jinx/ComfyUI
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Add ConditioningSetArea node.

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to apply conditioning/prompts only to a specific area of the image.

Add ConditioningCombine node.
so that multiple conditioning/prompts can be applied to the image at the
same time
This commit is contained in:
comfyanonymous
2023-01-26 12:06:48 -05:00
parent 52472cc88d
commit c4b02059d0
2 changed files with 146 additions and 13 deletions
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62
nodes.py
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@@ -4,6 +4,7 @@ import os
import sys
import json
import hashlib
import copy
from PIL import Image
from PIL.PngImagePlugin import PngInfo
@@ -33,7 +34,39 @@ class CLIPTextEncode:
FUNCTION = "encode"
def encode(self, clip, text):
return (clip.encode(text), )
return ([[clip.encode(text), {}]], )
class ConditioningCombine:
@classmethod
def INPUT_TYPES(s):
return {"required": {"conditioning_1": ("CONDITIONING", ), "conditioning_2": ("CONDITIONING", )}}
RETURN_TYPES = ("CONDITIONING",)
FUNCTION = "combine"
def combine(self, conditioning_1, conditioning_2):
return (conditioning_1 + conditioning_2, )
class ConditioningSetArea:
@classmethod
def INPUT_TYPES(s):
return {"required": {"conditioning": ("CONDITIONING", ),
"width": ("INT", {"default": 64, "min": 64, "max": 4096, "step": 64}),
"height": ("INT", {"default": 64, "min": 64, "max": 4096, "step": 64}),
"x": ("INT", {"default": 0, "min": 0, "max": 4096, "step": 64}),
"y": ("INT", {"default": 0, "min": 0, "max": 4096, "step": 64}),
"strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
}}
RETURN_TYPES = ("CONDITIONING",)
FUNCTION = "append"
def append(self, conditioning, width, height, x, y, strength, min_sigma=0.0, max_sigma=99.0):
c = copy.deepcopy(conditioning)
for t in c:
t[1]['area'] = (height // 8, width // 8, y // 8, x // 8)
t[1]['strength'] = strength
t[1]['min_sigma'] = min_sigma
t[1]['max_sigma'] = max_sigma
return (c, )
class VAEDecode:
def __init__(self, device="cpu"):
@@ -172,14 +205,21 @@ class KSampler:
noise = noise.to(self.device)
latent_image = latent_image.to(self.device)
if positive.shape[0] < noise.shape[0]:
positive = torch.cat([positive] * noise.shape[0])
positive_copy = []
negative_copy = []
if negative.shape[0] < noise.shape[0]:
negative = torch.cat([negative] * noise.shape[0])
positive = positive.to(self.device)
negative = negative.to(self.device)
for p in positive:
t = p[0]
if t.shape[0] < noise.shape[0]:
t = torch.cat([t] * noise.shape[0])
t = t.to(self.device)
positive_copy += [[t] + p[1:]]
for n in negative:
t = n[0]
if t.shape[0] < noise.shape[0]:
t = torch.cat([t] * noise.shape[0])
t = t.to(self.device)
negative_copy += [[t] + n[1:]]
if sampler_name in comfy.samplers.KSampler.SAMPLERS:
sampler = comfy.samplers.KSampler(model, steps=steps, device=self.device, sampler=sampler_name, scheduler=scheduler, denoise=denoise)
@@ -187,7 +227,7 @@ class KSampler:
#other samplers
pass
samples = sampler.sample(noise, positive, negative, cfg=cfg, latent_image=latent_image)
samples = sampler.sample(noise, positive_copy, negative_copy, cfg=cfg, latent_image=latent_image)
samples = samples.cpu()
model = model.cpu()
return (samples, )
@@ -272,7 +312,9 @@ NODE_CLASS_MAPPINGS = {
"EmptyLatentImage": EmptyLatentImage,
"LatentUpscale": LatentUpscale,
"SaveImage": SaveImage,
"LoadImage": LoadImage
"LoadImage": LoadImage,
"ConditioningCombine": ConditioningCombine,
"ConditioningSetArea": ConditioningSetArea,
}