mirror of
https://github.com/willmiao/ComfyUI-Lora-Manager.git
synced 2026-03-24 14:42:11 -03:00
docs: add frontend UI architecture and ComfyUI widget guidelines
- Document dual UI systems: standalone web UI and ComfyUI custom node widgets - Add ComfyUI widget development guidelines including styling and constraints - Update terminology in LoraRandomizerNode from 'frontend/backend' to 'fixed/always' for clarity - Include UI constraints for ComfyUI widgets: minimize vertical space, avoid dynamic height changes, keep UI simple
This commit is contained in:
Will Miao
@@ -2,8 +2,8 @@
|
||||
Lora Randomizer Node - Randomly selects LoRAs from a pool with configurable settings.
|
||||
|
||||
This node accepts optional pool_config input to filter available LoRAs, and outputs
|
||||
a LORA_STACK with randomly selected LoRAs. Supports both frontend roll (fixed selection)
|
||||
and backend roll (randomizes each execution).
|
||||
a LORA_STACK with randomly selected LoRAs. Returns UI updates with new random LoRAs
|
||||
and tracks the last used combination for reuse.
|
||||
"""
|
||||
|
||||
import logging
|
||||
@@ -44,7 +44,7 @@ class LoraRandomizerNode:
|
||||
Randomize LoRAs based on configuration and pool filters.
|
||||
|
||||
Args:
|
||||
randomizer_config: Dict with randomizer settings (count, strength ranges, roll mode)
|
||||
randomizer_config: Dict with randomizer settings (count, strength ranges, roll_mode)
|
||||
loras: List of LoRA dicts from LORAS widget (includes locked state)
|
||||
pool_config: Optional config from LoRA Pool node for filtering
|
||||
|
||||
@@ -53,41 +53,23 @@ class LoraRandomizerNode:
|
||||
"""
|
||||
from ..services.service_registry import ServiceRegistry
|
||||
|
||||
# Parse randomizer settings
|
||||
count_mode = randomizer_config.get("count_mode", "range")
|
||||
count_fixed = randomizer_config.get("count_fixed", 5)
|
||||
count_min = randomizer_config.get("count_min", 3)
|
||||
count_max = randomizer_config.get("count_max", 7)
|
||||
model_strength_min = randomizer_config.get("model_strength_min", 0.0)
|
||||
model_strength_max = randomizer_config.get("model_strength_max", 1.0)
|
||||
use_same_clip_strength = randomizer_config.get("use_same_clip_strength", True)
|
||||
clip_strength_min = randomizer_config.get("clip_strength_min", 0.0)
|
||||
clip_strength_max = randomizer_config.get("clip_strength_max", 1.0)
|
||||
roll_mode = randomizer_config.get("roll_mode", "frontend")
|
||||
roll_mode = randomizer_config.get("roll_mode", "always")
|
||||
logger.debug(f"[LoraRandomizerNode] roll_mode: {roll_mode}")
|
||||
|
||||
# Get lora scanner to access available loras
|
||||
scanner = await ServiceRegistry.get_lora_scanner()
|
||||
|
||||
# Backend roll mode: execute with input loras, return new random to UI
|
||||
if roll_mode == "backend":
|
||||
execution_stack = self._build_execution_stack_from_input(loras)
|
||||
if roll_mode == "fixed":
|
||||
ui_loras = loras
|
||||
else:
|
||||
scanner = await ServiceRegistry.get_lora_scanner()
|
||||
ui_loras = await self._generate_random_loras_for_ui(
|
||||
scanner, randomizer_config, loras, pool_config
|
||||
)
|
||||
logger.info(
|
||||
f"[LoraRandomizerNode] Backend roll: executing with input, returning new random to UI"
|
||||
)
|
||||
return {"result": (execution_stack,), "ui": {"loras": ui_loras}}
|
||||
|
||||
# Frontend roll mode: use current behavior (random selection for both)
|
||||
ui_loras = await self._generate_random_loras_for_ui(
|
||||
scanner, randomizer_config, loras, pool_config
|
||||
)
|
||||
execution_stack = self._build_execution_stack_from_input(ui_loras)
|
||||
logger.info(
|
||||
f"[LoraRandomizerNode] Frontend roll: executing with random selection"
|
||||
)
|
||||
return {"result": (execution_stack,), "ui": {"loras": ui_loras}}
|
||||
execution_stack = self._build_execution_stack_from_input(loras)
|
||||
|
||||
return {
|
||||
"result": (execution_stack,),
|
||||
"ui": {"loras": ui_loras, "last_used": loras},
|
||||
}
|
||||
|
||||
def _build_execution_stack_from_input(self, loras):
|
||||
"""
|
||||
@@ -121,9 +103,6 @@ class LoraRandomizerNode:
|
||||
|
||||
lora_stack.append((lora_path, model_strength, clip_strength))
|
||||
|
||||
logger.info(
|
||||
f"[LoraRandomizerNode] Built execution stack with {len(lora_stack)} LoRAs"
|
||||
)
|
||||
return lora_stack
|
||||
|
||||
async def _generate_random_loras_for_ui(
|
||||
@@ -158,16 +137,10 @@ class LoraRandomizerNode:
|
||||
else:
|
||||
target_count = random.randint(count_min, count_max)
|
||||
|
||||
logger.info(
|
||||
f"[LoraRandomizerNode] Generating random LoRAs, target count: {target_count}"
|
||||
)
|
||||
|
||||
# Extract locked LoRAs from input
|
||||
locked_loras = [lora for lora in input_loras if lora.get("locked", False)]
|
||||
locked_count = len(locked_loras)
|
||||
|
||||
logger.info(f"[LoraRandomizerNode] Locked LoRAs: {locked_count}")
|
||||
|
||||
# Get available loras from cache
|
||||
try:
|
||||
cache_data = await scanner.get_cached_data(force_refresh=False)
|
||||
@@ -185,10 +158,6 @@ class LoraRandomizerNode:
|
||||
available_loras, pool_config, scanner
|
||||
)
|
||||
|
||||
logger.info(
|
||||
f"[LoraRandomizerNode] Available LoRAs after filtering: {len(available_loras)}"
|
||||
)
|
||||
|
||||
# Calculate how many new LoRAs to select
|
||||
slots_needed = target_count - locked_count
|
||||
|
||||
@@ -208,10 +177,6 @@ class LoraRandomizerNode:
|
||||
if slots_needed > len(available_pool):
|
||||
slots_needed = len(available_pool)
|
||||
|
||||
logger.info(
|
||||
f"[LoraRandomizerNode] Selecting {slots_needed} new LoRAs from {len(available_pool)} available"
|
||||
)
|
||||
|
||||
# Random sample
|
||||
selected = []
|
||||
if slots_needed > 0:
|
||||
@@ -243,9 +208,6 @@ class LoraRandomizerNode:
|
||||
# Merge with locked LoRAs
|
||||
result_loras.extend(locked_loras)
|
||||
|
||||
logger.info(
|
||||
f"[LoraRandomizerNode] Final random LoRA count: {len(result_loras)}"
|
||||
)
|
||||
return result_loras
|
||||
|
||||
async def _apply_pool_filters(self, available_loras, pool_config, scanner):
|
||||
@@ -331,19 +293,21 @@ class LoraRandomizerNode:
|
||||
available_loras = lora_service.filter_set.apply(available_loras, criteria)
|
||||
|
||||
# Apply license filters
|
||||
# Note: no_credit_required=True means filter out models where credit is NOT required
|
||||
# (i.e., keep only models where credit IS required)
|
||||
if no_credit_required:
|
||||
available_loras = [
|
||||
lora
|
||||
for lora in available_loras
|
||||
if not lora.get("civitai", {}).get("allowNoCredit", True)
|
||||
if not (lora.get("license_flags", 127) & (1 << 0))
|
||||
]
|
||||
|
||||
# allow_selling=True means keep only models where selling generated content is allowed
|
||||
if allow_selling:
|
||||
available_loras = [
|
||||
lora
|
||||
for lora in available_loras
|
||||
if lora.get("civitai", {}).get("allowCommercialUse", ["None"])[0]
|
||||
!= "None"
|
||||
if bool(lora.get("license_flags", 127) & (1 << 1))
|
||||
]
|
||||
|
||||
return available_loras
|
||||
|
||||
@@ -384,19 +384,21 @@ class LoraService(BaseModelService):
|
||||
available_loras = self.filter_set.apply(available_loras, criteria)
|
||||
|
||||
# Apply license filters
|
||||
# Note: no_credit_required=True means filter out models where credit is NOT required
|
||||
# (i.e., keep only models where credit IS required)
|
||||
if no_credit_required:
|
||||
available_loras = [
|
||||
lora
|
||||
for lora in available_loras
|
||||
if not lora.get("civitai", {}).get("allowNoCredit", True)
|
||||
if not (lora.get("license_flags", 127) & (1 << 0))
|
||||
]
|
||||
|
||||
# allow_selling=True means keep only models where selling generated content is allowed
|
||||
if allow_selling:
|
||||
available_loras = [
|
||||
lora
|
||||
for lora in available_loras
|
||||
if lora.get("civitai", {}).get("allowCommercialUse", ["None"])[0]
|
||||
!= "None"
|
||||
if bool(lora.get("license_flags", 127) & (1 << 1))
|
||||
]
|
||||
|
||||
return available_loras
|
||||
|
||||
Reference in New Issue
Block a user