Png optimized?

app.py

@@ -20,7 +20,7 @@ def inference(img, size, type):
     INPUT_DIR = "/tmp/input_image" + str(_id) + "/"
     OUTPUT_DIR = "/tmp/output_image" + str(_id) + "/"
     img_in_path = os.path.join(INPUT_DIR, "1.jpg")
-    img_out_path = os.path.join(OUTPUT_DIR, "1_out.jpg")
+    img_out_path = os.path.join(OUTPUT_DIR, "1_out.png")
     run_cmd(f"rm -rf {INPUT_DIR}")
     run_cmd(f"rm -rf {OUTPUT_DIR}")
     run_cmd(f"mkdir {INPUT_DIR}")
@@ -47,7 +47,7 @@ def inference(img, size, type):
     return [img_out]
 
 input_image = gr.Image(type="pil", label="Input")
-upscale_type = gr.Radio(["Manga v2", "Anime", "General"], label="Select the type of picture you want to upscale:", value="Manga v2")
+upscale_type = gr.Radio(["Manga", "Anime", "General"], label="Select the type of picture you want to upscale:", value="Manga v2")
 upscale_size = gr.Radio(["x4", "x2"], label="Upscale by:", value="x4")
 output_image = gr.Gallery(label="Generated images", show_label=False, elem_id="gallery").style(grid=[2], height="auto")
 

inference.py

@@ -13,9 +13,7 @@ def is_cuda():
 
 model_type = sys.argv[3]
 
-if model_type == "Manga v1":
-    model_path = "4x_eula_digimanga_bw_v1_860k.pth"
-elif model_type == "Anime":
+if model_type == "Anime":
     model_path = "4x-AnimeSharp.pth"
 else:
     model_path = "4x-UniScaleV2_Sharp.pth"
@@ -55,4 +53,4 @@ with torch.no_grad():
     output = np.transpose(output[[2, 1, 0], :, :], (1, 2, 0))
     output = (output * 255.0).round()
 print('Finished upscaling, saving image.')
-cv2.imwrite(output_dir, output, [int(cv2.IMWRITE_JPEG_QUALITY), 95])
+cv2.imwrite(output_dir, output, [int(cv2.IMWRITE_PNG_COMPRESSION), 5])