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Add Lora Loader node support for Nunchaku SVDQuant FLUX model architecture with template workflow. Fixes #255

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Will Miao
2025-06-29 23:57:50 +08:00
parent 6472e00fb0
commit 71762d788f
5 changed files with 105 additions and 12 deletions
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46
py/nodes/lora_loader.py
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@@ -2,14 +2,14 @@ import logging
from nodes import LoraLoader
from comfy.comfy_types import IO # type: ignore
import asyncio
from .utils import FlexibleOptionalInputType, any_type, get_lora_info, extract_lora_name, get_loras_list
from .utils import FlexibleOptionalInputType, any_type, get_lora_info, extract_lora_name, get_loras_list, nunchaku_load_lora
logger = logging.getLogger(__name__)
class LoraManagerLoader:
NAME = "Lora Loader (LoraManager)"
CATEGORY = "Lora Manager/loaders"
@classmethod
def INPUT_TYPES(cls):
return {
@@ -37,19 +37,39 @@ class LoraManagerLoader:
clip = kwargs.get('clip', None)
lora_stack = kwargs.get('lora_stack', None)
# Check if model is a Nunchaku Flux model - simplified approach
is_nunchaku_model = False
try:
model_wrapper = model.model.diffusion_model
# Check if model is a Nunchaku Flux model using only class name
if model_wrapper.__class__.__name__ == "ComfyFluxWrapper":
is_nunchaku_model = True
logger.info("Detected Nunchaku Flux model")
except (AttributeError, TypeError):
# Not a model with the expected structure
pass
# First process lora_stack if available
if lora_stack:
for lora_path, model_strength, clip_strength in lora_stack:
# Apply the LoRA using the provided path and strengths
model, clip = LoraLoader().load_lora(model, clip, lora_path, model_strength, clip_strength)
# Apply the LoRA using the appropriate loader
if is_nunchaku_model:
# Use our custom function for Flux models
model = nunchaku_load_lora(model, lora_path, model_strength)
# clip remains unchanged for Nunchaku models
else:
# Use default loader for standard models
model, clip = LoraLoader().load_lora(model, clip, lora_path, model_strength, clip_strength)
# Extract lora name for trigger words lookup
lora_name = extract_lora_name(lora_path)
_, trigger_words = asyncio.run(get_lora_info(lora_name))
all_trigger_words.extend(trigger_words)
# Add clip strength to output if different from model strength
if abs(model_strength - clip_strength) > 0.001:
# Add clip strength to output if different from model strength (except for Nunchaku models)
if not is_nunchaku_model and abs(model_strength - clip_strength) > 0.001:
loaded_loras.append(f"{lora_name}: {model_strength},{clip_strength}")
else:
loaded_loras.append(f"{lora_name}: {model_strength}")
@@ -68,11 +88,17 @@ class LoraManagerLoader:
# Get lora path and trigger words
lora_path, trigger_words = asyncio.run(get_lora_info(lora_name))
# Apply the LoRA using the resolved path with separate strengths
model, clip = LoraLoader().load_lora(model, clip, lora_path, model_strength, clip_strength)
# Apply the LoRA using the appropriate loader
if is_nunchaku_model:
# For Nunchaku models, use our custom function
model = nunchaku_load_lora(model, lora_path, model_strength)
# clip remains unchanged
else:
# Use default loader for standard models
model, clip = LoraLoader().load_lora(model, clip, lora_path, model_strength, clip_strength)
# Include clip strength in output if different from model strength
if abs(model_strength - clip_strength) > 0.001:
# Include clip strength in output if different from model strength and not a Nunchaku model
if not is_nunchaku_model and abs(model_strength - clip_strength) > 0.001:
loaded_loras.append(f"{lora_name}: {model_strength},{clip_strength}")
else:
loaded_loras.append(f"{lora_name}: {model_strength}")