nodes/Extraltodeus-ComfyUI-AutomaticCFG
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Add files via upload

Browse Source
This commit is contained in:
Extraltodeus
2024-04-15 08:11:31 +02:00
committed by GitHub
parent 0eeca7f0ac
commit cc57bdf9c3
1 changed files with 79 additions and 18 deletions
Download Patch File Download Diff File Expand all files Collapse all files
nodes.py
+79 -18
View File
@@ -5,11 +5,13 @@ import math
original_sampling_function = deepcopy(comfy.samplers.sampling_function)
minimum_sigma_to_disable_uncond = 1
no_uncond_at_all = False
def sampling_function_patched(model, x, timestep, uncond, cond, cond_scale, model_options={}, seed=None):
if math.isclose(cond_scale, 1.0) and model_options.get("disable_cfg1_optimization", False) == False or timestep[0] <= minimum_sigma_to_disable_uncond:
if math.isclose(cond_scale, 1.0) and model_options.get("disable_cfg1_optimization", False) == False or timestep[0] <= minimum_sigma_to_disable_uncond or no_uncond_at_all:
uncond_ = None
cond_scale = 1
if not no_uncond_at_all:
cond_scale = 1
else:
uncond_ = uncond
@@ -54,13 +56,16 @@ class advancedDynamicCFG:
return {"required": {
"model": ("MODEL",),
"center_mean_post_cfg" : ("BOOLEAN", {"default": True}),
"center_mean_to_sigma" : ("BOOLEAN", {"default": True}),
"automatic_cfg" : (["None","soft","hard","include_boost"], {"default": "hard"},),
"center_mean_to_sigma" : ("BOOLEAN", {"default": False}),
"automatic_cfg" : (["None","soft","hard","progressive","include_boost"], {"default": "hard"},),
"sigma_boost" : ("BOOLEAN", {"default": True}),
"sigma_boost_percentage": ("FLOAT", {"default": 6.86, "min": 0.0, "max": 100.0, "step": 0.01, "round": 0.01}),
"lerp_uncond" : ("BOOLEAN", {"default": False}),
"lerp_uncond_strength": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 10.0, "step": 0.1, "round": 0.1}),
# "debug_print" : ("BOOLEAN", {"default": False}),
"lerp_uncond_strength": ("FLOAT", {"default": 1, "min": 0.0, "max": 10.0, "step": 0.1, "round": 0.1}),
"post_cfg_scale" : ("BOOLEAN", {"default": False}),
"post_cfg_scale_value": ("FLOAT", {"default": 0, "min": 0.0, "max": 100.0, "step": 0.1, "round": 0.1}),
"no_uncond_mode" : ("BOOLEAN", {"default": False}),
"debug_print" : ("BOOLEAN", {"default": False}),
}}
RETURN_TYPES = ("MODEL",)
FUNCTION = "patch"
@@ -68,15 +73,17 @@ class advancedDynamicCFG:
CATEGORY = "model_patches"
def patch(self, model, center_mean_post_cfg, center_mean_to_sigma,
automatic_cfg, sigma_boost, sigma_boost_percentage, lerp_uncond=False, lerp_uncond_strength=1, debug_print=False):
automatic_cfg, sigma_boost, sigma_boost_percentage, lerp_uncond=False, lerp_uncond_strength=1,
post_cfg_scale=False, post_cfg_scale_value=8, no_uncond_mode=False, debug_print=False):
global minimum_sigma_to_disable_uncond
global minimum_sigma_to_disable_uncond, no_uncond_at_all
no_uncond_at_all = no_uncond_mode
model_sampling = model.model.model_sampling
sigmin = model_sampling.sigma(model_sampling.timestep(model_sampling.sigma_min))
sigmax = model_sampling.sigma(model_sampling.timestep(model_sampling.sigma_max))
low_sigma_threshold = (sigmax - sigmin) / 100 * sigma_boost_percentage
if sigma_boost_percentage > 0 and sigma_boost:
minimum_sigma_to_disable_uncond = (sigmax - sigmin) / 100 * sigma_boost_percentage
minimum_sigma_to_disable_uncond = low_sigma_threshold
comfy.samplers.sampling_function = sampling_function_patched
print(f"Sampling function patched. Trigger when sigmas are at: {round(minimum_sigma_to_disable_uncond.item(),4)}")
else:
@@ -90,13 +97,19 @@ class advancedDynamicCFG:
input_x = args["input"]
cond_pred = args["cond_denoised"]
uncond_pred = args["uncond_denoised"]
sigma = args["sigma"][0]
if lerp_uncond:
uncond_pred = torch.lerp(cond_pred,uncond_pred,lerp_uncond_strength)
# uncond_pred = uncond_pred * cond_pred.norm() / uncond_pred.norm()
lerp_weight = lerp_uncond_strength if lerp_uncond_strength > 0 else max(sigma.item(), 1)
if lerp_weight != 1:
uncond_pred = torch.lerp(cond_pred, uncond_pred, lerp_weight)
cond = input_x - cond_pred
uncond = input_x - uncond_pred
sigma = args["sigma"][0]
if no_uncond_mode:
self.last_cfg_ht_one = cond_scale
return cond
if sigma == sigmax or cond_scale > 1:
self.last_cfg_ht_one = cond_scale
@@ -126,13 +139,18 @@ class advancedDynamicCFG:
min_val = abs(torch.mean(min_values).item())
elif automatic_cfg == "hard" or automatic_cfg == "include_boost":
min_val = torch.mean(torch.abs(min_values)).item()
elif automatic_cfg == "progressive":
min_val = torch.mean(torch.abs(min_values)).item()
s_progression = map_sigma(sigma, sigmax, sigmin)
target_intensity = 1.1 * target_intensity * s_progression + 0.9 * target_intensity * (1 - s_progression)
denoised_range = (max_val + min_val) / 2
scale_correction = target_intensity / denoised_range
scale_correction = target_intensity / denoised_range
tmp_scale = reference_cfg * scale_correction
if debug_print:
print(f"c{c}: {tmp_scale} / {scale_correction}")
print(f"denoised_range: {denoised_range}")
if cond_scale > 1:
denoised_tmp[b][c] = uncond[b][c] + tmp_scale * (cond[b][c] - uncond[b][c])
@@ -152,11 +170,33 @@ class advancedDynamicCFG:
denoised = center_latent_mean_values(denoised, False, mult)
return denoised
def rescale_post_cfg(args):
denoised = args["denoised"]
sigma = args["sigma"][0]
if sigma <= minimum_sigma_to_disable_uncond:
return denoised
for b in range(len(denoised)):
for c in range(len(denoised[b])): #TODO make a function for the scaling
channel = denoised[b][c]
max_values = torch.topk(channel, k=int(len(channel)*top_k), largest=True ).values
min_values = torch.topk(channel, k=int(len(channel)*top_k), largest=False).values
max_val = torch.mean(max_values).item()
min_val = torch.mean(torch.abs(min_values)).item()
denoised_range = (max_val + min_val) / 2
if no_uncond_mode or post_cfg_scale_value == 0:
target_intensity = self.last_cfg_ht_one / 10
else:
target_intensity = post_cfg_scale_value / 10
scale_correction = target_intensity / denoised_range
denoised[b][c] = channel * scale_correction
return denoised
m = model.clone()
m.set_model_sampler_cfg_function(linear_cfg, disable_cfg1_optimization=False)
if center_mean_post_cfg:
if center_mean_post_cfg or no_uncond_mode:
m.set_model_sampler_post_cfg_function(center_mean_latent_post_cfg)
if post_cfg_scale or no_uncond_mode:
m.set_model_sampler_post_cfg_function(rescale_post_cfg)
return (m, )
class simpleDynamicCFG:
@@ -182,7 +222,7 @@ class simpleDynamicCFGlerpUncond:
return {"required": {
"model": ("MODEL",),
"boost" : ("BOOLEAN", {"default": True}),
"negative_strength": ("FLOAT", {"default": 1, "min": 0.0, "max": 10.0, "step": 0.1, "round": 0.1}),
"negative_strength": ("FLOAT", {"default": 1, "min": 0.0, "max": 5.0, "step": 0.1, "round": 0.1}),
}}
RETURN_TYPES = ("MODEL",)
FUNCTION = "patch"
@@ -191,5 +231,26 @@ class simpleDynamicCFGlerpUncond:
def patch(self, model, boost, negative_strength):
advcfg = advancedDynamicCFG()
m = advcfg.patch(model, False, False, "hard", boost, 6.86, negative_strength != 1, negative_strength / 2)[0]
# automatic_cfg="progressive" if negative_strength == 1 else "hard"
m = advcfg.patch(model=model, center_mean_post_cfg=False, center_mean_to_sigma=False,
automatic_cfg="hard", sigma_boost=boost, sigma_boost_percentage=6.86,
lerp_uncond=negative_strength != 1, lerp_uncond_strength=negative_strength)[0]
return (m, )
class simpleDynamicCFGNoUncond:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"model": ("MODEL",),
}}
RETURN_TYPES = ("MODEL",)
FUNCTION = "patch"
CATEGORY = "model_patches"
def patch(self, model):
advcfg = advancedDynamicCFG()
m = advcfg.patch(model=model, center_mean_post_cfg=True, center_mean_to_sigma=False,
automatic_cfg="None", sigma_boost="None", sigma_boost_percentage=6.86,
no_uncond_mode=True)[0]
return (m, )