Update app.py

app.py

@@ -126,12 +126,12 @@ def classify_gallery(flux_loras):
     sorted_gallery = sorted(flux_loras, key=lambda x: x.get("likes", 0), reverse=True)
     return [(item["image"], item["title"]) for item in sorted_gallery], sorted_gallery
 
-def infer_with_lora_wrapper(input_image, prompt, selected_index, custom_lora, seed=42, randomize_seed=False, guidance_scale=2.5, lora_scale=1.75, flux_loras=None, progress=gr.Progress(track_tqdm=True)):
+def infer_with_lora_wrapper(input_image, prompt, selected_index, custom_lora, seed=42, randomize_seed=False, steps=28, guidance_scale=2.5, lora_scale=1.75, flux_loras=None, progress=gr.Progress(track_tqdm=True)):
     """Wrapper function to handle state serialization"""
-    return infer_with_lora(input_image, prompt, selected_index, custom_lora, seed, randomize_seed, guidance_scale, lora_scale, flux_loras, progress)
+    return infer_with_lora(input_image, prompt, selected_index, custom_lora, seed, randomize_seed, steps, guidance_scale, lora_scale, flux_loras, progress)
 
 @spaces.GPU
-def infer_with_lora(input_image, prompt, selected_index, custom_lora, seed=42, randomize_seed=False, guidance_scale=2.5, lora_scale=1.0, flux_loras=None, progress=gr.Progress(track_tqdm=True)):
+def infer_with_lora(input_image, prompt, selected_index, custom_lora, seed=42, randomize_seed=False, steps=28, guidance_scale=2.5, lora_scale=1.0, flux_loras=None, progress=gr.Progress(track_tqdm=True)):
     """Generate image with selected LoRA"""
     global current_lora, pipe
     
@@ -181,6 +181,7 @@ def infer_with_lora(input_image, prompt, selected_index, custom_lora, seed=42, r
             image=input_image, 
             prompt=prompt,
             guidance_scale=guidance_scale,
+            num_inference_steps=steps,
             generator=torch.Generator().manual_seed(seed),
         ).images[0]
         
@@ -291,6 +292,13 @@ with gr.Blocks(css=css) as demo:
                     step=1,
                     value=0,
                 )
+                steps = gr.Slider(
+                    label="Steps",
+                    minimum=1,
+                    maximum=40,
+                    value=28,
+                    step=1
+                )
                 randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
                 guidance_scale = gr.Slider(
                     label="Guidance Scale",
@@ -328,7 +336,7 @@ with gr.Blocks(css=css) as demo:
     gr.on(
         triggers=[run_button.click, prompt.submit],
         fn=infer_with_lora_wrapper,
-        inputs=[input_image, prompt, selected_state, custom_loaded_lora, seed, randomize_seed, guidance_scale, lora_scale, gr_flux_loras],
+        inputs=[input_image, prompt, selected_state, custom_loaded_lora, seed, randomize_seed, steps, guidance_scale, lora_scale, gr_flux_loras],
         outputs=[result, seed, reuse_button]
     )