Update

app.py

@@ -1,6 +1,5 @@
 import gradio as gr
 import torch
-import argparse
 from omegaconf import OmegaConf
 from gligen.task_grounded_generation import grounded_generation_box, load_ckpt, load_common_ckpt
 
@@ -17,23 +16,6 @@ from huggingface_hub import hf_hub_download
 hf_hub_download = partial(hf_hub_download, library_name="gligen_demo")
 
 
-arg_bool = lambda x: x.lower() == 'true'
-
-
-def parse_option():
-    parser = argparse.ArgumentParser('GLIGen Demo', add_help=False)
-    parser.add_argument("--folder", type=str,  default="create_samples", help="path to OUTPUT")
-    parser.add_argument("--official_ckpt", type=str,  default='ckpts/sd-v1-4.ckpt', help="")
-    parser.add_argument("--guidance_scale", type=float,  default=5, help="")
-    parser.add_argument("--alpha_scale", type=float,  default=1, help="scale tanh(alpha). If 0, the behaviour is same as original model")
-    parser.add_argument("--load-text-box-generation", type=arg_bool, default=True, help="Load text-box generation pipeline.")
-    parser.add_argument("--load-text-box-inpainting", type=arg_bool, default=True, help="Load text-box inpainting pipeline.")
-    parser.add_argument("--load-text-image-box-generation", type=arg_bool, default=True, help="Load text-image-box generation pipeline.")
-    args = parser.parse_args()
-    return args
-args = parse_option()
-
-
 def load_from_hf(repo_id, filename='diffusion_pytorch_model.bin', subfolder=None):
     cache_file = hf_hub_download(repo_id=repo_id, filename=filename, subfolder=subfolder)
     return torch.load(cache_file, map_location='cpu')
@@ -47,7 +29,7 @@ def load_ckpt_config_from_hf(modality):
 def ckpt_load_helper(modality, is_inpaint, is_style, common_instances=None):
     pretrained_ckpt_gligen, config = load_ckpt_config_from_hf(modality)
     config = OmegaConf.create( config["_content"] ) # config used in training
-    config.update( vars(args) )
+    config.alpha_scale = 1.0
     config.model['params']['is_inpaint'] = is_inpaint
     config.model['params']['is_style'] = is_style