diff --git a/README.md b/README.md index 01140aa..2480f73 100644 --- a/README.md +++ b/README.md @@ -12,7 +12,7 @@ This a ComfyUI node for batch creative interpolation. The goal is to allow you t Here's a workflow to get started with: https://banodoco.s3.amazonaws.com/creative_interpolation_example.json -You'll need to drop the input images into the 'creative_interpolation_input' folder. +You'll need to drop the input images into the 'creative_interpolation_input' folder in numerical order - 0.png, 1.png, etc. The main settings are: diff --git a/control/nodes.py b/control/nodes.py index e2a04ac..2e78b2e 100644 --- a/control/nodes.py +++ b/control/nodes.py @@ -164,7 +164,8 @@ class BatchCreativeInterpolationNode: "images": ("IMAGE", ), "frames_per_keyframe": ("INT", {"default": 16, "min": 4, "max": 64, "step": 1}), "length_of_key_frame_influence": ("FLOAT", {"default": 1.1, "min": 0.0, "max": 2.0, "step": 0.001}), - "cn_strength": ("FLOAT", {"default": 0.9, "min": 0.0, "max": 1.0, "step": 0.01}), + "cn_strength": ("FLOAT", {"default": 0.9, "min": 0.0, "max": 1.0, "step": 0.01}), + "soft_scaled_cn_weights_multipler": ("FLOAT", {"default": 0.85, "min": 0.0, "max": 10.0, "step": 0.01}), "interpolation": (["ease-in", "ease-out", "ease-in-out"],), }, "optional": { @@ -177,7 +178,7 @@ class BatchCreativeInterpolationNode: CATEGORY = "ComfyUI-AnimateDiff-Creative-Interpolation 🎞️🅟🅞🅜/Batch" - def combined_function(self, positive, negative, control_net_name, images, length_of_key_frame_influence,cn_strength,frames_per_keyframe,interpolation): + def combined_function(self, positive, negative, control_net_name, images, length_of_key_frame_influence,cn_strength,frames_per_keyframe,soft_scaled_cn_weights_multipler,interpolation): def calculate_keyframe_peaks_and_influence(frames_per_keyframe, number_of_keyframes, length_of_influence, new_influence_range=(0,4)): number_of_frames = frames_per_keyframe * number_of_keyframes @@ -248,7 +249,7 @@ class BatchCreativeInterpolationNode: latent_keyframe, = latent_keyframe_interpolation_node.load_keyframe(batch_index_from, strength_from, batch_index_to_excl, strength_to, interpolation,return_at_midpoint) scaled_soft_control_net_weights = ScaledSoftControlNetWeights() - control_net_weights, _ = scaled_soft_control_net_weights.load_weights(0.85, False) + control_net_weights, _ = scaled_soft_control_net_weights.load_weights(soft_scaled_cn_weights_multipler, False) timestep_keyframe_node = TimestepKeyframeNode() timestep_keyframe, = timestep_keyframe_node.load_keyframe(