Update app.py

app.py

@@ -22,17 +22,7 @@ if not os.path.exists('GFPGANv1.4.pth'):
 
 device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
 model_path = 'GFPGANv1.4.pth'
-gfpgan = GFPGANer(
-    model_path=model_path,
-    upscale_factor=4,
-    arch='clean',
-    channel_multiplier=2,
-    model_name='GPFGAN',
-    device=device
-)
-
-def enable_lora(lora_add, basemodel):
-    return basemodel if not lora_add else lora_add
+gfpgan = GFPGANer(model_path=model_path, upscale_factor=4, arch='clean', channel_multiplier=2, model_name='GPFGAN', device=device)
 
 async def generate_image(prompt, model, lora_word, width, height, scales, steps, seed):
     try:
@@ -47,19 +37,19 @@ async def generate_image(prompt, model, lora_word, width, height, scales, steps,
         print(f"Error generating image: {e}")
         return None, None
 
-def get_upscale_finegrain(prompt, img_path, upscale_factor):
+def get_upscale_gfpgan(prompt, img_path):
     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]
+        img = gfpgan.enhance(img_path)
+        return img
     except Exception as e:
         print(f"Error upscale image: {e}")
         return None
 
-def get_upscale_gfpgan(prompt, img_path):
+def get_upscale_finegrain(prompt, img_path, upscale_factor):
     try:
-        img = gfpgan.enhance(img_path)
-        return img
+        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
@@ -74,10 +64,10 @@ async def gen(prompt, basemodel, width, height, scales, steps, seed, upscale_fac
     image.save(image_path, format="JPEG")
     
     if process_upscale:
-        if upscale_model == "FineGrain":
-            upscale_image = get_upscale_finegrain(prompt, image_path, upscale_factor)
-        elif upscale_model == "GPFGAN":
+        if upscale_model == "GPFGAN":
             upscale_image = get_upscale_gfpgan(prompt, image_path)
+        elif upscale_model == "Finegrain":
+            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]
@@ -100,25 +90,20 @@ with gr.Blocks(css=css, theme="Nymbo/Nymbo_Theme") as demo:
                 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)
-                upscale_model = gr.Radio(label="Modelo de Escalado", choices=["FineGrain", "GPFGAN"], value="GPFGAN")
+                upscale_model = gr.Radio(label="Modelo de Escalado", choices=["GPFGAN", "Finegrain"], value="GPFGAN")
                 
                 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,
-                    outputs=[output_res],
-                    queue=False
-                ).then(
+                    width = gr.Slider(label="Ancho", minimum=512, maximum=1280, step=8, value=512)
+                    height = gr.Slider(label="Alto", minimum=512, maximum=1280, step=8, value=512)
+                    scales = gr.Slider(label="Escalado", minimum=1, maximum=20, step=1, value=10)
+                    steps = gr.Slider(label="Pasos", minimum=1, maximum=100, step=1, value=20)
+                    seed = gr.Number(label="Semilla", value=-1)
+
+                btn = gr.Button("Generar")
+                btn.click(
                     fn=gen,
-                    inputs=[prompt, basemodel_choice, width, height, scales, steps, seed, upscale_factor, process_upscale, lora_model_choice, process_lora, upscale_model],
-                    outputs=[output_res]
+                    inputs=[prompt, basemodel_choice, width, height, scales, steps, seed, upscale_factor, process_upscale, lora_model_choice, process_lora, upscale_model,],
+                    outputs=output_res,
                 )
 
 demo.launch()