Update app.py

app.py

@@ -12,6 +12,7 @@ from gradio_client import Client, handle_file
 from huggingface_hub import login
 from gradio_imageslider import ImageSlider
 
+
 translator = Translator()
 HF_TOKEN = os.environ.get("HF_TOKEN", None)
 basemodel = "black-forest-labs/FLUX.1-schnell"
@@ -19,44 +20,78 @@ MAX_SEED = np.iinfo(np.int32).max
 CSS = "footer { visibility: hidden; }"
 JS = "function () { gradioURL = window.location.href; if (!gradioURL.endsWith('?__theme=dark')) { window.location.replace(gradioURL + '?__theme=dark'); } }"
 
-def enable_lora(lora_add): return basemodel if not lora_add else lora_add
+
+def enable_lora(lora_add): 
+    return basemodel if not lora_add else lora_add
+
+
 async def generate_image(prompt, model, lora_word, width, height, scales, steps, seed):
-    if seed == -1: seed = random.randint(0, MAX_SEED)
+    if seed == -1: 
+        seed = random.randint(0, MAX_SEED)
     seed = int(seed)
     text = str(translator.translate(prompt, 'English')) + "," + lora_word
     client = AsyncInferenceClient()
-    try: image = await client.text_to_image(prompt=text, height=height, width=width, guidance_scale=scales, num_inference_steps=steps, model=model)
-    except Exception as e: raise gr.Error(f"Error in {e}")
+    try: 
+        image = await client.text_to_image(prompt=text, height=height, width=width, guidance_scale=scales, num_inference_steps=steps, model=model)
+    except Exception as e: 
+        raise gr.Error(f"Error in {e}")
     return image, seed
 
-async def gen(prompt, lora_add, lora_word, width, height, scales, steps, seed, upscale_factor, progress):
+
+async def gen(prompt, lora_add, lora_word, width, height, scales, steps, seed, upscale_factor, process_upscale):
     model = enable_lora(lora_add)
     image, seed = await generate_image(prompt, model, lora_word, width, height, scales, steps, seed)
     image_path = "temp_image.png"
     image.save(image_path)
-    upscale_image = get_upscale_finegrain(prompt, image_path, upscale_factor)
-    return upscale_image, seed
+    
+    if process_upscale:
+        upscale_image = get_upscale_finegrain(prompt, image_path, upscale_factor)
+    else:
+        upscale_image = image_path
+    
+    return [image_path, upscale_image]
+
 
 def get_upscale_finegrain(prompt, img_path, upscale_factor):
     client = Client("finegrain/finegrain-image-enhancer")
     result = client.predict(input_image=handle_file(img_path), prompt=prompt, negative_prompt="", seed=42, upscale_factor=upscale_factor, controlnet_scale=0.6, controlnet_decay=1, condition_scale=6, tile_width=112, tile_height=144, denoise_strength=0.35, num_inference_steps=18, solver="DDIM", api_name="/process")
     return result[1]
 
-with gr.Blocks(css=CSS, js=JS, theme="Nymbo/Nymbo_Theme") as demo:
-    gr.HTML("<h1><center>Flux Lab Light</center></h1>"); 
-    with gr.Row(): 
-        with gr.Column(scale=4): 
-            with gr.Row(): img = gr.Image(type="filepath", label='flux Generated Image', height=600); 
-            with gr.Row(): prompt = gr.Textbox(label='Enter Your Prompt (Multi-Languages)', placeholder="Enter prompt...", scale=6); sendBtn = gr.Button(scale=1, variant='primary'); 
-        with gr.Accordion("Advanced Options", open=True): 
-            with gr.Column(scale=1): 
-                width = gr.Slider(label="Width", minimum=512, maximum=1280, step=8, value=768); 
-                height = gr.Slider(label="Height", minimum=512, maximum=1280, step=8, value=1024); 
-                scales = gr.Slider(label="Guidance", minimum=3.5, maximum=7, step=0.1, value=3.5); 
-                steps = gr.Slider(label="Steps", minimum=1, maximum=100, step=1, value=24); 
-                seed = gr.Slider(label="Seeds", minimum=-1, maximum=MAX_SEED, step=1, value=-1); 
-                lora_add = gr.Textbox(label="Add Flux LoRA", info="Copy the HF LoRA model name here", lines=1, placeholder="Please use Warm status model"); 
-                lora_word = gr.Textbox(label="Add Flux LoRA Trigger Word", info="Add the Trigger Word", lines=1, value=""); 
+
+css = """
+#col-container{
+    margin: 0 auto;
+    max-width: 1024px;
+}
+"""
+
+
+with gr.Blocks(css=css) as demo:
+    with gr.Column(elem_id="col-container"):
+        gr.Markdown("# Flux Upscaled")
+        gr.Markdown("Step 1: Generate image with FLUX schnell; Step 2: UpScale with Finegrain Image-Enhancer")
+        with gr.Group():
+            prompt = gr.Textbox(label="Prompt")
+            with gr.Row():
+                lora_add = gr.Textbox(label="Add Flux LoRA", info="Copy the HF LoRA model name here", lines=1, placeholder="Please use Warm status model")
+                lora_word = gr.Textbox(label="Add Flux LoRA Trigger Word", info="Add the Trigger Word", lines=1, value="")
+                width = gr.Slider(label="Width", minimum=512, maximum=1280, step=8, value=768)
+                height = gr.Slider(label="Height", minimum=512, maximum=1280, step=8, value=1024)
+                scales = gr.Slider(label="Guidance", minimum=3.5, maximum=7, step=0.1, value=3.5)
+                steps = gr.Slider(label="Steps", minimum=1, maximum=100, step=1, value=24)
+                seed = gr.Slider(label="Seeds", minimum=-1, maximum=MAX_SEED, step=1, value=-1)
                 upscale_factor = gr.Radio(label="UpScale Factor", choices=[2, 3, 4], value=2, scale=2)
-    gr.on([prompt.submit, sendBtn.click], gen, [prompt, lora_add, lora_word, width, height, scales, steps, seed, upscale_factor], [img, seed])
-demo.queue(api_open=False).launch(show_api=False, share=False)
+                process_upscale = gr.Checkbox(label="Process Upscale", value=True)
+                submit_btn = gr.Button("Submit", scale=1)
+            output_res = ImageSlider(label="Flux / Upscaled")
+
+        submit_btn.click(
+            fn=lambda: None,
+            inputs=None,
+            outputs=[output_res],
+            queue=False
+        ).then(
+            fn=gen,
+            inputs=[prompt, lora_add, lora_word, width, height, scales, steps, seed, upscale_factor, process_upscale],
+            outputs=[output_res]
+        )