diff --git a/iz_helpers/InfZoomConfig.py b/iz_helpers/InfZoomConfig.py
index 44d9783..46167c1 100644
--- a/iz_helpers/InfZoomConfig.py
+++ b/iz_helpers/InfZoomConfig.py
@@ -30,11 +30,11 @@ class InfZoomConfig():
     overmask:int
     outpaintStrategy: str
     blend_image:Image
-    blend_mode:str
+    blend_mode:int #0: None, 1: Blend, 2: AlphaComposite, 3: LumaWipe
     blend_gradient_size:int
     blend_invert_do:bool
     blend_color:str
-    audio_filename=None,
+    audio_filename:str=None
     inpainting_denoising_strength:float=1
     inpainting_full_res:int =0
     inpainting_padding:int=0
diff --git a/iz_helpers/run.py b/iz_helpers/run.py
index a70c9a9..3dd64f7 100644
--- a/iz_helpers/run.py
+++ b/iz_helpers/run.py
@@ -98,6 +98,625 @@ class InfZoomer:
     fnOutpaintMainFrames: Callable
     fnInterpolateFrames: Callable
 
+    def create_zoom(self):
+        for i in range(self.C.batchcount):
+            print(f"Batch {i+1}/{self.C.batchcount}")
+            result = self.create_zoom_single()
+        return result
+
+    def create_zoom_single(self):
+
+        self.main_frames.append(self.prepareInitImage())
+
+        load_model_from_setting("infzoom_inpainting_model", self.C.progress, "Loading Model for inpainting/img2img: ")
+
+        processed = self.fnOutpaintMainFrames()
+
+        if (self.C.upscale_do): 
+            self.doUpscaling()
+
+        if self.C.video_zoom_mode:
+            self.main_frames = self.main_frames[::-1]
+
+        if not self.outerZoom:
+            self.contVW = ContinuousVideoWriter(
+                self.out_config["video_filename"], 
+                self.main_frames[0],
+                self.C.video_frame_rate,
+                int(self.C.video_start_frame_dupe_amount), 
+                self.C.video_ffmpeg_opts
+            )
+        
+        self.fnInterpolateFrames() # changes main_frame and writes to video
+
+        if self.C.audio_filename is not None:
+            self.out_config["video_filename"] = add_audio_to_video(self.out_config["video_filename"], self.C.audio_filename, str.replace(self.out_config["video_filename"], ".mp4", "_audio.mp4"), find_ffmpeg_binary())
+
+        print("Video saved in: " + os.path.join(script_path, self.out_config["video_filename"]))
+
+        return (
+            self.out_config["video_filename"],
+            self.main_frames,
+            processed.js(),
+            plaintext_to_html(processed.info),
+            plaintext_to_html(""),
+        )
+
+    def doUpscaling(self):
+        for idx,mf in enumerate(self.main_frames):
+            print (f"\033[KInfZoom: Upscaling mainframe: {idx}   \r",end="")
+            self.main_frames[idx]=do_upscaleImg(mf, self.C.upscale_do, self.C.upscaler_name, self.C.upscale_by)
+
+        self.mask_width = math.trunc(self.mask_width*self.C.upscale_by)
+        self.mask_height = math.trunc(self.mask_height *self.C.upscale_by)
+
+        if self.C.outpaintStrategy == "Corners":
+            self.width  = self.main_frames[0].width-2*self.mask_width 
+            self.height = self.main_frames[0].height-2*self.mask_height
+        else:
+            self.width  = self.main_frames[0].width
+            self.height = self.main_frames[0].height
+
+    def prepareInitImage(self) -> Image:
+        if self.C.custom_init_image:
+            current_image = Image.new(mode="RGBA", size=(self.width, self.height))
+            current_image = current_image.convert("RGB")
+            current_image = cv2_to_pil(cv2.resize(
+                    pil_to_cv2(self.C.custom_init_image),
+                    (self.width, self.height),
+                    interpolation=cv2.INTER_AREA            
+                )
+            )
+            self.save2Collect(current_image, f"init_custom.png")
+        else:
+            if self.prompt_images[min(k for k in self.prompt_images.keys() if k >= 0)] == "":
+                load_model_from_setting("infzoom_txt2img_model", self.C.progress, "Loading Model for txt2img: ")
+                processed, self.current_seed = self.renderFirstFrame()
+                if len(processed.images) > 0:
+                    current_image = processed.images[0]
+                    self.save2Collect(current_image, f"init_txt2img.png")
+            else:
+                print("using image 0 as Initial keyframe")
+                current_image = open_image(self.prompt_images[min(k for k in self.prompt_images.keys() if k >= 0)])
+                current_image = cv2_to_pil(cv2.resize(
+                        pil_to_cv2(current_image),
+                        (self.width, self.height),
+                        interpolation=cv2.INTER_AREA            
+                    )
+                )
+                self.save2Collect(current_image, f"init_custom.png")
+
+        return current_image
+
+    def renderFirstFrame(self):
+        pr = self.getInitialPrompt()
+
+        return renderTxt2Img(
+                f"{self.C.common_prompt_pre}\n{pr}\n{self.C.common_prompt_suf}".strip(),
+                self.C.negative_prompt,
+                self.C.sampler,
+                self.C.num_inference_steps,
+                self.C.guidance_scale,
+                self.current_seed,
+                self.width,
+                self.height
+        )
+
+    def getInitialPrompt(self):
+        return self.prompts[min(k for k in self.prompts.keys() if k >= 0)]
+    
+
+    def outpaint_steps_cornerStrategy(self):
+        current_image = self.main_frames[-1]
+
+        # just 30 radius to get inpaint connected between outer and innter motive
+        masked_image = create_mask_with_circles(
+            current_image, 
+            self.mask_width, self.mask_height, 
+            overmask=self.C.overmask, 
+            radius=min(self.mask_height,self.mask_height)*0.2
+        )
+
+        new_width= masked_image.width
+        new_height=masked_image.height
+
+        outpaint_steps=self.C.num_outpainting_steps
+        for i in range(outpaint_steps):
+            print (f"Outpaint step: {str(i + 1)}/{str(outpaint_steps)} Seed: {str(self.current_seed)}")
+            current_image = self.main_frames[-1]
+
+            #keyframes are not outpainted
+            paste_previous_image = not self.prompt_image_is_keyframe[(i + 1)]
+            print(f"paste_prev_image: {paste_previous_image} {i} {i + 1}")
+
+            if self.C.custom_exit_image and ((i + 1) == outpaint_steps):
+                current_image = cv2_to_pil(cv2.resize(
+                    pil_to_cv2(self.C.custom_exit_image),
+                    (self.C.width, self.C.height), 
+                    interpolation=cv2.INTER_AREA
+                    )
+                )
+                
+                if 0 == self.outerZoom:
+                    exit_img = current_image.convert("RGB")
+
+                self.save2Collect(current_image, self.out_config, f"exit_img.png")
+                paste_previous_image = False
+            else:
+                if self.prompt_images[max(k for k in self.prompt_images.keys() if k <= (i + 1))] == "":
+                    expanded_image = cv2_to_pil(
+                        cv2.resize(pil_to_cv2(current_image),
+                                 (new_width,new_height),
+                                 interpolation=cv2.INTER_AREA
+                        )
+                    )
+
+                    #expanded_image = Image.new("RGB",(new_width,new_height),"black")
+                    expanded_image.paste(current_image, (self.mask_width,self.mask_height))
+                    pr = self.prompts[max(k for k in self.prompts.keys() if k <= i)]
+                
+                    processed, newseed = renderImg2Img(
+                        f"{self.C.common_prompt_pre}\n{pr}\n{self.C.common_prompt_suf}".strip(),
+                        self.C.negative_prompt,
+                        self.C.sampler,
+                        self.C.num_inference_steps,
+                        self.C.guidance_scale,
+                        -1, # try to avoid massive repeatings: self.current_seed,
+                        new_width,  #outpaintsizeW
+                        new_height,  #outpaintsizeH
+                        expanded_image,
+                        masked_image,
+                        self.C.inpainting_denoising_strength,
+                        self.C.inpainting_mask_blur,
+                        self.C.inpainting_fill_mode,
+                        False, # self.C.inpainting_full_res,
+                        0 #self.C.inpainting_padding,
+                    )
+
+                    if len(processed.images) > 0:
+                        expanded_image = processed.images[0]
+                        zoomed_img = cv2_to_pil(cv2.resize(
+                            pil_to_cv2(expanded_image),
+                            (self.width,self.height), 
+                            interpolation=cv2.INTER_AREA
+                            )
+                        )
+                    #
+                else:
+                    # use prerendered image, known as keyframe. Resize to target size
+                    print(f"image {i + 1} is a keyframe: {not paste_previous_image}")
+                    current_image = open_image(self.prompt_images[(i + 1)])
+                    current_image = resize_and_crop_image(current_image, self.width, self.height).convert("RGBA")
+
+                    # if keyframe is last frame, use it as exit image
+                    if (not paste_previous_image) and ((i + 1) == outpaint_steps):
+                        exit_img = current_image
+                        print("using keyframe as exit image")
+                    else:
+                        # apply predefined or generated alpha mask to current image:
+                        if self.prompt_alpha_mask_images[max(k for k in self.prompt_alpha_mask_images.keys() if k <= (i + 1))] != "":
+                            current_image_amask = open_image(self.prompt_alpha_mask_images[max(k for k in self.prompt_alpha_mask_images.keys() if  k <= (i + 1))])
+                        else:
+                            current_image_gradient_ratio = (self.C.blend_gradient_size / 100)
+                            current_image_amask = draw_gradient_ellipse(current_image.width, current_image.height, current_image_gradient_ratio, 0.0, 2.5)
+                        current_image = apply_alpha_mask(current_image, current_image_amask)
+                        self.main_frames.append(current_image)
+                    self.save2Collect(current_image, f"key_frame_{i + 1}.png")                    
+                
+                if paste_previous_image and i > 0:
+                    if self.prompt_alpha_mask_images[max(k for k in self.prompt_alpha_mask_images.keys() if k <= (i + 1))] != "":
+                        current_image_amask = open_image(self.prompt_alpha_mask_images[max(k for k in self.prompt_alpha_mask_images.keys() if  k <= (i + 1))])
+                    else:
+                        current_image_gradient_ratio = (self.C.blend_gradient_size / 100)
+                        current_image_amask = draw_gradient_ellipse(self.main_frames[-1].width, self.main_frames[-1].height, current_image_gradient_ratio, 0.0, 2.5)
+                    current_image = apply_alpha_mask(self.main_frames[-1], current_image_amask)
+                    expanded_image.paste(current_image, (self.mask_width,self.mask_height))
+                    zoomed_img = cv2_to_pil(cv2.resize(
+                        pil_to_cv2(expanded_image),
+                        (self.width,self.height), 
+                        interpolation=cv2.INTER_AREA
+                        )
+                    )
+                        
+                    if self.outerZoom:
+                        self.main_frames[-1] = expanded_image # replace small image
+                        self.save2Collect(processed.images[0], f"outpaint_step_{i}.png")
+                        
+                        if (i < outpaint_steps-1):
+                            self.main_frames.append(zoomed_img)   # prepare next frame with former content
+
+                    else:
+                        zoomed_img = cv2_to_pil(cv2.resize(
+                                expanded_image,
+                                (self.width,self.height),
+                                interpolation=cv2.INTER_AREA
+                            )
+                        )
+                        self.main_frames.append(zoomed_img)
+                        processed.images[0]=self.main_frames[-1]
+                        self.save2Collect(processed.images[0], f"outpaint_step_{i}.png")
+
+        if exit_img is not None:
+            self.main_frames.append(exit_img)
+
+        return processed
+    
+
+    def outpaint_steps_v8hid(self):
+
+        self.main_frames = [self.C.init_img.convert("RGBA")]
+        prev_image = self.C.init_img.convert("RGBA")
+        exit_img = self.C.custom_exit_image.convert("RGBA") if self.C.custom_exit_image else None
+
+        for i in range(self.C.num_outpainting_steps):
+            print (f"Outpaint step: {str(i + 1)} / {str(self.C.num_outpainting_steps)} Seed: {str(self.current_seed)}")
+        
+            current_image = self.main_frames[-1]
+            current_image = shrink_and_paste_on_blank(
+                current_image, self.mask_width, self.mask_height
+            )
+
+            mask_image = np.array(current_image)[:, :, 3]
+            mask_image = Image.fromarray(255 - mask_image).convert("RGB")
+
+            #keyframes are not inpainted
+            paste_previous_image = not self.prompt_image_is_keyframe[(i + 1)]
+            print(f"paste_prev_image: {paste_previous_image} {i} {i + 1}")
+
+            if self.C.custom_exit_image and ((i + 1) == self.C.num_outpainting_steps):
+                current_image = cv2_to_pil(
+                    cv2.resize( pil_to_cv2(
+                        self.C.custom_exit_image),
+                        (self.width, self.height), 
+                        interpolation=cv2.INTER_AREA)
+                )                
+                exit_img = current_image.convert("RGB")
+                # print("using Custom Exit Image")
+                self.save2Collect(current_image, f"exit_img.png")
+
+                paste_previous_image = False
+            else:
+                if self.prompt_images[max(k for k in self.prompt_images.keys() if k <= (i + 1))] == "":
+                    pr = self.prompts[max(k for k in self.prompts.keys() if k <= i)]
+                    processed, seed = renderImg2Img(
+                        f"{self.C.common_prompt_pre}\n{pr}\n{self.C.common_prompt_suf}".strip(),
+                        self.C.negative_prompt,
+                        self.C.sampler,
+                        self.C.num_inference_steps,
+                        self.C.guidance_scale,
+                        self.current_seed,
+                        self.width,
+                        self.height,
+                        current_image,
+                        mask_image,
+                        self.C.inpainting_denoising_strength,
+                        self.C.inpainting_mask_blur,
+                        self.C.inpainting_fill_mode,
+                        self.C.inpainting_full_res,
+                        self.C.inpainting_padding,
+                    )
+
+                    if len(processed.images) > 0:
+                        self.main_frames.append(processed.images[0].convert("RGB"))
+                        self.save2Collect(processed.images[0], f"outpain_step_{i}.png")
+                else:
+                     # use prerendered image, known as keyframe. Resize to target size
+                    print(f"image {i + 1} is a keyframe: {not paste_previous_image}")
+                    current_image = open_image(self.prompt_images[(i + 1)])
+                    current_image = resize_and_crop_image(current_image, self.width, self.height).convert("RGBA")
+
+                    # if keyframe is last frame, use it as exit image
+                    if (not paste_previous_image) and ((i + 1) == self.C.outpaint_steps):
+                        exit_img = current_image
+                        print("using keyframe as exit image")
+                    else:
+                        # apply predefined or generated alpha mask to current image:
+                        if self.prompt_alpha_mask_images[max(k for k in self.prompt_alpha_mask_images.keys() if k <= (i + 1))] != "":
+                            current_image_amask = open_image(self.prompt_alpha_mask_images[max(k for k in self.prompt_alpha_mask_images.keys() if  k <= (i + 1))])
+                        else:
+                            current_image_gradient_ratio = (self.C.blend_gradient_size / 100)
+                            current_image_amask = draw_gradient_ellipse(current_image.width, current_image.height, current_image_gradient_ratio, 0.0, 2.5)
+                        current_image = apply_alpha_mask(current_image, current_image_amask)
+                        self.main_frames.append(current_image)
+                    self.save2Collect(current_image, f"key_frame_{i + 1}.png")
+
+                # TODO: seed behavior
+            
+            # paste current image with alpha layer on previous image to merge : paste on i         
+            if paste_previous_image and i > 0:
+                # apply predefined or generated alpha mask to current image: 
+                # current image must be redefined as most current image in frame stack
+                # use previous image alpha mask if available
+                if self.prompt_alpha_mask_images[max(k for k in self.prompt_alpha_mask_images.keys() if k <= (i + 1))] != "":
+                    current_image_amask = open_image(self.prompt_alpha_mask_images[max(k for k in self.prompt_alpha_mask_images.keys() if  k <= (i + 1))])
+                else:
+                    current_image_gradient_ratio = (self.C.blend_gradient_size / 100)
+                    current_image_amask = draw_gradient_ellipse(self.main_frames[i + 1].width, self.main_frames[i + 1].height, current_image_gradient_ratio, 0.0, 2.5)
+                current_image = apply_alpha_mask(self.main_frames[i + 1], current_image_amask)
+
+                #handle previous image alpha layer
+                #prev_image = (main_frames[i] if main_frames[i] else main_frames[0])
+                ## apply available alpha mask of previous image (inverted)
+                if self.prompt_alpha_mask_images[max(k for k in self.prompt_alpha_mask_images.keys() if k <= (i))] != "":
+                    prev_image_amask = open_image(self.prompt_alpha_mask_images[max(k for k in self.prompt_alpha_mask_images.keys() if  k <= (i))])
+                else:
+                    prev_image_gradient_ratio = (self.C.blend_gradient_size / 100)
+                    prev_image_amask = draw_gradient_ellipse(prev_image.width, prev_image.height, prev_image_gradient_ratio, 0.0, 2.5)
+                #prev_image = apply_alpha_mask(prev_image, prev_image_amask, invert = True)
+
+                # merge previous image with current image
+                corrected_frame = crop_inner_image(
+                    current_image, self.mask_width, self.mask_height
+                )
+                prev = Image.new(prev_image.mode, (self.width, self.height), (255,255,255,255))
+                prev.paste(apply_alpha_mask(self.main_frames[i], prev_image_amask))
+                corrected_frame.paste(prev, mask=prev)
+                
+                self.main_frames[i] = corrected_frame
+                self.save2Collect(corrected_frame, f"main_frame_gradient_{i + 0}")
+
+        if exit_img is not None:
+            self.main_frames.append(exit_img)
+        return processed
+
+    def calculate_interpolation_steps_linear(self, original_size, target_size, steps):
+        width, height = original_size
+        target_width, target_height = target_size
+
+        if width <= 0 or height <= 0 or target_width <= 0 or target_height <= 0 or steps <= 0:
+            return []
+
+        width_step = (width - target_width) / (steps+1)     #+1 enforce steps BETWEEN keyframe, dont reach the target size. interval  like []
+        height_step = (height - target_height) / (steps+1)
+
+        scaling_steps = [(round(width - i * width_step), round(height - i * height_step)) for i in range(1,steps+1)]
+        #scaling_steps.insert(0,original_size) # initial size is in the list
+        return scaling_steps
+
+   
+    def interpolateFramesOuterZoom(self):
+
+        if 0 == self.C.video_zoom_mode:
+            current_image = self.main_frames[0]
+            next_image = self.main_frames[1]
+        elif 1 == self.C.video_zoom_mode:
+            current_image = self.main_frames[-1]
+            next_image = self.main_frames[-2]
+        else:
+            raise ValueError("unsupported Zoom mode in INfZoom")
+
+        outzoomSize = (self.width+self.mask_width*2, self.height+self.mask_height*2)
+        target_size = (self.width, self.height) # mask border, hide blipping
+
+        scaling_steps = self.calculate_interpolation_steps_linear(outzoomSize, target_size, self.num_interpol_frames)
+        print(f"Before: {scaling_steps}, length: {len(scaling_steps)}")
+
+        # all sizes EVEN
+        for i,s in enumerate(scaling_steps):
+            scaling_steps[i] = (s[0]+s[0]%2, s[1]+s[1]%2)
+
+        print(f"After EVEN: {scaling_steps}, length: {len(scaling_steps)}")
+        for s in scaling_steps:
+            print(f"Ratios: {str(s[0]/s[1])}",end=";")
+
+        self.contVW = ContinuousVideoWriter(self.out_config["video_filename"], 
+                                            self.cropCenterTo(current_image,(target_size)),
+                                            self.cropCenterTo(next_image,(target_size)),
+                                            self.C.video_frame_rate,int(self.C.video_start_frame_dupe_amount-1),
+                                            self.C.video_ffmpeg_opts,
+                                            self.C.blend_invert_do,
+                                            self.C.blend_image,
+                                            self.C.blend_mode,
+                                            self.C.blend_gradient_size,
+                                            self.C.blend_color)
+
+        for i in range(len(self.main_frames)):
+            if 0 == self.C.video_zoom_mode:
+                current_image = self.main_frames[0+i]
+                previous_image = self.main_frames[i-1]
+            else:
+                current_image = self.main_frames[-1-i]
+                previous_image = self.main_frames[0-i]
+
+            lastFrame = self.cropCenterTo(current_image,target_size)
+            nextToLastFrame = self.cropCenterTo(previous_image,target_size)
+
+            if self.C.blend_mode == 0:
+                self.contVW.append([lastFrame])
+
+                cv2_image = pil_to_cv2(current_image)
+
+                # Resize and crop using OpenCV2
+                for j in range(self.num_interpol_frames):
+                    print(f"\033[KInfZoom: Interpolate frame(CV2): main/inter: {i}/{j}   \r", end="")
+                    resized_image = cv2.resize(
+                        cv2_image,
+                        (scaling_steps[j][0], scaling_steps[j][1]),
+                        interpolation=cv2.INTER_AREA
+                    )
+                    cropped_image_cv2 = cv2_crop_center(resized_image, target_size)
+                    cropped_image_pil = cv2_to_pil(cropped_image_cv2)
+                
+                    self.contVW.append([cropped_image_pil])
+                    lastFrame = cropped_image_pil
+            
+        self.contVW.finish(lastFrame,
+                           nextToLastFrame,
+                           int(self.C.video_last_frame_dupe_amount),
+                           self.C.blend_invert_do,
+                           self.C.blend_image,
+                           self.C.blend_mode,
+                           self.C.blend_gradient_size,
+                           self.C.blend_color)
+
+        """ USING PIL:
+        for i in range(len(self.main_frames)):
+            if 0 == self.C.video_zoom_mode:
+                current_image = self.main_frames[0+i]
+            else:
+                current_image = self.main_frames[-1-i]
+
+            self.contVW.append([
+                self.cropCenterTo(current_image,(self.width, self.height))
+            ])
+
+            # interpolation steps between 2 inpainted images (=sequential zoom and crop)
+            for j in range(self.num_interpol_frames - 1):
+                print (f"\033[KInfZoom: Interpolate frame: main/inter: {i}/{j}   \r",end="")
+                #todo: howto zoomIn when writing each frame; self.main_frames are inverted, howto interpolate?
+                scaled_image = current_image.resize(scaling_steps[j], Image.LANCZOS)                    
+                cropped_image = self.cropCenterTo(scaled_image,(self.width, self.height))
+
+                self.contVW.append([cropped_image])
+        """
+
+    def interpolateFramesSmallCenter(self):
+
+        if self.C.video_zoom_mode:
+            firstImage = self.main_frames[0]
+            nextImage = self.main_frames[1]
+        else:
+            firstImage = self.main_frames[-1]
+            nextImage = self.main_frames[-2]
+
+        self.contVW = ContinuousVideoWriter(self.out_config["video_filename"], 
+                                (firstImage,(self.width,self.height)),
+                                (nextImage,(self.width,self.height)),
+                                self.C.video_frame_rate,int(self.C.video_start_frame_dupe_amount),
+                                self.C.video_ffmpeg_opts,
+                                self.C.blend_invert_do,
+                                self.C.blend_image,
+                                self.C.blend_mode,
+                                self.C.blend_gradient_size,
+                                self.C.blend_color)
+
+        for i in range(len(self.main_frames) - 1):
+            # interpolation steps between 2 inpainted images (=sequential zoom and crop)
+            for j in range(self.num_interpol_frames - 1):
+
+                print (f"\033[KInfZoom: Interpolate frame: main/inter: {i}/{j}   \r",end="")
+                #todo: howto zoomIn when writing each frame; self.main_frames are inverted, howto interpolate?
+                if self.C.video_zoom_mode:
+                    current_image = self.main_frames[i + 1]
+                else:
+                    current_image = self.main_frames[i + 1]
+
+
+                interpol_image = current_image
+                self.save2Collect(interpol_image, f"interpol_img_{i}_{j}].png")
+
+                interpol_width = math.ceil(
+                    ( 1 - (1 - 2 * self.mask_width / self.width) **(1 - (j + 1) / self.num_interpol_frames) ) 
+                    * self.width / 2
+                )
+
+                interpol_height = math.ceil(
+                    ( 1 - (1 - 2 * self.mask_height / self.height) ** (1 - (j + 1) / self.num_interpol_frames) )
+                    * self.height/2
+                )
+
+                interpol_image = interpol_image.crop(
+                    (
+                        interpol_width,
+                        interpol_height,
+                        self.width - interpol_width,
+                        self.height - interpol_height,
+                    )
+                )
+
+                interpol_image = interpol_image.resize((self.width, self.height))
+                self.save2Collect(interpol_image, f"interpol_resize_{i}_{j}.png")
+
+                # paste the higher resolution previous image in the middle to avoid drop in quality caused by zooming
+                interpol_width2 = math.ceil(
+                    (1 - (self.width - 2 * self.mask_width) / (self.width - 2 * interpol_width))
+                    / 2 * self.width
+                )
+
+                interpol_height2 = math.ceil(
+                    (1 - (self.height - 2 * self.mask_height) / (self.height - 2 * interpol_height))
+                    / 2 * self.height
+                )
+
+                prev_image_fix_crop = shrink_and_paste_on_blank(
+                    self.main_frames[i], interpol_width2, interpol_height2
+                )
+
+                interpol_image.paste(prev_image_fix_crop, mask=prev_image_fix_crop)
+                self.save2Collect(interpol_image, f"interpol_prevcrop_{i}_{j}.png")
+
+                self.contVW.append([interpol_image])
+
+            self.contVW.append([current_image])
+
+
+    def prepare_output_path(self):
+        isCollect = shared.opts.data.get("infzoom_collectAllResources", False)
+        output_path = shared.opts.data.get("infzoom_outpath", "outputs")
+
+        save_path = os.path.join(
+            output_path, shared.opts.data.get("infzoom_outSUBpath", "infinite-zooms")
+        )
+
+        if isCollect:
+            save_path = os.path.join(save_path, "iz_collect" + str(int(time.time())))
+
+        if not os.path.exists(save_path):
+            os.makedirs(save_path)
+
+        video_filename = os.path.join(
+            save_path, "infinite_zoom_" + str(int(time.time())) + ".mp4"
+        )
+
+        return {
+            "isCollect": isCollect,
+            "save_path": save_path,
+            "video_filename": video_filename,
+        }
+
+
+    def save2Collect(self, img, name):
+        if self.out_config["isCollect"]:
+            img.save(f'{self.out_config["save_path"]}/{name}.png')
+
+
+    def frame2Collect(self,all_frames):
+        self.save2Collect(all_frames[-1], self.out_config, f"frame_{len(all_frames)}")
+
+
+    def frames2Collect(self, all_frames):
+        for i, f in enumerate(all_frames):
+            self.save2Collect(f, self.out_config, f"frame_{i}")
+
+
+    def crop_inner_image(self, outpainted_img, width_offset, height_offset):
+        width, height = outpainted_img.size
+
+        center_x, center_y = int(width / 2), int(height / 2)
+
+        # Crop the image to the center
+        cropped_img = outpainted_img.crop(
+            (
+                center_x - width_offset,
+                center_y - height_offset,
+                center_x + width_offset,
+                center_y + height_offset,
+            )
+        )
+        prev_step_img = cropped_img.resize((width, height), resample=Image.LANCZOS)
+        # resized_img = resized_img.filter(ImageFilter.SHARPEN)
+
+        return prev_step_img
+
+    def cropCenterTo(self, im: Image, toSize: tuple[int,int]):
+        width, height = im.size
+        left = (width - toSize[0])//2
+        top = (height - toSize[1])//2
+        right = (width + toSize[0])//2
+        bottom = (height + toSize[1])//2
+        return im.crop((left, top, right, bottom))
+
+##########################################################################################################################
 def outpaint_steps(
     width,
     height,
@@ -317,6 +936,9 @@ def create_zoom(
             upscale_do,
             upscaler_name,
             upscale_by,
+            overmask,
+            outpaintStrategy,
+            outpaint_amount_px,
             blend_image,
             blend_mode,
             blend_gradient_size,
@@ -394,6 +1016,9 @@ def create_zoom_single(
     upscale_do,
     upscaler_name,
     upscale_by,
+    overmask,
+    outpaintStrategy,
+    outpaint_amount_px,
     blend_image,
     blend_mode,
     blend_gradient_size,
@@ -641,7 +1266,7 @@ def create_zoom_single(
         plaintext_to_html(processed.info),
         plaintext_to_html(""),
     )
-
+#################################################################################################################
 def create_mask_with_circles(original_image, border_width, border_height, overmask: int, radius=4):
     # Create a new image with border and draw a mask
     new_width = original_image.width + 2 * border_width
diff --git a/iz_helpers/run_interface.py b/iz_helpers/run_interface.py
index 9403128..f796fab 100644
--- a/iz_helpers/run_interface.py
+++ b/iz_helpers/run_interface.py
@@ -32,6 +32,12 @@ def createZoom(
     overmask:int,
     outpaintStrategy:str,
     outpaint_amount_px: int,
+    blend_image:Image,
+    blend_mode:int,
+    blend_gradient_size:int,
+    blend_invert_do:bool,
+    blend_color:str,
+    audio_filename:str,
     inpainting_denoising_strength:float=1,
     inpainting_full_res:int =0,
     inpainting_padding:int=0,
@@ -42,7 +48,7 @@ def createZoom(
         prompts_array,
         common_prompt_suf,
         negative_prompt,
-        num_outpainting_steps,
+        num_outpainting_steps if custom_exit_image == None else (num_outpainting_steps + 1),
         guidance_scale,
         num_inference_steps,
         custom_init_image,
@@ -65,6 +71,12 @@ def createZoom(
         upscale_by,
         overmask,
         outpaintStrategy,
+        blend_image,
+        blend_mode,
+        blend_gradient_size,
+        blend_invert_do,
+        blend_color,
+        audio_filename,
         inpainting_denoising_strength,
         inpainting_full_res,
         inpainting_padding,
diff --git a/iz_helpers/ui.py b/iz_helpers/ui.py
index 2a7a32d..10eb1ff 100644
--- a/iz_helpers/ui.py
+++ b/iz_helpers/ui.py
@@ -386,7 +386,7 @@ Our best experience and trade-off is the R-ERSGAn4x upscaler.
                 )
 
         generate_btn.click(
-            fn=wrap_gradio_gpu_call(create_zoom, extra_outputs=[None, "", ""]),
+            fn=wrap_gradio_gpu_call(createZoom, extra_outputs=[None, "", ""]),
             inputs=[
                 main_common_prompt_pre,
                 main_prompts,
diff --git a/iz_helpers/video.py b/iz_helpers/video.py
index 21283cc..04a469a 100644
--- a/iz_helpers/video.py
+++ b/iz_helpers/video.py
@@ -74,7 +74,7 @@ class ContinuousVideoWriter:
 
     _writer = None
     
-    def __init__(self, file_path, initframe, fps, start_frame_dupe_amount=15, video_ffmpeg_opts="" ):
+    def __init__(self, file_path, initframe, nextframe, fps, start_frame_dupe_amount=15, video_ffmpeg_opts="", blend_invert: bool = False, blend_image= None, blend_type:int = 0, blend_gradient_size: int = 63, blend_color = "#ffff00" ):
         """
         Writes initial frame to a new mp4 video file
         :param file_path: Path to output video, must end with .mp4
@@ -87,7 +87,19 @@ class ContinuousVideoWriter:
             ffopts= video_ffmpeg_opts.split(" ")
 
         writer = imageio.get_writer(file_path, fps=fps, macro_block_size=None, ffmpeg_params=ffopts)
-        start_frames = [initframe] * start_frame_dupe_amount
+        # Duplicate the start frames
+        if blend_type != 0:
+            if blend_image is None:
+                blend_image = draw_gradient_ellipse(*initframe.size, blend_gradient_size)
+        
+            if blend_type == 1:
+                start_frames = blend_images(initframe, nextframe, math.ceil(start_frame_dupe_amount), blend_invert)
+            elif blend_type == 2:
+                start_frames = alpha_composite_images(initframe, nextframe, blend_image, math.ceil(start_frame_dupe_amount), blend_invert)
+            elif blend_type == 3:
+                start_frames = PSLumaWipe_images2(initframe, nextframe, blend_image, math.ceil(start_frame_dupe_amount), blend_invert,blend_color)            
+        else:
+            start_frames = [initframe] * start_frame_dupe_amount
         for f in start_frames:
             writer.append_data(np.array(f))
         self._writer = writer
@@ -100,13 +112,22 @@ class ContinuousVideoWriter:
         for i,f in enumerate(frames):
             self._writer.append_data(np.array(f))
     
-    def finish(self, frame, last_frame_dupe_amount=30 ):
+    def finish(self, exitframe, next_to_last_frame, last_frame_dupe_amount=30, blend_invert: bool = False, blend_image= None, blend_type:int = 0, blend_gradient_size: int = 63, blend_color = "#ffff00"  ):
         """
         Closes the file writer.
         """
-        for i in range(last_frame_dupe_amount):
-            self._writer.append_data(np.array(frame))
-        
+        # Duplicate the exit frames
+        if blend_type != 0:
+            if blend_type == 1:
+                end_frames = blend_images(next_to_last_frame, exitframe, math.ceil(last_frame_dupe_amount), blend_invert)
+            elif blend_type == 2:
+                end_frames = alpha_composite_images(next_to_last_frame, exitframe, blend_image, math.ceil(last_frame_dupe_amount), blend_invert)
+            elif blend_type == 3:
+                end_frames = PSLumaWipe_images2(next_to_last_frame, exitframe, blend_image, math.ceil(last_frame_dupe_amount), blend_invert, blend_color)
+        else:
+            end_frames = [exitframe] * last_frame_dupe_amount
+        for f in end_frames:
+            self._writer.append_data(np.array(f))       
         self._writer.close()
 
 def add_audio_to_video(video_path, audio_path, output_path, ffmpeg_location = 'ffmpeg'):