Update app.py

app.py

@@ -1,4 +1,5 @@
 import os
+import torch
 import gradio as gr
 import numpy as np
 import random
@@ -16,90 +17,70 @@ MAX_SEED = np.iinfo(np.int32).max
 HF_TOKEN = os.environ.get("HF_TOKEN")
 HF_TOKEN_UPSCALER = os.environ.get("HF_TOKEN_UPSCALER")
 
-def enable_lora(lora_add, basemodel):
-    return basemodel if not lora_add else lora_add
+if not os.path.exists('GFPGANv1.4.pth'): os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth -P .")
+
+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)
 
 async def generate_image(prompt, model, lora_word, width, height, scales, steps, 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
+	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
+def get_upscale_gfpgan(prompt, img_path):
+	try: return gfpgan.enhance(img_path)
+	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 = enable_lora(lora_model, basemodel) if process_lora else basemodel
-    image, seed = await generate_image(prompt, model, "", width, height, scales, steps, seed)
-    if image is None:
-        return [None, None]
-    
-    image_path = "temp_image.png"
-    try:
-        image.save(image_path, format="PNG")
-    except Exception as e:
-        print(f"Error al guardar la imagen: {e}")
-        return [None, None]
-    
-    if process_upscale:
-        upscale_image = get_upscale_finegrain(prompt, image_path, upscale_factor)
-        if upscale_image is None:
-            return [image_path, image_path]
-        upscale_image_path = "upscale_image.png"
-        try:
-            upscale_image.save(upscale_image_path, format="PNG")
-        except Exception as e:
-            print(f"Error al guardar la imagen escalada: {e}")
-            return [image_path, None]  
-        return [image_path, upscale_image_path]
+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
 
-css = """
-#col-container{ margin: 0 auto; max-width: 1024px;}
-"""
+async def gen(prompt, basemodel, width, height, scales, steps, seed, upscale_factor, process_upscale, lora_model, process_lora, upscale_model):
+	model = enable_lora(lora_model, basemodel) if process_lora else basemodel
+	image, seed = await generate_image(prompt, model, "", width, height, scales, steps, seed)
+	if image is None: return [None, None]
+	image_path = "temp_image.jpg"; image.save(image_path, format="JPEG")
+	if process_upscale:
+		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]
+	else: return [image_path, image_path]
 
+css = """#col-container{ margin: 0 auto; max-width: 1024px;}"""
 with gr.Blocks(css=css, theme="Nymbo/Nymbo_Theme") as demo:
-    with gr.Column(elem_id="col-container"):
-        with gr.Row():
-            with gr.Column(scale=3):
-                output_res = ImageSlider(label="Flux / Upscaled")
-            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")
+	with gr.Column(elem_id="col-container"):
+		with gr.Row():
+			with gr.Column(scale=3): output_res = ImageSlider(label="Flux / Upscaled")
+			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)
+                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(
-                    fn=gen,
-                    inputs=[prompt, basemodel_choice, width, height, scales, steps, seed, upscale_factor, process_upscale, lora_model_choice, process_lora],
-                    outputs=[output_res]
-                )
+					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,
+				)
 
-demo.launch()
+	demo.launch()