Update app.py

app.py

@@ -12,72 +12,77 @@ from gradio_client import Client, handle_file
 from huggingface_hub import login
 from gradio_imageslider import ImageSlider
 
-translator = Translator()
+MAX_SEED = np.iinfo(np.int32).max
 HF_TOKEN = os.environ.get("HF_TOKEN")
 HF_TOKEN_UPSCALER = os.environ.get("HF_TOKEN_UPSCALER")
-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, basemodel): 
+def enable_lora(lora_add, basemodel):
     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)
-    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)
-    return image, seed
+    try:
+        if seed == -1:
+            seed = random.randint(0, MAX_SEED)
+        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)
+        return image, seed
+    except Exception as e:
+        print(f"Error generating image: {e}")
+        return None, None
+
+def get_upscale_finegrain(prompt, img_path, upscale_factor):
+    try:
+        client = Client("finegrain/finegrain-image-enhancer", hf_token=HF_TOKEN_UPSCALER)
+        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 upscale image: {e}")
+        return None
 
 async def gen(prompt, basemodel, width, height, scales, steps, seed, upscale_factor, process_upscale, lora_model, process_lora):
-    model = lora_model
+    model = enable_lora(lora_model, basemodel) if process_lora else basemodel
     image, seed = await generate_image(prompt, model, "", width, height, scales, steps, seed)
-    image_path = "temp_image.png"
-    image.save(image_path)
+    if image is None:
+        return [None, None]
+    
+    image_path = "temp_image.jpg"
+    image.save(image_path, format="JPEG")
     
     if process_upscale:
         upscale_image = get_upscale_finegrain(prompt, image_path, upscale_factor)
+        upscale_image_path = "upscale_image.jpg"
+        upscale_image.save(upscale_image_path, format="JPEG")
+        return [image_path, upscale_image_path]
     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", hf_token=HF_TOKEN_UPSCALER)
-    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]
+        return [image_path, image_path]
 
 css = """
-#col-container{
-    margin: 0 auto;
-    max-width: 1024px;
-}
+#col-container{ margin: 0 auto; max-width: 1024px;}
 """
 
-with gr.Blocks(css=CSS, js=JS, theme="Nymbo/Nymbo_Theme") as demo:
+with gr.Blocks(css=css, theme="Nymbo/Nymbo_Theme") as demo:
     with gr.Column(elem_id="col-container"):
-        gr.Markdown("Flux Upscaled +LORA")
         with gr.Row():
-            with gr.Column(scale=1.5):
+            with gr.Column(scale=3):
                 output_res = ImageSlider(label="Flux / Upscaled")
-            with gr.Column(scale=0.8):
-                prompt = gr.Textbox(label="Prompt")
-                basemodel_choice = gr.Dropdown(label="Base Model", choices=["black-forest-labs/FLUX.1-schnell", "black-forest-labs/FLUX.1-DEV"], value="black-forest-labs/FLUX.1-schnell")
-                lora_model_choice = gr.Dropdown(label="LORA Model", choices=["Shakker-Labs/FLUX.1-dev-LoRA-add-details", "XLabs-AI/flux-RealismLora"])
-                process_lora = gr.Checkbox(label="Process LORA", value=True)
-                upscale_factor = gr.Radio(label="UpScale Factor", choices=[2, 4, 8], value=2, scale=2)
-                process_upscale = gr.Checkbox(label="Process Upscale", value=False)
+            with gr.Column(scale=2):
+                prompt = gr.Textbox(label="Descripción de imágen")
+                basemodel_choice = gr.Dropdown(label="Modelo", choices=["black-forest-labs/FLUX.1-schnell", "black-forest-labs/FLUX.1-DEV"], value="black-forest-labs/FLUX.1-schnell")
+                lora_model_choice = gr.Dropdown(label="LORA Realismo", choices=["Shakker-Labs/FLUX.1-dev-LoRA-add-details", "XLabs-AI/flux-RealismLora"], value="XLabs-AI/flux-RealismLora")
+                process_lora = gr.Checkbox(label="Procesar LORA")
+                process_upscale = gr.Checkbox(label="Procesar Escalador")
+                upscale_factor = gr.Radio(label="Factor de Escala", choices=[2, 4, 8], value=2)
                 
-                with gr.Accordion(label="Advanced Options", open=False):
-                    width = gr.Slider(label="Width", minimum=512, maximum=1280, step=8, value=512)
-                    height = gr.Slider(label="Height", minimum=512, maximum=1280, step=8, value=512)
-                    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)
-                    
-                submit_btn = gr.Button("Submit", scale=1)
+                with gr.Accordion(label="Opciones Avanzadas", open=False):
+                    width = gr.Slider(label="Ancho", minimum=512, maximum=1280, step=8, value=1280)
+                    height = gr.Slider(label="Alto", minimum=512, maximum=1280, step=8, value=768)
+                    scales = gr.Slider(label="Escalas", minimum=3.5, maximum=7, step=0.1, value=3.5)
+                    steps = gr.Slider(label="Pasos", minimum=1, maximum=100, step=1, value=24)
+                    seed = gr.Slider(label="Semillas", minimum=-1, maximum=MAX_SEED, step=1, value=-1)
+            
+                submit_btn = gr.Button("Crear", scale=1)
                 submit_btn.click(
                     fn=lambda: None,
                     inputs=None,
@@ -88,4 +93,5 @@ with gr.Blocks(css=CSS, js=JS, theme="Nymbo/Nymbo_Theme") as demo:
                     inputs=[prompt, basemodel_choice, width, height, scales, steps, seed, upscale_factor, process_upscale, lora_model_choice, process_lora],
                     outputs=[output_res]
                 )
+
 demo.launch()