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outpainting_mk_2.py

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+import math
+
+import numpy as np
+import skimage
+
+import modules.scripts as scripts
+import gradio as gr
+from PIL import Image, ImageDraw
+
+from modules import images
+from modules.processing import Processed, process_images
+from modules.shared import opts, state
+
+
+# this function is taken from https://github.com/parlance-zz/g-diffuser-bot
+def get_matched_noise(_np_src_image, np_mask_rgb, noise_q=1, color_variation=0.05):
+    # helper fft routines that keep ortho normalization and auto-shift before and after fft
+    def _fft2(data):
+        if data.ndim > 2:  # has channels
+            out_fft = np.zeros((data.shape[0], data.shape[1], data.shape[2]), dtype=np.complex128)
+            for c in range(data.shape[2]):
+                c_data = data[:, :, c]
+                out_fft[:, :, c] = np.fft.fft2(np.fft.fftshift(c_data), norm="ortho")
+                out_fft[:, :, c] = np.fft.ifftshift(out_fft[:, :, c])
+        else:  # one channel
+            out_fft = np.zeros((data.shape[0], data.shape[1]), dtype=np.complex128)
+            out_fft[:, :] = np.fft.fft2(np.fft.fftshift(data), norm="ortho")
+            out_fft[:, :] = np.fft.ifftshift(out_fft[:, :])
+
+        return out_fft
+
+    def _ifft2(data):
+        if data.ndim > 2:  # has channels
+            out_ifft = np.zeros((data.shape[0], data.shape[1], data.shape[2]), dtype=np.complex128)
+            for c in range(data.shape[2]):
+                c_data = data[:, :, c]
+                out_ifft[:, :, c] = np.fft.ifft2(np.fft.fftshift(c_data), norm="ortho")
+                out_ifft[:, :, c] = np.fft.ifftshift(out_ifft[:, :, c])
+        else:  # one channel
+            out_ifft = np.zeros((data.shape[0], data.shape[1]), dtype=np.complex128)
+            out_ifft[:, :] = np.fft.ifft2(np.fft.fftshift(data), norm="ortho")
+            out_ifft[:, :] = np.fft.ifftshift(out_ifft[:, :])
+
+        return out_ifft
+
+    def _get_gaussian_window(width, height, std=3.14, mode=0):
+        window_scale_x = float(width / min(width, height))
+        window_scale_y = float(height / min(width, height))
+
+        window = np.zeros((width, height))
+        x = (np.arange(width) / width * 2. - 1.) * window_scale_x
+        for y in range(height):
+            fy = (y / height * 2. - 1.) * window_scale_y
+            if mode == 0:
+                window[:, y] = np.exp(-(x ** 2 + fy ** 2) * std)
+            else:
+                window[:, y] = (1 / ((x ** 2 + 1.) * (fy ** 2 + 1.))) ** (std / 3.14)  # hey wait a minute that's not gaussian
+
+        return window
+
+    def _get_masked_window_rgb(np_mask_grey, hardness=1.):
+        np_mask_rgb = np.zeros((np_mask_grey.shape[0], np_mask_grey.shape[1], 3))
+        if hardness != 1.:
+            hardened = np_mask_grey[:] ** hardness
+        else:
+            hardened = np_mask_grey[:]
+        for c in range(3):
+            np_mask_rgb[:, :, c] = hardened[:]
+        return np_mask_rgb
+
+    width = _np_src_image.shape[0]
+    height = _np_src_image.shape[1]
+    num_channels = _np_src_image.shape[2]
+
+    _np_src_image[:] * (1. - np_mask_rgb)
+    np_mask_grey = (np.sum(np_mask_rgb, axis=2) / 3.)
+    img_mask = np_mask_grey > 1e-6
+    ref_mask = np_mask_grey < 1e-3
+
+    windowed_image = _np_src_image * (1. - _get_masked_window_rgb(np_mask_grey))
+    windowed_image /= np.max(windowed_image)
+    windowed_image += np.average(_np_src_image) * np_mask_rgb  # / (1.-np.average(np_mask_rgb))  # rather than leave the masked area black, we get better results from fft by filling the average unmasked color
+
+    src_fft = _fft2(windowed_image)  # get feature statistics from masked src img
+    src_dist = np.absolute(src_fft)
+    src_phase = src_fft / src_dist
+
+    # create a generator with a static seed to make outpainting deterministic / only follow global seed
+    rng = np.random.default_rng(0)
+
+    noise_window = _get_gaussian_window(width, height, mode=1)  # start with simple gaussian noise
+    noise_rgb = rng.random((width, height, num_channels))
+    noise_grey = (np.sum(noise_rgb, axis=2) / 3.)
+    noise_rgb *= color_variation  # the colorfulness of the starting noise is blended to greyscale with a parameter
+    for c in range(num_channels):
+        noise_rgb[:, :, c] += (1. - color_variation) * noise_grey
+
+    noise_fft = _fft2(noise_rgb)
+    for c in range(num_channels):
+        noise_fft[:, :, c] *= noise_window
+    noise_rgb = np.real(_ifft2(noise_fft))
+    shaped_noise_fft = _fft2(noise_rgb)
+    shaped_noise_fft[:, :, :] = np.absolute(shaped_noise_fft[:, :, :]) ** 2 * (src_dist ** noise_q) * src_phase  # perform the actual shaping
+
+    brightness_variation = 0.  # color_variation # todo: temporarily tieing brightness variation to color variation for now
+    contrast_adjusted_np_src = _np_src_image[:] * (brightness_variation + 1.) - brightness_variation * 2.
+
+    # scikit-image is used for histogram matching, very convenient!
+    shaped_noise = np.real(_ifft2(shaped_noise_fft))
+    shaped_noise -= np.min(shaped_noise)
+    shaped_noise /= np.max(shaped_noise)
+    shaped_noise[img_mask, :] = skimage.exposure.match_histograms(shaped_noise[img_mask, :] ** 1., contrast_adjusted_np_src[ref_mask, :], channel_axis=1)
+    shaped_noise = _np_src_image[:] * (1. - np_mask_rgb) + shaped_noise * np_mask_rgb
+
+    matched_noise = shaped_noise[:]
+
+    return np.clip(matched_noise, 0., 1.)
+
+
+
+class Script(scripts.Script):
+    def title(self):
+        return "Outpainting mk2"
+
+    def show(self, is_img2img):
+        return is_img2img
+
+    def ui(self, is_img2img):
+        if not is_img2img:
+            return None
+
+        info = gr.HTML("<p style=\"margin-bottom:0.75em\">Recommended settings: Sampling Steps: 80-100, Sampler: Euler a, Denoising strength: 0.8</p>")
+
+        pixels = gr.Slider(label="Pixels to expand", minimum=8, maximum=256, step=8, value=128, elem_id=self.elem_id("pixels"))
+        mask_blur = gr.Slider(label='Mask blur', minimum=0, maximum=64, step=1, value=8, elem_id=self.elem_id("mask_blur"))
+        direction = gr.CheckboxGroup(label="Outpainting direction", choices=['left', 'right', 'up', 'down'], value=['left', 'right', 'up', 'down'], elem_id=self.elem_id("direction"))
+        noise_q = gr.Slider(label="Fall-off exponent (lower=higher detail)", minimum=0.0, maximum=4.0, step=0.01, value=1.0, elem_id=self.elem_id("noise_q"))
+        color_variation = gr.Slider(label="Color variation", minimum=0.0, maximum=1.0, step=0.01, value=0.05, elem_id=self.elem_id("color_variation"))
+
+        return [info, pixels, mask_blur, direction, noise_q, color_variation]
+
+    def run(self, p, _, pixels, mask_blur, direction, noise_q, color_variation):
+        initial_seed_and_info = [None, None]
+
+        process_width = p.width
+        process_height = p.height
+
+        p.inpaint_full_res = False
+        p.inpainting_fill = 1
+        p.do_not_save_samples = True
+        p.do_not_save_grid = True
+
+        left = pixels if "left" in direction else 0
+        right = pixels if "right" in direction else 0
+        up = pixels if "up" in direction else 0
+        down = pixels if "down" in direction else 0
+
+        if left > 0 or right > 0:
+            mask_blur_x = mask_blur
+        else:
+            mask_blur_x = 0
+
+        if up > 0 or down > 0:
+            mask_blur_y = mask_blur
+        else:
+            mask_blur_y = 0
+
+        p.mask_blur_x = mask_blur_x*4
+        p.mask_blur_y = mask_blur_y*4
+
+        init_img = p.init_images[0]
+        target_w = math.ceil((init_img.width + left + right) / 64) * 64
+        target_h = math.ceil((init_img.height + up + down) / 64) * 64
+
+        if left > 0:
+            left = left * (target_w - init_img.width) // (left + right)
+
+        if right > 0:
+            right = target_w - init_img.width - left
+
+        if up > 0:
+            up = up * (target_h - init_img.height) // (up + down)
+
+        if down > 0:
+            down = target_h - init_img.height - up
+
+        def expand(init, count, expand_pixels, is_left=False, is_right=False, is_top=False, is_bottom=False):
+            is_horiz = is_left or is_right
+            is_vert = is_top or is_bottom
+            pixels_horiz = expand_pixels if is_horiz else 0
+            pixels_vert = expand_pixels if is_vert else 0
+
+            images_to_process = []
+            output_images = []
+            for n in range(count):
+                res_w = init[n].width + pixels_horiz
+                res_h = init[n].height + pixels_vert
+                process_res_w = math.ceil(res_w / 64) * 64
+                process_res_h = math.ceil(res_h / 64) * 64
+
+                img = Image.new("RGB", (process_res_w, process_res_h))
+                img.paste(init[n], (pixels_horiz if is_left else 0, pixels_vert if is_top else 0))
+                mask = Image.new("RGB", (process_res_w, process_res_h), "white")
+                draw = ImageDraw.Draw(mask)
+                draw.rectangle((
+                    expand_pixels + mask_blur_x if is_left else 0,
+                    expand_pixels + mask_blur_y if is_top else 0,
+                    mask.width - expand_pixels - mask_blur_x if is_right else res_w,
+                    mask.height - expand_pixels - mask_blur_y if is_bottom else res_h,
+                ), fill="black")
+
+                np_image = (np.asarray(img) / 255.0).astype(np.float64)
+                np_mask = (np.asarray(mask) / 255.0).astype(np.float64)
+                noised = get_matched_noise(np_image, np_mask, noise_q, color_variation)
+                output_images.append(Image.fromarray(np.clip(noised * 255., 0., 255.).astype(np.uint8), mode="RGB"))
+
+                target_width = min(process_width, init[n].width + pixels_horiz) if is_horiz else img.width
+                target_height = min(process_height, init[n].height + pixels_vert) if is_vert else img.height
+                p.width = target_width if is_horiz else img.width
+                p.height = target_height if is_vert else img.height
+
+                crop_region = (
+                    0 if is_left else output_images[n].width - target_width,
+                    0 if is_top else output_images[n].height - target_height,
+                    target_width if is_left else output_images[n].width,
+                    target_height if is_top else output_images[n].height,
+                )
+                mask = mask.crop(crop_region)
+                p.image_mask = mask
+
+                image_to_process = output_images[n].crop(crop_region)
+                images_to_process.append(image_to_process)
+
+            p.init_images = images_to_process
+
+            latent_mask = Image.new("RGB", (p.width, p.height), "white")
+            draw = ImageDraw.Draw(latent_mask)
+            draw.rectangle((
+                expand_pixels + mask_blur_x * 2 if is_left else 0,
+                expand_pixels + mask_blur_y * 2 if is_top else 0,
+                mask.width - expand_pixels - mask_blur_x * 2 if is_right else res_w,
+                mask.height - expand_pixels - mask_blur_y * 2 if is_bottom else res_h,
+            ), fill="black")
+            p.latent_mask = latent_mask
+
+            proc = process_images(p)
+
+            if initial_seed_and_info[0] is None:
+                initial_seed_and_info[0] = proc.seed
+                initial_seed_and_info[1] = proc.info
+
+            for n in range(count):
+                output_images[n].paste(proc.images[n], (0 if is_left else output_images[n].width - proc.images[n].width, 0 if is_top else output_images[n].height - proc.images[n].height))
+                output_images[n] = output_images[n].crop((0, 0, res_w, res_h))
+
+            return output_images
+
+        batch_count = p.n_iter
+        batch_size = p.batch_size
+        p.n_iter = 1
+        state.job_count = batch_count * ((1 if left > 0 else 0) + (1 if right > 0 else 0) + (1 if up > 0 else 0) + (1 if down > 0 else 0))
+        all_processed_images = []
+
+        for i in range(batch_count):
+            imgs = [init_img] * batch_size
+            state.job = f"Batch {i + 1} out of {batch_count}"
+
+            if left > 0:
+                imgs = expand(imgs, batch_size, left, is_left=True)
+            if right > 0:
+                imgs = expand(imgs, batch_size, right, is_right=True)
+            if up > 0:
+                imgs = expand(imgs, batch_size, up, is_top=True)
+            if down > 0:
+                imgs = expand(imgs, batch_size, down, is_bottom=True)
+
+            all_processed_images += imgs
+
+        all_images = all_processed_images
+
+        combined_grid_image = images.image_grid(all_processed_images)
+        unwanted_grid_because_of_img_count = len(all_processed_images) < 2 and opts.grid_only_if_multiple
+        if opts.return_grid and not unwanted_grid_because_of_img_count:
+            all_images = [combined_grid_image] + all_processed_images
+
+        res = Processed(p, all_images, initial_seed_and_info[0], initial_seed_and_info[1])
+
+        if opts.samples_save:
+            for img in all_processed_images:
+                images.save_image(img, p.outpath_samples, "", res.seed, p.prompt, opts.samples_format, info=res.info, p=p)
+
+        if opts.grid_save and not unwanted_grid_because_of_img_count:
+            images.save_image(combined_grid_image, p.outpath_grids, "grid", res.seed, p.prompt, opts.grid_format, info=res.info, short_filename=not opts.grid_extended_filename, grid=True, p=p)
+
+        return res

poor_mans_outpainting.py

@@ -0,0 +1,146 @@
+import math
+
+import modules.scripts as scripts
+import gradio as gr
+from PIL import Image, ImageDraw
+
+from modules import images, devices
+from modules.processing import Processed, process_images
+from modules.shared import opts, state
+
+
+class Script(scripts.Script):
+    def title(self):
+        return "Poor man's outpainting"
+
+    def show(self, is_img2img):
+        return is_img2img
+
+    def ui(self, is_img2img):
+        if not is_img2img:
+            return None
+
+        pixels = gr.Slider(label="Pixels to expand", minimum=8, maximum=256, step=8, value=128, elem_id=self.elem_id("pixels"))
+        mask_blur = gr.Slider(label='Mask blur', minimum=0, maximum=64, step=1, value=4, elem_id=self.elem_id("mask_blur"))
+        inpainting_fill = gr.Radio(label='Masked content', choices=['fill', 'original', 'latent noise', 'latent nothing'], value='fill', type="index", elem_id=self.elem_id("inpainting_fill"))
+        direction = gr.CheckboxGroup(label="Outpainting direction", choices=['left', 'right', 'up', 'down'], value=['left', 'right', 'up', 'down'], elem_id=self.elem_id("direction"))
+
+        return [pixels, mask_blur, inpainting_fill, direction]
+
+    def run(self, p, pixels, mask_blur, inpainting_fill, direction):
+        initial_seed = None
+        initial_info = None
+
+        p.mask_blur = mask_blur * 2
+        p.inpainting_fill = inpainting_fill
+        p.inpaint_full_res = False
+
+        left = pixels if "left" in direction else 0
+        right = pixels if "right" in direction else 0
+        up = pixels if "up" in direction else 0
+        down = pixels if "down" in direction else 0
+
+        init_img = p.init_images[0]
+        target_w = math.ceil((init_img.width + left + right) / 64) * 64
+        target_h = math.ceil((init_img.height + up + down) / 64) * 64
+
+        if left > 0:
+            left = left * (target_w - init_img.width) // (left + right)
+        if right > 0:
+            right = target_w - init_img.width - left
+
+        if up > 0:
+            up = up * (target_h - init_img.height) // (up + down)
+
+        if down > 0:
+            down = target_h - init_img.height - up
+
+        img = Image.new("RGB", (target_w, target_h))
+        img.paste(init_img, (left, up))
+
+        mask = Image.new("L", (img.width, img.height), "white")
+        draw = ImageDraw.Draw(mask)
+        draw.rectangle((
+            left + (mask_blur * 2 if left > 0 else 0),
+            up + (mask_blur * 2 if up > 0 else 0),
+            mask.width - right - (mask_blur * 2 if right > 0 else 0),
+            mask.height - down - (mask_blur * 2 if down > 0 else 0)
+        ), fill="black")
+
+        latent_mask = Image.new("L", (img.width, img.height), "white")
+        latent_draw = ImageDraw.Draw(latent_mask)
+        latent_draw.rectangle((
+             left + (mask_blur//2 if left > 0 else 0),
+             up + (mask_blur//2 if up > 0 else 0),
+             mask.width - right - (mask_blur//2 if right > 0 else 0),
+             mask.height - down - (mask_blur//2 if down > 0 else 0)
+        ), fill="black")
+
+        devices.torch_gc()
+
+        grid = images.split_grid(img, tile_w=p.width, tile_h=p.height, overlap=pixels)
+        grid_mask = images.split_grid(mask, tile_w=p.width, tile_h=p.height, overlap=pixels)
+        grid_latent_mask = images.split_grid(latent_mask, tile_w=p.width, tile_h=p.height, overlap=pixels)
+
+        p.n_iter = 1
+        p.batch_size = 1
+        p.do_not_save_grid = True
+        p.do_not_save_samples = True
+
+        work = []
+        work_mask = []
+        work_latent_mask = []
+        work_results = []
+
+        for (y, h, row), (_, _, row_mask), (_, _, row_latent_mask) in zip(grid.tiles, grid_mask.tiles, grid_latent_mask.tiles):
+            for tiledata, tiledata_mask, tiledata_latent_mask in zip(row, row_mask, row_latent_mask):
+                x, w = tiledata[0:2]
+
+                if x >= left and x+w <= img.width - right and y >= up and y+h <= img.height - down:
+                    continue
+
+                work.append(tiledata[2])
+                work_mask.append(tiledata_mask[2])
+                work_latent_mask.append(tiledata_latent_mask[2])
+
+        batch_count = len(work)
+        print(f"Poor man's outpainting will process a total of {len(work)} images tiled as {len(grid.tiles[0][2])}x{len(grid.tiles)}.")
+
+        state.job_count = batch_count
+
+        for i in range(batch_count):
+            p.init_images = [work[i]]
+            p.image_mask = work_mask[i]
+            p.latent_mask = work_latent_mask[i]
+
+            state.job = f"Batch {i + 1} out of {batch_count}"
+            processed = process_images(p)
+
+            if initial_seed is None:
+                initial_seed = processed.seed
+                initial_info = processed.info
+
+            p.seed = processed.seed + 1
+            work_results += processed.images
+
+
+        image_index = 0
+        for y, h, row in grid.tiles:
+            for tiledata in row:
+                x, w = tiledata[0:2]
+
+                if x >= left and x+w <= img.width - right and y >= up and y+h <= img.height - down:
+                    continue
+
+                tiledata[2] = work_results[image_index] if image_index < len(work_results) else Image.new("RGB", (p.width, p.height))
+                image_index += 1
+
+        combined_image = images.combine_grid(grid)
+
+        if opts.samples_save:
+            images.save_image(combined_image, p.outpath_samples, "", initial_seed, p.prompt, opts.samples_format, info=initial_info, p=p)
+
+        processed = Processed(p, [combined_image], initial_seed, initial_info)
+
+        return processed
+