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script/ContorlNet_I2I_sequence_toyxyz_V2.py

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+import copy
+import os
+import shutil
+
+import cv2
+import gradio as gr
+import numpy as np
+import modules.scripts as scripts
+
+from modules import images, processing
+from modules.processing import process_images, Processed
+from modules.shared import opts
+from PIL import Image, ImageFilter, ImageColor, ImageOps
+from pathlib import Path
+from typing import List, Tuple, Iterable
+
+
+#Returns a list of images located in the input path. For ControlNet iamges
+def get_all_frames_from_path(path):
+    if not os.path.isdir(path):
+        return None
+    frame_list = []
+    for filename in sorted(os.listdir(path)):
+        if filename.endswith(".jpg") or filename.endswith(".png"):
+            img_path = os.path.join(path, filename)
+            img = cv2.imread(img_path)
+            if img is not None:
+                frame_list.append(img)
+    frame_list.insert(0, frame_list[0])            
+    return frame_list  
+    
+
+#Returns a list of images located in the input path. For Color iamges
+def get_images_from_path(path):
+    if not os.path.isdir(path):
+        return None
+    images = []
+    for filename in os.listdir(path):
+        if filename.endswith('.jpg') or filename.endswith('.png'):
+            img_path = os.path.join(path, filename)
+            img = Image.open(img_path)
+            images.append(img)
+    images.append(images[-1])
+    images.insert(0, images[0])
+    return images
+
+#Returns the number of the smallest number in the entire image sequence list. For ControlNet
+def get_min_frame_num(video_list):
+    min_frame_num = -1
+    for video in video_list:
+        if video is None:
+            continue
+        else:
+            frame_num = len(video)
+            print(frame_num)
+            if min_frame_num < 0:
+                min_frame_num = frame_num
+            elif frame_num < min_frame_num:
+                min_frame_num = frame_num
+    return min_frame_num
+
+
+#Blende method
+
+
+def basic(target, blend, opacity):
+    return target * opacity + blend * (1-opacity)
+
+def blender(func):
+    def blend(target, blend, opacity=1, *args):
+        res = func(target, blend, *args)
+        res = basic(res, blend, opacity)
+        return np.clip(res, 0, 1)
+    return blend
+
+
+class Blend:
+    @classmethod
+    def method(cls, name):
+        return getattr(cls, name)
+    
+    normal = basic
+    
+    @staticmethod
+    @blender
+    def darken(target, blend, *args):
+        return np.minimum(target, blend)
+    
+    @staticmethod
+    @blender
+    def multiply(target, blend, *args):
+        return target * blend
+    
+    @staticmethod
+    @blender
+    def color_burn(target, blend, *args):
+        return 1 - (1-target)/blend
+    
+    @staticmethod
+    @blender
+    def linear_burn(target, blend, *args):
+        return target+blend-1
+    
+    @staticmethod
+    @blender
+    def lighten(target, blend, *args):
+        return np.maximum(target, blend)
+    
+    @staticmethod
+    @blender
+    def screen(target, blend, *args):
+        return 1 - (1-target) * (1-blend)
+    
+    @staticmethod
+    @blender
+    def color_dodge(target, blend, *args):
+        return target/(1-blend)
+    
+    @staticmethod
+    @blender
+    def linear_dodge(target, blend, *args):
+        return target+blend
+    
+    @staticmethod
+    @blender
+    def overlay(target, blend, *args):
+        return  (target>0.5) * (1-(2-2*target)*(1-blend)) +\
+                (target<=0.5) * (2*target*blend)
+    
+    @staticmethod
+    @blender
+    def soft_light(target, blend, *args):
+        return  (blend>0.5) * (1 - (1-target)*(1-(blend-0.5))) +\
+                (blend<=0.5) * (target*(blend+0.5))
+    
+    @staticmethod
+    @blender
+    def hard_light(target, blend, *args):
+        return  (blend>0.5) * (1 - (1-target)*(2-2*blend)) +\
+                (blend<=0.5) * (2*target*blend)
+    
+    @staticmethod
+    @blender
+    def vivid_light(target, blend, *args):
+        return  (blend>0.5) * (1 - (1-target)/(2*blend-1)) +\
+                (blend<=0.5) * (target/(1-2*blend))
+    
+    @staticmethod
+    @blender
+    def linear_light(target, blend, *args):
+        return  (blend>0.5) * (target + 2*(blend-0.5)) +\
+                (blend<=0.5) * (target + 2*blend)
+    
+    @staticmethod
+    @blender
+    def pin_light(target, blend, *args):
+        return  (blend>0.5) * np.maximum(target,2*(blend-0.5)) +\
+                (blend<=0.5) * np.minimum(target,2*blend)
+    
+    @staticmethod
+    @blender
+    def difference(target, blend, *args):
+        return np.abs(target - blend)
+    
+    @staticmethod
+    @blender
+    def exclusion(target, blend, *args):
+        return 0.5 - 2*(target-0.5)*(blend-0.5)
+
+blend_methods = [i for i in Blend.__dict__.keys() if i[0]!='_' and i!='method']
+
+
+
+def blend_images(base_img, blend_img, blend_method, blend_opacity, do_invert):
+    
+    img_base = np.array(base_img.convert("RGB")).astype(np.float64)/255
+    
+    if do_invert:
+        img_to_blend = ImageOps.invert(blend_img.convert('RGB'))       
+    else:
+        img_to_blend = blend_img
+                
+    img_to_blend = img_to_blend.resize((int(base_img.width), int(base_img.height)))
+                
+    img_to_blend = np.array(img_to_blend.convert("RGB")).astype(np.float64)/255
+            
+    img_blended = Blend.method(blend_method)(img_to_blend, img_base, blend_opacity)
+                
+    img_blended *= 255
+                
+    img_blended = Image.fromarray(img_blended.astype(np.uint8), mode='RGB')
+    
+    return img_blended
+
+
+#Define UI and script properties.
+class Script(scripts.Script):  
+    
+    def title(self):
+        return "controlnet I2I sequence_toyxyz_v2"
+
+    def show(self, is_img2img):
+        return is_img2img
+
+    def ui(self, is_img2img):
+
+        ctrls_group = ()
+        max_models = opts.data.get("control_net_max_models_num", 1)
+        
+        input_list = []
+
+        with gr.Group():
+            with gr.Accordion("ControlNet-I2I-sequence-toyxyz", open = True): 
+                with gr.Column():
+                                    
+                    feed_prev_frame = gr.Checkbox(value=False, label="Feed previous frame / Reduce flickering by feeding the previous frame image generated by Img2Img")
+                    
+                    use_init_img = gr.Checkbox(value=False, label="Blend color image / Blend the color image sequence with the initial Img2Img image or previous frame")
+                    
+                    use_TemporalNet = gr.Checkbox(value=False, label="Use TemporalNet / Using TemporalNet to reduce flicker between image sequences. Add TemporalNet in addition to the multi-controlnet you need. It should be placed at the end of the controlnet list.")
+
+                    blendmode = gr.Dropdown(blend_methods, value='normal', label='Blend mode / Choose how to blend the color image with the Previous frame or Img2Img initial image')
+                    
+                    opacityvalue = gr.Slider(0, 1, value=0, label="Opacity / Previous frame or Img2Img initial image + (color image * opacity)", info="Choose betwen 0 and 1")
+                    
+
+                    for i in range(max_models):
+                        input_path = gr.Textbox(label=f"ControlNet-{i}", placeholder="image sequence path")
+                        input_list.append(input_path)
+                    
+                    tone_image_path = gr.Textbox(label=f"Color_Image / Color images to be used for Img2Img in sequence", placeholder="image sequence path")
+                    
+                    output_path = gr.Textbox(label=f"Output_path / Deletes the contents located in the path, and creates a new path if it does not exist", placeholder="Output path")
+
+        ctrls_group += tuple(input_list) + (use_TemporalNet, use_init_img, opacityvalue, blendmode, feed_prev_frame, tone_image_path, output_path)
+
+        return ctrls_group
+        
+
+
+    #Image Generate Definition
+    def run(self, p, *args):
+
+        path = p.outpath_samples
+        
+        output_path = args[-1]  # get the last argument, which is the output path
+        
+        feedprev = args[-3]
+        
+        blendm = args[-4]
+        
+        opacityval = args[-5]
+        
+        useinit = args[-6]
+
+        usetempo = args[-7]
+        
+        
+        # Check whether the output path exists, if it does, delete it and create a new one.
+        if os.path.isdir(output_path):
+            for file in os.scandir(output_path):
+                os.remove(file.path)
+        else :    
+            os.mkdir(output_path)
+
+        #Get the number of controlnet models.
+        video_num = opts.data.get("control_net_max_models_num", 1)
+
+        # Get the ControlNet image sequence list.
+        image_list = [get_all_frames_from_path(image) for image in args[:video_num]]
+        
+        # Get a list of color image sequences.
+        color_image_list = get_images_from_path(args[-2])
+        
+        # Get the first frame
+        previmg = p.init_images
+
+        tempoimg = p.init_images[0]
+        
+        #If img2img color correction is enabled in webui settings, color correction is performed based on the first frame.
+        initial_color_corrections = [processing.setup_color_correction(p.init_images[0])]
+        
+        #Save initial img2img image
+        initial_image = p.init_images[0]
+        
+        # Get the total number of frames.
+        frame_num = get_min_frame_num(image_list)
+
+        # image processing
+        if frame_num > 0:
+            output_image_list = []
+            
+            for frame in range(frame_num):
+                copy_p = copy.copy(p)
+                copy_p.control_net_input_image = []
+                for video in image_list:
+                    if video is None:
+                        continue
+                    copy_p.control_net_input_image.append(video[frame])
+
+                    if usetempo == True :
+                        copy_p.control_net_input_image.append(tempoimg)
+
+                    
+                    if color_image_list and feedprev == False:
+                        
+                        if frame<len(color_image_list):
+                            tone_image = color_image_list[frame+1]
+                        
+                        if useinit:
+                            tone_image = blend_images(initial_image, tone_image, blendm, opacityval, False)
+                            
+                        p.init_images = [tone_image.convert("RGB")]
+                        
+                proc = process_images(copy_p) 
+                
+                
+                
+                if feedprev == True and useinit == False:
+                    if previmg is None:
+                        continue
+                    else:
+                        previmg = proc.images[0]
+                        
+                        if frame == 0:
+                            previmg = initial_image
+                            
+                        p.init_images = [previmg]
+                        
+                        if opts.img2img_color_correction:
+                            p.color_corrections = initial_color_corrections
+                            
+                            
+                if feedprev == True and color_image_list and useinit:
+                    if previmg is None:
+                        continue
+                    else:
+                        previmg = proc.images[0]
+                        
+                        if frame == 0:
+                            previmg = initial_image
+                            
+                        previmg = blend_images(previmg, color_image_list[frame+1], blendm, opacityval, False)
+
+                            
+                        p.init_images = [previmg]
+
+                        if opts.img2img_color_correction:
+                            p.color_corrections = initial_color_corrections
+
+                img = proc.images[0]
+
+                if usetempo == True :
+                    if frame > 0 :
+                        tempoimg = proc.images[0]
+
+
+                #Save image
+                if(frame>0):
+                    images.save_image(img, output_path, f"Frame_{frame}")
+                copy_p.close()
+                
+
+        else:
+            proc = process_images(p)
+        
+        return proc

script/loopback_wave.py

@@ -0,0 +1,345 @@
+import os
+import platform
+import numpy as np
+from tqdm import trange
+import math
+import subprocess as sp
+import string
+import random
+from functools import reduce
+import re
+
+import modules.scripts as scripts
+import gradio as gr
+
+from modules import processing, shared, sd_samplers, images
+from modules.processing import Processed
+from modules.sd_samplers import samplers
+from modules.shared import opts, cmd_opts, state
+import subprocess
+
+
+wave_completed_regex = r'@wave_completed\(([\-]?[0-9]*\.?[0-9]+), ?([\-]?[0-9]*\.?[0-9]+)\)'
+wave_remaining_regex = r'@wave_remaining\(([\-]?[0-9]*\.?[0-9]+), ?([\-]?[0-9]*\.?[0-9]+)\)'
+
+def run_cmd(cmd):
+    cmd = list(map(lambda arg: str(arg), cmd))
+    print("Executing %s" % " ".join(cmd))
+    popen_params = {"stdout": sp.DEVNULL, "stderr": sp.PIPE, "stdin": sp.DEVNULL}
+
+    if os.name == "nt":
+       popen_params["creationflags"] = 0x08000000
+
+    proc = sp.Popen(cmd, **popen_params)
+    out, err = proc.communicate()  # proc.wait()
+    proc.stderr.close()
+
+    if proc.returncode:
+        raise IOError(err.decode("utf8"))
+
+    del proc
+
+def encode_video(input_pattern, starting_number, output_dir, fps, quality, encoding, create_segments, segment_duration, ffmpeg_path):
+    two_pass = (encoding == "VP9 (webm)")
+    alpha_channel = ("webm" in encoding)
+    suffix = "webm" if "webm" in encoding else "mp4"
+    output_location = output_dir + f".{suffix}"
+
+    encoding_lib = {
+      "VP9 (webm)": "libvpx-vp9",
+      "VP8 (webm)": "libvpx",
+      "H.264 (mp4)": "libx264",
+      "H.265 (mp4)": "libx265",
+    }[encoding]
+
+    args = [
+        "-framerate", fps,
+        "-start_number", int(starting_number),
+        "-i", input_pattern, 
+        "-c:v", encoding_lib, 
+        "-b:v","0", 
+        "-crf", quality,
+        ]
+
+    if encoding_lib == "libvpx-vp9":
+        args += ["-pix_fmt", "yuva420p"]
+        
+    if(ffmpeg_path == ""):
+        ffmpeg_path = "ffmpeg"
+        if(platform.system == "Windows"):
+            ffmpeg_path += ".exe"
+
+    print("\n\n")
+    if two_pass:
+        first_pass_args = args + [
+            "-pass", "1",
+            "-an", 
+            "-f", "null",
+            os.devnull
+        ]
+
+        second_pass_args = args + [
+            "-pass", "2",
+            output_location
+        ]
+
+        print("Running first pass ffmpeg encoding")       
+
+        run_cmd([ffmpeg_path] + first_pass_args)
+        print("Running second pass ffmpeg encoding.  This could take awhile...")
+        run_cmd([ffmpeg_path] + second_pass_args)
+    else:
+        print("Running ffmpeg encoding.  This could take awhile...")
+        run_cmd([ffmpeg_path] + args + [output_location])
+
+    if(create_segments):
+      print("Segmenting video")
+      run_cmd([ffmpeg_path] + [
+          "-i", output_location,
+          "-f", "segment",
+          "-segment_time", segment_duration,
+          "-vcodec", "copy",
+          "-acodec", "copy",
+          f"{output_dir}.%d.{suffix}"
+      ])
+      
+def set_weights(match_obj, wave_progress):
+  weight_0 = 0
+  weight_1 = 0
+  if match_obj.group(1) is not None:
+    weight_0 = float(match_obj.group(1))
+  if match_obj.group(2) is not None:
+    weight_1 = float(match_obj.group(2))
+    
+  max_weight = max(weight_0, weight_1)
+  min_weight = min(weight_0, weight_1)
+  
+  weight_range = max_weight - min_weight
+  weight = min_weight + weight_range * wave_progress
+  return str(weight)
+
+
+class Script(scripts.Script):
+    def title(self):
+        return "Loopback Wave V1.4.1"
+
+    def show(self, is_img2img):
+        return is_img2img
+
+    def ui(self, is_img2img):
+        frames = gr.Slider(minimum=1, maximum=2048, step=1, label='Frames', value=100)
+        frames_per_wave = gr.Slider(minimum=0, maximum=120, step=1, label='Frames Per Wave', value=20)
+        denoising_strength_change_amplitude = gr.Slider(minimum=0, maximum=1, step=0.01, label='Max additional denoise', value=0.6)
+        denoising_strength_change_offset = gr.Number(minimum=0, maximum=180, step=1, label='Wave offset (ignore this if you don\'t know what it means)', value=0)
+        initial_image_number = gr.Number(minimum=0, label='Initial generated image number', value=0)
+
+        save_prompts = gr.Checkbox(label='Save prompts as text file', value=True)
+        prompts = gr.Textbox(label="Prompt Changes", lines=5, value="")
+
+        save_video = gr.Checkbox(label='Save results as video', value=True)
+        output_dir = gr.Textbox(label="Video Name", lines=1, value="")
+        video_fps = gr.Slider(minimum=1, maximum=120, step=1, label='Frames per second', value=10)
+        video_quality = gr.Slider(minimum=0, maximum=60, step=1, label='Video Quality (crf)', value=40)
+        video_encoding = gr.Dropdown(label='Video encoding', value="VP9 (webm)", choices=["VP9 (webm)", "VP8 (webm)", "H.265 (mp4)", "H.264 (mp4)"])
+        ffmpeg_path = gr.Textbox(label="ffmpeg binary.  Only set this if it fails otherwise.", lines=1, value="")
+
+        segment_video = gr.Checkbox(label='Cut video in to segments', value=True)
+        video_segment_duration = gr.Slider(minimum=10, maximum=60, step=1, label='Video Segment Duration (seconds)', value=20)
+
+
+        return [frames, denoising_strength_change_amplitude, frames_per_wave, denoising_strength_change_offset,initial_image_number, prompts, save_prompts, save_video, output_dir, video_fps, video_quality, video_encoding, ffmpeg_path, segment_video, video_segment_duration]
+
+    def run(self, p, frames, denoising_strength_change_amplitude, frames_per_wave, denoising_strength_change_offset, initial_image_number, prompts: str,save_prompts, save_video, output_dir, video_fps, video_quality, video_encoding, ffmpeg_path, segment_video, video_segment_duration):
+        processing.fix_seed(p)
+        batch_count = p.n_iter
+        p.extra_generation_params = {
+            "Max Additional Denoise": denoising_strength_change_amplitude,
+            "Frames per wave": frames_per_wave,
+            "Wave Offset": denoising_strength_change_offset,
+        }
+
+        # We save them ourselves for the sake of ffmpeg
+        p.do_not_save_samples = True
+
+        changes_dict = {}
+
+
+        p.batch_size = 1
+        p.n_iter = 1
+
+        output_images, info = None, None
+        initial_seed = None
+        initial_info = None
+
+        grids = []
+        all_images = []
+        original_init_image = p.init_images
+        state.job_count = frames * batch_count
+
+        initial_color_corrections = [processing.setup_color_correction(p.init_images[0])]
+        initial_denoising_strength = p.denoising_strength
+
+        if(output_dir==""):
+            output_dir = str(p.seed)
+        else:
+            output_dir = output_dir + "-" + str(p.seed)
+
+        loopback_wave_path = os.path.join(p.outpath_samples, "loopback-wave")
+        loopback_wave_images_path = os.path.join(loopback_wave_path, output_dir)
+
+        os.makedirs(loopback_wave_images_path, exist_ok=True)
+
+        p.outpath_samples = loopback_wave_images_path
+        
+        prompts = prompts.strip()
+        
+        if save_prompts:
+            with open(loopback_wave_images_path + "-prompts.txt", "w") as f:
+                generation_settings = [
+                  "Generation Settings",
+                  f"Total Frames: {frames}",
+                  f"Frames Per Wave: {frames_per_wave}",
+                  f"Wave Offset: {denoising_strength_change_offset}",
+                  f"Base Denoising Strength: {initial_denoising_strength}",
+                  f"Max Additional Denoise: {denoising_strength_change_amplitude}",
+                  f"Initial Image Number: {initial_image_number}",
+                  "",
+                  "Video Encoding Settings",
+                  f"Save Video: {save_video}"
+                ]
+                
+                if save_video:
+                  generation_settings = generation_settings + [
+                    f"Framerate: {video_fps}",
+                    f"Quality: {video_quality}",
+                    f"Encoding: {video_encoding}",
+                    f"Create Segmented Video: {segment_video}"
+                  ]
+                  
+                  if segment_video:
+                    generation_settings = generation_settings + [f"Segment Duration: {video_segment_duration}"]
+                 
+                generation_settings = generation_settings + [
+                  "",
+                  "Prompt Details",
+                  "Initial Prompt:" + p.prompt,
+                  "",
+                  "Negative Prompt:" + p.negative_prompt,
+                  "",
+                  "Frame change prompts:",
+                  prompts
+                ]
+                  
+
+
+                f.write('\n'.join(generation_settings))
+
+        if prompts:
+            lines = prompts.split("\n")
+            for prompt_line in lines:
+              params = prompt_line.split("::")
+              if len(params) == 2:
+                changes_dict[params[0]] = { "prompt": params[1] }
+              elif len(params) == 3:
+                changes_dict[params[0]] = { "seed": params[1], "prompt": params[2] }
+              else:
+                raise IOError(f"Invalid input in prompt line: {prompt_line}")
+        
+        raw_prompt = p.prompt
+                
+        for n in range(batch_count):
+            history = []
+
+            # Reset to original init image at the start of each batch
+            p.init_images = original_init_image
+            
+            seed_state = "adding"
+            current_seed = p.seed
+
+            for i in range(frames):
+                current_seed = p.seed
+                state.job = ""
+                
+                if str(i) in changes_dict:
+                  raw_prompt = changes_dict[str(i)]["prompt"]
+                  state.job = "New prompt: %s\n" % raw_prompt
+                                    
+                  if "seed" in changes_dict[str(i)]:
+                    current_seed = changes_dict[str(i)]["seed"]
+                    
+                    if current_seed.startswith("+"):
+                      seed_state = "adding"
+                      current_seed = current_seed.strip("+")
+                    elif current_seed.startswith("-"):
+                      seed_state = "subtracting"
+                      current_seed = current_seed.strip("-")
+                    else:
+                      seed_state = "constant"
+                      
+                    current_seed = int(current_seed)
+                    p.seed = current_seed
+                      
+                      
+                  
+                p.n_iter = 1
+                p.batch_size = 1
+                p.do_not_save_grid = True
+
+                if opts.img2img_color_correction:
+                    p.color_corrections = initial_color_corrections
+                    
+                    
+                wave_progress = float(1)/(float(frames_per_wave - 1))*float(((float(i)%float(frames_per_wave)) + ((float(1)/float(180))*denoising_strength_change_offset)))
+                print(wave_progress)
+                new_prompt = re.sub(wave_completed_regex, lambda x: set_weights(x, wave_progress), raw_prompt)
+                new_prompt = re.sub(wave_remaining_regex, lambda x: set_weights(x, 1 - wave_progress), new_prompt)
+                p.prompt = new_prompt
+                
+                print(new_prompt)
+
+                denoising_strength_change_rate = 180/frames_per_wave
+
+                cos = abs(math.cos(math.radians(i*denoising_strength_change_rate + denoising_strength_change_offset)))
+                p.denoising_strength = initial_denoising_strength + denoising_strength_change_amplitude - (cos * denoising_strength_change_amplitude)
+
+                state.job += f"Iteration {i + 1}/{frames}, batch {n + 1}/{batch_count}. Denoising Strength: {p.denoising_strength}"
+
+                processed = processing.process_images(p)
+
+                if initial_seed is None:
+                    initial_seed = processed.seed
+                    initial_info = processed.info
+
+                init_img = processed.images[0]
+
+                p.init_images = [init_img]
+                
+                if seed_state == "adding":
+                  p.seed = processed.seed + 1
+                elif seed_state == "subtracting":
+                  p.seed = processed.seed - 1
+                  
+                image_number = int(initial_image_number + i)
+                images.save_image(init_img, p.outpath_samples, "", processed.seed, processed.prompt, forced_filename=str(image_number))
+
+                history.append(init_img)
+
+            grid = images.image_grid(history, rows=1)
+            if opts.grid_save:
+                images.save_image(grid, p.outpath_grids, "grid", initial_seed, p.prompt, opts.grid_format, info=info, short_filename=not opts.grid_extended_filename, grid=True, p=p)
+
+            grids.append(grid)
+            all_images += history
+
+        if opts.return_grid:
+            all_images = grids + all_images
+
+        if save_video:
+            input_pattern = os.path.join(loopback_wave_images_path, "%d.png")
+            encode_video(input_pattern, initial_image_number, loopback_wave_images_path, video_fps, video_quality, video_encoding, segment_video, video_segment_duration, ffmpeg_path)
+
+        processed = Processed(p, all_images, initial_seed, initial_info)
+
+        return processed
+
+

script/run_n_times.py

@@ -0,0 +1,24 @@
+import math
+import os
+import sys
+import traceback
+
+import modules.scripts as scripts
+import gradio as gr
+
+from modules.processing import Processed, process_images
+
+class Script(scripts.Script):
+    def title(self):
+        return "Run n times"
+
+    def ui(self, is_img2img):
+        n = gr.Textbox(label="n")
+        return [n]
+
+    def run(self, p, n):
+        for x in range(int(n)):
+            p.seed = -1
+            proc = process_images(p)
+            image = proc.images
+        return Processed(p, image, p.seed, proc.info)