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Implement my alternative take on CFG++ as the euler_pp sampler.

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Add euler_ancestral_pp which is the ancestral version of euler with the
same modification.
This commit is contained in:
comfyanonymous
2024-06-25 07:41:52 -04:00
parent 90aebb6c86
commit 69d710e40f
3 changed files with 56 additions and 26 deletions
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25
comfy_extras/nodes_advanced_samplers.py
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@@ -82,29 +82,6 @@ def sample_euler_cfgpp(model, x, sigmas, extra_args=None, callback=None, disable
x = denoised + sigmas[i + 1] * d
return x
@torch.no_grad()
def sample_euler_cfgpp_alt(model, x, sigmas, extra_args=None, callback=None, disable=None):
extra_args = {} if extra_args is None else extra_args
temp = [0]
def post_cfg_function(args):
temp[0] = args["uncond_denoised"]
return args["denoised"]
model_options = extra_args.get("model_options", {}).copy()
extra_args["model_options"] = comfy.model_patcher.set_model_options_post_cfg_function(model_options, post_cfg_function, disable_cfg1_optimization=True)
s_in = x.new_ones([x.shape[0]])
for i in trange(len(sigmas) - 1, disable=disable):
sigma_hat = sigmas[i]
denoised = model(x, sigma_hat * s_in, **extra_args)
d = to_d(x - denoised + temp[0], sigma_hat, denoised)
if callback is not None:
callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigma_hat, 'denoised': denoised})
dt = sigmas[i + 1] - sigma_hat
# Euler method
x = x + d * dt
return x
class SamplerEulerCFGpp:
@classmethod
@@ -122,7 +99,7 @@ class SamplerEulerCFGpp:
if version == "regular":
sampler = comfy.samplers.KSAMPLER(sample_euler_cfgpp)
else:
sampler = comfy.samplers.KSAMPLER(sample_euler_cfgpp_alt)
sampler = comfy.samplers.ksampler("euler_pp")
return (sampler, )
NODE_CLASS_MAPPINGS = {