i

modules/core.py

@@ -10,7 +10,7 @@ import numpy as np
 import comfy.model_management
 import comfy.utils
 
-from comfy.sd import load_checkpoint_guess_config
+from comfy.sd import load_checkpoint_guess_config, load_lora_for_models
 from nodes import VAEDecode, EmptyLatentImage, CLIPTextEncode
 from comfy.sample import prepare_mask, broadcast_cond, load_additional_models, cleanup_additional_models
 from modules.samplers_advanced import KSampler, KSamplerWithRefiner
@@ -39,6 +39,16 @@ def load_model(ckpt_filename):
     return StableDiffusionModel(unet=unet, clip=clip, vae=vae, clip_vision=clip_vision)
 
 
+@torch.no_grad()
+def load_lora(model, lora_filename, strength_model=1.0, strength_clip=1.0):
+    if strength_model == 0 and strength_clip == 0:
+        return model
+
+    lora = comfy.utils.load_torch_file(lora_filename, safe_load=True)
+    model.unet, model.clip = comfy.sd.load_lora_for_models(model.unet, model.clip, lora, strength_model, strength_clip)
+    return model
+
+
 @torch.no_grad()
 def encode_prompt_condition(clip, prompt):
     return opCLIPTextEncode.encode(clip=clip, text=prompt)[0]

modules/default_pipeline.py

@@ -2,13 +2,16 @@ import modules.core as core
 import os
 import torch
 
-from modules.path import modelfile_path
+from modules.path import modelfile_path, lorafile_path
 
 
 xl_base_filename = os.path.join(modelfile_path, 'sd_xl_base_1.0.safetensors')
 xl_refiner_filename = os.path.join(modelfile_path, 'sd_xl_refiner_1.0.safetensors')
+xl_base_offset_lora_filename = os.path.join(lorafile_path, 'sd_xl_offset_example-lora_1.0.safetensors')
 
 xl_base = core.load_model(xl_base_filename)
+xl_base = core.load_lora(xl_base, xl_base_offset_lora_filename, strength_model=0.618, strength_clip=0.0)
+
 xl_refiner = core.load_model(xl_refiner_filename)
 del xl_base.vae