Add an experimental node to sharpen latents.

Can be used with LatentApplyOperationCFG for interesting results.
This commit is contained in:
comfyanonymous 2024-10-16 05:25:31 -04:00
parent c695c4af7f
commit f71cfd2687
1 changed files with 50 additions and 0 deletions

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@ -221,6 +221,55 @@ class LatentOperationTonemapReinhard:
return normalized_latent * new_magnitude
return (tonemap_reinhard,)
class LatentOperationSharpen:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"sharpen_radius": ("INT", {
"default": 9,
"min": 1,
"max": 31,
"step": 1
}),
"sigma": ("FLOAT", {
"default": 1.0,
"min": 0.1,
"max": 10.0,
"step": 0.1
}),
"alpha": ("FLOAT", {
"default": 0.1,
"min": 0.0,
"max": 5.0,
"step": 0.01
}),
}}
RETURN_TYPES = ("LATENT_OPERATION",)
FUNCTION = "op"
CATEGORY = "latent/advanced/operations"
EXPERIMENTAL = True
def op(self, sharpen_radius, sigma, alpha):
def sharpen(latent, **kwargs):
luminance = (torch.linalg.vector_norm(latent, dim=(1)) + 1e-6)[:,None]
normalized_latent = latent / luminance
channels = latent.shape[1]
kernel_size = sharpen_radius * 2 + 1
kernel = comfy_extras.nodes_post_processing.gaussian_kernel(kernel_size, sigma, device=luminance.device)
center = kernel_size // 2
kernel *= alpha * -10
kernel[center, center] = kernel[center, center] - kernel.sum() + 1.0
padded_image = torch.nn.functional.pad(normalized_latent, (sharpen_radius,sharpen_radius,sharpen_radius,sharpen_radius), 'reflect')
sharpened = torch.nn.functional.conv2d(padded_image, kernel.repeat(channels, 1, 1).unsqueeze(1), padding=kernel_size // 2, groups=channels)[:,:,sharpen_radius:-sharpen_radius, sharpen_radius:-sharpen_radius]
return luminance * sharpened
return (sharpen,)
NODE_CLASS_MAPPINGS = {
"LatentAdd": LatentAdd,
"LatentSubtract": LatentSubtract,
@ -231,4 +280,5 @@ NODE_CLASS_MAPPINGS = {
"LatentApplyOperation": LatentApplyOperation,
"LatentApplyOperationCFG": LatentApplyOperationCFG,
"LatentOperationTonemapReinhard": LatentOperationTonemapReinhard,
"LatentOperationSharpen": LatentOperationSharpen,
}