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.
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@ -122,10 +122,34 @@ class LatentBatch:
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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])])
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return (samples_out,)
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class LatentBatchSeedBehavior:
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@classmethod
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def INPUT_TYPES(s):
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return {"required": { "samples": ("LATENT",),
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"seed_behavior": (["random", "fixed"],),}}
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RETURN_TYPES = ("LATENT",)
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FUNCTION = "op"
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CATEGORY = "latent/advanced"
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def op(self, samples, seed_behavior):
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samples_out = samples.copy()
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latent = samples["samples"]
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if seed_behavior == "random":
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if 'batch_index' in samples_out:
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samples_out.pop('batch_index')
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elif seed_behavior == "fixed":
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batch_number = samples_out.get("batch_index", [0])[0]
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samples_out["batch_index"] = [batch_number] * latent.shape[0]
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return (samples_out,)
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NODE_CLASS_MAPPINGS = {
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"LatentAdd": LatentAdd,
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"LatentSubtract": LatentSubtract,
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"LatentMultiply": LatentMultiply,
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"LatentInterpolate": LatentInterpolate,
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"LatentBatch": LatentBatch,
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"LatentBatchSeedBehavior": LatentBatchSeedBehavior,
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}
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