Increased optimization steps

app.py

@@ -159,13 +159,14 @@ def inference(
         end_step_size=final_step_size,
         loss_stopping_value=loss_threshold,
         num_clusters_per_box=num_clusters_per_subject,
+        max_resolution=32,
     )
 
     register_attention_editor_diffusers(model, editor)
     return model(prompts, latents=start_code, guidance_scale=classifier_free_guidance_scale).images
 
 
-@spaces.GPU(duration=300)
+@spaces.GPU(duration=400)
 def generate(
     prompt,
     subject_token_indices,
@@ -299,7 +300,7 @@ def main():
                 with gr.Column():
                     gr.HTML(ADVANCED_OPTION_DESCRIPTION)
                     batch_size = gr.Slider(minimum=1, maximum=5, step=1, value=1, label="Number of samples (limited to one sample on current space)")
-                    num_guidance_steps = gr.Slider(minimum=5, maximum=20, step=1, value=5, label="Number of timesteps to perform guidance")
+                    num_guidance_steps = gr.Slider(minimum=5, maximum=20, step=1, value=8, label="Number of timesteps to perform guidance")
                     init_step_size = gr.Slider(minimum=0, maximum=50, step=0.5, value=30, label="Initial step size")
                     final_step_size = gr.Slider(minimum=0, maximum=20, step=0.5, value=15, label="Final step size")
                     num_clusters_per_subject = gr.Slider(minimum=0, maximum=5, step=0.5, value=3, label="Number of clusters per subject")