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Simplify Seed Variance Enhancer implementation

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Co-authored-by: lstein <111189+lstein@users.noreply.github.com>
This commit is contained in:
copilot-swe-agent[bot]
2025-12-28 14:32:57 +00:00
parent 6266e0e89d
commit 7d65cdfc16
1 changed files with 6 additions and 31 deletions
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37
invokeai/app/invocations/seed_variance_enhancer.py
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@@ -21,15 +21,6 @@ from invokeai.backend.stable_diffusion.diffusion.conditioning_data import (
)
class NoiseInsertMode(str, Enum):
"""When to apply noise during the generation process."""
BEGINNING = "noise on beginning steps"
ENDING = "noise on ending steps"
ALL = "noise on all steps"
DISABLED = "disabled"
class MaskStartPosition(str, Enum):
"""Which end of the prompt will be protected from noise."""
@@ -49,10 +40,12 @@ class SeedVarianceEnhancerInvocation(BaseInvocation):
"""Adds random noise to Z-Image conditioning embeddings to increase output diversity.
This node compensates for low seed variance by adding controlled noise to the conditioning
embeddings during specific steps of generation. Works specifically with Z-Image models.
embeddings. Works specifically with Z-Image models.
The noise can be applied to beginning steps, ending steps, or all steps. Applying noise
only to beginning steps (default) allows the model to pivot back toward prompt adherence.
Typical settings for Z-Image Turbo:
- randomize_percent: 50%
- strength: 15-40
- Experiment with different values for your specific prompts
Masking features allow protecting portions of the prompt from noise exposure.
"""
@@ -69,18 +62,7 @@ class SeedVarianceEnhancerInvocation(BaseInvocation):
)
strength: float = InputField(
default=20.0,
description="Scale of the random noise. Typical range: 15-40 for Z-Image.",
)
noise_insert: NoiseInsertMode = InputField(
default=NoiseInsertMode.BEGINNING,
description="Which steps of generation process use the noisy embedding.",
)
steps_switchover_percent: float = InputField(
default=20.0,
ge=1.0,
le=99.0,
description="Percentage of steps before switching between noisy and original embeddings. "
"Formula: (100/TOTAL_STEPS) * STEPS - 1",
description="Scale of the random noise. Typical range: 15-40 for Z-Image Turbo.",
)
seed: int = InputField(
default=0,
@@ -107,13 +89,6 @@ class SeedVarianceEnhancerInvocation(BaseInvocation):
# Load the conditioning data
conditioning_data = context.conditioning.load(self.conditioning.conditioning_name)
# Early return if disabled
if self.noise_insert == NoiseInsertMode.DISABLED:
if self.log_statistics:
context.logger.info("Seed Variance Enhancer is disabled. Passing conditioning through unchanged.")
self._log_statistics(context, conditioning_data)
return ZImageConditioningOutput(conditioning=self.conditioning)
# Early return if strength is zero
if self.strength == 0:
if self.log_statistics: