nodes/Extraltodeus-pre_cfg_comfy_nodes_for_ComfyUI
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Extraltodeus
2024-08-14 14:53:02 +02:00
committed by GitHub
parent 49743fe484
commit 23451ad201
1 changed files with 45 additions and 21 deletions
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nodes.py
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@@ -30,6 +30,20 @@ def get_sigma_min_max(model):
sigma_max = model_sampling.sigma(model_sampling.timestep(model_sampling.sigma_max)).item()
return sigma_min, sigma_max
@torch.no_grad()
def make_new_uncond_at_scale(cond,uncond,cond_scale,new_scale):
new_scale_ratio = (new_scale - 1) / (cond_scale - 1)
return cond * (1 - new_scale_ratio) + uncond * new_scale_ratio
@torch.no_grad()
def make_new_uncond_at_scale_co(conds_out,cond_scale,new_scale):
new_scale_ratio = (new_scale - 1) / (cond_scale - 1)
return conds_out[0] * (1 - new_scale_ratio) + conds_out[1] * new_scale_ratio
@torch.no_grad()
def get_denoised_at_scale(x_orig,cond,uncond,cond_scale):
return x_orig - ((x_orig - uncond) + cond_scale * ((x_orig - cond) - (x_orig - uncond)))
class pre_cfg_perp_neg:
@classmethod
def INPUT_TYPES(s):
@@ -1085,8 +1099,8 @@ class gradient_scaling_pre_cfg_node:
@classmethod
def INPUT_TYPES(s):
return {"required": {"model": ("MODEL",),
"maximum_scale": ("FLOAT", {"default": 80, "min": 3.0, "max": 1000.0, "step": 1, "round": 1/100, "tooltip":"It is an equivalent to the CFG scale."}),
"minimum_scale": ("FLOAT", {"default": 4.5, "min": 1.0, "max": 10.0, "step": 1/2, "round": 1/100, "tooltip":"It is an equivalent to the CFG scale."}),
"maximum_scale": ("FLOAT", {"default": 80, "min": 0.0, "max": 1000.0, "step": 1, "round": 1/100, "tooltip":"It is an equivalent to the CFG scale."}),
"minimum_scale": ("FLOAT", {"default": 4.5, "min": 0.0, "max": 10.0, "step": 1/2, "round": 1/100, "tooltip":"It is an equivalent to the CFG scale."}),
"strength": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 10.0, "step": 1/10, "round": 1/10}),
"end_at_sigma": ("FLOAT", {"default": 0.28, "min": 0.0, "max": 1000.0, "step": 1/100, "round": 1/100}),
# "free_scale" : ("BOOLEAN", {"default": False}),
@@ -1094,6 +1108,9 @@ class gradient_scaling_pre_cfg_node:
# "noise_add_diff" : ("BOOLEAN", {"default": True}),
# "split_channels" : ("BOOLEAN", {"default": False}),
"invert_mask" : ("BOOLEAN", {"default": False}),
# "no_input" : (["rand","rev","cond","uncond","swap","r_swap","diff","add_diff","rand_rev","rev_cond","rand_cond","rev_cond_sp","cond_rev_sp"],),
# "start_at_sigma": ("FLOAT", {"default": 15, "min": 0.0, "max": 1000.0, "step": 1/100, "round": 1/100}),
# "end_at_sigma": ("FLOAT", {"default": 0.28, "min": 0.0, "max": 1000.0, "step": 1/100, "round": 1/100}),
},
"optional":{
"input_mask": ("MASK", {"tooltip":"If only a mask is connected the scale becomes a CFG scale of what is being masked.\nWhen a latent is connected the mask defines what will be modified by the node."},),
@@ -1105,15 +1122,6 @@ class gradient_scaling_pre_cfg_node:
CATEGORY = "model_patches/Pre CFG"
@torch.no_grad()
def make_new_uncond_at_scale(self,cond,uncond,cond_scale,new_scale):
new_scale_ratio = (new_scale - 1) / (cond_scale - 1)
return cond * (1 - new_scale_ratio) + uncond * new_scale_ratio
@torch.no_grad()
def get_denoised_at_scale(self,x_orig,cond,uncond,cond_scale):
return x_orig - ((x_orig - uncond) + cond_scale * ((x_orig - cond) - (x_orig - uncond)))
def get_latent_guidance_mask_channel(self,x_orig,cond,uncond,guide,minimum_scale,maximum_scale,noise_add_diff):
scales = torch.zeros_like(x_orig, device=x_orig.device)
for b in range(cond.shape[0]):
@@ -1123,8 +1131,8 @@ class gradient_scaling_pre_cfg_node:
@torch.no_grad()
def get_latent_guidance_mask(self,x_orig,cond,uncond,guide,minimum_scale,maximum_scale,noise_add_diff):
low_denoised = self.get_denoised_at_scale(x_orig,cond,uncond,minimum_scale)
high_denoised = self.get_denoised_at_scale(x_orig,cond,uncond,maximum_scale)
low_denoised = get_denoised_at_scale(x_orig,cond,uncond,minimum_scale)
high_denoised = get_denoised_at_scale(x_orig,cond,uncond,maximum_scale)
if noise_add_diff:
guide = guide + (guide - (x_orig * guide.norm() / x_orig.norm()))
guide = guide / guide.norm()
@@ -1132,9 +1140,7 @@ class gradient_scaling_pre_cfg_node:
high_diff = (high_denoised - guide * high_denoised.norm()).abs()
return torch.clamp(low_diff / high_diff, min=0, max=1)
def patch(self, model, maximum_scale, minimum_scale, invert_mask, strength, end_at_sigma, noise_add_diff=True, converging_scales=False, split_channels=False, free_scale=False, input_mask=None, input_latent=None):
# if input_mask is None and input_latent is None:
# return (model,)
def patch(self, model, maximum_scale, minimum_scale, invert_mask, strength, end_at_sigma, start_at_sigma=99999, no_input="swap", noise_add_diff=True, converging_scales=False, split_channels=False, free_scale=False, input_mask=None, input_latent=None):
sigma_min, sigma_max = get_sigma_min_max(model)
model_sampling = model.model.model_sampling
scaling_function = self.get_latent_guidance_mask_channel if split_channels else self.get_latent_guidance_mask
@@ -1152,6 +1158,24 @@ class gradient_scaling_pre_cfg_node:
elif input_mask is None:
random_guidance = True
snc = lambda x: x / x.norm()
trl = lambda x: torch.randn_like(x,device=x.device)
no_input_operations = {
"rand": lambda x, y, o, z, s: snc(trl(x)) * x.norm(),
"rev": lambda x, y, o, z, s: x * -1,
"cond": lambda x, y, o, z, s: snc(y) * x.norm(),
"uncond": lambda x, y, o, z, s: snc(o) * x.norm() * -1,
"swap": lambda x, y, o, z, s: no_input_operations["cond"](x, y, o, z, s) if s > 0.36 else no_input_operations["uncond"](x, y, o, z, s),
"r_swap": lambda x, y, o, z, s: no_input_operations["cond"](x, y, o, z, s) if s <= 0.36 else no_input_operations["uncond"](x, y, o, z, s),
"diff": lambda x, y, o, z, s: snc(y - o) * x.norm(),
"add_diff": lambda x, y, o, z, s: snc(y + y - o) * x.norm(),
"rand_rev": lambda x, y, o, z, s: snc(trl(x)) * x.norm(),
"rev_cond": lambda x, y, o, z, s: (snc(x) * -1 + snc(y) * 0.5) * x.norm() / 1.5,
"rand_cond": lambda x, y, o, z, s: (snc(x) * -1 + snc(trl(x)) * 0.5) * x.norm() / 1.5,
"rev_cond_sp": lambda x, y, o, z, s: no_input_operations["rev"](x,y,z) * z + (1 - z) * no_input_operations["cond"](x,y,z),
"cond_rev_sp": lambda x, y, o, z, s: no_input_operations["rev"](x,y,z) * (1 - z) + z * no_input_operations["cond"](x,y,z),
}
@torch.no_grad()
def pre_cfg_patch(args):
nonlocal mask_as_weight, latent_as_guidance
@@ -1161,7 +1185,7 @@ class gradient_scaling_pre_cfg_node:
sigma = args["sigma"][0]
sp = min(1,max(0,sigma_to_percent(model_sampling, sigma - sigma_min * 3) + 1 / 100)) ** 2
if not torch.any(conds_out[1]) or sigma <= end_at_sigma or (converging_scales and sp == 1):
if not torch.any(conds_out[1]) or sigma <= end_at_sigma or sigma > start_at_sigma or (converging_scales and sp == 1):
return conds_out
if converging_scales:
@@ -1175,7 +1199,7 @@ class gradient_scaling_pre_cfg_node:
mask_as_weight = F.interpolate(mask_as_weight, size=(conds_out[1].shape[-2], conds_out[1].shape[-1]), mode='bilinear', align_corners=False)
if random_guidance:
latent_as_guidance = torch.randn_like(conds_out[0],device=conds_out[0].device) * 5
latent_as_guidance = no_input_operations[no_input](x_orig.clone(),conds_out[0].clone(),conds_out[1].clone(),sp,sigma/sigma_max)
if latent_as_guidance is not None:
if latent_as_guidance.shape[-2:] != conds_out[1].shape[-2:]:
@@ -1193,11 +1217,11 @@ class gradient_scaling_pre_cfg_node:
global_multiplier = global_multiplier * mask_as_weight
target_scales = target_scales * global_multiplier + torch.full_like(target_scales, cond_scale) * (1 - global_multiplier)
conds_out[1] = self.make_new_uncond_at_scale(conds_out[0],conds_out[1],cond_scale,target_scales)
conds_out[1] = make_new_uncond_at_scale(conds_out[0],conds_out[1],cond_scale,target_scales)
return conds_out
else:
target_scales = maximum_scale * mask_as_weight * strength + torch.full_like(conds_out[1], cond_scale) * (1 - mask_as_weight * strength)
conds_out[1] = self.make_new_uncond_at_scale(conds_out[0],conds_out[1],cond_scale,maximum_scale)
conds_out[1] = make_new_uncond_at_scale(conds_out[0],conds_out[1],cond_scale,maximum_scale)
return conds_out
m = model.clone()
@@ -1299,4 +1323,4 @@ class GradientRGBImage:
mask_gradient = gradient.expand(1, height, width, 1).squeeze(-1)
return (rgb_image,mask_gradient,)
return (rgb_image,mask_gradient,)