Upload folder using huggingface_hub (#5)

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Abysz-LAB-Ext/LICENSE

@@ -0,0 +1,674 @@
+                    GNU GENERAL PUBLIC LICENSE
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Abysz-LAB-Ext/README.md

@@ -0,0 +1,53 @@
+# Abysz deflicking & temporal coherence lab. 
+## Automatic1111 Extension. Beta 0.1.9
+
+![0 1 9](https://user-images.githubusercontent.com/112580728/228404036-5dbf11f1-51e3-4fb0-9c72-c3c2c0deb00e.png)
+|
+![ABYSZLAB09b](https://user-images.githubusercontent.com/112580728/226314389-ac838672-4af0-4d94-bde8-26fd83610a5f.png)
+
+## How DFI works:
+
+https://user-images.githubusercontent.com/112580728/226049549-e61bddb3-88ea-4953-893d-9993dd165180.mp4
+
+# Requirements
+
+OpenCV: ```pip install opencv-python```
+
+Imagemagick library: https://imagemagick.org/script/download.php
+
+## Basic guide:
+Differential frame interpolation analyzes the stability of the original video, and processes the generated video with that information. Example, if your original background is static, it will force the generated video to respect that, acting as a complex deflicker. It is an aggressive process, for which we need and will have a lot of control.
+
+Gui version 0.0.6 includes the following parameters.
+
+**Frame refresh frequency:** Every how many frames the interpolation is reduced. It allows to keep more information of the generated video, and avoid major ghosting.
+
+**Refresh Strength:** Opacity % of the interpolated information. 0 refreshes the entire frame, with no changes. Here you control how much change you allow overall.
+
+**DFI Strength:** Amount of information that tries to force. 4-6 recommended.
+
+**DFI Deghost:** A variable that generally reduces the areas affected by DFI. This can reduce ghosting without changing DFI strength.
+
+**Smooth:** Smoothes the interpolation. High values reduce the effectiveness of the process.
+
+**Source denoise:** Improves scanning in noisy sources.
+
+(DEFLICKERS PLAYGROUND ADDED)
+(FUSE AND VIDEO EXTRACT ADDED)
+
+# USE STRATEGIES:
+
+### Basic: 
+The simplest use is to find the balance between deflicking and deghosting. However, this is not efficient.
+
+## Multipass:
+The most efficient way to use this tool is to allow a certain amount of corruption and ghosting, in exchange for more stable video. Once we have that base, we must use a second step in Stable Diffusion, at low denoising (1-4). In most cases, this brings back much of the detail, but retains the stability we've gained.
+
+# Multibatch-controlnet: 
+The best, best way to use this tool is to use our "stabilized" video in img2img, and the original (REAL) video in controlnet HED. Then use a parallel batch to retrieve details. This considerably improves the multipass technique. Unfortunately, that function is not available in the controlnet gui as of this writing.
+
+# TODO
+Automatic1111 extension. Given my limited knowledge of programming, I had trouble getting my script to interact within A1111. I hope soon to solve details to integrate this tool.
+Also, there are many important utilities that are in development, waiting to be added soon, such as polar rendering (like "front/back", but more complex), gif viewer, source analysis, preprocessing, etc.
+
+

Abysz-LAB-Ext/scripts/Abysz_Lab.py

@@ -0,0 +1,1202 @@
+import gradio as gr
+import subprocess
+import os
+import imageio
+import numpy as np
+from gradio.outputs import Image
+from PIL import Image
+import sys
+import cv2
+import shutil
+import time
+import math
+
+from modules import shared
+from modules import scripts
+from modules import script_callbacks
+
+
+class Script(scripts.Script):
+    def title(self):
+        return "Abysz LAB"
+
+    def show(self, is_img2img):
+        return scripts.AlwaysVisible
+     
+    def ui(self, is_img2img):
+        return []
+        
+def main(ruta_entrada_1, ruta_entrada_2, ruta_salida, denoise_blur, dfi_strength, dfi_deghost, test_mode, inter_denoise, inter_denoise_size, inter_denoise_speed, fine_blur, frame_refresh_frequency, refresh_strength, smooth, frames_limit):
+            
+        maskD = os.path.join(os.getcwd(), 'extensions', 'Abysz-LAB-Ext', 'scripts', 'Run', 'MaskD')
+        maskS = os.path.join(os.getcwd(), 'extensions', 'Abysz-LAB-Ext', 'scripts', 'Run', 'MaskS')
+        #output = os.path.join(os.getcwd(), 'extensions', 'Abysz-LAB-Ext', 'scripts', 'Run', 'Output')
+        source = os.path.join(os.getcwd(), 'extensions', 'Abysz-LAB-Ext', 'scripts', 'Run', 'Source')
+        #gen = os.path.join(os.getcwd(), 'extensions', 'Abysz-LAB-Ext', 'scripts', 'Run', 'Gen')
+
+        # verificar si las carpetas existen y eliminarlas si es el caso
+        if os.path.exists(source): # verificar si existe la carpeta source
+            shutil.rmtree(source) # eliminar la carpeta source y su contenido
+        if os.path.exists(maskS): # verificar si existe la carpeta maskS
+            shutil.rmtree(maskS) # eliminar la carpeta maskS y su contenido
+        if os.path.exists(maskD): # verificar si existe la carpeta maskS
+            shutil.rmtree(maskD) # eliminar la carpeta maskS y su contenido
+                
+        os.makedirs(source, exist_ok=True)
+        os.makedirs(maskS, exist_ok=True)
+        os.makedirs(ruta_salida, exist_ok=True)
+        os.makedirs(maskD, exist_ok=True)
+        #os.makedirs(gen, exist_ok=True)
+        
+        
+        def copy_images(ruta_entrada_1, ruta_entrada_2, frames_limit=0):
+            # Copiar todas las imágenes de la carpeta ruta_entrada_1 a la carpeta Source
+            count = 0
+            
+            archivos = os.listdir(ruta_entrada_1)
+            archivos_ordenados = sorted(archivos)
+            
+            for i, file in enumerate(archivos_ordenados):
+                if file.endswith(".jpg") or file.endswith(".jpeg") or file.endswith(".png"):
+                    img = Image.open(os.path.join(ruta_entrada_1, file))
+                    rgb_img = img.convert('RGB')
+                    rgb_img.save(os.path.join("./extensions/Abysz-LAB-Ext/scripts/Run/Source", "{:04d}.jpeg".format(i+1)), "jpeg", quality=100)
+                    count += 1
+                    if frames_limit > 0 and count >= frames_limit:
+                        break
+                                     
+                        
+        # Llamar a la función copy_images para copiar las imágenes
+        copy_images(ruta_entrada_1,ruta_salida, frames_limit)
+        
+        def sresize(ruta_entrada_2):
+            gen_folder = ruta_entrada_2
+            
+            # Carpeta donde se encuentran las imágenes de FULL
+            full_folder = "./extensions/Abysz-LAB-Ext/scripts/Run/Source"
+            
+            # Obtener la primera imagen en la carpeta Gen
+            gen_images = os.listdir(gen_folder)
+            gen_image_path = os.path.join(gen_folder, gen_images[0])
+            gen_image = cv2.imread(gen_image_path)
+            gen_height, gen_width = gen_image.shape[:2]
+            gen_aspect_ratio = gen_width / gen_height
+            
+            # Recorrer todas las imágenes en la carpeta FULL
+            for image_name in sorted(os.listdir(full_folder)): 
+                image_path = os.path.join(full_folder, image_name)
+                image = cv2.imread(image_path)
+                height, width = image.shape[:2]
+                aspect_ratio = width / height
+            
+                if aspect_ratio != gen_aspect_ratio:
+                    if aspect_ratio > gen_aspect_ratio:
+                        # La imagen es más ancha que la imagen de Gen
+                        crop_width = int(height * gen_aspect_ratio)
+                        x = int((width - crop_width) / 2)
+                        image = image[:, x:x+crop_width]
+                    else:
+                        # La imagen es más alta que la imagen de Gen
+                        crop_height = int(width / gen_aspect_ratio)
+                        y = int((height - crop_height) / 2)
+                        image = image[y:y+crop_height, :]
+            
+                # Redimensionar la imagen de FULL a la resolución de la imagen de Gen
+                image = cv2.resize(image, (gen_width, gen_height))
+            
+                # Guardar la imagen redimensionada en la carpeta FULL
+                cv2.imwrite(os.path.join(full_folder, image_name), image)
+        
+        sresize(ruta_entrada_2)
+            
+        def s_g_rename(ruta_entrada_2):
+                                
+            gen_dir = ruta_entrada_2 # ruta de la carpeta "Source"
+            
+            # Obtener una lista de los nombres de archivo en la carpeta ruta_entrada_2
+            files2 = os.listdir(gen_dir)
+            files2 = sorted(files2) # ordenar alfabéticamente la lista
+            # Renombrar cada archivo
+            for i, file_name in enumerate(files2):
+                old_path = os.path.join(gen_dir, file_name) # ruta actual del archivo
+                new_file_name = f"{i+1:04d}rename" # nuevo nombre de archivo con formato %04d
+                new_path = os.path.join(gen_dir, new_file_name + os.path.splitext(file_name)[1]) # nueva ruta del archivo
+                try:
+                    os.rename(old_path, new_path)
+                except FileExistsError:
+                    print(f"El archivo {new_file_name} ya existe. Se omite su renombre.")
+                    
+            # Obtener una lista de los nombres de archivo en la carpeta ruta_entrada_2
+            files2 = os.listdir(gen_dir)
+            files2 = sorted(files2) # ordenar alfabéticamente la lista
+            # Renombrar cada archivo
+            for i, file_name in enumerate(files2):
+                old_path = os.path.join(gen_dir, file_name) # ruta actual del archivo
+                new_file_name = f"{i+1:04d}" # nuevo nombre de archivo con formato %04d
+                new_path = os.path.join(gen_dir, new_file_name + os.path.splitext(file_name)[1]) # nueva ruta del archivo
+                try:
+                    os.rename(old_path, new_path)
+                except FileExistsError:
+                    print(f"El archivo {new_file_name} ya existe. Se omite su renombre.")
+        
+        s_g_rename(ruta_entrada_2)
+        
+        # Obtener el primer archivo de la carpeta ruta_entrada_2
+        gen_files = os.listdir(ruta_entrada_2)
+        if gen_files:
+            first_gen_file = gen_files[0]
+
+            # Copiar el archivo a la carpeta "Output" y reemplazar si ya existe
+            #output_file = "Output" + first_gen_file
+            #shutil.copyfile(ruta_entrada_2 + first_gen_file, output_file)
+            output_file = os.path.join(ruta_salida, first_gen_file)
+            shutil.copyfile(os.path.join(ruta_entrada_2, first_gen_file), output_file)
+        #subprocess call
+        def denoise(denoise_blur):
+            if denoise_blur < 1: # Condición 1: strength debe ser mayor a 1
+                return
+                         
+            denoise_kernel = denoise_blur
+            # Obtener la lista de nombres de archivos en la carpeta source
+            files = os.listdir("./extensions/Abysz-LAB-Ext/scripts/Run/Source")
+            
+            # Crear una carpeta destino si no existe
+            #if not os.path.exists("dest"):
+            #   os.mkdir("dest")
+            
+            # Recorrer cada archivo en la carpeta source
+            for file in files:
+                # Leer la imagen con opencv
+                img = cv2.imread(os.path.join("./extensions/Abysz-LAB-Ext/scripts/Run/Source", file))
+            
+                # Aplicar el filtro de blur con un tamaño de kernel 5x5
+                dst = cv2.bilateralFilter(img, denoise_kernel, 31, 31)
+                
+                # Eliminar el archivo original
+                #os.remove(os.path.join("SourceDFI", file))
+            
+                # Guardar la imagen resultante en la carpeta destino con el mismo nombre
+                cv2.imwrite(os.path.join("./extensions/Abysz-LAB-Ext/scripts/Run/Source", file), dst)
+                
+        denoise(denoise_blur)    
+        
+        # Definir la carpeta donde están los archivos
+        carpeta = './extensions/Abysz-LAB-Ext/scripts/Run/Source'
+        
+        # Crear la carpeta MaskD si no existe
+        os.makedirs('./extensions/Abysz-LAB-Ext/scripts/Run/MaskD', exist_ok=True)
+        
+        # Inicializar contador
+        contador = 1
+        
+        umbral_size = dfi_strength
+        # Iterar a través de los archivos de imagen en la carpeta Source
+        for filename in sorted(os.listdir(carpeta)):
+            # Cargar la imagen actual y la siguiente en escala de grises
+            if contador > 1:
+                siguiente = cv2.imread(os.path.join(carpeta, filename), cv2.IMREAD_GRAYSCALE)
+                diff = cv2.absdiff(anterior, siguiente)
+        
+                # Aplicar un umbral y guardar la imagen resultante en la carpeta MaskD. Menos es más.
+                umbral = umbral_size
+                umbralizado = cv2.threshold(diff, umbral, 255, cv2.THRESH_BINARY_INV)[1] # Invertir los colores
+                cv2.imwrite(os.path.join('./extensions/Abysz-LAB-Ext/scripts/Run/MaskD', f'{contador-1:04d}.png'), umbralizado)
+        
+            anterior = cv2.imread(os.path.join(carpeta, filename), cv2.IMREAD_GRAYSCALE)
+            contador += 1
+            
+            #Actualmente, el tipo de umbralización es cv2.THRESH_BINARY_INV, que invierte los colores de la imagen umbralizada. 
+            #Puedes cambiarlo a otro tipo de umbralización, 
+            #como cv2.THRESH_BINARY, cv2.THRESH_TRUNC, cv2.THRESH_TOZERO o cv2.THRESH_TOZERO_INV.
+        
+        
+        # Obtener la lista de los nombres de los archivos en la carpeta MaskD
+        files = os.listdir("./extensions/Abysz-LAB-Ext/scripts/Run/MaskD")
+        # Definir la carpeta donde están los archivos
+        carpeta = "./extensions/Abysz-LAB-Ext/scripts/Run/MaskD"
+        blur_kernel = smooth
+        
+        # Iterar sobre cada archivo
+        for file in files:
+            if dfi_deghost == 0:
+                
+                continue
+            # Leer la imagen de la carpeta MaskD
+            #img = cv2.imread("MaskD" + file)
+            img = cv2.imread(os.path.join("./extensions/Abysz-LAB-Ext/scripts/Run/MaskD", file))
+            
+            # Invertir la imagen usando la función bitwise_not()
+            img_inv = cv2.bitwise_not(img)
+            
+            kernel_size = dfi_deghost
+            
+            # Dilatar la imagen usando la función dilate()
+            kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (kernel_size, kernel_size)) # Puedes cambiar el tamaño y la forma del kernel según tus preferencias
+            img_dil = cv2.dilate(img_inv, kernel)
+            
+            # Volver a invertir la imagen usando la función bitwise_not()
+            img_out = cv2.bitwise_not(img_dil)
+            
+            # Sobrescribir la imagen en la carpeta MaskD con el mismo nombre que el original
+            #cv2.imwrite("MaskD" + file, img_out)
+            #cv2.imwrite(os.path.join("MaskD", file, img_out))
+            filename = os.path.join("./extensions/Abysz-LAB-Ext/scripts/Run/MaskD", file)
+            cv2.imwrite(filename, img_out)
+    
+        # Iterar a través de los archivos de imagen en la carpeta MaskD
+        if smooth > 1:
+            for imagen in os.listdir(carpeta):
+                if imagen.endswith(".jpg") or imagen.endswith(".png") or imagen.endswith(".jpeg"):
+                    # Leer la imagen
+                    img = cv2.imread(os.path.join(carpeta, imagen))
+                    # Aplicar el filtro
+                    img = cv2.GaussianBlur(img, (blur_kernel,blur_kernel),0)
+                    # Guardar la imagen con el mismo nombre
+                    cv2.imwrite(os.path.join(carpeta, imagen), img)
+        
+        
+        # INICIO DEL BATCH Obtener el nombre del archivo en MaskD sin ninguna extensión
+        # Agregar una variable de contador de bucles
+        loop_count = 0
+        
+        # Agregar un bucle while para ejecutar el código en bucle infinito
+        while True:
+        
+            mask_files = sorted(os.listdir(maskD))
+            if not mask_files:
+                print(f"No frames left")
+                # Eliminar las carpetas Source, MaskS y MaskD si no hay más archivos para procesar
+                shutil.rmtree(maskD)
+                shutil.rmtree(maskS)
+                shutil.rmtree(source)
+                break
+            
+            extra_mod = fine_blur
+            
+            mask = mask_files[0]
+            maskname = os.path.splitext(mask)[0]
+            
+            maskp_path = os.path.join(maskD, mask) 
+
+            img = cv2.imread(maskp_path, cv2.IMREAD_GRAYSCALE) # leer la imagen en escala de grises
+            n_white_pix = np.sum(img == 255) # contar los píxeles que son iguales a 255 (blanco)
+            total_pix = img.size # obtener el número total de píxeles en la imagen
+            percentage = (n_white_pix / total_pix) * 100 # calcular el porcentaje de píxeles blancos
+            percentage = round(percentage, 1) # redondear el porcentaje a 1 decimal
+            
+            # calcular la variable extra
+            extra = 100 - percentage # restar el porcentaje a 100
+            extra = extra / 3 # dividir el resultado por 3
+            extra = math.ceil(extra) # redondear hacia arriba al entero más cercano
+            if extra % 2 == 0: # verificar si el número es par
+                extra = extra + 1 # sumarle 1 para hacerlo impar
+            
+            # Dynamic Blur
+            imgb = cv2.imread(maskp_path) # leer la imagen con opencv
+            img_blur = cv2.GaussianBlur(imgb, (extra,extra),0)
+
+            # guardar la imagen modificada con el mismo nombre y ruta
+            cv2.imwrite(maskp_path, img_blur)
+
+            # Obtener la ruta de la imagen en la subcarpeta de output que tiene el mismo nombre que la imagen en MaskD
+            output_files = [f for f in os.listdir(ruta_salida) if os.path.splitext(f)[0] == maskname]
+            if not output_files:
+                print(f"No se encontró en {ruta_salida} una imagen con el mismo nombre que {maskname}.")
+                exit(1)
+            
+            output_file = os.path.join(ruta_salida, output_files[0])
+            
+            # Aplicar el comando magick composite con las opciones deseadas
+            composite_command = f"magick composite -compose CopyOpacity {os.path.join(maskD, mask)} {output_file} {os.path.join(maskS, 'result.png')}"
+            os.system(composite_command)
+            
+            # Obtener el nombre del archivo en output sin ninguna extensión
+            name = os.path.splitext(os.path.basename(output_file))[0]
+            
+            # Renombrar el archivo result.png con el nombre del archivo en output y la extensión .png
+            os.rename(os.path.join(maskS, 'result.png'), os.path.join(maskS, f"{name}.png"))
+            
+            #Guardar el directorio actual en una variable
+            original_dir = os.getcwd()
+            
+            #Cambiar al directorio de la carpeta MaskS
+            os.chdir(maskS)
+            
+            #Iterar a través de los archivos de imagen en la carpeta MaskS
+            for imagen in sorted(os.listdir(".")):
+                # Obtener el nombre de la imagen sin la extensión
+                nombre, extension = os.path.splitext(imagen)
+                # Obtener solo el número de la imagen
+                numero = ''.join(filter(str.isdigit, nombre))
+                # Definir el nombre de la siguiente imagen
+                siguiente = f"{int(numero)+1:0{len(numero)}}{extension}"
+                # Renombrar la imagen
+                os.rename(imagen, siguiente)
+            
+            # Volver al directorio original
+            os.chdir(original_dir)
+            
+            # Establecer un valor predeterminado para disolución
+            if frame_refresh_frequency < 1:
+                dissolve = percentage
+            else:
+                dissolve = 100 if loop_count % frame_refresh_frequency != 0 else refresh_strength
+                    
+        
+            # Obtener el nombre del archivo en MaskS sin la extensión
+            maskS_files = [f for f in os.listdir(maskS) if os.path.isfile(os.path.join(maskS, f)) and f.endswith('.png')]
+            if maskS_files:
+                filename = os.path.splitext(maskS_files[0])[0]
+            else:
+                print(f"No se encontraron archivos de imagen en la carpeta '{maskS}'")
+                filename = ''[0]
+            
+            # Salir del bucle si no hay más imágenes que procesar
+            if not filename:
+                break
+            
+            # Obtener la extensión del archivo en Gen con el mismo nombre
+            gen_files = [f for f in os.listdir(ruta_entrada_2) if os.path.isfile(os.path.join(ruta_entrada_2, f)) and f.startswith(filename)]
+            if gen_files:
+                ext = os.path.splitext(gen_files[0])[1]
+            else:
+                print(f"No se encontró ningún archivo con el nombre '{filename}' en la carpeta '{ruta_entrada_2}'")
+                ext = ''
+                                
+            # Componer la imagen de MaskS y Gen con disolución (si está definido) y guardarla en la carpeta de salida
+            os.system(f"magick composite {'-dissolve ' + str(dissolve) + '%' if dissolve is not None else ''} {maskS}/{filename}.png {ruta_entrada_2}/{filename}{ext} {ruta_salida}/{filename}{ext}")
+            
+            denoise_loop = inter_denoise_speed
+            kernel1 = inter_denoise
+            kernel2 = inter_denoise_size
+            
+            # Demo plus bilateral
+            if loop_count % denoise_loop == 0:
+                # listar archivos en la carpeta de salida
+                archivos = os.listdir(ruta_salida)
+                # obtener el último archivo
+                ultimo_archivo = os.path.join(ruta_salida, archivos[-1])
+                # cargar imagen con opencv
+                imagen = cv2.imread(ultimo_archivo)
+                # aplicar filtro bilateral
+                imagen_filtrada = cv2.bilateralFilter(imagen, kernel1, kernel2, kernel2)
+                # sobreescribir el original
+                cv2.imwrite(ultimo_archivo, imagen_filtrada)
+            
+            # Obtener el nombre del archivo más bajo en la carpeta MaskD
+            maskd_files = [f for f in os.listdir(maskD) if os.path.isfile(os.path.join(maskD, f)) and f.startswith('')]
+            if maskd_files:
+                maskd_file = os.path.join(maskD, sorted(maskd_files)[0])
+                os.remove(maskd_file)
+            
+            # Obtener el nombre del archivo más bajo en la carpeta MaskS
+            masks_files = [f for f in os.listdir(maskS) if os.path.isfile(os.path.join(maskS, f)) and f.startswith('')]
+            if masks_files:
+                masks_file = os.path.join(maskS, sorted(masks_files)[0])
+                os.remove(masks_file)
+                                
+            # Aumentar el contador de bucles
+            loop_count += 1
+            
+def dyndef(ruta_entrada_3, ruta_salida_1, ddf_strength):
+    if ddf_strength <= 0: # Condición 1: strength debe ser mayor a 0
+        return
+    imgs = []
+    files = sorted(os.listdir(ruta_entrada_3))
+    
+    for file in files:
+        img = cv2.imread(os.path.join(ruta_entrada_3, file))
+        imgs.append(img)
+    
+    for idx in range(len(imgs)-1, 0, -1):
+        current_img = imgs[idx]
+        prev_img = imgs[idx-1]
+        alpha = ddf_strength
+        
+        current_img = cv2.addWeighted(current_img, alpha, prev_img, 1-alpha, 0)
+        imgs[idx] = current_img
+        
+        if not os.path.exists(ruta_salida_1):
+            os.makedirs(ruta_salida_1)
+            
+        output_path = os.path.join(ruta_salida_1, files[idx]) # Usa el mismo nombre que el original
+        cv2.imwrite(output_path, current_img)
+    
+    # Copia el primer archivo de los originales al finalizar el proceso
+    shutil.copy(os.path.join(ruta_entrada_3, files[0]), os.path.join(ruta_salida_1, files[0]))
+
+
+
+def overlay_images(image1_path, image2_path, over_strength):
+      
+    opacity = over_strength
+    
+    # Abrir las imágenes
+    image1 = Image.open(image1_path).convert('RGBA')
+    image2 = Image.open(image2_path).convert('RGBA')
+
+    # Alinear el tamaño de las imágenes
+    if image1.size != image2.size:
+        image2 = image2.resize(image1.size)
+
+    # Convertir las imágenes en matrices NumPy
+    np_image1 = np.array(image1).astype(np.float64) / 255.0
+    np_image2 = np.array(image2).astype(np.float64) / 255.0
+
+    # Aplicar el método de fusión "overlay" a las imágenes
+    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 overlay(target, blend, *args):
+            return  (target>0.5) * (1-(2-2*target)*(1-blend)) +\
+                    (target<=0.5) * (2*target*blend)
+
+    blended_image = Blend.overlay(np_image1, np_image2, opacity)
+
+    # Convertir la matriz de vuelta a una imagen PIL
+    blended_image = Image.fromarray((blended_image * 255).astype(np.uint8), 'RGBA').convert('RGB')
+
+    # Guardar la imagen resultante
+    return blended_image
+    
+def overlay_images2(image1_path, image2_path, fuse_strength):
+      
+    opacity = fuse_strength
+    
+    try:
+        image1 = Image.open(image1_path).convert('RGBA')
+        image2 = Image.open(image2_path).convert('RGBA')
+    except:
+        print("No more frames to fuse.")
+        return
+
+    # Alinear el tamaño de las imágenes
+    if image1.size != image2.size:
+        image1 = image1.resize(image2.size)
+
+    # Convertir las imágenes en matrices NumPy
+    np_image1 = np.array(image1).astype(np.float64) / 255.0
+    np_image2 = np.array(image2).astype(np.float64) / 255.0
+
+    # Aplicar el método de fusión "overlay" a las imágenes
+    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 overlay(target, blend, *args):
+            return  (target>0.5) * (1-(2-2*target)*(1-blend)) +\
+                    (target<=0.5) * (2*target*blend)
+
+    blended_image = Blend.overlay(np_image1, np_image2, opacity)
+
+    # Convertir la matriz de vuelta a una imagen PIL
+    blended_image = Image.fromarray((blended_image * 255).astype(np.uint8), 'RGBA').convert('RGB')
+
+    # Guardar la imagen resultante
+    return blended_image
+
+def overlay_run(ruta_entrada_3, ruta_salida_1, ddf_strength, over_strength):
+    if over_strength <= 0: # Condición 1: strength debe ser mayor a 0
+            return
+       
+    # Si ddf_strength y/o over_strength son mayores a 0, utilizar ruta_salida_1 en lugar de ruta_entrada_3
+    if ddf_strength > 0:
+        ruta_entrada_3 = ruta_salida_1    
+        
+    if not os.path.exists("overtemp"):
+            os.makedirs("overtemp")
+            
+    if not os.path.exists(ruta_salida_1):
+            os.makedirs(ruta_salida_1)
+            
+    gen_path = ruta_entrada_3
+    images = sorted(os.listdir(gen_path))
+    image1_path = os.path.join(gen_path, images[0])
+    image2_path = os.path.join(gen_path, images[1])
+    
+     
+    fused_image = overlay_images(image1_path, image2_path, over_strength)
+    fuseover_path = "overtemp"
+    filename = os.path.basename(image1_path)
+    fused_image.save(os.path.join(fuseover_path, filename))
+    
+    
+    # Obtener una lista de todos los archivos en la carpeta "Gen"
+    gen_files = sorted(os.listdir(ruta_entrada_3))
+    
+    for i in range(len(gen_files) - 1):
+        image1_path = os.path.join(ruta_entrada_3, gen_files[i])
+        image2_path = os.path.join(ruta_entrada_3, gen_files[i+1])
+        blended_image = overlay_images(image1_path, image2_path, over_strength)
+        blended_image.save(os.path.join("overtemp", gen_files[i+1]))
+    
+    
+    # Definimos la ruta de la carpeta "overtemp"
+    ruta_overtemp = "overtemp"
+    
+    # Movemos todos los archivos de la carpeta "overtemp" a la carpeta "ruta_salida"
+    for archivo in os.listdir(ruta_overtemp):
+        origen = os.path.join(ruta_overtemp, archivo)
+        destino = os.path.join(ruta_salida_1, archivo)
+        shutil.move(origen, destino)
+        
+    # Ajustar contraste y brillo para cada imagen en la carpeta de entrada
+    if over_strength >= 0.4:
+        for nombre_archivo in os.listdir(ruta_salida_1):
+            # Cargar imagen
+            ruta_archivo = os.path.join(ruta_salida_1, nombre_archivo)
+            img = cv2.imread(ruta_archivo)
+        
+            # Ajustar contraste y brillo
+            alpha = 1  # Factor de contraste (mayor que 1 para aumentar el contraste)
+            beta = 10  # Valor de brillo (entero positivo para aumentar el brillo)
+            img_contrast = cv2.convertScaleAbs(img, alpha=alpha, beta=beta)
+        
+            # Guardar imagen resultante en la carpeta de salida
+            ruta_salida = os.path.join(ruta_salida_1, nombre_archivo)
+            cv2.imwrite(ruta_salida, img_contrast)
+
+def over_fuse(ruta_entrada_4, ruta_entrada_5, ruta_salida_2, fuse_strength):
+    # Obtener una lista de todos los archivos en la carpeta "Gen"
+    gen_files = os.listdir(ruta_entrada_4)
+    
+    # Ordenar la lista de archivos alfabéticamente
+    gen_files.sort()
+    
+    # Obtener una lista de todos los archivos en la carpeta "Source"
+    source_files = os.listdir(ruta_entrada_5)
+    
+    # Ordenar la lista de archivos alfabéticamente
+    source_files.sort()
+    
+    if not os.path.exists(ruta_salida_2):
+            os.makedirs(ruta_salida_2)
+            
+    for i in range(len(gen_files)):
+        image1_path = os.path.join(ruta_entrada_4, gen_files[i])
+        image2_path = os.path.join(ruta_entrada_5, source_files[i])
+        blended_image = overlay_images2(image1_path, image2_path, fuse_strength)
+        try:
+            blended_image.save(os.path.join(ruta_salida_2, gen_files[i]))
+        except Exception as e:
+            print("Error al guardar la imagen:", str(e))
+            print("No more frames to fuse")
+            break
+
+
+def norm(ruta_entrada_3, ruta_salida_1, ddf_strength, over_strength, norm_strength):
+    if norm_strength <= 0: # Condición 1: Norm_strength debe ser mayor a 0
+        return
+    
+    # Si ddf_strength y/o over_strength son mayores a 0, utilizar ruta_salida_1 en lugar de ruta_entrada_3
+    if ddf_strength > 0 or over_strength > 0:
+        ruta_entrada_3 = ruta_salida_1
+    
+    # Crear la carpeta GenOverNorm si no existe
+    if not os.path.exists("normtemp"):
+        os.makedirs("normtemp")
+        
+    if not os.path.exists(ruta_salida_1):
+        os.makedirs(ruta_salida_1)
+            
+    # Obtener una lista de todas las imágenes en la carpeta FuseOver
+    img_list = os.listdir(ruta_entrada_3)
+    img_list.sort() # Ordenar la lista en orden ascendente
+        
+    # Iterar a través de las imágenes
+    for i in range(len(img_list)-1):
+        # Cargar las dos imágenes a fusionar
+        img1 = cv2.imread(os.path.join(ruta_entrada_3, img_list[i]))
+        img2 = cv2.imread(os.path.join(ruta_entrada_3, img_list[i+1]))
+    
+        # Calcular la luminosidad promedio de cada imagen
+        avg1 = np.mean(cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY))
+        avg2 = np.mean(cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY))
+    
+        # Calcular los pesos para cada imagen
+        weight1 = avg1 / (avg1 + avg2)
+        weight2 = avg2 / (avg1 + avg2)
+    
+        # Fusionar las imágenes utilizando los pesos
+        result = cv2.addWeighted(img1, weight1, img2, weight2, 0)
+                                
+        # Guardar la imagen resultante en la carpeta GenOverNorm con el mismo nombre que la imagen original
+        cv2.imwrite(os.path.join("normtemp", img_list[i+1]), result)
+    
+    # Copiar la primera imagen en la carpeta GenOverNorm para mantener la secuencia completa
+    img0 = cv2.imread(os.path.join(ruta_entrada_3, img_list[0]))
+    cv2.imwrite(os.path.join("normtemp", img_list[0]), img0)
+    
+    # Definimos la ruta de la carpeta "overtemp"
+    ruta_overtemp = "normtemp"
+    
+    # Movemos todos los archivos de la carpeta "overtemp" a la carpeta "ruta_salida"
+    for archivo in os.listdir(ruta_overtemp):
+        origen = os.path.join(ruta_overtemp, archivo)
+        destino = os.path.join(ruta_salida_1, archivo)
+        shutil.move(origen, destino)
+        
+def deflickers(ruta_entrada_3, ruta_salida_1, ddf_strength, over_strength, norm_strength):
+    dyndef(ruta_entrada_3, ruta_salida_1, ddf_strength)
+    overlay_run(ruta_entrada_3, ruta_salida_1, ddf_strength, over_strength)
+    norm(ruta_entrada_3, ruta_salida_1, ddf_strength, over_strength, norm_strength)
+    
+def extract_video(ruta_entrada_6, ruta_salida_3, fps_count):
+
+    # Ruta del archivo de video
+    filename = ruta_entrada_6
+
+    # Directorio donde se guardarán los frames extraídos
+    output_dir = ruta_salida_3
+
+    # Abrir el archivo de video
+    cap = cv2.VideoCapture(filename)
+
+    # Obtener los FPS originales del video
+    fps = cap.get(cv2.CAP_PROP_FPS)
+
+    # Si fps_count es 0, utilizar los FPS originales
+    if fps_count == 0:
+        fps_count = fps
+
+    # Calcular el tiempo entre cada frame a extraer en milisegundos
+    frame_time = int(round(1000 / fps_count))
+
+    # Crear el directorio de salida si no existe
+    if not os.path.exists(output_dir):
+        os.makedirs(output_dir)
+
+    # Inicializar el contador de frames
+    frame_count = 0
+
+    # Inicializar el tiempo del último frame extraído
+    last_frame_time = 0
+
+    # Iterar sobre los frames del video
+    while True:
+        # Leer el siguiente frame
+        ret, frame = cap.read()
+
+        # Si no se pudo leer un frame, salir del loop
+        if not ret:
+            break
+
+        # Calcular el tiempo actual del frame en milisegundos
+        current_frame_time = int(round(cap.get(cv2.CAP_PROP_POS_MSEC)))
+
+        # Si todavía no ha pasado suficiente tiempo desde el último frame extraído, saltar al siguiente frame
+        if current_frame_time - last_frame_time < frame_time:
+            continue
+
+        # Incrementar el contador de frames
+        frame_count += 1
+
+        # Construir el nombre del archivo de salida
+        output_filename = os.path.join(output_dir, 'frame_{:04d}.jpeg'.format(frame_count))
+
+        # Guardar el frame como una imagen
+        cv2.imwrite(output_filename, frame)
+
+        # Actualizar el tiempo del último frame extraído
+        last_frame_time = current_frame_time
+
+    # Cerrar el archivo de video
+    cap.release()
+
+    # Mostrar información sobre el proceso finalizado
+    print("Extracted {} frames.".format(frame_count))
+
+def test_dfi(ruta_entrada_1, ruta_entrada_2, denoise_blur, dfi_strength, dfi_deghost, test_mode, smooth):
+        
+        
+        maskD = os.path.join(os.getcwd(), 'extensions', 'Abysz-LAB-Ext', 'scripts', 'Run', 'MaskDT')
+        #maskS = os.path.join(os.getcwd(), 'extensions', 'Abysz-LAB-Ext', 'scripts', 'Run', 'MaskST')
+        #output = os.path.join(os.getcwd(), 'extensions', 'Abysz-lab', 'scripts', 'Run', 'Output')
+        source = os.path.join(os.getcwd(), 'extensions', 'Abysz-LAB-Ext', 'scripts', 'Run', 'SourceT')
+        #gen = os.path.join(os.getcwd(), 'extensions', 'Abysz-LAB-Ext', 'scripts', 'Run', 'GenT')
+        
+        # verificar si las carpetas existen y eliminarlas si es el caso
+        if os.path.exists(source): # verificar si existe la carpeta source
+            shutil.rmtree(source) # eliminar la carpeta source y su contenido
+        #if os.path.exists(maskS): # verificar si existe la carpeta maskS
+        #    shutil.rmtree(maskS) # eliminar la carpeta maskS y su contenido
+        if os.path.exists(maskD): # verificar si existe la carpeta maskS
+            shutil.rmtree(maskD) # eliminar la carpeta maskS y su contenido
+        #if os.path.exists(gen): # verificar si existe la carpeta maskS
+        #    shutil.rmtree(gen) # eliminar la carpeta maskS y su contenido
+        #if os.path.exists(output): # verificar si existe la carpeta maskS
+        #    shutil.rmtree(output) # eliminar la carpeta maskS y su contenido
+            
+    
+        os.makedirs(source, exist_ok=True)
+        #os.makedirs(maskS, exist_ok=True)
+        #os.makedirs(output, exist_ok=True)
+        os.makedirs(maskD, exist_ok=True)
+        #os.makedirs(gen, exist_ok=True)
+        
+        
+        def copy_images(ruta_entrada_1, ruta_entrada_2):
+            if test_mode == 0:
+                # Usar el primer formato
+                indices = [10, 11, 20, 21, 30, 31] # Los índices de las imágenes que quieres copiar
+            else:
+                test_frames = test_mode
+                # Usar el segundo formato
+                indices = list(range(test_frames)) # Los primeros 30 índices
+            # Copiar todas las imágenes de la carpeta ruta_entrada_1 a la carpeta Source
+            for i in indices:
+                file = os.listdir(ruta_entrada_1)[i] # Obtener el nombre del archivo en el índice i
+                if file.endswith(".jpg") or file.endswith(".jpeg") or file.endswith(".png"): # Verificar que sea una imagen
+                    img = Image.open(os.path.join(ruta_entrada_1, file)) # Abrir la imagen
+                    rgb_img = img.convert('RGB') # Convertir a RGB
+                    rgb_img.save(os.path.join("./extensions/Abysz-LAB-Ext/scripts/Run/SourceT", "{:04d}.jpeg".format(i+1)), "jpeg", quality=100) # Guardar la imagen en la carpeta destino
+                  
+        # Llamar a la función copy_images para copiar las imágenes
+        copy_images(ruta_entrada_1, ruta_entrada_2)
+        
+        # Carpeta donde se encuentran las imágenes de Gen
+        def sresize(ruta_entrada_2):
+            gen_folder = ruta_entrada_2
+            
+            # Carpeta donde se encuentran las imágenes de FULL
+            full_folder = "./extensions/Abysz-LAB-Ext/scripts/Run/SourceT"
+            
+            # Obtener la primera imagen en la carpeta Gen
+            gen_images = os.listdir(gen_folder)
+            gen_image_path = os.path.join(gen_folder, gen_images[0])
+            gen_image = cv2.imread(gen_image_path)
+            gen_height, gen_width = gen_image.shape[:2]
+            gen_aspect_ratio = gen_width / gen_height
+            
+            # Recorrer todas las imágenes en la carpeta FULL
+            for image_name in os.listdir(full_folder):
+                image_path = os.path.join(full_folder, image_name)
+                image = cv2.imread(image_path)
+                height, width = image.shape[:2]
+                aspect_ratio = width / height
+            
+                if aspect_ratio != gen_aspect_ratio:
+                    if aspect_ratio > gen_aspect_ratio:
+                        # La imagen es más ancha que la imagen de Gen
+                        crop_width = int(height * gen_aspect_ratio)
+                        x = int((width - crop_width) / 2)
+                        image = image[:, x:x+crop_width]
+                    else:
+                        # La imagen es más alta que la imagen de Gen
+                        crop_height = int(width / gen_aspect_ratio)
+                        y = int((height - crop_height) / 2)
+                        image = image[y:y+crop_height, :]
+            
+                # Redimensionar la imagen de FULL a la resolución de la imagen de Gen
+                image = cv2.resize(image, (gen_width, gen_height))
+            
+                # Guardar la imagen redimensionada en la carpeta FULL
+                cv2.imwrite(os.path.join(full_folder, image_name), image)
+        
+        sresize(ruta_entrada_2)
+            
+        def denoise(denoise_blur):
+            if denoise_blur < 1:
+                return
+                
+            denoise_kernel = denoise_blur
+            # Obtener la lista de nombres de archivos en la carpeta source
+            files = os.listdir("./extensions/Abysz-LAB-Ext/scripts/Run/SourceT")
+            
+            # Crear una carpeta destino si no existe
+            #if not os.path.exists("dest"):
+            #   os.mkdir("dest")
+            
+            # Recorrer cada archivo en la carpeta source
+            for file in files:
+                # Leer la imagen con opencv
+                img = cv2.imread(os.path.join("./extensions/Abysz-LAB-Ext/scripts/Run/SourceT", file))
+            
+                # Aplicar el filtro de blur con un tamaño de kernel 5x5
+                dst = cv2.bilateralFilter(img, denoise_kernel, 31, 31)
+                
+                # Eliminar el archivo original
+                #os.remove(os.path.join("SourceDFI", file))
+            
+                # Guardar la imagen resultante en la carpeta destino con el mismo nombre
+                cv2.imwrite(os.path.join("./extensions/Abysz-LAB-Ext/scripts/Run/SourceT", file), dst)
+                
+        denoise(denoise_blur)    
+                  
+            
+        # Definir la carpeta donde están los archivos
+        carpeta = './extensions/Abysz-LAB-Ext/scripts/Run/SourceT'
+        
+        # Crear la carpeta MaskD si no existe
+        os.makedirs('./extensions/Abysz-LAB-Ext/scripts/Run/MaskDT', exist_ok=True)
+        
+        # Inicializar número de imagen
+        numero = 1
+        
+        umbral_size = dfi_strength
+        # Iterar a través de los archivos de imagen en la carpeta Source
+        for filename in sorted(os.listdir(carpeta)):
+            if test_mode == 0:
+                # Cargar la imagen actual en escala de grises
+                actual = cv2.imread(os.path.join(carpeta, filename), cv2.IMREAD_GRAYSCALE)
+                
+                # Si el número de imagen es par, procesar la imagen actual y la anterior
+                if numero % 2 == 0:
+                    diff = cv2.absdiff(anterior, actual)
+            
+                    # Aplicar un umbral y guardar la imagen resultante en la carpeta MaskD con el mismo nombre que el original. Menos es más.
+                    umbral = umbral_size
+                    umbralizado = cv2.threshold(diff, umbral, 255, cv2.THRESH_BINARY_INV)[1] # Invertir los colores
+                    cv2.imwrite(os.path.join('./extensions/Abysz-LAB-Ext/scripts/Run/MaskDT', filename), umbralizado)
+                    
+                # Guardar la imagen actual como anterior para el siguiente ciclo
+                anterior = actual
+                
+                # Incrementar el número de imagen para alternar entre pares e impares
+                numero += 1
+        
+            else:
+                       
+                # Iterar a través de los archivos de imagen en la carpeta Source
+                for filename in sorted(os.listdir(carpeta)):
+                    # Cargar la imagen actual y la siguiente en escala de grises
+                    
+                    if numero > 1:
+                        siguiente = cv2.imread(os.path.join(carpeta, filename), cv2.IMREAD_GRAYSCALE)
+                        diff = cv2.absdiff(anterior, siguiente)
+                
+                        # Aplicar un umbral y guardar la imagen resultante en la carpeta MaskD. Menos es más.
+                        umbral = umbral_size
+                        umbralizado = cv2.threshold(diff, umbral, 255, cv2.THRESH_BINARY_INV)[1] # Invertir los colores
+                        cv2.imwrite(os.path.join('./extensions/Abysz-LAB-Ext/scripts/Run/MaskDT', filename), umbralizado)
+                
+                    anterior = cv2.imread(os.path.join(carpeta, filename), cv2.IMREAD_GRAYSCALE)
+                    numero += 1    
+        
+              
+        
+        # Obtener la lista de los nombres de los archivos en la carpeta MaskD
+        files = os.listdir("./extensions/Abysz-LAB-Ext/scripts/Run/MaskDT")
+        # Definir la carpeta donde están los archivos
+        carpeta = "./extensions/Abysz-LAB-Ext/scripts/Run/MaskDT"
+        blur_kernel = smooth
+        
+        # Iterar sobre cada archivo
+        for file in files:
+            if dfi_deghost == 0:
+                
+                continue
+            # Leer la imagen de la carpeta MaskD
+            #img = cv2.imread("MaskD" + file)
+            img = cv2.imread(os.path.join("./extensions/Abysz-LAB-Ext/scripts/Run/MaskDT", file))
+            
+            # Invertir la imagen usando la función bitwise_not()
+            img_inv = cv2.bitwise_not(img)
+            
+            kernel_size = dfi_deghost
+            
+            # Dilatar la imagen usando la función dilate()
+            kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (kernel_size, kernel_size)) # Puedes cambiar el tamaño y la forma del kernel según tus preferencias
+            img_dil = cv2.dilate(img_inv, kernel)
+            
+            # Volver a invertir la imagen usando la función bitwise_not()
+            img_out = cv2.bitwise_not(img_dil)
+            
+            # Sobrescribir la imagen en la carpeta MaskD con el mismo nombre que el original
+            #cv2.imwrite("MaskD" + file, img_out)
+            #cv2.imwrite(os.path.join("MaskD", file, img_out))
+            filename = os.path.join("./extensions/Abysz-LAB-Ext/scripts/Run/MaskDT", file)
+            cv2.imwrite(filename, img_out)
+    
+        # Iterar a través de los archivos de imagen en la carpeta MaskD
+        if smooth > 1:
+            for imagen in os.listdir(carpeta):
+                if imagen.endswith(".jpg") or imagen.endswith(".png") or imagen.endswith(".jpeg"):
+                    # Leer la imagen
+                    img = cv2.imread(os.path.join(carpeta, imagen))
+                    # Aplicar el filtro
+                    img = cv2.GaussianBlur(img, (blur_kernel,blur_kernel),0)
+                    # Guardar la imagen con el mismo nombre
+                    cv2.imwrite(os.path.join(carpeta, imagen), img)
+                    
+        if test_mode == 0:         
+            nombres = os.listdir("./extensions/Abysz-LAB-Ext/scripts/Run/MaskDT") # obtener los nombres de los archivos en la carpeta MaskDT
+            ancho = 0 # variable para guardar el ancho acumulado de las ventanas
+            for i, nombre in enumerate(nombres): # recorrer cada nombre de archivo
+                imagen = cv2.imread("./extensions/Abysz-LAB-Ext/scripts/Run/MaskDT/" + nombre) # leer la imagen correspondiente
+                h, w, c = imagen.shape # obtener el alto, ancho y canales de la imagen
+                aspect_ratio = w / h # calcular la relación de aspecto
+                cv2.namedWindow(nombre, cv2.WINDOW_NORMAL) # crear una ventana con el nombre del archivo
+                ancho_ventana = 630 # definir un ancho fijo para las ventanas
+                alto_ventana = int(ancho_ventana / aspect_ratio) # calcular el alto proporcional al ancho y a la relación de aspecto
+                cv2.resizeWindow(nombre, ancho_ventana, alto_ventana) # cambiar el tamaño de la ventana según las dimensiones calculadas 
+                cv2.moveWindow(nombre, ancho, 0) # mover la ventana a una posición horizontal según el ancho acumulado 
+                cv2.imshow(nombre, imagen) # mostrar la imagen en la ventana
+                cv2.setWindowProperty(nombre,cv2.WND_PROP_TOPMOST,1.0) # poner la ventana en primer plano con un valor double 
+                ancho += ancho_ventana + 10 # aumentar el ancho acumulado en 410 píxeles para la siguiente ventana 
+            cv2.waitKey(4000) # esperar a que se presione una tecla para cerrar todas las ventanas 
+            cv2.destroyAllWindows() # cerrar todas las ventanas abiertas por OpenCV 
+        
+        
+        else:
+            
+            # Directorio de entrada de imágenes
+            ruta_entrada = "./extensions/Abysz-LAB-Ext/scripts/Run/MaskDT"
+            
+            # Obtener el tamaño de la primera imagen en el directorio de entrada
+            img_path = os.path.join(ruta_entrada, os.listdir(ruta_entrada)[0])
+            img = cv2.imread(img_path)
+            img_size = (img.shape[1], img.shape[0])
+            
+            # Fps del video
+            fps = 10
+            
+            # Crear objeto VideoWriter
+            fourcc = cv2.VideoWriter_fourcc(*'mp4v')
+            video_salida = cv2.VideoWriter('output.mp4', fourcc, fps, img_size)
+            
+            # Crear ventana con nombre "video"
+            cv2.namedWindow("video")
+            
+            # Establecer la ventana en primer plano
+            cv2.setWindowProperty("video", cv2.WND_PROP_TOPMOST,1.0)
+
+            # Crear ventana de visualización
+            # Leer imágenes en el directorio y agregarlas al video de salida
+            for file in sorted(os.listdir(ruta_entrada)):
+                if file.endswith(".jpg") or file.endswith(".jpeg") or file.endswith(".png"): # Verificar que sea una imagen
+                    img = cv2.imread(os.path.join(ruta_entrada, file)) # Leer la imagen
+                    #img_resized = cv2.resize(img, img_size) # Redimensionar la imagen
+                    video_salida.write(img) # Agregar la imagen al video
+                    
+            # Liberar el objeto VideoWriter
+            video_salida.release()
+            
+            # Crear objeto VideoCapture para leer el archivo de video recién creado
+            video_capture = cv2.VideoCapture('output.mp4')
+            
+            # Crear ventana con nombre "video"
+            cv2.namedWindow("video")
+            
+            # Establecer la ventana en primer plano
+            cv2.setWindowProperty("video", cv2.WND_PROP_FULLSCREEN, cv2.WINDOW_NORMAL)
+            
+            # Mostrar el video en una ventana
+            while True:
+                ret, img = video_capture.read()
+                if ret:
+                    cv2.imshow('video', img)
+                    cv2.waitKey(int(1000/fps))
+                else:
+                    break
+            
+            # Liberar el objeto VideoCapture y cerrar la ventana de visualización
+            video_capture.release()
+            cv2.destroyAllWindows()
+
+def dfi_video(ruta_salida):
+    # Directorio de entrada de imágenes
+    ruta_entrada = ruta_salida
+    
+    # Obtener el tamaño de la primera imagen en el directorio de entrada
+    img_path = os.path.join(ruta_entrada, os.listdir(ruta_entrada)[0])
+    img = cv2.imread(img_path)
+    img_size = (img.shape[1], img.shape[0])
+    
+    # Fps del video
+    fps = 15
+    
+    # Crear objeto VideoWriter
+    fourcc = cv2.VideoWriter_fourcc(*'mp4v')
+    video_salida = cv2.VideoWriter('output.mp4', fourcc, fps, img_size)
+    
+    # Crear ventana con nombre "video"
+    cv2.namedWindow("video")
+    
+    # Establecer la ventana en primer plano
+    cv2.setWindowProperty("video", cv2.WND_PROP_TOPMOST,1.0)
+
+    # Crear ventana de visualización
+    # Leer imágenes en el directorio y agregarlas al video de salida
+    for file in sorted(os.listdir(ruta_entrada)):
+        if file.endswith(".jpg") or file.endswith(".jpeg") or file.endswith(".png"): # Verificar que sea una imagen
+            img = cv2.imread(os.path.join(ruta_entrada, file)) # Leer la imagen
+            #img_resized = cv2.resize(img, img_size) # Redimensionar la imagen
+            video_salida.write(img) # Agregar la imagen al video
+            
+    # Liberar el objeto VideoWriter
+    video_salida.release()
+    
+    # Crear objeto VideoCapture para leer el archivo de video recién creado
+    video_capture = cv2.VideoCapture('output.mp4')
+    
+    # Crear ventana con nombre "video"
+    cv2.namedWindow("video")
+    
+    # Establecer la ventana en primer plano
+    cv2.setWindowProperty("video", cv2.WND_PROP_FULLSCREEN, cv2.WINDOW_NORMAL)
+    
+    # Mostrar el video en una ventana
+    while True:
+        ret, img = video_capture.read()
+        if ret:
+            cv2.imshow('video', img)
+            cv2.waitKey(int(1000/fps))
+        else:
+            break
+    
+    # Liberar el objeto VideoCapture y cerrar la ventana de visualización
+    video_capture.release()
+    cv2.destroyAllWindows()
+    
+    
+def add_tab():
+    print('LAB')
+    with gr.Blocks(analytics_enabled=False) as demo:
+        with gr.Tabs():
+            with gr.Tab("Main"):
+                with gr.Row():
+                    with gr.Column():
+                        with gr.Column():
+                            gr.Markdown("# Abysz LAB 0.1.9 Temporal coherence tools")
+                            gr.Markdown("## DFI Render")
+                        with gr.Column():
+                            ruta_entrada_1 = gr.Textbox(label="Original/reference frames folder", placeholder="RAW frames, or generated ones. (Read the strategies in the guide)")
+                            ruta_entrada_2 = gr.Textbox(label="Generated frames folder", placeholder="The frames of AI generated video")
+                            ruta_salida = gr.Textbox(label="Output folder", placeholder="Remember that each generation overwrites previous frames in the same folder.")
+                        with gr.Accordion("Info", open=False):
+                            gr.Markdown("This process detects static areas between frames (white) and moving areas (black). Use preview map and you will understand this. Basically, it will force the white areas to stay the same on the next frame.")
+                            gr.Markdown("DFI Tolerance adjusts how stiff this process is. Higher = more rigidity + corruption. Lower = more flexible, less corruption, but allows more flick. ")
+                            gr.Markdown("As complement, you can clean the map, to reduce detail and noise, or fatten/expand the areas detected by DFI. It is better that you use preview many times to experience how it works.")
+                            gr.Markdown("### IMPORTANT: The general algorithm is optimized to maintain a balance between deflicking and corruption, so that it is easier to use StableDiffusion at low denoising to reconstruct lost detail while preserving the stability gained.")
+                        with gr.Row():
+                            denoise_blur = gr.Slider(minimum=0, maximum=30, value=0, step=1, label="Map Denoise")
+                            dfi_strength = gr.Slider(minimum=0.5, maximum=20, value=5, step=0.5, label="DFI Tolerance")
+                            dfi_deghost = gr.Slider(minimum=0, maximum=50, value=0, step=1, label="DFI Expand")
+                        with gr.Accordion("Info", open=False):
+                            gr.Markdown("Here you can preview examples of the motion map for those parameters. It is useful, for example, to adjust denoise if you see that it detects unnecessary graininess. Keep in mind that what you see represents movement between two frames.")
+                            gr.Markdown("A good balance point is to throttle DFI until you find just a few things in areas that should be static. If you force it to be TOO clean, it will mostly increase the overall corruption.")
+                        with gr.Row():
+                            dfi_test = gr.Button(value="Preview DFI Map")
+                            test_mode = gr.Slider(minimum=0, maximum=100, value=0, step=1, label="Preview amount. 0 = Quick shot")
+                        with gr.Accordion("Advanced", open=False):
+                            with gr.Accordion("Info", open=False):
+                                gr.Markdown("**Inter Denoise:** Reduces render pixelation generated by corruption. However, be careful. It's resource hungry, and might remove excess detail. Not recommended to change size or FPD, but to use Stable Diffusion to remove the pixelation later.")
+                                gr.Markdown("**Inter Blur:** Fine tunes the dynamic blur algorithm for DFI map. Lower = Stronger blur effects. Between 2-3 recommended.")
+                                gr.Markdown("**Corruption Refresh:** To reduce the distortion generated by the process, you can recover original information every X number of frames. Lower number = faster refresh.")
+                                gr.Markdown("**Corruption Preserve:** Here you decide how much corruption keep in each corruption refresh. Low values will recover more of the original frame, with its changes and flickering, in exchange for reducing corruption. You must find the balance that works best for your goal.")
+                                gr.Markdown("**Smooth:** This smoothes the edges of the interpolated areas. Low values are currently recommended until the algorithm is updated.")
+                            with gr.Row():
+                                inter_denoise = gr.Slider(minimum=1, maximum=25, value=9, step=1, label="Inter Denoise")
+                                inter_denoise_size = gr.Slider(minimum=1, maximum=25, value=9, step=2, label="Inter Denoise Size")
+                                inter_denoise_speed = gr.Slider(minimum=1, maximum=15, value=3, step=1, label="Inter Denoise FPD")
+                                fine_blur = gr.Slider(minimum=1, maximum=5, value=3, step=0.1, label="Inter Blur")
+                            gr.Markdown("### The new dynamic algorithm will handle these parameters. Activate them only for manual control.")
+                            with gr.Row():
+                                frame_refresh_frequency = gr.Slider(minimum=0, maximum=30, value=0, step=1, label="Corruption Refresh (Lower = Faster)")
+                                refresh_strength = gr.Slider(minimum=0, maximum=100, value=0, step=5, label="Corruption Preserve")
+                                smooth = gr.Slider(minimum=1, maximum=99, value=1, step=2, label="Smooth")
+                        with gr.Row():
+                            frames_limit = gr.Number(label="Frames to render. 0=ALL")
+                            run_button = gr.Button(value="Run DFI", variant="primary")
+                            output_placeholder = gr.Textbox(label="Status", placeholder="STAND BY...")
+                        video_dfi = gr.Button(value="Show output folder video")
+                    with gr.Column():
+                        with gr.Column():
+                            gr.Markdown("# |")
+                            gr.Markdown("## Deflickers Playground")
+                            with gr.Column():
+                                ruta_entrada_3 = gr.Textbox(label="Frames folder", placeholder="Frames to process")
+                                ruta_salida_1 = gr.Textbox(label="Output folder", placeholder="Processed frames")
+                                with gr.Accordion("Info", open=False):
+                                    gr.Markdown("I made this series of deflickers based on the standard that Vegas Pro includes. You can use them together or separately. Be careful when mixing them.")
+                                    gr.Markdown("**Blend:** Blends a percentage between frames. This can soften transitions and highlights. 50 is half of each frame. 80 or 20 are recommended values.")
+                                    gr.Markdown("**Overlay:** Use the overlay image blending mode. Note that it works particularly good at mid-high values, wich will modify the overall contrast. You will have to decide what works for you.")
+                                    gr.Markdown("**Normalize:** Calculates the average between frames to merge them. It may be more practical if you don't have a specific Blend deflicker value in mind.")
+                                ddf_strength = gr.Slider(minimum=0, maximum=1, value=0, step=0.01, label="BLEND (0=Off)")
+                                over_strength = gr.Slider(minimum=0, maximum=1, value=0, step=0.01, label="OVERLAY (0=Off)")
+                                norm_strength = gr.Slider(minimum=0, maximum=1, value=0, step=1, label="NORMALIZE (0=Off))")
+                                dfk_button = gr.Button(value="Deflickers")
+            with gr.Tab("LAB Tools"):
+                with gr.Column():
+                    gr.Markdown("## Style Fuse")
+                    with gr.Accordion("Info", open=False):
+                                    gr.Markdown("With this you can merge two sets of frames with overlay technique. For example, you can take a style video that is just lights and/or colors, and overlay it on top of another video.")
+                                    gr.Markdown("The resulting video will be useful for use in Img2Img Batch and that the AI render preserves these added color and lighting details, along with the details of the original video.")
+                    with gr.Row():
+                        ruta_entrada_4 = gr.Textbox(label="Style frames", placeholder="Style to fuse")
+                        ruta_entrada_5 = gr.Textbox(label="Video frames", placeholder="Frames to process")
+                    with gr.Row():
+                        ruta_salida_2 = gr.Textbox(label="Output folder", placeholder="Processed frames")   
+                        fuse_strength = gr.Slider(minimum=0.1, maximum=1, value=0.5, step=0.01, label="Fuse Strength")
+                        fuse_button = gr.Button(value="Fuse")
+                    gr.Markdown("## Video extract")
+                    with gr.Row():
+                        ruta_entrada_6 = gr.Textbox(label="Video path", placeholder="Remember to use same fps as generated video for DFI")
+                        ruta_salida_3 = gr.Textbox(label="Output folder", placeholder="Processed frames")
+                    with gr.Row():
+                        fps_count = gr.Number(label="Fps. 0=Original")
+                        vidextract_button = gr.Button(value="Extract")
+                    output_placeholder2 = gr.Textbox(label="Status", placeholder="STAND BY...") 
+            with gr.Tab("Guide"):
+                with gr.Column():
+                    gr.Markdown("# What DFI does?")
+                    with gr.Accordion("Info", open=False):
+                        gr.Markdown("DFI processing analyzes the motion of the original video, and attempts to force that information into the generated video. Demo on https://github.com/AbyszOne/Abysz-LAB-Ext")
+                        gr.Markdown("In short, this will reduce flicker in areas of the video that don't need to change, but SD does. For example, for a man smoking, leaning against a pole, it will detect that the pole is static, and will try to prevent it from changing as much as possible.")
+                        gr.Markdown("This is an aggressive process that requires a lot of control for each context. Read the recommended strategies.")
+                        gr.Markdown("Although Video to Video is the most efficient way, a DFI One Shot method is under experimental development as well.")
+                    gr.Markdown("# Usage strategies")
+                    with gr.Accordion("Info", open=False):
+                        gr.Markdown("If you get enough understanding of the tool, you can achieve a much more stable and clean enough rendering. However, this is quite demanding.")
+                        gr.Markdown("Instead, a much friendlier and faster way to use this tool is as an intermediate step. For this, you can allow a reasonable degree of corruption in exchange for more general stability. ")
+                        gr.Markdown("You can then clean up the corruption and recover details with a second step in Stable Diffusion at low denoising (0.2-0.4), using the same parameters and seed.")
+                        gr.Markdown("In this way, the final result will have the stability that we have gained, maintaining final detail. If you find a balanced workflow, you will get something at least much more coherent and stable than the raw AI render.")
+                        gr.Markdown("**OPTIONAL:** Although not ideal, you can use the same AI generated video as the source, instead of the RAW. The trick is to use DFI and denoise to wash out map details so that you reduce low/mid changes between frames. If you only need a soft deflick, it is a valid option.")
+        
+        dt_inputs=[ruta_entrada_1, ruta_entrada_2, denoise_blur, dfi_strength, dfi_deghost, test_mode, smooth]
+        run_inputs=[ruta_entrada_1, ruta_entrada_2, ruta_salida, denoise_blur, dfi_strength, dfi_deghost, test_mode, inter_denoise, inter_denoise_size, inter_denoise_speed, fine_blur, frame_refresh_frequency, refresh_strength, smooth, frames_limit]
+        dfk_inputs=[ruta_entrada_3, ruta_salida_1, ddf_strength, over_strength, norm_strength]
+        fuse_inputs=[ruta_entrada_4, ruta_entrada_5, ruta_salida_2, fuse_strength]
+        ve_inputs=[ruta_entrada_6, ruta_salida_3, fps_count]
+        
+        dfi_test.click(fn=test_dfi, inputs=dt_inputs, outputs=output_placeholder)
+        run_button.click(fn=main, inputs=run_inputs, outputs=output_placeholder)
+        video_dfi.click(fn=dfi_video, inputs=ruta_salida, outputs=output_placeholder)
+        dfk_button.click(fn=deflickers, inputs=dfk_inputs, outputs=output_placeholder)
+        fuse_button.click(fn=over_fuse, inputs=fuse_inputs, outputs=output_placeholder2)
+        vidextract_button.click(fn=extract_video, inputs=ve_inputs, outputs=output_placeholder2)
+    return [(demo, "Abysz LAB", "demo")]
+        
+script_callbacks.on_ui_tabs(add_tab)

SD-CN-Animation/.gitignore

@@ -0,0 +1,6 @@
+__pycache__/
+out/
+videos/
+FP_Res/
+result.mp4
+*.pth

SD-CN-Animation/FloweR/model.py

@@ -0,0 +1,191 @@
+import torch
+import torch.nn as nn
+import torch.functional as F
+
+# Define the model
+class FloweR(nn.Module):
+  def __init__(self, input_size = (384, 384), window_size = 4):
+    super(FloweR, self).__init__()
+
+    self.input_size = input_size
+    self.window_size = window_size
+
+    # 2 channels for optical flow
+    # 1 channel for occlusion mask
+    # 3 channels for next frame prediction 
+    self.out_channels = 6
+    
+
+    #INPUT: 384 x 384 x 4 * 3 
+
+    ### DOWNSCALE ###
+    self.conv_block_1 = nn.Sequential(
+      nn.Conv2d(3 * self.window_size, 128, kernel_size=3, stride=1, padding='same'),
+      nn.ReLU(),
+    ) # 384 x 384 x 128
+
+    self.conv_block_2 = nn.Sequential(
+      nn.AvgPool2d(2),
+      nn.Conv2d(128, 128, kernel_size=3, stride=1, padding='same'),
+      nn.ReLU(),
+    ) # 192 x 192 x 128
+
+    self.conv_block_3 = nn.Sequential(
+      nn.AvgPool2d(2),
+      nn.Conv2d(128, 128, kernel_size=3, stride=1, padding='same'),
+      nn.ReLU(),
+    ) # 96 x 96 x 128
+
+    self.conv_block_4 = nn.Sequential(
+      nn.AvgPool2d(2),
+      nn.Conv2d(128, 128, kernel_size=3, stride=1, padding='same'),
+      nn.ReLU(),
+    ) # 48 x 48 x 128
+
+    self.conv_block_5 = nn.Sequential(
+      nn.AvgPool2d(2),
+      nn.Conv2d(128, 128, kernel_size=3, stride=1, padding='same'),
+      nn.ReLU(),
+    ) # 24 x 24 x 128
+
+    self.conv_block_6 = nn.Sequential(
+      nn.AvgPool2d(2),
+      nn.Conv2d(128, 128, kernel_size=3, stride=1, padding='same'),
+      nn.ReLU(),
+    ) # 12 x 12 x 128
+
+    self.conv_block_7 = nn.Sequential(
+      nn.AvgPool2d(2),
+      nn.Conv2d(128, 128, kernel_size=3, stride=1, padding='same'),
+      nn.ReLU(),
+    ) # 6 x 6 x 128
+
+    self.conv_block_8 = nn.Sequential(
+      nn.AvgPool2d(2),
+      nn.Conv2d(128, 128, kernel_size=3, stride=1, padding='same'),
+      nn.ReLU(),
+    ) # 3 x 3 x 128 - 9 input tokens
+
+    ### Transformer part ###
+    # To be done
+
+    ### UPSCALE ###
+    self.conv_block_9 = nn.Sequential(
+      nn.Upsample(scale_factor=2),
+      nn.Conv2d(128, 128, kernel_size=3, stride=1, padding='same'),
+      nn.ReLU(),
+    ) # 6 x 6 x 128
+
+    self.conv_block_10 = nn.Sequential(
+      nn.Upsample(scale_factor=2),
+      nn.Conv2d(128, 128, kernel_size=3, stride=1, padding='same'),
+      nn.ReLU(),
+    ) # 12 x 12 x 128
+
+    self.conv_block_11 = nn.Sequential(
+      nn.Upsample(scale_factor=2),
+      nn.Conv2d(128, 128, kernel_size=3, stride=1, padding='same'),
+      nn.ReLU(),
+    ) # 24 x 24 x 128
+
+    self.conv_block_12 = nn.Sequential(
+      nn.Upsample(scale_factor=2),
+      nn.Conv2d(128, 128, kernel_size=3, stride=1, padding='same'),
+      nn.ReLU(),
+    ) # 48 x 48 x 128
+
+    self.conv_block_13 = nn.Sequential(
+      nn.Upsample(scale_factor=2),
+      nn.Conv2d(128, 128, kernel_size=3, stride=1, padding='same'),
+      nn.ReLU(),
+    ) # 96 x 96 x 128
+
+    self.conv_block_14 = nn.Sequential(
+      nn.Upsample(scale_factor=2),
+      nn.Conv2d(128, 128, kernel_size=3, stride=1, padding='same'),
+      nn.ReLU(),
+    ) # 192 x 192 x 128
+    
+    self.conv_block_15 = nn.Sequential(
+      nn.Upsample(scale_factor=2),
+      nn.Conv2d(128, 128, kernel_size=3, stride=1, padding='same'),
+      nn.ReLU(),
+    ) # 384 x 384 x 128
+
+    self.conv_block_16 = nn.Conv2d(128, self.out_channels, kernel_size=3, stride=1, padding='same')
+
+  def forward(self, input_frames):
+
+    if input_frames.size(1) != self.window_size: 
+      raise Exception(f'Shape of the input is not compatable. There should be exactly {self.window_size} frames in an input video.')
+
+    h, w = self.input_size
+    # batch, frames, height, width, colors
+    input_frames_permuted = input_frames.permute((0, 1, 4, 2, 3))
+    # batch, frames, colors, height, width
+
+    in_x = input_frames_permuted.reshape(-1, self.window_size * 3, self.input_size[0], self.input_size[1])
+
+    ### DOWNSCALE ###
+    block_1_out = self.conv_block_1(in_x)        # 384 x 384 x 128
+    block_2_out = self.conv_block_2(block_1_out) # 192 x 192 x 128
+    block_3_out = self.conv_block_3(block_2_out) # 96 x 96 x 128
+    block_4_out = self.conv_block_4(block_3_out) # 48 x 48 x 128
+    block_5_out = self.conv_block_5(block_4_out) # 24 x 24 x 128
+    block_6_out = self.conv_block_6(block_5_out) # 12 x 12 x 128
+    block_7_out = self.conv_block_7(block_6_out) # 6 x 6 x 128
+    block_8_out = self.conv_block_8(block_7_out) # 3 x 3 x 128
+
+    ### UPSCALE ###
+    block_9_out = block_7_out + self.conv_block_9(block_8_out)    # 6 x 6 x 128
+    block_10_out = block_6_out + self.conv_block_10(block_9_out)  # 12 x 12 x 128
+    block_11_out = block_5_out + self.conv_block_11(block_10_out) # 24 x 24 x 128
+    block_12_out = block_4_out + self.conv_block_12(block_11_out) # 48 x 48 x 128
+    block_13_out = block_3_out + self.conv_block_13(block_12_out) # 96 x 96 x 128
+    block_14_out = block_2_out + self.conv_block_14(block_13_out) # 192 x 192 x 128
+    block_15_out = block_1_out + self.conv_block_15(block_14_out) # 384 x 384 x 128
+
+    block_16_out = self.conv_block_16(block_15_out) # 384 x 384 x (2 + 1 + 3)
+    out = block_16_out.reshape(-1, self.out_channels, self.input_size[0], self.input_size[1])
+
+    ### for future model training ###
+    device = out.get_device()
+
+    pred_flow = out[:,:2,:,:] * 255  # (-255, 255)
+    pred_occl = (out[:,2:3,:,:] + 1) / 2 # [0, 1]
+    pred_next = out[:,3:6,:,:]
+
+    # Generate sampling grids
+
+    # Create grid to upsample input
+    '''    
+    d = torch.linspace(-1, 1, 8)
+    meshx, meshy = torch.meshgrid((d, d))
+    grid = torch.stack((meshy, meshx), 2)
+    grid = grid.unsqueeze(0) '''
+    
+    grid_y, grid_x = torch.meshgrid(torch.arange(0, h), torch.arange(0, w))
+    flow_grid = torch.stack((grid_x, grid_y), dim=0).float()
+    flow_grid = flow_grid.unsqueeze(0).to(device=device)
+    flow_grid = flow_grid + pred_flow
+
+    flow_grid[:, 0, :, :] = 2 * flow_grid[:, 0, :, :] / (w - 1) - 1
+    flow_grid[:, 1, :, :] = 2 * flow_grid[:, 1, :, :] / (h - 1) - 1
+    # batch, flow_chanels, height, width
+    flow_grid = flow_grid.permute(0, 2, 3, 1)
+    # batch, height, width, flow_chanels
+
+    previous_frame = input_frames_permuted[:, -1, :, :, :]
+    sampling_mode = "bilinear" if self.training else "nearest" 
+    warped_frame = torch.nn.functional.grid_sample(previous_frame, flow_grid, mode=sampling_mode, padding_mode="reflection", align_corners=False)
+    alpha_mask = torch.clip(pred_occl * 10, 0, 1) * 0.04
+    pred_next = torch.clip(pred_next, -1, 1)
+    warped_frame = torch.clip(warped_frame, -1, 1)
+    next_frame = pred_next * alpha_mask + warped_frame * (1 - alpha_mask)
+
+    res = torch.cat((pred_flow / 255, pred_occl * 2 - 1, next_frame), dim=1)
+
+    # batch, channels, height, width
+    res = res.permute((0, 2, 3, 1))
+    # batch, height, width, channels
+    return res

SD-CN-Animation/LICENSE

@@ -0,0 +1,22 @@
+License
+
+Copyright (c) 2023 Alexey Borsky
+
+The Software is subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
+
+This repository can only be used for personal/research/non-commercial purposes. 
+However, for commercial requests, please contact us directly at 
+[email protected]. This restriction applies only to the code itself, all
+derivative works made using this repository (i.e. images and video) can be 
+used for any purposes without restrictions. 

SD-CN-Animation/RAFT/LICENSE

@@ -0,0 +1,29 @@
+BSD 3-Clause License
+
+Copyright (c) 2020, princeton-vl
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are met:
+
+* Redistributions of source code must retain the above copyright notice, this
+  list of conditions and the following disclaimer.
+
+* Redistributions in binary form must reproduce the above copyright notice,
+  this list of conditions and the following disclaimer in the documentation
+  and/or other materials provided with the distribution.
+
+* Neither the name of the copyright holder nor the names of its
+  contributors may be used to endorse or promote products derived from
+  this software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

SD-CN-Animation/RAFT/corr.py

@@ -0,0 +1,91 @@
+import torch
+import torch.nn.functional as F
+from RAFT.utils.utils import bilinear_sampler, coords_grid
+
+try:
+    import alt_cuda_corr
+except:
+    # alt_cuda_corr is not compiled
+    pass
+
+
+class CorrBlock:
+    def __init__(self, fmap1, fmap2, num_levels=4, radius=4):
+        self.num_levels = num_levels
+        self.radius = radius
+        self.corr_pyramid = []
+
+        # all pairs correlation
+        corr = CorrBlock.corr(fmap1, fmap2)
+
+        batch, h1, w1, dim, h2, w2 = corr.shape
+        corr = corr.reshape(batch*h1*w1, dim, h2, w2)
+        
+        self.corr_pyramid.append(corr)
+        for i in range(self.num_levels-1):
+            corr = F.avg_pool2d(corr, 2, stride=2)
+            self.corr_pyramid.append(corr)
+
+    def __call__(self, coords):
+        r = self.radius
+        coords = coords.permute(0, 2, 3, 1)
+        batch, h1, w1, _ = coords.shape
+
+        out_pyramid = []
+        for i in range(self.num_levels):
+            corr = self.corr_pyramid[i]
+            dx = torch.linspace(-r, r, 2*r+1, device=coords.device)
+            dy = torch.linspace(-r, r, 2*r+1, device=coords.device)
+            delta = torch.stack(torch.meshgrid(dy, dx), axis=-1)
+
+            centroid_lvl = coords.reshape(batch*h1*w1, 1, 1, 2) / 2**i
+            delta_lvl = delta.view(1, 2*r+1, 2*r+1, 2)
+            coords_lvl = centroid_lvl + delta_lvl
+
+            corr = bilinear_sampler(corr, coords_lvl)
+            corr = corr.view(batch, h1, w1, -1)
+            out_pyramid.append(corr)
+
+        out = torch.cat(out_pyramid, dim=-1)
+        return out.permute(0, 3, 1, 2).contiguous().float()
+
+    @staticmethod
+    def corr(fmap1, fmap2):
+        batch, dim, ht, wd = fmap1.shape
+        fmap1 = fmap1.view(batch, dim, ht*wd)
+        fmap2 = fmap2.view(batch, dim, ht*wd) 
+        
+        corr = torch.matmul(fmap1.transpose(1,2), fmap2)
+        corr = corr.view(batch, ht, wd, 1, ht, wd)
+        return corr  / torch.sqrt(torch.tensor(dim).float())
+
+
+class AlternateCorrBlock:
+    def __init__(self, fmap1, fmap2, num_levels=4, radius=4):
+        self.num_levels = num_levels
+        self.radius = radius
+
+        self.pyramid = [(fmap1, fmap2)]
+        for i in range(self.num_levels):
+            fmap1 = F.avg_pool2d(fmap1, 2, stride=2)
+            fmap2 = F.avg_pool2d(fmap2, 2, stride=2)
+            self.pyramid.append((fmap1, fmap2))
+
+    def __call__(self, coords):
+        coords = coords.permute(0, 2, 3, 1)
+        B, H, W, _ = coords.shape
+        dim = self.pyramid[0][0].shape[1]
+
+        corr_list = []
+        for i in range(self.num_levels):
+            r = self.radius
+            fmap1_i = self.pyramid[0][0].permute(0, 2, 3, 1).contiguous()
+            fmap2_i = self.pyramid[i][1].permute(0, 2, 3, 1).contiguous()
+
+            coords_i = (coords / 2**i).reshape(B, 1, H, W, 2).contiguous()
+            corr, = alt_cuda_corr.forward(fmap1_i, fmap2_i, coords_i, r)
+            corr_list.append(corr.squeeze(1))
+
+        corr = torch.stack(corr_list, dim=1)
+        corr = corr.reshape(B, -1, H, W)
+        return corr / torch.sqrt(torch.tensor(dim).float())

SD-CN-Animation/RAFT/extractor.py

@@ -0,0 +1,267 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+class ResidualBlock(nn.Module):
+    def __init__(self, in_planes, planes, norm_fn='group', stride=1):
+        super(ResidualBlock, self).__init__()
+  
+        self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, padding=1, stride=stride)
+        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, padding=1)
+        self.relu = nn.ReLU(inplace=True)
+
+        num_groups = planes // 8
+
+        if norm_fn == 'group':
+            self.norm1 = nn.GroupNorm(num_groups=num_groups, num_channels=planes)
+            self.norm2 = nn.GroupNorm(num_groups=num_groups, num_channels=planes)
+            if not stride == 1:
+                self.norm3 = nn.GroupNorm(num_groups=num_groups, num_channels=planes)
+        
+        elif norm_fn == 'batch':
+            self.norm1 = nn.BatchNorm2d(planes)
+            self.norm2 = nn.BatchNorm2d(planes)
+            if not stride == 1:
+                self.norm3 = nn.BatchNorm2d(planes)
+        
+        elif norm_fn == 'instance':
+            self.norm1 = nn.InstanceNorm2d(planes)
+            self.norm2 = nn.InstanceNorm2d(planes)
+            if not stride == 1:
+                self.norm3 = nn.InstanceNorm2d(planes)
+
+        elif norm_fn == 'none':
+            self.norm1 = nn.Sequential()
+            self.norm2 = nn.Sequential()
+            if not stride == 1:
+                self.norm3 = nn.Sequential()
+
+        if stride == 1:
+            self.downsample = None
+        
+        else:    
+            self.downsample = nn.Sequential(
+                nn.Conv2d(in_planes, planes, kernel_size=1, stride=stride), self.norm3)
+
+
+    def forward(self, x):
+        y = x
+        y = self.relu(self.norm1(self.conv1(y)))
+        y = self.relu(self.norm2(self.conv2(y)))
+
+        if self.downsample is not None:
+            x = self.downsample(x)
+
+        return self.relu(x+y)
+
+
+
+class BottleneckBlock(nn.Module):
+    def __init__(self, in_planes, planes, norm_fn='group', stride=1):
+        super(BottleneckBlock, self).__init__()
+  
+        self.conv1 = nn.Conv2d(in_planes, planes//4, kernel_size=1, padding=0)
+        self.conv2 = nn.Conv2d(planes//4, planes//4, kernel_size=3, padding=1, stride=stride)
+        self.conv3 = nn.Conv2d(planes//4, planes, kernel_size=1, padding=0)
+        self.relu = nn.ReLU(inplace=True)
+
+        num_groups = planes // 8
+
+        if norm_fn == 'group':
+            self.norm1 = nn.GroupNorm(num_groups=num_groups, num_channels=planes//4)
+            self.norm2 = nn.GroupNorm(num_groups=num_groups, num_channels=planes//4)
+            self.norm3 = nn.GroupNorm(num_groups=num_groups, num_channels=planes)
+            if not stride == 1:
+                self.norm4 = nn.GroupNorm(num_groups=num_groups, num_channels=planes)
+        
+        elif norm_fn == 'batch':
+            self.norm1 = nn.BatchNorm2d(planes//4)
+            self.norm2 = nn.BatchNorm2d(planes//4)
+            self.norm3 = nn.BatchNorm2d(planes)
+            if not stride == 1:
+                self.norm4 = nn.BatchNorm2d(planes)
+        
+        elif norm_fn == 'instance':
+            self.norm1 = nn.InstanceNorm2d(planes//4)
+            self.norm2 = nn.InstanceNorm2d(planes//4)
+            self.norm3 = nn.InstanceNorm2d(planes)
+            if not stride == 1:
+                self.norm4 = nn.InstanceNorm2d(planes)
+
+        elif norm_fn == 'none':
+            self.norm1 = nn.Sequential()
+            self.norm2 = nn.Sequential()
+            self.norm3 = nn.Sequential()
+            if not stride == 1:
+                self.norm4 = nn.Sequential()
+
+        if stride == 1:
+            self.downsample = None
+        
+        else:    
+            self.downsample = nn.Sequential(
+                nn.Conv2d(in_planes, planes, kernel_size=1, stride=stride), self.norm4)
+
+
+    def forward(self, x):
+        y = x
+        y = self.relu(self.norm1(self.conv1(y)))
+        y = self.relu(self.norm2(self.conv2(y)))
+        y = self.relu(self.norm3(self.conv3(y)))
+
+        if self.downsample is not None:
+            x = self.downsample(x)
+
+        return self.relu(x+y)
+
+class BasicEncoder(nn.Module):
+    def __init__(self, output_dim=128, norm_fn='batch', dropout=0.0):
+        super(BasicEncoder, self).__init__()
+        self.norm_fn = norm_fn
+
+        if self.norm_fn == 'group':
+            self.norm1 = nn.GroupNorm(num_groups=8, num_channels=64)
+            
+        elif self.norm_fn == 'batch':
+            self.norm1 = nn.BatchNorm2d(64)
+
+        elif self.norm_fn == 'instance':
+            self.norm1 = nn.InstanceNorm2d(64)
+
+        elif self.norm_fn == 'none':
+            self.norm1 = nn.Sequential()
+
+        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3)
+        self.relu1 = nn.ReLU(inplace=True)
+
+        self.in_planes = 64
+        self.layer1 = self._make_layer(64,  stride=1)
+        self.layer2 = self._make_layer(96, stride=2)
+        self.layer3 = self._make_layer(128, stride=2)
+
+        # output convolution
+        self.conv2 = nn.Conv2d(128, output_dim, kernel_size=1)
+
+        self.dropout = None
+        if dropout > 0:
+            self.dropout = nn.Dropout2d(p=dropout)
+
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
+            elif isinstance(m, (nn.BatchNorm2d, nn.InstanceNorm2d, nn.GroupNorm)):
+                if m.weight is not None:
+                    nn.init.constant_(m.weight, 1)
+                if m.bias is not None:
+                    nn.init.constant_(m.bias, 0)
+
+    def _make_layer(self, dim, stride=1):
+        layer1 = ResidualBlock(self.in_planes, dim, self.norm_fn, stride=stride)
+        layer2 = ResidualBlock(dim, dim, self.norm_fn, stride=1)
+        layers = (layer1, layer2)
+        
+        self.in_planes = dim
+        return nn.Sequential(*layers)
+
+
+    def forward(self, x):
+
+        # if input is list, combine batch dimension
+        is_list = isinstance(x, tuple) or isinstance(x, list)
+        if is_list:
+            batch_dim = x[0].shape[0]
+            x = torch.cat(x, dim=0)
+
+        x = self.conv1(x)
+        x = self.norm1(x)
+        x = self.relu1(x)
+
+        x = self.layer1(x)
+        x = self.layer2(x)
+        x = self.layer3(x)
+
+        x = self.conv2(x)
+
+        if self.training and self.dropout is not None:
+            x = self.dropout(x)
+
+        if is_list:
+            x = torch.split(x, [batch_dim, batch_dim], dim=0)
+
+        return x
+
+
+class SmallEncoder(nn.Module):
+    def __init__(self, output_dim=128, norm_fn='batch', dropout=0.0):
+        super(SmallEncoder, self).__init__()
+        self.norm_fn = norm_fn
+
+        if self.norm_fn == 'group':
+            self.norm1 = nn.GroupNorm(num_groups=8, num_channels=32)
+            
+        elif self.norm_fn == 'batch':
+            self.norm1 = nn.BatchNorm2d(32)
+
+        elif self.norm_fn == 'instance':
+            self.norm1 = nn.InstanceNorm2d(32)
+
+        elif self.norm_fn == 'none':
+            self.norm1 = nn.Sequential()
+
+        self.conv1 = nn.Conv2d(3, 32, kernel_size=7, stride=2, padding=3)
+        self.relu1 = nn.ReLU(inplace=True)
+
+        self.in_planes = 32
+        self.layer1 = self._make_layer(32,  stride=1)
+        self.layer2 = self._make_layer(64, stride=2)
+        self.layer3 = self._make_layer(96, stride=2)
+
+        self.dropout = None
+        if dropout > 0:
+            self.dropout = nn.Dropout2d(p=dropout)
+        
+        self.conv2 = nn.Conv2d(96, output_dim, kernel_size=1)
+
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
+            elif isinstance(m, (nn.BatchNorm2d, nn.InstanceNorm2d, nn.GroupNorm)):
+                if m.weight is not None:
+                    nn.init.constant_(m.weight, 1)
+                if m.bias is not None:
+                    nn.init.constant_(m.bias, 0)
+
+    def _make_layer(self, dim, stride=1):
+        layer1 = BottleneckBlock(self.in_planes, dim, self.norm_fn, stride=stride)
+        layer2 = BottleneckBlock(dim, dim, self.norm_fn, stride=1)
+        layers = (layer1, layer2)
+    
+        self.in_planes = dim
+        return nn.Sequential(*layers)
+
+
+    def forward(self, x):
+
+        # if input is list, combine batch dimension
+        is_list = isinstance(x, tuple) or isinstance(x, list)
+        if is_list:
+            batch_dim = x[0].shape[0]
+            x = torch.cat(x, dim=0)
+
+        x = self.conv1(x)
+        x = self.norm1(x)
+        x = self.relu1(x)
+
+        x = self.layer1(x)
+        x = self.layer2(x)
+        x = self.layer3(x)
+        x = self.conv2(x)
+
+        if self.training and self.dropout is not None:
+            x = self.dropout(x)
+
+        if is_list:
+            x = torch.split(x, [batch_dim, batch_dim], dim=0)
+
+        return x

SD-CN-Animation/RAFT/raft.py

@@ -0,0 +1,144 @@
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from RAFT.update import BasicUpdateBlock, SmallUpdateBlock
+from RAFT.extractor import BasicEncoder, SmallEncoder
+from RAFT.corr import CorrBlock, AlternateCorrBlock
+from RAFT.utils.utils import bilinear_sampler, coords_grid, upflow8
+
+try:
+    autocast = torch.cuda.amp.autocast
+except:
+    # dummy autocast for PyTorch < 1.6
+    class autocast:
+        def __init__(self, enabled):
+            pass
+        def __enter__(self):
+            pass
+        def __exit__(self, *args):
+            pass
+
+
+class RAFT(nn.Module):
+    def __init__(self, args):
+        super(RAFT, self).__init__()
+        self.args = args
+
+        if args.small:
+            self.hidden_dim = hdim = 96
+            self.context_dim = cdim = 64
+            args.corr_levels = 4
+            args.corr_radius = 3
+        
+        else:
+            self.hidden_dim = hdim = 128
+            self.context_dim = cdim = 128
+            args.corr_levels = 4
+            args.corr_radius = 4
+
+        if 'dropout' not in self.args:
+            self.args.dropout = 0
+
+        if 'alternate_corr' not in self.args:
+            self.args.alternate_corr = False
+
+        # feature network, context network, and update block
+        if args.small:
+            self.fnet = SmallEncoder(output_dim=128, norm_fn='instance', dropout=args.dropout)        
+            self.cnet = SmallEncoder(output_dim=hdim+cdim, norm_fn='none', dropout=args.dropout)
+            self.update_block = SmallUpdateBlock(self.args, hidden_dim=hdim)
+
+        else:
+            self.fnet = BasicEncoder(output_dim=256, norm_fn='instance', dropout=args.dropout)        
+            self.cnet = BasicEncoder(output_dim=hdim+cdim, norm_fn='batch', dropout=args.dropout)
+            self.update_block = BasicUpdateBlock(self.args, hidden_dim=hdim)
+
+    def freeze_bn(self):
+        for m in self.modules():
+            if isinstance(m, nn.BatchNorm2d):
+                m.eval()
+
+    def initialize_flow(self, img):
+        """ Flow is represented as difference between two coordinate grids flow = coords1 - coords0"""
+        N, C, H, W = img.shape
+        coords0 = coords_grid(N, H//8, W//8, device=img.device)
+        coords1 = coords_grid(N, H//8, W//8, device=img.device)
+
+        # optical flow computed as difference: flow = coords1 - coords0
+        return coords0, coords1
+
+    def upsample_flow(self, flow, mask):
+        """ Upsample flow field [H/8, W/8, 2] -> [H, W, 2] using convex combination """
+        N, _, H, W = flow.shape
+        mask = mask.view(N, 1, 9, 8, 8, H, W)
+        mask = torch.softmax(mask, dim=2)
+
+        up_flow = F.unfold(8 * flow, [3,3], padding=1)
+        up_flow = up_flow.view(N, 2, 9, 1, 1, H, W)
+
+        up_flow = torch.sum(mask * up_flow, dim=2)
+        up_flow = up_flow.permute(0, 1, 4, 2, 5, 3)
+        return up_flow.reshape(N, 2, 8*H, 8*W)
+
+
+    def forward(self, image1, image2, iters=12, flow_init=None, upsample=True, test_mode=False):
+        """ Estimate optical flow between pair of frames """
+
+        image1 = 2 * (image1 / 255.0) - 1.0
+        image2 = 2 * (image2 / 255.0) - 1.0
+
+        image1 = image1.contiguous()
+        image2 = image2.contiguous()
+
+        hdim = self.hidden_dim
+        cdim = self.context_dim
+
+        # run the feature network
+        with autocast(enabled=self.args.mixed_precision):
+            fmap1, fmap2 = self.fnet([image1, image2])        
+        
+        fmap1 = fmap1.float()
+        fmap2 = fmap2.float()
+        if self.args.alternate_corr:
+            corr_fn = AlternateCorrBlock(fmap1, fmap2, radius=self.args.corr_radius)
+        else:
+            corr_fn = CorrBlock(fmap1, fmap2, radius=self.args.corr_radius)
+
+        # run the context network
+        with autocast(enabled=self.args.mixed_precision):
+            cnet = self.cnet(image1)
+            net, inp = torch.split(cnet, [hdim, cdim], dim=1)
+            net = torch.tanh(net)
+            inp = torch.relu(inp)
+
+        coords0, coords1 = self.initialize_flow(image1)
+
+        if flow_init is not None:
+            coords1 = coords1 + flow_init
+
+        flow_predictions = []
+        for itr in range(iters):
+            coords1 = coords1.detach()
+            corr = corr_fn(coords1) # index correlation volume
+
+            flow = coords1 - coords0
+            with autocast(enabled=self.args.mixed_precision):
+                net, up_mask, delta_flow = self.update_block(net, inp, corr, flow)
+
+            # F(t+1) = F(t) + \Delta(t)
+            coords1 = coords1 + delta_flow
+
+            # upsample predictions
+            if up_mask is None:
+                flow_up = upflow8(coords1 - coords0)
+            else:
+                flow_up = self.upsample_flow(coords1 - coords0, up_mask)
+            
+            flow_predictions.append(flow_up)
+
+        if test_mode:
+            return coords1 - coords0, flow_up
+            
+        return flow_predictions

SD-CN-Animation/RAFT/update.py

@@ -0,0 +1,139 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+class FlowHead(nn.Module):
+    def __init__(self, input_dim=128, hidden_dim=256):
+        super(FlowHead, self).__init__()
+        self.conv1 = nn.Conv2d(input_dim, hidden_dim, 3, padding=1)
+        self.conv2 = nn.Conv2d(hidden_dim, 2, 3, padding=1)
+        self.relu = nn.ReLU(inplace=True)
+
+    def forward(self, x):
+        return self.conv2(self.relu(self.conv1(x)))
+
+class ConvGRU(nn.Module):
+    def __init__(self, hidden_dim=128, input_dim=192+128):
+        super(ConvGRU, self).__init__()
+        self.convz = nn.Conv2d(hidden_dim+input_dim, hidden_dim, 3, padding=1)
+        self.convr = nn.Conv2d(hidden_dim+input_dim, hidden_dim, 3, padding=1)
+        self.convq = nn.Conv2d(hidden_dim+input_dim, hidden_dim, 3, padding=1)
+
+    def forward(self, h, x):
+        hx = torch.cat([h, x], dim=1)
+
+        z = torch.sigmoid(self.convz(hx))
+        r = torch.sigmoid(self.convr(hx))
+        q = torch.tanh(self.convq(torch.cat([r*h, x], dim=1)))
+
+        h = (1-z) * h + z * q
+        return h
+
+class SepConvGRU(nn.Module):
+    def __init__(self, hidden_dim=128, input_dim=192+128):
+        super(SepConvGRU, self).__init__()
+        self.convz1 = nn.Conv2d(hidden_dim+input_dim, hidden_dim, (1,5), padding=(0,2))
+        self.convr1 = nn.Conv2d(hidden_dim+input_dim, hidden_dim, (1,5), padding=(0,2))
+        self.convq1 = nn.Conv2d(hidden_dim+input_dim, hidden_dim, (1,5), padding=(0,2))
+
+        self.convz2 = nn.Conv2d(hidden_dim+input_dim, hidden_dim, (5,1), padding=(2,0))
+        self.convr2 = nn.Conv2d(hidden_dim+input_dim, hidden_dim, (5,1), padding=(2,0))
+        self.convq2 = nn.Conv2d(hidden_dim+input_dim, hidden_dim, (5,1), padding=(2,0))
+
+
+    def forward(self, h, x):
+        # horizontal
+        hx = torch.cat([h, x], dim=1)
+        z = torch.sigmoid(self.convz1(hx))
+        r = torch.sigmoid(self.convr1(hx))
+        q = torch.tanh(self.convq1(torch.cat([r*h, x], dim=1)))        
+        h = (1-z) * h + z * q
+
+        # vertical
+        hx = torch.cat([h, x], dim=1)
+        z = torch.sigmoid(self.convz2(hx))
+        r = torch.sigmoid(self.convr2(hx))
+        q = torch.tanh(self.convq2(torch.cat([r*h, x], dim=1)))       
+        h = (1-z) * h + z * q
+
+        return h
+
+class SmallMotionEncoder(nn.Module):
+    def __init__(self, args):
+        super(SmallMotionEncoder, self).__init__()
+        cor_planes = args.corr_levels * (2*args.corr_radius + 1)**2
+        self.convc1 = nn.Conv2d(cor_planes, 96, 1, padding=0)
+        self.convf1 = nn.Conv2d(2, 64, 7, padding=3)
+        self.convf2 = nn.Conv2d(64, 32, 3, padding=1)
+        self.conv = nn.Conv2d(128, 80, 3, padding=1)
+
+    def forward(self, flow, corr):
+        cor = F.relu(self.convc1(corr))
+        flo = F.relu(self.convf1(flow))
+        flo = F.relu(self.convf2(flo))
+        cor_flo = torch.cat([cor, flo], dim=1)
+        out = F.relu(self.conv(cor_flo))
+        return torch.cat([out, flow], dim=1)
+
+class BasicMotionEncoder(nn.Module):
+    def __init__(self, args):
+        super(BasicMotionEncoder, self).__init__()
+        cor_planes = args.corr_levels * (2*args.corr_radius + 1)**2
+        self.convc1 = nn.Conv2d(cor_planes, 256, 1, padding=0)
+        self.convc2 = nn.Conv2d(256, 192, 3, padding=1)
+        self.convf1 = nn.Conv2d(2, 128, 7, padding=3)
+        self.convf2 = nn.Conv2d(128, 64, 3, padding=1)
+        self.conv = nn.Conv2d(64+192, 128-2, 3, padding=1)
+
+    def forward(self, flow, corr):
+        cor = F.relu(self.convc1(corr))
+        cor = F.relu(self.convc2(cor))
+        flo = F.relu(self.convf1(flow))
+        flo = F.relu(self.convf2(flo))
+
+        cor_flo = torch.cat([cor, flo], dim=1)
+        out = F.relu(self.conv(cor_flo))
+        return torch.cat([out, flow], dim=1)
+
+class SmallUpdateBlock(nn.Module):
+    def __init__(self, args, hidden_dim=96):
+        super(SmallUpdateBlock, self).__init__()
+        self.encoder = SmallMotionEncoder(args)
+        self.gru = ConvGRU(hidden_dim=hidden_dim, input_dim=82+64)
+        self.flow_head = FlowHead(hidden_dim, hidden_dim=128)
+
+    def forward(self, net, inp, corr, flow):
+        motion_features = self.encoder(flow, corr)
+        inp = torch.cat([inp, motion_features], dim=1)
+        net = self.gru(net, inp)
+        delta_flow = self.flow_head(net)
+
+        return net, None, delta_flow
+
+class BasicUpdateBlock(nn.Module):
+    def __init__(self, args, hidden_dim=128, input_dim=128):
+        super(BasicUpdateBlock, self).__init__()
+        self.args = args
+        self.encoder = BasicMotionEncoder(args)
+        self.gru = SepConvGRU(hidden_dim=hidden_dim, input_dim=128+hidden_dim)
+        self.flow_head = FlowHead(hidden_dim, hidden_dim=256)
+
+        self.mask = nn.Sequential(
+            nn.Conv2d(128, 256, 3, padding=1),
+            nn.ReLU(inplace=True),
+            nn.Conv2d(256, 64*9, 1, padding=0))
+
+    def forward(self, net, inp, corr, flow, upsample=True):
+        motion_features = self.encoder(flow, corr)
+        inp = torch.cat([inp, motion_features], dim=1)
+
+        net = self.gru(net, inp)
+        delta_flow = self.flow_head(net)
+
+        # scale mask to balence gradients
+        mask = .25 * self.mask(net)
+        return net, mask, delta_flow
+
+
+

SD-CN-Animation/RAFT/utils/augmentor.py

@@ -0,0 +1,246 @@
+import numpy as np
+import random
+import math
+from PIL import Image
+
+import cv2
+cv2.setNumThreads(0)
+cv2.ocl.setUseOpenCL(False)
+
+import torch
+from torchvision.transforms import ColorJitter
+import torch.nn.functional as F
+
+
+class FlowAugmentor:
+    def __init__(self, crop_size, min_scale=-0.2, max_scale=0.5, do_flip=True):
+        
+        # spatial augmentation params
+        self.crop_size = crop_size
+        self.min_scale = min_scale
+        self.max_scale = max_scale
+        self.spatial_aug_prob = 0.8
+        self.stretch_prob = 0.8
+        self.max_stretch = 0.2
+
+        # flip augmentation params
+        self.do_flip = do_flip
+        self.h_flip_prob = 0.5
+        self.v_flip_prob = 0.1
+
+        # photometric augmentation params
+        self.photo_aug = ColorJitter(brightness=0.4, contrast=0.4, saturation=0.4, hue=0.5/3.14)
+        self.asymmetric_color_aug_prob = 0.2
+        self.eraser_aug_prob = 0.5
+
+    def color_transform(self, img1, img2):
+        """ Photometric augmentation """
+
+        # asymmetric
+        if np.random.rand() < self.asymmetric_color_aug_prob:
+            img1 = np.array(self.photo_aug(Image.fromarray(img1)), dtype=np.uint8)
+            img2 = np.array(self.photo_aug(Image.fromarray(img2)), dtype=np.uint8)
+
+        # symmetric
+        else:
+            image_stack = np.concatenate([img1, img2], axis=0)
+            image_stack = np.array(self.photo_aug(Image.fromarray(image_stack)), dtype=np.uint8)
+            img1, img2 = np.split(image_stack, 2, axis=0)
+
+        return img1, img2
+
+    def eraser_transform(self, img1, img2, bounds=[50, 100]):
+        """ Occlusion augmentation """
+
+        ht, wd = img1.shape[:2]
+        if np.random.rand() < self.eraser_aug_prob:
+            mean_color = np.mean(img2.reshape(-1, 3), axis=0)
+            for _ in range(np.random.randint(1, 3)):
+                x0 = np.random.randint(0, wd)
+                y0 = np.random.randint(0, ht)
+                dx = np.random.randint(bounds[0], bounds[1])
+                dy = np.random.randint(bounds[0], bounds[1])
+                img2[y0:y0+dy, x0:x0+dx, :] = mean_color
+
+        return img1, img2
+
+    def spatial_transform(self, img1, img2, flow):
+        # randomly sample scale
+        ht, wd = img1.shape[:2]
+        min_scale = np.maximum(
+            (self.crop_size[0] + 8) / float(ht), 
+            (self.crop_size[1] + 8) / float(wd))
+
+        scale = 2 ** np.random.uniform(self.min_scale, self.max_scale)
+        scale_x = scale
+        scale_y = scale
+        if np.random.rand() < self.stretch_prob:
+            scale_x *= 2 ** np.random.uniform(-self.max_stretch, self.max_stretch)
+            scale_y *= 2 ** np.random.uniform(-self.max_stretch, self.max_stretch)
+        
+        scale_x = np.clip(scale_x, min_scale, None)
+        scale_y = np.clip(scale_y, min_scale, None)
+
+        if np.random.rand() < self.spatial_aug_prob:
+            # rescale the images
+            img1 = cv2.resize(img1, None, fx=scale_x, fy=scale_y, interpolation=cv2.INTER_LINEAR)
+            img2 = cv2.resize(img2, None, fx=scale_x, fy=scale_y, interpolation=cv2.INTER_LINEAR)
+            flow = cv2.resize(flow, None, fx=scale_x, fy=scale_y, interpolation=cv2.INTER_LINEAR)
+            flow = flow * [scale_x, scale_y]
+
+        if self.do_flip:
+            if np.random.rand() < self.h_flip_prob: # h-flip
+                img1 = img1[:, ::-1]
+                img2 = img2[:, ::-1]
+                flow = flow[:, ::-1] * [-1.0, 1.0]
+
+            if np.random.rand() < self.v_flip_prob: # v-flip
+                img1 = img1[::-1, :]
+                img2 = img2[::-1, :]
+                flow = flow[::-1, :] * [1.0, -1.0]
+
+        y0 = np.random.randint(0, img1.shape[0] - self.crop_size[0])
+        x0 = np.random.randint(0, img1.shape[1] - self.crop_size[1])
+        
+        img1 = img1[y0:y0+self.crop_size[0], x0:x0+self.crop_size[1]]
+        img2 = img2[y0:y0+self.crop_size[0], x0:x0+self.crop_size[1]]
+        flow = flow[y0:y0+self.crop_size[0], x0:x0+self.crop_size[1]]
+
+        return img1, img2, flow
+
+    def __call__(self, img1, img2, flow):
+        img1, img2 = self.color_transform(img1, img2)
+        img1, img2 = self.eraser_transform(img1, img2)
+        img1, img2, flow = self.spatial_transform(img1, img2, flow)
+
+        img1 = np.ascontiguousarray(img1)
+        img2 = np.ascontiguousarray(img2)
+        flow = np.ascontiguousarray(flow)
+
+        return img1, img2, flow
+
+class SparseFlowAugmentor:
+    def __init__(self, crop_size, min_scale=-0.2, max_scale=0.5, do_flip=False):
+        # spatial augmentation params
+        self.crop_size = crop_size
+        self.min_scale = min_scale
+        self.max_scale = max_scale
+        self.spatial_aug_prob = 0.8
+        self.stretch_prob = 0.8
+        self.max_stretch = 0.2
+
+        # flip augmentation params
+        self.do_flip = do_flip
+        self.h_flip_prob = 0.5
+        self.v_flip_prob = 0.1
+
+        # photometric augmentation params
+        self.photo_aug = ColorJitter(brightness=0.3, contrast=0.3, saturation=0.3, hue=0.3/3.14)
+        self.asymmetric_color_aug_prob = 0.2
+        self.eraser_aug_prob = 0.5
+        
+    def color_transform(self, img1, img2):
+        image_stack = np.concatenate([img1, img2], axis=0)
+        image_stack = np.array(self.photo_aug(Image.fromarray(image_stack)), dtype=np.uint8)
+        img1, img2 = np.split(image_stack, 2, axis=0)
+        return img1, img2
+
+    def eraser_transform(self, img1, img2):
+        ht, wd = img1.shape[:2]
+        if np.random.rand() < self.eraser_aug_prob:
+            mean_color = np.mean(img2.reshape(-1, 3), axis=0)
+            for _ in range(np.random.randint(1, 3)):
+                x0 = np.random.randint(0, wd)
+                y0 = np.random.randint(0, ht)
+                dx = np.random.randint(50, 100)
+                dy = np.random.randint(50, 100)
+                img2[y0:y0+dy, x0:x0+dx, :] = mean_color
+
+        return img1, img2
+
+    def resize_sparse_flow_map(self, flow, valid, fx=1.0, fy=1.0):
+        ht, wd = flow.shape[:2]
+        coords = np.meshgrid(np.arange(wd), np.arange(ht))
+        coords = np.stack(coords, axis=-1)
+
+        coords = coords.reshape(-1, 2).astype(np.float32)
+        flow = flow.reshape(-1, 2).astype(np.float32)
+        valid = valid.reshape(-1).astype(np.float32)
+
+        coords0 = coords[valid>=1]
+        flow0 = flow[valid>=1]
+
+        ht1 = int(round(ht * fy))
+        wd1 = int(round(wd * fx))
+
+        coords1 = coords0 * [fx, fy]
+        flow1 = flow0 * [fx, fy]
+
+        xx = np.round(coords1[:,0]).astype(np.int32)
+        yy = np.round(coords1[:,1]).astype(np.int32)
+
+        v = (xx > 0) & (xx < wd1) & (yy > 0) & (yy < ht1)
+        xx = xx[v]
+        yy = yy[v]
+        flow1 = flow1[v]
+
+        flow_img = np.zeros([ht1, wd1, 2], dtype=np.float32)
+        valid_img = np.zeros([ht1, wd1], dtype=np.int32)
+
+        flow_img[yy, xx] = flow1
+        valid_img[yy, xx] = 1
+
+        return flow_img, valid_img
+
+    def spatial_transform(self, img1, img2, flow, valid):
+        # randomly sample scale
+
+        ht, wd = img1.shape[:2]
+        min_scale = np.maximum(
+            (self.crop_size[0] + 1) / float(ht), 
+            (self.crop_size[1] + 1) / float(wd))
+
+        scale = 2 ** np.random.uniform(self.min_scale, self.max_scale)
+        scale_x = np.clip(scale, min_scale, None)
+        scale_y = np.clip(scale, min_scale, None)
+
+        if np.random.rand() < self.spatial_aug_prob:
+            # rescale the images
+            img1 = cv2.resize(img1, None, fx=scale_x, fy=scale_y, interpolation=cv2.INTER_LINEAR)
+            img2 = cv2.resize(img2, None, fx=scale_x, fy=scale_y, interpolation=cv2.INTER_LINEAR)
+            flow, valid = self.resize_sparse_flow_map(flow, valid, fx=scale_x, fy=scale_y)
+
+        if self.do_flip:
+            if np.random.rand() < 0.5: # h-flip
+                img1 = img1[:, ::-1]
+                img2 = img2[:, ::-1]
+                flow = flow[:, ::-1] * [-1.0, 1.0]
+                valid = valid[:, ::-1]
+
+        margin_y = 20
+        margin_x = 50
+
+        y0 = np.random.randint(0, img1.shape[0] - self.crop_size[0] + margin_y)
+        x0 = np.random.randint(-margin_x, img1.shape[1] - self.crop_size[1] + margin_x)
+
+        y0 = np.clip(y0, 0, img1.shape[0] - self.crop_size[0])
+        x0 = np.clip(x0, 0, img1.shape[1] - self.crop_size[1])
+
+        img1 = img1[y0:y0+self.crop_size[0], x0:x0+self.crop_size[1]]
+        img2 = img2[y0:y0+self.crop_size[0], x0:x0+self.crop_size[1]]
+        flow = flow[y0:y0+self.crop_size[0], x0:x0+self.crop_size[1]]
+        valid = valid[y0:y0+self.crop_size[0], x0:x0+self.crop_size[1]]
+        return img1, img2, flow, valid
+
+
+    def __call__(self, img1, img2, flow, valid):
+        img1, img2 = self.color_transform(img1, img2)
+        img1, img2 = self.eraser_transform(img1, img2)
+        img1, img2, flow, valid = self.spatial_transform(img1, img2, flow, valid)
+
+        img1 = np.ascontiguousarray(img1)
+        img2 = np.ascontiguousarray(img2)
+        flow = np.ascontiguousarray(flow)
+        valid = np.ascontiguousarray(valid)
+
+        return img1, img2, flow, valid

SD-CN-Animation/RAFT/utils/flow_viz.py

@@ -0,0 +1,132 @@
+# Flow visualization code used from https://github.com/tomrunia/OpticalFlow_Visualization
+
+
+# MIT License
+#
+# Copyright (c) 2018 Tom Runia
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy
+# of this software and associated documentation files (the "Software"), to deal
+# in the Software without restriction, including without limitation the rights
+# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+# copies of the Software, and to permit persons to whom the Software is
+# furnished to do so, subject to conditions.
+#
+# Author: Tom Runia
+# Date Created: 2018-08-03
+
+import numpy as np
+
+def make_colorwheel():
+    """
+    Generates a color wheel for optical flow visualization as presented in:
+        Baker et al. "A Database and Evaluation Methodology for Optical Flow" (ICCV, 2007)
+        URL: http://vision.middlebury.edu/flow/flowEval-iccv07.pdf
+
+    Code follows the original C++ source code of Daniel Scharstein.
+    Code follows the the Matlab source code of Deqing Sun.
+
+    Returns:
+        np.ndarray: Color wheel
+    """
+
+    RY = 15
+    YG = 6
+    GC = 4
+    CB = 11
+    BM = 13
+    MR = 6
+
+    ncols = RY + YG + GC + CB + BM + MR
+    colorwheel = np.zeros((ncols, 3))
+    col = 0
+
+    # RY
+    colorwheel[0:RY, 0] = 255
+    colorwheel[0:RY, 1] = np.floor(255*np.arange(0,RY)/RY)
+    col = col+RY
+    # YG
+    colorwheel[col:col+YG, 0] = 255 - np.floor(255*np.arange(0,YG)/YG)
+    colorwheel[col:col+YG, 1] = 255
+    col = col+YG
+    # GC
+    colorwheel[col:col+GC, 1] = 255
+    colorwheel[col:col+GC, 2] = np.floor(255*np.arange(0,GC)/GC)
+    col = col+GC
+    # CB
+    colorwheel[col:col+CB, 1] = 255 - np.floor(255*np.arange(CB)/CB)
+    colorwheel[col:col+CB, 2] = 255
+    col = col+CB
+    # BM
+    colorwheel[col:col+BM, 2] = 255
+    colorwheel[col:col+BM, 0] = np.floor(255*np.arange(0,BM)/BM)
+    col = col+BM
+    # MR
+    colorwheel[col:col+MR, 2] = 255 - np.floor(255*np.arange(MR)/MR)
+    colorwheel[col:col+MR, 0] = 255
+    return colorwheel
+
+
+def flow_uv_to_colors(u, v, convert_to_bgr=False):
+    """
+    Applies the flow color wheel to (possibly clipped) flow components u and v.
+
+    According to the C++ source code of Daniel Scharstein
+    According to the Matlab source code of Deqing Sun
+
+    Args:
+        u (np.ndarray): Input horizontal flow of shape [H,W]
+        v (np.ndarray): Input vertical flow of shape [H,W]
+        convert_to_bgr (bool, optional): Convert output image to BGR. Defaults to False.
+
+    Returns:
+        np.ndarray: Flow visualization image of shape [H,W,3]
+    """
+    flow_image = np.zeros((u.shape[0], u.shape[1], 3), np.uint8)
+    colorwheel = make_colorwheel()  # shape [55x3]
+    ncols = colorwheel.shape[0]
+    rad = np.sqrt(np.square(u) + np.square(v))
+    a = np.arctan2(-v, -u)/np.pi
+    fk = (a+1) / 2*(ncols-1)
+    k0 = np.floor(fk).astype(np.int32)
+    k1 = k0 + 1
+    k1[k1 == ncols] = 0
+    f = fk - k0
+    for i in range(colorwheel.shape[1]):
+        tmp = colorwheel[:,i]
+        col0 = tmp[k0] / 255.0
+        col1 = tmp[k1] / 255.0
+        col = (1-f)*col0 + f*col1
+        idx = (rad <= 1)
+        col[idx]  = 1 - rad[idx] * (1-col[idx])
+        col[~idx] = col[~idx] * 0.75   # out of range
+        # Note the 2-i => BGR instead of RGB
+        ch_idx = 2-i if convert_to_bgr else i
+        flow_image[:,:,ch_idx] = np.floor(255 * col)
+    return flow_image
+
+
+def flow_to_image(flow_uv, clip_flow=None, convert_to_bgr=False):
+    """
+    Expects a two dimensional flow image of shape.
+
+    Args:
+        flow_uv (np.ndarray): Flow UV image of shape [H,W,2]
+        clip_flow (float, optional): Clip maximum of flow values. Defaults to None.
+        convert_to_bgr (bool, optional): Convert output image to BGR. Defaults to False.
+
+    Returns:
+        np.ndarray: Flow visualization image of shape [H,W,3]
+    """
+    assert flow_uv.ndim == 3, 'input flow must have three dimensions'
+    assert flow_uv.shape[2] == 2, 'input flow must have shape [H,W,2]'
+    if clip_flow is not None:
+        flow_uv = np.clip(flow_uv, 0, clip_flow)
+    u = flow_uv[:,:,0]
+    v = flow_uv[:,:,1]
+    rad = np.sqrt(np.square(u) + np.square(v))
+    rad_max = np.max(rad)
+    epsilon = 1e-5
+    u = u / (rad_max + epsilon)
+    v = v / (rad_max + epsilon)
+    return flow_uv_to_colors(u, v, convert_to_bgr)

SD-CN-Animation/RAFT/utils/frame_utils.py

@@ -0,0 +1,137 @@
+import numpy as np
+from PIL import Image
+from os.path import *
+import re
+
+import cv2
+cv2.setNumThreads(0)
+cv2.ocl.setUseOpenCL(False)
+
+TAG_CHAR = np.array([202021.25], np.float32)
+
+def readFlow(fn):
+    """ Read .flo file in Middlebury format"""
+    # Code adapted from:
+    # http://stackoverflow.com/questions/28013200/reading-middlebury-flow-files-with-python-bytes-array-numpy
+
+    # WARNING: this will work on little-endian architectures (eg Intel x86) only!
+    # print 'fn = %s'%(fn)
+    with open(fn, 'rb') as f:
+        magic = np.fromfile(f, np.float32, count=1)
+        if 202021.25 != magic:
+            print('Magic number incorrect. Invalid .flo file')
+            return None
+        else:
+            w = np.fromfile(f, np.int32, count=1)
+            h = np.fromfile(f, np.int32, count=1)
+            # print 'Reading %d x %d flo file\n' % (w, h)
+            data = np.fromfile(f, np.float32, count=2*int(w)*int(h))
+            # Reshape data into 3D array (columns, rows, bands)
+            # The reshape here is for visualization, the original code is (w,h,2)
+            return np.resize(data, (int(h), int(w), 2))
+
+def readPFM(file):
+    file = open(file, 'rb')
+
+    color = None
+    width = None
+    height = None
+    scale = None
+    endian = None
+
+    header = file.readline().rstrip()
+    if header == b'PF':
+        color = True
+    elif header == b'Pf':
+        color = False
+    else:
+        raise Exception('Not a PFM file.')
+
+    dim_match = re.match(rb'^(\d+)\s(\d+)\s$', file.readline())
+    if dim_match:
+        width, height = map(int, dim_match.groups())
+    else:
+        raise Exception('Malformed PFM header.')
+
+    scale = float(file.readline().rstrip())
+    if scale < 0: # little-endian
+        endian = '<'
+        scale = -scale
+    else:
+        endian = '>' # big-endian
+
+    data = np.fromfile(file, endian + 'f')
+    shape = (height, width, 3) if color else (height, width)
+
+    data = np.reshape(data, shape)
+    data = np.flipud(data)
+    return data
+
+def writeFlow(filename,uv,v=None):
+    """ Write optical flow to file.
+    
+    If v is None, uv is assumed to contain both u and v channels,
+    stacked in depth.
+    Original code by Deqing Sun, adapted from Daniel Scharstein.
+    """
+    nBands = 2
+
+    if v is None:
+        assert(uv.ndim == 3)
+        assert(uv.shape[2] == 2)
+        u = uv[:,:,0]
+        v = uv[:,:,1]
+    else:
+        u = uv
+
+    assert(u.shape == v.shape)
+    height,width = u.shape
+    f = open(filename,'wb')
+    # write the header
+    f.write(TAG_CHAR)
+    np.array(width).astype(np.int32).tofile(f)
+    np.array(height).astype(np.int32).tofile(f)
+    # arrange into matrix form
+    tmp = np.zeros((height, width*nBands))
+    tmp[:,np.arange(width)*2] = u
+    tmp[:,np.arange(width)*2 + 1] = v
+    tmp.astype(np.float32).tofile(f)
+    f.close()
+
+
+def readFlowKITTI(filename):
+    flow = cv2.imread(filename, cv2.IMREAD_ANYDEPTH|cv2.IMREAD_COLOR)
+    flow = flow[:,:,::-1].astype(np.float32)
+    flow, valid = flow[:, :, :2], flow[:, :, 2]
+    flow = (flow - 2**15) / 64.0
+    return flow, valid
+
+def readDispKITTI(filename):
+    disp = cv2.imread(filename, cv2.IMREAD_ANYDEPTH) / 256.0
+    valid = disp > 0.0
+    flow = np.stack([-disp, np.zeros_like(disp)], -1)
+    return flow, valid
+
+
+def writeFlowKITTI(filename, uv):
+    uv = 64.0 * uv + 2**15
+    valid = np.ones([uv.shape[0], uv.shape[1], 1])
+    uv = np.concatenate([uv, valid], axis=-1).astype(np.uint16)
+    cv2.imwrite(filename, uv[..., ::-1])
+    
+
+def read_gen(file_name, pil=False):
+    ext = splitext(file_name)[-1]
+    if ext == '.png' or ext == '.jpeg' or ext == '.ppm' or ext == '.jpg':
+        return Image.open(file_name)
+    elif ext == '.bin' or ext == '.raw':
+        return np.load(file_name)
+    elif ext == '.flo':
+        return readFlow(file_name).astype(np.float32)
+    elif ext == '.pfm':
+        flow = readPFM(file_name).astype(np.float32)
+        if len(flow.shape) == 2:
+            return flow
+        else:
+            return flow[:, :, :-1]
+    return []

SD-CN-Animation/RAFT/utils/utils.py

@@ -0,0 +1,82 @@
+import torch
+import torch.nn.functional as F
+import numpy as np
+from scipy import interpolate
+
+
+class InputPadder:
+    """ Pads images such that dimensions are divisible by 8 """
+    def __init__(self, dims, mode='sintel'):
+        self.ht, self.wd = dims[-2:]
+        pad_ht = (((self.ht // 8) + 1) * 8 - self.ht) % 8
+        pad_wd = (((self.wd // 8) + 1) * 8 - self.wd) % 8
+        if mode == 'sintel':
+            self._pad = [pad_wd//2, pad_wd - pad_wd//2, pad_ht//2, pad_ht - pad_ht//2]
+        else:
+            self._pad = [pad_wd//2, pad_wd - pad_wd//2, 0, pad_ht]
+
+    def pad(self, *inputs):
+        return [F.pad(x, self._pad, mode='replicate') for x in inputs]
+
+    def unpad(self,x):
+        ht, wd = x.shape[-2:]
+        c = [self._pad[2], ht-self._pad[3], self._pad[0], wd-self._pad[1]]
+        return x[..., c[0]:c[1], c[2]:c[3]]
+
+def forward_interpolate(flow):
+    flow = flow.detach().cpu().numpy()
+    dx, dy = flow[0], flow[1]
+
+    ht, wd = dx.shape
+    x0, y0 = np.meshgrid(np.arange(wd), np.arange(ht))
+
+    x1 = x0 + dx
+    y1 = y0 + dy
+    
+    x1 = x1.reshape(-1)
+    y1 = y1.reshape(-1)
+    dx = dx.reshape(-1)
+    dy = dy.reshape(-1)
+
+    valid = (x1 > 0) & (x1 < wd) & (y1 > 0) & (y1 < ht)
+    x1 = x1[valid]
+    y1 = y1[valid]
+    dx = dx[valid]
+    dy = dy[valid]
+
+    flow_x = interpolate.griddata(
+        (x1, y1), dx, (x0, y0), method='nearest', fill_value=0)
+
+    flow_y = interpolate.griddata(
+        (x1, y1), dy, (x0, y0), method='nearest', fill_value=0)
+
+    flow = np.stack([flow_x, flow_y], axis=0)
+    return torch.from_numpy(flow).float()
+
+
+def bilinear_sampler(img, coords, mode='bilinear', mask=False):
+    """ Wrapper for grid_sample, uses pixel coordinates """
+    H, W = img.shape[-2:]
+    xgrid, ygrid = coords.split([1,1], dim=-1)
+    xgrid = 2*xgrid/(W-1) - 1
+    ygrid = 2*ygrid/(H-1) - 1
+
+    grid = torch.cat([xgrid, ygrid], dim=-1)
+    img = F.grid_sample(img, grid, align_corners=True)
+
+    if mask:
+        mask = (xgrid > -1) & (ygrid > -1) & (xgrid < 1) & (ygrid < 1)
+        return img, mask.float()
+
+    return img
+
+
+def coords_grid(batch, ht, wd, device):
+    coords = torch.meshgrid(torch.arange(ht, device=device), torch.arange(wd, device=device))
+    coords = torch.stack(coords[::-1], dim=0).float()
+    return coords[None].repeat(batch, 1, 1, 1)
+
+
+def upflow8(flow, mode='bilinear'):
+    new_size = (8 * flow.shape[2], 8 * flow.shape[3])
+    return  8 * F.interpolate(flow, size=new_size, mode=mode, align_corners=True)

SD-CN-Animation/examples/flower_1.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a8db0719d9f215b775ae1b5dae912a425bc010f0586b41894c14bb8ad042711e
+size 1259280

SD-CN-Animation/examples/flower_11.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7499401998e41c65471963d6cbd70568908dd83a8c957a43940df99be7c52026
+size 1328049

SD-CN-Animation/examples/girl_to_jc.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4d09ded8b44f7e30d55d5d6245d9ec7fa3b95e970a8c29d2c544b6c288341e39
+size 5274033

SD-CN-Animation/examples/girl_to_wc.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:bd730de667b8e7ea5af2dddcf129095694349f126e06291c9b1c2bb7d49843a8
+size 5630710