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feat(lora_randomizer): implement dual seed mechanism for batch queue synchronization, fixes #773

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- Add execution_seed and next_seed parameters to support deterministic randomization across batch executions
- Separate UI display generation from execution stack generation to maintain consistency in batch queues
- Update LoraService to accept optional seed parameter for reproducible randomization
- Ensure each execution with a different seed produces unique results without affecting global random state
This commit is contained in:
Will Miao
2026-01-21 00:52:08 +08:00
parent 50c012ae33
commit fd06086a05
7 changed files with 134 additions and 19 deletions
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22
py/services/lora_service.py
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@@ -231,6 +231,7 @@ class LoraService(BaseModelService):
use_recommended_strength: bool = False,
recommended_strength_scale_min: float = 0.5,
recommended_strength_scale_max: float = 1.0,
seed: Optional[int] = None,
) -> List[Dict]:
"""
Get random LoRAs with specified strength ranges.
@@ -250,6 +251,7 @@ class LoraService(BaseModelService):
use_recommended_strength: Whether to use recommended strength from usage_tips
recommended_strength_scale_min: Minimum scale factor for recommended strength
recommended_strength_scale_max: Maximum scale factor for recommended strength
seed: Optional random seed for reproducible/unique randomization per execution
Returns:
List of LoRA dicts with randomized strengths
@@ -257,6 +259,10 @@ class LoraService(BaseModelService):
import random
import json
# Use a local Random instance to avoid affecting global random state
# This ensures each execution with a different seed produces different results
rng = random.Random(seed)
def get_recommended_strength(lora_data: Dict) -> Optional[float]:
"""Parse usage_tips JSON and extract recommended strength"""
try:
@@ -286,7 +292,7 @@ class LoraService(BaseModelService):
if count_mode == "fixed":
target_count = count
else:
target_count = random.randint(count_min, count_max)
target_count = rng.randint(count_min, count_max)
# Get available loras from cache
cache = await self.scanner.get_cached_data(force_refresh=False)
@@ -320,7 +326,7 @@ class LoraService(BaseModelService):
# Random sample
selected = []
if slots_needed > 0:
selected = random.sample(available_pool, slots_needed)
selected = rng.sample(available_pool, slots_needed)
# Generate random strengths for selected LoRAs
result_loras = []
@@ -328,17 +334,17 @@ class LoraService(BaseModelService):
if use_recommended_strength:
recommended_strength = get_recommended_strength(lora)
if recommended_strength is not None:
scale = random.uniform(
scale = rng.uniform(
recommended_strength_scale_min, recommended_strength_scale_max
)
model_str = round(recommended_strength * scale, 2)
else:
model_str = round(
random.uniform(model_strength_min, model_strength_max), 2
rng.uniform(model_strength_min, model_strength_max), 2
)
else:
model_str = round(
random.uniform(model_strength_min, model_strength_max), 2
rng.uniform(model_strength_min, model_strength_max), 2
)
if use_same_clip_strength:
@@ -346,17 +352,17 @@ class LoraService(BaseModelService):
elif use_recommended_strength:
recommended_clip_strength = get_recommended_clip_strength(lora)
if recommended_clip_strength is not None:
scale = random.uniform(
scale = rng.uniform(
recommended_strength_scale_min, recommended_strength_scale_max
)
clip_str = round(recommended_clip_strength * scale, 2)
else:
clip_str = round(
random.uniform(clip_strength_min, clip_strength_max), 2
rng.uniform(clip_strength_min, clip_strength_max), 2
)
else:
clip_str = round(
random.uniform(clip_strength_min, clip_strength_max), 2
rng.uniform(clip_strength_min, clip_strength_max), 2
)
result_loras.append(