Update app.py

app.py

@@ -14,6 +14,7 @@ from gradio_imageslider import ImageSlider
 
 MAX_SEED = np.iinfo(np.int32).max
 
+
 def enable_lora(lora_add, basemodel):
     return basemodel if not lora_add else lora_add
 
@@ -24,41 +25,19 @@ async def generate_image(prompt, model, lora_word, width, height, scales, steps,
         seed = int(seed)
         text = str(Translator().translate(prompt, 'English')) + "," + lora_word
         client = AsyncInferenceClient()
-        image = await client.text_to_image(
-            prompt=text,
-            height=height,
-            width=width,
-            guidance_scale=scales,
-            num_inference_steps=steps,
-            model=model
-        )
+        image = await client.text_to_image(prompt=text, height=height, width=width, guidance_scale=scales, num_inference_steps=steps, model=model)
         return image, seed
     except Exception as e:
-        print(f"Error generating image: {e}")
+        print(f"Error generando imagen: {e}")
         return None, None
 
 def get_upscale_finegrain(prompt, img_path, upscale_factor):
     try:
         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"
-        )
+        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]
     except Exception as e:
-        print(f"Error scaling image: {e}")
+        print(f"Error escalando imagen: {e}")
         return None
 
 async def gen(prompt, basemodel, width, height, scales, steps, seed, upscale_factor, process_upscale, lora_model, process_lora):
@@ -82,10 +61,6 @@ async def gen(prompt, basemodel, width, height, scales, steps, seed, upscale_fac
     else:
         return [image_path, image_path]
 
-# Helper to run async functions synchronously
-def run_async(fn, *args, **kwargs):
-    return asyncio.run(fn(*args, **kwargs))
-
 css = """
 #col-container{ margin: 0 auto; max-width: 1024px;}
 """
@@ -97,27 +72,8 @@ with gr.Blocks(css=css) as demo:
                 output_res = ImageSlider(label="Flux / Upscaled")
             with gr.Column(scale=2):
                 prompt = gr.Textbox(label="Image Description")
-                basemodel_choice = gr.Dropdown(
-                    label="Model",
-                    choices=[
-                        "black-forest-labs/FLUX.1-schnell",
-                        "black-forest-labs/FLUX.1-DEV",
-                        "enhanceaiteam/Flux-uncensored",
-                        "Shakker-Labs/FLUX.1-dev-ControlNet-Union-Pro",
-                        "Shakker-Labs/FLUX.1-dev-LoRA-add-details",
-                        "city96/FLUX.1-dev-gguf"
-                    ],
-                    value="black-forest-labs/FLUX.1-schnell"
-                )
-                lora_model_choice = gr.Dropdown(
-                    label="LoRA",
-                    choices=[
-                        "Shakker-Labs/FLUX.1-dev-LoRA-add-details",
-                        "XLabs-AI/flux-RealismLora",
-                        "enhanceaiteam/Flux-uncensored"
-                    ],
-                    value="XLabs-AI/flux-RealismLora"
-                )
+                basemodel_choice = gr.Dropdown(label="Model", choices=["black-forest-labs/FLUX.1-schnell", "black-forest-labs/FLUX.1-DEV", "enhanceaiteam/Flux-uncensored", "Shakker-Labs/FLUX.1-dev-ControlNet-Union-Pro", "Shakker-Labs/FLUX.1-dev-LoRA-add-details", "city96/FLUX.1-dev-gguf"], value="black-forest-labs/FLUX.1-schnell")
+                lora_model_choice = gr.Dropdown(label="LoRA", choices=["Shakker-Labs/FLUX.1-dev-LoRA-add-details", "XLabs-AI/flux-RealismLora", "enhanceaiteam/Flux-uncensored"], value="XLabs-AI/flux-RealismLora")
                 process_lora = gr.Checkbox(label="LoRA Process")
                 process_upscale = gr.Checkbox(label="Scale Process")
                 upscale_factor = gr.Radio(label="Scaling Factor", choices=[2, 4, 8], value=2)
@@ -130,12 +86,5 @@ with gr.Blocks(css=css) as demo:
                     seed = gr.Number(label="Seed", value=-1)
     
                 btn = gr.Button("Generate")
-                btn.click(
-                    fn=lambda *inputs: run_async(gen, *inputs),
-                    inputs=[
-                        prompt, basemodel_choice, width, height, scales, steps, seed,
-                        upscale_factor, process_upscale, lora_model_choice, process_lora
-                    ],
-                    outputs=output_res
-                )
-    demo.launch()
+                btn.click(fn=gen, inputs=[prompt, basemodel_choice, width, height, scales, steps, seed, upscale_factor, process_upscale, lora_model_choice, process_lora], outputs=output_res,)
+    demo.launch()