Update app.py

app.py

@@ -311,23 +311,6 @@ def generate_image(prompt_mash, steps, seed, cfg_scale, width, height, progress)
             good_vae=good_vae,
         ):
             yield img
-
-#def generate_image_to_image(prompt_mash, image_input_path, image_strength, steps, cfg_scale, width, height, seed):
-#    pipe_i2i.to("cuda")
-#    generator = torch.Generator(device="cuda").manual_seed(seed)
-#    image_input = load_image(image_input_path)
-#    final_image = pipe_i2i(
-#        prompt=prompt_mash,
-#        image=image_input,
-#        strength=image_strength,
-#        num_inference_steps=steps,
-#        guidance_scale=cfg_scale,
-#        width=width,
-#        height=height,
-#        generator=generator,
-#        joint_attention_kwargs={"scale": 1.0},
-#        output_type="pil",
-#    ).images[0]
     return img
 
 @spaces.GPU(duration=75)
@@ -352,10 +335,10 @@ def run_lora(prompt, cfg_scale, steps, selected_indices, lora_scale_1, lora_scal
     # Unload previous LoRA weights
     with calculateDuration("Unloading LoRA"):
         pipe.unload_lora_weights()
-#        pipe_i2i.unload_lora_weights()
+
         
     print(pipe.get_active_adapters())
-    # Load LoRA weights with respective scales
+
     lora_names = []
     lora_weights = []
     with calculateDuration("Loading LoRA weights"):
@@ -373,35 +356,25 @@ def run_lora(prompt, cfg_scale, steps, selected_indices, lora_scale_1, lora_scal
                 low_cpu_mem_usage=True,
                 adapter_name=lora_name
             )
-#            if image_input is not None: pipe_i2i = pipe_to_use
-#            else: pipe = pipe_to_use
+
         print("Loaded LoRAs:", lora_names)
         print("Adapter weights:", lora_weights)
-#        if image_input is not None:
-#            pipe_i2i.set_adapters(lora_names, adapter_weights=lora_weights)
-#        else:
+
         pipe.set_adapters(lora_names, adapter_weights=lora_weights)
-#    print(pipe.get_active_adapters())
-    # Set random seed for reproducibility
+
     with calculateDuration("Randomizing seed"):
         if randomize_seed:
             seed = random.randint(0, MAX_SEED)
 
-# Generate image
-#    if image_input is not None:
-#        final_image = generate_image_to_image(prompt_mash, steps, cfg_scale, width, height, seed)
-#        yield final_image, seed, gr.update(visible=False)
-#    else:
         image_generator = generate_image(prompt_mash, steps, seed, cfg_scale, width, height, progress)
-# Consume the generator to get the final image
-#        final_image = None
+
         step_counter = 0
         for image in image_generator:
             step_counter += 1
             final_image = image
             progress_bar = f'<div class="progress-container"><div class="progress-bar" style="--current: {step_counter}; --total: {steps};"></div></div>'
             yield image, seed, gr.update(value=progress_bar, visible=True)
-#            yield final_image, seed, gr.update(value=progress_bar, visible=False)
+
 
 run_lora.zerogpu = True