e4db1fc892
- Fixed duration_seconds input - Both duration settings are now shown by default - Audio latent fix
117 lines
4.6 KiB
Python
117 lines
4.6 KiB
Python
import torch
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import comfy.utils
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from comfy_api.latest import io
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class LTXKeyframer(io.ComfyNode):
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@classmethod
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def define_schema(cls):
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inputs = [
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io.Vae.Input("vae", tooltip="Video VAE used to encode the images"),
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io.Latent.Input("latent", tooltip="Video latent to insert images into"),
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io.Image.Input("multi_input", tooltip="Batched images from MultiImageLoader"),
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]
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inputs.append(io.Int.Input("num_images", default=1, min=0, max=50, step=1, display_name="images_loaded", tooltip="Select how many index/strength widgets to configure."))
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for i in range(1, 51): # 1 to 50 images
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inputs.extend([
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io.Int.Input(
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f"insert_frame_{i}",
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default=0,
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min=-9999,
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max=9999,
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step=1,
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tooltip=f"Frame insert_frame for image {i} (in pixel space).",
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optional=True,
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),
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io.Float.Input(
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f"strength_{i}",
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default=1.0,
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min=0.0,
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max=1.0,
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step=0.01,
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tooltip=f"Strength for image {i}.",
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optional=True,
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),
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])
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return io.Schema(
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node_id="LTXKeyframer",
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display_name="LTX Keyframer",
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category="WhatDreamsCost",
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description="Replaces video latent frames with the encoded input images. Number of widgets is dynamically configured.",
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inputs=inputs,
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outputs=[
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io.Latent.Output(display_name="latent", tooltip="The video latent with the images inserted and latent noise mask updated."),
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],
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)
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@classmethod
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def execute(cls, vae, latent, multi_input, num_images, **kwargs) -> io.NodeOutput:
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samples = latent["samples"].clone()
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scale_factors = vae.downscale_index_formula
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_, height_scale_factor, width_scale_factor = scale_factors
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batch, _, latent_frames, latent_height, latent_width = samples.shape
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width = latent_width * width_scale_factor
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height = latent_height * height_scale_factor
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# Get existing noise mask if present, otherwise create new one
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if "noise_mask" in latent:
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conditioning_latent_frames_mask = latent["noise_mask"].clone()
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else:
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conditioning_latent_frames_mask = torch.ones(
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(batch, 1, latent_frames, 1, 1),
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dtype=torch.float32,
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device=samples.device,
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)
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batch_size = multi_input.shape[0] if multi_input is not None else 0
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# We process inputs up to num_images, extracting values from kwargs
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for i in range(1, num_images + 1):
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# Skip if this image index exceeds the batch
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if i > batch_size:
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continue
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image = multi_input[i-1:i] # Extract the single image frame from the batch
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if image is None:
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continue
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insert_frame = kwargs.get(f"insert_frame_{i}")
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if insert_frame is None:
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continue
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strength = kwargs.get(f"strength_{i}", 1.0)
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if image.shape[1] != height or image.shape[2] != width:
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pixels = comfy.utils.common_upscale(image.movedim(-1, 1), width, height, "bilinear", "center").movedim(1, -1)
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else:
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pixels = image
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encode_pixels = pixels[:, :, :, :3]
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t = vae.encode(encode_pixels)
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# Convert pixel frame insert_frame to latent insert_frame
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time_scale_factor = scale_factors[0]
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# Handle negative indexing in pixel space
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pixel_frame_count = (latent_frames - 1) * time_scale_factor + 1
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if insert_frame < 0:
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insert_frame = pixel_frame_count + insert_frame
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# Convert to latent insert_frame
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latent_idx = insert_frame // time_scale_factor
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# Clamp to valid range
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latent_idx = max(0, min(latent_idx, latent_frames - 1))
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# Calculate end insert_frame, ensuring we don't exceed latent_frames
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end_index = min(latent_idx + t.shape[2], latent_frames)
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# Replace samples at the specified insert_frame range
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samples[:, :, latent_idx:end_index] = t[:, :, :end_index - latent_idx]
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# Update mask at the specified insert_frame range
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conditioning_latent_frames_mask[:, :, latent_idx:end_index] = 1.0 - strength
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return io.NodeOutput({"samples": samples, "noise_mask": conditioning_latent_frames_mask}) |