Upload 2 files

app.py

@@ -66,6 +66,7 @@ def generate_image(prompt, trigger_word, steps, seed, cfg_scale, width, height,
     pipe.to("cuda")
     generator = torch.Generator(device="cuda").manual_seed(seed)
     
+    progress(0, desc="Start Inference.")
     with calculateDuration("Generating image"):
         # Generate image
         image = pipe(
@@ -84,6 +85,7 @@ def run_lora(prompt, cfg_scale, steps, selected_index, randomize_seed, seed, wid
     if selected_index is None and not is_valid_lora(lora_json):
         gr.Info("LoRA isn't selected.")
     #    raise gr.Error("You must select a LoRA before proceeding.")
+    progress(0, desc="Preparing Inference.")
 
     if is_valid_lora(lora_json):
         with calculateDuration("Loading LoRA weights"):

mod.py

@@ -78,11 +78,11 @@ def change_base_model(repo_id: str, progress=gr.Progress(track_tqdm=True)):
     global last_model
     try:
         if repo_id == last_model or not is_repo_name(repo_id) or not is_repo_exists(repo_id): return
-        progress(0, f"Loading model: {repo_id}")
+        progress(0, desc=f"Loading model: {repo_id}")
         clear_cache()
         pipe = DiffusionPipeline.from_pretrained(repo_id, torch_dtype=torch.bfloat16)
         last_model = repo_id
-        progress(1, f"Model loaded: {repo_id}")
+        progress(1, desc=f"Model loaded: {repo_id}")
     except Exception as e:
         print(e)
     return gr.update(visible=True)