Update app.py

app.py

@@ -9,6 +9,16 @@ try:
 except ImportError:
     print("Flash attention not available - using standard attention (this is fine)")
 
+# Ensure PEFT is available for LoRA
+try:
+    import peft
+    print("PEFT library is available")
+except ImportError:
+    print("Installing PEFT for LoRA support...")
+    subprocess.run([sys.executable, "-m", "pip", "install", "peft>=0.7.0"], check=True)
+    import peft
+    print("PEFT installed successfully")
+
 import spaces
 import argparse
 import os
@@ -122,14 +132,34 @@ pipe = FluxPipeline.from_pretrained(
     torch_dtype=torch.bfloat16
 )
 
+# Track whether LoRA was loaded successfully
+LORA_LOADED = False
+
 print("Loading LoRA weights...")
-pipe.load_lora_weights(
-    hf_hub_download(
+try:
+    # Method 1: Try loading with file path
+    lora_path = hf_hub_download(
         "ByteDance/Hyper-SD", 
         "Hyper-FLUX.1-dev-8steps-lora.safetensors"
     )
-)
-pipe.fuse_lora(lora_scale=0.125)
+    pipe.load_lora_weights(lora_path)
+    pipe.fuse_lora(lora_scale=0.125)
+    LORA_LOADED = True
+    print("LoRA weights loaded and fused successfully (Method 1)")
+except Exception as e1:
+    print(f"Method 1 failed: {e1}")
+    try:
+        # Method 2: Try loading directly from repo
+        pipe.load_lora_weights("ByteDance/Hyper-SD", weight_name="Hyper-FLUX.1-dev-8steps-lora.safetensors")
+        pipe.fuse_lora(lora_scale=0.125)
+        LORA_LOADED = True
+        print("LoRA weights loaded and fused successfully (Method 2)")
+    except Exception as e2:
+        print(f"Method 2 failed: {e2}")
+        print("WARNING: Could not load LoRA weights. Continuing without LoRA.")
+        print("The model will still work but may require more inference steps for good quality.")
+        print("Recommended: Use 20-30 inference steps instead of 8.")
+
 pipe.to(device="cuda", dtype=torch.bfloat16)
 
 # Safety checker initialization
@@ -387,12 +417,14 @@ with gr.Blocks(theme=gr.themes.Soft(), css=css) as demo:
                     )
                 
                 with gr.Row():
+                    # Adjust default steps based on whether LoRA is loaded
+                    default_steps = 8 if LORA_LOADED else 20
                     steps = gr.Slider(
-                        label="Inference Steps",
+                        label="Inference Steps" + (" (LoRA Enabled)" if LORA_LOADED else " (No LoRA - More Steps Recommended)"),
                         minimum=6,
-                        maximum=25,
+                        maximum=50,
                         step=1,
-                        value=8
+                        value=default_steps
                     )
                     scales = gr.Slider(
                         label="Guidance Scale",