Fixed starting issue + added README
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peter942
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# ComfyUI-Advanced-ControlNet
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These custom nodes allow for scheduling ControlNet strength across latents in the same batch (WORKING) and across timesteps (IN PROGRESS).
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# ComfyUI-Creative-Interpolation (Beta)
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Custom weights can also be applied to ControlNets and T2IAdapters to mimic the "My prompt is more important" functionality in AUTOMATIC1111's ControlNet extension.
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This a ComfyUI node for batch creative interpolation. The goal is to allow you to input a batch of images, and to provide a range of simple settings to control how the images are interpolated between.
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TODO:
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- Other handy nodes
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- Finish and update this README for other workflows
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## Installation
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## Workflows
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1. If you haven't already, download [ComfyUI](https://github.com/comfyanonymous/ComfyUI) and [Comfy Manager](https://github.com/ltdrdata/ComfyUI-Manager).
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1. go to your custom_nodes folder and run: git clone https://github.com/peteromallet/ComfyUI-Creative-Interpolation.git
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2. Download Controlnet tile from Comfy Manager: control_v11f1e_sd15_tile_fp16.safetensors
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### AnimateDiff Workflows
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***Latent Keyframes*** identify which latents in a batch the ControlNet should apply to, and at what strength. They connect to a ***Timestep Keyframe*** to identify at what point in the generation to kick in (for basic use, start_percent on the Timestep Keyframe should be 0.0). Latent Keyframe nodes can be chained to apply the ControlNet to multiple keyframes at various strengths.
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## Usage
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Here's a workflow to get started with: https://banodoco.s3.amazonaws.com/creative_interpolation_example.json
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You'll need to drop the input images into the 'creative_interpolation_input' folder.
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The main settings are:
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- frames_per_key_frame: How many frames to generate between each main key frame you provide.
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- length_of_key_frame_influence: How many frames to apply the ControlNet for after each key frame - the larger the number, the the wider range the input images will influence.
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- cn_strength: How strong the control of the ControlNet should overall.
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The **batch_size should be equal to the frames_per_key_frame * number_of_key_frames + 4** - the additional 4 is to accommodate for buffer frames that improve consistency.
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Also, **there's currently a bug where it needs to be restarted after each batch**. This will be fixed soon.
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As an example, here's a batch of input images:
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And here's what it looks like when the length_of_key_frame_influence is set to 1.1:
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https://github.com/Fannovel16/ComfyUI-Frame-Interpolation
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While here's while that looks like at 0.8:
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These settings can be so powerful I believe - please share what works for you and your results!
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## Coming Soon
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- Fix restarting bug
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- Clean up code
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- Simplify settings
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- Nuanced settings for each key frame
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- Improvements to structure and consistency
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- Better control over style
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## Want to give feedback, share creations, or join our community?
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You can drop into our Discord here: https://discord.com/invite/8Wx9dFu5tP
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## Credits
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This code draws heavily from [Kosinkadink's ComfyUI-Advanced-ControlNet](https://github.com/Kosinkadink/ComfyUI-Advanced-ControlNet), while the workflows uses [Kosinkadink's Animatediff Evolved](https://github.com/Kosinkadink/ComfyUI-AnimateDiff-Evolved), Cubiq's [IPAdapter_plus](https://github.com/cubiq/ComfyUI_IPAdapter_plus), Fizzledorf's [Fizznodes](https://github.com/FizzleDorf/ComfyUI_FizzNodes), Fannovel16's [Frame Interpolation](https://github.com/Fannovel16/ComfyUI-Frame-Interpolation) and more. Thanks to all and of course the Animatediff team, Controlnet, others, and of course our supportive community!
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@@ -144,7 +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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"revert_direction_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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+26
-27
@@ -11,9 +11,6 @@ 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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@@ -165,9 +162,9 @@ class BatchCreativeInterpolationNode:
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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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"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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"interpolation": (["ease-in", "ease-out", "ease-in-out"],),
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},
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"optional": {
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@@ -182,8 +179,7 @@ class BatchCreativeInterpolationNode:
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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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def calculate_keyframe_peaks_and_influence(frames_per_keyframe, number_of_keyframes, length_of_influence, new_influence_range=(0,4)):
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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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@@ -198,7 +194,7 @@ class BatchCreativeInterpolationNode:
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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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peaks.append(number_of_frames - 1) # 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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@@ -206,13 +202,16 @@ class BatchCreativeInterpolationNode:
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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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influence_ranges = [new_influence_range]
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# Loop through each keyframe to calculate its influence range
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# Shift the subsequent influence ranges
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shift = new_influence_range[1]
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# Loop through each keyframe to calculate its shifted 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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# Calculate the start and end influence around the peak, shifted by the new range's end
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start_influence = max(shift, int(peak - scaled_interval / 2.0) + shift)
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end_influence = min(number_of_frames + shift, int(peak + scaled_interval / 2.0) + shift)
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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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@@ -220,21 +219,27 @@ class BatchCreativeInterpolationNode:
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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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for i, (start, end) in enumerate(influence_ranges):
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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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if i == 0: # buffer image
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image = images[0]
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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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strength_to = 1.0
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return_at_midpoint = False
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elif i == 1: # First image
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image = images[0]
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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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image = images[i-1]
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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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return_at_midpoint = False
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else: # Middle images
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image = images[i-1]
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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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@@ -242,11 +247,6 @@ class BatchCreativeInterpolationNode:
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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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@@ -261,8 +261,7 @@ class BatchCreativeInterpolationNode:
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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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