Merge branch 'outpaint' of https://github.com/Kyle0654/InvokeAI into Kyle0654-outpaint

ldm/generate.py

@@ -295,6 +295,13 @@ class Generate:
             catch_interrupts = False,
             hires_fix        = False,
             use_mps_noise    = False,
+            # Seam settings for outpainting
+            seam_size: int   = 0,
+            seam_blur: int   = 0,
+            seam_strength: float = 0.7,
+            seam_steps: int  = 10,
+            tile_size: int   = 32,
+            force_outpaint: bool = False,
             **args,
     ):   # eat up additional cruft
         """
@@ -459,7 +466,13 @@ class Generate:
                 embiggen_tiles=embiggen_tiles,
                 inpaint_replace=inpaint_replace,
                 mask_blur_radius=mask_blur_radius,
-                safety_checker=checker
+                safety_checker=checker,
+                seam_size = seam_size,
+                seam_blur = seam_blur,
+                seam_strength = seam_strength,
+                seam_steps = seam_steps,
+                tile_size = tile_size,
+                force_outpaint = force_outpaint
             )
 
             if init_color:
@@ -648,7 +661,7 @@ class Generate:
         if inpainting_model_in_use:
             return self._make_omnibus()
 
-        if (init_image is not None) and (mask_image is not None):
+        if ((init_image is not None) and (mask_image is not None)) or force_outpaint:
             return self._make_inpaint()
         
         if init_image is not None:
@@ -925,8 +938,9 @@ class Generate:
         image = ImageOps.exif_transpose(image)
         return image
 
-    def _create_init_image(self, image, width, height, fit=True):
+    def _create_init_image(self, image: Image.Image, width, height, fit=True):
-        image = image.convert('RGB')
+        if image.mode != 'RGBA':
+            image = image.convert('RGB')
         image = self._fit_image(image, (width, height)) if fit else self._squeeze_image(image)
         return image
 

ldm/invoke/generator/img2img.py

@@ -30,7 +30,7 @@ class Img2Img(Generator):
         )
 
         if isinstance(init_image, PIL.Image.Image):
-            init_image = self._image_to_tensor(init_image)
+            init_image = self._image_to_tensor(init_image.convert('RGB'))
 
         scope = choose_autocast(self.precision)
         with scope(self.model.device.type):

ldm/invoke/generator/inpaint.py

@@ -2,12 +2,13 @@
 ldm.invoke.generator.inpaint descends from ldm.invoke.generator
 '''
 
+import math
 import torch
 import torchvision.transforms as T
 import numpy as  np
 import cv2 as cv
 import PIL
-from PIL import Image, ImageFilter
+from PIL import Image, ImageFilter, ImageOps
 from skimage.exposure.histogram_matching import match_histograms
 from einops import rearrange, repeat
 from ldm.invoke.devices             import choose_autocast
@@ -24,11 +25,128 @@ class Inpaint(Img2Img):
         self.mask_blur_radius = 0
         super().__init__(model, precision)
 
+    # Outpaint support code
+    def get_tile_images(self, image: np.ndarray, width=8, height=8):
+        _nrows, _ncols, depth = image.shape
+        _strides = image.strides
+
+        nrows, _m = divmod(_nrows, height)
+        ncols, _n = divmod(_ncols, width)
+        if _m != 0 or _n != 0:
+            return None
+
+        return np.lib.stride_tricks.as_strided(
+            np.ravel(image),
+            shape=(nrows, ncols, height, width, depth),
+            strides=(height * _strides[0], width * _strides[1], *_strides),
+            writeable=False
+        )
+
+    def tile_fill_missing(self, im: Image.Image, tile_size: int = 16, seed: int = None) -> Image:
+        a = np.asarray(im, dtype=np.uint8)
+
+        tile_size = (tile_size, tile_size)
+
+        # Get the image as tiles of a specified size
+        tiles = self.get_tile_images(a,*tile_size).copy()
+
+        # Get the mask as tiles
+        tiles_mask = tiles[:,:,:,:,3]
+
+        # Find any mask tiles with any fully transparent pixels (we will be replacing these later)
+        tmask_shape = tiles_mask.shape
+        tiles_mask = tiles_mask.reshape(math.prod(tiles_mask.shape))
+        n,ny = (math.prod(tmask_shape[0:2])), math.prod(tmask_shape[2:])
+        tiles_mask = (tiles_mask > 0)
+        tiles_mask = tiles_mask.reshape((n,ny)).all(axis = 1)
+
+        # Get RGB tiles in single array and filter by the mask
+        tshape = tiles.shape
+        tiles_all = tiles.reshape((math.prod(tiles.shape[0:2]), * tiles.shape[2:]))
+        filtered_tiles = tiles_all[tiles_mask]
+
+        if len(filtered_tiles) == 0:
+            return im
+
+        # Find all invalid tiles and replace with a random valid tile
+        replace_count = (tiles_mask == False).sum()
+        rng = np.random.default_rng(seed = seed)
+        tiles_all[np.logical_not(tiles_mask)] = filtered_tiles[rng.choice(filtered_tiles.shape[0], replace_count),:,:,:]
+
+        # Convert back to an image
+        tiles_all = tiles_all.reshape(tshape)
+        tiles_all = tiles_all.swapaxes(1,2)
+        st = tiles_all.reshape((math.prod(tiles_all.shape[0:2]), math.prod(tiles_all.shape[2:4]), tiles_all.shape[4]))
+        si = Image.fromarray(st, mode='RGBA')
+
+        return si
+
+
+    def mask_edge(self, mask: Image, edge_size: int, edge_blur: int) -> Image:
+        npimg = np.asarray(mask, dtype=np.uint8)
+
+        # Detect any partially transparent regions
+        npgradient = np.uint8(255 * (1.0 - np.floor(np.abs(0.5 - np.float32(npimg) / 255.0) * 2.0)))
+
+        # Detect hard edges
+        npedge = cv.Canny(npimg, threshold1=100, threshold2=200)
+
+        # Combine
+        npmask = npgradient + npedge
+
+        # Expand 
+        npmask = cv.dilate(npmask, np.ones((3,3), np.uint8), iterations = int(edge_size / 2))
+
+        new_mask = Image.fromarray(npmask)
+
+        if edge_blur > 0:
+            new_mask = new_mask.filter(ImageFilter.BoxBlur(edge_blur))
+
+        return ImageOps.invert(new_mask)
+
+
+    def seam_paint(self,
+        im: Image.Image,
+        seam_size: int,
+        seam_blur: int,
+        prompt,sampler,steps,cfg_scale,ddim_eta,
+        conditioning,strength,
+        noise
+    ) -> Image.Image:
+        hard_mask = self.pil_image.split()[-1].copy()
+        mask = self.mask_edge(hard_mask, seam_size, seam_blur)
+
+        make_image = self.get_make_image(
+            prompt,
+            sampler,
+            steps,
+            cfg_scale,
+            ddim_eta,
+            conditioning,
+            init_image = im.copy().convert('RGBA'),
+            mask_image = mask.convert('RGB'), # Code currently requires an RGB mask
+            strength = strength,
+            mask_blur_radius = 0,
+            seam_size = 0
+        )
+
+        result = make_image(noise)
+
+        return result
+
+
     @torch.no_grad()
     def get_make_image(self,prompt,sampler,steps,cfg_scale,ddim_eta,
                        conditioning,init_image,mask_image,strength,
                        mask_blur_radius: int = 8,
-                       step_callback=None,inpaint_replace=False, **kwargs):
+                       # Seam settings - when 0, doesn't fill seam
+                       seam_size: int = 0,
+                       seam_blur: int = 0,
+                       seam_strength: float = 0.7,
+                       seam_steps: int = 10,
+                       tile_size: int = 32,
+                       step_callback=None,
+                       inpaint_replace=False, **kwargs):
         """
         Returns a function returning an image derived from the prompt and
         the initial image + mask.  Return value depends on the seed at
@@ -37,7 +155,17 @@ class Inpaint(Img2Img):
 
         if isinstance(init_image, PIL.Image.Image):
             self.pil_image = init_image
-            init_image = self._image_to_tensor(init_image)
+
+            # Fill missing areas of original image
+            init_filled = self.tile_fill_missing(
+                self.pil_image.copy(),
+                seed = self.seed,
+                tile_size = tile_size
+            )
+            init_filled.paste(init_image, (0,0), init_image.split()[-1])
+
+            # Create init tensor
+            init_image = self._image_to_tensor(init_filled.convert('RGB'))
 
         if isinstance(mask_image, PIL.Image.Image):
             self.pil_mask = mask_image
@@ -106,38 +234,56 @@ class Inpaint(Img2Img):
                 mask                       = mask_image,
                 init_latent                = self.init_latent
             )
-            return self.sample_to_image(samples)
+
+            result = self.sample_to_image(samples)
+
+            # Seam paint if this is our first pass (seam_size set to 0 during seam painting)
+            if seam_size > 0:
+                result = self.seam_paint(
+                    result,
+                    seam_size,
+                    seam_blur,
+                    prompt,
+                    sampler,
+                    seam_steps,
+                    cfg_scale,
+                    ddim_eta,
+                    conditioning,
+                    seam_strength,
+                    x_T)
+
+            return result
 
         return make_image
 
-    def sample_to_image(self, samples)->Image.Image:
-        gen_result = super().sample_to_image(samples).convert('RGB')
-
-        if self.pil_image is None or self.pil_mask is None:
-            return gen_result
-        
-        pil_mask = self.pil_mask
-        pil_image = self.pil_image
-        mask_blur_radius = self.mask_blur_radius
 
+    def color_correct(self, image: Image.Image, base_image: Image.Image, mask: Image.Image, mask_blur_radius: int) -> Image.Image:
         # Get the original alpha channel of the mask if there is one.
         # Otherwise it is some other black/white image format ('1', 'L' or 'RGB')
-        pil_init_mask = pil_mask.getchannel('A') if pil_mask.mode == 'RGBA' else pil_mask.convert('L')
+        pil_init_mask = mask.getchannel('A') if mask.mode == 'RGBA' else mask.convert('L')
-        pil_init_image = pil_image.convert('RGBA') # Add an alpha channel if one doesn't exist
+        pil_init_image = base_image.convert('RGBA') # Add an alpha channel if one doesn't exist
 
         # Build an image with only visible pixels from source to use as reference for color-matching.
-        # Note that this doesn't use the mask, which would exclude some source image pixels from the
+        init_rgb_pixels = np.asarray(base_image.convert('RGB'), dtype=np.uint8)
-        # histogram and cause slight color changes.
+        init_a_pixels = np.asarray(pil_init_image.getchannel('A'), dtype=np.uint8)
-        init_rgb_pixels = np.asarray(pil_image.convert('RGB'), dtype=np.uint8).reshape(pil_image.width * pil_image.height, 3)
+        init_mask_pixels = np.asarray(pil_init_mask, dtype=np.uint8)
-        init_a_pixels = np.asarray(pil_init_image.getchannel('A'), dtype=np.uint8).reshape(pil_init_mask.width * pil_init_mask.height)
-        init_rgb_pixels = init_rgb_pixels[init_a_pixels > 0]
-        init_rgb_pixels = init_rgb_pixels.reshape(1, init_rgb_pixels.shape[0], init_rgb_pixels.shape[1]) # Filter to just pixels that have any alpha, this is now our histogram
 
-        # Get numpy version
+        # Get numpy version of result
-        np_gen_result = np.asarray(gen_result, dtype=np.uint8)
+        np_image = np.asarray(image, dtype=np.uint8)
+
+        # Mask and calculate mean and standard deviation
+        mask_pixels = init_a_pixels * init_mask_pixels > 0
+        np_init_rgb_pixels_masked = init_rgb_pixels[mask_pixels, :]
+        np_image_masked = np_image[mask_pixels, :]
+
+        init_means = np_init_rgb_pixels_masked.mean(axis=0)
+        init_std = np_init_rgb_pixels_masked.std(axis=0)
+        gen_means = np_image_masked.mean(axis=0)
+        gen_std = np_image_masked.std(axis=0)
 
         # Color correct
-        np_matched_result = match_histograms(np_gen_result, init_rgb_pixels, channel_axis=-1)
+        np_matched_result = np_image.copy()
+        np_matched_result[:,:,:] = (((np_matched_result[:,:,:].astype(np.float32) - gen_means[None,None,:]) / gen_std[None,None,:]) * init_std[None,None,:] + init_means[None,None,:]).clip(0, 255).astype(np.uint8)
         matched_result = Image.fromarray(np_matched_result, mode='RGB')
 
         # Blur the mask out (into init image) by specified amount
@@ -150,6 +296,16 @@ class Inpaint(Img2Img):
             blurred_init_mask = pil_init_mask
 
         # Paste original on color-corrected generation (using blurred mask)
-        matched_result.paste(pil_image, (0,0), mask = blurred_init_mask)
+        matched_result.paste(base_image, (0,0), mask = blurred_init_mask)
         return matched_result
 
+
+    def sample_to_image(self, samples)->Image.Image:
+        gen_result = super().sample_to_image(samples).convert('RGB')
+
+        if self.pil_image is None or self.pil_mask is None:
+            return gen_result
+        
+        corrected_result = self.color_correct(gen_result, self.pil_image, self.pil_mask, self.mask_blur_radius)
+
+        return corrected_result