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peter942
@@ -144,6 +144,7 @@ class LatentKeyframeInterpolationNode:
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"strength_from": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.0001}, ),
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"strength_to": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.0001}, ),
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"interpolation": (["linear", "ease-in", "ease-out", "ease-in-out"], ),
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"return_at_midpoint": ("BOOLEAN", {"default": False}),
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},
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"optional": {
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"prev_latent_keyframe": ("LATENT_KEYFRAME", ),
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@@ -160,7 +161,10 @@ class LatentKeyframeInterpolationNode:
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batch_index_to_excl: int,
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strength_to: float,
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interpolation: str,
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prev_latent_keyframe: LatentKeyframeGroup=None):
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revert_direction_at_midpoint: bool=False,
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prev_latent_keyframe: LatentKeyframeGroup=None
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):
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if (batch_index_from > batch_index_to_excl):
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raise ValueError("batch_index_from must be less than or equal to batch_index_to.")
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@@ -174,18 +178,24 @@ class LatentKeyframeInterpolationNode:
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steps = batch_index_to_excl - batch_index_from
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diff = strength_to - strength_from
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if interpolation == "linear":
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weights = np.linspace(strength_from, strength_to, steps)
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elif interpolation == "ease-in":
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if revert_direction_at_midpoint:
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index = np.linspace(0, 1, steps // 2 + 1)
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else:
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index = np.linspace(0, 1, steps)
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if interpolation == "linear":
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weights = np.linspace(strength_from, strength_to, len(index))
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elif interpolation == "ease-in":
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weights = diff * np.power(index, 2) + strength_from
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elif interpolation == "ease-out":
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index = np.linspace(0, 1, steps)
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weights = diff * (1 - np.power(1 - index, 2)) + strength_from
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elif interpolation == "ease-in-out":
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index = np.linspace(0, 1, steps)
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weights = diff * ((1 - np.cos(index * np.pi)) / 2) + strength_from
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if revert_direction_at_midpoint:
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weights = np.concatenate([weights, weights[-2::-1]])
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for i in range(steps):
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keyframe = LatentKeyframe(batch_index_from + i, float(weights[i]))
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logger.info(f"keyframe {batch_index_from + i}:{weights[i]}")
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+123
-1
@@ -11,6 +11,9 @@ from .deprecated_nodes import LoadImagesFromDirectory
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from .logger import logger
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class TimestepKeyframeNode:
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@classmethod
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def INPUT_TYPES(s):
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@@ -22,7 +25,7 @@ class TimestepKeyframeNode:
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"control_net_weights": ("CONTROL_NET_WEIGHTS", ),
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"t2i_adapter_weights": ("T2I_ADAPTER_WEIGHTS", ),
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"latent_keyframe": ("LATENT_KEYFRAME", ),
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"prev_timestep_keyframe": ("TIMESTEP_KEYFRAME", ),
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"prev_timestep_keyframe": ("TIMESTEP_KEYFRAME", ),
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}
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}
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@@ -125,12 +128,14 @@ class AdvancedControlNetApply:
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out = []
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for conditioning in [positive, negative]:
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c = []
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for t in conditioning:
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d = t[1].copy()
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prev_cnet = d.get('control', None)
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if prev_cnet in cnets:
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c_net = cnets[prev_cnet]
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else:
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c_net = control_net.copy().set_cond_hint(control_hint, strength, (1.0 - start_percent, 1.0 - end_percent))
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# set cond hint mask
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@@ -150,9 +155,124 @@ class AdvancedControlNetApply:
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out.append(c)
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return (out[0], out[1])
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class CombinedNode:
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"positive": ("CONDITIONING", ),
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"negative": ("CONDITIONING", ),
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"control_net_name": (folder_paths.get_filename_list("controlnet"), ),
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"images": ("IMAGE", ),
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"length_of_key_frame_influence": ("FLOAT", {"default": 1.1, "min": 0.0, "max": 2.0, "step": 0.001}),
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"cn_strength": ("FLOAT", {"default": 0.9, "min": 0.0, "max": 1.0, "step": 0.01}),
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"frames_per_keyframe": ("INT", {"default": 16, "min": 4, "max": 64, "step": 1}),
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"interpolation": (["ease-in", "ease-out", "ease-in-out"],),
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},
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"optional": {
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}
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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 = "combined_function"
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CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/combined"
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def combined_function(self, positive, negative, control_net_name, images, length_of_key_frame_influence,cn_strength,frames_per_keyframe,interpolation):
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def calculate_keyframe_peaks_and_influence(frames_per_keyframe, number_of_keyframes, length_of_influence):
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number_of_frames = frames_per_keyframe * number_of_keyframes
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# Calculate the interval between keyframes
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interval = (number_of_frames - 1) // (number_of_keyframes - 1)
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# Determine if we need to adjust the interval because of a remainder
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adjustment = (number_of_frames - 1) % (number_of_keyframes - 1)
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# Calculate the peak frames for each keyframe
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peaks = [0] # The first keyframe is always at the first frame
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for i in range(1, number_of_keyframes - 1): # We already know the first and last keyframe peaks
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peak = peaks[-1] + interval
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# If we have a remainder, we distribute it among the first keyframes
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if i <= adjustment:
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peak += 1
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peaks.append(peak)
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peaks.append(number_of_frames) # The last keyframe is always at the last frame
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# Calculate the full interval between keyframes
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full_interval = (number_of_frames - 1) / (number_of_keyframes - 1)
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# Calculate the scaled interval based on the length_of_influence
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scaled_interval = full_interval * length_of_influence
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# Initialize the list to store the influence range for each keyframe
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influence_ranges = []
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# Loop through each keyframe to calculate its influence range
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for i, peak in enumerate(peaks):
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# Calculate the start and end influence around the peak
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start_influence = max(0, int(peak - scaled_interval / 2.0))
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end_influence = min(number_of_frames, int(peak + scaled_interval / 2.0))
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# Add the influence range as a tuple (start, end) to the list
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influence_ranges.append((start_influence, end_influence))
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return influence_ranges
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influence_ranges = calculate_keyframe_peaks_and_influence(frames_per_keyframe, len(images), length_of_key_frame_influence)
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for i, image in enumerate(images):
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batch_index_from, batch_index_to_excl = influence_ranges[i]
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if i == 0: # First image
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strength_from = 1.0
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strength_to = 0.0
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return_at_midpoint = False
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elif i == len(images) - 1: # Last image
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strength_from = 0.0
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strength_to = 1.0
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return_at_midpoint = False
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else: # Middle images
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strength_from = 0.0
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strength_to = 1.0
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return_at_midpoint = True
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latent_keyframe_interpolation_node = LatentKeyframeInterpolationNode()
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latent_keyframe, = latent_keyframe_interpolation_node.load_keyframe(batch_index_from, strength_from, batch_index_to_excl, strength_to, interpolation,return_at_midpoint)
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if i == len(images) - 1:
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for i in range(10):
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keyframe = TimestepKeyframe(batch_index_to_excl + i, 1.0)
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timestep_keyframe.add(keyframe)
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scaled_soft_control_net_weights = ScaledSoftControlNetWeights()
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control_net_weights, _ = scaled_soft_control_net_weights.load_weights(0.85, False)
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timestep_keyframe_node = TimestepKeyframeNode()
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timestep_keyframe, = timestep_keyframe_node.load_keyframe(
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start_percent=0.0,
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control_net_weights=control_net_weights,
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t2i_adapter_weights=None,
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latent_keyframe=latent_keyframe,
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prev_timestep_keyframe=None
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)
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control_net_loader = ControlNetLoaderAdvanced()
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control_net, = control_net_loader.load_controlnet(control_net_name, timestep_keyframe)
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apply_advanced_control_net = AdvancedControlNetApply()
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positive, negative = apply_advanced_control_net.apply_controlnet(positive, negative, control_net, image.unsqueeze(0), cn_strength, 0.0, 1.0)
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return (positive, negative)
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# NODE MAPPING
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NODE_CLASS_MAPPINGS = {
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# Combined
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"CombinedNode": CombinedNode,
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# Keyframes
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"TimestepKeyframe": TimestepKeyframeNode,
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"LatentKeyframe": LatentKeyframeNode,
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@@ -175,6 +295,8 @@ NODE_CLASS_MAPPINGS = {
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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# Combined
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"CombinedNode": "Combined 🛂🅐🅒🅝",
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# Keyframes
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"TimestepKeyframe": "Timestep Keyframe 🛂🅐🅒🅝",
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"LatentKeyframe": "Latent Keyframe 🛂🅐🅒🅝",
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