fix sub_idxs
This commit is contained in:
logtd
+145
-149
@@ -10,194 +10,190 @@ N_MAX_OBJECTS = 30
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def get_point_from_box(bbox):
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x0, y0, x1, y1 = bbox[0], bbox[1], bbox[2], bbox[3]
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return [(x0 + x1) / 2.0, (y0 + y1) / 2.0]
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x0, y0, x1, y1 = bbox[0], bbox[1], bbox[2], bbox[3]
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return [(x0 + x1) / 2.0, (y0 + y1) / 2.0]
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def get_empty_binary_mask(img_width, img_height):
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return np.zeros((img_width, img_height, 1))
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return np.zeros((img_width, img_height, 1))
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def sample_random_points_from_mask(mask, k=N_SCRIBBLE_POINTS):
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mask = mask[:, :, 0]
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# Find the coordinates of non-zero pixels in the binary mask
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nonzero_coords = np.transpose(np.nonzero(mask))
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mask = mask[:, :, 0]
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# Find the coordinates of non-zero pixels in the binary mask
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nonzero_coords = np.transpose(np.nonzero(mask))
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# Randomly sample 'k' points
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# return all zeros if there is no non-zero pixel
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if len(nonzero_coords) == 0:
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xy_points = [0 for _ in range(k * 2)]
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# Randomly sample 'k' points
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# return all zeros if there is no non-zero pixel
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if len(nonzero_coords) == 0:
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xy_points = [0 for _ in range(k * 2)]
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return xy_points
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# randomly sample with replacement if there are not enough non-zero pixels
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if len(nonzero_coords) < k and len(nonzero_coords) > 0:
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random_indices = np.random.choice(len(nonzero_coords), k, replace=True)
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# randomly sample withiout replacement if there are enough non-zero pixels
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else:
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random_indices = np.random.choice(
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len(nonzero_coords), k, replace=False)
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sampled_points = nonzero_coords[random_indices]
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# order the points by their distance to (0, 0)
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# center = np.array([mask.shape[0] // 2, mask.shape[1] // 2])
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center = np.array([0, 0])
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sampled_points = sorted(sampled_points, key=lambda x: np.linalg.norm(
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np.array(x) - center)) # np.linalg.norm
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# concatenate x and y coordinates and return them as a list
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# [x1,y1,x2,y2,...,x_k,y_k]
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xy_points = []
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for x in sampled_points:
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xy_points.append(float(x[1]))
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xy_points.append(float(x[0]))
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return xy_points
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# randomly sample with replacement if there are not enough non-zero pixels
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if len(nonzero_coords) < k and len(nonzero_coords) > 0:
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random_indices = np.random.choice(len(nonzero_coords), k, replace=True)
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# randomly sample withiout replacement if there are enough non-zero pixels
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else:
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random_indices = np.random.choice(
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len(nonzero_coords), k, replace=False)
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sampled_points = nonzero_coords[random_indices]
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# order the points by their distance to (0, 0)
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# center = np.array([mask.shape[0] // 2, mask.shape[1] // 2])
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center = np.array([0, 0])
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sampled_points = sorted(sampled_points, key=lambda x: np.linalg.norm(
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np.array(x) - center)) # np.linalg.norm
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# concatenate x and y coordinates and return them as a list
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# [x1,y1,x2,y2,...,x_k,y_k]
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xy_points = []
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for x in sampled_points:
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xy_points.append(float(x[1]))
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xy_points.append(float(x[0]))
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return xy_points
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def convert_points(points, img_width, img_height):
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# convert polygons/scribbless' coordinates to the relative values (0, 1)
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for i in range(len(points)):
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if i % 2 == 0:
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points[i] = min(points[i] / img_width, 1.0)
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else:
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points[i] = min(points[i] / img_height, 1.0)
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return points
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# convert polygons/scribbless' coordinates to the relative values (0, 1)
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for i in range(len(points)):
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if i % 2 == 0:
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points[i] = min(points[i] / img_width, 1.0)
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else:
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points[i] = min(points[i] / img_height, 1.0)
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return points
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def sample_sparse_points_from_mask(mask, k=256):
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n_points = k
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n_polygons = n_points // 2 # half points should be sampled from the polygons
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mask = mask[:, :, 0]
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# sample sparse points from the polygons (boundary)
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polygons = binary_mask_to_polygon(mask, tolerance=0.0)
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# concatenate polygons to a single list
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polygons_single = []
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for polygon in polygons:
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polygons_single += polygon
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if len(polygons_single) != 0:
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# uniformly sample points from the polygon
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polygons_single = np.array(polygons_single).reshape(-1, 2)
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indexes = np.linspace(0, polygons_single.shape[0] - 1, n_polygons)
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indexes = list([int(i) for i in indexes])
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n_points = k
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n_polygons = n_points // 2 # half points should be sampled from the polygons
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mask = mask[:, :, 0]
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# sample sparse points from the polygons (boundary)
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polygons = binary_mask_to_polygon(mask, tolerance=0.0)
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# concatenate polygons to a single list
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polygons_single = []
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for polygon in polygons:
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polygons_single += polygon
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if len(polygons_single) != 0:
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# uniformly sample points from the polygon
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polygons_single = np.array(polygons_single).reshape(-1, 2)
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indexes = np.linspace(0, polygons_single.shape[0] - 1, n_polygons)
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indexes = list([int(i) for i in indexes])
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polygons_single = polygons_single[indexes]
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sampled_polygons = [(x[0], x[1]) for x in polygons_single]
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else:
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return [0 for _ in range(256 * 2)]
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polygons_single = polygons_single[indexes]
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sampled_polygons = [(x[0], x[1]) for x in polygons_single]
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else:
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return [0 for _ in range(256 * 2)]
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# sample sparse points from the mask
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n_inside_points = n_points - len(sampled_polygons)
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inside_points = sample_uniform_sparse_points(mask, n_inside_points)
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# sample sparse points from the mask
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n_inside_points = n_points - len(sampled_polygons)
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inside_points = sample_uniform_sparse_points(mask, n_inside_points)
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# combine inside_points and sampled_polygons
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xy_points = inside_points + sampled_polygons
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# combine inside_points and sampled_polygons
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xy_points = inside_points + sampled_polygons
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# order the points by their distance to (0, 0)
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center = np.array([0, 0])
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xy_points = sorted(xy_points, key=lambda x: np.linalg.norm(
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np.array(x) - center)) # np.linalg.norm
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# order the points by their distance to (0, 0)
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center = np.array([0, 0])
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xy_points = sorted(xy_points, key=lambda x: np.linalg.norm(
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np.array(x) - center)) # np.linalg.norm
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# return the sampled points
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sampled_points = []
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for x in xy_points:
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sampled_points.append(x[0])
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sampled_points.append(x[1])
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return sampled_points
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# return the sampled points
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sampled_points = []
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for x in xy_points:
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sampled_points.append(x[0])
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sampled_points.append(x[1])
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return sampled_points
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# [x0, y0, x1, y1]
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def get_grounding_input_from_coords(coords, img_width, img_height):
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x0, y0, x1, y1, coord_width, coord_height = coords
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location = [x0 / coord_width, y0 / coord_height,
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x1 / coord_width, y1 / coord_height]
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x0, y0, x1, y1, coord_width, coord_height = coords
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location = [x0 / coord_width, y0 / coord_height,
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x1 / coord_width, y1 / coord_height]
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point = get_point_from_box(location)
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binary_mask = get_empty_binary_mask(img_width, img_height)
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point = get_point_from_box(location)
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binary_mask = get_empty_binary_mask(img_width, img_height)
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scribble = sample_random_points_from_mask(binary_mask, k=N_SCRIBBLE_POINTS)
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scribble = convert_points(scribble, img_width, img_height)
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scribble = sample_random_points_from_mask(binary_mask, k=N_SCRIBBLE_POINTS)
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scribble = convert_points(scribble, img_width, img_height)
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polygon = sample_sparse_points_from_mask(binary_mask, k=N_POLYGON_POINTS)
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polygon = convert_points(polygon, img_width, img_height)
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polygon = sample_sparse_points_from_mask(binary_mask, k=N_POLYGON_POINTS)
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polygon = convert_points(polygon, img_width, img_height)
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segment = resize(binary_mask.astype(np.float32),
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(img_width, img_height)).squeeze()
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# segment = np.stack(segment).astype(np.float32).squeeze() if len(segment) > 0 else segment
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segment = resize(binary_mask.astype(np.float32),
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(img_width, img_height)).squeeze()
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# segment = np.stack(segment).astype(np.float32).squeeze() if len(segment) > 0 else segment
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return dict(
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polygon=polygon,
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scribble=scribble,
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segment=segment,
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box=location,
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point=point,
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)
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return dict(
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polygon=polygon,
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scribble=scribble,
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segment=segment,
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box=location,
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point=point,
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)
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def create_zero_input_tensors(n_frames, img_width, img_height):
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masks = torch.zeros(n_frames, N_MAX_OBJECTS)
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text_masks = torch.zeros(n_frames, N_MAX_OBJECTS)
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text_embeddings = torch.zeros(n_frames, N_MAX_OBJECTS, 768)
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box_embeddings = torch.zeros(n_frames, N_MAX_OBJECTS, 4)
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polygon_embeddings = torch.zeros(
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n_frames, N_MAX_OBJECTS, N_POLYGON_POINTS * 2)
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scribble_embeddings = torch.zeros(
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n_frames, N_MAX_OBJECTS, N_SCRIBBLE_POINTS * 2)
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segment_embeddings = torch.zeros(
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n_frames, N_MAX_OBJECTS, img_width, img_height) # TODO: width height order
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point_embeddings = torch.zeros(n_frames, N_MAX_OBJECTS, 2)
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masks = torch.zeros(n_frames, N_MAX_OBJECTS)
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text_masks = torch.zeros(n_frames, N_MAX_OBJECTS)
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text_embeddings = torch.zeros(n_frames, N_MAX_OBJECTS, 768)
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box_embeddings = torch.zeros(n_frames, N_MAX_OBJECTS, 4)
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polygon_embeddings = torch.zeros(
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n_frames, N_MAX_OBJECTS, N_POLYGON_POINTS * 2)
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scribble_embeddings = torch.zeros(
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n_frames, N_MAX_OBJECTS, N_SCRIBBLE_POINTS * 2)
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segment_embeddings = torch.zeros(
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n_frames, N_MAX_OBJECTS, img_width, img_height) # TODO: width height order
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point_embeddings = torch.zeros(n_frames, N_MAX_OBJECTS, 2)
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return dict(
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masks=masks,
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text_masks=text_masks,
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prompts=text_embeddings,
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boxes=box_embeddings,
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polygons=polygon_embeddings,
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scribbles=scribble_embeddings,
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segments=segment_embeddings,
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points=point_embeddings
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)
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return dict(
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masks=masks,
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text_masks=text_masks,
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prompts=text_embeddings,
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boxes=box_embeddings,
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polygons=polygon_embeddings,
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scribbles=scribble_embeddings,
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segments=segment_embeddings,
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points=point_embeddings
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)
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def get_attn_mask(img_size=64):
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return torch.zeros(N_MAX_OBJECTS, img_size, img_size)
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return torch.zeros(N_MAX_OBJECTS, img_size, img_size)
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def prepare_embeddings(conds, latent_shape, idxs, use_masked_att=False):
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batch_size, _, latent_height, latent_width = latent_shape
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embeddings = create_zero_input_tensors(
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batch_size, latent_width, latent_height)
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if use_masked_att:
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embeddings['att_masks'] = torch.zeros(
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batch_size, N_MAX_OBJECTS, latent_width, latent_height)
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batch_size, _, latent_height, latent_width = latent_shape
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embeddings = create_zero_input_tensors(
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batch_size, latent_width, latent_height)
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if use_masked_att:
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embeddings['att_masks'] = torch.zeros(
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batch_size, N_MAX_OBJECTS, latent_width, latent_height)
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for idx in idxs:
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for cond_idx, cond in enumerate(conds):
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if cond['positions'][idx] is None:
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continue
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for grounding_idx, frame_idx in enumerate(idxs):
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for cond_idx, cond in enumerate(conds):
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if cond['positions'][frame_idx] is None:
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continue
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grounding = get_grounding_input_from_coords(
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cond['positions'][idx], latent_width, latent_height)
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embeddings['masks'][idx][cond_idx] = 1
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embeddings['text_masks'][idx][cond_idx] = 1
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embeddings['prompts'][idx][cond_idx] = cond['cond_pooled']
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embeddings['boxes'][idx][cond_idx] = torch.tensor(
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grounding['box'])
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embeddings['polygons'][idx][cond_idx] = torch.tensor(
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grounding['polygon'])
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embeddings['scribbles'][idx][cond_idx] = torch.tensor(
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grounding['scribble'])
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embeddings['segments'][idx][cond_idx] = torch.tensor(
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grounding['segment'])
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embeddings['points'][idx][cond_idx] = torch.tensor(
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grounding['point'])
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grounding = get_grounding_input_from_coords(
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cond['positions'][frame_idx], latent_width, latent_height)
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embeddings['masks'][grounding_idx][cond_idx] = 1
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embeddings['text_masks'][grounding_idx][cond_idx] = 1
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embeddings['prompts'][grounding_idx][cond_idx] = cond['cond_pooled']
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embeddings['boxes'][grounding_idx][cond_idx] = torch.tensor(
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grounding['box'])
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embeddings['polygons'][grounding_idx][cond_idx] = torch.tensor(
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grounding['polygon'])
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embeddings['scribbles'][grounding_idx][cond_idx] = torch.tensor(
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grounding['scribble'])
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embeddings['segments'][grounding_idx][cond_idx] = torch.tensor(
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grounding['segment'])
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embeddings['points'][grounding_idx][cond_idx] = torch.tensor(
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grounding['point'])
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if use_masked_att:
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box = grounding['box']
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x1, y1, x2, y2 = int(np.round(box[0] * latent_width)), int(np.round(box[1] * latent_height)), int(
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np.round(box[2] * latent_width)), int(np.round(box[3] * latent_height))
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embeddings['att_masks'][idx][cond_idx][x1:x2, y1:y2] = 1
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if use_masked_att:
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box = grounding['box']
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x1, y1, x2, y2 = int(np.round(box[0] * latent_width)), int(np.round(box[1] * latent_height)), int(
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np.round(box[2] * latent_width)), int(np.round(box[3] * latent_height))
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embeddings['att_masks'][grounding_idx][cond_idx][x1:x2, y1:y2] = 1
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return embeddings
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def get_model_inputs():
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return
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return embeddings
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@@ -3,79 +3,75 @@ from ..conditioning.instance_conditioning import InstanceConditioning
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class InstanceDiffusionTrackingPromptNode:
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@classmethod
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def INPUT_TYPES(s):
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return {"required": {"positive": ("CONDITIONING", ),
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"negative": ("CONDITIONING", ),
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"clip": ("CLIP", ),
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"tracking": ("TRACKING", ),
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"positionnet": ("POSITIONNET", ),
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"fusers": ("FUSERS", ),
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"fusers_batch_size": ("INT", {"default": 5, "min": 0, "max": 10000, "step": 8}),
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"positive_text": ("STRING", {"multiline": True}),
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"negative_text": ("STRING", {"multiline": True}),
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}}
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RETURN_TYPES = ("CONDITIONING", "CONDITIONING")
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RETURN_NAMES = ("positive", "negative")
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FUNCTION = "append"
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@classmethod
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def INPUT_TYPES(s):
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return {"required": {"positive": ("CONDITIONING", ),
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"negative": ("CONDITIONING", ),
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"clip": ("CLIP", ),
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"tracking": ("TRACKING", ),
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"positionnet": ("POSITIONNET", ),
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"fusers": ("FUSERS", ),
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"fusers_batch_size": ("INT", {"default": 5, "min": 0, "max": 10000, "step": 8}),
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"positive_text": ("STRING", {"multiline": True}),
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"negative_text": ("STRING", {"multiline": True}),
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}}
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RETURN_TYPES = ("CONDITIONING", "CONDITIONING")
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RETURN_NAMES = ("positive", "negative")
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FUNCTION = "append"
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CATEGORY = "instance/conditioning"
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CATEGORY = "instance/conditioning"
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def _get_position_conds(self, clip, tracking, text):
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# Get prompts and their class id and trakcer id
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prompt_pairs = extract_prompts(text)
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def _get_position_conds(self, clip, tracking, text):
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# Get prompts and their class id and trakcer id
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prompt_pairs = extract_prompts(text)
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# Go through prompt pairs, encode prompts, and join with positions from tracking
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position_conds = []
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for tracker_id, class_id, prompt in prompt_pairs:
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_, cond_pooled = clip.encode_from_tokens(
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clip.tokenize(prompt), return_pooled=True)
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# A tracker_id of -1 means that it is prompting all instances of a single class
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if tracker_id != -1:
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position_cond = {'cond_pooled': cond_pooled, 'positions':
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tracking[class_id][tracker_id]}
|
||||
position_conds.append(position_cond)
|
||||
else:
|
||||
for tracker_id in tracking[class_id]:
|
||||
position_cond = {'cond_pooled': cond_pooled,
|
||||
'positions': tracking[class_id][tracker_id]}
|
||||
position_conds.append(position_cond)
|
||||
# Go through prompt pairs, encode prompts, and join with positions from tracking
|
||||
position_conds = []
|
||||
for tracker_id, class_id, prompt in prompt_pairs:
|
||||
_, cond_pooled = clip.encode_from_tokens(
|
||||
clip.tokenize(prompt), return_pooled=True)
|
||||
# A tracker_id of -1 means that it is prompting all instances of a single class
|
||||
if tracker_id != -1:
|
||||
position_cond = {'cond_pooled': cond_pooled, 'positions':
|
||||
tracking[class_id][tracker_id]}
|
||||
position_conds.append(position_cond)
|
||||
else:
|
||||
for tracker_id in tracking[class_id]:
|
||||
position_cond = {'cond_pooled': cond_pooled,
|
||||
'positions': tracking[class_id][tracker_id]}
|
||||
position_conds.append(position_cond)
|
||||
|
||||
return position_conds
|
||||
return position_conds
|
||||
|
||||
def _apply_position_conds(self, position_conds, conditioning, fusers, positionnet, fusers_batch_size):
|
||||
# Add prompts+embeddings to the input conditionings
|
||||
cond_out = []
|
||||
for t in conditioning:
|
||||
n = [t[0], t[1].copy()]
|
||||
cond = n[1]
|
||||
prev = []
|
||||
has_instance = 'instance_diffusion' in cond
|
||||
instance_conditioning = conditioning['instance_diffusion'] if has_instance else InstanceConditioning(
|
||||
fusers, positionnet, fusers_batch_size)
|
||||
cond['instance_diffusion'] = instance_conditioning
|
||||
instance_conditioning.add_conds(position_conds)
|
||||
def _apply_position_conds(self, position_conds, conditioning, fusers, positionnet, fusers_batch_size):
|
||||
# Add prompts+embeddings to the input conditionings
|
||||
cond_out = []
|
||||
for t in conditioning:
|
||||
n = [t[0], t[1].copy()]
|
||||
cond = n[1]
|
||||
prev = []
|
||||
has_instance = 'instance_diffusion' in cond
|
||||
instance_conditioning = conditioning['instance_diffusion'] if has_instance else InstanceConditioning(
|
||||
fusers, positionnet, fusers_batch_size)
|
||||
cond['instance_diffusion'] = instance_conditioning
|
||||
instance_conditioning.add_conds(position_conds)
|
||||
|
||||
cond['instance_diffusion'] = {
|
||||
'conds': prev + position_conds,
|
||||
}
|
||||
cond['gligen'] = ('position', instance_conditioning, None)
|
||||
|
||||
cond['gligen'] = ('position', instance_conditioning, None)
|
||||
cond_out.append(n)
|
||||
|
||||
cond_out.append(n)
|
||||
return cond_out
|
||||
|
||||
return cond_out
|
||||
def append(self, positive, negative, clip, tracking, fusers, positionnet, fusers_batch_size, positive_text, negative_text):
|
||||
|
||||
def append(self, positive, negative, clip, tracking, fusers, positionnet, fusers_batch_size, positive_text, negative_text):
|
||||
positive_positions = self._get_position_conds(
|
||||
clip, tracking, positive_text)
|
||||
positive = self._apply_position_conds(
|
||||
positive_positions, positive, fusers, positionnet, fusers_batch_size)
|
||||
|
||||
positive_positions = self._get_position_conds(
|
||||
clip, tracking, positive_text)
|
||||
positive = self._apply_position_conds(
|
||||
positive_positions, positive, fusers, positionnet, fusers_batch_size)
|
||||
negative_positions = self._get_position_conds(
|
||||
clip, tracking, negative_text)
|
||||
negative = self._apply_position_conds(
|
||||
negative_positions, negative, fusers, positionnet, fusers_batch_size)
|
||||
|
||||
negative_positions = self._get_position_conds(
|
||||
clip, tracking, negative_text)
|
||||
negative = self._apply_position_conds(
|
||||
negative_positions, negative, fusers, positionnet, fusers_batch_size)
|
||||
|
||||
return (positive, negative)
|
||||
return (positive, negative)
|
||||
|
||||
Reference in New Issue
Block a user