Update app.py

app.py

@@ -7,7 +7,6 @@ from diffusers import  DiffusionPipeline, FlowMatchEulerDiscreteScheduler, Autoe
 from transformers import CLIPTextModel, CLIPTokenizer,T5EncoderModel, T5TokenizerFast
 from live_preview_helpers import calculate_shift, retrieve_timesteps, flux_pipe_call_that_returns_an_iterable_of_images
 
-
 dtype = torch.bfloat16
 device = "cuda" if torch.cuda.is_available() else "cpu"
 
@@ -21,7 +20,6 @@ MAX_IMAGE_SIZE = 2048
 
 pipe.flux_pipe_call_that_returns_an_iterable_of_images = flux_pipe_call_that_returns_an_iterable_of_images.__get__(pipe)
 
-
 @spaces.GPU()
 def infer(prompt, seed=42, randomize_seed=False, width=1024, height=1024, guidance_scale=3.5, num_inference_steps=28, lora_id=None, lora_scale=0.95, progress=gr.Progress(track_tqdm=True)):
     if randomize_seed:
@@ -68,31 +66,10 @@ def infer(prompt, seed=42, randomize_seed=False, width=1024, height=1024, guidan
         # Unload LoRA weights if they were loaded
         if lora_id:
             pipe.unload_lora_weights()
+    
 
 
 
-examples = [
-    "a tiny astronaut hatching from an egg on the moon",
-    "a cat holding a sign that says hello world",
-    "an anime illustration of a wiener schnitzel",
-]
-
-css = """
-#col-container {
-    margin: 0 auto;
-    max-width: 960px;
-}
-.generate-btn {
-    background: linear-gradient(90deg, #4B79A1 0%, #283E51 100%) !important;
-    border: none !important;
-    color: white !important;
-}
-.generate-btn:hover {
-    transform: translateY(-2px);
-    box-shadow: 0 5px 15px rgba(0,0,0,0.2);
-}
-"""
-
 # with gr.Blocks(css=css) as app:
 #     gr.HTML("<center><h1>FLUX.1-Dev with LoRA support</h1></center>")
 #     with gr.Column(elem_id="col-container"):
@@ -147,105 +124,3 @@ css = """
 #         # text_button.click(infer, inputs=[text_prompt, seed, randomize_seed, width, height, cfg, steps, custom_lora, lora_scale], outputs=[image_output,seed_output, seed])
 
 # app.launch(share=True)
-
-
-with gr.Blocks(css=css) as demo:
-    
-    with gr.Column(elem_id="col-container"):
-        gr.Markdown(f"""# FLUX.1 [dev] LoRA
-12B param rectified flow transformer guidance-distilled from [FLUX.1 [pro]](https://blackforestlabs.ai/)  
-[[non-commercial license](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md)] [[blog](https://blackforestlabs.ai/announcing-black-forest-labs/)] [[model](https://huggingface.co/black-forest-labs/FLUX.1-dev)]
-        """)
-        
-        with gr.Row():
-            
-            prompt = gr.Text(
-                label="Prompt",
-                show_label=False,
-                max_lines=1,
-                placeholder="Enter your prompt",
-                container=False,
-            )
-            
-            run_button = gr.Button("Run", scale=0)
-        
-        result = gr.Image(label="Result", show_label=False)
-        
-        with gr.Accordion("Advanced Settings", open=False):
-            
-            seed = gr.Slider(
-                label="Seed",
-                minimum=0,
-                maximum=MAX_SEED,
-                step=1,
-                value=0,
-            )
-            
-            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
-            
-            with gr.Row():
-                
-                width = gr.Slider(
-                    label="Width",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=8,
-                    value=1024,
-                )
-                
-                height = gr.Slider(
-                    label="Height",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=8,
-                    value=1024,
-                )
-            
-            with gr.Row():
-
-                guidance_scale = gr.Slider(
-                    label="Guidance Scale",
-                    minimum=1,
-                    maximum=15,
-                    step=0.1,
-                    value=3.5,
-                )
-  
-                num_inference_steps = gr.Slider(
-                    label="Number of inference steps",
-                    minimum=1,
-                    maximum=50,
-                    step=1,
-                    value=28,
-                )
-
-            with gr.Row():
-                lora_id = gr.Textbox(
-                    label="LoRA Model ID (HuggingFace path)",
-                    placeholder="username/lora-model",
-                    max_lines=1
-                )
-                lora_scale = gr.Slider(
-                    label="LoRA Scale",
-                    minimum=0,
-                    maximum=2,
-                    step=0.01,
-                    value=0.95,
-                )
-        
-        gr.Examples(
-            examples = examples,
-            fn = infer,
-            inputs = [prompt],
-            outputs = [result, seed],
-            cache_examples="lazy"
-        )
-
-    gr.on(
-        triggers=[run_button.click, prompt.submit],
-        fn = infer,
-        inputs = [prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps,lora_id,lora_scale],
-        outputs = [result, seed]
-    )
-
-demo.launch()