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Added option to specify list of scales as comma-separated string

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This commit is contained in:
GodOfNothing 2025-03-24 18:09:10 +03:00
parent 9f9db7fc29
commit c868cb2055
1 changed files with 12 additions and 15 deletions
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27
comfy_extras/nodes_advanced_samplers.py
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@ -58,18 +58,14 @@ class SamplerLCMUpscale:
@torch.no_grad()
def sample_lcm_scalewise(model, x, sigmas, extra_args=None, callback=None, disable=None, total_upscale=2.0, upscale_method="bislerp", upscale_steps=None):
def sample_lcm_scalewise(model, x, sigmas, extra_args=None, callback=None, disable=None, upscales=None, upscale_method="bicubic"):
extra_args = {} if extra_args is None else extra_args
seed = extra_args.get("seed", None)
if upscale_steps is None:
upscale_steps = max(len(sigmas) // 2 + 1, 2)
else:
upscale_steps += 1
upscale_steps = min(upscale_steps, len(sigmas) + 1)
upscales = np.linspace(1.0, total_upscale, upscale_steps)[1:]
if upscales is not None:
# Resolution is increased on each step except the last one
assert len(upscales) == len(sigmas) - 2
orig_shape = x.size()
s_in = x.new_ones([x.shape[0]])
for i in trange(len(sigmas) - 1, disable=disable):
@ -97,8 +93,7 @@ class SamplerLCMScalewise:
return {
"required":
{
"scale_ratio": ("FLOAT", {"default": 1.0, "min": 1.0, "max": 4.0, "step": 0.25}),
"scale_steps": ("INT", {"default": -1, "min": -1, "max": 1000, "step": 1}),
"upscales": ("STRING", {"default": ""}),
"upscale_method": (s.upscale_methods,),
}
}
@ -107,10 +102,12 @@ class SamplerLCMScalewise:
FUNCTION = "get_sampler"
def get_sampler(self, scale_ratio, scale_steps, upscale_method):
if scale_steps < 0:
scale_steps = None
sampler = comfy.samplers.KSAMPLER(sample_lcm_scalewise, extra_options={"total_upscale": scale_ratio, "upscale_steps": scale_steps, "upscale_method": upscale_method})
def get_sampler(self, upscales, upscale_method):
# Turn comma-separated list into string
upscales = [float(value) for value in upscales.split(',')]
if len(upscales) == 0:
upscales = None
sampler = comfy.samplers.KSAMPLER(sample_lcm_scalewise, extra_options={"upscales": upscales, "upscale_method": upscale_method})
return (sampler, )