Update app.py

app.py

@@ -17,23 +17,20 @@ lora_repo = "kaytoo2022/kaytoo2022-flux"
 trigger_word = ""  # Leave trigger_word blank if not used.
 pipe.load_lora_weights(lora_repo, adapter_name='kaytoo')
 
-lora_repo_ghibsky = "aleksa-codes/flux-ghibsky-illustration"
-pipe.load_lora_weights(lora_repo_ghibsky, adapter_name='ghibsky')
-
-pipe.set_adapters(["kaytoo", "ghibsky"], adapter_weights=[0.7, 0.3])
+# pipe.set_adapters(["kaytoo"], adapter_weights=[0.85])
 
 pipe.to("cuda")
 
 MAX_SEED = 2**32-1
 
 @spaces.GPU(duration=80)
-def run_lora(prompt, cfg_scale, steps, randomize_seed, seed, width, height, lora_scale, lora_scale_ghibsky, progress=gr.Progress(track_tqdm=True)):
+def run_lora(prompt, cfg_scale, steps, randomize_seed, seed, width, height, lora_scale, progress=gr.Progress(track_tqdm=True)):
     # Set random seed for reproducibility
     if randomize_seed:
         seed = random.randint(0, MAX_SEED)
     generator = torch.Generator(device="cuda").manual_seed(seed)
 
-    pipe.set_adapters(["kaytoo", "ghibsky"], adapter_weights=[lora_scale, lora_scale_ghibsky])
+    # pipe.set_adapters(["kaytoo"], adapter_weights=[lora_scale])
 
     # Update progress bar (0% saat mulai)
     progress(0, "Starting image generation...")
@@ -52,7 +49,7 @@ def run_lora(prompt, cfg_scale, steps, randomize_seed, seed, width, height, lora
         width=width,
         height=height,
         generator=generator,
-        # joint_attention_kwargs={"scale": lora_scale},
+        joint_attention_kwargs={"scale": lora_scale},
     ).images[0]
 
     # Final update (100%)
@@ -69,12 +66,11 @@ example_width = 1152
 example_height = 896
 example_seed = 3981632454
 example_lora_scale = 0.8
-example_lora_scale_ghibsky = 0.3
 
 def load_example():
     # Load example image from file
     example_image = Image.open(example_image_path)
-    return example_prompt, example_cfg_scale, example_steps, True, example_seed, example_width, example_height, example_lora_scale, example_lora_scale_ghibsky, example_image
+    return example_prompt, example_cfg_scale, example_steps, True, example_seed, example_width, example_height, example_lora_scale, example_image
 
 with gr.Blocks() as app:
     gr.Markdown("# Flux Lora Image Generator")
@@ -89,17 +85,16 @@ with gr.Blocks() as app:
             randomize_seed = gr.Checkbox(True, label="Randomize seed")
             seed = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=example_seed)
             lora_scale = gr.Slider(label="LoRA Scale", minimum=0, maximum=1, step=0.01, value=example_lora_scale)
-            lora_scale_ghibsky = gr.Slider(label="LoRA Scale (Ghibli sky)", minimum=0, maximum=1, step=0.01, value=example_lora_scale_ghibsky)
         with gr.Column(scale=1):
             result = gr.Image(label="Generated Image")
             gr.Markdown("Generate images using Flux and a text prompt.\nUse `b3lla dog` in the prompt to trigger generating an image of Bella the dog.\n[[non-commercial license, Flux.1 Dev](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md)]")
 
     # Automatically load example data and image when the interface is launched
-    app.load(load_example, inputs=[], outputs=[prompt, cfg_scale, steps, randomize_seed, seed, width, height, lora_scale, lora_scale_ghibsky, result])
+    app.load(load_example, inputs=[], outputs=[prompt, cfg_scale, steps, randomize_seed, seed, width, height, lora_scale, result])
     
     generate_button.click(
         run_lora,
-        inputs=[prompt, cfg_scale, steps, randomize_seed, seed, width, height, lora_scale, lora_scale_ghibsky],
+        inputs=[prompt, cfg_scale, steps, randomize_seed, seed, width, height, lora_scale],
         outputs=[result, seed]
     )