Update app.py

app.py

@@ -4,6 +4,7 @@ Hugging Face Spaces Script for Basic Stable Diffusion 1.5 Gradio App
 Adapted from user's local script and HF Spaces template.
 Supports Hub models and CPU/GPU selection based on available hardware.
 Includes Attention Slicing optimization toggle.
+Corrected progress callback format.
 """
 
 import gradio as gr
@@ -40,9 +41,8 @@ DEFAULT_HUB_MODELS = [
     "runwayml/stable-diffusion-v1-5",
     "SG161222/Realistic_Vision_V6.0_B1_noVAE", # Example popular 1.5 model
     "nitrosocke/Ghibli-Diffusion",
-    "Bilal326/SD_1.5_DragonWarriorV2",
-    "emilianJR/epiCRealism",
-    "Yntec/RevAnimatedV2Rebirth"
+    "danyloylo/sd1.5-ghibli-style-05",
+    "Bilal326/SD_1.5_DragonWarriorV2"
     # "CompVis/stable-diffusion-v1-4", # Example SD 1.4 model (might behave slightly differently)
     # Add other diffusers-compatible SD1.5 models here
 ]
@@ -165,8 +165,7 @@ def infer(
     seed,                     # From seed_input (now a Slider)
     randomize_seed,           # From randomize_seed_checkbox
     enable_attention_slicing, # New input for the optimization toggle
-    # Removed track_tqdm=True which can conflict with callbacks
-    progress=gr.Progress(),   # <-- Corrected Progress initialization
+    progress=gr.Progress(),   # Corrected Progress initialization (removed track_tqdm=True)
 ):
     """Generates an image using the selected model and parameters on the chosen device."""
     global current_pipeline, current_model_id, current_device_loaded, SCHEDULER_MAP, MAX_SEED
@@ -428,7 +427,8 @@ def infer(
             generator=generator, # Pass the generator (which might be None)
             # Pass progress object for tqdm tracking in Gradio
             callback_steps=max(1, num_inference_steps_int // 20), # Update progress bar periodically
-            callback=lambda step, timestep, latents: progress((step / num_inference_steps_int, f"Step {step}/{num_inference_steps_int}")),
+            # --- CORRECTED CALLBACK FORMAT ---
+            callback=lambda step, timestep, latents: progress((step, num_inference_steps_int), desc=f"Step {step}/{num_inference_steps_int}"),
 
             # Add VAE usage here if needed for specific models that require it
             # vae=...
@@ -450,6 +450,7 @@ def infer(
     except ValueError as ve:
          # Handle specific value errors like invalid parameters
          print(f"Parameter Error: {ve}")
+         # This is the line that was re-raising the error, it remains here.
          raise gr.Error(f"Invalid Parameter: {ve}")
     except Exception as e:
         # Catch any other unexpected errors during generation