update labels

html/locale_en.json

@@ -118,6 +118,7 @@
   {"id":"","label":"Tiling","localized":"","hint":"Produce an image that can be tiled"},
   {"id":"","label":"full quality","localized":"","hint":"Use full quality VAE to decode latent samples"},
   {"id":"","label":"face restore","localized":"","hint":"Run processed image through additional face restoration model"},
+  {"id":"","label":"hidiffusion","localized":"","hint":"HiDiffusion allows creation of high-resolution images using your standard models without duplicates/distorsions and improved performance"},
   {"id":"","label":"HDR Clamp","localized":"","hint":"Adjusts the level of nonsensical details by pruning values that deviate significantly from the distribution mean. It is particularly useful for enhancing generation at higher guidance scales, identifying outliers early in the process and applying mathematical adjustments based on the Range (Boundary) and Threshold settings. Think of it as setting the range within which you want your image values to be, and adjusting the threshold determines which values should be brought back into that range"},
   {"id":"","label":"HDR Maximize","localized":"","hint":"Calculates a 'normalization factor' by dividing the maximum tensor value by the specified range multiplied by 4. This factor is then used to shift the channels within the given boundary, ensuring maximum dynamic range for subsequent processing. The objective is to optimize dynamic range for external applications like Photoshop, particularly for adjusting levels, contrast, and brightness"},
   {"id":"","label":"Enable second pass","localized":"","hint":"Use a similar process as image to image to upscale and/or add detail to the final image. Optionally uses refiner model to enhance image details."},

modules/ui_postprocessing.py

@@ -76,7 +76,7 @@ def create_ui():
                     with gr.Row():
                         vqa_image = gr.Image(type='pil', label="Image")
                     with gr.Row():
-                        vqa_question = gr.Textbox(label="Question", placeholder="Descirbe the image")
+                        vqa_question = gr.Textbox(label="Question", placeholder="Describe the image")
                     with gr.Row():
                         vqa_answer = gr.Textbox(label="Answer", lines=3)
                     with gr.Row(elem_id='interrogate_buttons_query'):