Delete ltx_keyframer.py

ltx_keyframer.py

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