fix KSamplerVariationsWithNoise after recent comfy update
This commit is contained in:
matt3o
+52
-52
@@ -96,7 +96,7 @@ class ImageResize:
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width = ow
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if height == 0:
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height = oh
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if multiple_of > 1:
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width = width - (width % multiple_of)
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height = height - (height % multiple_of)
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@@ -108,7 +108,7 @@ class ImageResize:
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outputs = comfy.utils.lanczos(outputs, width, height)
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else:
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outputs = F.interpolate(outputs, size=(height, width), mode=interpolation)
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outputs = pb(outputs)
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return(outputs, outputs.shape[2], outputs.shape[1],)
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@@ -122,7 +122,7 @@ class ImageFlip:
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"axis": (["x", "y", "xy"],),
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}
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}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "execute"
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CATEGORY = "essentials"
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@@ -150,7 +150,7 @@ class ImageCrop:
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"y_offset": ("INT", { "default": 0, "min": -99999, "step": 1, }),
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}
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}
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RETURN_TYPES = ("IMAGE","INT","INT",)
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RETURN_NAMES = ("IMAGE","x","y",)
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FUNCTION = "execute"
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@@ -161,7 +161,7 @@ class ImageCrop:
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width = min(ow, width)
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height = min(oh, height)
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if "center" in position:
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x = round((ow-width) / 2)
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y = round((oh-height) / 2)
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@@ -173,10 +173,10 @@ class ImageCrop:
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x = 0
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if "right" in position:
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x = ow-width
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x += x_offset
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y += y_offset
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x2 = x+width
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y2 = y+height
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@@ -202,7 +202,7 @@ class ImageDesaturate:
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"factor": ("FLOAT", { "default": 1.00, "min": 0.00, "max": 1.00, "step": 0.05, }),
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}
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}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "execute"
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CATEGORY = "essentials"
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@@ -221,7 +221,7 @@ class ImagePosterize:
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"threshold": ("FLOAT", { "default": 0.50, "min": 0.00, "max": 1.00, "step": 0.05, }),
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}
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}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "execute"
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CATEGORY = "essentials"
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@@ -244,7 +244,7 @@ class ImageEnhanceDifference:
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"exponent": ("FLOAT", { "default": 0.75, "min": 0.00, "max": 1.00, "step": 0.05, }),
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}
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}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "execute"
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CATEGORY = "essentials"
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@@ -271,7 +271,7 @@ class ImageExpandBatch:
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"method": (["expand", "repeat all", "repeat first", "repeat last"],)
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}
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}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "execute"
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CATEGORY = "essentials"
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@@ -318,7 +318,7 @@ class ImageListToBatch:
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"image": ("IMAGE",),
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}
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}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "execute"
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INPUT_IS_LIST = True
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@@ -340,7 +340,7 @@ class ImageListToBatch:
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#image[i] = pb(transforms(img))
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out = torch.cat(out, dim=0)
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return (out,)
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class ExtractKeyframes:
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@@ -385,7 +385,7 @@ class MaskFlip:
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"axis": (["x", "y", "xy"],),
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}
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}
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RETURN_TYPES = ("MASK",)
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FUNCTION = "execute"
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CATEGORY = "essentials"
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@@ -409,7 +409,7 @@ class MaskBlur:
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"amount": ("FLOAT", { "default": 6.0, "min": 0, "step": 0.5, }),
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}
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}
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RETURN_TYPES = ("MASK",)
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FUNCTION = "execute"
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CATEGORY = "essentials"
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@@ -418,7 +418,7 @@ class MaskBlur:
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size = int(6 * amount +1)
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if size % 2 == 0:
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size+= 1
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blurred = mask.unsqueeze(1)
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blurred = T.GaussianBlur(size, amount)(blurred)
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blurred = blurred.squeeze(1)
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@@ -431,14 +431,14 @@ class MaskPreview(SaveImage):
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self.type = "temp"
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self.prefix_append = "_temp_" + ''.join(random.choice("abcdefghijklmnopqrstupvxyz") for x in range(5))
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self.compress_level = 4
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {"mask": ("MASK",), },
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"hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},
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}
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FUNCTION = "execute"
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CATEGORY = "essentials"
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@@ -455,7 +455,7 @@ class MaskBatch:
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"mask2": ("MASK",),
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}
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}
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RETURN_TYPES = ("MASK",)
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FUNCTION = "execute"
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CATEGORY = "essentials"
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@@ -463,7 +463,7 @@ class MaskBatch:
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def execute(self, mask1, mask2):
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if mask1.shape[1:] != mask2.shape[1:]:
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mask2 = F.interpolate(mask2.unsqueeze(1), size=(mask1.shape[1], mask1.shape[2]), mode="bicubic").squeeze(1)
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out = torch.cat((mask1, mask2), dim=0)
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return (out,)
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@@ -477,7 +477,7 @@ class MaskExpandBatch:
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"method": (["expand", "repeat all", "repeat first", "repeat last"],)
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}
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}
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RETURN_TYPES = ("MASK",)
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FUNCTION = "execute"
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CATEGORY = "essentials"
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@@ -532,7 +532,7 @@ class MaskFromColor:
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"threshold": ("INT", { "default": 0, "min": 0, "max": 127, "step": 1, }),
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}
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}
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RETURN_TYPES = ("MASK",)
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FUNCTION = "execute"
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CATEGORY = "essentials"
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@@ -560,7 +560,7 @@ class MaskFromBatch:
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"length": ("INT", { "default": -1, "min": -1, "step": 1, }),
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}
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}
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RETURN_TYPES = ("MASK",)
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FUNCTION = "execute"
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CATEGORY = "essentials"
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@@ -582,7 +582,7 @@ class ImageFromBatch:
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"length": ("INT", { "default": -1, "min": -1, "step": 1, }),
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}
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}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "execute"
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CATEGORY = "essentials"
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@@ -604,7 +604,7 @@ class ImageCompositeFromMaskBatch:
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"mask": ("MASK", )
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}
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}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "execute"
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CATEGORY = "essentials"
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@@ -616,12 +616,12 @@ class ImageCompositeFromMaskBatch:
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image_to = p(image_to)
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image_to = comfy.utils.common_upscale(image_to, image_from.shape[2], image_from.shape[1], upscale_method='bicubic', crop='center')
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image_to = pb(image_to)
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if frames < image_from.shape[0]:
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image_from = image_from[:frames]
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elif frames > image_from.shape[0]:
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image_from = torch.cat((image_from, image_from[-1].unsqueeze(0).repeat(frames-image_from.shape[0], 1, 1, 1)), dim=0)
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mask = mask.unsqueeze(3).repeat(1, 1, 1, 3)
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if image_from.shape[1] != mask.shape[1] or image_from.shape[2] != mask.shape[2]:
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@@ -647,7 +647,7 @@ class TransitionMask:
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"timing_function": (["linear", "in", "out", "in-out"],)
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}
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}
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RETURN_TYPES = ("MASK",)
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FUNCTION = "execute"
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CATEGORY = "essentials"
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@@ -727,12 +727,12 @@ class TransitionMask:
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frame[:,:] = progress
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out.append(frame)
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if end_frame < frames:
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out = out + [torch.full((height, width), 1.0, dtype=torch.float32, device="cpu")] * (frames - end_frame)
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out = torch.stack(out, dim=0)
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return (out, )
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def min_(tensor_list):
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@@ -740,7 +740,7 @@ def min_(tensor_list):
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x = torch.stack(tensor_list)
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mn = x.min(axis=0)[0]
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return torch.clamp(mn, min=0)
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def max_(tensor_list):
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# return the element-wise max of the tensor list.
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x = torch.stack(tensor_list)
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@@ -774,22 +774,22 @@ class ImageCAS:
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g = img[..., 2:, :-2]
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h = img[..., 2:, 1:-1]
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i = img[..., 2:, 2:]
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# Computing contrast
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cross = (b, d, e, f, h)
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mn = min_(cross)
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mx = max_(cross)
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diag = (a, c, g, i)
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mn2 = min_(diag)
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mx2 = max_(diag)
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mx = mx + mx2
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mn = mn + mn2
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# Computing local weight
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inv_mx = torch.reciprocal(mx + EPSILON)
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amp = inv_mx * torch.minimum(mn, (2 - mx))
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# scaling
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amp = torch.sqrt(amp)
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w = - amp * (amount * (1/5 - 1/8) + 1/8)
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@@ -799,7 +799,7 @@ class ImageCAS:
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output = output.clamp(0, 1)
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#output = torch.nan_to_num(output) # this seems the only way to ensure there are no NaNs
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output = pb(output)
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output = pb(output)
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return (output,)
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@@ -867,7 +867,7 @@ class SimpleMath:
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return (round(result), result, )
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class ModelCompile():
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class ModelCompile():
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@classmethod
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def INPUT_TYPES(s):
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return {
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@@ -878,7 +878,7 @@ class ModelCompile():
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"mode": (["default", "reduce-overhead", "max-autotune", "max-autotune-no-cudagraphs"],),
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},
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}
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RETURN_TYPES = ("MODEL", )
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FUNCTION = "execute"
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CATEGORY = "essentials"
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@@ -944,7 +944,7 @@ class DebugTensorShape:
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shapes.append(list(tensor.shape))
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tensorShape(tensor)
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print(f"\033[96mShapes found: {shapes}\033[0m")
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return (None,)
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@@ -972,7 +972,7 @@ class BatchCount:
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count = len(batch)
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return (count, )
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class ImageSeamCarving:
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@classmethod
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def INPUT_TYPES(cls):
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@@ -1018,14 +1018,14 @@ class ImageSeamCarving:
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for i in range(img.shape[0]):
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resized = seam_carving(
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T.ToPILImage()(img[i]),
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size=(width, height),
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size=(width, height),
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energy_mode=energy,
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order=order,
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keep_mask=T.ToPILImage()(keep_mask[i]) if keep_mask is not None else None,
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drop_mask=T.ToPILImage()(drop_mask[i]) if drop_mask is not None else None,
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)
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out.append(T.ToTensor()(resized))
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out = torch.stack(out)
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out = pb(out)
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@@ -1159,7 +1159,7 @@ def expand_mask(mask, expand, tapered_corners):
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return torch.stack(out, dim=0)
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class KSamplerVariationsWithNoise:
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class KSamplerVariationsWithNoise:
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@classmethod
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def INPUT_TYPES(s):
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return {"required": {
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@@ -1209,12 +1209,12 @@ class KSamplerVariationsWithNoise:
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# Calculate sigma
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comfy.model_management.load_model_gpu(model)
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real_model = model.model
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sampler = comfy.samplers.KSampler(real_model, steps=steps, device=device, sampler=sampler_name, scheduler=scheduler, denoise=1.0, model_options=model.model_options)
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model_patcher = comfy.model_patcher.ModelPatcher(model.model, load_device=device, offload_device=comfy.model_management.unet_offload_device())
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sampler = comfy.samplers.KSampler(model_patcher, steps=steps, device=device, sampler=sampler_name, scheduler=scheduler, denoise=1.0, model_options=model.model_options)
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sigmas = sampler.sigmas
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sigma = sigmas[start_at_step] - sigmas[end_at_step]
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sigma /= model.model.latent_format.scale_factor
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sigma = sigma.cpu().numpy()
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sigma = sigma.detach().cpu().item()
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work_latent = latent_image.copy()
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work_latent["samples"] = latent_image["samples"].clone() + slerp_noise * sigma
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@@ -1230,7 +1230,7 @@ class KSamplerVariationsWithNoise:
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class SDXLEmptyLatentSizePicker:
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def __init__(self):
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self.device = comfy.model_management.intermediate_device()
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@classmethod
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def INPUT_TYPES(s):
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return {"required": {
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@@ -1273,7 +1273,7 @@ class ImageApplyLUT:
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# TODO: check if we can do without numpy
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def execute(self, image, lut_file, log_colorspace, clip_values, strength):
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from colour.io.luts.iridas_cube import read_LUT_IridasCube
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lut = read_LUT_IridasCube(os.path.join(LUTS_DIR, lut_file))
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lut.name = lut_file
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@@ -1309,7 +1309,7 @@ class ImageApplyLUT:
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if strength < 1.0:
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lut_img = strength * lut_img + (1 - strength) * img
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out.append(lut_img)
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out = torch.stack(out)
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return (out, )
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@@ -1340,7 +1340,7 @@ class DrawText:
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def execute(self, text, font, size, color, background_color, shadow_distance, shadow_blur, shadow_color, alignment, width, height):
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font = ImageFont.truetype(os.path.join(FONTS_DIR, font), size)
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lines = text.split("\n")
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# Calculate the width and height of the text
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@@ -1371,7 +1371,7 @@ class DrawText:
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draw = ImageDraw.Draw(image)
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draw.text((x, y), line, font=font, fill=color)
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if image_shadow is not None:
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draw = ImageDraw.Draw(image_shadow)
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draw.text((x + shadow_distance, y + shadow_distance), line, font=font, fill=shadow_color)
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@@ -1527,7 +1527,7 @@ class NoiseFromImage:
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# Convert image to grayscale mask
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noise_mask = noise_mask.mean(dim=3).unsqueeze(-1)
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# add color noise
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imgs = p(image.clone())
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if color_noise > 0:
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