nodes/Extraltodeus-ComfyUI-AutomaticCFG
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Update nodes.py

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Extraltodeus
2024-02-23 10:13:09 +01:00
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parent 4c88ee6e61
commit 6ef7851368
1 changed files with 0 additions and 39 deletions
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nodes.py
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@@ -36,42 +36,3 @@ class simpleDynamicCFG:
m.set_model_sampler_cfg_function(linear_cfg, disable_cfg1_optimization=True)
return (m, )
class simpleDynamicCFGperChannelMultiplier:
@classmethod
def INPUT_TYPES(s):
return {"required": {"model": ("MODEL",),
"dynamic_intensity_channel_1": ("FLOAT", {"default": 1, "min": 0.0, "max": 10.0, "step":0.01, "round": 0.01}),
"dynamic_intensity_channel_2": ("FLOAT", {"default": 1, "min": 0.0, "max": 10.0, "step":0.01, "round": 0.01}),
"dynamic_intensity_channel_3": ("FLOAT", {"default": 1, "min": 0.0, "max": 10.0, "step":0.01, "round": 0.01}),
"dynamic_intensity_channel_4": ("FLOAT", {"default": 1, "min": 0.0, "max": 10.0, "step":0.01, "round": 0.01}),
}}
RETURN_TYPES = ("MODEL",)
FUNCTION = "patch"
CATEGORY = "model_patches"
def patch(self, model, dynamic_intensity_channel_1,dynamic_intensity_channel_2,dynamic_intensity_channel_3,dynamic_intensity_channel_4):
base_target = 0.41903709829879954/model.model.latent_format.scale_factor
top_k = 0.25
dynamic_channels = [dynamic_intensity_channel_1,dynamic_intensity_channel_2,dynamic_intensity_channel_3,dynamic_intensity_channel_4]
def linear_cfg(args):
cond = args["cond"]
cond_scale = args["cond_scale"]
intensity_target = cond_scale/8
uncond = args["uncond"]
input_x = args["input"]
denoised_tmp = input_x-(uncond + 8*(cond - uncond))
for b in range(len(denoised_tmp)):
for c in range(len(denoised_tmp[b])):
channel = denoised_tmp[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(min_values).item()
denoised_range = (max_val+abs(min_val))/2
tmp_scale = 2*base_target*intensity_target/denoised_range*dynamic_channels[c]
denoised_tmp[b][c] = uncond[b][c] + tmp_scale * (cond[b][c] - uncond[b][c])
return denoised_tmp
m = model.clone()
m.set_model_sampler_cfg_function(linear_cfg, disable_cfg1_optimization=True)
return (m, )