testing with 2 loras

app.py

@@ -23,7 +23,7 @@ with open('loras.json', 'r') as f:
     loras = json.load(f)
 
 # Initialize the base model
-base_model = "stabilityai/stable-diffusion-xl-base-1.0"
+base_model = "stabilityai/stable-diffusion-xl-refiner-1.0"
 pipe = DiffusionPipeline.from_pretrained(base_model, torch_dtype=torch.float16)
 pipe.to("cuda")
 
@@ -92,7 +92,7 @@ def run_lora(prompt, negative_prompt, cfg_scale, steps, selected_index, schedule
     generator = torch.Generator(device="cuda").manual_seed(seed)
 
     # Generate image
-    logo_image = pipe(
+    image = pipe(
         prompt=f"{prompt} logo, logoredmaf, icons",
         negative_prompt=negative_prompt,
         num_inference_steps=steps,
@@ -105,24 +105,8 @@ def run_lora(prompt, negative_prompt, cfg_scale, steps, selected_index, schedule
 
     # Unload LoRA weights
     pipe.unload_lora_weights()
-    pipe.load_lora_weights("Abdullah-Habib/icon-lora", scale=1)
 
-    # Pass the generated logo through the icon LoRA
-    rounded_square_image = pipe(
-        prompt="rounded square icon",
-        image=logo_image,  # Pass the generated logo image
-        num_inference_steps=10,
-        guidance_scale=cfg_scale,
-        width=width,
-        height=height,
-        generator=generator,
-    ).images[0]
-
-    # Unload the icon LoRA
-    pipe.unload_lora_weights()
-
-    return rounded_square_image
-    # return image
+    return image
 
 with gr.Blocks(theme=gr.themes.Soft()) as app: