nodes/kaibioinfo-ComfyUI_AdvancedRefluxControl
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update nodes

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kaidu
2024-11-22 23:39:26 +01:00
parent be421dba58
commit 97f41ef9ae
1 changed files with 17 additions and 19 deletions
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nodes.py
+17 -19
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@@ -32,10 +32,8 @@ def automerge(tensor, threshold):
newTensor.append(torch.stack(tokens))
return torch.stack(newTensor)
DownScalingFactors = {"1:1": 1, "1:3":3, "1:9": 9}
STRENGTHS = ["very high", "medium", "low", "very low", "lowest"]
STRENGTHS_VALUES = [1,3,3,3,3]
WEIGHTING_VALUES = [1,1,0.75, 0.35,0.2]
STRENGTHS = ["very high", "medium", "low", "very low"]
STRENGTHS_VALUES = [1,3,4,5]
class StyleModelApplySimple:
@classmethod
@@ -51,15 +49,15 @@ class StyleModelApplySimple:
CATEGORY = "conditioning/style_model"
def apply_stylemodel(self, clip_vision_output, style_model, conditioning, image_strength):
cond = style_model.get_cond(clip_vision_output).flatten(start_dim=0, end_dim=1).unsqueeze(dim=0)
stren = STRENGTHS.index(image_strength)
factor = STRENGTHS_VALUES[stren]
weighting = WEIGHTING_VALUES[stren]
if factor>1:
downsampling_factor = STRENGTHS_VALUES[stren]
mode="area"
cond = style_model.get_cond(clip_vision_output).flatten(start_dim=0, end_dim=1).unsqueeze(dim=0)
if downsampling_factor>1:
(b,t,h)=cond.shape
m = int(np.sqrt(t))
cond=torch.nn.PixelUnshuffle(factor)(cond.view(b, m, m, h).transpose(-1, 1)).transpose(1,-1)
cond=cond.view(b,m//factor, m//factor, h, factor*factor).mean(-1).view(b,-1, h) * weighting
cond=torch.nn.functional.interpolate(cond.view(b, m, m, h).transpose(1,-1), size=(m//downsampling_factor, m//downsampling_factor), mode=mode)#
cond=cond.transpose(1,-1).reshape(b,-1,h)
c = []
for t in conditioning:
n = [torch.cat((t[0], cond), dim=1), t[1].copy()]
@@ -101,23 +99,23 @@ class StyleModelApplyAdvanced:
return {"required": {"conditioning": ("CONDITIONING", ),
"style_model": ("STYLE_MODEL", ),
"clip_vision_output": ("CLIP_VISION_OUTPUT", ),
"downscale": (["1:1", "1:3", "1:9"], {"default": "1:3"}),
"downsampling_factor": ("INT", {"default": 3, "min": 1, "max": 9}),
"mode": (["nearest", "bicubic", "bilinear","area", "nearest-exact" ], {"default": "area"}),
"merge_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
"clipWeight": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01})
}}
RETURN_TYPES = ("CONDITIONING",)
FUNCTION = "apply_stylemodel"
CATEGORY = "conditioning/style_model"
CATEGORY = "_for_testing/conditioning/style_model"
def apply_stylemodel(self, clip_vision_output, style_model, conditioning, downscale, merge_strength, clipWeight):
def apply_stylemodel(self, clip_vision_output, style_model, conditioning, downsampling_factor, mode, merge_strength, clipWeight):
cond = style_model.get_cond(clip_vision_output).flatten(start_dim=0, end_dim=1).unsqueeze(dim=0)
if downscale!="1:1":
if downsampling_factor>1:
(b,t,h)=cond.shape
m = int(np.sqrt(t))
factor = DownScalingFactors[downscale]
cond=torch.nn.PixelUnshuffle(factor)(cond.view(b, m, m, h).transpose(-1, 1)).transpose(1,-1)
cond=cond.view(b,m//factor, m//factor, h, factor*factor).mean(-1).view(b,-1, h)
cond=torch.nn.functional.interpolate(cond.view(b, m, m, h).transpose(1,-1), size=(m//downsampling_factor, m//downsampling_factor), mode=mode)#
cond=cond.transpose(1,-1).reshape(b,-1,h)
cond = automerge(cond, merge_strength)*clipWeight
c = []
@@ -139,7 +137,7 @@ NODE_CLASS_MAPPINGS = {
# A dictionary that contains the friendly/humanly readable titles for the nodes
NODE_DISPLAY_NAME_MAPPINGS = {
"StyleModelApplySimple": "Apply Style model (simple)",
"StyleModelApplySimple": "Apply style model (simple)",
"StyleModelApplyAdvanced": "Apply Style model (advanced)",
"StyleModelApplyInterpolation": "Apply style model (interpolation)"
"StyleModelApplyInterpolation": "Apply style model (advanced)"
}