Add a LatentBatchSeedBehavior node.

This lets you set it so the latents can use the same seed for the sampling
on every image in the batch.
This commit is contained in:
comfyanonymous 2024-01-26 23:13:02 -05:00
parent 2d105066df
commit fc196aac80
1 changed files with 24 additions and 0 deletions

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@ -122,10 +122,34 @@ class LatentBatch:
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])]) 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,) return (samples_out,)
class LatentBatchSeedBehavior:
@classmethod
def INPUT_TYPES(s):
return {"required": { "samples": ("LATENT",),
"seed_behavior": (["random", "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 = { NODE_CLASS_MAPPINGS = {
"LatentAdd": LatentAdd, "LatentAdd": LatentAdd,
"LatentSubtract": LatentSubtract, "LatentSubtract": LatentSubtract,
"LatentMultiply": LatentMultiply, "LatentMultiply": LatentMultiply,
"LatentInterpolate": LatentInterpolate, "LatentInterpolate": LatentInterpolate,
"LatentBatch": LatentBatch, "LatentBatch": LatentBatch,
"LatentBatchSeedBehavior": LatentBatchSeedBehavior,
} }