parser.add_argument("--use_tile_vae", action='store_true', default=False)#False

app.py

@@ -8,8 +8,6 @@ import numpy as np
 import torch
 from SUPIR.util import create_SUPIR_model, load_QF_ckpt
 from PIL import Image
-from llava.llava_agent import LLavaAgent
-from CKPT_PTH import LLAVA_MODEL_PATH
 import einops
 import copy
 import math
@@ -32,7 +30,7 @@ parser.add_argument("--no_llava", action='store_true', default=True)#False
 parser.add_argument("--use_image_slider", action='store_true', default=False)#False
 parser.add_argument("--log_history", action='store_true', default=False)
 parser.add_argument("--loading_half_params", action='store_true', default=False)#False
-parser.add_argument("--use_tile_vae", action='store_true', default=True)#False
+parser.add_argument("--use_tile_vae", action='store_true', default=False)#False
 parser.add_argument("--encoder_tile_size", type=int, default=512)
 parser.add_argument("--decoder_tile_size", type=int, default=64)
 parser.add_argument("--load_8bit_llava", action='store_true', default=False)
@@ -60,12 +58,7 @@ if torch.cuda.device_count() > 0:
     model.first_stage_model.denoise_encoder_s1 = copy.deepcopy(model.first_stage_model.denoise_encoder)
     model.current_model = 'v0-Q'
     ckpt_Q, ckpt_F = load_QF_ckpt(args.opt)
-
-    # load LLaVA
-    if use_llava:
-        llava_agent = LLavaAgent(LLAVA_MODEL_PATH, device=LLaVA_device, load_8bit=args.load_8bit_llava, load_4bit=False)
-    else:
-        llava_agent = None
+    llava_agent = None
 
 def check_upload(input_image):
     if input_image is None: