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Add seed parameter to CADS node #3

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Add seed parameter to CADS node #3

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10 changes: 4 additions & 6 deletions __init__.py
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Original file line number Diff line number Diff line change
Expand Up @@ -13,10 +13,6 @@ def randn_like(cond, generator=None):
class CADS:
current_step = 0

@classmethod
def IS_CHANGED(*args, **kwargs):
return float("nan")

@classmethod
def INPUT_TYPES(s):
return {
Expand All @@ -25,6 +21,7 @@ def INPUT_TYPES(s):
"noise_scale": ("FLOAT", {"min": 0.0, "max": 1.0, "step": 0.01, "default": 0.25}),
"t1": ("FLOAT", {"min": 0.0, "max": 1.0, "step": 0.01, "default": 0.6}),
"t2": ("FLOAT", {"min": 0.0, "max": 1.0, "step": 0.01, "default": 0.9}),
"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff})
},
"optional": {
"rescale": ("FLOAT", {"min": 0.0, "max": 1.0, "step": 0.01, "default": 0.0}),
Expand All @@ -40,11 +37,12 @@ def INPUT_TYPES(s):

CATEGORY = "utils"

def do(self, model, noise_scale, t1, t2, rescale=0.0, start_step=-1, total_steps=-1, apply_to="both", key="y"):
def do(self, model, noise_scale, t1, t2, seed, rescale=0.0, start_step=-1, total_steps=-1, apply_to="both", key="y"):
previous_wrapper = model.model_options.get("model_function_wrapper")

im = model.model.model_sampling
CADS.current_step = start_step
generator = torch.Generator(device="cpu").manual_seed(seed)

skip = None
if apply_to == "cond":
Expand Down Expand Up @@ -72,7 +70,7 @@ def cads_noise(gamma, y):
if y is None:
return None
s = noise_scale
noise = randn_like(y)
noise = randn_like(y, generator=generator)
gamma = torch.tensor(gamma).to(y)
psi = rescale
if psi > 0:
Expand Down