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Replace channel_rescale cfg drift method with sharpen, which well, ends up sharpening the image.

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Allow negative cfg drift multiplier
Allow negative rescale cfg phi
Allow negative spectral mod multiplier
Change default sharpness to 0
Move divisive norm to take place as the last step.
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Clybius
2024-01-02 15:56:10 -06:00
parent 2f25a1608a
commit 4eab09a3fa
1 changed files with 29 additions and 29 deletions
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sampler_mega_modifier.py
+29 -29
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@@ -615,17 +615,16 @@ def center_0channel(tensor): #https://birchlabs.co.uk/machine-learning#combating
tensor[:, [3]] += torch.copysign(torch.pow(torch.abs(mean_3), 1.5), mean_3)
return tensor# - tensor.mean(dim=(2,3), keepdim=True)
def channel_rescale(tensor):
"""Rescales channels according to a vision I had."""
std_dev_0 = tensor[:, [0, 1, 2, 3]].std()
mean_0 = tensor[:, [0, 1, 2, 3]].mean()
mean_3 = tensor[:, [3]].mean()
def channel_sharpen(tensor):
"""Centers on 0 to combat CFG drift."""
flattened = tensor.flatten(2)
flat_std = flattened.std(dim=(2)).unsqueeze(2).expand(flattened.shape)
flattened *= flat_std
flattened -= flattened.mean(dim=(2)).unsqueeze(2).expand(flattened.shape)
flattened /= flat_std
tensor = flattened.unflatten(2, tensor.shape[2:])
return tensor
tensor[:, [0, 1, 2, 3]] = (tensor[:, [0, 1, 2, 3]] - mean_0) / std_dev_0
std_dev = tensor[:, [3]].std()
tensor[:, [3]] = (tensor[:, [3]] * std_dev) + mean_3
return tensor# - tensor.mean(dim=(2,3), keepdim=True)
def center_012channel(tensor): #https://birchlabs.co.uk/machine-learning#combating-mean-drift-in-cfg
"""Centers on 0 to combat CFG drift."""
@@ -878,24 +877,24 @@ class ModelSamplerLatentMegaModifier:
@classmethod
def INPUT_TYPES(s):
return {"required": { "model": ("MODEL",),
"sharpness_multiplier": ("FLOAT", {"default": 2.0, "min": -100.0, "max": 100.0, "step": 0.1}),
"sharpness_multiplier": ("FLOAT", {"default": 0.0, "min": -100.0, "max": 100.0, "step": 0.1}),
"sharpness_method": (["anisotropic", "joint-anisotropic", "gaussian", "cas"], ),
"tonemap_multiplier": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 100.0, "step": 0.01}),
"tonemap_method": (["reinhard", "reinhard_perchannel", "arctan", "quantile", "gated", "cfg-mimic", "spatial-norm"], ),
"tonemap_percentile": ("FLOAT", {"default": 100.0, "min": 0.0, "max": 100.0, "step": 0.005}),
"contrast_multiplier": ("FLOAT", {"default": 0.0, "min": -100.0, "max": 100.0, "step": 0.1}),
"combat_method": (["subtract", "subtract_channels", "subtract_median", "rescale_channels"], ),
"combat_cfg_drift": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01}),
"rescale_cfg_phi": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01}),
"combat_method": (["subtract", "subtract_channels", "subtract_median", "sharpen"], ),
"combat_cfg_drift": ("FLOAT", {"default": 0.0, "min": -10.0, "max": 10.0, "step": 0.01}),
"rescale_cfg_phi": ("FLOAT", {"default": 0.0, "min": -10.0, "max": 10.0, "step": 0.01}),
"extra_noise_type": (["gaussian", "uniform", "perlin", "pink", "green", "pyramid"], ),
"extra_noise_method": (["add", "add_scaled", "speckle", "cads", "cads_rescaled", "cads_speckle", "cads_speckle_rescaled"], ),
"extra_noise_multiplier": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 100.0, "step": 0.1}),
"extra_noise_lowpass": ("INT", {"default": 100, "min": 0, "max": 1000, "step": 1}),
"divisive_norm_size": ("INT", {"default": 127, "min": 1, "max": 255, "step": 1}),
"divisive_norm_multiplier": ("FLOAT", {"default": 0, "min": 0, "max": 1, "step": 0.01}),
"divisive_norm_multiplier": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01}),
"spectral_mod_mode": (["hard_clamp", "soft_clamp"], ),
"spectral_mod_percentile": ("FLOAT", {"default": 5.0, "min": 0.0, "max": 50.0, "step": 0.01}),
"spectral_mod_multiplier": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 15.0, "step": 0.01}),
"spectral_mod_multiplier": ("FLOAT", {"default": 0.0, "min": -15.0, "max": 15.0, "step": 0.01}),
"affect_uncond": (["None", "Sharpness"], ),
"dyn_cfg_augmentation": (["None", "dyncfg-halfcosine", "dyncfg-halfcosine-mimic"], ),
},
@@ -1115,7 +1114,7 @@ class ModelSamplerLatentMegaModifier:
print("Could not tonemap, for the method was not found.")
# Spectral Modification
if spectral_mod_multiplier > 0:
if spectral_mod_multiplier > 0 or spectral_mod_multiplier < 0:
#alpha = 1. - (timestep / 999.0)[:, None, None, None].clone() # Get alpha multiplier, lower alpha at high sigmas/high noise
#alpha = spectral_mod_multiplier# User-input and weaken the strength so we don't annihilate the latent.
match spectral_mod_mode:
@@ -1144,16 +1143,9 @@ class ModelSamplerLatentMegaModifier:
x_rescaled = x_cfg * (ro_pos / ro_cfg)
x_final = rescale_cfg_phi * x_rescaled + (1.0 - rescale_cfg_phi) * x_cfg
if divisive_norm_multiplier > 0:
alpha = 1. - (timestep / 999.0)[:, None, None, None].clone()
alpha ** 0.025 # Alpha might as well be 1, but we want to protect the beginning steps (?).
alpha *= divisive_norm_multiplier
high_noise = divisive_normalization(x_final, (divisive_norm_size * 2) + 1)
x_final = high_noise * alpha + x_final * (1.0 - alpha)
if combat_cfg_drift > 0:
alpha = (1. - (timestep / 999.0)[:, None, None, None].clone()) * 2
alpha ** 0.025 # Alpha might as well be 1, but we want to protect the beginning steps (?).
if combat_cfg_drift > 0 or combat_cfg_drift < 0:
alpha = (1. - (timestep / 999.0)[:, None, None, None].clone())
alpha ** 0.025 # Alpha might as well be 1, but we want to protect the first steps (?).
alpha = alpha.clamp_(max=1)
match combat_method:
case "subtract":
@@ -1165,10 +1157,18 @@ class ModelSamplerLatentMegaModifier:
case "subtract_median":
combat_drift_func = center_latent_median
alpha *= combat_cfg_drift
case "rescale_channels":
combat_drift_func = center_rescale
case "sharpen":
combat_drift_func = channel_sharpen
alpha *= combat_cfg_drift
x_final = combat_drift_func(x_final) * alpha + x_final * (1.0 - alpha) # Mix the modified latent with the existing latent by the alpha
if divisive_norm_multiplier > 0:
alpha = 1. - (timestep / 999.0)[:, None, None, None].clone()
alpha ** 0.025 # Alpha might as well be 1, but we want to protect the beginning steps (?).
alpha *= divisive_norm_multiplier
high_noise = divisive_normalization(x_final, (divisive_norm_size * 2) + 1)
x_final = high_noise * alpha + x_final * (1.0 - alpha)
return x_input - (x - x_final * sigma / (sigma * sigma + 1.0) ** 0.5) # General formula for CFG. uncond + (cond - uncond) * cond_scale