+
class ComfyRoll_ImageOutput:
|
405 |
+
def __init__(self):
|
406 |
+
self.output_dir = folder_paths.get_output_directory()
|
407 |
+
self.type = "output"
|
408 |
+
|
409 |
+
@classmethod
|
410 |
+
def INPUT_TYPES(s):
|
411 |
+
return {"required":
|
412 |
+
{"images": ("IMAGE", ),
|
413 |
+
"output_type": (["Preview", "Save"],),
|
414 |
+
"filename_prefix": ("STRING", {"default": "ComfyUI"})},
|
415 |
+
"hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},
|
416 |
+
}
|
417 |
+
|
418 |
+
RETURN_TYPES = ()
|
419 |
+
FUNCTION = "save_images"
|
420 |
+
|
421 |
+
OUTPUT_NODE = True
|
422 |
+
|
423 |
+
CATEGORY = "Comfyroll/Legacy"
|
424 |
+
|
425 |
+
def save_images(self, images, filename_prefix="ComfyUI", output_type = "Preview", prompt=None, extra_pnginfo=None):
|
426 |
+
def map_filename(filename):
|
427 |
+
prefix_len = len(os.path.basename(filename_prefix))
|
428 |
+
prefix = filename[:prefix_len + 1]
|
429 |
+
try:
|
430 |
+
digits = int(filename[prefix_len + 1:].split('_')[0])
|
431 |
+
except:
|
432 |
+
digits = 0
|
433 |
+
return (digits, prefix)
|
434 |
+
|
435 |
+
def compute_vars(input):
|
436 |
+
input = input.replace("%width%", str(images[0].shape[1]))
|
437 |
+
input = input.replace("%height%", str(images[0].shape[0]))
|
438 |
+
return input
|
439 |
+
|
440 |
+
if output_type == "Save":
|
441 |
+
self.output_dir = folder_paths.get_output_directory()
|
442 |
+
self.type = "output"
|
443 |
+
elif output_type == "Preview":
|
444 |
+
self.output_dir = folder_paths.get_temp_directory()
|
445 |
+
self.type = "temp"
|
446 |
+
|
447 |
+
filename_prefix = compute_vars(filename_prefix)
|
448 |
+
|
449 |
+
subfolder = os.path.dirname(os.path.normpath(filename_prefix))
|
450 |
+
filename = os.path.basename(os.path.normpath(filename_prefix))
|
451 |
+
|
452 |
+
full_output_folder = os.path.join(self.output_dir, subfolder)
|
453 |
+
|
454 |
+
if os.path.commonpath((self.output_dir, os.path.abspath(full_output_folder))) != self.output_dir:
|
455 |
+
print("Saving image outside the output folder is not allowed.")
|
456 |
+
return {}
|
457 |
+
|
458 |
+
try:
|
459 |
+
counter = max(filter(lambda a: a[1][:-1] == filename and a[1][-1] == "_", map(map_filename, os.listdir(full_output_folder))))[0] + 1
|
460 |
+
except ValueError:
|
461 |
+
counter = 1
|
462 |
+
except FileNotFoundError:
|
463 |
+
os.makedirs(full_output_folder, exist_ok=True)
|
464 |
+
counter = 1
|
465 |
+
|
466 |
+
results = list()
|
467 |
+
for image in images:
|
468 |
+
i = 255. * image.cpu().numpy()
|
469 |
+
img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8))
|
470 |
+
metadata = PngInfo()
|
471 |
+
if prompt is not None:
|
472 |
+
metadata.add_text("prompt", json.dumps(prompt))
|
473 |
+
if extra_pnginfo is not None:
|
474 |
+
for x in extra_pnginfo:
|
475 |
+
metadata.add_text(x, json.dumps(extra_pnginfo[x]))
|
476 |
+
|
477 |
+
file = f"{filename}_{counter:05}_.png"
|
478 |
+
img.save(os.path.join(full_output_folder, file), pnginfo=metadata, compress_level=4)
|
479 |
+
results.append({
|
480 |
+
"filename": file,
|
481 |
+
"subfolder": subfolder,
|
482 |
+
"type": self.type
|
483 |
+
})
|
484 |
+
counter += 1
|
485 |
+
|
486 |
+
return { "ui": { "images": results } }
|
487 |
+
|
488 |
+
#---------------------------------------------------------------------------------------------------------------------------------------------------#
|
489 |
+
|
490 |
+
class CR_Int_Multiple_Of:
|
491 |
+
def __init__(self):
|
492 |
+
pass
|
493 |
+
|
494 |
+
@classmethod
|
495 |
+
def INPUT_TYPES(cls):
|
496 |
+
return {
|
497 |
+
"required": {
|
498 |
+
"integer": ("INT", {"default": 1, "min": -18446744073709551615, "max": 18446744073709551615}),
|
499 |
+
"multiple": ("FLOAT", {"default": 8, "min": 1, "max": 18446744073709551615}),
|
500 |
+
}
|
501 |
+
}
|
502 |
+
|
503 |
+
RETURN_TYPES =("INT",)
|
504 |
+
FUNCTION = "int_multiple_of"
|
505 |
+
|
506 |
+
CATEGORY = "Comfyroll/Math"
|
507 |
+
|
508 |
+
def int_multiple_of(self, integer, multiple=8):
|
509 |
+
if multiple == 0:
|
510 |
+
return (int(integer), )
|
511 |
+
integer = integer * multiple
|
512 |
+
return (int(integer), )
|
513 |
+
|
514 |
+
#---------------------------------------------------------------------------------------------------------------------------------------------------#
|
515 |
+
|
516 |
+
class ComfyRoll_AspectRatio:
|
517 |
+
def __init__(self):
|
518 |
+
pass
|
519 |
+
|
520 |
+
@classmethod
|
521 |
+
def INPUT_TYPES(s):
|
522 |
+
return {
|
523 |
+
"required": {
|
524 |
+
"width": ("INT", {"default": 512, "min": 64, "max": 2048}),
|
525 |
+
"height": ("INT", {"default": 512, "min": 64, "max": 2048}),
|
526 |
+
"aspect_ratio": (["custom", "1:1 square 512x512", "1:1 square 1024x1024", "2:3 portrait 512x768", "3:4 portrait 512x682", "3:2 landscape 768x512", "4:3 landscape 682x512", "16:9 cinema 910x512", "2:1 cinema 1024x512"],),
|
527 |
+
"swap_dimensions": (["Off", "On"],),
|
528 |
+
"upscale_factor1": ("FLOAT", {"default": 1, "min": 1, "max": 2000}),
|
529 |
+
"upscale_factor2": ("FLOAT", {"default": 1, "min": 1, "max": 2000}),
|
530 |
+
"batch_size": ("INT", {"default": 1, "min": 1, "max": 64})
|
531 |
+
}
|
532 |
+
}
|
533 |
+
RETURN_TYPES = ("INT", "INT", "FLOAT", "FLOAT", "INT")
|
534 |
+
#RETURN_NAMES = ("Width", "Height")
|
535 |
+
FUNCTION = "Aspect_Ratio"
|
536 |
+
|
537 |
+
CATEGORY = "Comfyroll/Image"
|
538 |
+
|
539 |
+
def Aspect_Ratio(self, width, height, aspect_ratio, swap_dimensions, upscale_factor1, upscale_factor2, batch_size):
|
540 |
+
if swap_dimensions == "Off":
|
541 |
+
if aspect_ratio == "2:3 portrait 512x768":
|
542 |
+
width, height = 512, 768
|
543 |
+
elif aspect_ratio == "3:2 landscape 768x512":
|
544 |
+
width, height = 768, 512
|
545 |
+
elif aspect_ratio == "1:1 square 512x512":
|
546 |
+
width, height = 512, 512
|
547 |
+
elif aspect_ratio == "1:1 square 1024x1024":
|
548 |
+
width, height = 1024, 1024
|
549 |
+
elif aspect_ratio == "16:9 cinema 910x512":
|
550 |
+
width, height = 910, 512
|
551 |
+
elif aspect_ratio == "3:4 portrait 512x682":
|
552 |
+
width, height = 512, 682
|
553 |
+
elif aspect_ratio == "4:3 landscape 682x512":
|
554 |
+
width, height = 682, 512
|
555 |
+
elif aspect_ratio == "2:1 cinema 1024x512":
|
556 |
+
width, height = 1024, 512
|
557 |
+
return(width, height, upscale_factor1, upscale_factor2, batch_size)
|
558 |
+
elif swap_dimensions == "On":
|
559 |
+
if aspect_ratio == "2:3 portrait 512x768":
|
560 |
+
width, height = 512, 768
|
561 |
+
elif aspect_ratio == "3:2 landscape 768x512":
|
562 |
+
width, height = 768, 512
|
563 |
+
elif aspect_ratio == "1:1 square 512x512":
|
564 |
+
width, height = 512, 512
|
565 |
+
elif aspect_ratio == "1:1 square 1024x1024":
|
566 |
+
width, height = 1024, 1024
|
567 |
+
elif aspect_ratio == "16:9 cinema 910x512":
|
568 |
+
width,height = 910, 512
|
569 |
+
elif aspect_ratio == "3:4 portrait 512x682":
|
570 |
+
width, height = 512, 682
|
571 |
+
elif aspect_ratio == "4:3 landscape 682x512":
|
572 |
+
width, height = 682, 512
|
573 |
+
elif aspect_ratio == "2:1 cinema 1024x512":
|
574 |
+
width, height = 1024, 512
|
575 |
+
return(height, width, upscale_factor1, upscale_factor2, batch_size)
|
576 |
+
|
577 |
+
#---------------------------------------------------------------------------------------------------------------------------------------------------#
|
578 |
+
|
579 |
+
class ComfyRoll_AspectRatio_SDXL:
|
580 |
+
def __init__(self):
|
581 |
+
pass
|
582 |
+
|
583 |
+
@classmethod
|
584 |
+
def INPUT_TYPES(s):
|
585 |
+
return {
|
586 |
+
"required": {
|
587 |
+
"width": ("INT", {"default": 1024, "min": 64, "max": 2048}),
|
588 |
+
"height": ("INT", {"default": 1024, "min": 64, "max": 2048}),
|
589 |
+
"aspect_ratio": (["custom", "square 1024x1024", "portrait 896x1152", "portrait 832x1216", "portrait 768x1344", "portrait 640 x 1536", "landscape 1152x896", "landscape 1216x832", "landscape 1344x768", "landscape 1536x640"],),
|
590 |
+
"swap_dimensions": (["Off", "On"],),
|
591 |
+
"upscale_factor1": ("FLOAT", {"default": 1, "min": 1, "max": 2000}),
|
592 |
+
"upscale_factor2": ("FLOAT", {"default": 1, "min": 1, "max": 2000}),
|
593 |
+
"batch_size": ("INT", {"default": 1, "min": 1, "max": 64})
|
594 |
+
}
|
595 |
+
}
|
596 |
+
RETURN_TYPES = ("INT", "INT", "FLOAT", "FLOAT", "INT")
|
597 |
+
#RETURN_NAMES = ("Width", "Height")
|
598 |
+
FUNCTION = "Aspect_Ratio"
|
599 |
+
|
600 |
+
CATEGORY = "Comfyroll/SDXL"
|
601 |
+
|
602 |
+
def Aspect_Ratio(self, width, height, aspect_ratio, swap_dimensions, upscale_factor1, upscale_factor2, batch_size):
|
603 |
+
if aspect_ratio == "square 1024x1024":
|
604 |
+
width, height = 1024, 1024
|
605 |
+
elif aspect_ratio == "portrait 896x1152":
|
606 |
+
width, height = 896, 1152
|
607 |
+
elif aspect_ratio == "portrait 832x1216":
|
608 |
+
width, height = 822, 1216
|
609 |
+
elif aspect_ratio == "portrait 768x1344":
|
610 |
+
width, height = 768, 1344
|
611 |
+
elif aspect_ratio == "portrait 640 x 1536":
|
612 |
+
width, height = 640, 1536
|
613 |
+
elif aspect_ratio == "landscape 1152x896":
|
614 |
+
width, height = 1152, 896
|
615 |
+
elif aspect_ratio == "landscape 1152x896":
|
616 |
+
width, height = 682, 512
|
617 |
+
elif aspect_ratio == "landscape 1216x832":
|
618 |
+
width, height = 1216, 832
|
619 |
+
elif aspect_ratio == "landscape 1344x768":
|
620 |
+
width, height = 1152, 896
|
621 |
+
elif aspect_ratio == "landscape 1536x640":
|
622 |
+
width, height = 1536, 640
|
623 |
+
|
624 |
+
if swap_dimensions == "On":
|
625 |
+
return(height, width, upscale_factor1, upscale_factor2, batch_size,)
|
626 |
+
else:
|
627 |
+
return(width, height, upscale_factor1, upscale_factor2, batch_size,)
|
628 |
+
|
629 |
+
#---------------------------------------------------------------------------------------------------------------------------------------------------#
|
630 |
+
|
631 |
+
class ComfyRoll_SeedToInt:
|
632 |
+
def __init__(self):
|
633 |
+
pass
|
634 |
+
|
635 |
+
@classmethod
|
636 |
+
def INPUT_TYPES(cls):
|
637 |
+
return {
|
638 |
+
"required": {
|
639 |
+
"seed": ("SEED", ),
|
640 |
+
}
|
641 |
+
}
|
642 |
+
|
643 |
+
RETURN_TYPES = ("INT",)
|
644 |
+
FUNCTION = "seed_to_int"
|
645 |
+
|
646 |
+
CATEGORY = "Comfyroll/Number"
|
647 |
+
|
648 |
+
def seed_to_int(self, seed):
|
649 |
+
return (seed.get('seed'),)
|
650 |
+
|
651 |
+
#---------------------------------------------------------------------------------------------------------------------------------------------------#
|
652 |
+
|
653 |
+
class Comfyroll_Color_Tint:
|
654 |
+
def __init__(self):
|
655 |
+
pass
|
656 |
+
|
657 |
+
@classmethod
|
658 |
+
def INPUT_TYPES(s):
|
659 |
+
return {
|
660 |
+
"required": {
|
661 |
+
"image": ("IMAGE",),
|
662 |
+
"strength": ("FLOAT", {
|
663 |
+
"default": 1.0,
|
664 |
+
"min": 0.1,
|
665 |
+
"max": 1.0,
|
666 |
+
"step": 0.1
|
667 |
+
}),
|
668 |
+
"mode": (["white", "black", "sepia", "red", "green", "blue", "cyan", "magenta", "yellow", "purple", "orange", "warm", "cool", "lime", "navy", "vintage", "rose", "teal", "maroon", "peach", "lavender", "olive"],),
|
669 |
+
},
|
670 |
+
}
|
671 |
+
|
672 |
+
RETURN_TYPES = ("IMAGE",)
|
673 |
+
FUNCTION = "color_tint"
|
674 |
+
|
675 |
+
CATEGORY = "Comfyroll/Image"
|
676 |
+
|
677 |
+
def color_tint(self, image: torch.Tensor, strength: float, mode: str = "sepia"):
|
678 |
+
if strength == 0:
|
679 |
+
return (image,)
|
680 |
+
|
681 |
+
sepia_weights = torch.tensor([0.2989, 0.5870, 0.1140]).view(1, 1, 1, 3).to(image.device)
|
682 |
+
|
683 |
+
mode_filters = {
|
684 |
+
"white": torch.tensor([1.0, 1.0, 1.0]),
|
685 |
+
"black": torch.tensor([0, 0, 0]),
|
686 |
+
"sepia": torch.tensor([1.0, 0.8, 0.6]),
|
687 |
+
"red": torch.tensor([1.0, 0.6, 0.6]),
|
688 |
+
"green": torch.tensor([0.6, 1.0, 0.6]),
|
689 |
+
"blue": torch.tensor([0.6, 0.8, 1.0]),
|
690 |
+
"cyan": torch.tensor([0.6, 1.0, 1.0]),
|
691 |
+
"magenta": torch.tensor([1.0, 0.6, 1.0]),
|
692 |
+
"yellow": torch.tensor([1.0, 1.0, 0.6]),
|
693 |
+
"purple": torch.tensor([0.8, 0.6, 1.0]),
|
694 |
+
"orange": torch.tensor([1.0, 0.7, 0.3]),
|
695 |
+
"warm": torch.tensor([1.0, 0.9, 0.7]),
|
696 |
+
"cool": torch.tensor([0.7, 0.9, 1.0]),
|
697 |
+
"lime": torch.tensor([0.7, 1.0, 0.3]),
|
698 |
+
"navy": torch.tensor([0.3, 0.4, 0.7]),
|
699 |
+
"vintage": torch.tensor([0.9, 0.85, 0.7]),
|
700 |
+
"rose": torch.tensor([1.0, 0.8, 0.9]),
|
701 |
+
"teal": torch.tensor([0.3, 0.8, 0.8]),
|
702 |
+
"maroon": torch.tensor([0.7, 0.3, 0.5]),
|
703 |
+
"peach": torch.tensor([1.0, 0.8, 0.6]),
|
704 |
+
"lavender": torch.tensor([0.8, 0.6, 1.0]),
|
705 |
+
"olive": torch.tensor([0.6, 0.7, 0.4]),
|
706 |
+
}
|
707 |
+
|
708 |
+
scale_filter = mode_filters[mode].view(1, 1, 1, 3).to(image.device)
|
709 |
+
|
710 |
+
grayscale = torch.sum(image * sepia_weights, dim=-1, keepdim=True)
|
711 |
+
tinted = grayscale * scale_filter
|
712 |
+
|
713 |
+
result = tinted * strength + image * (1 - strength)
|
714 |
+
return (result,)
|
715 |
+
|
716 |
+
#---------------------------------------------------------------------------------------------------------------------------------------------------#
|
717 |
+
|
718 |
+
class ComfyRoll_prompt_mixer:
|
719 |
+
def __init__(self):
|
720 |
+
pass
|
721 |
+
|
722 |
+
@classmethod
|
723 |
+
def INPUT_TYPES(s):
|
724 |
+
return {
|
725 |
+
"required":{
|
726 |
+
},
|
727 |
+
"optional":{
|
728 |
+
"prompt_positive": ("STRING", {"multiline": True, "default": "BASE_POSITIVE"}),
|
729 |
+
"prompt_negative": ("STRING", {"multiline": True, "default": "BASE_NEGATIVE"}),
|
730 |
+
"style_positive": ("STRING", {"multiline": True, "default": "REFINER_POSTIVE"}),
|
731 |
+
"style_negative": ("STRING", {"multiline": True, "default": "REFINER_NEGATIVE"}),
|
732 |
+
"preset": (["preset 1", "preset 2", "preset 3", "preset 4", "preset 5"],),
|
733 |
+
},
|
734 |
+
}
|
735 |
+
|
736 |
+
RETURN_TYPES = ("STRING", "STRING", "STRING", "STRING", "STRING", "STRING", )
|
737 |
+
RETURN_NAMES = ("pos_g", "pos_l", "pos_r", "neg_g", "neg_l", "neg_r", )
|
738 |
+
FUNCTION = "mixer"
|
739 |
+
|
740 |
+
CATEGORY = "Comfyroll/SDXL"
|
741 |
+
|
742 |
+
def mixer(self, prompt_positive, prompt_negative, style_positive, style_negative, preset):
|
743 |
+
if preset == "preset 1":
|
744 |
+
pos_g = prompt_positive
|
745 |
+
pos_l = prompt_positive
|
746 |
+
pos_r = prompt_positive
|
747 |
+
neg_g = prompt_negative
|
748 |
+
neg_l = prompt_negative
|
749 |
+
neg_r = prompt_negative
|
750 |
+
elif preset == "preset 2":
|
751 |
+
pos_g = prompt_positive
|
752 |
+
pos_l = style_positive
|
753 |
+
pos_r = prompt_positive
|
754 |
+
neg_g = prompt_negative
|
755 |
+
neg_l = style_negative
|
756 |
+
neg_r = prompt_negative
|
757 |
+
elif preset == "preset 3":
|
758 |
+
pos_g = style_positive
|
759 |
+
pos_l = prompt_positive
|
760 |
+
pos_r = style_positive
|
761 |
+
neg_g = style_negative
|
762 |
+
neg_l = prompt_negative
|
763 |
+
neg_r = style_negative
|
764 |
+
elif preset == "preset 4":
|
765 |
+
pos_g = prompt_positive + style_positive
|
766 |
+
pos_l = prompt_positive + style_positive
|
767 |
+
pos_r = prompt_positive + style_positive
|
768 |
+
neg_g = prompt_negative + style_negative
|
769 |
+
neg_l = prompt_negative + style_negative
|
770 |
+
neg_r = prompt_negative + style_negative
|
771 |
+
elif preset == "preset 5":
|
772 |
+
pos_g = prompt_positive
|
773 |
+
pos_l = prompt_positive
|
774 |
+
pos_r = style_positive
|
775 |
+
neg_g = prompt_negative
|
776 |
+
neg_l = prompt_negative
|
777 |
+
neg_r = style_negative
|
778 |
+
return (pos_g, pos_l, pos_r, neg_g, neg_l, neg_r, )
|
779 |
+
|
780 |
+
|
781 |
+
|
782 |
+
|
783 |
+
#---------------------------------------------------------------------------------------------------------------------------------------------------#
|
784 |
+
|
785 |
+
|
786 |
+
class Comfyroll_SDXLStyleText:
|
787 |
+
@classmethod
|
788 |
+
def INPUT_TYPES(s):
|
789 |
+
return {"required": {
|
790 |
+
"positive_style": ("STRING", {"default": "POS_STYLE", "multiline": True}),
|
791 |
+
"negative_style": ("STRING", {"default": "NEG_STYLE", "multiline": True}),
|
792 |
+
},
|
793 |
+
}
|
794 |
+
|
795 |
+
RETURN_TYPES = ("STRING", "STRING", )
|
796 |
+
RETURN_NAMES = ("positive_prompt_text_l", "negative_prompt_text_l" )
|
797 |
+
FUNCTION = "get_value"
|
798 |
+
|
799 |
+
CATEGORY = "Comfyroll/SDXL"
|
800 |
+
|
801 |
+
def get_value(self, positive_style, negative_style):
|
802 |
+
return (positive_style, negative_style,)
|
803 |
+
|
804 |
+
#---------------------------------------------------------------------------------------------------------------------------------------------------#
|
805 |
+
|
806 |
+
class Comfyroll_SDXLBasePromptEncoder:
|
807 |
+
@classmethod
|
808 |
+
def INPUT_TYPES(s):
|
809 |
+
return {"required": {
|
810 |
+
"base_clip": ("CLIP", ),
|
811 |
+
"pos_g": ("STRING", {"multiline": True, "default": "POS_G"}),
|
812 |
+
"pos_l": ("STRING", {"multiline": True, "default": "POS_L"}),
|
813 |
+
"neg_g": ("STRING", {"multiline": True, "default": "NEG_G"}),
|
814 |
+
"neg_l": ("STRING", {"multiline": True, "default": "NEG_L"}),
|
815 |
+
"preset": (["preset A", "preset B", "preset C"],),
|
816 |
+
"base_width": ("INT", {"default": 4096.0, "min": 0, "max": MAX_RESOLUTION, "step": 64}),
|
817 |
+
"base_height": ("INT", {"default": 4096.0, "min": 0, "max": MAX_RESOLUTION, "step": 64}),
|
818 |
+
"crop_w": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 64}),
|
819 |
+
"crop_h": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 64}),
|
820 |
+
"target_width": ("INT", {"default": 4096.0, "min": 0, "max": MAX_RESOLUTION, "step": 64}),
|
821 |
+
"target_height": ("INT", {"default": 4096.0, "min": 0, "max": MAX_RESOLUTION, "step": 64}),
|
822 |
+
},
|
823 |
+
}
|
824 |
+
|
825 |
+
RETURN_TYPES = ("CONDITIONING", "CONDITIONING", )
|
826 |
+
RETURN_NAMES = ("base_positive", "base_negative", )
|
827 |
+
FUNCTION = "encode"
|
828 |
+
|
829 |
+
CATEGORY = "Comfyroll/SDXL"
|
830 |
+
|
831 |
+
def encode(self, base_clip, pos_g, pos_l, neg_g, neg_l, base_width, base_height, crop_w, crop_h, target_width, target_height, preset,):
|
832 |
+
empty = base_clip.tokenize("")
|
833 |
+
|
834 |
+
# positive prompt
|
835 |
+
tokens1 = base_clip.tokenize(pos_g)
|
836 |
+
tokens1["l"] = base_clip.tokenize(pos_l)["l"]
|
837 |
+
|
838 |
+
if len(tokens1["l"]) != len(tokens1["g"]):
|
839 |
+
while len(tokens1["l"]) < len(tokens1["g"]):
|
840 |
+
tokens1["l"] += empty["l"]
|
841 |
+
while len(tokens1["l"]) > len(tokens1["g"]):
|
842 |
+
tokens1["g"] += empty["g"]
|
843 |
+
|
844 |
+
cond1, pooled1 = base_clip.encode_from_tokens(tokens1, return_pooled=True)
|
845 |
+
res1 = [[cond1, {"pooled_output": pooled1, "width": base_width, "height": base_height, "crop_w": crop_w, "crop_h": crop_h, "target_width": target_width, "target_height": target_height}]]
|
846 |
+
|
847 |
+
# negative prompt
|
848 |
+
tokens2 = base_clip.tokenize(neg_g)
|
849 |
+
tokens2["l"] = base_clip.tokenize(neg_l)["l"]
|
850 |
+
|
851 |
+
if len(tokens2["l"]) != len(tokens2["g"]):
|
852 |
+
while len(tokens2["l"]) < len(tokens2["g"]):
|
853 |
+
tokens2["l"] += empty["l"]
|
854 |
+
while len(tokens2["l"]) > len(tokens2["g"]):
|
855 |
+
tokens2["g"] += empty["g"]
|
856 |
+
|
857 |
+
cond2, pooled2 = base_clip.encode_from_tokens(tokens2, return_pooled=True)
|
858 |
+
res2 = [[cond2, {"pooled_output": pooled2, "width": base_width, "height": base_height, "crop_w": crop_w, "crop_h": crop_h, "target_width": target_width, "target_height": target_height}]]
|
859 |
+
|
860 |
+
# positive style
|
861 |
+
tokens2 = base_clip.tokenize(pos_l)
|
862 |
+
tokens2["l"] = base_clip.tokenize(neg_l)["l"]
|
863 |
+
|
864 |
+
if len(tokens2["l"]) != len(tokens2["g"]):
|
865 |
+
while len(tokens2["l"]) < len(tokens2["g"]):
|
866 |
+
tokens2["l"] += empty["l"]
|
867 |
+
while len(tokens2["l"]) > len(tokens2["g"]):
|
868 |
+
tokens2["g"] += empty["g"]
|
869 |
+
|
870 |
+
cond2, pooled2 = base_clip.encode_from_tokens(tokens2, return_pooled=True)
|
871 |
+
res3 = [[cond2, {"pooled_output": pooled2, "width": base_width, "height": base_height, "crop_w": crop_w, "crop_h": crop_h, "target_width": target_width, "target_height": target_height}]]
|
872 |
+
|
873 |
+
# negative style
|
874 |
+
tokens2 = base_clip.tokenize(neg_l)
|
875 |
+
tokens2["l"] = base_clip.tokenize(neg_l)["l"]
|
876 |
+
|
877 |
+
if len(tokens2["l"]) != len(tokens2["g"]):
|
878 |
+
while len(tokens2["l"]) < len(tokens2["g"]):
|
879 |
+
tokens2["l"] += empty["l"]
|
880 |
+
while len(tokens2["l"]) > len(tokens2["g"]):
|
881 |
+
tokens2["g"] += empty["g"]
|
882 |
+
|
883 |
+
cond2, pooled2 = base_clip.encode_from_tokens(tokens2, return_pooled=True)
|
884 |
+
res4 = [[cond2, {"pooled_output": pooled2, "width": base_width, "height": base_height, "crop_w": crop_w, "crop_h": crop_h, "target_width": target_width, "target_height": target_height}]]
|
885 |
+
|
886 |
+
if preset == "preset A":
|
887 |
+
base_positive = res1
|
888 |
+
base_negative = res2
|
889 |
+
elif preset == "preset B":
|
890 |
+
base_positive = res3
|
891 |
+
base_negative = res4
|
892 |
+
elif preset == "preset C":
|
893 |
+
base_positive = res1 + res3
|
894 |
+
base_negative = res2 + res4
|
895 |
+
|
896 |
+
return (base_positive, base_negative, )
|
897 |
+
|
898 |
+
|
899 |
+
|
900 |
+
#---------------------------------------------------------------------------------------------------------------------------------------------------#
|
901 |
+
|
902 |
+
|
903 |
+
class Comfyroll_Halftone_Grid:
|
904 |
+
@classmethod
|
905 |
+
def INPUT_TYPES(s):
|
906 |
+
return {"required": {
|
907 |
+
"width": ("INT", {"default": 512, "min": 64, "max": 2048}),
|
908 |
+
"height": ("INT", {"default": 512, "min": 64, "max": 2048}),
|
909 |
+
"dot_style": (["Accent","afmhot","autumn","binary","Blues","bone","BrBG","brg",
|
910 |
+
"BuGn","BuPu","bwr","cividis","CMRmap","cool","coolwarm","copper","cubehelix","Dark2","flag",
|
911 |
+
"gist_earth","gist_gray","gist_heat","gist_rainbow","gist_stern","gist_yarg","GnBu","gnuplot","gnuplot2","gray","Greens",
|
912 |
+
"Greys","hot","hsv","inferno","jet","magma","nipy_spectral","ocean","Oranges","OrRd",
|
913 |
+
"Paired","Pastel1","Pastel2","pink","PiYG","plasma","PRGn","prism","PuBu","PuBuGn",
|
914 |
+
"PuOr","PuRd","Purples","rainbow","RdBu","RdGy","RdPu","RdYlBu","RdYlGn","Reds","seismic",
|
915 |
+
"Set1","Set2","Set3","Spectral","spring","summer","tab10","tab20","tab20b","tab20c","terrain",
|
916 |
+
"turbo","twilight","twilight_shifted","viridis","winter","Wistia","YlGn","YlGnBu","YlOrBr","YlOrRd"],),
|
917 |
+
"reverse_dot_style": (["No", "Yes"],),
|
918 |
+
"dot_frequency": ("INT", {"default": 50, "min": 1, "max":200, "step": 1}),
|
919 |
+
"background_color": (["custom", "white", "black", "red", "green", "blue", "cyan", "magenta", "yellow", "purple", "orange", "lime", "navy", "teal", "maroon", "lavender", "olive"],),
|
920 |
+
"background_R": ("INT", {"default": 255, "min": 0, "max": 255, "step": 1}),
|
921 |
+
"background_G": ("INT", {"default": 255, "min": 0, "max": 255, "step": 1}),
|
922 |
+
"background_B": ("INT", {"default": 255, "min": 0, "max": 255, "step": 1}),
|
923 |
+
"x_pos": ("FLOAT", {"default": 0.5, "min": 0, "max": 1, "step": .01}),
|
924 |
+
"y_pos": ("FLOAT", {"default": 0.5, "min": 0, "max": 1, "step": .01}),
|
925 |
+
},
|
926 |
+
}
|
927 |
+
|
928 |
+
RETURN_TYPES = ("IMAGE", )
|
929 |
+
FUNCTION = "halftone"
|
930 |
+
|
931 |
+
CATEGORY = "Comfyroll/Image"
|
932 |
+
|
933 |
+
def halftone(self, width, height, dot_style, reverse_dot_style, dot_frequency, background_color, background_R, background_G, background_B, x_pos, y_pos):
|
934 |
+
if background_color == "custom":
|
935 |
+
bgc = (background_R/255, background_G/255, background_B/255)
|
936 |
+
else:
|
937 |
+
bgc = background_color
|
938 |
+
|
939 |
+
reverse = ""
|
940 |
+
|
941 |
+
if reverse_dot_style == "Yes":
|
942 |
+
reverse = "_r"
|
943 |
+
|
944 |
+
#img = Image.new(mode = 'RGB', size = (300, 200), color = (red, green, blue))
|
945 |
+
fig, ax = plt.subplots(figsize=(width/100,height/100))
|
946 |
+
#fig, ax = plt.subplots(figsize=(width/20,height/20))
|
947 |
+
|
948 |
+
|
949 |
+
dotsx = np.linspace(0, 1, dot_frequency)
|
950 |
+
dotsy = np.linspace(0, 1, dot_frequency)
|
951 |
+
|
952 |
+
X, Y = np.meshgrid(dotsx, dotsy)
|
953 |
+
|
954 |
+
dist = np.sqrt((X - x_pos)**2 + (Y - y_pos)**2)
|
955 |
+
|
956 |
+
fig.patch.set_facecolor(bgc)
|
957 |
+
ax.scatter(X, Y, c=dist, cmap=dot_style+reverse)
|
958 |
+
|
959 |
+
plt.axis('off')
|
960 |
+
plt.tight_layout(pad=0, w_pad=0, h_pad=0)
|
961 |
+
plt.autoscale(tight=True)
|
962 |
+
plt.show()
|
963 |
+
|
964 |
+
img_buf = io.BytesIO()
|
965 |
+
plt.savefig(img_buf, format='png')
|
966 |
+
img = Image.open(img_buf)
|
967 |
+
|
968 |
+
return(pil2tensor(img),)
|
969 |
+
|
970 |
+
#---------------------------------------------------------------------------------------------------------------------------------------------------#
|
971 |
+
|
972 |
+
|
973 |
+
|
974 |
+
class Comfyroll_LatentBatchSize:
|
975 |
+
|
976 |
+
def __init__(self):
|
977 |
+
pass
|
978 |
+
|
979 |
+
@classmethod
|
980 |
+
def INPUT_TYPES(s):
|
981 |
+
return {
|
982 |
+
"required": {
|
983 |
+
"latent": ("LATENT", ),
|
984 |
+
"batch_size": ("INT", {
|
985 |
+
"default": 2,
|
986 |
+
"min": 1,
|
987 |
+
"max": 16,
|
988 |
+
"step": 1,
|
989 |
+
}),
|
990 |
+
},
|
991 |
+
}
|
992 |
+
|
993 |
+
RETURN_TYPES = ("LATENT", )
|
994 |
+
|
995 |
+
FUNCTION = "batchsize"
|
996 |
+
|
997 |
+
OUTPUT_NODE = False
|
998 |
+
|
999 |
+
CATEGORY = "Comfyroll/Latent"
|
1000 |
+
|
1001 |
+
def batchsize(self, latent: tg.Sequence[tg.Mapping[tg.Text, torch.Tensor]], batch_size: int):
|
1002 |
+
samples = latent['samples']
|
1003 |
+
shape = samples.shape
|
1004 |
+
|
1005 |
+
sample_list = [samples] + [
|
1006 |
+
torch.clone(samples) for _ in range(batch_size - 1)
|
1007 |
+
]
|
1008 |
+
|
1009 |
+
return ({
|
1010 |
+
'samples': torch.cat(sample_list),
|
1011 |
+
}, )
|
1012 |
+
|
1013 |
+
|
1014 |
+
#---------------------------------------------------------------------------------------------------------------------------------------------------#
|
1015 |
+
|
1016 |
+
class Comfyroll_ApplyLoRA_Stack:
|
1017 |
+
|
1018 |
+
@classmethod
|
1019 |
+
def INPUT_TYPES(cls):
|
1020 |
+
return {"required": {"model": ("MODEL",),
|
1021 |
+
"clip": ("CLIP", ),
|
1022 |
+
"lora_stack": ("LORA_STACK", ),
|
1023 |
+
}
|
1024 |
+
}
|
1025 |
+
|
1026 |
+
RETURN_TYPES = ("MODEL", "CLIP",)
|
1027 |
+
RETURN_NAMES = ("MODEL", "CLIP", )
|
1028 |
+
FUNCTION = "apply_lora_stack"
|
1029 |
+
CATEGORY = "Comfyroll/IO"
|
1030 |
+
|
1031 |
+
def apply_lora_stack(self, model, clip, lora_stack=None,):
|
1032 |
+
|
1033 |
+
# Initialise the list
|
1034 |
+
lora_params = list()
|
1035 |
+
|
1036 |
+
# Extend lora_params with lora-stack items
|
1037 |
+
if lora_stack:
|
1038 |
+
lora_params.extend(lora_stack)
|
1039 |
+
else:
|
1040 |
+
return (model, clip,)
|
1041 |
+
|
1042 |
+
#print(lora_params)
|
1043 |
+
|
1044 |
+
# Initialise the model and clip
|
1045 |
+
model_lora = model
|
1046 |
+
clip_lora = clip
|
1047 |
+
|
1048 |
+
# Loop through the list
|
1049 |
+
for tup in lora_params:
|
1050 |
+
lora_name, strength_model, strength_clip = tup
|
1051 |
+
print(lora_name, strength_model, strength_clip)
|
1052 |
+
|
1053 |
+
lora_path = folder_paths.get_full_path("loras", lora_name)
|
1054 |
+
lora = comfy.utils.load_torch_file(lora_path, safe_load=True)
|
1055 |
+
|
1056 |
+
model_lora, clip_lora = comfy.sd.load_lora_for_models(model_lora, clip_lora, lora, strength_model, strength_clip)
|
1057 |
+
|
1058 |
+
return (model_lora, clip_lora,)
|
1059 |
+
|
1060 |
+
#---------------------------------------------------------------------------------------------------------------------------------------------------#
|
1061 |
+
|
1062 |
+
# Based on Efficiency Nodes
|
1063 |
+
class Comfyroll_LoRA_Stack:
|
1064 |
+
|
1065 |
+
loras = ["None"] + folder_paths.get_filename_list("loras")
|
1066 |
+
|
1067 |
+
@classmethod
|
1068 |
+
def INPUT_TYPES(cls):
|
1069 |
+
return {"required": {
|
1070 |
+
"switch_1": ([
|
1071 |
+
"Off",
|
1072 |
+
"On"],),
|
1073 |
+
"lora_name_1": (cls.loras,),
|
1074 |
+
"model_weight_1": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
|
1075 |
+
"clip_weight_1": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
|
1076 |
+
"switch_2": ([
|
1077 |
+
"Off",
|
1078 |
+
"On"],),
|
1079 |
+
"lora_name_2": (cls.loras,),
|
1080 |
+
"model_weight_2": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
|
1081 |
+
"clip_weight_2": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
|
1082 |
+
"switch_3": ([
|
1083 |
+
"Off",
|
1084 |
+
"On"],),
|
1085 |
+
"lora_name_3": (cls.loras,),
|
1086 |
+
"model_weight_3": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
|
1087 |
+
"clip_weight_3": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
|
1088 |
+
},
|
1089 |
+
"optional": {"lora_stack": ("LORA_STACK",)
|
1090 |
+
},
|
1091 |
+
}
|
1092 |
+
|
1093 |
+
RETURN_TYPES = ("LORA_STACK",)
|
1094 |
+
RETURN_NAMES = ("LORA_STACK",)
|
1095 |
+
FUNCTION = "lora_stacker"
|
1096 |
+
CATEGORY = "Comfyroll/IO"
|
1097 |
+
|
1098 |
+
def lora_stacker(self, lora_name_1, model_weight_1, clip_weight_1, switch_1, lora_name_2, model_weight_2, clip_weight_2, switch_2, lora_name_3, model_weight_3, clip_weight_3, switch_3, lora_stack=None):
|
1099 |
+
|
1100 |
+
# Initialise the list
|
1101 |
+
lora_list=list()
|
1102 |
+
|
1103 |
+
if lora_stack is not None:
|
1104 |
+
lora_list.extend([l for l in lora_stack if l[0] != "None"])
|
1105 |
+
|
1106 |
+
if lora_name_1 != "None" and switch_1 == "On":
|
1107 |
+
lora_list.extend([(lora_name_1, model_weight_1, clip_weight_1)]),
|
1108 |
+
|
1109 |
+
if lora_name_2 != "None" and switch_2 == "On":
|
1110 |
+
lora_list.extend([(lora_name_2, model_weight_2, clip_weight_2)]),
|
1111 |
+
|
1112 |
+
if lora_name_3 != "None" and switch_3 == "On":
|
1113 |
+
lora_list.extend([(lora_name_3, model_weight_3, clip_weight_3)]),
|
1114 |
+
|
1115 |
+
return (lora_list,)
|
1116 |
+
|
1117 |
+
|
1118 |
+
|
1119 |
+
#---------------------------------------------------------------------------------------------------------------------------------------------------#
|
1120 |
+
|
1121 |
+
|
1122 |
+
'''
|
1123 |
+
NODE_CLASS_MAPPINGS = {
|
1124 |
+
"CR Image Input Switch": ComfyRoll_InputImages,
|
1125 |
+
"CR Image Input Switch (4 way)": ComfyRoll_InputImages_4way,
|
1126 |
+
"CR Latent Input Switch": ComfyRoll_InputLatents,
|
1127 |
+
"CR Process Switch": ComfyRoll_InputLatentsText,
|
1128 |
+
"CR Conditioning Input Switch": ComfyRoll_InputConditioning,
|
1129 |
+
"CR Clip Input Switch": ComfyRoll_InputClip,
|
1130 |
+
"CR Model Input Switch": ComfyRoll_InputModel,
|
1131 |
+
"CR ControlNet Input Switch": ComfyRoll_InputControlNet,
|
1132 |
+
"CR Load LoRA": ComfyRoll_LoraLoader,
|
1133 |
+
"CR Apply ControlNet": ComfyRoll_ApplyControlNet,
|
1134 |
+
"CR Image Size": ComfyRoll_ImageSize_Float,
|
1135 |
+
"CR Image Output": ComfyRoll_ImageOutput,
|
1136 |
+
"CR Integer Multiple": CR_Int_Multiple_Of,
|
1137 |
+
"CR Aspect Ratio": ComfyRoll_AspectRatio,
|
1138 |
+
"CR Aspect Ratio SDXL": ComfyRoll_AspectRatio_SDXL,
|
1139 |
+
"CR Seed to Int": ComfyRoll_SeedToInt,
|
1140 |
+
"CR Color Tint": Comfyroll_Color_Tint,
|
1141 |
+
"CR SDXL Prompt Mixer": ComfyRoll_prompt_mixer,
|
1142 |
+
"CR SDXL Style Text": Comfyroll_SDXLStyleText,
|
1143 |
+
"CR SDXL Base Prompt Encoder": Comfyroll_SDXLBasePromptEncoder,
|
1144 |
+
"CR Hires Fix Process Switch": ComfyRoll_HiResFixSwitch,
|
1145 |
+
"CR Halftones" :Comfyroll_Halftone_Grid,
|
1146 |
+
"CR LoRA Stack":Comfyroll_LoRA_Stack,
|
1147 |
+
"CR Apply LoRA Stack":Comfyroll_ApplyLoRA_Stack,
|
1148 |
+
"CR Latent Batch Size":Comfyroll_LatentBatchSize
|
1149 |
+
}
|
1150 |
+
'''
|
1151 |
+
|
1152 |
+
#---------------------------------------------------------------------------------------------------------------------------------------------------#
|
1153 |
+
# Credits #
|
1154 |
+
# WASasquatch https://github.com/WASasquatch/was-node-suite-comfyui #
|
1155 |
+
# hnmr293 https://github.com/hnmr293/ComfyUI-nodes-hnmr #
|
1156 |
+
# SeargeDP https://github.com/SeargeDP/SeargeSDXL #
|
1157 |
+
# LucianoCirino https://github.com/LucianoCirino/efficiency-nodes-comfyui #
|
1158 |
+
# SLAPaper https://github.com/SLAPaper/ComfyUI-Image-Selector #
|
1159 |
+
#---------------------------------------------------------------------------------------------------------------------------------------------------#
|
1160 |
+
|
ComfyUI_Comfyroll_CustomNodes/Comfyroll_Pipe_Nodes.py
ADDED
@@ -0,0 +1,271 @@
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|
|
1 |
+
#---------------------------------------------------------------------------------------------------------------------------------------------------#
|
2 |
+
# Comfyroll Pipe Nodes by Akatsuzi https://github.com/RockOfFire/ComfyUI_Comfyroll_CustomNodes #
|
3 |
+
# for ComfyUI https://github.com/comfyanonymous/ComfyUI #
|
4 |
+
#---------------------------------------------------------------------------------------------------------------------------------------------------#
|
5 |
+
|
6 |
+
import os
|
7 |
+
import sys
|
8 |
+
import json
|
9 |
+
import torch
|
10 |
+
import comfy.sd
|
11 |
+
import comfy.utils
|
12 |
+
import numpy as np
|
13 |
+
|
14 |
+
#---------------------------------------------------------------------------------------------------------------------------------------------------#
|
15 |
+
|
16 |
+
class module_pipe_loader:
|
17 |
+
def __init__(self):
|
18 |
+
pass
|
19 |
+
|
20 |
+
@classmethod
|
21 |
+
def INPUT_TYPES(s):
|
22 |
+
return {
|
23 |
+
"required": {
|
24 |
+
#"model": ("MODEL",),
|
25 |
+
},
|
26 |
+
"optional": {
|
27 |
+
"model": ("MODEL",),
|
28 |
+
"pos": ("CONDITIONING",),
|
29 |
+
"neg": ("CONDITIONING",),
|
30 |
+
"latent": ("LATENT",),
|
31 |
+
"vae": ("VAE",),
|
32 |
+
"clip": ("CLIP",),
|
33 |
+
"controlnet": ("CONTROL_NET",),
|
34 |
+
"image": ("IMAGE",),
|
35 |
+
"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff})
|
36 |
+
},
|
37 |
+
}
|
38 |
+
|
39 |
+
RETURN_TYPES = ("PIPE_LINE", )
|
40 |
+
RETURN_NAMES = ("pipe", )
|
41 |
+
FUNCTION = "flush"
|
42 |
+
|
43 |
+
CATEGORY = "Comfyroll/Module"
|
44 |
+
|
45 |
+
def flush(self, model=0, pos=0, neg=0, latent=0, vae=0, clip=0, controlnet=0, image=0, seed=0):
|
46 |
+
pipe_line = (model, pos, neg, latent, vae, clip, controlnet, image, seed)
|
47 |
+
return (pipe_line, )
|
48 |
+
|
49 |
+
#---------------------------------------------------------------------------------------------------------------------------------------------------#
|
50 |
+
|
51 |
+
class module_input:
|
52 |
+
def __init__(self):
|
53 |
+
pass
|
54 |
+
|
55 |
+
@classmethod
|
56 |
+
def INPUT_TYPES(s):
|
57 |
+
return {
|
58 |
+
"required": {"pipe": ("PIPE_LINE",)},
|
59 |
+
}
|
60 |
+
|
61 |
+
RETURN_TYPES = ("PIPE_LINE", "MODEL", "CONDITIONING", "CONDITIONING", "LATENT", "VAE", "CLIP", "CONTROL_NET", "IMAGE", "INT")
|
62 |
+
RETURN_NAMES = ("pipe", "model", "pos", "neg", "latent", "vae", "clip", "controlnet", "image", "seed")
|
63 |
+
FUNCTION = "flush"
|
64 |
+
|
65 |
+
CATEGORY = "Comfyroll/Module"
|
66 |
+
|
67 |
+
def flush(self, pipe):
|
68 |
+
model, pos, neg, latent, vae, clip, controlnet, image, seed = pipe
|
69 |
+
return pipe, model, pos, neg, latent, vae, clip, controlnet, image, seed
|
70 |
+
|
71 |
+
#---------------------------------------------------------------------------------------------------------------------------------------------------#
|
72 |
+
|
73 |
+
class module_output:
|
74 |
+
def __init__(self):
|
75 |
+
pass
|
76 |
+
|
77 |
+
@classmethod
|
78 |
+
def INPUT_TYPES(s):
|
79 |
+
return {"required": {"pipe": ("PIPE_LINE",)},
|
80 |
+
"optional": {
|
81 |
+
"model": ("MODEL",),
|
82 |
+
"pos": ("CONDITIONING",),
|
83 |
+
"neg": ("CONDITIONING",),
|
84 |
+
"latent": ("LATENT",),
|
85 |
+
"vae": ("VAE",),
|
86 |
+
"clip": ("CLIP",),
|
87 |
+
"controlnet": ("CONTROL_NET",),
|
88 |
+
"image": ("IMAGE",),
|
89 |
+
"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff})
|
90 |
+
},
|
91 |
+
}
|
92 |
+
|
93 |
+
RETURN_TYPES = ("PIPE_LINE", )
|
94 |
+
RETURN_NAMES = ("pipe", )
|
95 |
+
FUNCTION = "flush"
|
96 |
+
|
97 |
+
CATEGORY = "Comfyroll/Module"
|
98 |
+
|
99 |
+
def flush(self, pipe, model=None, pos=None, neg=None, latent=None, vae=None, clip=None, controlnet=None, image=None, seed=None):
|
100 |
+
new_model, new_pos, new_neg, new_latent, new_vae, new_clip, new_controlnet, new_image, new_seed = pipe
|
101 |
+
|
102 |
+
if model is not None:
|
103 |
+
new_model = model
|
104 |
+
|
105 |
+
if pos is not None:
|
106 |
+
new_pos = pos
|
107 |
+
|
108 |
+
if neg is not None:
|
109 |
+
new_neg = neg
|
110 |
+
|
111 |
+
if latent is not None:
|
112 |
+
new_latent = latent
|
113 |
+
|
114 |
+
if vae is not None:
|
115 |
+
new_vae = vae
|
116 |
+
|
117 |
+
if clip is not None:
|
118 |
+
new_clip = clip
|
119 |
+
|
120 |
+
if controlnet is not None:
|
121 |
+
new_controlnet = controlnet
|
122 |
+
|
123 |
+
if image is not None:
|
124 |
+
new_image = image
|
125 |
+
|
126 |
+
if seed is not None:
|
127 |
+
new_seed = seed
|
128 |
+
|
129 |
+
pipe = new_model, new_pos, new_neg, new_latent, new_vae, new_clip, new_controlnet, new_image, new_seed
|
130 |
+
return (pipe, )
|
131 |
+
|
132 |
+
#---------------------------------------------------------------------------------------------------------------------------------------------------#
|
133 |
+
|
134 |
+
class image_pipe_in:
|
135 |
+
def __init__(self):
|
136 |
+
pass
|
137 |
+
|
138 |
+
@classmethod
|
139 |
+
def INPUT_TYPES(s):
|
140 |
+
return {
|
141 |
+
"required": {
|
142 |
+
#"model": ("MODEL",),
|
143 |
+
},
|
144 |
+
"optional": {
|
145 |
+
"image": ("IMAGE",),
|
146 |
+
"width": ("INT", {"default": 512, "min": 64, "max": 2048}),
|
147 |
+
"height": ("INT", {"default": 512, "min": 64, "max": 2048}),
|
148 |
+
"upscale_factor": ("FLOAT", {"default": 1, "min": 1, "max": 2000})
|
149 |
+
},
|
150 |
+
}
|
151 |
+
|
152 |
+
RETURN_TYPES = ("PIPE_LINE", )
|
153 |
+
RETURN_NAMES = ("pipe", )
|
154 |
+
FUNCTION = "flush"
|
155 |
+
|
156 |
+
CATEGORY = "Comfyroll/Module"
|
157 |
+
|
158 |
+
def flush(self, image=0, width=0, height=0, upscale_factor=0):
|
159 |
+
pipe_line = (image, width, height, upscale_factor)
|
160 |
+
return (pipe_line, )
|
161 |
+
|
162 |
+
#---------------------------------------------------------------------------------------------------------------------------------------------------#
|
163 |
+
|
164 |
+
class image_pipe_edit:
|
165 |
+
def __init__(self):
|
166 |
+
pass
|
167 |
+
|
168 |
+
@classmethod
|
169 |
+
def INPUT_TYPES(s):
|
170 |
+
return {"required": {"pipe": ("PIPE_LINE",)},
|
171 |
+
"optional": {
|
172 |
+
"image": ("IMAGE",),
|
173 |
+
"width": ("INT", {"default": 512, "min": 64, "max": 2048}),
|
174 |
+
"height": ("INT", {"default": 512, "min": 64, "max": 2048}),
|
175 |
+
"upscale_factor": ("FLOAT", {"default": 1, "min": 1, "max": 2000})
|
176 |
+
},
|
177 |
+
}
|
178 |
+
|
179 |
+
RETURN_TYPES = ("PIPE_LINE", )
|
180 |
+
RETURN_NAMES = ("pipe", )
|
181 |
+
FUNCTION = "flush"
|
182 |
+
|
183 |
+
CATEGORY = "Comfyroll/Module"
|
184 |
+
|
185 |
+
def flush(self, pipe, image=None, width=None, height=None, upscale_factor=None):
|
186 |
+
new_image, new_width, new_height, new_upscale_factor = pipe
|
187 |
+
|
188 |
+
if image is not None:
|
189 |
+
new_image = image
|
190 |
+
|
191 |
+
if width is not None:
|
192 |
+
new_width = width
|
193 |
+
|
194 |
+
if height is not None:
|
195 |
+
new_height = height
|
196 |
+
|
197 |
+
if upscale_factor is not None:
|
198 |
+
new_upscale_factor = upscale_factor
|
199 |
+
|
200 |
+
pipe = new_image, new_width, new_height, new_upscale_factor
|
201 |
+
return (pipe, )
|
202 |
+
|
203 |
+
#---------------------------------------------------------------------------------------------------------------------------------------------------#
|
204 |
+
|
205 |
+
class image_pipe_out:
|
206 |
+
def __init__(self):
|
207 |
+
pass
|
208 |
+
|
209 |
+
@classmethod
|
210 |
+
def INPUT_TYPES(s):
|
211 |
+
return {
|
212 |
+
"required": {"pipe": ("PIPE_LINE",)},
|
213 |
+
}
|
214 |
+
|
215 |
+
RETURN_TYPES = ("PIPE_LINE", "IMAGE", "INT", "INT", "FLOAT",)
|
216 |
+
RETURN_NAMES = ("pipe", "image", "width", "height", "upscale_factor")
|
217 |
+
FUNCTION = "flush"
|
218 |
+
|
219 |
+
CATEGORY = "Comfyroll/Module"
|
220 |
+
|
221 |
+
def flush(self, pipe):
|
222 |
+
#if switch == "Off":
|
223 |
+
#return (pipe, )
|
224 |
+
#else:
|
225 |
+
image, width, height, upscale_factor = pipe
|
226 |
+
return pipe, image, width, height, upscale_factor
|
227 |
+
|
228 |
+
#---------------------------------------------------------------------------------------------------------------------------------------------------#
|
229 |
+
|
230 |
+
class input_switch_pipe:
|
231 |
+
def __init__(self):
|
232 |
+
pass
|
233 |
+
|
234 |
+
@classmethod
|
235 |
+
def INPUT_TYPES(cls):
|
236 |
+
return {
|
237 |
+
"required": {
|
238 |
+
"Input": ("INT", {"default": 1, "min": 1, "max": 2}),
|
239 |
+
"pipe1": ("PIPE_LINE",),
|
240 |
+
"pipe2": ("PIPE_LINE",)
|
241 |
+
}
|
242 |
+
}
|
243 |
+
|
244 |
+
RETURN_TYPES = ("PIPE_LINE",)
|
245 |
+
OUTPUT_NODE = True
|
246 |
+
FUNCTION = "InputSwitchPipe"
|
247 |
+
|
248 |
+
CATEGORY = "Comfyroll/Module"
|
249 |
+
|
250 |
+
def InputSwitchPipe(self, Input, pipe1, pipe2):
|
251 |
+
if Input == 1:
|
252 |
+
return (pipe1, )
|
253 |
+
else:
|
254 |
+
return (pipe2, )
|
255 |
+
|
256 |
+
#---------------------------------------------------------------------------------------------------------------------------------------------------#
|
257 |
+
'''
|
258 |
+
NODE_CLASS_MAPPINGS_2 = {
|
259 |
+
"CR Module Pipe Loader": module_pipe_loader,
|
260 |
+
"CR Module Input": module_input,
|
261 |
+
"CR Module Output": module_output,
|
262 |
+
"CR Image Pipe In": image_pipe_in,
|
263 |
+
"CR Image Pipe Edit": image_pipe_edit,
|
264 |
+
"CR Image Pipe Out": image_pipe_out,
|
265 |
+
"CR Pipe Switch": input_switch_pipe,
|
266 |
+
}
|
267 |
+
'''
|
268 |
+
#---------------------------------------------------------------------------------------------------------------------------------------------------#
|
269 |
+
# Credits
|
270 |
+
# TinyTerra https://github.com/TinyTerra/ComfyUI_tinyterraNodes #
|
271 |
+
#---------------------------------------------------------------------------------------------------------------------------------------------------#
|
ComfyUI_Comfyroll_CustomNodes/README.md
ADDED
@@ -0,0 +1,89 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
# Comfyroll Custom Nodes
|
2 |
+
|
3 |
+
These nodes were originally made for use in the Comfyroll Template Workflows.
|
4 |
+
|
5 |
+
[ComfyUI Template Workflows](https://civitai.com/models/59806/comfyroll-template-workflows)
|
6 |
+
|
7 |
+
[Comfyroll Pro Templates](https://civitai.com/models/85619/comfyroll-pro-template)
|
8 |
+
|
9 |
+
The nodes can be used in any ComfyUI workflow.
|
10 |
+
|
11 |
+
# Installation
|
12 |
+
|
13 |
+
If you have a previous version of the Comfyroll nodes from the Comfyroll Worflow Templates download, please delete this before installing these nodes.
|
14 |
+
|
15 |
+
1. cd custom_nodes
|
16 |
+
2. git clone https://github.com/RockOfFire/ComfyUI_Comfyroll_CustomNodes.git
|
17 |
+
3. Restart ComfyUI
|
18 |
+
|
19 |
+
You can also install the nodes using the following methods:
|
20 |
+
* install using [ComfyUI Manager](https://github.com/ltdrdata/ComfyUI-Manager)
|
21 |
+
* download from [CivitAI](https://civitai.com/models/87609/comfyroll-custom-nodes-for-comfyui)
|
22 |
+
|
23 |
+
# List of Custom Nodes
|
24 |
+
|
25 |
+
__Logic__
|
26 |
+
* CR Image Input Switch
|
27 |
+
* CR Image Input Switch (4 way)
|
28 |
+
* CR Latent Input Switch
|
29 |
+
* CR Conditioning Input Switch
|
30 |
+
* CR Clip Input Switch
|
31 |
+
* CR Model Input Switch
|
32 |
+
* CR ControlNet Input Switch
|
33 |
+
|
34 |
+
__Process__
|
35 |
+
* CR Img2Img Process Switch
|
36 |
+
* CR Hires Fix Process Switch
|
37 |
+
|
38 |
+
__IO__
|
39 |
+
* CR Load LoRA
|
40 |
+
|
41 |
+
__Maths__
|
42 |
+
* CR Integer Multiple
|
43 |
+
|
44 |
+
__Number__
|
45 |
+
* CR Seed to Int
|
46 |
+
|
47 |
+
__Image__
|
48 |
+
* CR Image Size
|
49 |
+
* CR Aspect Ratio
|
50 |
+
* CR Color Tint
|
51 |
+
|
52 |
+
__Conditioning__
|
53 |
+
* CR Apply ControlNet
|
54 |
+
|
55 |
+
__SDXL__
|
56 |
+
* CR Aspect Ratio SDXL
|
57 |
+
* CR SDXL Prompt Mixer
|
58 |
+
* CR SDXL Style Text
|
59 |
+
* CR SDXL Base Prompt Encoder
|
60 |
+
|
61 |
+
__Module__
|
62 |
+
* CR Module Pipe Loader
|
63 |
+
* CR Module Input
|
64 |
+
* CR Module Output
|
65 |
+
* CR Image Pipe In
|
66 |
+
* CR Image Pipe Edit
|
67 |
+
* CR Image Pipe Out
|
68 |
+
* CR Pipe Switch
|
69 |
+
|
70 |
+
|
71 |
+
![Custom Nodes](/images/custom_nodes_image1.png)
|
72 |
+
|
73 |
+
![Custom Nodes](/images/custom_nodes_image2.jpg)
|
74 |
+
|
75 |
+
![Custom Nodes](/images/custom_nodes_image3.JPG)
|
76 |
+
|
77 |
+
![Custom Nodes](/images/custom_nodes_image4.JPG)
|
78 |
+
|
79 |
+
# Credits
|
80 |
+
|
81 |
+
comfyanonymous/[ComfyUI](https://github.com/comfyanonymous/ComfyUI) - A powerful and modular stable diffusion GUI.
|
82 |
+
|
83 |
+
WASasquatch/[was-node-suite-comfyui](https://github.com/WASasquatch/was-node-suite-comfyui) - A powerful custom node extensions of ComfyUI.
|
84 |
+
|
85 |
+
TinyTerra/[ComfyUI_tinyterraNodes](https://github.com/TinyTerra/ComfyUI_tinyterraNodes) - A selection of nodes for Stable Diffusion ComfyUI
|
86 |
+
|
87 |
+
hnmr293/[ComfyUI-nodes-hnmr](https://github.com/hnmr293/ComfyUI-nodes-hnmr) - ComfyUI custom nodes - merge, grid (aka xyz-plot) and others
|
88 |
+
|
89 |
+
SeargeDP/[SeargeSDXL](https://github.com/SeargeDP) - ComfyUI custom nodes - Prompt nodes and Conditioning nodes
|
ComfyUI_Comfyroll_CustomNodes/__init__.py
ADDED
@@ -0,0 +1,48 @@
|
|
|
|
|
|
|
|
|
|
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from .Comfyroll_Nodes import *
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from .Comfyroll_Pipe_Nodes import *
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#from .Comfyroll_Test_Nodes import *
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NODE_CLASS_MAPPINGS = {
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"CR Module Pipe Loader": module_pipe_loader,
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"CR Module Input": module_input,
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"CR Module Output": module_output,
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"CR Image Pipe In": image_pipe_in,
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"CR Image Pipe Edit": image_pipe_edit,
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"CR Image Pipe Out": image_pipe_out,
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"CR Pipe Switch": input_switch_pipe,
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"CR Image Input Switch": ComfyRoll_InputImages,
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"CR Image Input Switch (4 way)": ComfyRoll_InputImages_4way,
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"CR Latent Input Switch": ComfyRoll_InputLatents,
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"CR Conditioning Input Switch": ComfyRoll_InputConditioning,
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"CR Clip Input Switch": ComfyRoll_InputClip,
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"CR Model Input Switch": ComfyRoll_InputModel,
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"CR ControlNet Input Switch": ComfyRoll_InputControlNet,
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"CR Load LoRA": ComfyRoll_LoraLoader,
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"CR Apply ControlNet": ComfyRoll_ApplyControlNet,
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"CR Image Size": ComfyRoll_ImageSize_Float,
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"CR Image Output": ComfyRoll_ImageOutput,
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"CR Integer Multiple": CR_Int_Multiple_Of,
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"CR Aspect Ratio": ComfyRoll_AspectRatio,
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"CR Aspect Ratio SDXL": ComfyRoll_AspectRatio_SDXL,
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"CR Seed to Int": ComfyRoll_SeedToInt,
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"CR Color Tint": Comfyroll_Color_Tint,
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"CR SDXL Prompt Mixer": ComfyRoll_prompt_mixer,
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"CR SDXL Style Text": Comfyroll_SDXLStyleText,
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"CR SDXL Base Prompt Encoder": Comfyroll_SDXLBasePromptEncoder,
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"CR Img2Img Process Switch": ComfyRoll_InputLatentsText,
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"CR Hires Fix Process Switch": ComfyRoll_HiResFixSwitch,
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"CR Halftone Grid" : Comfyroll_Halftone_Grid,
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"CR Latent Batch Size": Comfyroll_LatentBatchSize,
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"CR LoRA Stack":Comfyroll_LoRA_Stack,
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"CR Apply LoRA Stack":Comfyroll_ApplyLoRA_Stack,
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### test nodes
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#"CR Latent Upscale (Iterative)":Comfyroll_LatentUpscaleIterative,
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#"CR KSampler (Iterative)":Comfyroll_Iterative_KSampler,
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#"CR Load Image Sequence":Comfyroll_LoadImageSequence,
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#"CR Switch": Comfyroll_Comfyroll_Switch_Test,
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#"CR Halftone Image":Comfyroll_ConvertImageToHalftone,
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}
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__all__ = ['NODE_CLASS_MAPPINGS']
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print("\033[34mComfyroll Custom Nodes: \033[92mLoaded\033[0m")
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ComfyUI_Comfyroll_CustomNodes/images/custom_nodes_image1.png
ADDED
![]() |
Git LFS Details
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ComfyUI_Comfyroll_CustomNodes/images/custom_nodes_image2.jpg
ADDED
![]() |
Git LFS Details
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ComfyUI_Comfyroll_CustomNodes/images/custom_nodes_image3.JPG
ADDED
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Git LFS Details
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ComfyUI_Comfyroll_CustomNodes/images/custom_nodes_image4.JPG
ADDED
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Git LFS Details
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