diff --git a/scripts/openvino_accelerate.py b/scripts/openvino_accelerate.py
index 2bcd5002681..759d38eabb3 100644
--- a/scripts/openvino_accelerate.py
+++ b/scripts/openvino_accelerate.py
@@ -53,6 +53,7 @@
     StableDiffusionXLImg2ImgPipeline,
     StableDiffusionXLInpaintPipeline,
     ControlNetModel,
+    StableDiffusionLatentUpscalePipeline,
     DDIMScheduler,
     DPMSolverMultistepScheduler,
     EulerAncestralDiscreteScheduler,
@@ -535,6 +536,23 @@ class NoWatermark:
     def apply_watermark(self, img):
         return img
 
+
+def get_diffusers_upscaler(upscaler: str):
+    torch._dynamo.reset()
+    openvino_clear_caches()
+    model_name = "stabilityai/sd-x2-latent-upscaler"
+    print("OpenVINO Script: loading upscaling model: " + model_name)
+    sd_model = StableDiffusionLatentUpscalePipeline.from_pretrained(model_name, torch_dtype=torch.float32)
+    sd_model.safety_checker = None
+    sd_model.cond_stage_key = functools.partial(cond_stage_key, shared.sd_model)
+    sd_model.unet = torch.compile(sd_model.unet, backend="openvino")
+    sd_model.vae.decode = torch.compile(sd_model.vae.decode, backend="openvino")
+    shared.sd_diffusers_model = sd_model
+    del sd_model
+
+    return shared.sd_diffusers_model
+
+
 def get_diffusers_sd_model(model_config, vae_ckpt, sampler_name, enable_caching, openvino_device, mode, is_xl_ckpt, refiner_ckpt, refiner_frac):
     if (model_state.recompile == 1):
         model_state.partition_id = 0
@@ -770,7 +788,8 @@ def init_new(self, all_prompts, all_seeds, all_subseeds):
     else:
         raise RuntimeError(f"bad number of images passed: {len(imgs)}; expecting {self.batch_size} or less")
 
-def process_images_openvino(p: StableDiffusionProcessing, model_config, vae_ckpt, sampler_name, enable_caching, openvino_device, mode, is_xl_ckpt, refiner_ckpt, refiner_frac) -> Processed:
+def process_images_openvino(p: StableDiffusionProcessing, model_config, vae_ckpt, sampler_name, enable_caching, override_hires, upscaler, hires_steps, d_strength, openvino_device, mode, is_xl_ckpt, refiner_ckpt, refiner_frac) -> Processed:
+    """this is the main loop that both txt2img and img2img use; it calls func_init once inside all the scopes and func_sample once per batch"""
 
     """this is the main loop that both txt2img and img2img use; it calls func_init once inside all the scopes and func_sample once per batch"""
     if (mode == 0 and p.enable_hr):
@@ -1092,6 +1111,23 @@ def callback(iter, t, latents):
 
     devices.torch_gc()
 
+    # Hight resolutuon mode
+    if override_hires:
+        if upscaler == "Latent":
+            model_state.mode = -1
+            shared.sd_diffusers_model = get_diffusers_upscaler(upscaler)
+            img_idx = slice(len(output_images)) if p.batch_size == 1 else slice(1, len(output_images))
+            output_images[img_idx] = shared.sd_diffusers_model(
+                        image=output_images[img_idx],
+                        prompt=p.prompts,
+                        negative_prompt=p.negative_prompts,
+                        num_inference_steps=hires_steps,
+                        guidance_scale=p.cfg_scale,
+                        generator=generator,
+                        callback = callback,
+                        callback_steps = 1,
+            ).images
+
     res = Processed(
         p,
         images_list=output_images,
@@ -1102,7 +1138,8 @@ def callback(iter, t, latents):
         index_of_first_image=index_of_first_image,
         infotexts=infotexts,
     )
-
+    if override_hires:
+        res.info = res.info + f", Hires upscaler: {upscaler}, Denoising strength: {d_strength}"
     res.info = res.info + ", Warm up time: " + str(round(warmup_duration, 2)) + " secs "
 
     if (generation_rate >= 1.0):
@@ -1116,6 +1153,9 @@ def callback(iter, t, latents):
 
     return res
 
+def on_change(mode):
+    return gr.update(visible=mode)
+
 class Script(scripts.Script):
     def title(self):
         return "Accelerate with OpenVINO"
@@ -1170,6 +1210,12 @@ def get_refiner_list():
         override_sampler = gr.Checkbox(label="Override the sampling selection from the main UI (Recommended as only below sampling methods have been validated for OpenVINO)", value=True)
         sampler_name = gr.Radio(label="Select a sampling method", choices=["Euler a", "Euler", "LMS", "Heun", "DPM++ 2M", "LMS Karras", "DPM++ 2M Karras", "DDIM", "PLMS"], value="Euler a")
         enable_caching = gr.Checkbox(label="Cache the compiled models on disk for faster model load in subsequent launches (Recommended)", value=True, elem_id=self.elem_id("enable_caching"))
+        override_hires = gr.Checkbox(label="Override the Hires.fix selection from the main UI (Recommended as only below upscalers have been validated for OpenVINO)", value=False, visible=self.is_txt2img)
+        with gr.Group(visible=False) as hires:
+            with gr.Row():
+                upscaler = gr.Dropdown(label="Upscaler", choices=["Latent"], value="Latent")
+                hires_steps = gr.Slider(1, 150, value=10, step=1, label="Steps")
+                d_strength = gr.Slider(0, 1, value=0.5, step=0.01, label="Strength")
         warmup_status = gr.Textbox(label="Device", interactive=False, visible=False)
         vae_status = gr.Textbox(label="VAE", interactive=False, visible=False)
         gr.Markdown(
@@ -1184,6 +1230,8 @@ def get_refiner_list():
         So it's normal for the first inference after a settings change to be slower, while subsequent inferences use the optimized compiled model and run faster.
         """)
 
+        override_hires.change(on_change, override_hires, hires)
+
         def device_change(choice):
             if (model_state.device == choice):
                 return gr.update(value="Device selected is " + choice, visible=True)
@@ -1206,9 +1254,9 @@ def refiner_ckpt_change(choice):
             else:
                 model_state.refiner_ckpt = choice
         refiner_ckpt.change(refiner_ckpt_change, refiner_ckpt)
-        return [model_config, vae_ckpt, openvino_device, override_sampler, sampler_name, enable_caching, is_xl_ckpt, refiner_ckpt, refiner_frac]
+        return [model_config, vae_ckpt, openvino_device, override_sampler, sampler_name, enable_caching, override_hires, upscaler, hires_steps, d_strength, is_xl_ckpt, refiner_ckpt, refiner_frac]
 
-    def run(self, p, model_config, vae_ckpt, openvino_device, override_sampler, sampler_name, enable_caching, is_xl_ckpt, refiner_ckpt, refiner_frac):
+    def run(self, p, model_config, vae_ckpt, openvino_device, override_sampler, sampler_name, enable_caching, override_hires, upscaler, hires_steps, d_strength, is_xl_ckpt, refiner_ckpt, refiner_frac):
         os.environ["OPENVINO_TORCH_BACKEND_DEVICE"] = str(openvino_device)
 
         if enable_caching:
@@ -1225,14 +1273,12 @@ def run(self, p, model_config, vae_ckpt, openvino_device, override_sampler, samp
         mode = 0
         if self.is_txt2img:
             mode = 0
-            processed = process_images_openvino(p, model_config, vae_ckpt, p.sampler_name, enable_caching, openvino_device, mode, is_xl_ckpt, refiner_ckpt, refiner_frac)
+            processed = process_images_openvino(p, model_config, vae_ckpt, p.sampler_name, enable_caching, override_hires, upscaler, hires_steps, d_strength, openvino_device, mode, is_xl_ckpt, refiner_ckpt, refiner_frac)
         else:
             if p.image_mask is None:
                 mode = 1
             else:
                 mode = 2
             p.init = functools.partial(init_new, p)
-            processed = process_images_openvino(p, model_config, vae_ckpt, p.sampler_name, enable_caching, openvino_device, mode, is_xl_ckpt, refiner_ckpt, refiner_frac)
+            processed = process_images_openvino(p, model_config, vae_ckpt, p.sampler_name, enable_caching, override_hires, upscaler, hires_steps, d_strength, openvino_device, mode, is_xl_ckpt, refiner_ckpt, refiner_frac)
         return processed
-
-