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Update nodes.py
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kijai
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125
nodes.py
125
nodes.py
@ -3124,6 +3124,129 @@ class StableZero123_BatchSchedule:
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latent = torch.zeros([batch_size, 4, height // 8, width // 8])
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return (final_positive, final_negative, {"samples": latent})
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def linear_interpolate(start, end, fraction):
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return start + (end - start) * fraction
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class SV3D_BatchSchedule:
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@classmethod
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def INPUT_TYPES(s):
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return {"required": { "clip_vision": ("CLIP_VISION",),
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"init_image": ("IMAGE",),
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"vae": ("VAE",),
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"width": ("INT", {"default": 576, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}),
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"height": ("INT", {"default": 576, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}),
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"batch_size": ("INT", {"default": 21, "min": 1, "max": 4096}),
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"interpolation": (["linear", "ease_in", "ease_out", "ease_in_out"],),
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"azimuth_points_string": ("STRING", {"default": "0:(0.0),\n9:(180.0),\n20:(360.0)\n", "multiline": True}),
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"elevation_points_string": ("STRING", {"default": "0:(0.0),\n9:(0.0),\n20:(0.0)\n", "multiline": True}),
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}}
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RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT")
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RETURN_NAMES = ("positive", "negative", "latent")
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FUNCTION = "encode"
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CATEGORY = "KJNodes"
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def encode(self, clip_vision, init_image, vae, width, height, batch_size, azimuth_points_string, elevation_points_string, interpolation):
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output = clip_vision.encode_image(init_image)
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pooled = output.image_embeds.unsqueeze(0)
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pixels = comfy.utils.common_upscale(init_image.movedim(-1,1), width, height, "bilinear", "center").movedim(1,-1)
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encode_pixels = pixels[:,:,:,:3]
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t = vae.encode(encode_pixels)
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def ease_in(t):
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return t * t
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def ease_out(t):
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return 1 - (1 - t) * (1 - t)
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def ease_in_out(t):
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return 3 * t * t - 2 * t * t * t
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# Parse the azimuth input string into a list of tuples
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azimuth_points = []
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azimuth_points_string = azimuth_points_string.rstrip(',\n')
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for point_str in azimuth_points_string.split(','):
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frame_str, azimuth_str = point_str.split(':')
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frame = int(frame_str.strip())
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azimuth = float(azimuth_str.strip()[1:-1])
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azimuth_points.append((frame, azimuth))
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# Sort the points by frame number
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azimuth_points.sort(key=lambda x: x[0])
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# Parse the elevation input string into a list of tuples
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elevation_points = []
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elevation_points_string = elevation_points_string.rstrip(',\n')
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for point_str in elevation_points_string.split(','):
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frame_str, elevation_str = point_str.split(':')
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frame = int(frame_str.strip())
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elevation_val = float(elevation_str.strip()[1:-1])
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elevation_points.append((frame, elevation_val))
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# Sort the points by frame number
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elevation_points.sort(key=lambda x: x[0])
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# Index of the next point to interpolate towards
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next_point = 1
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next_elevation_point = 1
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elevations = []
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azimuths = []
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# For azimuth interpolation
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for i in range(batch_size):
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# Find the interpolated azimuth for the current frame
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while next_point < len(azimuth_points) and i >= azimuth_points[next_point][0]:
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next_point += 1
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if next_point == len(azimuth_points):
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next_point -= 1
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prev_point = max(next_point - 1, 0)
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if azimuth_points[next_point][0] != azimuth_points[prev_point][0]:
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fraction = (i - azimuth_points[prev_point][0]) / (azimuth_points[next_point][0] - azimuth_points[prev_point][0])
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# Apply the ease function to the fraction
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if interpolation == "ease_in":
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fraction = ease_in(fraction)
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elif interpolation == "ease_out":
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fraction = ease_out(fraction)
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elif interpolation == "ease_in_out":
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fraction = ease_in_out(fraction)
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interpolated_azimuth = linear_interpolate(azimuth_points[prev_point][1], azimuth_points[next_point][1], fraction)
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else:
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interpolated_azimuth = azimuth_points[prev_point][1]
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# Interpolate the elevation
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next_elevation_point = 1
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while next_elevation_point < len(elevation_points) and i >= elevation_points[next_elevation_point][0]:
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next_elevation_point += 1
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if next_elevation_point == len(elevation_points):
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next_elevation_point -= 1
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prev_elevation_point = max(next_elevation_point - 1, 0)
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if elevation_points[next_elevation_point][0] != elevation_points[prev_elevation_point][0]:
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fraction = (i - elevation_points[prev_elevation_point][0]) / (elevation_points[next_elevation_point][0] - elevation_points[prev_elevation_point][0])
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# Apply the ease function to the fraction
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if interpolation == "ease_in":
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fraction = ease_in(fraction)
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elif interpolation == "ease_out":
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fraction = ease_out(fraction)
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elif interpolation == "ease_in_out":
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fraction = ease_in_out(fraction)
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interpolated_elevation = linear_interpolate(elevation_points[prev_elevation_point][1], elevation_points[next_elevation_point][1], fraction)
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else:
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interpolated_elevation = elevation_points[prev_elevation_point][1]
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azimuths.append(interpolated_azimuth)
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elevations.append(interpolated_elevation)
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print("azimuths", azimuths)
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print("elevations", elevations)
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# Structure the final output
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final_positive = [[pooled, {"concat_latent_image": t, "elevation": elevations, "azimuth": azimuths}]]
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final_negative = [[torch.zeros_like(pooled), {"concat_latent_image": torch.zeros_like(t),"elevation": elevations, "azimuth": azimuths}]]
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latent = torch.zeros([batch_size, 4, height // 8, width // 8])
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return (final_positive, final_negative, {"samples": latent})
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class ImageBatchRepeatInterleaving:
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RETURN_TYPES = ("IMAGE",)
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@ -3819,6 +3942,7 @@ NODE_CLASS_MAPPINGS = {
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"SoundReactive": SoundReactive,
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"GenerateNoise": GenerateNoise,
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"StableZero123_BatchSchedule": StableZero123_BatchSchedule,
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"SV3D_BatchSchedule": SV3D_BatchSchedule,
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"GetImagesFromBatchIndexed": GetImagesFromBatchIndexed,
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"ImageBatchRepeatInterleaving": ImageBatchRepeatInterleaving,
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"NormalizedAmplitudeToMask": NormalizedAmplitudeToMask,
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@ -3889,6 +4013,7 @@ NODE_DISPLAY_NAME_MAPPINGS = {
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"SoundReactive": "SoundReactive",
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"GenerateNoise": "GenerateNoise",
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"StableZero123_BatchSchedule": "StableZero123_BatchSchedule",
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"SV3D_BatchSchedule": "SV3D_BatchSchedule",
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"GetImagesFromBatchIndexed": "GetImagesFromBatchIndexed",
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"ImageBatchRepeatInterleaving": "ImageBatchRepeatInterleaving",
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"NormalizedAmplitudeToMask": "NormalizedAmplitudeToMask",
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