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import comfy.utils
import torch
def reshape_latent_to(target_shape, latent):
if latent.shape[1:] != target_shape[1:]:
latent = comfy.utils.common_upscale(latent, target_shape[3], target_shape[2], "bilinear", "center")
return comfy.utils.repeat_to_batch_size(latent, target_shape[0])
class LatentAdd:
@classmethod
def INPUT_TYPES(s):
return {"required": { "samples1": ("LATENT",), "samples2": ("LATENT",)}}
RETURN_TYPES = ("LATENT",)
FUNCTION = "op"
CATEGORY = "latent/advanced"
def op(self, samples1, samples2):
samples_out = samples1.copy()
s1 = samples1["samples"]
s2 = samples2["samples"]
s2 = reshape_latent_to(s1.shape, s2)
samples_out["samples"] = s1 + s2
return (samples_out,)
class LatentSubtract:
@classmethod
def INPUT_TYPES(s):
return {"required": { "samples1": ("LATENT",), "samples2": ("LATENT",)}}
RETURN_TYPES = ("LATENT",)
FUNCTION = "op"
CATEGORY = "latent/advanced"
def op(self, samples1, samples2):
samples_out = samples1.copy()
s1 = samples1["samples"]
s2 = samples2["samples"]
s2 = reshape_latent_to(s1.shape, s2)
samples_out["samples"] = s1 - s2
return (samples_out,)
class LatentMultiply:
@classmethod
def INPUT_TYPES(s):
return {"required": { "samples": ("LATENT",),
"multiplier": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
}}
RETURN_TYPES = ("LATENT",)
FUNCTION = "op"
CATEGORY = "latent/advanced"
def op(self, samples, multiplier):
samples_out = samples.copy()
s1 = samples["samples"]
samples_out["samples"] = s1 * multiplier
return (samples_out,)
class LatentInterpolate:
@classmethod
def INPUT_TYPES(s):
return {"required": { "samples1": ("LATENT",),
"samples2": ("LATENT",),
"ratio": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
}}
RETURN_TYPES = ("LATENT",)
FUNCTION = "op"
CATEGORY = "latent/advanced"
def op(self, samples1, samples2, ratio):
samples_out = samples1.copy()
s1 = samples1["samples"]
s2 = samples2["samples"]
s2 = reshape_latent_to(s1.shape, s2)
m1 = torch.linalg.vector_norm(s1, dim=(1))
m2 = torch.linalg.vector_norm(s2, dim=(1))
s1 = torch.nan_to_num(s1 / m1)
s2 = torch.nan_to_num(s2 / m2)
t = (s1 * ratio + s2 * (1.0 - ratio))
mt = torch.linalg.vector_norm(t, dim=(1))
st = torch.nan_to_num(t / mt)
samples_out["samples"] = st * (m1 * ratio + m2 * (1.0 - ratio))
return (samples_out,)
class LatentBatch:
@classmethod
def INPUT_TYPES(s):
return {"required": { "samples1": ("LATENT",), "samples2": ("LATENT",)}}
RETURN_TYPES = ("LATENT",)
FUNCTION = "batch"
CATEGORY = "latent/batch"
def batch(self, samples1, samples2):
samples_out = samples1.copy()
s1 = samples1["samples"]
s2 = samples2["samples"]
if s1.shape[1:] != s2.shape[1:]:
s2 = comfy.utils.common_upscale(s2, s1.shape[3], s1.shape[2], "bilinear", "center")
s = torch.cat((s1, s2), dim=0)
samples_out["samples"] = s
samples_out["batch_index"] = samples1.get("batch_index", [x for x in range(0, s1.shape[0])]) + samples2.get("batch_index", [x for x in range(0, s2.shape[0])])
return (samples_out,)
class LatentBatchSeedBehavior:
@classmethod
def INPUT_TYPES(s):
return {"required": { "samples": ("LATENT",),
"seed_behavior": (["random", "fixed"],{"default": "fixed"}),}}
RETURN_TYPES = ("LATENT",)
FUNCTION = "op"
CATEGORY = "latent/advanced"
def op(self, samples, seed_behavior):
samples_out = samples.copy()
latent = samples["samples"]
if seed_behavior == "random":
if 'batch_index' in samples_out:
samples_out.pop('batch_index')
elif seed_behavior == "fixed":
batch_number = samples_out.get("batch_index", [0])[0]
samples_out["batch_index"] = [batch_number] * latent.shape[0]
return (samples_out,)
NODE_CLASS_MAPPINGS = {
"LatentAdd": LatentAdd,
"LatentSubtract": LatentSubtract,
"LatentMultiply": LatentMultiply,
"LatentInterpolate": LatentInterpolate,
"LatentBatch": LatentBatch,
"LatentBatchSeedBehavior": LatentBatchSeedBehavior,
}
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