Update app.py

app.py

@@ -17,52 +17,73 @@ MAX_SEED = np.iinfo(np.int32).max
 HF_TOKEN = os.environ.get("HF_TOKEN")
 HF_TOKEN_UPSCALER = os.environ.get("HF_TOKEN_UPSCALER")
 
-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 .")
+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_gfpgan(prompt, img_path):
-	try: return gfpgan.enhance(img_path)
-	except Exception as e: print(f"Error upscale image: {e}"); return None
+    try:
+        img = gfpgan.enhance(img_path)
+        return img
+    except Exception as e:
+        print(f"Error upscale image: {e}")
+        return 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
+    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, 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]
+    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;}
+"""
 
-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")
+    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")